Kaiserslautern - Fachbereich Informatik
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Planar force or pressure is a fundamental physical aspect during any people-vs-people and people-vs-environment activities and interactions. It is as significant as the more established linear and angular acceleration (usually acquired by inertial measurement units). There have been several studies involving planar pressure in the discipline of activity recognition, as reviewed in the first chapter. These studies have shown that planar pressure is a promising sensing modality for activity recognition. However, they still take a niche part in the entire discipline, using ad hoc systems and data analysis methods. Mostly these studies were not followed by further elaborative works. The situation calls for a general framework that can help push planar pressure sensing into the mainstream.
This dissertation systematically investigates using planar pressure distribution sensing technology for ubiquitous and wearable activity recognition purposes. We propose a generic Textile Pressure Mapping (TPM) Framework, which encapsulates (1) design knowledge and guidelines, (2) a multi-layered tool including hardware, software and algorithms, and (3) an ensemble of empirical study examples. Through validation with various empirical studies, the unified TPM framework covers the full scope of application recognition, including the ambient, object, and wearable subspaces.
The hardware part constructs a general architecture and implementations in the large-scale and mobile directions separately. The software toolkit consists of four heterogeneous tiers: driver, data processing, machine learning, visualization/feedback. The algorithm chapter describes generic data processing techniques and a unified TPM feature set. The TPM framework offers a universal solution for other researchers and developers to evaluate TPM sensing modality in their application scenarios.
The significant findings from the empirical studies have shown that TPM is a versatile sensing modality. Specifically, in the ambient subspace, a sports mat or carpet with TPM sensors embedded underneath can distinguish different sports activities or different people's gait based on the dynamic change of body-print; a pressure sensitive tablecloth can detect various dining actions by the force propagated from the cutlery through the plates to the tabletop. In the object subspace, swirl office chairs with TPM sensors under the cover can be used to detect the seater's real-time posture; TPM can be used to detect emotion-related touch interactions for smart objects, toys or robots. In the wearable subspace, TPM sensors can be used to perform pressure-based mechanomyography to detect muscle and body movement; it can also be tailored to cover the surface of a soccer shoe to distinguish different kicking angles and intensities.
All the empirical evaluations have resulted in accuracies well-above the chance level of the corresponding number of classes, e.g., the `swirl chair' study has classification accuracy of 79.5% out of 10 posture classes and in the `soccer shoe' study the accuracy is 98.8% among 17 combinations of angle and intensity.
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.
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.
Today, information systems are often distributed to achieve high availability and low latency.
These systems can be realized by building on a highly available database to manage the distribution of data.
However, it is well known that high availability and low latency are not compatible with strong consistency guarantees.
For application developers, the lack of strong consistency on the database layer can make it difficult to reason about their programs and ensure that applications work as intended.
We address this problem from the perspective of formal verification.
We present a specification technique, which allows specifying functional properties of the application.
In addition to data invariants, we support history properties.
These let us express relations between events, including invocations of the application API and operations on the database.
To address the verification problem, we have developed a proof technique that handles concurrency using invariants and thereby reduces the problem to sequential verification.
The underlying system semantics, technique and its soundness proof are all formalized in the interactive theorem prover Isabelle/HOL.
Additionally, we have developed a tool named Repliss which uses the proof technique to enable partially automated verification and testing of applications.
For verification, Repliss generates verification conditions via symbolic execution and then uses an SMT solver to discharge them.
With the technological advancement in the field of robotics, it is now quite practical to acknowledge the actuality of social robots being a part of human's daily life in the next decades. Concerning HRI, the basic expectations from a social robot are to perceive words, emotions, and behaviours, in order to draw several conclusions and adapt its behaviour to realize natural HRI. Henceforth, assessment of human personality traits is essential to bring a sense of appeal and acceptance towards the robot during interaction.
Knowledge of human personality is highly relevant as far as natural and efficient HRI is concerned. The idea is taken from human behaviourism, with humans behaving differently based on the personality trait of the communicating partners. This thesis contributes to the development of personality trait assessment system for intelligent human-robot interaction.
The personality trait assessment system is organized in three separate levels. The first level, known as perceptual level, is responsible for enabling the robot to perceive, recognize and understand human actions in the surrounding environment in order to make sense of the situation. Using psychological concepts and theories, several percepts have been extracted. A study has been conducted to validate the significance of these percepts towards personality traits.
The second level, known as affective level, helps the robot to connect the knowledge acquired in the first level to make higher order evaluations such as assessment of human personality traits. The affective system of the robot is responsible for analysing human personality traits. To the best of our knowledge, this thesis is the first work in the field of human-robot interaction that presents an automatic assessment of human personality traits in real-time using visual information. Using psychology and cognitive studies, many theories has been studied. Two theories have been been used to build the personality trait assessment system: Big Five personality traits assessment and temperament framework for personality traits assessment.
By using the information from the perceptual and affective level, the last level, known as behavioural level, enables the robot to synthesize an appropriate behaviour adapted to human personality traits. Multiple experiments have been conducted with different scenarios. It has been shown that the robot, ROBIN, assesses personality traits correctly during interaction and uses the similarity-attraction principle to behave with similar personality type. For example, if the person is found out to be extrovert, the robot also behaves like an extrovert. However, it also uses the complementary attraction theory to adapt its behaviour and complement the personality of the interaction partner. For example, if the person is found out to be self-centred, the robot behaves like an agreeable in order to flourish human-robot interaction.
This thesis focuses on novel methods to establish the utility of wearable devices along with machine learning and pattern recognition methods for formal education and address the open research questions posed by existing methods. Firstly, state-of-the-art methods are proposed to analyse the cognitive activities in the learning process, i.e., reading, writing, and their correlation. Furthermore, this thesis presents real-time applications in wearable space as an experimental tool in Physics education, and an air-writing system.
There are two critical components in analysing the reading behaviour, i.e., WHERE a person looks at (gaze analysis) and WHAT a person looks at (content analysis). This thesis proposes novel methods to classify the reading content to address the WHAT AT component. The proposed methods are based on a hybrid approach, which fuses the traditional computer vision methods with deep neural networks. These methods, when evaluated on publicly available datasets, yield state-of-the-art results to define the structure of the document images. Moreover, extensive efforts were made to refine and correct ICDAR2017-POD dataset along with a completely new FFD dataset.
Traditionally, handwriting research focuses on character and number recognition without looking into the type of writing, i.e. text, math, and drawing. This thesis reports multiple contributions for on-line handwriting classification. First, it presents a public dataset for on-line handwriting classification OnTabWriter, collected using iPen and an iPad. In addition, a new feature set is introduced for on-line handwriting classification to establish the benchmark on the proposed dataset to classify handwriting as plain text, mathematical expression, and plot/graph. An ablation study is made to evaluate the performance of the proposed feature set in comparison to existing feature sets. Lastly, this thesis evaluates the importance of context for on-line handwriting classification.
Analysing reading and writing activities individually is not enough to provide insights to identify the student's expertise unless their correlations are analysed. This thesis presents a study where reading data from wearable eye-trackers and writing data from sensor pen are analysed together in correlation to correlate the expertise of the users in Physics education with their actual knowledge. Initial results show a strong correlation between individual's expertise and understanding of the subject.
Augmented reality & virtual applications can play a vital role in making classroom environments more interactive and engaging both for teachers and learners. To validate the hypothesis, different applications are developed and evaluated. First, smart glasses are used as an experimental tool in Physics education to help the learners perform experiments by providing assistance and feedback on head mounted display in understanding acoustics concepts. Second, a real-time application of air-writing with the finger on an imaginary canvas using a single IMU as the FAirWrite system is also presented. FAirWrite system is further equipped with DL methods to classify the air-written characters.
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.
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.
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.