For many years real-time task models have focused the timing constraints on execution windows defined by earliest start times and deadlines for feasibility.
However, the utility of some application may vary among scenarios which yield correct behavior, and maximizing this utility improves the resource utilization.
For example, target sensitive applications have a target point where execution results in maximized utility, and an execution window for feasibility.
Execution around this point and within the execution window is allowed, albeit at lower utility.
The intensity of the utility decay accounts for the importance of the application.
Examples of such applications include multimedia and control; multimedia application are very popular nowadays and control applications are present in every automated system.
In this thesis, we present a novel real-time task model which provides for easy abstractions to express the timing constraints of target sensitive RT applications: the gravitational task model.
This model uses a simple gravity pendulum (or bob pendulum) system as a visualization model for trade-offs among target sensitive RT applications.
We consider jobs as objects in a pendulum system, and the target points as the central point.
Then, the equilibrium state of the physical problem is equivalent to the best compromise among jobs with conflicting targets.
Analogies with well-known systems are helpful to fill in the gap between application requirements and theoretical abstractions used in task models.
For instance, the so-called nature algorithms use key elements of physical processes to form the basis of an optimization algorithm.
Examples include the knapsack problem, traveling salesman problem, ant colony optimization, and simulated annealing.
We also present a few scheduling algorithms designed for the gravitational task model which fulfill the requirements for on-line adaptivity.
The scheduling of target sensitive RT applications must account for timing constraints, and the trade-off among tasks with conflicting targets.
Our proposed scheduling algorithms use the equilibrium state concept to order the execution sequence of jobs, and compute the deviation of jobs from their target points for increased system utility.
The execution sequence of jobs in the schedule has a significant impact on the equilibrium of jobs, and dominates the complexity of the problem --- the optimum solution is NP-hard.
We show the efficacy of our approach through simulations results and 3 target sensitive RT applications enhanced with the gravitational task model.
Advanced sensing systems, sophisticated algorithms, and increasing computational resources continuously enhance the advanced driver assistance systems (ADAS). To date, despite that some vehicle based approaches to driver fatigue/drowsiness detection have been realized and deployed, objectively and reliably detecting the fatigue/drowsiness state of driver without compromising driving experience still remains challenging. In general, the choice of input sensorial information is limited in the state-of-the-art work. On the other hand, smart and safe driving, as representative future trends in the automotive industry worldwide, increasingly demands the new dimensional human-vehicle interactions, as well as the associated behavioral and bioinformatical data perception of driver. Thus, the goal of this research work is to investigate the employment of general and custom 3D-CMOS sensing concepts for the driver status monitoring, and to explore the improvement by merging/fusing this information with other salient customized information sources for gaining robustness/reliability. This thesis presents an effective multi-sensor approach with novel features to driver status monitoring and intention prediction aimed at drowsiness detection based on a multi-sensor intelligent assistance system -- DeCaDrive, which is implemented on an integrated soft-computing system with multi-sensing interfaces in a simulated driving environment. Utilizing active illumination, the IR depth camera of the realized system can provide rich facial and body features in 3D in a non-intrusive manner. In addition, steering angle sensor, pulse rate sensor, and embedded impedance spectroscopy sensor are incorporated to aid in the detection/prediction of driver's state and intention. A holistic design methodology for ADAS encompassing both driver- and vehicle-based approaches to driver assistance is discussed in the thesis as well. Multi-sensor data fusion and hierarchical SVM techniques are used in DeCaDrive to facilitate the classification of driver drowsiness levels based on which a warning can be issued in order to prevent possible traffic accidents. The realized DeCaDrive system achieves up to 99.66% classification accuracy on the defined drowsiness levels, and exhibits promising features such as head/eye tracking, blink detection, gaze estimation that can be utilized in human-vehicle interactions. However, the driver's state of "microsleep" can hardly be reflected in the sensor features of the implemented system. General improvements on the sensitivity of sensory components and on the system computation power are required to address this issue. Possible new features and development considerations for DeCaDrive are discussed as well in the thesis aiming to gain market acceptance in the future.
At present the standardization of third generation (3G) mobile radio systems is the subject of worldwide research activities. These systems will cope with the market demand for high data rate services and the system requirement for exibility concerning the offered services and the transmission qualities. However, there will be de ciencies with respect to high capacity, if 3G mobile radio systems exclusively use single antennas. Very promising technique developed for increasing the capacity of 3G mobile radio systems the application is adaptive antennas. In this thesis, the benefits of using adaptive antennas are investigated for 3G mobile radio systems based on Time Division CDMA (TD-CDMA), which forms part of the European 3G mobile radio air interface standard adopted by the ETSI, and is intensively studied within the standardization activities towards a worldwide 3G air interface standard directed by the 3GPP (3rd Generation Partnership Project). One of the most important issues related to adaptive antennas is the analysis of the benefits of using adaptive antennas compared to single antennas. In this thesis, these bene ts are explained theoretically and illustrated by computer simulation results for both data detection, which is performed according to the joint detection principle, and channel estimation, which is applied according to the Steiner estimator, in the TD-CDMA uplink. The theoretical explanations are based on well-known solved mathematical problems. The simulation results illustrating the benefits of adaptive antennas are produced by employing a novel simulation concept, which offers a considerable reduction of the simulation time and complexity, as well as increased exibility concerning the use of different system parameters, compared to the existing simulation concepts for TD-CDMA. Furthermore, three novel techniques are presented which can be used in systems with adaptive antennas for additionally improving the system performance compared to single antennas. These techniques concern the problems of code-channel mismatch, of user separation in the spatial domain, and of intercell interference, which, as it is shown in the thesis, play a critical role on the performance of TD-CDMA with adaptive antennas. Finally, a novel approach for illustrating the performance differences between the uplink and downlink of TD-CDMA based mobile radio systems in a straightforward manner is presented. Since a cellular mobile radio system with adaptive antennas is considered, the ultimate goal is the investigation of the overall system efficiency rather than the efficiency of a single link. In this thesis, the efficiency of TD-CDMA is evaluated through its spectrum efficiency and capacity, which are two closely related performance measures for cellular mobile radio systems. Compared to the use of single antennas, the use of adaptive antennas allows impressive improvements of both spectrum efficiency and capacity. Depending on the mobile radio channel model and the user velocity, improvement factors range from six to 10.7 for the spectrum efficiency, and from 6.7 to 12.6 for the spectrum capacity of TD-CDMA. Thus, adaptive antennas constitute a promising technique for capacity increase of future mobile communications systems.
Real-time systems are systems that have to react correctly to stimuli from the environment within given timing constraints.
Today, real-time systems are employed everywhere in industry, not only in safety-critical systems but also in, e.g., communication, entertainment, and multimedia systems.
With the advent of multicore platforms, new challenges on the efficient exploitation of real-time systems have arisen:
First, there is the need for effective scheduling algorithms that feature low overheads to improve the use of the computational resources of real-time systems.
The goal of these algorithms is to ensure timely execution of tasks, i.e., to provide runtime guarantees.
Additionally, many systems require their scheduling algorithm to flexibly react to unforeseen events.
Second, the inherent parallelism of multicore systems leads to contention for shared hardware resources and complicates system analysis.
At any time, multiple applications run with varying resource requirements and compete for the scarce resources of the system.
As a result, there is a need for an adaptive resource management.
Achieving and implementing an effective and efficient resource management is a challenging task.
The main goal of resource management is to guarantee a minimum resource availability to real-time applications.
A further goal is to fulfill global optimization objectives, e.g., maximization of the global system performance, or the user perceived quality of service.
In this thesis, we derive methods based on the slot shifting algorithm.
Slot shifting provides flexible scheduling of time-constrained applications and can react to unforeseen events in time-triggered systems.
For this reason, we aim at designing slot shifting based algorithms targeted for multicore systems to tackle the aforementioned challenges.
The main contribution of this thesis is to present two global slot shifting algorithms targeted for multicore systems.
Additionally, we extend slot shifting algorithms to improve their runtime behavior, or to handle non-preemptive firm aperiodic tasks.
In a variety of experiments, the effectiveness and efficiency of the algorithms are evaluated and confirmed.
Finally, the thesis presents an implementation of a slot-shifting-based logic into a resource management framework for multicore systems.
Thus, the thesis closes the circle and successfully bridges the gap between real-time scheduling theory and real-world implementations.
We prove applicability of the slot shifting algorithm to effectively and efficiently perform adaptive resource management on multicore systems.
The recently established technologies in the areas of distributed measurement and intelligent
information processing systems, e.g., Cyber Physical Systems (CPS), Ambient
Intelligence/Ambient Assisted Living systems (AmI/AAL), the Internet of Things
(IoT), and Industry 4.0 have increased the demand for the development of intelligent
integrated multi-sensory systems as to serve rapid growing markets [1, 2]. These increase
the significance of complex measurement systems, that incorporate numerous advanced
methodological implementations including electronics circuit, signal processing,
and multi-sensory information fusion. In particular, in multi-sensory cognition applications,
to design such systems, the skill-required tasks, e.g., method selection, parameterization,
model analysis, and processing chain construction are elaborated with immense
effort, which conventionally are done manually by the expert designer. Moreover, the
strong technological competition imposes even more complicated design problems with
multiple constraints, e.g., cost, speed, power consumption,
exibility, and reliability.
Thus, the conventional human expert based design approach may not be able to cope
with the increasing demand in numbers, complexity, and diversity. To alleviate the issue,
the design automation approach has been the topic for numerous research works [3-14]
and has been commercialized to several products [15-18]. Additionally, the dynamic
adaptation of intelligent multi-sensor systems is the potential solution for developing
dependable and robust systems. Intrinsic evolution approach and self-x properties ,
which include self-monitoring, -calibrating/trimming, and -healing/repairing, are among
the best candidates for the issue. Motivated from the ongoing research trends and based
on the background of our research work [12, 13] among the pioneers in this topic, the
research work of the thesis contributes to the design automation of intelligent integrated
In this research work, the Design Automation for Intelligent COgnitive system with self-
X properties, the DAICOX, architecture is presented with the aim of tackling the design
effort and to providing high quality and robust solutions for multi-sensor intelligent
systems. Therefore, the DAICOX architecture is conceived with the defined goals as
Perform front to back complete processing chain design with automated method
selection and parameterization,
Provide a rich choice of pattern recognition methods to the design method pool,
Associate design information via interactive user interface and visualization along
with intuitive visual programming,
Deliver high quality solutions outperforming conventional approaches by using
Gain the adaptability, reliability and robustness of designed solutions with self-x
Derived from the goals, several scientific methodological developments and implementations,
particularly in the areas of pattern recognition and computational intelligence,
will be pursued as part of the DAICOX architecture in the research work of this thesis.
The method pool is aimed to contain a rich choice of methods and algorithms covering
data acquisition and sensor configuration, signal processing and feature computation,
dimensionality reduction, and classification. These methods will be selected and parameterized
automatically by the DAICOX design optimization to construct a multi-sensory
cognition processing chain. A collection of non-parametric feature quality assessment
functions for the purpose of Dimensionality Reduction (DR) process will be presented.
In addition, to standard DR methods, the variations of feature selection method, in
particular, feature weighting will be proposed. Three different classification categories
shall be incorporated in the method pool. Hierarchical classification approach will be
proposed and developed to serve as a multi-sensor fusion architecture at the decision
level. Beside multi-class classification, one-class classification methods, e.g., One-Class
SVM and NOVCLASS will be presented to extend functionality of the solutions, in particular,
anomaly and novelty detection. DAICOX is conceived to effectively handle the
problem of method selection and parameter setting for a particular application yielding
high performance solutions. The processing chain construction tasks will be carried
out by meta-heuristic optimization methods, e.g., Genetic Algorithms (GA) and Particle
Swarm Optimization (PSO), with multi-objective optimization approach and model
analysis for robust solutions. In addition, to the automated system design mechanisms,
DAICOX will facilitate the design tasks with intuitive visual programming and various
options of visualization. Design database concept of DAICOX is aimed to allow the
reusability and extensibility of the designed solutions gained from previous knowledge.
Thus, the cooperative design of machine and knowledge from the design expert can also
be utilized for obtaining fully enhanced solutions. In particular, the integration of self-x
properties as well as intrinsic optimization into the system is proposed to gain enduring
reliability and robustness. Hence, DAICOX will allow the inclusion of dynamically
reconfigurable hardware instances to the designed solutions in order to realize intrinsic
optimization and self-x properties.
As a result from the research work in this thesis, a comprehensive intelligent multisensor
system design architecture with automated method selection, parameterization,
and model analysis is developed with compliance to open-source multi-platform software.It is integrated with an intuitive design environment, which includes visual programming
concept and design information visualizations. Thus, the design effort is minimized as
investigated in three case studies of different application background, e.g., food analysis
(LoX), driving assistance (DeCaDrive), and magnetic localization. Moreover, DAICOX
achieved better quality of the solutions compared to the manual approach in all cases,
where the classification rate was increased by 5.4%, 0.06%, and 11.4% in the LoX,
DeCaDrive, and magnetic localization case, respectively. The design time was reduced
by 81.87% compared to the conventional approach by using DAICOX in the LoX case
study. At the current state of development, a number of novel contributions of the thesis
are outlined below.
Automated processing chain construction and parameterization for the design of
signal processing and feature computation.
Novel dimensionality reduction methods, e.g., GA and PSO based feature selection
and feature weighting with multi-objective feature quality assessment.
A modification of non-parametric compactness measure for feature space quality
Decision level sensor fusion architecture based on proposed hierarchical classification
approach using, i.e., H-SVM.
A collection of one-class classification methods and a novel variation, i.e.,
Automated design toolboxes supporting front to back design with automated
model selection and information visualization.
In this research work, due to the complexity of the task, neither all of the identified goals
have been comprehensively reached yet nor has the complete architecture definition been
fully implemented. Based on the currently implemented tools and frameworks, ongoing
development of DAICOX is pursuing towards the complete architecture. The potential
future improvements are the extension of method pool with a richer choice of methods
and algorithms, processing chain breeding via graph based evolution approach, incorporation
of intrinsic optimization, and the integration of self-x properties. According to
these features, DAICOX will improve its aptness in designing advanced systems to serve
the increasingly growing technologies of distributed intelligent measurement systems, in
particular, CPS and Industrie 4.0.
Wireless Sensor Networks (WSN) are dynamically-arranged networks typically composed of a large number of arbitrarily-distributed sensor nodes with computing capabilities contributing to –at least– one common application. The main characteristic of these networks is that of being functionally constrained due to a scarce availability of resources and strong dependence on uncontrollable environmental factors. These conditions introduce severe restrictions on the applicability of classic real-time methods aiming at guaranteeing time-bounded communications. Existing real-time solutions tend to apply concepts that were originally not conceived for sensor networks, idealizing realistic application scenarios and overlooking at important design limitations. This results in a number of misleading practices contributing to approaches of restricted validity in real-world scenarios. Amending the confrontation between WSNs and real-time objectives starts with a review of the basic fundamentals of existing approaches. In doing so, this thesis presents an alternative approach based on a generalized timeliness notion suitable to the particularities of WSNs. The new conceptual notion allows the definition of feasible real-time objectives opening a new scope of possibilities not constrained to idealized systems. The core of this thesis is based on the definition and application of Quality of Service (QoS) trade-offs between timeliness and other significant QoS metrics. The analysis of local and global trade-offs provides a step-by-step methodology identifying the correlations between these quality metrics. This association enables the definition of alternative trade-off configurations (set points) influencing the quality performance of the network at selected instants of time. With the basic grounds established, the above concepts are embedded in a simple routing protocol constituting a proof of concept for the validity of the presented analysis. Extensive evaluations under realistic scenarios are driven on simulation environments as well as real testbeds, validating the consistency of this approach.
Photonic crystals are inhomogeneous dielectric media with periodic variation of the refractive index. A photonic crystal gives us new tools for the manipulation of photons and thus has received great interests in a variety of fields. Photonic crystals are expected to be used in novel optical devices such as thresholdless laser diodes, single-mode light emitting diodes, small waveguides with low-loss sharp bends, small prisms, and small integrated optical circuits. They can be operated in some aspects as "left handed materials" which are capable of focusing transmitted waves into a sub-wavelength spot due to negative refraction. The thesis is focused on the applications of photonic crystals in communications and optical imaging: • Photonic crystal structures for potential dispersion management in optical telecommunication systems • 2D non-uniform photonic crystal waveguides with a square lattice for wide-angle beam refocusing using negative refraction • 2D non-uniform photonic crystal slabs with triangular lattice for all-angle beam refocusing • Compact phase-shifted band-pass transmission filter based on photonic crystals
Die industrielle Oberflächeninspektion und insbesondere die Defekterkennung ist ein wichtiges Anwendungsgebiet für die automatische Bildverarbeitung (BV). Für den Entwurf und die Konfiguration der entsprechenden Softwaresysteme, in der Regel anwendungsspezifische Einzellösungen, werden im industriellen Umfeld zumeist entweder firmeneigene Bildverarbeitungsbibliotheken, kommerzielle oder freie Toolboxen verwendet. In der Regel beinhalten diese u.a. Standardalgorithmen der Bildverarbeitung in modularer Form, z. B. Filter- oder Schwellwertoperatoren. Die einzelnen BV-Methoden werden in der Regel nach dem Prinzip der visuellen Programmierung in einer grafischen Entwicklungsumgebung ausgewählt und zu einer BV-Kette bzw. einem -Graph zusammengesetzt. Dieses Prinzip ermöglicht es auch einem Programmierunkundigen, BV-Systeme zu erstellen und zu konfigurieren. Eine gewisse Grundkenntnis der Methoden der Bildverarbeitung ist jedoch notwendig. Je nach Aufgabenstellung und Erfahrung des Systementwicklers erfordern manueller Entwurf und Konfiguration eines BV-Systems erheblichen Zeiteinsatz. Diese Arbeit beschäftigt sich mit automatischen Entwurfs-, Konfigurations- und Optimierungsmöglichkeiten dieser modularen BV-Systeme, die es auch einem ungeübten Endnutzer ermöglichen, adäquate Lösungen zu generieren mit dem Ziel, ein effizienteres Entwurfswerkzeug für Bildverarbeitungssysteme mit neuen und verbesserten Eigenschaften zu schaffen. Die Methodenauswahl und Parameteroptimierung reicht von der Bildvorverarbeitung und -verbesserung mittels BV-Algorithmen bis hin zu ggf. eingesetzten Klassifikatoren, wie Nächste-Nachbar-Klassifikator (NNK) und Support-Vektor-Maschinen (SVM) und verschiedenen Bewertungsfunktionen. Der flexible Einsatz verschiedener Klassifikations- und Bewertungsmethoden ermöglicht einen automatischen problemspezifischen Entwurf und die Optimierung des BV-Systems für Aufgaben der Fehlerdetektion und Texturanalyse für 2d-Bilder, sowie die Trennung von Objekten und Hintergrund für 2d- und 3d-Grauwertbilder. Für die Struktur- und Parameteroptimierung des BV-Systems werden Evolutionäre Algorithmen (EA) und Partikelschwarmoptimierung (PSO) verwendet.
Um die in der Automatisierung zunehmenden Anforderungen an Vorschubachsen hinsichtlich Dynamik, Präzision und Wartungsaufwand bei niedriger Bauhöhe und kleiner werdendem Bauvolumen gerecht zu werden, kommen immer mehr Synchron-Linearmotoren in Zahnspulentechnik mit Permanentmagneterregung in Werkzeugmaschinen zum Einsatz. Als hauptsächlicher Vorteil gegenüber der rotierenden Antriebslösung mit Getriebeübersetzung und Kugelrollspindel wird die direkte Kraftübertragung ohne Bewegungswandler genannt. Der Übergang vom konventionellen linearen Antriebssystem zum Direktantriebssystem eröffnet dem Werkzeugmaschinenherstellern und den Industrieanwendungen eine Vielzahl neuer Möglichkeiten durch beeindruckende Verfahrgeschwindigkeit und hohes Beschleunigungsvermögen sowie Positionier- und Wiederholgenauigkeit und bietet darüber hinaus die Chance zu einer weiteren Produktivitäts- und Qualitätssteigerung. Um alle dieser Vorteile ausnutzen zu können, muss der Antrieb zuerst hinsichtlich der für Linearmotoren typisch Kraftwelligkeit optimiert werden. Die Suche nach wirtschaftlichen und praxistauglichen Gegenmaßnahmen ist ein aktuelles Forschungsthema in der Antriebstechnik. In der vorliegenden Arbeit werden die Kraftschwankungen infolge Nutung, Endeffekt und elektrischer Durchflutung in PM-Synchron-Linearmotor rechnerisch und messtechnisch untersucht. Ursachen und Eigenschaften der Kraftwelligkeit werden beschrieben und Einflussparameter aufgezeigt. Es besteht die Möglichkeit, die Kraftwelligkeit durch bestimmte Maßnahmen zu beeinflussen, z. B. mit Hilfe des Kraftwelligkeitsausgleichs bestehend aus ferromagnetischem Material oder durch gegenseitigen Ausgleich mehrerer zusammengekoppelter Primärteile. Wie die Untersuchungen gezeigt haben, ist eine Abstimmung der Einflussparameter auf analytischem Weg kaum möglich, in der Praxis führt das auf eine experimentell-iterative Optimierung mit FEM-Unterstützung. Die gute Übereinstimmung zwischen Messung und Simulation bietet einen klaren Hinweis, dass die hier vorgestellten Maßnahmen als geeignet angesehen werden können, sie ermöglichen eine Kraftwelligkeitsreduzierung von ursprünglichen 3-5% bis auf 1%, wobei eine leichte Herabsetzung der Kraftdichte in Kauf genommen werden muss. Beim Maschinenentwurf muss rechtzeitig ermittelt werden, welches Kompensationsverfahren günstig ist bezüglich der vorgesehenen Anwendungen.
Analog sensor electronics requires special care during design in order to increase the quality and precision of the signal, and the life time of the product. Nevertheless, it can experience static deviations due to the manufacturing tolerances, and dynamic deviations due to operating in non-ideal environment. Therefore, the advanced applications such as MEMS technology employs calibration loop to deal with the deviations, but unfortunately, it is considered only in the digital domain, which cannot cope with all the analog deviations such as saturation of the analog signal, etc. On the other hand, rapid-prototyping is essential to decrease the development time, and the cost of the products for small quantities. Recently, evolvable hardware has been developed with the motivation to cope with the mentioned sensor electronic problems. However the industrial specifications and requirements are not considered in the hardware learning loop. Indeed, it minimizes the error between the required output and the real output generated due to given test signal. The aim of this thesis is to synthesize the generic organic-computing sensor electronics and return hardware with predictable behavior for embedded system applications that gains the industrial acceptance; therefore, the hardware topology is constrained to the standard hardware topologies, the hardware standard specifications are included in the optimization, and hierarchical optimization are abstracted from the synthesis tools to evolve first the building blocks, then evolve the abstract level that employs these optimized blocks. On the other hand, measuring some of the industrial specifications needs expensive equipments and some others are time consuming which is not fortunate for embedded system applications. Therefore, the novel approach "mixtrinsic multi-objective optimization" is proposed that simulates/estimates the set of the specifications that is hard to be measured due to the cost or time requirements, while it measures intrinsically the set of the specifications that has high sensitivity to deviations. These approaches succeed to optimize the hardware to meet the industrial specifications with low cost measurement setup which is essential for embedded system applications.