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Termination of Rewriting
(1994)
More and more, term rewriting systems are applied in computer science aswell as in mathematics. They are based on directed equations which may be used as non-deterministic functional programs. Termination is a key property for computing with termrewriting systems.In this thesis, we deal with different classes of so-called simplification orderings which areable to prove the termination of term rewriting systems. Above all, we focus on the problemof applying these termination methods to examples occurring in practice. We introduce aformalism that allows clear representations of orderings. The power of classical simplifica-tion orderings - namely recursive path orderings, path and decomposition orderings, Knuth-Bendix orderings and polynomial orderings - is improved. Further, we restrict these orderingssuch that they are compatible with underlying AC-theories by extending well-known methodsas well as by developing new techniques. For automatically generating all these orderings,heuristic-based algorithms are given. A comparison of these orderings with respect to theirpowers and their time complexities concludes the theoretical part of this thesis. Finally, notonly a detailed statistical evaluation of examples but also a brief introduction into the designof a software tool representing the integration of the specified approaches is given.
Zur Planung und Steuerung von komplexen rechnerintegrierten Fertigungssystemen (CIM) ist die Abarbeitung vieler extrem aufwendiger Algorithmen notwendig. Aus dem Bereich der Fertigungssteuerung zählt die Generierung von Maschinenbelegungsplänen (scheduling) dazu. Zur Steigerung der Lösungsgeschwindigkeit bzw. zum Erreichen exakter Ergebnisse bietet sich der massive Einsatz von Rechenparallelität an. Mit Parallelrechnern ist durch die gleichzeitige Verwendung von vielen Prozessoren potentiell eine sehr große Leistungssteigerung zu erreichen. Dafür muß jedoch die vorhandene Parallelität effektiv genutzt werden. Die dazu erforderliche Verteilung der anstehenden Arbeit auf eine große Menge von Prozessoren heißt Lastverteilung und stellt den Kern dieser Arbeit dar. Als allgemeiner Algorithmus zur Lösung kombinatorischer Optimierungs-probleme wird das Branch-and-bound-Verfahren eingesetzt und auf fein-körnigen Parallelrechnerarchitekturen ausgeführt. Zur Lastverteilung werden folgende drei Ansätze verfolgt und untersucht: " Statische Lastverteilung: Es werden mehrere Methoden zur Initialisierung der Prozessoren, welche vor dem eigentlichen Optimierungsalgorithmus ausgeführt werden, analysiert. Es zeigt sich, daß sich die statische Last-verteilung überproportional stark auf die Laufzeit des nachfolgenden Branch-and-bound-Algorithmus auswirkt. Es ist daher wichtig, der bisher unterschätzten statischen Lastverteilung für die parallele Baumsuche mit realen Problemstellungen, besondere Aufmerksamkeit zu schenken. " Dynamische Lastverteilung: Es wird ein vereinfachtes, gut skalierbares Flüssigkeitsmodell als erste synchrone lokale Lastverteilung entwickelt, welche besonders für Parallelrechner mit kurzer Verzögerungszeit beim Aufbau von Kommunikationsverbindungen effizient ist. Die Methode wird mit dem bekannten, aus dem Asynchronen übertragenen, Mittelungs-Ansatz verglichen. Zum analytischen Vergleich wird als ein realistischeres Aufwandsmaß die Kommunikationsmenge statt der üblichen Anzahl von Kommunikationsschritte verwendet. Der in der Prozessoranzahl bisher benötigte quadratische Zeitaufwand wird durch das Flüssigkeitsmodell auf einen linearen Aufwand reduziert, wobei das Flüssigkeitsmodell auch bzgl. der konstanten Zeitfaktoren signifikant effizienter ist. " Implizite Lastverteilung: Zur Vermeidung von Wartezeiten der unbe-teiligten Prozessoren während der Lastverteilung wird der Lastverteilungs-prozeß mit dem Branch-and-bound-Prozeß verschmolzen. Das neuartige Konzept der k-Expansion unterstützt eine automatische Lastverteilung und approximiert eine globale Suchstrategie. Zur Validierung der Ergebnisse werden Simulationen und Experimente mit einem Satz von Benchmark-Problemen durchgeführt. Der zugrunde liegende SIMD-Rechner ist eine MasPar MP-1 mit 16.384 Prozessoren in einem 2- dimensionalen Torus. Als exemplarische, NP-harte Anwendungsdomäne werden statische, non-operationale Planungsprobleme betrachtet.
In dieser Dissertation wird das Konzept der Gröbnerbasen für endlich erzeugte Monoid-und Gruppenringe verallgemeinert. Dabei werden Reduktionsmethoden sowohl zurDarstellung der Monoid- beziehungsweise Gruppenelemente, als auch zur Beschreibungder Rechtsidealkongruenz in den entsprechenden Monoid- beziehungsweise Gruppenrin-gen benutzt. Da im allgemeinen Monoide und insbesondere Gruppen keine zulässigenOrdnungen mehr erlauben, treten bei der Definition einer geeigneten Reduktionsrela-tion wesentliche Probleme auf: Zum einen ist es schwierig, die Terminierung einer Re-duktionsrelation zu garantieren, zum anderen sind Reduktionsschritte nicht mehr mitMultiplikationen verträglich und daher beschreiben Reduktionen nicht mehr unbedingteine Rechtsidealkongruenz. In dieser Arbeit werden verschiedene Möglichkeiten Reduk-tionsrelationen zu definieren aufgezeigt und im Hinblick auf die beschriebenen Problemeuntersucht. Dabei wird das Konzept der Saturierung, d.h. eine Polynommenge so zu er-weitern, daß man die von ihr erzeugte Rechtsidealkongruenz durch Reduktion erfassenkann, benutzt, um Charakterisierungen von Gröbnerbasen bezüglich der verschiedenenReduktionen durch s-Polynome zu geben. Mithilfe dieser Konzepte ist es gelungenfür spezielle Klassen von Monoiden, wie z.B. endliche, kommutative oder freie, undverschiedene Klassen von Gruppen, wie z.B. endliche, freie, plain, kontext-freie odernilpotente, unter Ausnutzung struktureller Eigenschaften spezielle Reduktionsrelatio-nen zu definieren und terminierende Algorithmen zur Berechnung von Gröbnerbasenbezüglich dieser Reduktionsrelationen zu entwickeln.
Zur Zeit haben Industrieroboter nur eine sehr begrenzte Wahrnehmung ihrer Umwelt. Wenn sich Menschen im Arbeitsraum des Roboters aufhalten sind sie daher gefährdet. Durch eine Einteilung der möglichen Roboterbewegung in verschiedene Klassen kann gezeigt werden, dass die für einen Menschen im Arbeitsraum gefährlichste Bewegung die freie Transferbewegung ist. Daher besteht die betrachtete Aufgabe darin, diese Transferbewegung eines Manipulators durchzuführen, ohne mit dynamischen Hindernissen, wie zum Beispiel Menschen, zu kollidieren. Das vorgestellte SIMERO-System realisiert eine globale Ganzarmkollisionsvermeidung auf der Basis von Bildern stationärer Kameras. Das System gliedert sich in die vier Hauptkomponenten Bildverarbeitung, Robotermodellierung, Kollisionserkennung und Bahnplanung. Diese Komponenten werden im einzelnen vorgestellt.
Die formale Spezifikation von Kommunikationssystemen stellt durch die mit ihr verbundene Abstraktion und Präzision eine wichtige Grundlage für die formale Verifikation von Systemeigenschaften dar. Diese Abstraktion begrenzt jedoch auch die Ausdrucksfähigkeit der formalen Beschreibungstechnik und kann somit zu problemunangemessenen Spezifikationen führen. Wir untersuchen anhand der formalen Beschreibungstechnik Estelle zunächst zwei solche Aspekte. Beide führen speziell in Hinsicht auf die Domäne von Estelle, der Spezifikation von Kommunikationsprotokollen, zu schwerwiegenden Beeinträchtigungen der Ausdrucksfähigkeit. Eines dieser Defizite zeigt sich bei dem Versuch, in Estelle ein offenes System wie z. B. eine Protokollmaschine oder einen Kommunikationsdienst zu spezifizieren. Da Estelle-Spezifikationen nur geschlossene Systeme beschreiben können, werden solche Komponenten immer nur als Teil einer fest vorgegebenen Umgebung spezifiziert und besitzen auch nur in dieser eine formale Syntax und Semantik. Als Lösung für dieses Problem führen wir die kompatible syntaktische und semantische Estelle-Erweiterung Open-Estelle ein, die eine formale Spezifikation solcher offener Systeme und ihres Imports in verschiedene Umgebungen ermöglicht. Ein anderes Defizit in der Ausdrucksfähigkeit von Estelle ergibt sich aus der strengen Typprüfung. Wir werden zeigen, dass es in heterogenen, hierarchisch strukturierten Kommunikationssystemen im Zusammenhang mit den dort auftretenden horizontalen und vertikalen Typkompositionen zu einer unangemessenen Modellierung von Nutzdatentypen an den Dienstschnittstellen kommt. Dieses Problem erweist sich beim Versuch einer generischen und nutzdatentypunabhängigen Spezifikation eines offenen Systems (z. B. mit Open-Estelle) sogar als fatal. Deshalb führen wir die kompatible Containertyp-Erweiterung ein, durch die eine formale Spezifikation nutzdatentypunabhängiger und somit generischer Schnittstellen von Diensten und Protokollmaschinen ermöglicht wird. Als Grundlage für unsere Implementierungs- und Optimierungsexperimente führen wir den „eXperimental Estelle Compiler“ (XEC) ein. Er ermöglicht aufgrund seines Implementierungskonzeptes eine sehr flexible Modellierung des Systemmanagements und ist insbesondere für die Realisierung verschiedener Auswahloptimierungen geeignet. XEC ist zudem mit verschiedenen Statistik- und Monitoring-Funktionalitäten ausgestattet, durch die eine effiziente quantitative Analyse der durchgeführten Implementierungsexperimente möglich ist. Neben dem vollständigen Sprachumfang von Estelle unterstützt XEC auch die meisten der hier eingeführten Estelle-Erweiterungen. Neben der Korrektheit ist die Effizienz automatisch generierter Implementierungen eine wichtige Anforderung im praktischen Einsatz. Hier zeigt sich jedoch, dass viele der in formalen Protokollspezifikationen verwendeten Konstrukte nur schwer semantikkonform und zugleich effizient implementiert werden können. Entsprechend untersuchen wir anhand des Kontrollflusses und der Handhabung von Nutzdaten, wie die spezifizierten Operationen effizient implementiert werden können, ohne das Abstraktionsniveau senken zu müssen. Die Optimierung des Kontrollflusses geschieht dabei ausgehend von der effizienten Realisierung der Basisoperationen der von XEC erzeugten Implementierungen primär anhand der Transitionsauswahl, da diese speziell bei komplexen Spezifikationen einen erheblichen Teil der Ausführungszeit bansprucht. Wir entwickeln dazu verschiedene heuristische Optimierungen der globalen Auswahl und der modullokalen Auswahl und werten diese sowohl analytisch wie auch experimentell aus. Wesentliche Ansatzpunkte sind dabei verschiedene ereignisgesteuerte Auswahlverfahren auf globaler Ebene und die Reduktion der zu untersuchenden Transitionen auf lokaler Ebene. Die Überprüfung der Ergebnisse anhand der ausführungszeitbezogenen Leistungsbewertung bestätigt diese Ergebnisse. Hinsichtlich der effizienten Handhabung von Daten untersuchen wir unterschiedliche Ansätze auf verschiedenen Ebenen, die jedoch in den meisten Fällen eine problemunangemessene Ausrichtung der Spezifikation auf die effiziente Datenübertragung erfordern. Eine überraschend elegante, problemorientierte und effiziente Lösung ergibt sich jedoch auf Basis der Containertyp-Erweiterung, die ursprünglich zur Steigerung des Abstraktionsniveaus eingeführt wurde. Dieses Ergebnis widerlegt die Vorstellung, dass Maßnahmen zur Steigerung der effizienten Implementierbarkeit auch immer durch eine Senkung des Abstraktionsniveaus erkauft werden müssen.
In dieser Arbeit wird gezeigt, wie durch eine Automatisierung von Software-Entwicklungsaktivitäten sowohl Effizienz- als auch Qualitätsgewinne erzielt und komplexe Aktivitäten beherrschbar gemacht werden können. Dazu wird zunächst eine solide Basis für eine modellbasierte Software-Entwicklung geschaffen. Nach der Identifikation der Probleme der bisher üblicherweise eingesetzten Metamodellierung wird eine verbesserte Multiebenenmodellierung vorgeschlagen, welche die explizite Angabe der Instanziierbarkeit (Tiefe und Automatismus der Instanziierung) der Modellelemente erlaubt und damit eine deutliche Vereinfachung und bessere Verständlichkeit der Metamodelle ermöglicht. Zur operationalen Beschreibung von Modelltransformationen im Kontext dieser Multiebenenmodellierung wird sodann die Aktionssprache AL++ konzipiert. Insbesondere durch die Einführung von Sprachelementen für die Handhabung von Relationen und Attributen und die Aufnahme von Reflexionskonstrukten in die Sprache AL++ werden Transformationen kompakt und generisch beschreibbar. Anwendung finden diese Ansätze in der modellbasierten Entwicklung reaktiver Systeme. Dazu wird eine existierende Entwicklungsmethode erweitert, um eine durchgängige Automatisierung realisieren zu können. Die wichtigste Erweiterung ist dabei die modifizierte Automatenmodellierung, bei welcher erweiterte Endliche Automaten durch die Komposition getrennt modellierter Zustandsübergänge spezifiziert werden, was eine eindeutige Verfolgbarkeit zu den Anforderungen erlaubt. Eingesetzt werden obige Techniken für die statische Analyse von Spezifikationen, wobei insbesondere die automatische Detektion von Feature-Interaktionen (also die Feststellung kritischer Wechselwirkungen zwischen Produktmerkmalen) in dieser Form erstmalig für den Bereich der reaktiven Systeme durchgeführt wird. Daneben werden automatisierte dynamische Analysen auf der Basis generierter Prototypen betrachtet. Die Analyseergebnisse können automatisiert für die Modifikation und Neukonstruktion der Prototypen genutzt werden, womit Software-Entwicklungsexperimente vollständig in einem „virtuellen Labor“ durchgeführt werden können. Wichtigstes experimentelles Ergebnis ist, dass eine statische Parametrisierung einer „intelligenten“ Temperaturregelung möglich ist und daher eine Reduktion der notwendigen Produktmerkmale (und damit der Komplexität) erreicht werden kann. In Fallstudien wird am Ende der Arbeit nachgewiesen, dass alleine durch die automatische Erzeugung von Entwicklungsdokumenten und die konsistente Änderung vorhergehender Dokumente durch die in dieser Arbeit implementierten Werkzeuge ein Effizienzgewinn von 54 % erreicht werden kann. Die Erstellung der eingesetzten Werkzeuge hätte sich dabei bereits nach zwei ähnlichen Projekten bezahlt gemacht.
Im Informationszeitalter haben die Menschen überall und jederzeit Zugang zu einer kontinuierlich ansteigenden Fülle von Informationen. Hierzu trägt vor allem die explosionsartig wachsende globale Vernetzung der Welt, insbesondere das Internet, maßgeblich bei. Die Transformation der verfügbaren Informationen in Wissen sowie die effiziente Nutzung dieses Wissens stellen dabei entscheidende Faktoren für den Erfolg eines Unternehmens oder eines Einzelnen dar. Es stellt sich also die Frage: Leben wir im Informationszeitalter? Diese Frage erinnert an die von Immanuel Kant in [65] gestellte Frage "Leben wir jetzt in einem aufgeklärten Zeitalter?" und dessen Antwort "Nein, aber wohl in einem Zeitalter der Aufklärung.". Entsprechend lässt sich auch die Frage "Leben wir in einem informierten Zeitalter?" mit "Nein, aber wohl in einem Zeitalter der Information" beantworten (vergleiche [14]). Das Problem, dass sich die überwältigende Fülle an Information ohne geeignete Hilfsmittel vom Menschen nicht oder nur schwer beherrschen lässt, hat im Laufe des letzten Jahrzehnts maßgeblich zur Entwicklung des äußerst dynamischen Forschungs- und Anwendungsgebietes der Visualisierung als Teilgebiet der Computergrafik beigetragen. Der Grund hierfür liegt in der Tatsache, dass der Mensch wesentlich besser mit visuellen Eindrücken als mit abstrakten Zahlen oder Fakten umgehen kann. Die Erkennung von Mustern in Daten (z. B. Gruppierungen und Häufungen) wird durch die Visualisierung stark vereinfacht und lässt vielmals Zusammenhänge zwischen Daten überhaupt erst greifbar werden. Unter computergestützter Visualisierung versteht man die in der Regel interaktive grafische Umsetzung von Daten. Handelt es sich dabei um physikalische Daten (z. B. entstanden durch Messvorgänge), so spricht man von Scientific Visualization. Handelt es sich eher um abstrakte bzw. nicht-physikalische Daten, so ordnet man die entsprechenden Verfahren der Information Visualization zu. Beide Teilgebiete der Visualisierung verfolgen jedoch das gemeinsame Ziel, Informationen dem Menschen sichtbar und verständlich zu machen und verwenden hierzu geeignete visuelle Paradigmen, häufig verbunden mit entsprechenden Interaktionsmöglichkeiten. Die vorliegende wissenschaftliche Arbeit ist in den Bereich der angewandten Computergrafik, speziell der interaktiven Visualisierung, einzuordnen. Die primären Ziele lagen dabei in der Übertragung des Begriffes kontextsensitiv auf den Bereich der Visualisierung zur Sicherstellung effizienter und kontextsensitiver Visualisierungsapplikationen sowie die Anwendung in aktuellen praktischen Aufgabenstellungen. Die Umsetzung einer kontextsensitiven Visualisierung gelingt im Rahmen dieser Arbeit durch die zukunftsweisende Kopplung von Visualisierungspipeline und Agententechnologie. Basierend auf der Identifikation zentraler Szenarien der kontextsensitiven Visualisierung wird eine agentenbasierte Visualisierungskontrolle durch intelligente Überwachung und Regelung der Visualisierungspipeline vorgestellt. Nach einer Zusammenfassung der relevanten Grundlagen aus den Gebieten der Visualisierung und der Agententechnologie folgen eine theoretische Klassifizierung und ein Überblick über existierende Systeme und Anwendungen aus beiden Bereichen. Anschließend wird das im Rahmen dieser Arbeit erarbeitete Paradigma der kontextsensitiven Visualisierung vorgestellt und die praktische, komponentenbasierte Umsetzung erläutert. Einen nicht unerheblichen Anteil der Arbeit machen drei innovative, auf der kontextsensitiven Visualisierung basierende Visualisierungsapplikationen aus, welche die Möglichkeiten und die Funktionsfähigkeit der entwickelten Architektur aufzeigen. Die Entwicklung einer plattformunabhängigen interaktiven Visualisierung beschäftigt sich insbesondere mit dem Auffinden der aktuell maximal möglichen Performance durch Abwägung der gegenläufigen Hauptparameter Qualität und Interaktivität und behandelt damit vor allem den System- und Interaktionskontext. Der Gedanke der plattformunabhängigen interaktiven Visualisierung wird anschließend auf mobile Informationssysteme ausgeweitet. Hier ist neben den Performanceaspekten vor allem die Art des Ausgabemediums, d. h. der Darstellungskontext, ein entscheidender Faktor. Die dritte Anwendung stellt eine agentenbasierte Applikation für die Bekleidungsindustrie in Form eines interaktiven Individual-Katalogs dar und behandelt insbesondere den Daten- und den Benutzerkontext. Eine kurze Zusammenfassung sowie ein Ausblick auf geplante zukünftige Entwicklungen runden letztlich die Betrachtungen ab.
Ultraschall ist eines der am häufigsten genutzen, bildgebenden Verfahren in der Kardiologie. Dies ist durch die günstige Erzeugung, die Nicht-Invasivität und die Unschädlichkeit für die Patienten begründet. Nachteilig an den existierenden Geräten ist der Umstand, daß lediglich zwei-dimensionale Bilder generiert werden können. Zusätzlich können diese Bilder aufgrund anatomischer Gegebenheiten nicht aus einer wahlfreien Position akquiriert werden. Dies erschwert die Analyse der Daten und folglich die Diagnose. Mit dieser Arbeit wurden neue, algorithmische Aspekte des vier-dimensionalen, kardiologischen Ultraschalls ausgehend von der Akquisition der Rohdaten, deren Synchronisation und Rekonstruktion bis hin zur Visualisierung bearbeitet. In einem zusätzlichen Kapitel wurde eine neue Technik zur weiteren Aufwertung der Visualisierung, sowie zur visuellen Bearbeitung der Ultraschalldaten entwickelt. Durch die hier entwickelten Verfahren ist es möglich bestimmte Einschränkungen des kardiologischen Ultraschalls aufzuheben oder zumindest zu mildern. Hierunter zählen vor allem die Einschränkung auf zwei-dimensionale Schnittbilder, sowie die eingeschränkte Sichtwahl.
Automated theorem proving is a search problem and, by its undecidability, a very difficult one. The challenge in the development of a practically successful prover is the mapping of the extensively developed theory into a program that runs efficiently on a computer. Starting from a level-based system model for automated theorem provers, in this work we present different techniques that are important for the development of powerful equational theorem provers. The contributions can be divided into three areas: Architecture. We present a novel prover architecture that is based on a set-based compression scheme. With moderate additional computational costs we achieve a substantial reduction of the memory requirements. Further wins are architectural clarity, the easy provision of proof objects, and a new way to parallelize a prover which shows respectable speed-ups in practice. The compact representation paves the way to new applications of automated equational provers in the area of verification systems. Algorithms. To improve the speed of a prover we need efficient solutions for the most time-consuming sub-tasks. We demonstrate improvements of several orders of magnitude for two of the most widely used term orderings, LPO and KBO. Other important contributions are a novel generic unsatisfiability test for ordering constraints and, based on that, a sufficient ground reducibility criterion with an excellent cost-benefit ratio. Redundancy avoidance. The notion of redundancy is of central importance to justify simplifying inferences which are used to prune the search space. In our experience with unfailing completion, the usual notion of redundancy is not strong enough. In the presence of associativity and commutativity, the provers often get stuck enumerating equations that are permutations of each other. By extending and refining the proof ordering, many more equations can be shown redundant. Furthermore, our refinement of the unfailing completion approach allows us to use redundant equations for simplification without the need to consider them for generating inferences. We describe the efficient implementation of several redundancy criteria and experimentally investigate their influence on the proof search. The combination of these techniques results in a considerable improvement of the practical performance of a prover, which we demonstrate with extensive experiments for the automated theorem prover Waldmeister. The progress achieved allows the prover to solve problems that were previously out of reach. This considerably enhances the potential of the prover and opens up the way for new applications.
Die Computerisierung der Gesellschaft bedingt ein ständiges Zunehmen der Geschwindigkeit, mit der neue Daten erzeugt werden. Parallel zu dieser Entwicklung steigt der Bedarf an geeigneten Analyseverfahren, die in diesen großen und oftmals heterogenen Datenmengen Muster finden, Zusammenhänge entdecken und damit Wissen erzeugen. Das in dieser Arbeit entwickelte Verfahren findet die passende Struktur in einer ungeordneten, abstrakten Datenmenge, ordnet die zugrunde liegenden Informationen und bündelt diese somit für eine gezielte Anwendung. Dieser Prozess des Information Clustering ist zweistufig, es erfolgt zuerst ein generelles Clustering, an das sich eine interpretierende Visualisierung anschliesst. Für das Clustering wird das Verfahren der Voronoidiagramme erweitert. Durch den Einsatz einer generellen Distanzfunktion wird die Modellierung der durch die großen Datenmengen entstehenden multidimensionalen Parameter sowie weiterer Gewichte ermöglicht. Eine anschließende Visualisierung aus dem Bereich der Informationsvisualisierung unterstützt die Interpretation der neu gewonnenen Informationen. Für die praktische Anwendung wird die Stadtplanung betrachtet. In der Stadtplanung wird das Modell des Planungsablaufes eingesetzt, mit dem verschiedene Planungsalternativen erzeugt werden. Dieses Modell ist jedoch zu starr, um den dynamischen Anforderungen in der Realität gerecht zu werden. Das Information Clustering erweitert den klassischen Planungsablauf, die Flexibilität des Modells wird dadurch erhöht und die Komplexität reduziert. Das Ergebnis der Berechnung ist genau eine Planungsalternative, die sämtliche Eingabeparameter kanalisiert.
The validity of formulas w.r.t. a specification over first-order logic with a semantics based on all models is semi-decidable. Therefore, we may implement a proof procedure which finds a proof for every valid formula fully automatically. But this semantics often lacks intuition: Some pathological models such as the trivial model may produce unexpected results w.r.t. validity. Instead, we may consider just a class of special models, for instance, the class of all data models. Proofs are then performed using induction. But, inductive validity is not semi-decidable -- even for first-order logic. This theoretical drawback manifests itself in practical limitations: There are theorems that cannot be proved by induction directly but only generalizations can be proved. For their definition, we may have to extend the specification. Therefore, we cannot expect to prove interesting theorems fully automatically. Instead, we have to support user-interaction in a suitable way. In this thesis, we aim at developing techniques that enhance automatic proof control of (inductive) theorem provers and that enable user-interaction in a suitable way. We integrate our new proof techniques into the inductive theorem prover QuodLibet and validate them with various case studies. Essentially, we introduce the following three proof techniques: -We integrate a decision procedure for linear arithmetic into QuodLibet in a close way by defining new inference rules that perform the elementary steps of the decision procedure. This allows us to implement well-known heuristics for automatic proof control. Furthermore, we are able to provide special purpose tactics that support the manual speculation of lemmas if a proof attempt gets stuck. The integration improves the ability of the theorem prover to prove theorems automatically as well as its efficiency. Our approach is competitive with other approaches regarding efficiency; it provides advantages regarding the speculation of lemmas. -The automatic proof control searches for a proof by applying inference rules. The search space is not only infinite, but grows dramatically with the depth of the search. In contrast to this, checking and analyzing performed proofs is very efficient. As the search space also has a high redundancy, it is reasonable to reuse subproofs found during proof search. We define new notions for the contribution of proof steps to a proof. These notions enable the derivation of pruned proofs and the identification of superfluous subformulas in theorems. A proof may be reused in two ways: upward propagation prunes a proof by eliminating superfluous proof steps; sideward reuse closes an open proof obligation by replaying an already found proof. -For interactive theorem provers, it is essential not only to prove automatically as many lemmas as possible but also to restrict proof search in such a way that the proof process stops within a reasonable amount of time. We introduce different markings in the goals to be proved and the lemmas to be applied to restrict proof search in a flexible way: With a forbidden marking, we can simulate well-known approaches for applying conditional lemmas. A mandatory marking provides a new heuristics which is inspired by local contribution of proof steps. With obligatory and generous markings, we can fine-tune the degree of efficiency and extent of proof search manually. With an elaborate case study, we show the benefits of the different techniques, in particular the synergetic effect of their combination.
Modelling languages are important in the process of software development. The suitability of a modelling language for a project depends on its applicability to the target domain. Here, domain-specific languages have an advantage over more general modelling languages. On the other hand, modelling languages like the Unified Modeling Language can be used in a wide range of domains, which supports the reuse of development knowledge between projects. This thesis treats the syntactical and semantical harmonisation of modelling languages and their combined use, and the handling of complexity of modelling languages by providing language subsets - called language profiles - with tailor-made formal semantics definitions, generated by a profile tool. We focus on the widely-used modelling languages SDL and UML, and formal semantics definitions specified using Abstract State Machines.
Embedded systems have become ubiquitous in everyday life, and especially in the automotive industry. New applications challenge their design by introducing a new class of problems that are based on a detailed analysis of the environmental situation. Situation analysis systems rely on models and algorithms of the domain of computational geometry. The basic model is usually an Euclidean plane, which contains polygons to represent the objects of the environment. Usual implementations of computational geometry algorithms cannot be directly used for safety-critical systems. First, a strict analysis of their correctness is indispensable and second, nonfunctional requirements with respect to the limited resources must be considered. This thesis proposes a layered approach to a polygon-processing system. On top of rational numbers, a geometry kernel is formalised at first. Subsequently, geometric primitives form a second layer of abstraction that is used for plane sweep and polygon algorithms. These layers do not only divide the whole system into manageable parts but make it possible to model problems and reason about them at the appropriate level of abstraction. This structure is used for the verification as well as the implementation of the developed polygon-processing library.
Software stellt ein komplexes Werkzeug dar, das durch seine umfassenden Möglichkeiten die moderne Gesellschaft entscheidend geprägt hat. Daraus ergibt sich eine Abhängigkeit von Funktion und Fehlfunktion der Software, die eine an den funktionalen Anforderungen orientierte Entwicklung und Qualitätssicherung der Software notwendig macht. Die vorliegende Arbeit schafft durch Formalisierung und Systematisierung der Verfahren im funktionsorientierten Test eine fundierte Basis für eine Hinwendung zu den funktionsorientierten Techniken in Softwareentwicklung und –qualitätssicherung. Hierzu wird in der Arbeit zunächst ein formales Modell für das Vorgehen im dynamischen Test beschrieben, das sich an der Begriffsbildung der Literatur und dem Verständnis der Praxis orientiert. Das Modell beruht auf wenigen zentralen Annahmen, eignet sich für formale Untersuchungen und Nachweise und ist wegen seiner sehr allgemein gehaltenen Definitionen breit anwendbar und einfach erweiterbar. Auf dieser Basis werden Vorgehen und Verfahren zum funktionsorientierten Test analysiert. Zunächst wird dazu das Vorgehen im funktionsorientierten Test im Rahmen des Modells dargestellt. Darauf aufbauend werden zentrale Verfahren des funktionsorientierten Tests analysiert, die zum Gegenstand die systematische Prüfung der Umsetzung von weitgehend informal beschriebenen Anforderungen in einem Softwareprodukt haben. Betrachtet werden Verfahren der funktionalen Partitionierung, der funktionalen Äquivalenzklassenanalyse und Grenzwertbildung, Verfahren zur Prüfung von kausalen Zusammenhängen zwischen Ursachen und Wirkungen, Verfahren zur Prüfung von graphisch spezifizierter Funktionalität in Syntaxdiagrammen, Aktivitätsdiagrammen, Sequenz- und Kollaborationsdiagrammen und Petrinetzen, Verfahren zum Test zustandsbasierter Systeme sowie Ansätze einer funktionalen Dekomposition. Die Analyse und Diskussion der bekannten Verfahren im formalisierten Rahmenwerk führt zu zahlreichen Ergebnissen und Verfahrensergänzungen. So zeigt sich, dass in den klassischen, informalen Beschreibungen häufig Unklarheiten bestehen. Diese werden hier adressiert und durch Angabe von Kriterien präzisiert, Optimierungsmöglichkeiten werden aufgezeigt. Darüber hinaus wird an der einheitlichen formalen Darstellung der in der Literatur meist separat betrachteten Verfahren deutlich, welche Vergleichbarkeit zwischen den Verfahren besteht, welche Verfahrenskombinationen sinnvoll sind und wie durch ein kombiniert funktions- und strukturorientiertes Vorgehen eine hohe Aussagekraft in der analytischen Qualitätssicherung erreicht werden kann. Bei der Formulierung der Verfahren im Rahmen des Modells wird herausgearbeitet, wo zur Verfahrensdurchführung die kreative Leistung des Testers notwendig ist und welche Anteile formalisiert und damit automatisiert unterstützt werden können. Diese Betrachtungen bilden die Grundlage für die Skizzierung einer integrierten Entwicklungsumgebung, in der ein funktionsorientiertes Vorgehen in Entwicklung und Qualitätssicherung umgesetzt wird: Hier helfen funktionsorientierte Beschreibungsformen bei der Angabe der Spezifikation, ihrer Verfeinerung und ihrer Vervollständigung, sie unterstützen die Entwicklung durch Modellbildung, sie liefern die Basis für eine funktionsorientierte Testdatenselektion mit Adäquatheitsprüfung, sie können bei geeigneter Interpretierbarkeit über den Datenbereichen zur automatisierten Testfallgenerierung genutzt werden und unterstützen als suboptimale Testorakel eine automatisierte Auswertung des dynamischen Tests. Diese Skizze zeigt die praktische Umsetzbarkeit der vorwiegend theoretischen Ergebnisse dieser Arbeit und setzt einen Impuls für ein verstärktes Aufgreifen funktionsorientierter Techniken in Wissenschaft und Praxis.
Nowadays, accounting, charging and billing users' network resource consumption are commonly used for the purpose of facilitating reasonable network usage, controlling congestion, allocating cost, gaining revenue, etc. In traditional IP traffic accounting systems, IP addresses are used to identify the corresponding consumers of the network resources. However, there are some situations in which IP addresses cannot be used to identify users uniquely, for example, in multi-user systems. In these cases, network resource consumption can only be ascribed to the owners of these hosts instead of corresponding real users who have consumed the network resources. Therefore, accurate accountability in these systems is practically impossible. This is a flaw of the traditional IP address based IP traffic accounting technique. This dissertation proposes a user based IP traffic accounting model which can facilitate collecting network resource usage information on the basis of users. With user based IP traffic accounting, IP traffic can be distinguished not only by IP addresses but also by users. In this dissertation, three different schemes, which can achieve the user based IP traffic accounting mechanism, are discussed in detail. The inband scheme utilizes the IP header to convey the user information of the corresponding IP packet. The Accounting Agent residing in the measured host intercepts IP packets passing through it. Then it identifies the users of these IP packets and inserts user information into the IP packets. With this mechanism, a meter located in a key position of the network can intercept the IP packets tagged with user information, extract not only statistic information, but also IP addresses and user information from the IP packets to generate accounting records with user information. The out-of-band scheme is a contrast scheme to the in-band scheme. It also uses an Accounting Agent to intercept IP packets and identify the users of IP traffic. However, the user information is transferred through a separated channel, which is different from the corresponding IP packets' transmission. The Multi-IP scheme provides a different solution for identifying users of IP traffic. It assigns each user in a measured host a unique IP address. Through that, an IP address can be used to identify a user uniquely without ambiguity. This way, traditional IP address based accounting techniques can be applied to achieve the goal of user based IP traffic accounting. In this dissertation, a user based IP traffic accounting prototype system developed according to the out-of-band scheme is also introduced. The application of user based IP traffic accounting model in the distributed computing environment is also discussed.
In urban planning, sophisticated simulation models are key tools to estimate future population growth for measuring the impact of planning decisions on urban developments and the environment. Simulated population projections usually result in large, macro-scale, multivariate geospatial data sets. Millions of records have to be processed, stored, and visualized to help planners explore and analyze complex population patterns. We introduce a database driven framework for visualizing geospatial multidimensional simulation data based on the output from UrbanSim, a software for the analysis and planning of urban developments. The designed framework is extendable and aims at integrating empirical-stochastic methods and urban simulation models with techniques developed for information visualization and cartography. First, we develop an empirical model for the estimation of residential building types based on demographic household characteristics. The predicted dwelling type information is important for the analysis of future material use, carbon footprint calculations, and for visualizing simultaneously the results of land usage, density, and other significant parameters in 3D space. Our model uses multinomial logistic regression to derive building types at different scales. The estimated regression coefficients are applied to UrbanSim output in order to predict residential building types. The simulation results and the estimated building types are managed in an object-relational geodatabase. From the database, density, building types, and significant demographic variables are visually encoded as scalable, georeferenced 3D geometries and displayed on top of aerial photographs in a Google Earth visual synthesis. The geodatabase can be accessed and the visualization parameters can be chosen through a web-based user interface. The geometries are encoded in KML, Google's markup language, as ready-to-visualize data sets. The goal is to enhance human cognition by displaying abstract representations of multidimensional data sets in a realistic context and thus to support decision making in planning processes.
Feature Based Visualization
(2007)
In this thesis we apply powerful mathematical tools such as interval arithmetic for applications in computational geometry, visualization and computer graphics, leading to robust, general and efficient algorithms. We present a completely novel approach for computing the arrangement of arbitrary implicit planar curves and perform ray casting of arbitrary implicit functions by jointly achieving, for the first time, robustness, efficiency and flexibility. Indeed we are able to render even the most difficult implicits in real-time with guaranteed topology and at high resolution. We use subdivision and interval arithmetic as key-ingredients to guarantee robustness. The presented framework is also well-suited for applications to large and unstructured data sets due to the inherent adaptivity of the techniques that are used. We also approach the topic of tensors by collaborating with mechanical engineers on comparative tensor visualization and provide them with helpful visualization paradigms to interpret the data.
The visualization of numerical fluid flow datasets is essential to the engineering processes that motivate their computational simulation. To address the need for visual representations that convey meaningful relations and enable a deep understanding of flow structures, the discipline of Flow Visualization has produced many methods and schemes that are tailored to a variety of visualization tasks. The ever increasing complexity of modern flow simulations, however, puts an enormous demand on these methods. The study of vortex breakdown, for example, which is a highly transient and inherently three-dimensional flow pattern with substantial impact wherever it appears, has driven current techniques to their limits. In this thesis, we propose several novel visualization methods that significantly advance the state of the art in the visualization of complex flow structures. First, we propose a novel scheme for the construction of stream surfaces from the trajectories of particles embedded in a flow. These surfaces are extremely useful since they naturally exploit coherence between neighboring trajectories and are highly illustrative in nature. We overcome the limitations of existing stream surface algorithms that yield poor results in complex flows, and show how the resulting surfaces can be used a building blocks for advanced flow visualization techniques. Moreover, we present a visualization method that is based on moving section planes that travel through a dataset and sample the flow. By considering the changes to the flow topology on the plane as it moves, we obtain a method of visualizing topological structures in three-dimensional flows that are not accessible by conventional topological methods. On the same algorithmic basis, we construct an algorithm for the tracking of critical points in such flows, thereby enabling the treatment of time-dependent datasets. Last, we address some problems with the recently introduced Lagrangian techniques. While conceptually elegant and generally applicable, they suffer from an enormous computational cost that we significantly use by developing an adaptive approximation algorithm. This allows the application of such methods on very large and complex numerical simulations. Throughout this thesis, we will be concerned with flow visualization aspect of general practical significance but we will particularly emphasize the remarkably challenging visualization of the vortex breakdown phenomenon.
Sound surrounds us all the time and in every place in our daily life, may it be pleasant music in a concert hall or disturbing noise emanating from a busy street in front of our home. The basic properties are the same for both kinds of sound, namely sound waves propagating from a source, but we perceive it in different ways depending on our current mood or if the sound is wanted or not. In this thesis both pleasant sound as well as disturbing noise is examined by means of simulating the sound and visualizing the results thereof. However, although the basic properties of music and traffic noise are the same, one is interested in different features. For example, in a concert hall, the reverberation time is an important quality measure, but if noise is considered only the resulting sound level, for example on ones balcony, is of interest. Such differences are reflected in different methods of simulation and required visualizations, therefore this thesis is divided into two parts. The first part about room acoustics deals with the simulation and novel visualizations for indoor sound and acoustic quality measures, such as definition (original "Deutlichkeit") and clarity index (original "Klarheitsmaß"). For the simulation two different methods, a geometric (phonon tracing) and a wave based (FEM) approach, are applied and compared. The visualization techniques give insight into the sound behaviour and the acoustic quality of a room from a global as well as a listener based viewpoint. Furthermore, an acoustic rendering equation is presented, which is used to render interference effects for different frequencies. Last but not least a novel visualization approach for low frequency sound is presented, which enables the topological analysis of pressure fields based on room eigenfrequencies. The second part about environmental noise is concerned with the simulation and visualization of outdoor sound with a focus on traffic noise. The simulation instruction prescribed by national regulations is discussed in detail, and an approach for the computation of noise volumes, as well as an extension to the simulation, allowing interactive noise calculation, are presented. Novel visualization and interaction techniques for the calculated noise data, incorporated in an interactive three dimensional environment, enabling the easy comprehension of noise problems, are presented. Furthermore additional information can be integrated into the framework to enhance the visualization of noise and the usability of the framework for different usages.
Today’s high-resolution digital images and videos require large amounts of storage space and transmission bandwidth. To cope with this, compression methods are necessary that reduce the required space while at the same time minimize visual artifacts. We propose a compression method based on a piecewise linear color interpolation induced by a triangulation of the image domain. We present methods to speed up significantly the optimization process for finding the triangulation. Furthermore, we extend the method to digital videos. Laser scanners to capture the surface of three-dimensional objects are widely used in industry nowadays, e.g., for reverse engineering or quality measurement. Hand-held scanning devices have the advantage that the laser device can be moved to any position, permitting a scan of complex objects. But operating a hand-held laser scanner is challenging. The operator has to keep track of the scanned regions in his mind, and has no feedback of the sample density unless he starts the surface reconstruction after finishing the scan. We present a system to support the operator by computing and rendering high-quality surface meshes of the captured data online, i.e., while he is still scanning, and in real time. Furthermore, it color-codes the rendered surface to reflect the surface quality. Thereby, instant feedback is provided, resulting in better scans in less time.
Layout analysis--the division of page images into text blocks, lines, and determination of their reading order--is a major performance limiting step in large scale document digitization projects. This thesis addresses this problem in several ways: it presents new performance measures to identify important classes of layout errors, evaluates the performance of state-of-the-art layout analysis algorithms, presents a number of methods to reduce the error rate and catastrophic failures occurring during layout analysis, and develops a statistically motivated, trainable layout analysis system that addresses the needs of large-scale document analysis applications. An overview of the key contributions of this thesis is as follows. First, this thesis presents an efficient local adaptive thresholding algorithm that yields the same quality of binarization as that of state-of-the-art local binarization methods, but runs in time close to that of global thresholding methods, independent of the local window size. Tests on the UW-1 dataset demonstrate a 20-fold speedup compared to traditional local thresholding techniques. Then, this thesis presents a new perspective for document image cleanup. Instead of trying to explicitly detect and remove marginal noise, the approach focuses on locating the page frame, i.e. the actual page contents area. A geometric matching algorithm is presented to extract the page frame of a structured document. It is demonstrated that incorporating page frame detection step into document processing chain results in a reduction in OCR error rates from 4.3% to 1.7% (n=4,831,618 characters) on the UW-III dataset and layout-based retrieval error rates from 7.5% to 5.3% (n=815 documents) on the MARG dataset. The performance of six widely used page segmentation algorithms (x-y cut, smearing, whitespace analysis, constrained text-line finding, docstrum, and Voronoi) on the UW-III database is evaluated in this work using a state-of-the-art evaluation methodology. It is shown that current evaluation scores are insufficient for diagnosing specific errors in page segmentation and fail to identify some classes of serious segmentation errors altogether. Thus, a vectorial score is introduced that is sensitive to, and identifies, the most important classes of segmentation errors (over-, under-, and mis-segmentation) and what page components (lines, blocks, etc.) are affected. Unlike previous schemes, this evaluation method has a canonical representation of ground truth data and guarantees pixel-accurate evaluation results for arbitrary region shapes. Based on a detailed analysis of the errors made by different page segmentation algorithms, this thesis presents a novel combination of the line-based approach by Breuel with the area-based approach of Baird which solves the over-segmentation problem in area-based approaches. This new approach achieves a mean text-line extraction error rate of 4.4% (n=878 documents) on the UW-III dataset, which is the lowest among the analyzed algorithms. This thesis also describes a simple, fast, and accurate system for document image zone classification that results from a detailed comparative analysis of performance of widely used features in document analysis and content-based image retrieval. Using a novel combination of known algorithms, an error rate of 1.46% (n=13,811 zones) is achieved on the UW-III dataset in comparison to a state-of-the-art system that reports an error rate of 1.55% (n=24,177 zones) using more complicated techniques. In addition to layout analysis of Roman script documents, this work also presents the first high-performance layout analysis method for Urdu script. For that purpose a geometric text-line model for Urdu script is presented. It is shown that the method can accurately extract Urdu text-lines from documents of different layouts like prose books, poetry books, magazines, and newspapers. Finally, this thesis presents a novel algorithm for probabilistic layout analysis that specifically addresses the needs of large-scale digitization projects. The presented approach models known page layouts as a structural mixture model. A probabilistic matching algorithm is presented that gives multiple interpretations of input layout with associated probabilities. An algorithm based on A* search is presented for finding the most likely layout of a page, given its structural layout model. For training layout models, an EM-like algorithm is presented that is capable of learning the geometric variability of layout structures from data, without the need for a page segmentation ground-truth. Evaluation of the algorithm on documents from the MARG dataset shows an accuracy of above 95% for geometric layout analysis.
Computer-based simulation and visualization of acoustics of a virtual scene can aid during the design process of concert halls, lecture rooms, theaters, or living rooms. Because, not only the visual aspect of the room is important, but also its acoustics. In factory floors noise reduction is important since noise is hazardous to health. Despite the obvious dissimilarity between our aural and visual senses, many techniques required for the visualization of photo-realistic images and for the auralization of acoustic environments are quite similar. Both applications can be served by geometric methods such as particle- and ray tracing if we neglect a number of less important effects. By means of the simulation of room acoustics we want to predict the acoustic properties of a virtual model. For auralization, a pulse response filter needs to be assembled for each pair of source and listener positions. The convolution of this filter with an anechoic source signal provides the signal received at the listener position. Hence, the pulse response filter must contain all reverberations (echos) of a unit pulse, including their frequency decompositions due to absorption at different surface materials. For the room acoustic simulation a method named phonon tracing, since it is based on particles, is developed. The approach computes the energy or pressure decomposition for each particle (phonon) sent out from a sound source and uses this in a second pass (phonon collection) to construct the response filters for different listeners. This step can be performed in different precision levels. During the tracing step particle paths and additional information are stored in a so called phonon map. Using this map several sound visualization approaches were developed. From the visualization, the effect of different materials on the spectral energy / pressure distribution can be observed. The first few reflections already show whether certain frequency bands are rapidly absorbed. The absorbing materials can be identified and replaced in the virtual model, improving the overall acoustic quality of the simulated room. Furthermore an insight into the pressure / energy received at the listener position is possible. The phonon tracing algorithm as well as several sound visualization approaches are integrated into a common system utilizing Virtual Reality technologies in order to facilitate the immersion into the virtual scene. The system is a prototype developed within a project at the University of Kaiserslautern and is still a subject of further improvements. It consists of a stereoscopic back-projection system for visual rendering as well as professional audio equipment for auralization purposes.
Die vorliegende Arbeit beschäftigt sich mit der visuellen Kontrolle raumplanerischer Entwürfe. Grundlage der Überlegungen ist das gegenwärtige Verfahren, der Planungsprozess, das zur Erstellung der Entwürfe führt. Der Entscheidungsweg hin zum endgültigen Ergebnis erfolgt zurzeit noch ohne Rechnerunterstützung. Die in den Planungsprozess Involvierten stützen ihre Entscheidungen bspw. auf Pläne, eigene Erfahrungen und Statistiken und fertigen im Verlauf von Diskussionsrunden verschiedene Entwürfe an. Dieser Ablauf ist komplex, aufgrund der eingehenden Daten und der damit zusammenhängenden Diskussionen, und langwierig da erst nach einigen Iterationsschritten ein Ergebnis vorliegt. Die Arbeit verfolgt das Ziel, die Akteure durch eine Rechnerunterstützung schneller und zielgerichtet zu einer Entscheidungsfindung zu führen. Meine Untersuchung des Anwendungsumfeldes hat ergeben, dass dies nur möglich ist, wenn zum Einen das entstehende System in der Lage ist, die großen, heterogenen Datenmengen zu verarbeiten und andererseits die Visualisierung der Ergebnisse in einer Form erfolgt, die den Akteuren vom bisherigen Planungsprozess her bekannt ist. Die Visualisierung darf dabei keine bewertende Aussage treffen, sondern muss die Informationen der Analyse neutral in einem dem Nutzer bekannten Format abbilden. Als Ansatzpunkt stellt sich der informelle Bereich der Entscheidungsfindung dar. Es werden zwei Lösungswege aus dem Bereich der Clusteringalgorithmen verfolgt, die die großen Datenmengen verarbeiten und analysieren. Als Ergebnis erhalten die Akteure durch das Voronoi-Diagramm direkt einen Entwurf, der die Einschätzungen aller Akteure widerspiegelt und durch ein Übereinanderlegen mit der Karte des Plangebietes dem klassischen Format im Rahmen des Planungsprozesses entspricht. Dadurch wird die Akzeptanz der Rechnerunterstützung bei den Beteiligten des Planungsprozesses gesteigert. Sollte dieser Entwurf noch keine direkte Zustimmung finden, kann über die entwickelte Informationsvisualisierung eine Anzeige und in der Folge eine Anpassung der Eingangsgrößen erfolgen und somit sehr schnell ein neuer Entwurf entwickelt werden. Die Visualisierung übernimmt dabei die Funktion der bisher in Papierform erstellten Pläne im Entscheidungsprozess und bietet damit auch fachfremden Beteiligten eine visuelle Kontrollmöglichkeit der Qualität des Entwurfes. Insgesamt werden mit dem Tool IKone die Akteure in Anlehnung an die standardmäßigen Abläufe und visuellen Darstellungen mittels eines rechnergestützten Systems unterstützt.
In engineering and science, a multitude of problems exhibit an inherently geometric nature. The computational assessment of such problems requires an adequate representation by means of data structures and processing algorithms. One of the most widely adopted and recognized spatial data structures is the Delaunay triangulation which has its canonical dual in the Voronoi diagram. While the Voronoi diagram provides a simple and elegant framework to model spatial proximity, the core of which is the concept of natural neighbors, the Delaunay triangulation provides robust and efficient access to it. This combination explains the immense popularity of Voronoi- and Delaunay-based methods in all areas of science and engineering. This thesis addresses aspects from a variety of applications that share their affinity to the Voronoi diagram and the natural neighbor concept. First, an idea for the generalization of B-spline surfaces to unstructured knot sets over Voronoi diagrams is investigated. Then, a previously proposed method for \(C^2\) smooth natural neighbor interpolation is backed with concrete guidelines for its implementation. Smooth natural neighbor interpolation is also one of many applications requiring derivatives of the input data. The generation of derivative information in scattered data with the help of natural neighbors is described in detail. In a different setting, the computation of a discrete harmonic function in a point cloud is considered, and an observation is presented that relates natural neighbor coordinates to a continuous dependency between discrete harmonic functions and the coordinates of the point cloud. Attention is then turned to integrating the flexibility and meritable properties of natural neighbor interpolation into a framework that allows the algorithmically transparent and smooth extrapolation of any known natural neighbor interpolant. Finally, essential properties are proved for a recently introduced novel finite element tessellation technique in which a Delaunay triangulation is transformed into a unique polygonal tessellation.
We study the extension of techniques from Inductive Logic Programming (ILP) to temporal logic programming languages. Therefore we present two temporal logic programming languages and analyse the learnability of programs from these languages from finite sets of examples. In first order temporal logic the following topics are analysed: - How can we characterize the denotational semantics of programs? - Which proof techniques are best suited? - How complex is the learning task? In propositional temporal logic we analyse the following topics: - How can we use well known techniques from model checking in order to refine programs? - How complex is the learning task? In both cases we present estimations for the VC-dimension of selected classes of programs.
Most software systems are described in high-level model or programming languages. Their runtime behavior, however, is determined by the compiled code. For uncritical software, it may be sufficient to test the runtime behavior of the code. For safety-critical software, there is an additional aggravating factor resulting from the fact that the code must satisfy the formal specification which reflects the safety policy of the software consumer and that the software producer is obliged to demonstrate that the code is correct with respect to the specification using formal verification techniques. In this scenario, it is of great importance that static analyses and formal methods can be applied on the source code level, because this level is more abstract and better suited for such techniques. However, the results of the analyses and the verification can only be carried over to the machine code level, if we can establish the correctness of the translation. Thus, compilation is a crucial step in the development of software systems and formally verified translation correctness is essential to close the formalization chain from high-level formal methods to the machine-code level. In this thesis, I propose an approach to certifying compilers which achieves the aim of closing the formalization chain from high-level formal methods to the machine-code level by applying techniques from mathematical logic and programming language semantics. I propose an approach called foundational translation validation (FTV) in which the software producer implements an FTV system comprising a compiler and a specification and verification framework (SVF) which is implemented in higher-order logic (HOL). The most important part of the SVF is an explicit translation contract which comprises the formalizations of the source and the target languages of the compiler and the formalization of a binary translation correctness predicate corrTrans(S,T) for source programs S and target programs T. The formalizations of the languages are realized as deep embeddings in HOL. This enables one to declare the whole program in a formalized language as a HOL constant. The predicate formally specifies when T is considered to be a correct translation of S. Its definition is explicitly based on the program semantics definitions provided by the translation contract. Subsequent to the translation, the compiler translates the source and the target programs into their syntactic representations as HOL constants, S and T, and generates a proof of corrTrans(S,T). We call a compiler which follows the FTV approach a proof generating compiler. Our approach borrows the idea of representing programs in correctness proofs as logic constants from the foundational proof-carrying code (FPCC) approach. Novel features that distinquish our approach from further approaches to certifying compilers, such as proof-carrying code (PCC) and translation validation (TV) are the following: Firstly, the presence of an explicit translation contract formalized in HOL: The approaches PCC and TV do not formalize a translation contract explicitly. Instead of this, they incorporate operational semantics and translation correctness criterion in translation validation tools on the programming language level. Secondly, representation of programs in correctness proofs as logic constants: The approaches PCC and the TV translate programs into their representations as semantic abstractions that serve as inputs for translation validation tools. Thirdly, certification of program transformation chains: Unlike the TV approach, which certifies single program transformations, the FTV approach achieves the aim of certifying whole chains of program transformations. This is possible due to the fact that the translation contract provides, for all programming languages involved in the program transformation chain, definitions of program semantics functions which map programs to mathematical objects that are elements of a set with an (at least) partial order "<=". Then, the proof makes use of the fact that the relation "<=" is transitive. In this thesis, the feasibility of the FTV approach is exemplified by the implementation of an FTV system. The system comprises a compiler front-end that certifies its optimization phase and an accompanying SVF that is implemented in the theorem prover Isabelle/HOL. The compiler front-end translates programs in a small C-like programming language, performs three optimizations: constant folding, dead assignment elimination, and loop invariant hoisting, and generates translation certificates in the form of Isabelle/HOL theories. The main focus of the thesis is on the description of the SVF and its translation verification techniques.
Interactive visualization of large structured and unstructured data sets is a permanent challenge for scientific visualization. Large data sets are for example created by magnetic resonance imaging (MRI), computed tomography (CT), Computational fluid dynamics (CFD) finite element method (FEM), and computer aided design (CAD). For visualizing those data sets not only accelerated rasterization by means of using specialized hardware i.e. graphics cards is of interest, but also ray casting, as it is perfectly suited for scientific visualization. Ray casting does not only support many rendering modes (e.g., opaque rendering, semi transparent rendering, iso surface rendering, maximum intensity projection, x-ray, absorption emitter model, ...) for which it allows the creation of high quality images, but it also supports many primitives (e.g., not only triangles but also spheres, curved iso surfaces, NURBS, implicit functions, ...). It furthermore scales basically linear to the amount of processor cores used and - this makes it highly interesting for the visualization of large data sets - it scales for static scenes sublinear to data size. Interactive ray casting is currently not widely used within the scientifc visualization community. This is mainly based on historical reasons, as just a few years ago no applicable interactive ray casters for commodity hardware did exist. Interactive scientific visualization has only been possible by using graphics cards or specialized and/or expensive hardware. The goal of this work is to broaden the possibilies for interactive scientific visualization, by showing that interactive CPU based ray casting is today feasible on commodity hardware and that it may efficiently be used together with GPU based rasterization. In this thesis it is first shown that interactive CPU based ray casters may efficiently be integrated into already existing OpenGL frameworks. This is achieved through an OpenGL friendly interface that supports multiple threads and single instruction multiple data (SIMD) operations. For the visualization of rectilinear (and not necessarily cartesian) grids are new implicit kd-trees introduced. They have fast construction times, low memory requirements, and allow ontoday's commodity desktop machines interactive iso surface ray tracing and maximum intensity projection of large scalar fields. A new interactive SIMD ray tracing technique for large tetrahedral meshes is introduced. It is very portable and general and is therefore suited for portation upon different (future) hardware and for usage upon several applications. The thesis ends with a real life commercial application which shows that CPU-based ray casting has already reached the state where it may outperform GPU-based rasterization for scientific visualization.
Knowledge discovery from large and complex collections of today’s scientific datasets is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the increasing number of data dimensions and data objects is presenting tremendous challenges for data analysis and effective data exploration methods and tools. Researchers are overwhelmed with data and standard tools are often insufficient to enable effective data analysis and knowledge discovery. The main objective of this thesis is to provide important new capabilities to accelerate scientific knowledge discovery form large, complex, and multivariate scientific data. The research covered in this thesis addresses these scientific challenges using a combination of scientific visualization, information visualization, automated data analysis, and other enabling technologies, such as efficient data management. The effectiveness of the proposed analysis methods is demonstrated via applications in two distinct scientific research fields, namely developmental biology and high-energy physics. Advances in microscopy, image analysis, and embryo registration enable for the first time measurement of gene expression at cellular resolution for entire organisms. Analysis of highdimensional spatial gene expression datasets is a challenging task. By integrating data clustering and visualization, analysis of complex, time-varying, spatial gene expression patterns and their formation becomes possible. The analysis framework MATLAB and the visualization have been integrated, making advanced analysis tools accessible to biologist and enabling bioinformatic researchers to directly integrate their analysis with the visualization. Laser wakefield particle accelerators (LWFAs) promise to be a new compact source of highenergy particles and radiation, with wide applications ranging from medicine to physics. To gain insight into the complex physical processes of particle acceleration, physicists model LWFAs computationally. The datasets produced by LWFA simulations are (i) extremely large, (ii) of varying spatial and temporal resolution, (iii) heterogeneous, and (iv) high-dimensional, making analysis and knowledge discovery from complex LWFA simulation data a challenging task. To address these challenges this thesis describes the integration of the visualization system VisIt and the state-of-the-art index/query system FastBit, enabling interactive visual exploration of extremely large three-dimensional particle datasets. Researchers are especially interested in beams of high-energy particles formed during the course of a simulation. This thesis describes novel methods for automatic detection and analysis of particle beams enabling a more accurate and efficient data analysis process. By integrating these automated analysis methods with visualization, this research enables more accurate, efficient, and effective analysis of LWFA simulation data than previously possible.
Adaptive Extraction and Representation of Geometric Structures from Unorganized 3D Point Sets
(2009)
The primary emphasis of this thesis concerns the extraction and representation of intrinsic properties of three-dimensional (3D) unorganized point clouds. The points establishing a point cloud as it mainly emerges from LiDaR (Light Detection and Ranging) scan devices or by reconstruction from two-dimensional (2D) image series represent discrete samples of real world objects. Depending on the type of scenery the data is generated from the resulting point cloud may exhibit a variety of different structures. Especially, in the case of environmental LiDaR scans the complexity of the corresponding point clouds is relatively high. Hence, finding new techniques allowing the efficient extraction and representation of the underlying structural entities becomes an important research issue of recent interest. This thesis introduces new methods regarding the extraction and visualization of structural features like surfaces and curves (e.g. ridge-lines, creases) from 3D (environmental) point clouds. One main part concerns the extraction of curve-like features from environmental point data sets. It provides a new method supporting a stable feature extraction by incorporating a probability-based point classification scheme that characterizes individual points regarding their affiliation to surface-, curve- and volume-like structures. Another part is concerned with the surface reconstruction from (environmental) point clouds exhibiting objects that are more or less complex. A new method providing multi-resolutional surface representations from regular point clouds is discussed. Following the applied principles of this approach a volumetric surface reconstruction method based on the proposed classification scheme is introduced. It allows the reconstruction of surfaces from highly unstructured and noisy point data sets. Furthermore, contributions in the field of reconstructing 3D point clouds from 2D image series are provided. In addition, a discussion concerning the most important properties of (environmental) point clouds with respect to feature extraction is presented.
This PhD thesis aims at finding a global robot navigation strategy for rugged off-road terrain which is robust against inaccurate self-localization, scalable to large environments, but also cost-efficient, e.g. able to generate navigation paths which optimize a cost measure closely related to terrain traversability. In order to meet this goal, aspects of both metrical and topological navigation techniques are combined. A primarily topological map is extended with the previously lacking capability of cost-efficient path planning and map extension. Further innovations include a multi-dimensional cost measure for topological edges, a method to learn these costs based on live feedback from the robot and a set of extrapolation methods to predict the traversability costs for untraversed edges. The thesis presents two sophisticated new image analysis techniques to optimize cost prediction based on the shape and appearance of surrounding terrain. Experimental results indicate that the proposed global navigation system is indeed able to perform cost-efficient, large scale path planning. At the same time, the need to maintain a fine-grained, global world model which would reduce the scalability of the approach is avoided.
Modern digital imaging technologies, such as digital microscopy or micro-computed tomography, deliver such large amounts of 2D and 3D-image data that manual processing becomes infeasible. This leads to a need for robust, flexible and automatic image analysis tools in areas such as histology or materials science, where microstructures are being investigated (e.g. cells, fiber systems). General-purpose image processing methods can be used to analyze such microstructures. These methods usually rely on segmentation, i.e., a separation of areas of interest in digital images. As image segmentation algorithms rarely adapt well to changes in the imaging system or to different analysis problems, there is a demand for solutions that can easily be modified to analyze different microstructures, and that are more accurate than existing ones. To address these challenges, this thesis contributes a novel statistical model for objects in images and novel algorithms for the image-based analysis of microstructures. The first contribution is a novel statistical model for the locations of objects (e.g. tumor cells) in images. This model is fully trainable and can therefore be easily adapted to many different image analysis tasks, which is demonstrated by examples from histology and materials science. Using algorithms for fitting this statistical model to images results in a method for locating multiple objects in images that is more accurate and more robust to noise and background clutter than standard methods. On simulated data at high noise levels (peak signal-to-noise ratio below 10 dB), this method achieves detection rates up to 10% above those of a watershed-based alternative algorithm. While objects like tumor cells can be described well by their coordinates in the plane, the analysis of fiber systems in composite materials, for instance, requires a fully three dimensional treatment. Therefore, the second contribution of this thesis is a novel algorithm to determine the local fiber orientation in micro-tomographic reconstructions of fiber-reinforced polymers and other fibrous materials. Using simulated data, it will be demonstrated that the local orientations obtained from this novel method are more robust to noise and fiber overlap than those computed using an established alternative gradient-based algorithm, both in 2D and 3D. The property of robustness to noise of the proposed algorithm can be explained by the fact that a low-pass filter is used to detect local orientations. But even in the absence of noise, depending on fiber curvature and density, the average local 3D-orientation estimate can be about 9° more accurate compared to that alternative gradient-based method. Implementations of that novel orientation estimation method require repeated image filtering using anisotropic Gaussian convolution filters. These filter operations, which other authors have used for adaptive image smoothing, are computationally expensive when using standard implementations. Therefore, the third contribution of this thesis is a novel optimal non-orthogonal separation of the anisotropic Gaussian convolution kernel. This result generalizes a previous one reported elsewhere, and allows for efficient implementations of the corresponding convolution operation in any dimension. In 2D and 3D, these implementations achieve an average performance gain by factors of 3.8 and 3.5, respectively, compared to a fast Fourier transform-based implementation. The contributions made by this thesis represent improvements over state-of-the-art methods, especially in the 2D-analysis of cells in histological resections, and in the 2D and 3D-analysis of fibrous materials.
In robotics, information is often regarded as a means to an end. The question of how to structure information and how to bridge the semantic gap between different levels of abstraction in a uniform way is still widely regarded as a technical issue. Ignoring these challenges appears to lead robotics into a similar stasis as experienced in the software industry of the late 1960s. From the beginning of the software crisis until today, numerous methods, techniques, and tools for managing the increasing complexity of software systems have evolved. The attempt to transfer several of these ideas towards applications in robotics yielded various control architectures, frameworks, and process models. These attempts mainly provide modularisation schemata which suggest how to decompose a complex system into less complex subsystems. The schematisation of representation and information flow however is mostly ignored. In this work, a set of design schemata is proposed which is embedded into an action/perception-oriented design methodology to promote thorough abstractions between distinct levels of control. Action-oriented design decomposes control systems top-down and sensor data is extracted from the environment as required. This comes with the problem that information is often condensed in a premature fashion. That way, sensor processing is dependent on the control system design resulting in a monolithical system structure with limited options for reusability. In contrast, perception-oriented design constructs control systems bottom-up starting with the extraction of environment information from sensor data. The extracted entities are placed into structures which evolve with the development of the sensor processing algorithms. In consequence, the control system is strictly dependent on the sensor processing algorithms which again results in a monolithic system. In their particular domain, both design approaches have great advantages but fail to create inherently modular systems. The design approach proposed in this work combines the strengths of action orientation and perception orientation into one coherent methodology without inheriting their weaknesses. More precisely, design schemata for representation, translation, and fusion of environmental information are developed which establish thorough abstraction mechanisms between components. The explicit introduction of abstractions particularly supports extensibility and scalability of robot control systems by design.
We tackle the problem of obtaining statistics on content and structure of XML documents by using summaries which may provide cardinality estimations for XML query expressions. Our focus is a data-centric processing scenario in which we use a query engine to process such query expressions. We provide three new summary structures called LESS (Leaf-Element-in-Subtree), LWES (Level-Wide Element Summarization), and EXsum (Element-centered XML Summarization) which are targeted to base an estimation process in an XML query optimizer. Each of these collects structural statistical information of XML documents, and the latter (EXsum) gathers, in addition, statistics on document content. Estimation procedures and/or heuristics for specic types of query expressions of each proposed approach are developed. We have incorporated and implemented our proposals in XTC, a native XML database management system (XDBMS). With this common implementation base, we present an empirical and comparative study in which our proposals are stressed against others published in the literature, which are also incorporated into the XTC. Furthermore, an analysis is made based on criteria pertinent to a query optimizer process.
A prime motivation for using XML to directly represent pieces of information is the ability of supporting ad-hoc or 'schema-later' settings. In such scenarios, modeling data under loose data constraints is essential. Of course, the flexibility of XML comes at a price: the absence of a rigid, regular, and homogeneous structure makes many aspects of data management more challenging. Such malleable data formats can also lead to severe information quality problems, because the risk of storing inconsistent and incorrect data is greatly increased. A prominent example of such problems is the appearance of the so-called fuzzy duplicates, i.e., multiple and non-identical representations of a real-world entity. Similarity joins correlating XML document fragments that are similar can be used as core operators to support the identification of fuzzy duplicates. However, similarity assessment is especially difficult on XML datasets because structure, besides textual information, may exhibit variations in document fragments representing the same real-world entity. Moreover, similarity computation is substantially more expensive for tree-structured objects and, thus, is a serious performance concern. This thesis describes the design and implementation of an effective, flexible, and high-performance XML-based similarity join framework. As main contributions, we present novel structure-conscious similarity functions for XML trees - either considering XML structure in isolation or combined with textual information -, mechanisms to support the selection of relevant information from XML trees and organization of this information into a suitable format for similarity calculation, and efficient algorithms for large-scale identification of similar, set-represented objects. Finally, we validate the applicability of our techniques by integrating our framework into a native XML database management system; in this context we address several issues around the integration of similarity operations into traditional database architectures.
Due to remarkable technological advances in the last three decades the capacity of computer systems has improved tremendously. Considering Moore's law, the number of transistors on integrated circuits has doubled approximately every two years and the trend is continuing. Likewise, developments in storage density, network bandwidth, and compute capacity show similar patterns. As a consequence, the amount of data that can be processed by today's systems has increased by orders of magnitude. At the same time, however, the resolution of screens has hardly increased by a factor of ten. Thus, there is a gap between the amount of data that can be processed and the amount of data that can be visualized. Large high-resolution displays offer a way to deal with this gap and provide a significantly increased screen area by combining the images of multiple smaller display devices. The main objective of this dissertation is the development of new visualization and interaction techniques for large high-resolution displays.
Beim funktionsorientierten Testen von Steuergeräten im automobilen Bereich ist das Expertenwissen aufgrund der hohen Komplexität der Testfälle unersetzlich. Bei Basistesttechniken wie der Grenzwertanalyse ist die Absicht eines Testfalls implizit durch die Technik gegeben. Beim Expertenwissen wird jedoch zur Zeit zu jedem erstellten Testfall zusätzlich ein Prosatext verfasst um die Testabsicht anzugeben. Diese Prosabeschreibung ist anfällig für Mehrdeutigkeiten, fällt bei jedem Testentwickler unterschiedlich aus und der inhaltliche Bezug zum Testfall ist lose. Ziel der Arbeit ist eine Spezifikationssprache für die Testfallbeschreibung zu entwerfen um die Nachteile der natürlichen Sprache zu minimieren und testablaufspezifische Sprachelemente zu definieren, so dass sie als ein Grundgerüst für einen Testfall verwendet werden kann. Dazu wird aus der Einsatzumgebung (Systemspezifikation, Testimplementierung und Testprozessthemen) Sprachelemente für die Beschreibung abgeleitet und Ansätze für die Überführung der Beschreibung in die Testimplementierung betrachtet. Das Ergebnis ist eine Testfall-Spezifikationssprache, die auf formaler Grundlage basiert und u.a. in eine graphische Sicht überführt werden kann. Ähnlich der UML wird der Mehrwert erst durch eine werkzeugunterstützte Eingabe deutlich: So sind die Testentwickler in der Lage, einheitliche, formale, wieder verwendbare, verständliche Testfälle zu definieren.
The recognition of patterns and structures has gained importance for dealing with the growing amount of data being generated by sensors and simulations. Most existing methods for pattern recognition are tailored for scalar data and non-correlated data of higher dimensions. The recognition of general patterns in flow structures is possible, but not yet practically usable, due to the high computation effort. The main goal of this work is to present methods for comparative visualization of flow data, amongst others, based on a new method for efficient pattern recognition on flow data. This work is structured in three parts: At first, a known feature-based approach for pattern recognition on flow data, the Clifford convolution, has been applied to color edge detection, and been extended to non-uniform grids. However, this method is still computationally expensive for a general pattern recognition, since the recognition algorithm has to be applied for numerous different scales and orientations of the query pattern. A more efficient and accurate method for pattern recognition on flow data is presented in the second part. It is based upon a novel mathematical formulation of moment invariants for flow data. The common moment invariants for pattern recognition are not applicable on flow data, since they are only invariant on non-correlated data. Because of the spatial correlation of flow data, the moment invariants had to be redefined with different basis functions to satisfy the demands for an invariant mapping of flow data. The computation of the moment invariants is done by a multi-scale convolution of the complete flow field with the basis functions. This pre-processing computation time almost equals the time for the pattern recognition of one single general pattern with the former algorithms. However, after having computed the moments once, they can be indexed and used as a look-up-table to recognize any desired pattern quickly and interactively. This results in a flexible and easy-to-use tool for the analysis of patterns in 2d flow data. For an improved rendering of the recognized features, an importance driven streamline algorithm has been developed. The density of the streamlines can be adjusted by using importance maps. The result of a pattern recognition can be used as such a map, for example. Finally, new comparative flow visualization approaches utilizing the streamline approach, the flow pattern matching, and the moment invariants are presented.
Multi-Field Visualization
(2011)
Modern science utilizes advanced measurement and simulation techniques to analyze phenomena from fields such as medicine, physics, or mechanics. The data produced by application of these techniques takes the form of multi-dimensional functions or fields, which have to be processed in order to provide meaningful parts of the data to domain experts. Definition and implementation of such processing techniques with the goal to produce visual representations of portions of the data are topic of research in scientific visualization or multi-field visualization in the case of multiple fields. In this thesis, we contribute novel feature extraction and visualization techniques that are able to convey data from multiple fields created by scientific simulations or measurements. Furthermore, our scalar-, vector-, and tensor field processing techniques contribute to scattered field processing in general and introduce novel ways of analyzing and processing tensorial quantities such as strain and displacement in flow fields, providing insights into field topology. We introduce novel mesh-free extraction techniques for visualization of complex-valued scalar fields in acoustics that aid in understanding wave topology in low frequency sound simulations. The resulting structures represent regions with locally minimal sound amplitude and convey wave node evolution and sound cancellation in time-varying sound pressure fields, which is considered an important feature in acoustics design. Furthermore, methods for flow field feature extraction are presented that facilitate analysis of velocity and strain field properties by visualizing deformation of infinitesimal Lagrangian particles and macroscopic deformation of surfaces and volumes in flow. The resulting adaptive manifolds are used to perform flow field segmentation which supports multi-field visualization by selective visualization of scalar flow quantities. The effects of continuum displacement in scattered moment tensor fields can be studied by a novel method for multi-field visualization presented in this thesis. The visualization method demonstrates the benefit of clustering and separate views for the visualization of multiple fields.
In urban planning, both measuring and communicating sustainability are among the most recent concerns. Therefore, the primary emphasis of this thesis concerns establishing metrics and visualization techniques in order to deal with indicators of sustainability.
First, this thesis provides a novel approach for measuring and monitoring two indicators of sustainability - urban sprawl and carbon footprints – at the urban neighborhood scale. By designating different sectors of relevant carbon emissions as well as different household categories, this thesis provides detailed information about carbon emissions in order to estimate impacts of daily consumption decisions and travel behavior by household type. Regarding urban sprawl, a novel gridcell-based indicator model is established, based on different dimensions of urban sprawl.
Second, this thesis presents a three-step-based visualization method, addressing predefined requirements for geovisualizations and visualizing those indicator results, introduced above. This surface-visualization combines advantages from both common GIS representation and three-dimensional representation techniques within the field of urban planning, and is assisted by a web-based graphical user interface which allows for accessing the results by the public.
In addition, by focusing on local neighborhoods, this thesis provides an alternative approach in measuring and visualizing both indicators by utilizing a Neighborhood Relation Diagram (NRD), based on weighted Voronoi diagrams. Thus, the user is able to a) utilize original census data, b) compare direct impacts of indicator results on the neighboring cells, and c) compare both indicators of sustainability visually.
Im Bereich der Automobilelektronik ist eine Zunahme an Fahrerassistenzsystemen zu bemerken, die den Fahrer neben einer warnenden Funktion durch autonomes aktives Eingreifen in seiner Fahraufgabe unterstützen. Dadurch entsteht eine hohe Anforderung an die funktionale Sicherheit dieser Systeme, um ein einwandfreies Verhalten in allen Fahrsituationen zu garantieren und sicherheitskritische Situationen zu vermeiden oder zu entschärfen. Die funktionale Sicherheit derartiger Fahrerassistenzsysteme muss u. a. durch adäquate Testmethoden und einen effizienten Umgang damit innerhalb der etablierten industriellen Entwicklungsprozesse erhöht und sichergestellt werden.
Diese Arbeit bietet einen Überblick über existierende wissenschaftliche wie industrielle Ansätze zum Testen von Automobilelektronik sowie über aktive Fahrerassistenzsysteme. Der Schwerpunkt wird dabei auf diejenigen Systeme gelegt, die Informationen über ihre Umgebung aus Kamerasensoren gewinnen. Aus der Herausforderung, die funktionale Absicherung derart sicherheitskritischer Systeme zu gewährleisten, werden spezifische Anforderungen abgeleitet. Aus dem „Delta“ zwischen Anforderungen und Stand der Technik ergibt sich ein Handlungsbedarf, um neue Methoden und für deren Anwendung nötige Vorgehensweisen und Werkzeuge zu erforschen bzw. bestehende zu erweitern.
Die Methode des „Visual Loop Tests“ wird dafür vorgestellt. Sie kann durch die Anwendung sog. Grafik-Engines als neuer Bestandteil der Test-Technologien zur Absicherung eingesetzt werden. Dabei werden fotorealistische Grafiken zur Stimulation der Assistenzsysteme erzeugt. Die für die effiziente Anwendung dieser Technologien benötigten neuen Vorgehensweisen zur Beschreibung und Erzeugung von Testfällen in einem visuell repräsentierbaren Format werden erarbeitet.
Dadurch können moderne Assistenzfunktionen gleichzeitig effizienter, zuverlässiger, sicherer und kostengünstiger entwickelt werden und die Sicherheit auf den Straßen wird erhöht. Die erste empirische Bewertung im Rahmen der prototypischen Umsetzung bestärkt diese Einschätzung.
Today, polygonal models occur everywhere in graphical applications, since they are easy
to render and to compute and a very huge set of tools are existing for generation and
manipulation of polygonal data. But modern scanning devices that allow a high quality
and large scale acquisition of complex real world models often deliver a large set of
points as resulting data structure of the scanned surface. A direct triangulation of those
point clouds does not always result in good models. They often contain problems like
holes, self-intersections and non manifold structures. Also one often looses important
surface structures like sharp corners and edges during a usual surface reconstruction.
So it is suitable to stay a little longer in the point based world to analyze the point cloud
data with respect to such features and apply a surface reconstruction method afterwards
that is known to construct continuous and smooth surfaces and extend it to reconstruct
sharp features.
Predicting secondary structures of RNA molecules is one of the fundamental problems of and thus a challenging task in computational structural biology. Existing prediction methods basically use the dynamic programming principle and are either based on a general thermodynamic model or on a specific probabilistic model, traditionally realized by a stochastic context-free grammar. To date, the applied grammars were rather simple and small and despite the fact that statistical approaches have become increasingly appreciated over the past years, a corresponding sampling algorithm based on a stochastic RNA structure model has not yet been devised. In addition, basically all popular state-of-the-art tools for computational structure prediction have the same worst-case time and space requirements of O(n^3) and O(n^2) for sequence length n, limiting their applicability for practical purposes due to the often quite large sizes of native RNA molecules. Accordingly, the prime demand imposed by biologists on computational prediction procedures is to reach a reduced waiting time for results that are not significantly less accurate.
We here deal with all of these issues, by describing algorithms and performing comprehensive studies that are based on sophisticated stochastic context-free grammars of similar complexity as those underlying thermodynamic prediction approaches, where all of our methods indeed make use of the concept of sampling. We also employ the approximation technique known from theoretical computer science in order to reach a heuristic worst-case speedup for RNA folding.
Particularly, we start by describing a way for deriving a sequence-independent random sampler for an arbitrary class of RNAs by means of (weighted) unranking. The resulting algorithm may generate any secondary structure of a given fixed size n in only O(n·log(n)) time, where the results are observed to be accurate, validating its practical applicability.
With respect to RNA folding, we present a novel probabilistic sampling algorithm that generates statistically representative and reproducible samples of the entire ensemble of feasible structures for a particular input sequence. This method actually samples the possible foldings from a distribution implied by a suitable (traditional or length-dependent) grammar. Notably, we also propose several (new) ways for obtaining predictions from generated samples. Both variants have the same worst-case time and space complexities of O(n^3) and O(n^2) for sequence length n. Nevertheless, evaluations of our sampling methods show that they are actually capable of producing accurate (prediction) results.
In an attempt to resolve the long-standing problem of reducing the time complexity of RNA folding algorithms without sacrificing much of the accuracy of the results, we invented an innovative heuristic statistical sampling method that can be implemented to require only O(n^2) time for generating a fixed-size sample of candidate structures for a given sequence of length n. Since a reasonable prediction can still efficiently be obtained from the generated sample set, this approach finally reduces the worst-case time complexity by a liner factor compared to all existing precise methods. Notably, we also propose a novel (heuristic) sampling strategy as opposed to the common one typically applied for statistical sampling, which may produce more accurate results for particular settings. A validation of our heuristic sampling approach by comparison to several leading RNA secondary structure prediction tools indicates that it is capable of producing competitive predictions, but may require the consideration of large sample sizes.
The safety of embedded systems is becoming more and more important nowadays. Fault Tree Analysis (FTA) is a widely used technique for analyzing the safety of embedded systems. A standardized tree-like structure called a Fault Tree (FT) models the failures of the systems. The Component Fault Tree (CFT) provides an advanced modeling concept for adapting the traditional FTs to the hierarchical architecture model in system design. Minimal Cut Set (MCS) analysis is a method that works for qualitative analysis based on the FTs. Each MCS represents a minimal combination of component failures of a system called basic events, which may together cause the top-level system failure. The ordinary representations of MCSs consist of plain text and data tables with little additional supporting visual and interactive information. Importance analysis based on FTs or CFTs estimates the contribution of each potential basic event to a top-level system failure. The resulting importance values of basic events are typically represented in summary views, e.g., data tables and histograms. There is little visual integration between these forms and the FT (or CFT) structure. The safety of a system can be improved using an iterative process, called the safety improvement process, based on FTs taking relevant constraints into account, e.g., cost. Typically, relevant data regarding the safety improvement process are presented across multiple views with few interactive associations. In short, the ordinary representation concepts cannot effectively facilitate these analyses.
We propose a set of visualization approaches for addressing the issues above mentioned in order to facilitate those analyses in terms of the representations.
Contribution:
1. To support the MCS analysis, we propose a matrix-based visualization that allows detailed data of the MCSs of interest to be viewed while maintaining a satisfactory overview of a large number of MCSs for effective navigation and pattern analysis. Engineers can also intuitively analyze the influence of MCSs of a CFT.
2. To facilitate the importance analysis based on the CFT, we propose a hybrid visualization approach that combines the icicle-layout-style architectural views with the CFT structure. This approach facilitates to identify the vulnerable components taking the hierarchies of system architecture into account and investigate the logical failure propagation of the important basic events.
3. We propose a visual safety improvement process that integrates an enhanced decision tree with a scatter plot. This approach allows one to visually investigate the detailed data related to individual steps of the process while maintaining the overview of the process. The approach facilitates to construct and analyze improvement solutions of the safety of a system.
Using our visualization approaches, the MCS analysis, the importance analysis, and the safety improvement process based on the CFT can be facilitated.
Generic layout analysis--process of decomposing document image into homogeneous regions for a collection of diverse document images--has many important applications in document image analysis and understanding such as preprocessing of degraded warped, camera-captured document images, high performance layout analysis of document images containing complex cursive scripts, and word spotting in historical document images at page level. Many areas in this field like generic text line extraction method are considered as elusive goals so far, still beyond the reach of the state-of-the-art methods [NJ07, LSZT07, KB06]. This thesis addresses this problem in such a way that it presents generic, domain-independent, text line extraction and text and non-text segmentation methods, and then describes some important applications, that were developed based on these methods. An overview of the key contributions of this thesis is as follows.
The first part of this thesis presents a generic text line extraction method using a combination of matched filtering and ridge detection techniques, which are commonly used in computer vision. Unlike the state-of-the-art text line extraction methods in the literature, the generic text line extraction method can be equally and robustly applied to a large variety of document image classes including scanned and camera-captured documents, binary and grayscale documents, typed-text and handwritten documents, historical and contemporary documents, and documents containing different scripts. Different standard datasets are selected for performance evaluation that belong to different categories of document images such as the UW-III [GHHP97] dataset of scanned documents, the ICDAR 2007 [GAS07] and the UMD [LZDJ08] datasets of handwritten documents, the DFKI-I [SB07] dataset of camera-captured documents, Arabic/Urdu script documents dataset, and German calligraphic (Fraktur) script historical documents dataset. The generic text line extraction method achieves 86% (n = 23,763 text lines in 650 documents) text line detection accuracy which is better than the aggregate accuracy of 73% of the best performing domain-specific state-of-the-art methods. To the best of the author's knowledge, it is the first general-purpose text line extraction method that can be equally used for a diverse collection of documents.
This thesis also presents an active contour (snake) based curled text line extraction method for warped, camera-captured document images. The presented approach is applied to DFKI-I [SB07] dataset of camera-captured, Latin script document images for curled text line extraction. It achieves above 95% (n = 3,091 text lines in 102 documents) text line detection accuracy, which is significantly better than the competing state-of-the-art curled text line extraction methods. The presented text line extraction method can also be applied to document images containing different scripts like Chinese, Devanagari, and Arabic after small modifications.
The second part of this thesis presents an improved version of the state-of-the-art multiresolution morphology (Leptonica) based text and non-text segmentation method [Blo91], which is a domain-independent page segmentation approach and can be equally applied to a diverse collection of binarized document images. It is demonstrated that the presented improvements result in an increase in segmentation accuracy from 93% to 99% (n = 113 documents).
This thesis also introduces a discriminative learning based approach for page segmentation, where a self-tunable multi-layer perceptron (MLP) classifier [BS10] is trained for distinguishing between text and non-text connected components. Unlike other classification based page segmentation approaches in the literature, the connected components based discriminative learning based approach is faster than pixel based classification methods and does not require a block segmentation method beforehand. A segmentation accuracy of $96\%$ ($n = 113$ documents) is achieved in comparison to the state-of-the-art multiresolution morphology (Leptonica) based page segmentation method [Blo91] that achieves a segmentation accuracy of 93%. In addition to text and non-text segmentation of Latin script documents, the presented approach can also be adapted for document images containing other scripts as well as for other specialized layout analysis tasks such as digit and non-digit segmentation [HBSB12], orientation detection [RBSB09], and body-text and side-note segmentation [BAESB12].
Finally, this thesis presents important applications of the two generic layout analysis techniques, ridge-based text line extraction method and the multi-resolution morphology based text and non-text segmentation method, discussed above. First, a complete preprocessing pipeline is described for removing different types of degradations from grayscale warped, camera-captured document images that includes removal of grayscale degradations such as non-uniform shadows and blurring through binarization, noise cleanup applying page frame detection, and document rectification using monocular dewarping. Each of these preprocessing steps shows significant improvement in comparison to the analyzed state-of-the-art methods in the literature. Second, a high performance layout analysis method is described for complex Arabic script document images written in different languages such as Arabic, Urdu, and Persian and different styles for example Naskh and Nastaliq. The presented layout analysis system is robust against different types of document image degradations and shows better performance for text and non-text segmentation, text line extraction, and reading order determination on a variety of Arabic and Urdu document images as compared to the state-of-the-art methods. It can be used for large scale Arabic and Urdu documents' digitization processes. These applications demonstrate that the layout analysis methods, ridge-based text line extraction and the multi-resolution morphology based text and non-text segmentation, are generic and can be applied easily to a large collection of diverse document images.
Dealing with information in modern times involves users to cope with hundreds of thousands of documents, such as articles, emails, Web pages, or News feeds.
Above all information sources, the World Wide Web presents information seekers with great challenges.
It offers more text in natural language than one is capable to read.
The key idea for this research intends to provide users with adaptable filtering techniques, supporting them in filtering out the specific information items they need.
Its realization focuses on developing an Information Extraction system,
which adapts to a domain of concern, by interpreting the contained formalized knowledge.
Utilizing the Resource Description Framework (RDF), which is the Semantic Web's formal language for exchanging information,
allows extending information extractors to incorporate the given domain knowledge.
Because of this, formal information items from the RDF source can be recognized in the text.
The application of RDF allows a further investigation of operations on recognized information items, such as disambiguating and rating the relevance of these.
Switching between different RDF sources allows changing the application scope of the Information Extraction system from one domain of concern to another.
An RDF-based Information Extraction system can be triggered to extract specific kinds of information entities by providing it with formal RDF queries in terms of the SPARQL query language.
Representing extracted information in RDF extends the coverage of the Semantic Web's information degree and provides a formal view on a text from the perspective of the RDF source.
In detail, this work presents the extension of existing Information Extraction approaches by incorporating the graph-based nature of RDF.
Hereby, the pre-processing of RDF sources allows extracting statistical information models dedicated to support specific information extractors.
These information extractors refine standard extraction tasks, such as the Named Entity Recognition, by using the information provided by the pre-processed models.
The post-processing of extracted information items enables representing these results in RDF format or lists, which can now be ranked or filtered by relevance.
Post-processing also comprises the enrichment of originating natural language text sources with extracted information items by using annotations in RDFa format.
The results of this research extend the state-of-the-art of the Semantic Web.
This work contributes approaches for computing customizable and adaptable RDF views on the natural language content of Web pages.
Finally, due to the formal nature of RDF, machines can interpret these views allowing developers to process the contained information in a variety of applications.
The automatic analysis and retrieval of technical line drawings is hindered by many challenges such as: the large amount of contextual clutter around the symbols within the drawings, degradation, transformations on the symbols in drawings, large databases of drawings
and large alphabets of symbols. The core tasks required for the analysis of technical line
drawings are: symbol recognition, spotting and retrieval. The current systems for performing these tasks have poor performance due to the mentioned challenges. This dissertation
presents a number of methods that address these challenges. These methods achieve both
accurate and efficient symbol spotting and retrieval in technical line drawings, and perform
significantly better than state-of-the-art methods on the same problems. An overview of
the key contributions of this dissertation is given in the following.
First, this dissertation presents a geometric matching-based method for symbol recognition
and spotting. The method performs recognition in the presence of large amounts of contextual clutter, and provides precise localization of the recognized symbols. On standard
databases such as GREC-2005 and GREC-2011, the method achieves up to 10% higher
recall and up to 28% higher precision than state-of-the-art methods on the spotting task,
and achieves up to 7% higher recognition accuracy on the isolated recognition task. The
method is based on a geometric matching approach, which is flexible enough to incorporate
improvements on the matching strategy, feature types and information on the features. The
method also includes an adaptive preprocessing algorithm that deals with a wide variety
of noise types.
In order to improve the performance of the spotting method when dealing with degraded
drawings, two novel methods are presented in this dissertation. Both methods are based on
combining geometric matching with machine learning techniques. The geometric matching
is used to automatically generate training data that contain information on how well the
features of the queries are matched in both the true and the false matches found by the
spotting method. The first method learns the feature weights of the different query symbols
by linear discriminant analysis (LDA). The weighted query features are used in the spotting
method and result in 27% higher average precision than the original method, with a speedup
factor of 2. The second method uses SVM classification as a post-spotting step to distinguish
the true from the false matches in the spotting method. The use of the classification step
further improves the average precision of the spotting method by 20.6%.
This dissertation also presents methods for content analysis of line drawings. First, a
method for accurate and consistent detection (95.8%) of regions of interest (ROIs) is presented. The method is based on statistical feature grouping. The ROI-finding method is
identified as an important part of a symbol retrieval system: the better the detected ROIs,the higher the performance of a retrieval system. The ROI-finding method is also used to
improve the performance of the geometric-based spotting system.
Second, a symbol clustering method for building a compact and accurate representation of
a large database of technical drawings is presented. This method uses the output from the
ROI-finding method as input, and uses geometric matching as a similarity measure. The
method achieves high accuracy (90.1% recall, 94.3% precision) in forming clusters of symbols. The representatives of the clusters (34 symbols) are used as key entries to a symbol
index, which is identified as the outcome of an off-line stage of a symbol retrieval system.
Finally, an efficient and high performing large scale symbol retrieval system is presented
in this dissertation. The system follows the bag of visual words (BoVW) model, but with
using methods that are suitable to line drawings. The system uses the symbol index to
represent a database of drawings. During the on-line query retrieval stage, the query is
analyzed by the ROI-finding method, matched with the key entries of the symbol index via
geometric matching, and finally, a spatial verification step is performed on the retrieved
matches. The system achieves a query lookup time that is independent of the size of the
database, and is instead dependent on the size of the symbol index. The system achieves up
to 10% higher recall and up to 28% higher precision than state-of-the-art spotting systems
on similar databases.
Overall, these contributions are major advancements in the research of graphics recognition.
The hope is that, such contributions provide the basis for the development of reliable and
accurate performing applications for browsing, querying or classification of line drawings
for the benefit of end users.
Fluid extraction is a typical chemical process where two types of fluids are mixed together. The high complexity of this process which involves droplet coalescence, breakup, mass transfer, and counter-current flow often makes design difficult. The industrial design of these processes is still based on expensive mini-plant and pilot plant experiments. Therefore, there is a strong need for research into the stimulation of fluid-fluid interaction processes using computational fluid dynamics (CFD).
Previous multi-phase fluid simulations have focused on the development of models that couple mass and momentum using the Navier-Stokes equation. Recent population balance models (PBM) have proved to be important methods for analyzing droplet breakage and collisions. A combination of CFD and PBM facilitates the simulation of flow property by solving coupling equations, and the calculation of the droplet size and numbers. In our study, we successfully coupled an Euler-Euler CFD model with the breakup and coalescence models proposed by Luo and Svendsen (59).
The simulation output of extraction columns provides a mathematical understand- ing of how fluids are mixed inside a mixing device. This mixing process shows that the dispersed phase of a flow generates large blobs and bubbles. Current mathemati- cal simulation results often fail to provide an intuitive representation of how well two different types of fluid interact, so intuitive and physically plausible visualization tech- niques are in high demand to help chemical engineers to explore and analyze bubble column simulation data. In chapter 3, we present the visualization tools we developed for extraction column data.
Fluid interfaces and free surfaces are topics of growing interest in the field of multi- phase computational fluid dynamics. However, the analysis of the flow field relative to the material interface shape and topology is a challenging task. In chapter 5, we present a technique that facilitates the visualization and analysis of complex material interface behaviors over time. To achieve this, we track the surface parameterization of time-varying material interfaces and identify locations where there are interactions between the material interfaces and fluid particles. Splatting and surface visualization techniques produce an intuitive representation of the derived interface stability. Our results demonstrate that the interaction of a flow field with a material interface can be understood using appropriate extraction and visualization techniques, and that our techniques can help the analysis of mixing and material interface consistency.
In addition to texture-based methods for surface analysis, the interface of two- phase fluid can be considered as an implicit function of the density or volume fraction values. High-level visualization techniques such as topology-based methods can re- veal the hidden structure underlying simple simulation data, which will enhance and advance our understanding of multi-fluid simulation data. Recent feature-based vi- sualization approaches have explored the possibility of using Reeb graphs to analyze scalar field topologies(19, 107). In chapter 6, we present a novel interpolation scheme for interpolating point-based volume fraction data and we further explore the implicit fluid interface using a topology-based method.
Recent progresses and advances in the field of consumer electronics, driven by display
technologies and also the sector of mobile, hand-held devices, enable new ways in
presenting information to users, as well as new ways of user interaction, therefore
providing a basis for user-centered applications and work environments.
My thesis focuses on how arbitrary display environments can be utilized to improve
both the user experience, regarding perception of information, and also to provide
intuitive interaction possibilities. On the one hand advances in display technologies
provide the basis for new ways of visualizing content and collaborative work, on the
other hand forward-pressing developments in the consumer market, especially the
market of smart phones, offer potential to enhance usability in terms of interaction
and therefore can provide additional benefit for users.
Tiled display setups, combining both large screen real estate and high resolution,
provide new possibilities and chances to visualize large datasets and to facilitate col-
laboration in front of a large screen area. Furthermore these display setups present
several advantages over the traditional single-user-workspace environments: con-
trary to single-user-workspaces, multiple users are able to explore a dataset displayed
on a tiled display system, at the same time, thus allowing new forms of collabora-
tive work. Based on that, face-to-face discussions are enabled, an additional value
is added. Large displays also allow the utilization of the user’s spatial memory, al-
lowing physical navigation without the need of switching between different windows
to explore information.
With Tiled++ I contributed a versatile approach to address the bezel problem. The
bezel problem is one of the Top Ten research challenges in the research field of LCD-
based tiled wall setups. By applying the Tiled++ approach a large high resolution
Focus & Context screen is created, combining high resolution focus areas with low
resolution context information, projected onto the bezel area.
Additionally the field of user interaction poses an important challenge, especially
regarding the utilization of large tiled displays, since traditional keyboard & mouse
interaction devices reached their limits. My focus in this thesis is on Mobile HCI.Devices like mobile phones are utilized to interact with large displays, since they
feature various interaction modalities and preserve user mobility.
Large public displays, as a modernized form of traditional bulletin boards, also en-
able new ways of handling information, displaying content, and user interaction.
Utilized in hot spots, Digital Interactive Public Pinboards can provide an adequate
answer to questions like how to approach pressing issues like disaster and crisis man-
agement for both responders as well as citizens and also new ways of how to handle
information flow (contribution & distribution & accession). My contribution to the
research field of public display environments was the conception and implementa-
tion of an easy-to-use and easy-to-set-up architecture to overcome shortcomings of
current approaches and to cover the needs of aid personnel.
Although being a niche, Virtual Reality (VR) environments can provide additional
value for visualizing specific content. Disciplines like earth sciences & geology, me-
chanical engineering, design, and architecture can benefit from VR environments. In
order to consider the variety of users, I introduce a more intuitive and user friendly
interaction metaphor, the ARC metaphor.
Visualization challenges base on being able to cope with more and more complex
datasets and to bridge the gap between comprehensibility and loss of information.
Furthermore the visualization approach has to be reasonable, which is a crucial
factor when working in interdisciplinary teams, where the standard of knowledge
is diverse. Users have to be able to conceive the visualized content in a fast and
reliable way. My contribution are visualization approaches in the field of supportive
visualization.
Finally, my work illuminates how the synthesis of visualization, interaction and dis-
play technologies enhance the user experience. I promote a holistic view. The user
is brought back into the focus of attention, provided with a tool-set to support him,
without overextending the abilities of, for example, non-expert users, a crucial factor
in the more and more interdisciplinary field of computer science.
This thesis provides a fully automatic translation from synchronous programs to parallel software for different architectures, in particular, shared memory processing (SMP) and distributed memory systems. Thereby, we exploit characteristics of the synchronous model of computation (MoC) to reduce communication and to improve available parallelism and load-balancing by out-of-order (OOO) execution and data speculation.
Manual programming of parallel software requires the developers to partition a system into tasks and to add synchronization and communication. The model-based approach of development abstracts from details of the target architecture and allows to make decisions about the target architecture as late as possible. The synchronous MoC supports this approach by abstracting from time and providing implicit parallelism and synchronization. Existing compilation techniques translate synchronous programs into synchronous guarded actions (SGAs) which are an intermediate format abstracting from semantic problems in synchronous languages. Compilers for SGAs analyze causality problems, ensure logical correctness and the absence of schizophrenia problems. Hence, SGAs are a simplified and general starting point and keep the synchronous MoC at the same time. The instantaneous feedback in the synchronous MoC makes the mapping of these systems to parallel software a non-trivial task. In contrast, other MoCs such as data-flow processing networks (DPNs) directly match with parallel architectures. We translate the SGAs into DPNs,which represent a commonly used model to create parallel software. DPNs have been proposed as a programming model for distributed parallel systems that have communication paths with unpredictable latencies. The purely data-driven execution of DPNs does not require a global coordination and therefore DPNs can be easily mapped to parallel software for architectures with distributed memory. The generation of efficient parallel code from DPNs challenges compiler design with two issues: To perfectly utilize a parallel system, the communication and synchronization has to be kept low, and the utilization of the computational units has to be balanced. The variety of hardware architectures and dynamic execution techniques in processing units of these systems make a statically balanced distributed execution impossible.
The synchronous MoC is still reflected in our generated DPNs, which exhibits characteristics that allow optimizations concerning the previously mentioned issues. In particular, we apply a general communication reduction and OOO execution to achieve a dynamically balanced execution which is inspired from hardware design.
There is a growing trend for ever larger wireless sensor networks (WSNs) consisting of thousands or tens of thousands of sensor nodes (e.g., [91, 79]). We believe this trend will continue and thus scalability plays a crucial role in all protocols and mechanisms for WSNs. Another trend in many modern WSN applications is the time sensitivity to information from sensors to sinks. In particular, WSNs are a central part of the vision of cyber-physical systems and as these are basically closed-loop systems many WSN applications will have to operate under stringent timing requirements. Hence, it is crucial to develop algorithms that minimize the worst-case delay in WSNs. In addition, almost all WSNs consist of battery-powered nodes, and thus energy-efficiency clearly remains another premier goal in order to keep network lifetime high. This dissertation presents and evaluates designs for WSNs using multiple sinks to achieve high lifetime and low delay. Firstly, we investigate random and deterministic node placement strategies for large-scale and time-sensitive WSNs. In particular, we focus on tiling-based deterministic node placement strategies and analyze their effects on coverage, lifetime, and delay performance under both exact placement and stochastically disturbed placement. Next, we present sink placement strategies, which constitutes the main contributions of this dissertation. Static sinks will be placed and mobile sinks will be given a trajectory. A proper sink placement strategy can improve the performance of a WSN significantly. In general, the optimal sink placement with lifetime maximization is an NP-hard problem. The problem is even harder if delay is taken into account. In order to achieve both lifetime and delay goals, we focus on the problem of placing multiple (static) sinks such that the maximum worst-case delay is minimized while keeping the energy consumption as low as possible. Different target networks may need a corresponding sink placement strategy under differing levels of apriori assumptions. Therefore, we first develop an algorithm based on the Genetic Algorithm (GA) paradigm for known sensor nodes' locations. For a network where global information is not feasible we introduce a self-organized sink placement (SOSP) strategy. While GA-based sink placement achieves a near-optimal solution, SOSP provides a good sink placement strategy with a lower communication overhead. How to plan the trajectories of many mobile sinks in very large WSNs in order to simultaneously achieve lifetime and delay goals had not been treated so far in the literature. Therefore, we delve into this difficult problem and propose a heuristic framework using multiple orbits for the sinks' trajectories. The framework is designed based on geometric arguments to achieve both, high lifetime and low delay. In simulations, we compare two different instances of our framework, one conceived based on a load-balancing argument and one based on a distance minimization argument, with a set of different competitors spanning from statically placed sinks to battery-state aware strategies. We find our heuristics outperform the competitors in both, lifetime and delay. Furthermore, and probably even more important, the heuristic, while keeping its good delay and lifetime performance, scales well with an increasing number of sinks. In brief, the goal of this dissertation is to show that placing nodes and sinks in conventional WSNs as well as planning trajectories in mobility enabled WSNs carefully really pays off for large-scale and time-sensitive WSNs.
Due to tremendous improvements of high-performance computing resources as well
as numerical advances computational simulations became a common tool for modern
engineers. Nowadays, simulation of complex physics is more and more substituting a
large amount of physical experiments. While the vast compute power of large-scale
high-performance systems enabled for simulating more complex numerical equations,
handling the ever increasing amount of data with spatial and temporal resolution
burdens new challenges to scientists. Huge hardware and energy costs desire for
ecient utilization of high-performance systems. However, increasing complexity of
simulations raises the risk of failing simulations resulting in a single simulation to be
restarted multiple times. Computational Steering is a promising approach to interact
with running simulations which could prevent simulation crashes. The large amount
of data expands gaps in the amount of data that can be calculated and the amount of
data that can be processed. Extreme-scale simulations produce more data that can
even be stored. In this thesis, I propose several methods that enhance the process
of steering, exploring, visualizing, and analyzing ongoing numerical simulations.
Backward compatibility of class libraries ensures that an old implementation of a library can safely be replaced by a new implementation without breaking existing clients.
Formal reasoning about backward compatibility requires an adequate semantic model to compare the behavior of two library implementations.
In the object-oriented setting with inheritance and callbacks, finding such models is difficult as the interface between library implementations and clients are complex.
Furthermore, handling these models in a way to support practical reasoning requires appropriate verification tools.
This thesis proposes a formal model for library implementations and a reasoning approach for backward compatibility that is implemented using an automatic verifier. The first part of the thesis develops a fully abstract trace-based semantics for class libraries of a core sequential object-oriented language. Traces abstract from the control flow (stack) and data representation (heap) of the library implementations. The construction of a most general context is given that abstracts exactly from all possible clients of the library implementation.
Soundness and completeness of the trace semantics as well as the most general context are proven using specialized simulation relations on the operational semantics. The simulation relations also provide a proof method for reasoning about backward compatibility.
The second part of the thesis presents the implementation of the simulation-based proof method for an automatic verifier to check backward compatibility of class libraries written in Java. The approach works for complex library implementations, with recursion and loops, in the setting of unknown program contexts. The verification process relies on a coupling invariant that describes a relation between programs that use the old library implementation and programs that use the new library implementation. The thesis presents a specification language to formulate such coupling invariants. Finally, an application of the developed theory and tool to typical examples from the literature validates the reasoning and verification approach.
Modellgetriebene Entwicklung von Kommunikationsprotokollen für drahtlos vernetzte Regelungssysteme
(2013)
Funkvernetzte Sensorsysteme sind heutzutage allgegenwärtig.
Sie werden sowohl in Rauchmeldern, in Raumtemperaturüberwachungen und Sicherheitssystemen eingesetzt.
Das Sensorsystem soll seine Aufgabe zuverlässig und über viele Jahre ohne Batteriewechsel erfüllen.
Durch die Vernetzung der Sensorsysteme und ihre immer komplexer werdenden Aufgaben wird die Programmierung in einer maschinennahen Sprache immer aufwändiger.
Die modellgetriebene Entwicklung erhöht die Wartbarkeit und reduziert die Entwicklungszeit wodurch im Allgemeinen die Produktqualität steigt.
In Folge der höheren Komplexität, der Abstraktion von der konkreten Hardwareplattform und den immer kürzere Produktentwicklungszeiten bleibt oft keine Zeit für Energieoptimierung, wodurch die Batterielaufzeit geringer ausfällt, als dies möglich wäre.
In dieser Arbeit werden verschiedene Ansätze vorgestellt, die es ermöglichen, bereits während der Modellierung den Stromverbrauch zu berücksichtigen und diesen zu optimieren.
Am Beispiel des inversen Pendels, einem sehr instabilen Regelungssystem, wird dazu mit Hilfe der modellgetriebenen Entwicklung eine funkvernetzte, verteilte Regelung spezifiziert.
Der aus der Spezifikation erzeugte Kode wird direkt auf den Sensorknoten ausgeführt und muß dazu performant und zuverlässig sein, um die Echtzeitanforderungen des Regelungssystems zu erfüllen, aber gleichzeitig so wenig Energie wie möglich zu verbrauchen.
m die Zuverlässigkeit der verteilten Regelung zu gewährleisten ist eine deterministische kollisionsfreie Datenübertragung über das drahtlose Kommunikationsmedium erforderlich.
Die Synchronisation ist eine weitere Voraussetzung zur Ermittlung eines konsistenten Systemzustands.
Data integration aims at providing uniform access to heterogeneous data, managed by distributed source systems. Data sources can range from legacy systems, databases, and enterprise applications to web-scale data management systems. The materialized approach to data integration, extracts data from the sources, transforms and consolidates the data, and loads it into an integration system, where it is persistently stored and can be queried and analyzed.
To support materialized data integration, so called Extract-Transform-Load (ETL) systems have been built and are widely used to populate data warehouses today. While ETL is considered state-of-the-art in enterprise data warehousing, a new paradigm known as MapReduce has recently gained popularity for web-scale data transformations, such as web indexing or page rank computation.
The input data of both, ETL and MapReduce programs keeps changing over time, while business transactions are processed or the web is crawled, for instance. Hence, the results of ETL and MapReduce programs get stale and need to be recomputed from time to time. Recurrent computations over changing input data can be performed in two ways. The result may either be recomputed from scratch or recomputed in an incremental fashion. The idea behind the latter approach is to update the existing result in response to incremental changes in the input data. This is typically more efficient than the full recomputation approach, because reprocessing unchanged portions of the input data can often be avoided.
Incremental recomputation techniques have been studied by the database research community mainly in the context of the maintenance of materialized views and have been adopted by all major commercial database systems today. However, neither today's ETL tools nor MapReduce support incremental recomputation techniques. The situation of ETL and MapReduce programmers nowadays is thus much comparable to the situation of database programmers in the early 1990s. This thesis makes an effort to transfer incremental recomputation techniques into the ETL and MapReduce environments. This poses interesting research challenges, because these environments differ fundamentally from the relational world with regard to query and programming models, change data capture, transactional guarantees and consistency models. However, as this thesis will show, incremental recomputations are feasible in ETL and MapReduce and may lead to considerable efficiency improvements.
In recent years, recommender systems have been widely used for a variety of different kinds of items such as books, movies, and music. However, current recommendation approaches have often been criticized to suffer from overspecialization thus not enough considering a user’s diverse topics of interest. In this thesis we present a novel approach to extracting contextualized user profiles which enable recommendations taking into account a user’s full range of interests. The method applies algorithms from the domain of topic detection and tracking to automatically identify diverse user interests and to represent them with descriptive labels. That way manual annotations of interest topics by the users, e. g., from a predefined domain taxonomy, are no longer required. The approach has been tested in two scenarios: First, we implemented a content-based recommender system for an Enterprise 2.0 resource sharing platform where the contextualized user interest profiles have been used to generate recommendations with a high degree of inter-topic diversity. In an effort to harness the collective intelligence of the users, the resources in the system were described by making use of user-generated metadata. The evaluation experiments show that our approach is likely to capture a multitude of diverse interest topics per user. The labels extracted are specific for these topics and can be used to retrieve relevant on-topic resources. Second, a slightly adapted variation of the algorithm has been used to target music recommendations based on the user’s current mood. In this scenario music artists are described by using freely available Semantic Web data from the Linked Open Data cloud thus not requiring expensive metadata annotations by experts. The evaluation experiments conducted show that many users have a multitude of different preferred music styles. However a correlation between these music styles and music mood categories could not be observed. An integration of our proposed user profiles with existing user model ontologies seems promising for enabling context-sensitive recommendations.
An huge amount of computational models and programming languages have been proposed
for the description of embedded systems. In contrast to traditional sequential programming
languages, they cope directly with the requirements for embedded systems: direct support for
concurrent computations and periodic interaction with the environment are only some of the
features they offer. Synchronous languages are one class of languages for the development of
embedded systems and they follow the fundamental principle that the execution is divided into
a sequence of logical steps. Thereby, each step follows the simplification that the computation
of the outputs is finished directly when the inputs are available. This rigorous abstraction leads
to well-defined deterministic parallel composition in general, and to deterministic abortion
and suspension in imperative synchronous languages in particular. These key features also
allow to translate programs to hardware and software, and also formal verification techniques
like model checking can be easily applied.
Besides the advantages of imperative synchronous languages, also some drawbacks can
be listed. Over-synchronization is an effect being caused by parallel threads which have to
synchronize for each execution step, even if they do not communicate, since the synchronization
is implicitly forced by the control-flow. This thesis considers the idea of clock refinement to
introduce several abstraction layers for communication and synchronization in addition to the
existing single-clock abstraction. Thereby, clocks can be refined by several independent clocks
so that a controlled amount of asynchrony between subsequent synchronization points can be
exploited by compilers. The declarations of clocks form a tree, and clocks can be defined within
the threads of the parallel statement, which allows one to do independent computations based
on these clocks without synchronizing the threads. However, the synchronous abstraction is
kept at each level of the abstraction.
Clock refinement is introduced in this thesis as an extension to the imperative synchronous
language Quartz. Therefore, new program statements are introduced which allow to define
a new clock as a refinement of an existing one and to finish a step based on a certain clock.
Examples are considered to show the impact of the behavior of the new statements to
the already existing statements, before the semantics of this extension is formally defined.
Furthermore, the thesis presents a compile algorithm to translate programs to an intermediate
format, and to translate the intermediate format to a hardware description. The advantages
obtained by the new modeling feature are finally evaluated based on examples.
Embedded systems, ranging from very simple systems up to complex controllers, may
nowadays have quite challenging real-time requirements. Many embedded systems are reactive
systems that have to respond to environmental events and have to guarantee certain real-time
constrain. Their execution is usually divided into reaction steps, where in each step, the
system reads inputs from the environment and reacts to these by computing corresponding
outputs.
The synchronous Model of Computation (MoC) has proven to be well-suited for the
development of reactive real-time embedded systems whose paradigm directly reflects the
reactive nature of the systems it describes. Another advantage is the availability of formal
verification by model checking as a result of the deterministic execution based on a formal
semantics. Nevertheless, the increasing complexity of embedded systems requires to compensate
the natural disadvantages of model checking that suffers from the well-known state-space
explosion problem. It is therefore natural to try to integrate other verification methods with
the already established techniques. Hence, improvements to encounter these problems are
required, e.g., appropriate decomposition techniques, which encounter the disadvantages
of the model checking approach naturally. But defining decomposition techniques for synchronous
language is a difficult task, as a result of the inherent parallelism emerging from
the synchronous broadcast communication.
Inspired by the progress in the field of desynchronization of synchronous systems by
representing them in other MoCs, this work will investigate the possibility of adapting and use
methods and tools designed for other MoC for the verification of systems represented in the
synchronous MoC. Therefore, this work introduces the interactive verification of synchronous
systems based on the basic foundation of formal verification for sequential programs – the
Hoare calculus. Due to the different models of computation several problems have to be
solved. In particular due to the large amount of concurrency, several parts of the program
are active at the same point of time. In contrast to sequential programs, a decomposition
in the Hoare-logic style that is in some sense a symbolic execution from one control flow
location to another one requires the consideration of several flows here. Therefore, different
approaches for the interactive verification of synchronous systems are presented.
Additionally, the representation of synchronous systems by other MoCs and the influence
of the representation on the verification task by differently embedding synchronous system
in a single verification tool are elaborated.
The feasibility is shown by integration of the presented approach with the established
model checking methods by implementing the AIFProver on top of the Averest system.
Optical character recognition (OCR) of machine printed text is ubiquitously considered as a solved problem. However, error free OCR of degraded (broken and merged) and noisy text is still challenging for modern OCR systems. OCR of degraded text with high accuracy is very important due to many applications in business, industry and large scale document digitization projects. This thesis presents a new OCR method for degraded
text recognition by introducing a combined ANN/HMM OCR approach. The approach
provides significantly better performance in comparison with state-of-the-art HMM based OCR methods and existing open source OCR systems. In addition, the thesis introduces novel applications of ANNs and HMMs for document image preprocessing and recognition of low resolution text. Furthermore, the thesis provides psychophysical experiments to determine the effect of letter permutation in visual word recognition of Latin and Cursive
script languages.
HMMs and ANNs are widely employed pattern recognition paradigms and have been
used in numerous pattern classification problems. This work presents a simple and novel method for combining the HMMs and ANNs in application to segmentation free OCR of degraded text. HMMs and ANNs are powerful pattern recognition strategies and their combination is interesting to improve current state-of-the-art research in OCR. Mostly, previous attempts in combining the HMMs and ANNs were focused on applying ANNs
as approximation of the probability density function or as a neural vector quantizer for HMMs. These methods either require combined NN/HMM training criteria [ECBG-MZM11] or they use complex neural network architecture like time delay or space displacement neural networks [BLNB95]. However, in this work neural networks are used as discriminative feature extractor, in combination with novel text line scanning mechanism, to extract discriminative features from unsegmented text lines. The features are
processed by HMMs to provide segmentation free text line recognition. The ANN/HMM modules are trained separately on a common dataset by using standard machine learning procedures. The proposed ANN/HMM OCR system also realizes to some extent several cognitive reading based strategies during the OCR. On a dataset of 1,060 degraded text lines extracted from the widely used UNLV-ISRI benchmark database [TNBC99], the presented system achieves a 30% reduction in error rate as compared to Google’s Tesseract OCR system [Smi13] and 43% reduction in error as compared to OCRopus OCR system [Bre08], which are the best open source OCR systems available today.
In addition, this thesis introduces new applications of HMMs and ANNs in OCR and document images preprocessing. First, an HMMs-based segmentation free OCR approach is presented for recognition of low resolution text. OCR of low resolution text is quite important due to presence of low resolution text in screen-shots, web images and video captions. OCR of low resolution text is challenging because of antialiased rendering and use of very small font size. The characters in low resolution text are usually joined to each other and they may appear differently at different locations on computer screen. This
work presents the use of HMMs in optical recognition of low resolution isolated characters and text lines. The evaluation of the proposed method shows that HMMs-based OCR techniques works quite well and reaches the performance of specialized approaches for OCR of low resolution text.
Then, this thesis presents novel applications of ANNs for automatic script recognition and orientation detection. Script recognition determines the written script on the page for the application of an appropriate character recognition algorithm. Orientation detection detects and corrects the deviation of the document’s orientation angle from the horizontal direction. Both, script recognition and orientation detection, are important preprocessing steps in developing robust OCR systems. In this work, instead of extracting handcrafted features, convolutional neural networks are used to extract relevant discriminative features for each classification task. The proposed method resulted in more than 95% script recognition accuracy on various multi-script documents at connected component level
and 100% page orientation detection accuracy for Urdu documents.
Human reading is a nearly analogous cognitive process to OCR that involves decoding of printed symbols into meanings. Studying the cognitive reading behavior may help in building a robust machine reading strategy. This thesis presents a behavioral study that deals on how cognitive system works in visual recognition of words and permuted non-words. The objective of this study is to determine the impact of overall word shape
in visual word recognition process. The permutation is considered as a source of shape degradation and visual appearance of actual words can be distorted by changing the constituent letter positions inside the words. The study proposes a hypothesis that reading of words and permuted non-words are two distinct mental level processes, and people use
different strategies in handling permuted non-words as compared to normal words. The hypothesis is tested by conducting psychophysical experiments in visual recognition of words from orthographically different languages i.e. Urdu, German and English. Experimental data is analyzed using analysis of variance (ANOVA) and distribution free rank tests to determine significance differences in response time latencies for two classes of data. The results support the presented hypothesis and the findings are consistent with
the dual route theories of reading.
In the presented work, I evaluate if and how Virtual Reality (VR) technologies can be used to support researchers working in the geosciences by providing immersive, collaborative visualization systems as well as virtual tools for data analysis. Technical challenges encountered in the development of theses systems are identified and solutions for these are provided.
To enable geologists to explore large digital terrain models (DTMs) in an immersive, explorative fashion within a VR environment, a suitable terrain rendering algorithm is required. For realistic perception of planetary curvature at large viewer altitudes, spherical rendering of the surface is necessary. Furthermore, rendering must sustain interactive frame rates of about 30 frames per second to avoid sensory confusion of the user. At the same time, the data structures used for visualization should also be suitable for efficiently computing spatial properties such as height profiles or volumes in order to implement virtual analysis tools. To address these requirements, I have developed a novel terrain rendering algorithm based on tiled quadtree hierarchies using the HEALPix parametrization of a sphere. For evaluation purposes, the system is applied to a 500 GiB dataset representing the surface of Mars.
Considering the current development of inexpensive remote surveillance equipment such as quadcopters, it seems inevitable that these devices will play a major role in future disaster management applications. Virtual reality installations in disaster management headquarters which provide an immersive visualization of near-live, three-dimensional situational data could then be a valuable asset for rapid, collaborative decision making. Most terrain visualization algorithms, however, require a computationally expensive pre-processing step to construct a terrain database.
To address this problem, I present an on-the-fly pre-processing system for cartographic data. The system consists of a frontend for rendering and interaction as well as a distributed processing backend executing on a small cluster which produces tiled data in the format required by the frontend on demand. The backend employs a CUDA based algorithm on graphics cards to perform efficient conversion from cartographic standard projections to the HEALPix-based grid used by the frontend.
Measurement of spatial properties is an important step in quantifying geological phenomena. When performing these tasks in a VR environment, a suitable input device and abstraction for the interaction (a “virtual tool”) must be provided. This tool should enable the user to precisely select the location of the measurement even under a perspective projection. Furthermore, the measurement process should be accurate to the resolution of the data available and should not have a large impact on the frame rate in order to not violate interactivity requirements.
I have implemented virtual tools based on the HEALPix data structure for measurement of height profiles as well as volumes. For interaction, a ray-based picking metaphor was employed, using a virtual selection ray extending from the user’s hand holding a VR interaction device. To provide maximum accuracy, the algorithms access the quad-tree terrain database at the highest available resolution level while at the same time maintaining interactivity in rendering.
Geological faults are cracks in the earth’s crust along which a differential movement of rock volumes can be observed. Quantifying the direction and magnitude of such translations is an essential requirement in understanding earth’s geological history. For this purpose, geologists traditionally use maps in top-down projection which are cut (e.g. using image editing software) along the suspected fault trace. The two resulting pieces of the map are then translated in parallel against each other until surface features which have been cut by the fault motion come back into alignment. The amount of translation applied is then used as a hypothesis for the magnitude of the fault action. In the scope of this work it is shown, however, that performing this study in a top-down perspective can lead to the acceptance of faulty reconstructions, since the three-dimensional structure of topography is not considered.
To address this problem, I present a novel terrain deformation algorithm which allows the user to trace a fault line directly within a 3D terrain visualization system and interactively deform the terrain model while inspecting the resulting reconstruction from arbitrary perspectives. I demonstrate that the application of 3D visualization allows for a more informed interpretation of fault reconstruction hypotheses. The algorithm is implemented on graphics cards and performs real-time geometric deformation of the terrain model, guaranteeing interactivity with respect to all parameters.
Paleoceanography is the study of the prehistoric evolution of the ocean. One of the key data sources used in this research are coring experiments which provide point samples of layered sediment depositions at the ocean floor. The samples obtained in these experiments document the time-varying sediment concentrations within the ocean water at the point of measurement. The task of recovering the ocean flow patterns based on these deposition records is a challenging inverse numerical problem, however.
To support domain scientists working on this problem, I have developed a VR visualization tool to aid in the verification of model parameters by providing simultaneous visualization of experimental data from coring as well as the resulting predicted flow field obtained from numerical simulation. Earth is visualized as a globe in the VR environment with coring data being presented using a billboard rendering technique while the
time-variant flow field is indicated using Line-Integral-Convolution (LIC). To study individual sediment transport pathways and their correlation with the depositional record, interactive particle injection and real-time advection is supported.
We consider two major topics in this thesis: spatial domain partitioning which serves as a framework to simulate creep flows in representative volume elements.
First, we introduce a novel multi-dimensional space partitioning method. A new type of tree combines the advantages of the Octree and the KD-tree without having their disadvantages. We present a new data structure allowing local refinement, parallelization and proper restriction of transition ratios between nodes. Our technique has no dimensional restrictions at all. The tree's data structure is defined by a topological algebra based on the symbols \( A = \{ L, I, R \} \) that encode the partitioning steps. The set of successors is restricted such that each node has the partition of unity property to partition domains without overlap. With our method it is possible to construct a wide choice of spline spaces to compress or reconstruct scientific data such as pressure and velocity fields and multidimensional images. We present a generator function to build a tree that represents a voxel geometry. The space partitioning system is used as a framework to allow numerical computations. This work is triggered by the problem of representing, in a numerically appropriate way, huge three-dimensional voxel geometries that could have up to billions of voxels. These large datasets occure in situations where it is needed to deal with large representative volume elements (REV).
Second, we introduce a novel approach of variable arrangement for pressure and velocity to solve the Stokes equations. The basic idea of our method is to arrange variables in a way such that each cell is able to satisfy a given physical law independently from its neighbor cells. This is done by splitting velocity values to a left and right converging component. For each cell we can set up a small linear system that describes the momentum and mass conservation equations. This formulation allows to use the Gauß-Seidel algorithm to solve the global linear system. Our tree structure is used for spatial partitioning of the geometry and provides a proper initial guess. In addition, we introduce a method that uses the actual velocity field to refine the tree and improve the numerical accuracy where it is needed. We developed a novel approach rather than using existing approaches such as the SIMPLE algorithm, Lattice-Boltzmann methods or Exlicit jump methods since they are suited for regular grid structures. Other standard CFD approaches extract surfaces and creates tetrahedral meshes to solve on unstructured grids thus can not be applied to our datastructure. The discretization converges to the analytical solution with respect to grid refinement. We conclude a high strength in computational time and memory for high porosity geometries and a high strength in memory requirement for low porosity geometries.
Researchers and analysts in modern industrial and academic environments are faced with a daunting amount of multivariate data. While there has been significant development in the areas of data mining and knowledge
discovery, there is still the need for improved visualizations and generic solutions. The state-of-the-art in visual analytics and exploratory data visualization is to incorporate more profound analysis methods while focusing on improving interactive abilities, in order to support data analysts in gaining new insights through visual exploration and hypothesis building.
In the research field of exploratory data visualization, this thesis contributes new approaches in dimension reduction that tackle a number of shortcomings in state-of-the-art methods, such as interpretability and ambiguity. By combining methods from several disciplines, we describe how ambiguity can be countered effectively by visualizing coordinate values within a lower-dimensional embedding, thereby focusing on the display of the structural composition of high-dimensional data and on an intuitive depiction of inherent global relationships. We also describe how properties and alignment of high-dimensional manifolds can be analyzed in different levels of detail by means of a self-embedding hierarchy of local projections, each using full degree of freedom, while keeping the global context.
To the application field of air quality research, the thesis provides novel means for the research of aerosol source contributions. Triggered by this particularly challenging application problem, we instigate a new research direction in the area of visual analytics by describing a methodology to model-based visual analysis that (i) allows the scientist to be “in the loop” of computations and (ii) enables him to verify and control the analysis process, in order to steer computations towards physical meaning. Careful reflection of our work in this application has led us to derive key design choices that underlie and transcend beyond application-specific solutions. As a result, we describe a general design methodology to computing parameters of a pre-defined analytical model that map to multivariate data. Core applications areas that can benefit from our approach are within engineering disciplines, such as civil, chemical, electrical, and mechanical engineering, as well as in geology, physics, and biology.
Mechanical ventilation of patients with severe lung injury is an important clinical treatment to ensure proper lung oxygenation and to mitigate the extent of collapsed lung regions. While current imaging technologies such as Computed Tomography (CT) and chest X-ray allow for a thorough inspection of the thorax, they are limited to static pictures and exhibit several disadvantages, including exposure to ionizing radiation and high cost. Electrical Impedance Tomography (EIT) is a novel method to determine functional processes inside the thorax such as lung ventilation and cardiac activity. EIT reconstructs the internal electrical conductivity distribution within the thorax from voltage measurements on the body surface. Conductivity changes correlate with important clinical parameters such as lung volume and perfusion. Current EIT systems and algorithms use simplified or generalized thorax models to solve the reconstruction problem, which reduce image quality and anatomical significance. In this thesis, the development of a clinically relevant workflow to compute sophisticated three-dimensional thorax models from patient-specific CT data is described. The method allows medical experts to generate a multi-material segmentation in an interactive and fast way, while a volumetric mesh is computed automatically from the segmentation. The significantly improved image quality and anatomical precision of EIT images reconstructed with these 3D models is reported, and the impact on clinical applicability is discussed. In addition, three projects concerning quantitative CT (qCT) measurements and multi-modal 3D visualization are presented, which demonstrate the importance and productivity of interdisciplinary research groups including computer scientists and medical experts. The results presented in this thesis contribute significantly to clinical research efforts to pave the way towards improved patient-specific treatments of lung injury using EIT and qCT.
This PhD-Thesis deals with the calculation and application of a new class of invariants, that can be used to recognize patterns in tensor fields (i.e. scalar fields, vector fields und matrix fields), and by the composition of scalar fields with delta-functions also to point-clouds.
In the first chapter an overview over already existing invariants is given.
In the second chapter the general definition of the new invariants is given:
starting with a tensor field a set of moment tensor is created via folding in tensor-product manner with different orders of the tensor product of the positional vector. From these, rotational invariant values are calculated via contraction of tensor products. An algorithm to get a complete and independent set of invariants from a given moment tensor set is described. Furthermore methods to make these sets of invariants invariant against translation, rotation, scaling, and affine transformation.
In the third chapter, a method to optimize the calculation of these sets of invariants is described: every invariant can be modeled as undirected graph comprising multiple sub-graphs representing partially contracted tensor products of the moment tensors.
The composition of the sets of invariants is optimized by a clever choice of the decomposition into sub-graphs, all paths creating a hyper-graph of sub-graphs where each node describes a composition step. Finally, C++-source-code is created, which optimized using the symmetry of the different tensors and tensor-products, and a comparison of the effort to other calculation methods of invariants is given.
The fourth chapter describes the application of the invariants to object recognition in point-clouds from 3D-scans. To do this, the invariants of sub-sets of point-clouds are stored for every known object. Afterwards, invariants are calculated from an unknown point-cloud and tried to find them in the database to assign it to one of the known objects. Benchmarks using three 3D-object databases are made testing time and recognition rate.
Three dimensional (3d) point data is used in industry for measurement and reverse engineering. Precise point data is usually acquired with triangulating laser scanners or high precision structured light scanners. Lower precision point data is acquired by real-time structured light devices or by stereo matching with multiple cameras. The basic principle of all these methods is the so-called triangulation of 3d coordinates from two dimensional (2d) camera images.
This dissertation contributes a method for multi-camera stereo matching that uses a system of four synchronized cameras. A GPU based stereo matching method is presented to achieve a high quality reconstruction at interactive frame rates. Good depth resolution is achieved by allowing large disparities between the images. A multi level approach on the GPU allows a fast processing of these large disparities. In reverse engineering, hand-held laser scanners are used for the scanning of complex shaped objects. The operator of the scanner can scan complex regions slower, multiple times, or from multiple angles to achieve a higher point density. Traditionally, computer aided design (CAD) geometry is reconstructed in a separate step after the scanning. Errors or missing parts in the scan prevent a successful reconstruction. The contribution of this dissertation is an on-line algorithm that allows the reconstruction during the scanning of an object. Scanned points are added to the reconstruction and improve it on-line. The operator can detect the areas in the scan where the reconstruction needs additional data.
First, the point data is thinned out using an octree based data structure. Local normals and principal curvatures are estimated for the reduced set of points. These local geometric values are used for segmentation using a region growing approach. Implicit quadrics are fitted to these segments. The canonical form of the quadrics provides the parameters of basic geometric primitives.
An improved approach uses so called accumulated means of local geometric properties to perform segmentation and primitive reconstruction in a single step. Local geometric values can be added and removed on-line to these means to get a stable estimate over a complete segment. By estimating the shape of the segment it is decided which local areas are added to a segment. An accumulated score estimates the probability for a segment to belong to a certain type of geometric primitive. A boundary around the segment is reconstructed using a growing algorithm that ensures that the boundary is closed and avoids self intersections.
If an automated system is tasked to provide services such as search or clustering of information on an information repository, the quality of the output depends a lot on the information that is available to the system in machine-readable form. Simple text, for example, is machine-readable only in a very limited sense. Advanced services typically need to derive other representations of the text (e.g., sets of keywords) as input for their core algorithms. Some services might need information that cannot be derived from the resource in question alone, but is available as separate metadata only, such as usage information. Annotations can be used to carry this information.
This thesis focuses on so-called ontology-based annotations. In contrast to other forms of annotations such as Tags (arbitrary strings that users can assign to resources), ontology-based annotations conform to a predefined data structure and class hierarchy. An advantage of this approach is that rich information can be stored in a well-structured way in the annotations; a drawback is that users need to be familiar with the hierarchy and other design decisions of the underlying ontology used for annotations.
Two scenarios are considered in this thesis:
First, a document-based scenario in which text annotations are used to represent both information about the text content and usage and user context information in a multi-user setting with mostly objective annotation criteria; second, a resource-based scenario whose annotation model focuses on multi-user settings with subjective annotation criteria, using (dis-)similarities in user annotations to derive user similarity metrics, and building personalized views from this information.
Finally, the prototypical systems that have been developed throughout this thesis get evaluated, proving the concepts presented in this thesis.
This thesis discusses several applications of computational topology to the visualization
of scalar fields. Scalar field data come from different measurements and simulations. The
intrinsic properties of this kind of data, which make the visualization of it to a complicated
task, are the large size and presence of noise. Computational topology is a powerful tool
for automatic feature extraction, which allows the user to interpret the information contained
in the dataset in a more efficient way. Utilizing it one can make the main purpose of
scientific visualization, namely extracting knowledge from data, a more convenient task.
Volume rendering is a class of methods designed for realistic visual representation of 3D
scalar fields. It is used in a wide range of applications with different data size, noise
rate and requirements on interactivity and flexibility. At the moment there is no known
technique which can meet the needs of every application domain, therefore development
of methods solving specific problems is required. One of such algorithms, designed for
rendering of noisy data with high frequencies is presented in the first part of this thesis.
The method works with multidimensional transfer functions and is especially suited for
functions exhibiting sharp features. Compared with known methods the presented algorithm
achieves better visual quality with a faster performance in presence of mentioned
features. An improvement on the method utilizing a topological theory, Morse theory, and
a topological construct, Morse-Smale complex, is also presented in this part of the thesis.
The improvement allows for performance speedup at a little precomputation and memory
cost.
The usage of topological methods for feature extraction on a real world dataset often
results in a very large feature space which easily leads to information overflow. Topology
simplification is designed to reduce the number of features and allow a domain expert
to concentrate on the most important ones. In the terms of Morse theory features are
represented by critical points. An importance measure which is usually used for removing
critical points is called homological persistence. Critical points are cancelled pairwise
according to their homological persistence value. In the presence of outlier-like noise
homological persistence has a clear drawback: the outliers get a high importance value
assigned and therefore are not being removed. In the second part of this thesis a new
importance measure is presented which is especially suited for data with outliers. This
importance measure is called scale space persistence. The algorithm for the computation
of this measure is based on the scale space theory known from the area of computer
vision. The development of a critical point in scale space gives information about its
spacial extent, therefore outliers can be distinguished from other critical points. The usage
of the presented importance measure is demonstrated on a real world application, crater
identification on a surface of Mars.
The third part of this work presents a system for general interactive topology analysis
and exploration. The development of such a system is motivated by the fact that topological
methods are often considered to be complicated and hard to understand, because
application of topology for visualization requires deep understanding of the mathematical
background behind it. A domain expert exploring the data using topology for feature
extraction needs an intuitive way to manipulate the exploration process. The presented
system is based on an intuitive notion of a scene graph, where the user can choose and
place the component blocks to achieve an individual result. This way the domain expert
can extract more knowledge from given data independent on the application domain. The
tool gives the possibility for calculation and simplification of the underlying topological
structure, Morse-Smale complex, and also the visualization of parts of it. The system also
includes a simple generic query language to acquire different structures of the topological
structure at different levels of hierarchy.
The fourth part of this dissertation is concentrated on an application of computational
geometry for quality assessment of a triangulated surface. Quality assessment of a triangulation
is called surface interrogation and is aimed for revealing intrinsic irregularities
of a surface. Curvature and continuity are the properties required to design a visually
pleasing geometric object. For example, a surface of a manufactured body usually should
be convex without bumps of wiggles. Conventional rendering methods hide the regions
of interest because of smoothing or interpolation. Two new methods which are presented
here: curvature estimation using local fitting with B´ezier patches and computation of reflection
lines for visual representation of continuity, are specially designed for assessment
problems. The examples and comparisons presented in this part of the thesis prove the
benefits of the introduced algorithms. The methods are also well suited for concurrent visualization
of the results from simulation and surface interrogation to reveal the possible
intrinsic relationship between them.
As the complexity of embedded systems continuously rises, their development becomes more and more challenging. One technique to cope with this complexity is the employment of virtual prototypes. The virtual prototypes are intended to represent the embedded system’s properties on different levels of detail like register transfer level or transaction level. Virtual prototypes can be used for different tasks throughout the development process. They can act as executable specification, can be used for architecture exploration, can ease system integration, and allow for pre- and post-silicon software development and verification. The optimization objectives for virtual prototypes and their creation process are manifold. Finding an appropriate trade-off between the simulation accuracy, the simulation performance, and the implementation effort is a major challenge, as these requirements are contradictory.
In this work, two new and complementary techniques for the efficient creation of accurate and high-performance SystemC based virtual prototypes are proposed: Advanced Temporal Decoupling (ATD) and Transparent Transaction Level Modeling (TTLM). The suitability for industrial environments is assured by the employment of common standards like SystemC TLM-2.0 and IP-XACT.
Advanced Temporal Decoupling enhances the simulation accuracy while retaining high simulation performance by allowing for cycle accurate simulation in the context of SystemC TLM-2.0 temporal decoupling. This is achieved by exploiting the local time warp arising in SystemC TLM-2.0 temporal decoupled models to support the computation of resource contention effects. In ATD, accesses to shared resource are managed by Temporal Decoupled Semaphores (TDSems) which are integrated into the modeled shared resources. The set of TDSems assures the correct execution order of shared resource accesses and incorporates timing effects resulting from shared resource access execution and resource conflicts. This is done by dynamically varying the data granularity of resource accesses based on information gathered from the local time warp. ATD facilitates modeling of a wide range of resource and resource access properties like preemptable and non-preemptable accesses, synchronous and asynchronous accesses, multiport resources, dynamic access priorities, interacting and cascaded resources, and user specified schedulers prioritizing simultaneous resource accesses.
Transparent Transaction Level Modeling focuses on the efficient creation of virtual prototypes by reducing the implementation effort and consists of a library and a code generator. The TTLM library adds a layer of convenience functions to ATD comprising various application programming interfaces for inter module communication, virtual prototype configuration and run time information extraction. The TTLM generator is used to automatically generate the structural code of the virtual prototype from the formal hardware specification language IP-XACT.
The applicability and benefits of the presented techniques are demonstrated using an image processing centric automotive application. Compared to an existing cycle accurate SystemC model, the implementation effort can be reduced by approximately 50% using TTLM. Applying ATD, the simulation performance can be increased by a factor of up to five while retaining cycle accuracy.
The recognition of day-to-day activities is still a very challenging and important research topic. During recent years, a lot of research has gone into designing and realizing smart environ- ments in different application areas such as health care, maintenance, sports or smart homes. As a result, a large amount of sensor modalities were developed, different types of activity and context recognition services were implemented and the resulting systems were benchmarked using state-of-the-art evaluation techniques. However, so far hardly any of these approaches have found their way into the market and consequently into the homes of real end-users on a large scale. The reason for this is, that almost all systems have one or more of the following characteristics in common: expensive high-end or prototype sensors are used which are not af- fordable or reliable enough for mainstream applications; many systems are deployed in highly instrumented environments or so-called "living labs", which are far from real-life scenarios and are often evaluated only in research labs; almost all systems are based on complex system con- figurations and/or extensive training data sets, which means that a large amount of data must be collected in order to install the system. Furthermore, many systems rely on a user and/or environment dependent training, which makes it even more difficult to install them on a large scale. Besides, a standardized integration procedure for the deployment of services in existing environments and smart homes has still not been defined. As a matter of fact, service providers use their own closed systems, which are not compatible with other systems, services or sensors. It is clear, that these points make it nearly impossible to deploy activity recognition systems in a real daily-life environment, to make them affordable for real users and to deploy them in hundreds or thousands of different homes.
This thesis works towards the solution of the above mentioned problems. Activity and context recognition systems designed for large-scale deployment and real-life scenarios are intro- duced. Systems are based on low-cost, reliable sensors and can be set up, configured and trained with little effort, even by technical laymen. It is because of these characteristics that we call our approach "minimally invasive". As a consequence, large amounts of training data, that are usu- ally required by many state-of-the-art approaches, are not necessary. Furthermore, all systems were integrated unobtrusively in real-world/similar to real-world environments and were evalu- ated under real-life, as well as similar to real-life conditions. The thesis addresses the following topics: First, a sub-room level indoor positioning system is introduced. The system is based on low-cost ceiling cameras and a simple computer vision tracking approach. The problem of user identification is solved by correlating modes of locomotion patterns derived from the trajectory of unidentified objects and on-body motion sensors. Afterwards, the issue of recognizing how and what mainstream household devices have been used for is considered. Based on a low-cost microphone, the water consumption of water-taps can be approximated by analyzing plumbing noise. Besides that, operating modes of mainstream electronic devices were recognized by using rule-based classifiers, electric current features and power measurement sensors. As a next step, the difficulty of spotting subtle, barely distinguishable hand activities and the resulting object interactions, within a data set containing a large amount of background data, is addressed. The problem is solved by introducing an on-body core system which is configured by simple, one-time physical measurements and minimal data collections. The lack of large training sets is compensated by fusing the system with activity and context recognition systems, that are able to reduce the search space observed. Amongst other systems, previously introduced approaches and ideas are revisited in this section. An in-depth evaluation shows the impact of each fusion procedure on the performance and run-time of the system. The approaches introduced are able to provide significantly better results than a state-of-the-art inertial system using large amounts of training data. The idea of using unobtrusive sensors has also been applied to the field of behavior analysis. Integrated smartphone sensors are used to detect behavioral changes of in- dividuals due to medium-term stress periods. Behavioral parameters related to location traces, social interactions and phone usage were analyzed to detect significant behavioral changes of individuals during stressless and stressful time periods. Finally, as a closing part of the the- sis, a standardization approach related to the integration of ambient intelligence systems (as introduced in this thesis) in real-life and large-scale scenarios is shown.
For many decades, the search for language classes that extend the
context-free laguages enough to include various languages that arise in
practice, while still keeping as many of the useful properties that
context-free grammars have - most notably cubic parsing time - has been
one of the major areas of research in formal language theory. In this thesis
we add a new family of classes to this field, namely
position-and-length-dependent context-free grammars. Our classes use the
approach of regulated rewriting, where derivations in a context-free base
grammar are allowed or forbidden based on, e.g., the sequence of rules used
in a derivation or the sentential forms, each rule is applied to. For our
new classes we look at the yield of each rule application, i.e. the
subword of the final word that eventually is derived from the symbols
introduced by the rule application. The position and length of the yield
in the final word define the position and length of the rule application and
each rule is associated a set of positions and lengths where it is allowed
to be applied.
We show that - unless the sets of allowed positions and lengths are really
complex - the languages in our classes can be parsed in the same time as
context-free grammars, using slight adaptations of well-known parsing
algorithms. We also show that they form a proper hierarchy above the
context-free languages and examine their relation to language classes
defined by other types of regulated rewriting.
We complete the treatment of the language classes by introducing pushdown
automata with position counter, an extension of traditional pushdown
automata that recognizes the languages generated by
position-and-length-dependent context-free grammars, and we examine various
closure and decidability properties of our classes. Additionally, we gather
the corresponding results for the subclasses that use right-linear resp.
left-linear base grammars and the corresponding class of automata, finite
automata with position counter.
Finally, as an application of our idea, we introduce length-dependent
stochastic context-free grammars and show how they can be employed to
improve the quality of predictions for RNA secondary structures.
Regular physical activity is essential to maintain or even improve an individual’s health. There exist various guidelines on how much individuals should do. Therefore, it is important to monitor performed physical activities during people’s daily routine in order to tell how far they meet professional recommendations. This thesis follows the goal to develop a mobile, personalized physical activity monitoring system applicable for everyday life scenarios. From the mentioned recommendations, this thesis concentrates on monitoring aerobic physical activity. Two main objectives are defined in this context. On the one hand, the goal is to estimate the intensity of performed activities: To distinguish activities of light, moderate or vigorous effort. On the other hand, to give a more detailed description of an individual’s daily routine, the goal is to recognize basic aerobic activities (such as walk, run or cycle) and basic postures (lie, sit and stand).
With recent progress in wearable sensing and computing the technological tools largely exist nowadays to create the envisioned physical activity monitoring system. Therefore, the focus of this thesis is on the development of new approaches for physical activity recognition and intensity estimation, which extend the applicability of such systems. In order to make physical activity monitoring feasible in everyday life scenarios, the thesis deals with questions such as 1) how to handle a wide range of e.g.
everyday, household or sport activities and 2) how to handle various potential users. Moreover, this thesis deals with the realistic scenario where either the currently performed activity or the current user is unknown during the development and training
phase of activity monitoring applications. To answer these questions, this thesis proposes and developes novel algorithms, models and evaluation techniques, and performs thorough experiments to prove their validity.
The contributions of this thesis are both of theoretical and of practical value. Addressing the challenge of creating robust activity monitoring systems for everyday life the concept of other activities is introduced, various models are proposed and validated. Another key challenge is that complex activity recognition tasks exceed the potential of existing classification algorithms. Therefore, this thesis introduces a confidence-based extension of the well known AdaBoost.M1 algorithm, called ConfAdaBoost.M1. Thorough experiments show its significant performance improvement compared to commonly used boosting methods. A further major theoretical contribution is the introduction and validation of a new general concept for the personalization of physical activity recognition applications, and the development of a novel algorithm (called Dependent Experts) based on this concept. A major contribution of practical value is the introduction of a new evaluation technique (called leave-one-activity-out) to simulate when performing previously unknown activities in a physical activity monitoring system. Furthermore, the creation and benchmarking of publicly available physical activity monitoring datasets within this thesis are directly benefiting the research community. Finally, the thesis deals with issues related to the implementation of the proposed methods, in order to realize the envisioned mobile system and integrate it into a full healthcare application for aerobic activity monitoring and support in daily life.
Optimal Multilevel Monte Carlo Algorithms for Parametric Integration and Initial Value Problems
(2015)
We intend to find optimal deterministic and randomized algorithms for three related problems: multivariate integration, parametric multivariate integration, and parametric initial value problems. The main interest is concentrated on the question, in how far randomization affects the precision of an approximation. We want to understand when and to which extent randomized algorithms are superior to deterministic ones.
All problems are studied for Banach space valued input functions. The analysis of Banach space valued problems is motivated by the investigation of scalar parametric problems; these can be understood as particular cases of Banach space valued problems. The gain achieved by randomization depends on the underlying Banach space.
For each problem, we introduce deterministic and randomized algorithms and provide the corresponding convergence analysis.
Moreover, we also provide lower bounds for the general Banach space valued settings, and thus, determine the complexity of the problems. It turns out that the obtained algorithms are order optimal in the deterministic setting. In the randomized setting, they are order optimal for certain classes of Banach spaces, which includes the L_p spaces and any finite dimensional Banach space. For general Banach spaces, they are optimal up to an arbitrarily small gap in the order of convergence.
The last couple of years have marked the entire field of information technology with the introduction of a new global resource, called data. Certainly, one can argue that large amounts of information and highly interconnected and complex datasets were available since the dawn of the computer and even centuries before. However, it has been only a few years since digital data has exponentially expended, diversified and interconnected into an overwhelming range of domains, generating an entire universe of zeros and ones. This universe represents a source of information with the potential of advancing a multitude of fields and sparking valuable insights. In order to obtain this information, this data needs to be explored, analyzed and interpreted.
While a large set of problems can be addressed through automatic techniques from fields like artificial intelligence, machine learning or computer vision, there are various datasets and domains that still rely on the human intuition and experience in order to parse and discover hidden information. In such instances, the data is usually structured and represented in the form of an interactive visual representation that allows users to efficiently explore the data space and reach valuable insights. However, the experience, knowledge and intuition of a single person also has its limits. To address this, collaborative visualizations allow multiple users to communicate, interact and explore a visual representation by building on the different views and knowledge blocks contributed by each person.
In this dissertation, we explore the potential of subjective measurements and user emotional awareness in collaborative scenarios as well as support flexible and user- centered collaboration in information visualization systems running on tabletop displays. We commence by introducing the concept of user-centered collaborative visualization (UCCV) and highlighting the context in which it applies. We continue with a thorough overview of the state-of-the-art in the areas of collaborative information visualization, subjectivity measurement and emotion visualization, combinable tabletop tangibles, as well as browsing history visualizations. Based on a new web browser history visualization for exploring user parallel browsing behavior, we introduce two novel user-centered techniques for supporting collaboration in co-located visualization systems. To begin with, we inspect the particularities of detecting user subjectivity through brain-computer interfaces, and present two emotion visualization techniques for touch and desktop interfaces. These visualizations offer real-time or post-task feedback about the users’ affective states, both in single-user and collaborative settings, thus increasing the emotional self-awareness and the awareness of other users’ emotions. For supporting collaborative interaction, a novel design for tabletop tangibles is described together with a set of specifically developed interactions for supporting tabletop collaboration. These ring-shaped tangibles minimize occlusion, support touch interaction, can act as interaction lenses, and describe logical operations through nesting operations. The visualization and the two UCCV techniques are each evaluated individually capturing a set of advantages and limitations of each approach. Additionally, the collaborative visualization supported by the two UCCV techniques is also collectively evaluated in three user studies that offer insight into the specifics of interpersonal interaction and task transition in collaborative visualization. The results show that the proposed collaboration support techniques do not only improve the efficiency of the visualization, but also help maintain the collaboration process and aid a balanced social interaction.
The main goal of this thesis is twofold. First, the thesis aims at bridging the gap between existing Pattern Recognition (PR) methods of automatic signature verification and the requirements for their application in forensic science. This gap, attributed by various factors ranging from system definition to evaluation, prevents automatic methods from being used by Forensic Handwriting Examiners (FHEs). Second, the thesis presents novel signature verification methods developed particularly considering the implications of forensic casework, and outperforming the state-of-the-art PR methods.
The first goal of the thesis is attributed by four important factors, i.e., data, terminology, output reporting, and how evaluation of automatic systems is carried out today. It is argued that traditionally the signature data used in PR are not actual/close representative of the real world data (especially that available in forensic cases). The systems trained on such data are, therefore, not suitable for forensic environments. This situation can be tackled by providing more realistic data to PR researchers. To this end, various signature and handwriting datasets are gathered in collaboration with FHEs and are made publicly available through the course of this thesis. A special attention is given to disguised signatures--where authentic authors purposefully make their signatures look like a forgery. This genre was at large neglected in PR research previously.
The terminology used, in the two communities - PR and FHEs, differ greatly. In fact, even in PR, there is no standard terminology and people often differ in the usage of various terms particularly related to various types of forged signatures/handwriting. The thesis presents a new terminology that is equally useful for both forensic scientists and PR researchers. The proposed terminology is hoped to increase the general acceptability of automatic signature analysis systems in forensic science.
The outputs reported by general signature verification systems are not acceptable for FHEs and courts as they are either binary (yes/no) or score (raw evidence) based on similarity/difference. The thesis describes that automatic systems should rather report the probability of observing the evidence (e.g., a certain similarity/difference score) given the signature belongs to the acclaimed identity, and the probability of observing the same evidence given the signature does not belong to the acclaimed identity. This will take automatic systems from hard decisions to soft decisions, thereby enabling them to report likelihood ratios that actually represent the evidential value of the score rather than the raw score (evidence).
When automatic systems report soft decisions (as in the form of likelihood ratios), the thesis argues that there must be some methods to evaluate such systems. This thesis presents one such adaptation. The thesis argues that the state-of-the-art evaluation methods, like equal error rate and area under curve, do not address the needs of forensic science. These needs require an assessment of the evidential value of signature verification, rather than a hard/pure classification (accept/reject binary decision). The thesis demonstrates and validates a relatively simple adaptation of the current verification methods based on the Bayesian inference dependent calibration of continuous scores rather than hard classifications (binary and/or score based classification).
The second goal of this thesis is to introduce various local features based techniques which are capable of performing signature verification in forensic cases and reporting results as anticipated by FHEs and courts. This is an important contribution of the thesis because of the following two reasons. First, to the best of author's knowledge, local feature descriptors are for the first time used for development of signature verification systems for forensic environments (particularly considering disguised signatures). Previously, such methods have been heavily used for recognition tasks, rather than verification of writing behaviors, such as character and digit recognition. Second, the proposed methods not only report the more traditional decisions (like scores-usually reported in PR) but also the Bayesian inference based likelihood ratios (suitable for courts and forensic cases).
Furthermore, the thesis also provides a detailed man vs. machine comparison for signature verification tasks. The men, in this comparison, are forensic scientists serving as forensic handwriting examiners and having experience of varying number of years. The machines are the local features based methods proposed in this thesis, along with various other state-of-the-art signature verification systems. The proposed methods clearly outperform the state-of-the-art systems, and sometimes the human experts.
Finally, the thesis details various tasks that have been performed in the areas closely related to signature verification and its application in forensic casework. These include, developing novel local feature based methods for extraction of signatures/handwritten text from document images, hyper-spectral image analysis for extraction of signatures from forensic documents, and analysis of on-line signatures acquired through specialized pens equipped with Accelerometer and Gyroscope. These tasks are important as they enable the thesis to take PR systems one step further close to direct application in forensic cases.
The goal of this work is to develop statistical natural language models and processing techniques
based on Recurrent Neural Networks (RNN), especially the recently introduced Long Short-
Term Memory (LSTM). Due to their adapting and predicting abilities, these methods are more
robust, and easier to train than traditional methods, i.e., words list and rule-based models. They
improve the output of recognition systems and make them more accessible to users for browsing
and reading. These techniques are required, especially for historical books which might take
years of effort and huge costs to manually transcribe them.
The contributions of this thesis are several new methods which have high-performance computing and accuracy. First, an error model for improving recognition results is designed. As
a second contribution, a hyphenation model for difficult transcription for alignment purposes
is suggested. Third, a dehyphenation model is used to classify the hyphens in noisy transcription. The fourth contribution is using LSTM networks for normalizing historical orthography.
A size normalization alignment is implemented to equal the size of strings, before the training
phase. Using the LSTM networks as a language model to improve the recognition results is
the fifth contribution. Finally, the sixth contribution is a combination of Weighted Finite-State
Transducers (WFSTs), and LSTM applied on multiple recognition systems. These contributions
will be elaborated in more detail.
Context-dependent confusion rules is a new technique to build an error model for Optical
Character Recognition (OCR) corrections. The rules are extracted from the OCR confusions
which appear in the recognition outputs and are translated into edit operations, e.g., insertions,
deletions, and substitutions using the Levenshtein edit distance algorithm. The edit operations
are extracted in a form of rules with respect to the context of the incorrect string to build an
error model using WFSTs. The context-dependent rules assist the language model to find the
best candidate corrections. They avoid the calculations that occur in searching the language
model and they also make the language model able to correct incorrect words by using context-
dependent confusion rules. The context-dependent error model is applied on the university of
Washington (UWIII) dataset and the Nastaleeq script in Urdu dataset. It improves the OCR
results from an error rate of 1.14% to an error rate of 0.68%. It performs better than the
state-of-the-art single rule-based which returns an error rate of 1.0%.
This thesis describes a new, simple, fast, and accurate system for generating correspondences
between real scanned historical books and their transcriptions. The alignment has many challenges, first, the transcriptions might have different modifications, and layout variations than the
original book. Second, the recognition of the historical books have misrecognition, and segmentation errors, which make the alignment more difficult especially the line breaks, and pages will
not have the same correspondences. Adapted WFSTs are designed to represent the transcription. The WFSTs process Fraktur ligatures and adapt the transcription with a hyphenations
model that allows the alignment with respect to the varieties of the hyphenated words in the line
breaks of the OCR documents. In this work, several approaches are implemented to be used for
the alignment such as: text-segments, page-wise, and book-wise approaches. The approaches
are evaluated on German calligraphic (Fraktur) script historical documents dataset from “Wan-
derungen durch die Mark Brandenburg” volumes (1862-1889). The text-segmentation approach
returns an error rate of 2.33% without using a hyphenation model and an error rate of 2.0%
using a hyphenation model. Dehyphenation methods are presented to remove the hyphen from
the transcription. They provide the transcription in a readable and reflowable format to be used
for alignment purposes. We consider the task as classification problem and classify the hyphens
from the given patterns as hyphens for line breaks, combined words, or noise. The methods are
applied on clean and noisy transcription for different languages. The Decision Trees classifier
returns better performance on UWIII dataset and returns an accuracy of 98%. It returns 97%
on Fraktur script.
A new method for normalizing historical OCRed text using LSTM is implemented for different texts, ranging from Early New High German 14th - 16th centuries to modern forms in New
High German applied on the Luther bible. It performed better than the rule-based word-list
approaches. It provides a transcription for various purposes such as part-of-speech tagging and
n-grams. Also two new techniques are presented for aligning the OCR results and normalize the
size by using adding Character-Epsilons or Appending-Epsilons. They allow deletion and insertion in the appropriate position in the string. In normalizing historical wordforms to modern
wordforms, the accuracy of LSTM on seen data is around 94%, while the state-of-the-art combined rule-based method returns 93%. On unseen data, LSTM returns 88% and the combined
rule-based method returns 76%. In normalizing modern wordforms to historical wordforms, the
LSTM delivers the best performance and returns 93.4% on seen data and 89.17% on unknown
data.
In this thesis, a deep investigation has been done on constructing high-performance language
modeling for improving the recognition systems. A new method to construct a language model
using LSTM is designed to correct OCR results. The method is applied on UWIII and Urdu
script. The LSTM approach outperforms the state-of-the-art, especially for unseen tokens
during training. On the UWIII dataset, the LSTM returns reduction in OCR error rates from
1.14% to 0.48%. On the Nastaleeq script in Urdu dataset, the LSTM reduces the error rate
from 6.9% to 1.58%.
Finally, the integration of multiple recognition outputs can give higher performance than a
single recognition system. Therefore, a new method for combining the results of OCR systems is
explored using WFSTs and LSTM. It uses multiple OCR outputs and votes for the best output
to improve the OCR results. It performs better than the ISRI tool, Pairwise of Multiple Sequence and it helps to improve the OCR results. The purpose is to provide correct transcription
so that it can be used for digitizing books, linguistics purposes, N-grams, and part-of-speech
tagging. The method consists of two alignment steps. First, two recognition systems are aligned
using WFSTs. The transducers are designed to be more flexible and compatible with the different symbols in line and page breaks to avoid the segmentation and misrecognition errors.
The LSTM model then is used to vote the best candidate correction of the two systems and
improve the incorrect tokens which are produced during the first alignment. The approaches
are evaluated on OCRs output from the English UWIII and historical German Fraktur dataset
which are obtained from state-of-the-art OCR systems. The Experiments show that the error
rate of ISRI-Voting is 1.45%, the error rate of the Pairwise of Multiple Sequence is 1.32%, the
error rate of the Line-to-Page alignment is 1.26% and the error rate of the LSTM approach has
the best performance with 0.40%.
The purpose of this thesis is to contribute methods providing correct transcriptions corresponding to the original book. This is considered to be the first step towards an accurate and
more effective use of the documents in digital libraries.
Today's ubiquity of visual content as driven by the availability of broadband Internet, low-priced storage, and the omnipresence of camera equipped mobile devices conveys much of our thinking and feeling as individuals and as a society. As a result the growth of video repositories is increasing at enourmous rates with content now being embedded and shared through social media. To make use of this new form of social multimedia, concept detection, the automatic mapping of semantic concepts and video content has to be extended such that concept vocabularies are synchronized with current real-world events, systems can perform scalable concept learning with thousands of concepts, and high-level information such as sentiment can be extracted from visual content. To catch up with these demands the following three contributions are made in this thesis: (i) concept detection is linked to trending topics, (ii) visual learning from web videos is presented including the proper treatment of tags as concept labels, and (iii) the extension of concept detection with adjective noun pairs for sentiment analysis is proposed.
In order for concept detection to satisfy users' current information needs, the notion of fixed concept vocabularies has to be reconsidered. This thesis presents a novel concept learning approach built upon dynamic vocabularies, which are automatically augmented with trending topics mined from social media. Once discovered, trending topics are evaluated by forecasting their future progression to predict high impact topics, which are then either mapped to an available static concept vocabulary or trained as individual concept detectors on demand. It is demonstrated in experiments on YouTube video clips that by a visual learning of trending topics, improvements of over 100% in concept detection accuracy can be achieved over static vocabularies (n=78,000).
To remove manual efforts related to training data retrieval from YouTube and noise caused by tags being coarse, subjective and context-depedent, this thesis suggests an automatic concept-to-query mapping for the retrieval of relevant training video material, and active relevance filtering to generate reliable annotations from web video tags. Here, the relevance of web tags is modeled as a latent variable, which is combined with an active learning label refinement. In experiments on YouTube, active relevance filtering is found to outperform both automatic filtering and active learning approaches, leading to a reduction of required label inspections by 75% as compared to an expert annotated training dataset (n=100,000).
Finally, it is demonstrated, that concept detection can serve as a key component to infer the sentiment reflected in visual content. To extend concept detection for sentiment analysis, adjective noun pairs (ANP) as novel entities for concept learning are proposed in this thesis. First a large-scale visual sentiment ontology consisting of 3,000 ANPs is automatically constructed by mining the web. From this ontology a mid-level representation of visual content – SentiBank – is trained to encode the visual presence of 1,200 ANPs. This novel approach of visual learning is validated in three independent experiments on sentiment prediction (n=2,000), emotion detection (n=807) and pornographic filtering (n=40,000). SentiBank is shown to outperform known low-level feature representations (sentiment prediction, pornography detection) or perform comparable to state-of-the art methods (emotion detection).
Altogether, these contributions extend state-of-the-art concept detection approaches such that concept learning can be done autonomously from web videos on a large-scale, and can cope with novel semantic structures such as trending topics or adjective noun pairs, adding a new dimension to the understanding of video content.
In this thesis, an approach is presented that turns the currently unstructured process of automotive hazard analysis and risk assessments (HRA), which relies on creativity techniques, into a structured, model-based approach that makes the HRA results less dependent on experts' experience, more consistent, and gives them higher quality. The challenge can be subdivided into two steps. The first step is to improve the HRA as it is performed in current practice. The second step is to go beyond the current practice and consider not only single service failures as relevant hazards, but also multiple service failures. For the first step, the most important aspect is to formalize the operational situation of the system and to determine its likelihood. Current approaches use natural-language textual descriptions, which makes it hard to ensure consistency and increase efficiency through reuse. Furthermore, due to ambiguity in natural language, it is difficult to ensure consistent likelihood estimates for situations.
The main aspect of the second step is that considering multiple service failures as hazards implies that one needs to analyze an exponential number of hazards. Due to the fact that hazard assessments are currently done purely manually, considering multiple service failures is not possible. The only way to approach this challenge is to formalize the HRA and make extensive use of automation support.
In SAHARA we handle these challenges by first introducing a model-based representation of an HRA with GOBI. Based on this, we formalized the representation of operational situations and their likelihood assessment in OASIS and HEAT, respectively. We show that more consistent situation assessments are possible and that situations (including their likelihood) can be efficiently reused. The second aspect, coping with multiple service failures, is addressed in ARID. We show that using our tool-supported HRA approach, 100% coverage of all possible hazards (including multiple service failures) can be achieved by relying on very limited manual effort. We furthermore show that not considering multiple service failures results in insufficient safety goals.
Sequential Consistency (SC) is the memory model traditionally applied by programmers and verification tools for the analysis of multithreaded programs.
SC guarantees that instructions of each thread are executed atomically and in program order.
Modern CPUs implement memory models that relax the SC guarantees: threads can execute instructions out of order, stores to the memory can be observed by different threads in different order.
As a result of these relaxations, multithreaded programs can show unexpected, potentially undesired behaviors, when run on real hardware.
The robustness problem asks if a program has the same behaviors under SC and under a relaxed memory model.
Behaviors are formalized in terms of happens-before relations — dataflow and control-flow relations between executed instructions.
Programs that are robust against a memory model produce the same results under this memory model and under SC.
This means, they only need to be verified under SC, and the verification results will carry over to the relaxed setting.
Interestingly, robustness is a suitable correctness criterion not only for multithreaded programs, but also for parallel programs running on computer clusters.
Parallel programs written in Partitioned Global Address Space (PGAS) programming model, when executed on cluster, consist of multiple processes, each running on its cluster node.
These processes can directly access memories of each other over the network, without the need of explicit synchronization.
Reorderings and delays introduced on the network level, just as the reorderings done by the CPUs, may result into unexpected behaviors that are hard to reproduce and fix.
Our first contribution is a generic approach for solving robustness against relaxed memory models.
The approach involves two steps: combinatorial analysis, followed by an algorithmic development.
The aim of combinatorial analysis is to show that among program computations violating robustness there is always a computation in a certain normal form, where reorderings are applied in a restricted way.
In the algorithmic development we work out a decision procedure for checking whether a program has violating normal-form computations.
Our second contribution is an application of the generic approach to widely implemented memory models, including Total Store Order used in Intel x86 and Sun SPARC architectures, the memory model of Power architecture, and the PGAS memory model.
We reduce robustness against TSO to SC state reachability for a modified input program.
Robustness against Power and PGAS is reduced to language emptiness for a novel class of automata — multiheaded automata.
The reductions lead to new decidability results.
In particular, robustness is PSPACE-complete for all the considered memory models.
Large displays become more and more popular, due to dropping prices. Their size and high resolution leverages collaboration and they are capable of dis- playing even large datasets in one view. This becomes even more interesting as the number of big data applications increases. The increased screen size and other properties of large displays pose new challenges to the Human- Computer-Interaction with these screens. This includes issues such as limited scalability to the number of users, diversity of input devices in general, leading to increased learning efforts for users, and more.
Using smart phones and tablets as interaction devices for large displays can solve many of these issues. Since they are almost ubiquitous today, users can bring their own device. This approach scales well with the number of users. These mobile devices are easy and intuitive to use and allow for new interaction metaphors, as they feature a wide array of input and output capabilities, such as touch screens, cameras, accelerometers, microphones, speakers, Near-Field Communication, WiFi, etc.
This thesis will present a concept to solve the issues posed by large displays. We will show proofs-of-concept, with specialized approaches showing the via- bility of the concept. A generalized, eyes-free technique using smart phones or tablets to interact with any kind of large display, regardless of hardware or software then overcomes the limitations of the specialized approaches. This is implemented in a large display application that is designed to run under a multitude of environments, including both 2D and 3D display setups. A special visualization method is used to combine 2D and 3D data in a single visualization.
Additionally the thesis will present several approaches to solve common is- sues with large display interaction, such as target sizes on large display getting too small, expensive tracking hardware, and eyes-free interaction through vir- tual buttons. These methods provide alternatives and context for the main contribution.
Information Visualization (InfoVis) and Human-Computer Interaction (HCI) have strong ties with each other. Visualization supports the human cognitive system by providing interactive and meaningful images of the underlying data. On the other side, the HCI domain cares about the usability of the designed visualization from the human perspectives. Thus, designing a visualization system requires considering many factors in order to achieve the desired functionality and the system usability. Achieving these goals will help these people in understanding the inside behavior of complex data sets in less time.
Graphs are widely used data structures to represent the relations between the data elements in complex applications. Due to the diversity of this data type, graphs have been applied in numerous information visualization applications (e.g., state transition diagrams, social networks, etc.). Therefore, many graph layout algorithms have been proposed in the literature to help in visualizing this rich data type. Some of these algorithms are used to visualize large graphs, while others handle the medium sized graphs. Regardless of the graph size, the resulting layout should be understandable from the users’ perspective and at the same time it should fulfill a list of aesthetic criteria to increase the representation readability. Respecting these two principles leads to produce a resulting graph visualization that helps the users in understanding and exploring the complex behavior of critical systems.
In this thesis, we utilize the graph visualization techniques in modeling the structural and behavioral aspects of embedded systems. Furthermore, we focus on evaluating the resulting representations from the users’ perspectives.
The core contribution of this thesis is a framework, called ESSAVis (Embedded Systems Safety Aspect Visualizer). This framework visualizes not only some of the safety aspects (e.g. CFT models) of embedded systems, but also helps the engineers and experts in analyzing the system safety critical situations. For this, the framework provides a 2Dplus3D environment in which the 2D represents the graph representation of the abstract data about the safety aspects of the underlying embedded system while the 3D represents the underlying system 3D model. Both views are integrated smoothly together in the 3D world fashion. In order to check the effectiveness and feasibility of the framework and its sub-components, we conducted many studies with real end users as well as with general users. Results of the main study that targeted the overall ESSAVis framework show high acceptance ratio and higher accuracy with better performance using the provided visual support of the framework.
The ESSAVis framework has been designed to be compatible with different 3D technologies. This enabled us to use the 3D stereoscopic depth of such technologies to encode nodes attributes in node-link diagrams. In this regard, we conducted an evaluation study to measure the usability of the stereoscopic depth cue approach, called the stereoscopic highlighting technique, against other selected visual cues (i.e., color, shape, and sizes). Based on the results, the thesis proposes the Reflection Layer extension to the stereoscopic highlighting technique, which was also evaluated from the users’ perspectives. Additionally, we present a new technique, called ExpanD (Expand in Depth), that utilizes the depth cue to show the structural relations between different levels of details in node-link diagrams. Results of this part opens a promising direction of the research in which visualization designers can get benefits from the richness of the 3D technologies in visualizing abstract data in the information visualization domain.
Finally, this thesis proposes the application of the ESSAVis frame- work as a visual tool in the educational training process of engineers for understanding the complex concepts. In this regard, we conducted an evaluation study with computer engineering students in which we used the visual representations produced by ESSAVis to teach the principle of the fault detection and the failure scenarios in embedded systems. Our work opens the directions to investigate many challenges about the design of visualization for educational purposes.
There are a number of designs for an online advertising system that allow for behavioral targeting without revealing user online behavior or user interest profiles to the ad network. Although these designs purport to be practical solutions, none of them adequately consider the role of ad auctions, which today are central to the operation of online advertising systems. Moreover, none of the proposed designs have been deployed in real-life settings. In this thesis, we present an effort to fill this gap. First, we address the challenge of running ad auctions that leverage user profiles while keeping the profile information private. We define the problem, broadly explore the solution space, and discuss the pros and cons of these solutions. We analyze the performance of our solutions using data from Microsoft Bing advertising auctions. We conclude that, while none of our auctions are ideal in all respects, they are adequate and practical solutions. Second, we build and evaluate a fully functional prototype of a practical privacy-preserving ad system at a reasonably large scale. With more than 13K opted-in users, our system was in operation for over two months serving an average of 4800 active users daily. During the last month alone, we registered 790K ad views, 417 clicks, and even a small number of product purchases. Our system obtained click-through rates comparable with those for Google display ads. In addition, our prototype is equipped with a differentially private analytics mechanism, which we used as the primary means for gathering experimental data. In this thesis, we describe our first-hand experience and lessons learned in running the world's first fully operational “private-by-design” behavioral advertising and analytics system.
This dissertation focuses on the visualization of urban microclimate data sets,
which describe the atmospheric impact of individual urban features. The application
and adaptation of visualization and analysis concepts to enhance the
insight into observational data sets used this specialized area are explored, motivated
through application problems encountered during active involvement
in urban microclimate research at the Arizona State University in Tempe, Arizona.
Besides two smaller projects dealing with the analysis of thermographs
recorded with a hand-held device and visualization techniques used for building
performance simulation results, the main focus of the work described in
this document is the development of a prototypic tool for the visualization
and analysis of mobile transect measurements. This observation technique involves
a sensor platform mounted to a vehicle, which is then used to traverse
a heterogeneous neighborhood to investigate the relationships between urban
form and microclimate. The resulting data sets are among the most complex
modes of in-situ observations due to their spatio-temporal dependence, their
multivariate nature, but also due to the various error sources associated with
moving platform observations.
The prototype enables urban climate researchers to preprocess their data,
to explore a single transect in detail, and to aggregate observations from multiple
traverses conducted over diverse routes for a visual delineation of climatic
microenvironments. Extending traditional analysis methods, the suggested visualization
tool provides techniques to relate the measured attributes to each
other and to the surrounding land cover structure. In addition to that, an
improved method for sensor lag correction is described, which shows the potential
to increase the spatial resolution of measurements conducted with slow
air temperature sensors.
In summary, the interdisciplinary approach followed in this thesis triggers
contributions to geospatial visualization and visual analytics, as well as to urban
climatology. The solutions developed in the course of this dissertation are
meant to support domain experts in their research tasks, providing means to
gain a qualitative overview over their specific data sets and to detect patterns,
which can then be further analyzed using domain-specific tools and methods.
Since their invention in the 1980s, behaviour-based systems have become very popular among roboticists. Their component-based nature facilitates the distributed implementation of systems, fosters reuse, and allows for early testing and integration. However, the distributed approach necessitates the interconnection of many components into a network in order to realise complex functionalities. This network is crucial to the correct operation of the robotic system. There are few sound design techniques for behaviour networks, especially if the systems shall realise task sequences. Therefore, the quality of the resulting behaviour-based systems is often highly dependant on the experience of their developers.
This dissertation presents a novel integrated concept for the design and verification of behaviour-based systems that realise task sequences. Part of this concept is a technique for encoding task sequences in behaviour networks. Furthermore, the concept provides guidance to developers of such networks. Based on a thorough analysis of methods for defining sequences, Moore machines have been selected for representing complex tasks. With the help of the structured workflow proposed in this work and the developed accompanying tool support, Moore machines defining task sequences can be transferred automatically into corresponding behaviour networks, resulting in less work for the developer and a lower risk of failure.
Due to the common integration of automatically and manually created behaviour-based components, a formal analysis of the final behaviour network is reasonable. For this purpose, the dissertation at hand presents two verification techniques and justifies the selection of model checking. A novel concept for applying model checking to behaviour-based systems is proposed according to which behaviour networks are modelled as synchronised automata. Based on such automata, properties of behaviour networks that realise task sequences can be verified or falsified. Extensive graphical tool support has been developed in order to assist the developer during the verification process.
Several examples are provided in order to illustrate the soundness of the presented design and verification techniques. The applicability of the integrated overall concept to real-world tasks is demonstrated using the control system of an autonomous bucket excavator. It can be shown that the proposed design concept is suitable for developing complex sophisticated behaviour networks and that the presented verification technique allows for verifying real-world behaviour-based systems.
Attention-awareness is a key topic for the upcoming generation of computer-human interaction. A human moves his or her eyes to visually attends to a particular region in a scene. Consequently, he or she can process visual information rapidly and efficiently without being overwhelmed by vast amount of information from the environment. Such a physiological function called visual attention provides a computer system with valuable information of the user to infer his or her activity and the surrounding environment. For example, a computer can infer whether the user is reading text or not by analyzing his or her eye movements. Furthermore, it can infer with which object he or she is interacting by recognizing the object the user is looking at. Recent developments of mobile eye tracking technologies enable us
to capture human visual attention in ubiquitous everyday environments. There are various types of applications where attention-aware systems may be effectively incorporated. Typical examples are augmented reality (AR) applications such as Wikitude which overlay virtual information onto physical objects. This type of AR application presents augmentative information of recognized objects to the user. However, if it presents information of all recognized objects at once, the over
ow of information could be obtrusive to the user. As a solution for such a problem, attention-awareness can be integrated into a system. If a
system knows to which object the user is attending, it can present only the information of
relevant objects to the user.
Towards attention-aware systems in everyday environments, this thesis presents approaches
for analysis of user attention to visual content. Using a state-of-the-art wearable eye tracking device, one can measure the user's eye movements in a mobile scenario. By capturing the user's eye gaze position in a scene and analyzing the image where the eyes focus, a computer can recognize the visual content the user is currently attending to. I propose several image analysis methods to recognize the user-attended visual content in a scene image. For example, I present an application called Museum Guide 2.0. In Museum Guide 2.0, image-based object recognition and eye gaze analysis are combined together to recognize user-attended objects in a museum scenario. Similarly, optical character recognition
(OCR), face recognition, and document image retrieval are also combined with eye gaze analysis to identify the user-attended visual content in respective scenarios. In addition to Museum Guide 2.0, I present other applications in which these combined frameworks are effectively used. The proposed applications show that the user can benefit from active information presentation which augments the attended content in a virtual environment with
a see-through head-mounted display (HMD).
In addition to the individual attention-aware applications mentioned above, this thesis
presents a comprehensive framework that combines all recognition modules to recognize the user-attended visual content when various types of visual information resources such as text, objects, and human faces are present in one scene. In particular, two processing strategies are proposed. The first one selects an appropriate image analysis module according to the user's current cognitive state. The second one runs all image analysis modules simultaneously and merges the analytic results later. I compare these two processing strategies in terms of user-attended visual content recognition when multiple visual information resources are present in the same scene.
Furthermore, I present novel interaction methodologies for a see-through HMD using eye gaze input. A see-through HMD is a suitable device for a wearable attention-aware system for everyday environments because the user can also view his or her physical environment
through the display. I propose methods for the user's attention engagement estimation with the display, eye gaze-driven proactive user assistance functions, and a method for interacting
with a multi-focal see-through display.
Contributions of this thesis include:
• An overview of the state-of-the-art in attention-aware computer-human interaction
and attention-integrated image analysis.
• Methods for the analysis of user-attended visual content in various scenarios.
• Demonstration of the feasibilities and the benefits of the proposed user-attended visual content analysis methods with practical user-supportive applications.
• Methods for interaction with a see-through HMD using eye gaze.
• A comprehensive framework for recognition of user-attended visual content in a complex
scene where multiple visual information resources are present.
This thesis opens a novel field of wearable computer systems where computers can understand the user attention in everyday environments and provide with what the user wants. I will show the potential of such wearable attention-aware systems for everyday
environments for the next generation of pervasive computer-human interaction.
Open distributed systems are a class of distributed systems where (i) only partial information about the environment, in which they are running, is present, (ii) new resources may become available at runtime, and (iii) a subsystem may become aware of other subsystems after some interaction. Modeling and implementing such systems correctly is a complex task due to the openness and the dynamicity aspects. One way to ensure that the resulting systems behave correctly is to utilize formal verification.
Formal verification requires an adequate semantic model of the implementation, a specification of the desired behavior, and a reasoning technique. The actor model is a semantic model that captures the challenging aspects of open distributed systems by utilizing actors as universal primitives to represent system entities and allowing them to create new actors and to communicate by sending directed messages as reply to received messages. To enable compositional reasoning, where the reasoning task is reduced to independent verification of the system parts, semantic entities at a higher level of abstraction than actors are needed.
This thesis proposes an automaton model and combines sound reasoning techniques to compositionally verify implementations of open actor systems. Based on I/O automata, the model allows automata to be created dynamically and captures dynamic changes in communication patterns. Each automaton represents either an actor or a group of actors. The specification of the desired behavior is given constructively as an automaton. As the basis for compositionality, we formalize a component notion based on the static structure of the implementation instead of the dynamic entities (the actors) occurring in the system execution. The reasoning proceeds in two stages. The first stage establishes the connection between the automata representing single actors and their implementation description by means of weakest liberal preconditions. The second stage employs this result as the basis for verifying whether a component specification is satisfied. The verification is done by building a simulation relation from the automaton representing the implementation to the component's automaton. Finally, we validate the compositional verification approach through a number of examples by proving correctness of their actor implementations with respect to system specifications.
In a networked system, the communication system is indispensable but often the weakest link w.r.t. performance and reliability. This, particularly, holds for wireless communication systems, where the error- and interference-prone medium and the character of network topologies implicate special challenges. However, there are many scenarios of wireless networks, in which a certain quality-of-service has to be provided despite these conditions. In this regard, distributed real-time systems, whose realization by wireless multi-hop networks becomes increasingly popular, are a particular challenge. For such systems, it is of crucial importance that communication protocols are deterministic and come with the required amount of efficiency and predictability, while additionally considering scarce hardware resources that are a major limiting factor of wireless sensor nodes. This, in turn, does not only place demands on the behavior of a protocol but also on its implementation, which has to comply with timing and resource constraints.
The first part of this thesis presents a deterministic protocol for wireless multi-hop networks with time-critical behavior. The protocol is referred to as Arbitrating and Cooperative Transfer Protocol (ACTP), and is an instance of a binary countdown protocol. It enables the reliable transfer of bit sequences of adjustable length and deterministically resolves contest among nodes based on a flexible priority assignment, with constant delays, and within configurable arbitration radii. The protocol's key requirement is the collision-resistant encoding of bits, which is achieved by the incorporation of black bursts. Besides revisiting black bursts and proposing measures to optimize their detection, robustness, and implementation on wireless sensor nodes, the first part of this thesis presents the mode of operation and time behavior of ACTP. In addition, possible applications of ACTP are illustrated, presenting solutions to well-known problems of distributed systems like leader election and data dissemination. Furthermore, results of experimental evaluations with customary wireless transceivers are outlined to provide evidence of the protocol's implementability and benefits.
In the second part of this thesis, the focus is shifted from concrete deterministic protocols to their model-driven development with the Specification and Description Language (SDL). Though SDL is well-established in the domain of telecommunication and distributed systems, the predictability of its implementations is often insufficient as previous projects have shown. To increase this predictability and to improve SDL's applicability to time-critical systems, real-time tasks, an approved concept in the design of real-time systems, are transferred to SDL and extended to cover node-spanning system tasks. In this regard, a priority-based execution and suspension model is introduced in SDL, which enables task-specific priority assignments in the SDL specification that are orthogonal to the static structure of SDL systems and control transition execution orders on design as well as on implementation level. Both the formal incorporation of real-time tasks into SDL and their implementation in a novel scheduling strategy are discussed in this context. By means of evaluations on wireless sensor nodes, evidence is provided that these extensions reduce worst-case execution times substantially, and improve the predictability of SDL implementations and the language's applicability to real-time systems.
In the digital era we live in, users can access an abundance of digital resources in their daily life. These digital resources can be located on the user's devices, in traditional repositories such as intranets or digital libraries, but also in open environments such as the World Wide Web.
To be able to efficiently work with this abundance of information, users need support to get access to the resources that are relevant to them. Access to digital resources can be supported in various ways. Whether we talk about technologies for browsing, searching, filtering, ranking, or recommending resources: what they all have in common is that they depend on the available information (i.e., resources and metadata). The accessibility of digital resources that meet a user's information need, and the existence and quality of metadata is crucial for the success of any information system.
This work focuses on how social media technologies can support the access to digital resources. In contrast to closed and controlled environments where only selected users have the rights to contribute digital resources and metadata, and where this contribution involves a social process of formal agreement of the relevant stakeholders, potentially any user can easily create and provide information in social media environments. This usually leads to a larger variety of resources and metadata, and allows for dynamics that would otherwise hardly be possible.
Most information systems still mainly rely on traditional top-down approaches where only selected stakeholders can contribute information. The main idea of this thesis is an approach that allows for introducing the characteristics of social media environments in such traditional contexts. The requirements for such an approach are being examined, as well as the benefits and potentials it can provide.
The ALOE infrastructure was developed according to the identified requirements and realises a Social Resource and Metadata Hub. Case studies and evaluation results are provided to show the impact of the approach on the user's behaviours and the creation of digital resources and metadata, and to justify the presented approach.
Maintaining complex software systems tends to be a costly activity where software engineers spend a significant amount of time trying to understand the system's structure and behavior. As early as the 1980s, operation and maintenance costs were already twice as expensive as the initial development costs incurred. Since then these costs have steadily increased. The focus of this thesis is to reduce these costs through novel interactive exploratory visualization concepts and to apply these modern techniques in the context of services offered by software quality analysis.
Costs associated with the understanding of software are governed by specific features of the system in terms of different domains, including re-engineering, maintenance, and evolution. These features are reflected in software measurements or inner qualities such as extensibility, reusability, modifiability, testability, compatability, or adatability. The presence or absence of these qualities determines how easily a software system can conform or be customized to meet new requirements. Consequently, the need arises to monitor and evaluate the qualitative state of a software system in terms of these qualities. Using metrics-based analysis, production costs and quality defects of the software can be recorded objectively and analyzed.
In practice, there exist a number of free and commercial tools that analyze the inner quality of a software system through the use of software metrics. However, most of these tools focus on software data mining and metrics (computational analysis) and only a few support visual analytical reasoning. Typically, computational analysis tools generate data and software visualization tools facilitate the exploration and explanation of this data through static or interactive visual representations. Tools that combine these two approaches focus only on well-known metrics and lack the ability to examine user defined metrics. Further, they are often confined to simple visualization methods and metaphors, including charts, histograms, scatter plots, and node-link diagrams.
The goal of this thesis is to develop methodologies that combine computational analysis methods together with sophisticated visualization methods and metaphors through an interactive visual analysis approach. This approach promotes an iterative knowledge discovery process through multiple views of the data where analysts select features of interest in one of the views and inspect data items of the select subset in all of the views. On the one hand, we introduce a novel approach for the visual analysis of software measurement data that captures complete facts of the system, employs a flow-based visual paradigm for the specification of software measurement queries, and presents measurement results through integrated software visualizations. This approach facilitates the on-demand computation of desired features and supports interactive knowledge discovery - the analyst can gain more insight into the data through activities that involve: building a mental model of the system; exploring expected and unexpected features and relations; and generating, verifying, or rejecting hypothesis with visual tools. On the other hand, we have also extended existing tools with additional views of the data for the presentation and interactive exploration of system artifacts and their inter-relations.
Contributions of this thesis have been integrated into two different prototype tools. First evaluations of these tools show that they can indeed improve the understanding of large and complex software systems.
Today’s pervasive availability of computing devices enabled with wireless communication and location- or inertial sensing capabilities is unprecedented. The number of smartphones sold worldwide are still growing and increasing numbers of sensor enabled accessories are available which a user can wear in the shoe or at the wrist for fitness tracking, or just temporarily puts on to measure vital signs. Despite this availability of computing and sensing hardware the merit of application seems rather limited regarding the full potential of information inherent to such senor deployments. Most applications build upon a vertical design which encloses a narrowly defined sensor setup and algorithms specifically tailored to suit the application’s purpose. Successful technologies, however, such as the OSI model, which serves as base for internet communication, have used a horizontal design that allows high level communication protocols to be run independently from the actual lower-level protocols and physical medium access. This thesis contributes to a more horizontal design of human activity recognition systems at two stages. First, it introduces an integrated toolchain to facilitate the entire process of building activity recognition systems and to foster sharing and reusing of individual components. At a second stage, a novel method for automatic integration of new sensors to increase a system’s performance is presented and discussed in detail.
The integrated toolchain is built around an efficient toolbox of parametrizable components for interfacing sensor hardware, synchronization and arrangement of data streams, filtering and extraction of features, classification of feature vectors, and interfacing output devices and applications. The toolbox emerged as open-source project through several research projects and is actively used by research groups. Furthermore, the toolchain supports recording, monitoring, annotation, and sharing of large multi-modal data sets for activity recognition through a set of integrated software tools and a web-enabled database.
The method for automatically integrating a new sensor into an existing system is, at its core, a variation of well-established principles of semi-supervised learning: (1) unsupervised clustering to discover structure in data, (2) assumption that cluster membership is correlated with class membership, and (3) obtaining at a small number of labeled data points for each cluster, from which the cluster labels are inferred. In most semi-supervised approaches, however, the labels are the ground truth provided by the user. By contrast, the approach presented in this thesis uses a classifier trained on an N-dimensional feature space (old classifier) to provide labels for a few points in an (N+1)-dimensional feature space which are used to generate a new, (N+1)-dimensional classifier. The different factors that make a distribution difficult to handle are discussed, a detailed description of heuristics designed to mitigate the influences of such factors is provided, and a detailed evaluation on a set of over 3000 sensor combinations from 3 multi-user experiments that have been used by a variety of previous studies of different activity recognition methods is presented.
Industrial design has a long history. With the introduction of Computer-Aided Engineering, industrial design was revolutionised. Due to the newly found support, the design workflow changed, and with the introduction of virtual prototyping, new challenges arose. These new engineering problems have triggered
new basic research questions in computer science.
In this dissertation, I present a range of methods which support different components of the virtual design cycle, from modifications of a virtual prototype and optimisation of said prototype, to analysis of simulation results.
Starting with a virtual prototype, I support engineers by supplying intuitive discrete normal vectors which can be used to interactively deform the control mesh of a surface. I provide and compare a variety of different normal definitions which have different strengths and weaknesses. The best choice depends on
the specific model and on an engineer’s priorities. Some methods have higher accuracy, whereas other methods are faster.
I further provide an automatic means of surface optimisation in the form of minimising total curvature. This minimisation reduces surface bending, and therefore, it reduces material expenses. The best results can be obtained for analytic surfaces, however, the technique can also be applied to real-world examples.
Moreover, I provide engineers with a curvature-aware technique to optimise mesh quality. This helps to avoid degenerated triangles which can cause numerical issues. It can be applied to any component of the virtual design cycle: as a direct modification of the virtual prototype (depending on the surface defini-
tion), during optimisation, or dynamically during simulation.
Finally, I have developed two different particle relaxation techniques that both support two components of the virtual design cycle. The first component for which they can be used is discretisation. To run computer simulations on a model, it has to be discretised. Particle relaxation uses an initial sampling,
and it improves it with the goal of uniform distances or curvature-awareness. The second component for which they can be used is the analysis of simulation results. Flow visualisation is a powerful tool in supporting the analysis of flow fields through the insertion of particles into the flow, and through tracing their movements. The particle seeding is usually uniform, e.g. for an integral surface, one could seed on a square. Integral surfaces undergo strong deformations, and they can have highly varying curvature. Particle relaxation redistributes the seeds on the surface depending on surface properties like local deformation or curvature.
Automata theory has given rise to a variety of automata models that consist
of a finite-state control and an infinite-state storage mechanism. The aim
of this work is to provide insights into how the structure of the storage
mechanism influences the expressiveness and the analyzability of the
resulting model. To this end, it presents generalizations of results about
individual storage mechanisms to larger classes. These generalizations
characterize those storage mechanisms for which the given result remains
true and for which it fails.
In order to speak of classes of storage mechanisms, we need an overarching
framework that accommodates each of the concrete storage mechanisms we wish
to address. Such a framework is provided by the model of valence automata,
in which the storage mechanism is represented by a monoid. Since the monoid
serves as a parameter to specifying the storage mechanism, our aim
translates into the question: For which monoids does the given
(automata-theoretic) result hold?
As a first result, we present an algebraic characterization of those monoids
over which valence automata accept only regular languages. In addition, it
turns out that for each monoid, this is the case if and only if valence
grammars, an analogous grammar model, can generate only context-free
languages.
Furthermore, we are concerned with closure properties: We study which
monoids result in a Boolean closed language class. For every language class
that is closed under rational transductions (in particular, those induced by
valence automata), we show: If the class is Boolean closed and contains any
non-regular language, then it already includes the whole arithmetical
hierarchy.
This work also introduces the class of graph monoids, which are defined by
finite graphs. By choosing appropriate graphs, one can realize a number of
prominent storage mechanisms, but also combinations and variants thereof.
Examples are pushdowns, counters, and Turing tapes. We can therefore relate
the structure of the graphs to computational properties of the resulting
storage mechanisms.
In the case of graph monoids, we study (i) the decidability of the emptiness
problem, (ii) which storage mechanisms guarantee semilinear Parikh images,
(iii) when silent transitions (i.e. those that read no input) can be
avoided, and (iv) which storage mechanisms permit the computation of
downward closures.
A wide range of methods and techniques have been developed over the years to manage the increasing
complexity of automotive Electrical/Electronic systems. Standardization is an example
of such complexity managing techniques that aims to minimize the costs, avoid compatibility
problems and improve the efficiency of development processes.
A well-known and -practiced standard in automotive industry is AUTOSAR (Automotive
Open System Architecture). AUTOSAR is a common standard among OEMs (Original Equipment
Manufacturer), suppliers and other involved companies. It was developed originally with
the goal of simplifying the overall development and integration process of Electrical/Electronic
artifacts from different functional domains, such as hardware, software, and vehicle communication.
However, the AUTOSAR standard, in its current status, is not able to manage the problems
in some areas of the system development. Validation and optimization process of system configuration
handled in this thesis are examples of such areas, in which the AUTOSAR standard
offers so far no mature solutions.
Generally, systems developed on the basis of AUTOSAR must be configured in a way that all
defined requirements are met. In most cases, the number of configuration parameters and their
possible settings in AUTOSAR systems are large, especially if the developed system is complex
with modules from various knowledge domains. The verification process here can consume a
lot of resources to test all possible combinations of configuration settings, and ideally find the
optimal configuration variant, since the number of test cases can be very high. This problem is
referred to in literature as the combinatorial explosion problem.
Combinatorial testing is an active and promising area of functional testing that offers ideas
to solve the combinatorial explosion problem. Thereby, the focus is to cover the interaction
errors by selecting a sample of system input parameters or configuration settings for test case
generation. However, the industrial acceptance of combinatorial testing is still weak because of
the deficiency of real industrial examples.
This thesis is tempted to fill this gap between the industry and the academy in the area
of combinatorial testing to emphasizes the effectiveness of combinatorial testing in verifying
complex configurable systems.
The particular intention of the thesis is to provide a new applicable approach to combinatorial
testing to fight the combinatorial explosion problem emerged during the verification and
performance measurement of transport protocol parallel routing of an AUTOSAR gateway. The
proposed approach has been validated and evaluated by means of two real industrial examples
of AUTOSAR gateways with multiple communication buses and two different degrees of complexity
to illustrate its applicability.
The Context and Its Importance: In safety and reliability analysis, the information generated by Minimal Cut Set (MCS) analysis is large.
The Top Level event (TLE) that is the root of the fault tree (FT) represents a hazardous state of the system being analyzed.
MCS analysis helps in analyzing the fault tree (FT) qualitatively-and quantitatively when accompanied with quantitative measures.
The information shows the bottlenecks in the fault tree design leading to identifying weaknesses of the system being examined.
Safety analysis (containing the MCS analysis) is especially important for critical systems, where harm can be done to the environment or human causing injuries, or even death during the system usage.
Minimal Cut Set (MCS) analysis is performed using computers and generating a lot of information.
This phase is called MCS analysis I in this thesis.
The information is then analyzed by the analysts to determine possible issues and to improve the design of the system regarding its safety as early as possible.
This phase is called MCS analysis II in this thesis.
The goal of my thesis was developing interactive visualizations to support MCS analysis II of one fault tree (FT).
The Methodology: As safety visualization-in this thesis, Minimal Cut Set analysis II visualization-is an emerging field and no complete checklist regarding Minimal Cut Set analysis II requirements and gaps were available from the perspective of visualization and interaction capabilities,
I have conducted multiple studies using different methods with different data sources (i.e., triangulation of methods and data) for determining these requirements and gaps before developing and evaluating visualizations and interactions supporting Minimal Cut Set analysis II.
Thus, the following approach was taken in my thesis:
1- First, a triangulation of mixed methods and data sources was conducted.
2- Then, four novel interactive visualizations and one novel interaction widget were developed.
3- Finally, these interactive visualizations were evaluated both objectively and subjectively (compared to multiple safety tools)
from the point of view of users and developers of the safety tools that perform MCS analysis I with respect to their degree in supporting MCS analysis II and from the point of non-domain people using empirical strategies.
The Spiral tool supports analysts with different visions, i.e., full vision, color deficiency protanopia, deuteranopia, and tritanopia. It supports 100 out of 103 (97%) requirements obtained from the triangulation and it fills 37 out of 39 (95%) gaps. Its usability was rated high (better than their best currently used tools) by the users of the safety and reliability tools (RiskSpectrum, ESSaRel, FaultTree+, and a self-developed tool) and at least similar to the best currently used tools from the point of view of the CAFTA tool developers. Its quality was higher regarding its degree of supporting MCS analysis II compared to the FaultTree+ tool. The time spent for discovering the critical MCSs from a problem size of 540 MCSs (with a worst case of all equal order) was less than a minute while achieving 99.5% accuracy. The scalability of the Spiral visualization was above 4000 MCSs for a comparison task. The Dynamic Slider reduces the interaction movements up to 85.71% of the previous sliders and solves the overlapping thumb issues by the sliders provides the 3D model view of the system being analyzed provides the ability to change the coloring of MCSs according to the color vision of the user provides selecting a BE (i.e., multi-selection of MCSs), thus, can observe the BEs' NoO and provides its quality provides two interaction speeds for panning and zooming in the MCS, BE, and model views provide a MCS, a BE, and a physical tab for supporting the analysis starting by the MCSs, the BEs, or the physical parts. It combines MCS analysis results and the model of an embedded system enabling the analysts to directly relate safety information with the corresponding parts of the system being analyzed and provides an interactive mapping between the textual information of the BEs and MCSs and the parts related to the BEs.
Verifications and Assessments: I have evaluated all visualizations and the interaction widget both objectively and subjectively, and finally evaluated the final Spiral visualization tool also both objectively and subjectively regarding its perceived quality and regarding its degree of supporting MCS analysis II.
Synapses play a central role in the information propagation in the nervous system. A better understanding of synaptic structures and processes is vital for advancing nervous disease research. This work is part of an interdisciplinary project that aims at the quantitative examination of components of the neuromuscular junction, a synaptic connection between a neuron and a muscle cell.
The research project is based on image stacks picturing neuromuscular junctions captured by modern electron microscopes, which permit the rapid acquisition of huge amounts of image data at a high level of detail. The large amount and sheer size of such microscopic data makes a direct visual examination infeasible, though.
This thesis presents novel problem-oriented interactive visualization techniques that support the segmentation and examination of neuromuscular junctions.
First, I introduce a structured data model for segmented surfaces of neuromuscular junctions to enable the computational analysis of their properties. However, surface segmentation of neuromuscular junctions is a very challenging task due to the extremely intricate character of the objects of interest. Hence, such problematic segmentations are often performed manually by non-experts and thus requires further inspection.
With NeuroMap, I develop a novel framework to support proofreading and correction of three-dimensional surface segmentations. To provide a clear overview and to ease navigation within the data, I propose the surface map, an abstracted two-dimensional representation using key features of the surface as landmarks. These visualizations are augmented with information about automated segmentation error estimates. The framework provides intuitive and interactive data correction mechanisms, which in turn permit the expeditious creation of high-quality segmentations.
While analyzing such segmented synapse data, the formulation of specific research questions is often impossible due to missing insight into the data. I address this problem by designing a generic parameter space for segmented structures from biological image data. Furthermore, I introduce a graphical interface to aid its exploration, combining both parameter selection as well as data representation.
When designing autonomous mobile robotic systems, there usually is a trade-off between the three opposing goals of safety, low-cost and performance.
If one of these design goals is approached further, it usually leads to a recession of one or even both of the other goals.
If for example the performance of a mobile robot is increased by making use of higher vehicle speeds, then the safety of the system is usually decreased, as, under the same circumstances, faster robots are often also more dangerous robots.
This decrease of safety can be mitigated by installing better sensors on the robot, which ensure the safety of the system, even at high speeds.
However, this solution is accompanied by an increase of system cost.
In parallel to mobile robotics, there is a growing amount of ambient and aware technology installations in today's environments - no matter whether in private homes, offices or factory environments.
Part of this technology are sensors that are suitable to assess the state of an environment.
For example, motion detectors that are used to automate lighting can be used to detect the presence of people.
This work constitutes a meeting point between the two fields of robotics and aware environment research.
It shows how data from aware environments can be used to approach the abovementioned goal of establishing safe, performant and additionally low-cost robotic systems.
Sensor data from aware technology, which is often unreliable due to its low-cost nature, is fed to probabilistic methods for estimating the environment's state.
Together with models, these methods cope with the uncertainty and unreliability associated with the sensor data, gathered from an aware environment.
The estimated state includes positions of people in the environment and is used as an input to the local and global path planners of a mobile robot, enabling safe, cost-efficient and performant mobile robot navigation during local obstacle avoidance as well as on a global scale, when planning paths between different locations.
The probabilistic algorithms enable graceful degradation of the whole system.
Even if, in the extreme case, all aware technology fails, the robots will continue to operate, by sacrificing performance while maintaining safety.
All the presented methods of this work have been validated using simulation experiments as well as using experiments with real hardware.
Requirements-Aware, Template-Based Protocol Graphs for Service-Oriented Network Architectures
(2016)
Rigidness of the Internet causes its architectural design issues such as interdependencies among the layers, no cross-layer information exchange, and applications dependency on the underlying protocols implementation.
G-Lab (i.e., http://www.german-lab.de/) is a research project for Future Internet Architecture (FIA), which focuses on problems of the Internet such as rigidness, mobility, and addressing. Where the focus of ICSY (i.e., www.icsy) was on providing the flexibility in future network architectures. An approach so-called Service Oriented Network Architecture (SONATE) is proposed to compose the protocols dynamically. SONATE is based on principles of the service-oriented architecture (SOA), where protocols are decomposed in software modules and later they are put together on demand to provide the desired service.
This composition of functionalities can be performed at various time-epochs (e.g., run-time, design-time, deployment-time). However, these epochs have trade-off in terms of the time-complexity (i.e., required setup time) and the provided flexibility. The design-time is the least time critical in comparison to other time phases, which makes it possible to utilize human-analytical capability. However, the design-time lacks the real-time knowledge of requirements and network conditions, what results in inflexible protocol graphs, and they cannot be changed at later stages on changing requirements. Contrary to the design-time, the run-time is most time critical where an application is waiting for a connection to be established, but at the same time it has maximum information to generate a protocol graph suitable to the given requirements.
Considering limitations above of different time-phases, in this thesis, a novel intermediate functional composition approach (i.e., Template-Based Composition) has been presented to generate requirements aware protocol graphs. The template-based composition splits the composition process across different time-phases to exploit the less time critical nature and human-analytical availability of the design-time, ability to instantaneously deploy new functionalities of the deployment time and maximum information availability of the run-time. The approach is successfully implemented , demonstrated and evaluated based on its performance to know the implications for the practical use.
Stochastic Network Calculus (SNC) emerged from two branches in the late 90s:
the theory of effective bandwidths and its predecessor the Deterministic Network
Calculus (DNC). As such SNC’s goal is to analyze queueing networks and support
their design and control.
In contrast to queueing theory, which strives for similar goals, SNC uses in-
equalities to circumvent complex situations, such as stochastic dependencies or
non-Poisson arrivals. Leaving the objective to compute exact distributions behind,
SNC derives stochastic performance bounds. Such a bound would, for example,
guarantee a system’s maximal queue length that is violated by a known small prob-
ability only.
This work includes several contributions towards the theory of SNC. They are
sorted into four main contributions:
(1) The first chapters give a self-contained introduction to deterministic net-
work calculus and its two branches of stochastic extensions. The focus lies on the
notion of network operations. They allow to derive the performance bounds and
simplifying complex scenarios.
(2) The author created the first open-source tool to automate the steps of cal-
culating and optimizing MGF-based performance bounds. The tool automatically
calculates end-to-end performance bounds, via a symbolic approach. In a second
step, this solution is numerically optimized. A modular design allows the user to
implement their own functions, like traffic models or analysis methods.
(3) The problem of the initial modeling step is addressed with the development
of a statistical network calculus. In many applications the properties of included
elements are mostly unknown. To that end, assumptions about the underlying
processes are made and backed by measurement-based statistical methods. This
thesis presents a way to integrate possible modeling errors into the bounds of SNC.
As a byproduct a dynamic view on the system is obtained that allows SNC to adapt
to non-stationarities.
(4) Probabilistic bounds are fundamentally different from deterministic bounds:
While deterministic bounds hold for all times of the analyzed system, this is not
true for probabilistic bounds. Stochastic bounds, although still valid for every time
t, only hold for one time instance at once. Sample path bounds are only achieved by
using Boole’s inequality. This thesis presents an alternative method, by adapting
the theory of extreme values.
(5) A long standing problem of SNC is the construction of stochastic bounds
for a window flow controller. The corresponding problem for DNC had been solved
over a decade ago, but remained an open problem for SNC. This thesis presents
two methods for a successful application of SNC to the window flow controller.
This thesis presents a novel, generic framework for information segmentation in document images.
A document image contains different types of information, for instance, text (machine printed/handwritten), graphics, signatures, and stamps.
It is necessary to segment information in documents so that to process such segmented information only when required in automatic document processing workflows.
The main contribution of this thesis is the conceptualization and implementation of an information segmentation framework that is based on part-based features.
The generic nature of the presented framework makes it applicable to a variety of documents (technical drawings, magazines, administrative, scientific, and academic documents) digitized using different methods (scanners, RGB cameras, and hyper-spectral imaging (HSI) devices).
A highlight of the presented framework is that it does not require large training sets, rather a few training samples (for instance, four pages) lead to high performance, i.e., better than previously existing methods.
In addition, the presented framework is simple and can be adapted quickly to new problem domains.
This thesis is divided into three major parts on the basis of document digitization method (scanned, hyper-spectral imaging, and camera captured) used.
In the area of scanned document images, three specific contributions have been realized.
The first of them is in the domain of signature segmentation in administrative documents.
In some workflows, it is very important to check the document authenticity before processing the actual content.
This can be done based on the available seal of authenticity, e.g., signatures.
However, signature verification systems expect pre-segmented signature image, while signatures are usually a part of document.
To use signature verification systems on document images, it is necessary to first segment signatures in documents.
This thesis shows that the presented framework can be used to segment signatures in administrative documents.
The system based on the presented framework is tested on a publicly available dataset where it outperforms the state-of-the-art methods and successfully segmented all signatures, while less than half of the found signatures are false positives.
This shows that it can be applied for practical use.
The second contribution in the area of scanned document images is segmentation of stamps in administrative documents.
A stamp also serves as a seal for documents authenticity.
However, the location of stamp on the document can be more arbitrary than a signature depending on the person sealing the document.
This thesis shows that a system based on our generic framework is able to extract stamps of any arbitrary shape and color.
The evaluation of the presented system on a publicly available dataset shows that it is also able to segment black stamps (that were not addressed in the past) with a recall and precision of 83% and 73%, respectively.
%Furthermore, to segment colored stamps, this thesis presents a novel feature set which is based on intensity gradient, is able to extract unseen, colored, arbitrary shaped, textual as well as graphical stamps, and outperforms the state-of-the-art methods.
The third contribution in the scanned document images is in the domain of information segmentation in technical drawings (architectural floorplans, maps, circuit diagrams, etc.) containing usually a large amount of graphics and comparatively less textual components. Further, as in technical drawings, text is overlapping with graphics.
Thus, automatic analysis of technical drawings uses text/graphics segmentation as a pre-processing step.
This thesis presents a method based on our generic information segmentation framework that is able to detect the text, which is touching graphical components in architectural floorplans and maps.
Evaluation of the method on a publicly available dataset of architectural floorplans shows that it is able to extract almost all touching text components with precision and recall of 71% and 95%, respectively.
This means that almost all of the touching text components are successfully extracted.
In the area of hyper-spectral document images, two contributions have been realized.
Unlike normal three channels RGB images, hyper-spectral images usually have multiple channels that range from ultraviolet to infrared regions including the visible region.
First, this thesis presents a novel automatic method for signature segmentation from hyper-spectral document images (240 spectral bands between 400 - 900 nm).
The presented method is based on a part-based key point detection technique, which does not use any structural information, but relies only on the spectral response of the document regardless of ink color and intensity.
The presented method is capable of segmenting (overlapping and non-overlapping) signatures from varying backgrounds like, printed text, tables, stamps, logos, etc.
Importantly, the presented method can extract signature pixels and not just the bounding boxes.
This is substantial when signatures are overlapping with text and/or other objects in image. Second, this thesis presents a new dataset comprising of 300 documents scanned using a high-resolution hyper-spectral scanner. Evaluation of the presented signature segmentation method on this hyper-spectral dataset shows that it is able to extract signature pixels with the precision and recall of 100% and 79%, respectively.
Further contributions have been made in the area of camera captured document images. A major problem in the development of Optical Character Recognition (OCR) systems for camera captured document images is the lack of labeled camera captured document images datasets. In the first place, this thesis presents a novel, generic, method for automatic ground truth generation/labeling of document images. The presented method builds large-scale (i.e., millions of images) datasets of labeled camera captured / scanned documents without any human intervention. The method is generic and can be used for automatic ground truth generation of (scanned and/or camera captured) documents in any language, e.g., English, Russian, Arabic, Urdu. The evaluation of the presented method, on two different datasets in English and Russian, shows that 99.98% of the images are correctly labeled in every case.
Another important contribution in the area of camera captured document images is the compilation of a large dataset comprising 1 million word images (10 million character images), captured in a real camera-based acquisition environment, along with the word and character level ground truth. The dataset can be used for training as well as testing of character recognition systems for camera-captured documents. Various benchmark tests are performed to analyze the behavior of different open source OCR systems on camera captured document images. Evaluation results show that the existing OCRs, which already get very high accuracies on scanned documents, fail on camera captured document images.
Using the presented camera-captured dataset, a novel character recognition system is developed which is based on a variant of recurrent neural networks, i.e., Long Short Term Memory (LSTM) that outperforms all of the existing OCR engines on camera captured document images with an accuracy of more than 95%.
Finally, this thesis provides details on various tasks that have been performed in the area closely related to information segmentation. This includes automatic analysis and sketch based retrieval of architectural floor plan images, a novel scheme for online signature verification, and a part-based approach for signature verification. With these contributions, it has been shown that part-based methods can be successfully applied to document image analysis.
Distributed systems are omnipresent nowadays and networking them is fundamental for the continuous dissemination and thus availability of data. Provision of data in real-time is one of the most important non-functional aspects that safety-critical networks must guarantee. Formal verification of data communication against worst-case deadline requirements is key to certification of emerging x-by-wire systems. Verification allows aircraft to take off, cars to steer by wire, and safety-critical industrial facilities to operate. Therefore, different methodologies for worst-case modeling and analysis of real-time systems have been established. Among them is deterministic Network Calculus (NC), a versatile technique that is applicable across multiple domains such as packet switching, task scheduling, system on chip, software-defined networking, data center networking and network virtualization. NC is a methodology to derive deterministic bounds on two crucial performance metrics of communication systems:
(a) the end-to-end delay data flows experience and
(b) the buffer space required by a server to queue all incoming data.
NC has already seen application in the industry, for instance, basic results have been used to certify the backbone network of the Airbus A380 aircraft.
The NC methodology for worst-case performance analysis of distributed real-time systems consists of two branches. Both share the NC network model but diverge regarding their respective derivation of performance bounds, i.e., their analysis principle. NC was created as a deterministic system theory for queueing analysis and its operations were later cast in a (min,+)-algebraic framework. This branch is known as algebraic Network Calculus (algNC). While algNC can efficiently compute bounds on delay and backlog, the algebraic manipulations do not allow NC to attain the most accurate bounds achievable for the given network model. These tight performance bounds can only be attained with the other, newly established branch of NC, the optimization-based analysis (optNC). However, the only optNC analysis that can currently derive tight bounds was proven to be computationally infeasible even for the analysis of moderately sized networks other than simple sequences of servers.
This thesis makes various contributions in the area of algNC: accuracy within the existing framework is improved, distributivity of the sensor network calculus analysis is established, and most significantly the algNC is extended with optimization principles. They allow algNC to derive performance bounds that are competitive with optNC. Moreover, the computational efficiency of the new NC approach is improved such that this thesis presents the first NC analysis that is both accurate and computationally feasible at the same time. It allows NC to scale to larger, more complex systems that require formal verification of their real-time capabilities.