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Whole-body vibrations (WBV) have adverse effects on ride comfort and human health. Suspension seats have an important influence on the WBV severity. In this study, WBV were measured on a medium-sized compact wheel loader (CWL) in its typical operations. The effect of short-term exposure to the WBV on the ride comfort was evaluated according to ISO 2631-1:1985 and ISO 2631-1:1997. ISO 2631-1:1997 and ISO 2631-5:2004 were adopted to evaluate the effect of long-term exposure to the WBV on the human health. Reasons for the different evaluation results obtained according to ISO 2631-1:1997 and ISO 2631-5:2004 were explained in this study. The WBV measurements were carried out in cases where the driver wore a lap belt or a four-point seat harness and in the case where the driver did not wear any safety belt. The seat effective amplitude transmissibility (SEAT) and the seat transmissibility in the frequency domain in these three cases were analyzed to investigate the effect of a safety belt on the seat transmissibility. Seat tests were performed on a multi-axis shaking table in laboratory to study the dynamic behavior of a suspension seat under the vibration excitations measured on the CWL. The WBV intensity was reduced by optimizing the vertical and the longitudinal seat suspension systems with the help of computational simulations. For the optimization multi-body models of the seat-dummy system in the laboratory seat tests and the seat-driver system in the field vibration measurements were built and validated.
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.
This work introduces a promising concept for the preparation of new nano-sized receptors. Mixed monolayer protected gold nanoparticles (AuNPs) for low molecular weight compounds were prepared featuring functional groups on their surfaces. It has been shown that these AuNPs can engage in interactions with peptides in aqueous media. Quantitative binding information was obtained from DOSY-NMR titrations indicating that nanoparticles containing a combination of three orthogonal functional groups are more efficient in binding to dipeptides than mono or difunctionalised analogues. The strategy is highly modular and easily allows adapting the receptor selectivity to a
given substrate by varying the type, number, and ratio of binding sites on the nanoparticle
surface.
Most of today’s wireless communication devices operate on unlicensed bands with uncoordinated spectrum access, with the consequence that RF interference and collisions are impairing the overall performance of wireless networks. In the classical design of network protocols, both packets in a collision are considered lost, such that channel access mechanisms attempt to avoid collisions proactively. However, with the current proliferation of wireless applications, e.g., WLANs, car-to-car networks, or the Internet of Things, this conservative approach is increasingly limiting the achievable network performance in practice. Instead of shunning interference, this thesis questions the notion of „harmful“ interference and argues that interference can, when generated in a controlled manner, be used to increase the performance and security of wireless systems. Using results from information theory and communications engineering, we identify the causes for reception or loss of packets and apply these insights to design system architectures that benefit from interference. Because the effect of signal propagation and channel fading, receiver design and implementation, and higher layer interactions on reception performance is complex and hard to reproduce by simulations, we design and implement an experimental platform for controlled interference generation to strengthen our theoretical findings with experimental results. Following this philosophy, we introduce and evaluate a system architecture that leverage interference.
First, we identify the conditions for successful reception of concurrent transmissions in wireless networks. We focus on the inherent ability of angular modulation receivers to reject interference when the power difference of the colliding signals is sufficiently large, the so-called capture effect. Because signal power fades over distance, the capture effect enables two or more sender–receiver pairs to transmit concurrently if they are positioned appropriately, in turn boosting network performance. Second, we show how to increase the security of wireless networks with a centralized network access control system (called WiFire) that selectively interferes with packets that violate a local security policy, thus effectively protecting legitimate devices from receiving such packets. WiFire’s working principle is as follows: a small number of specialized infrastructure devices, the guardians, are distributed alongside a network and continuously monitor all packet transmissions in the proximity, demodulating them iteratively. This enables the guardians to access the packet’s content before the packet fully arrives at the receiver. Using this knowledge the guardians classify the packet according to a programmable security policy. If a packet is deemed malicious, e.g., because its header fields indicate an unknown client, one or more guardians emit a limited burst of interference targeting the end of the packet, with the objective to introduce bit errors into it. Established communication standards use frame check sequences to ensure that packets are received correctly; WiFire leverages this built-in behavior to prevent a receiver from processing a harmful packet at all. This paradigm of „over-the-air“ protection without requiring any prior modification of client devices enables novel security services such as the protection of devices that cannot defend themselves because their performance limitations prohibit the use of complex cryptographic protocols, or of devices that cannot be altered after deployment.
This thesis makes several contributions. We introduce the first software-defined radio based experimental platform that is able to generate selective interference with the timing precision needed to evaluate the novel architectures developed in this thesis. It implements a real-time receiver for IEEE 802.15.4, giving it the ability to react to packets in a channel-aware way. Extending this system design and implementation, we introduce a security architecture that enables a remote protection of wireless clients, the wireless firewall. We augment our system with a rule checker (similar in design to Netfilter) to enable rule-based selective interference. We analyze the security properties of this architecture using physical layer modeling and validate our analysis with experiments in diverse environmental settings. Finally, we perform an analysis of concurrent transmissions. We introduce a new model that captures the physical properties correctly and show its validity with experiments, improving the state of the art in the design and analysis of cross-layer protocols for wireless networks.
Dual-Pivot Quicksort and Beyond: Analysis of Multiway Partitioning and Its Practical Potential
(2016)
Multiway Quicksort, i.e., partitioning the input in one step around several pivots, has received much attention since Java 7’s runtime library uses a new dual-pivot method that outperforms by far the old Quicksort implementation. The success of dual-pivot Quicksort is most likely due to more efficient usage of the memory hierarchy, which gives reason to believe that further improvements are possible with multiway Quicksort.
In this dissertation, I conduct a mathematical average-case analysis of multiway Quicksort including the important optimization to choose pivots from a sample of the input. I propose a parametric template algorithm that covers all practically relevant partitioning methods as special cases, and analyze this method in full generality. This allows me to analytically investigate in depth what effect the parameters of the generic Quicksort have on its performance. To model the memory-hierarchy costs, I also analyze the expected number of scanned elements, a measure for the amount of data transferred from memory that is known to also approximate the number of cache misses very well. The analysis unifies previous analyses of particular Quicksort variants under particular cost measures in one generic framework.
A main result is that multiway partitioning can reduce the number of scanned elements significantly, while it does not save many key comparisons; this explains why the earlier studies of multiway Quicksort did not find it promising. A highlight of this dissertation is the extension of the analysis to inputs with equal keys. I give the first analysis of Quicksort with pivot sampling and multiway partitioning on an input model with equal keys.
Software is becoming increasingly concurrent: parallelization, decentralization, and reactivity necessitate asynchronous programming in which processes communicate by posting messages/tasks to others’ message/task buffers. Asynchronous programming has been widely used to build fast servers and routers, embedded systems and sensor networks, and is the basis of Web programming using Javascript. Languages such as Erlang and Scala have adopted asynchronous programming as a fundamental concept with which highly scalable and highly reliable distributed systems are built.
Asynchronous programs are challenging to implement correctly: the loose coupling between asynchronously executed tasks makes the control and data dependencies difficult to follow. Even subtle design and programming mistakes on the programs have the capability to introduce erroneous or divergent behaviors. As asynchronous programs are typically written to provide a reliable, high-performance infrastructure, there is a critical need for analysis techniques to guarantee their correctness.
In this dissertation, I provide scalable verification and testing tools to make asyn- chronous programs more reliable. I show that the combination of counter abstraction and partial order reduction is an effective approach for the verification of asynchronous systems by presenting PROVKEEPER and KUAI, two scalable verifiers for two types of asynchronous systems. I also provide a theoretical result that proves a counter-abstraction based algorithm called expand-enlarge-check, is an asymptotically optimal algorithm for the coverability problem of branching vector addition systems as which many asynchronous programs can be modeled. In addition, I present BBS and LLSPLAT, two testing tools for asynchronous programs that efficiently uncover many subtle memory violation bugs.
The biodiversity of the cyanobacterial lichen flora of Vietnam is chronically understudied. Previous studies often neglected the lichens that inhabit lowlands especially outcrops and sand dunes that are common habitats in Vietnam.
A cyanolichen collection was gathered from lowlands of central and southern Vietnam to study their diversity and distribution. At the same time, cultured photobionts from those lichens were used for olyphasic taxonomic approach.
A total of 66 cyanolichens were recorded from lowland regions in central and southern of Vietnam, doubles the number of cyanolichens for Vietnam. 80% of them are new records for Vietnam in which a new species Pyrenopsis melanophthalma and two new unidentified lichinacean taxa were described.
A notably floristic segregation by habitats was indicated in the communities. Saxicolous Lichinales dominated in coastal outcrops that corresponded to 56% of lichen species richness. Lecanoralean cyanolichens and basidiolichens were found in the lowland forests. Precipitation correlated negatively to species richness in this study, indicating a competitive relationship.
Eleven cyanobacterial strains including 8 baeocyte-forming members of the genus Chroococcidiopsis and 3 heterocyte-forming species of the genera Nostoc and Scytonema were successfully isolated from lichens.
Phylogenetic and morphological analyses indicated that Chroococcidiopsis was the unique photobiont in Peltula. New mophological characters were found in two Chroococcidiopsis strains: (1) the purple content of cells in one photobiont strain that was isolated from a new lichinacean taxon, and (2) the pseudofilamentous feature by binary division from a strain that was isolated from Porocyphus dimorphus.
With respect to heterocyte-forming cyanobiont, Scytonema was confirmed as the photobiont in the ascolichen Heppia lutosa applying the polyphasic method. The genus Scytonema in the basidiolichens Cyphellostereum was morphologically examinated in lichen thalli. For the first time the intracellular haustorial system of basidiolichen genus Cyphellostereum was noted and investigated.
Phylogenetic analysis of photobiont strains Nostoc from Pannaria tavaresii and Parmeliella brisbanensis indicated that a high selectivity occurred in Parmeliella brisbanensis that were from different regions of the world, while low photobiont selectivity occurred among Pannaria tavaresii samples from different geographical regions.
The herewith presented dissertation is therefore an important contribution to the lichen flora of Vietnam and a significant improvement of the actual knowledge about cyanolichens in this country.
The task of printed Optical Character Recognition (OCR), though considered ``solved'' by many, still poses several challenges. The complex grapheme structure of many scripts, such as Devanagari and Urdu Nastaleeq, greatly lowers the performance of state-of-the-art OCR systems.
Moreover, the digitization of historical and multilingual documents still require much probing. Lack of benchmark datasets further complicates the development of reliable OCR systems. This thesis aims to find the answers to some of these challenges using contemporary machine learning technologies. Specifically, the Long Short-Term Memory (LSTM) networks, have been employed to OCR modern as well historical monolingual documents. The excellent OCR results obtained on these have led us to extend their application for multilingual documents.
The first major contribution of this thesis is to demonstrate the usability of LSTM networks for monolingual documents. The LSTM networks yield very good OCR results on various modern and historical scripts, without using sophisticated features and post-processing techniques. The set of modern scripts include modern English, Urdu Nastaleeq and Devanagari. To address the challenge of OCR of historical documents, this thesis focuses on Old German Fraktur script, medieval Latin script of the 15th century, and Polytonic Greek script. LSTM-based systems outperform the contemporary OCR systems on all of these scripts. To cater for the lack of ground-truth data, this thesis proposes a new methodology, combining segmentation-based and segmentation-free OCR approaches, to OCR scripts for which no transcribed training data is available.
Another major contribution of this thesis is the development of a novel multilingual OCR system. A unified framework for dealing with different types of multilingual documents has been proposed. The core motivation behind this generalized framework is the human reading ability to process multilingual documents, where no script identification takes place.
In this design, the LSTM networks recognize multiple scripts simultaneously without the need to identify different scripts. The first step in building this framework is the realization of a language-independent OCR system which recognizes multilingual text in a single step. This language-independent approach is then extended to script-independent OCR that can recognize multiscript documents using a single OCR model. The proposed generalized approach yields low error rate (1.2%) on a test corpus of English-Greek bilingual documents.
In summary, this thesis aims to extend the research in document recognition, from modern Latin scripts to Old Latin, to Greek and to other ``under-privilaged'' scripts such as Devanagari and Urdu Nastaleeq.
It also attempts to add a different perspective in dealing with multilingual documents.
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 [19],
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
multi-sensor systems.
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
listed below.
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
multi-objective optimization,
Gain the adaptability, reliability and robustness of designed solutions with self-x
properties,
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
assessment.
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.,
NOVCLASS-R.
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.