Fakultät für Informatik und Mathematik
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Institute
Advanced driver assistance systems play an important role in increasing the safety on today's roads. The knowledge about the other vehicles' positions is a fundamental prerequisite for numerous safety critical applications, making it possible to foresee critical situations, warn the driver or autonomously intervene. Forward collision avoidance systems, lane change assistants or adaptive cruise control are examples of safety relevant applications that require an accurate, continuous and reliable relative position of surrounding vehicles.
Currently, the positions of surrounding vehicles is estimated by measuring the distance with e.g. radar, laser scanners or camera systems. However, all these techniques have limitations in their perception range, as all of them can only detect objects in their line-of-sight. The limited perception range of today's vehicles can be extended in future by using cooperative approaches based on Vehicle-to-Vehicle (V2V) communication.
In this thesis, the capabilities of cooperative relative positioning for vehicles will be assessed in terms of its accuracy, continuity and reliability. A novel approach where Global Navigation Satellite System (GNSS) raw data is exchanged between the vehicles is presented. Vehicles use GNSS pseudorange and Doppler measurements from surrounding vehicles to estimate the relative positioning vector in a cooperative way. In this thesis, this approach is shown to outperform the absolute position subtraction as it is able to effectively cancel out common errors to both GNSS receivers. This is modeled theoretically and demonstrated empirically using simulated signals from a GNSS constellation simulator.
In order to cope with GNSS outages and to have a sufficiently good relative position estimate even in strong multipath environments, a sensor fusion approach is proposed. In addition to the GNSS raw data, inertial measurements from speedometers, accelerometers and turn rate sensors from each vehicle are exchanged over V2V communication links. A Bayesian approach is applied to consider the uncertainties inherently to each of the information sources. In a dynamic Bayesian network, the temporal relationship of the relative position estimate is predicted by using relative vehicle movement models.
Also real world measurements in highway, rural and urban scenarios are performed in the scope of this work to demonstrate the performance of the cooperative relative positioning approach based on sensor fusion. The results show that the relative position of another vehicle towards the ego vehicle can be estimated with sub-meter accuracy in highway scenarios. Here, good reliability and 90% availability with an uncertainty of less than 2.5m is achieved. In rural environments, drives through forests and towns are correctly bridged with the support of on-board sensors. In an urban environment, the difficult estimation of the ego vehicle heading has a mayor impact in the relative position estimate, yielding large errors in its longitudinal component.
Splinekurven sind oft das erste Mittel der Wahl, wenn Daten interpoliert oder approximiert werden sollen. Sie spielen in vielen praktischen Anwendungsbereichen eine wichtige Rolle und sind in Bereichen des CAD/CAM nicht mehr weg zu denken. In der vorliegenden Arbeit werden in diesem Kontext Bahnpunkte zur Steuerung von Werkzeugmaschinen untersucht. Die Analyse wird mit Hilfe eines Multiresolution (MRA) Ansatzes für Splinekurven mit adaptiven Knotenfolgen realisiert. Dieser MRA Ansatz basiert auf einer Least-Squares-Projektion zum Knotenentfernen und unterscheidet sich somit zu bekannten Ansätzen, die auf orthogonalen Komplementen aufbauen. Des Weiteren wird ein Konzept zur Approximation von Orientierungsdaten mittels homogenen Quaternionensplines vorgestellt. Diese Splines leben auf der Sphäre und lassen sich mittels Knotenentfernen bzw. einfügen verfeinern. Somit lässt sich das vorgestellte MRA–Analyseverfahren ebenfalls auf diese Kurven anwenden. Weiter konnte für diese Kurven eine konvexe Hülle–Eigenschaft nachgewiesen werden.
In our knowledge-driven society, the acquisition and the transfer of knowledge play a principal role. Web search engines are somehow tools for knowledge acquisition and transfer from the web to the user. The search engine results page (SERP) consists mainly of a list of links and snippets (excerpts from the results). The snippets are used to express, as efficiently as possible, the way a web page may be relevant to the query.
As an extension of the existing web, the semantic web or “web 3.0” is designed to convert the presently available web of unstructured documents into a web of data consumable by both human and machines. The resulting web of data and the current web of documents coexist and interconnect via multiple mechanisms, such as the embedded structured data, or the automatic annotation.
In this thesis, we introduce a new interactive artifact for the SERP: the “Semantic Snippet”. Semantic Snippets rely on the coexistence of the two webs to facilitate the transfer of knowledge to the user thanks to a semantic contextualization of the user’s information need. It makes apparent the relationships between the information need and the most relevant entities present in the web page.
The generation of semantic snippets is mainly based on the automatic annotation of the LOD1’s entities in web pages. The annotated entities have different level of impor- tance, usefulness and relevance. Even with state of the art solutions for the automatic annotations of LOD entities within web pages, there is still a lot of noise in the form of erroneous or off-topic annotations. Therefore, we propose a query-biased algorithm (LDRANK) for the ranking of these entities. LDRANK adopts a strategy based on the linear consensual combination of several sources of prior knowledge (any form of con- textual knowledge, like the textual descriptions for the nodes of the graph) to modify a PageRank-like algorithm.
For generating semantic snippets, we use LDRANK to find the more relevant entities in the web page. Then, we use a supervised learning algorithm to link each selected entity to excerpts from the web page that highlight the relationship between the entity and the original information need.
In order to evaluate our semantic snippets, we integrate them in ENsEN (Enhanced Search Engine), a software system that enhances the SERP with semantic snippets.
Finally, we use crowdsourcing to evaluate the usefulness and the efficiency of ENsEN.
Opportunistic networks (OppNets) are human-centric mobile ad-hoc networks, in which neither the topology nor the participating nodes are known in advance. Routing is dynamically planned following the store-carry-and-forward paradigm, which takes advantage of people mobility. This widens the range of communication and supports indirect end-to-end data delivery. But due to individuals’ mobility, OppNets are characterized by frequent communication disruptions and uncertain data delivery. Hence, these networks are mostly used for exchanging small messages like disaster alarms or traffic notifications. Other scenarios that require the exchange of larger data (e.g. video) are still challenging due to the characteristics of this kind of networks. However, there are still multimedia sharing scenarios where a user might need switching from infrastructural communications to an ad-hoc alternative. Examples are the cases of 1) absence of infrastructural networks in far rural areas, 2) high costs due to roaming or limited data volumes or 3) undesirable censorship by third parties while exchanging sensitive content. Consequently, we target in this thesis a video dissemination scheme in OppNets.
For the video delivery problem in the sparse opportunistic networks, we propose a solution with the objective of reducing the video playout delay, so that enabling the recipient to play the video content as soon as possible even if at a low quality. Furthermore, the received video reaches later a higher quality level, ensuring a better viewing experience.
The proposed solution encloses three contributions. The first one is given by granulating the videos at the source node into smaller parts, and associating them with unequal redundancy degrees. This is technically based on using the Scalable Video Coding (SVC), which encodes a video into several layers of unequal importance for viewing the content at different quality levels. Layers are routed using the Spray-and-Wait routing protocol, with different redundancy factors for the different layers depending on their importance degree. In this context as well, a video viewing QoE metric is proposed, which takes the values of the perceived video quality, delivery delay and network overhead into consideration, and on a scalable basis.
Second, we take advantage of the small units of the Network Abstraction Layer (NAL), which compose SVC layers. NAL units are packetized together under specific size constraints to optimize granularity. Packets sizes are tuned in an adaptive way, with regard to the dynamic network conditions. Each node is enabled to record a history of environmental information regarding the contacts and forwarding opportunities, and use this history to predict future opportunities and optimize the sizes accordingly.
Lastly, the receiver (destination) node is pushed into action by reacting to missing data parts in a composite ``backward'' loss concealment mechanism. So, the receiver asks first for the missing data from other nodes in the network in the form of request-response. Then, since the transmission is concerned with video content, video frame loss error concealment techniques are also exploited at the receiver side. Consequently, we propose to combine the two techniques in the loss concealment mechanism, which is enabled then to react to missing data parts.
To study the feasibility and the applicability of the proposed solutions, simulation-driven experiments are performed, and statistical results are collected and analyzed. Consequently, we have got promising results that show the applicability of video dissemination in opportunistic delay tolerant networks, and open the door for a range of possible future works.
In modern CMOS technology, process variations have significantly increased impact on the circuit behavior with continuously scaled transistor sizes. Manufactured devices tend to have different performances due to parameter variations during manufacturing and
in the operating context. Conventional tests generated regardless of variations could fail to rule out devices with low performance and even functional failure caused by extreme variations; the unreliability in shipped products is in turn raised. To tackle the problem, many existing test approaches have focused on identifying and testing a number of critical paths in the circuit, and aimed at the efficiency of the searching process. However, the statistical circuit model, which better describes the circuit timing behavior under variations, is not yet sufficiently investigated and employed by existing testing methodologies.
This thesis work proposes Opt-KLPG and MIRID, which can be utilized by a statistical delay testing flow. Opt-KLPG—a K Longest Paths Generation (KLPG) algorithm for optimal solutions under memory constraints—can pin-pointedly generate tests for small delay defects, which are common small timing deviations under process variations, based on the traditional KLPG algorithm. In contrast to KLPG, Opt-KLPG guarantees the optimality of the solution (the K longest sensitizable paths indeed). MIRID is a mixed-mode timing-aware simulator, incorporating effects of power-supply noise and combining an event-driven logic simulation engine with interfaces to provided electrical models. MIRID aims at evaluating delay tests in presence of process variations efficiently yet accurately, by performing logic simulation at the gate level while determining the gate delays using simplified electrical modes. The electrical models applied by the simulator focus on the IR drop effect. Electrical parameters mainly contributing to the effect
are incorporated into the model. The simulator is generic and flexible to be adapted by modifying the interfaces with minor effort. Both applications were verified in various aspects by experiments for academical/industrial circuits, and turned out to have satisfiable effectiveness and performance.
Employment of a very large number of antennas is seen as the key technology to provide future users with very high data rates. At the same time, the implementation complexity will rise due to large memories required and sophisticated signal processing algorithms employed. Continuous technology downscaling allows implementation of such complex digital designs. At the same time, its inherent variability and vulnerability to physical disturbances violate the assumption of perfectly reliable hardware operation.
This work considers Unique Word OFDM which represents the alternative to the standard Cyclic Prefix OFDM providing superior detection quality. The generalization of Unique Word OFDM to a MIMO system is performed which allows interpretation as a virtual massive MIMO system with only few physical antennas. Detection methods for the introduced generalization are discussed and their performance is quantified.
Because of the large memory size required, linear detection represents the cost and performance effective solution. The possible memory errors due to radiation effects or voltage scaling are addressed and the nonlinear MMSE detection algorithm is proposed. This algorithm keeps track of the memory errors and is able to significantly mitigate their effect on the quality of the estimated data.
Apart of memory issues, reliability of the actual computational hardware which constitutes the receiver is of concern in this work. An own implementation of the MMSE Sorted Givens Rotations is subjected to transient fault injection. The impact of faults in various parts of the implemented circuit on the detection performance is quantified. Most vulnerable components of the implemented circuit in terms of reliability are identified.
Security is another major address of this work, since most current implementations include cryptographic devices.
Fault-based attacks on such systems are known to be able to extract the secret key in feasible time.
The remaining part of this work addresses such fault injection-based malicious attacks. Countermeasures based on a combination of information and hardware redundancy are considered. Recently introduced robust codes target such attacks by providing guaranteed detection capability. The performance of these codes is assessed by application to actual cryptographic and general purpose circuits. The work introduces metrics that help to identify fault locations in the circuit which could escape detection with high probability. These locations are targeted by transistor resizing that renders fault injection unfeasible.
Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question & Answering. We propose a new collective, graph-based disambiguation algorithm utilizing semantic entity and document embeddings for robust entity disambiguation. Robust thereby refers to the property of achieving better than state-of-the-art results over a wide range of very different data sets. Our approach is also able to abstain if no appropriate entity can be found for a specific surface form. Our evaluation shows, that our approach achieves significantly (>5%) better results than all other publicly available disambiguation algorithms on 7 of 9 datasets without data set specific tuning. Moreover, we discuss the influence of the quality of the knowledge base on the disambiguation accuracy and indicate that our algorithm achieves better results than non-publicly available state-of-the-art algorithms.
DoSeR - A Knowledge-Base-Agnostic Framework for Entity Disambiguation Using Semantic Embeddings
(2016)
Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question & Answering. In this work, we propose DoSeR (Disambiguation of Semantic Resources), a (named) entity disambiguation framework that is knowledge-base-agnostic in terms of RDF (e.g. DBpedia) and entity-annotated document knowledge bases (e.g. Wikipedia). Initially, our framework automatically generates semantic entity embeddings given one or multiple knowledge bases. In the following, DoSeR accepts documents with a given set of surface forms as input and collectively links them to an entity in a knowledge base with a graph-based approach. We evaluate DoSeR on seven different data sets against publicly available, state-of-the-art (named) entity disambiguation frameworks. Our approach outperforms the state-of-the-art approaches that make use of RDF knowledge bases and/or entity-annotated document knowledge bases by up to 10% F1 measure.
A configurable system enables users to derive individual system variants based on a selection of configuration options. To cope with the often huge number of possible configurations, several analysis approaches (e.g., for verification of configurable systems) implement different strategies to account for configurability. One popular strategy—often applied in practice—is to use sampling (i.e.,analyzing only a subset of all system variants). While sampling reduces the analysis effort significantly, the information obtained is necessarily incomplete as some variants are not analyzed. A second strategy is to identify the common parts and the variable parts of a configurable system and analyze each part separately (called feature-based strategy). As a third strategy, researchers have begun to develop family-based analyses. Family-based approaches analyze the code base of a configurable system as a whole, rather than the individual variants or parts of the system, this way exploiting similarities among individual variants to reduce analysis effort. Each of these three strategies has advantages and disadvantages, which might even prevent its application (e.g., the family-based strategy typically needs much main memory). The goal of this thesis is to enable the efficient analysis of configuable systems, even if existing strategies fail (e.g., the family-based strategy, because of memory limitations). To this end, we designed a framework that models the key aspects of configurable-system analysis strategies, independent of their implementation and of the analyses techniques (e.g., type checking or model checking). Guided by our model, we developed a number of analysis strategies for configurable systems. To learn about advantages and disadvantages of individual strategies, we compared these in a series of empirical studies. In particular, we developed and evaluated a model-checking analysis and a data-flow analysis for configurable systems. One of our key findings is that family-based analysis outperforms most sampling heuristics with respect to analysis time, while being able to make definite statements about all variants of a configurable system. Furthermore, we identified advantages and disadvantages of analysis strategies and how to mitigate them by combining strategies. In our endeavor, we identified two key problems that are common to configurable-system analyses, and we developed supporting techniques to solve them. These techniques are general and are applicable beyond our research. In particular, we developed presence-condition simplification and variability encoding. Presence-condition simplification provides a simple method to reduce the size of the output or the internal data structure of configurable-systemanalyses. Variability encoding provides a means for transforming compile-time variability to run-time variability, which enables many family-based analyses.
Our key contributions are the model of analysis strategies for configurable systems and the corresponding empirical comparisons of strategies. Our findings are backed by empirical studies, which helped broaden the community knowledge on analyses of configurable systems (indicated by citations). For these evaluations, we prepared several subject systems, which have also been used already by other researchers. Furthermore, we developed several analysis tools and demonstrated their feasibility in practical application scenarios based on code from, for example, the Linux kernel. Our tools are based on variability-aware optimizations that enable levels of scalability on configurable systems that were not possible with other tools before.
The well-known Riemann Mapping Theorem states the existence of a conformal map of a simply connected proper domain of the complex plane onto the upper half plane. One of the main topics in geometric function theory is to investigate the behaviour of the mapping functions at the boundary of such domains. In this work, we always assume that a piecewise analytic boundary is given. Hereby, we have to distinguish regular and singular boundary points. While the asymptotic behaviour for regular boundary points can be investigated by using the Schwarz Reflection at analytic arcs, the situation for singular boundary points is far more complicated. In the latter scenario two cases have to be differentiated: analytic corners and analytic cusps. The first part of the thesis deals with the asymptotic behaviour at analytic corners where the opening angle is greater than 0. The results of Lichtenstein and Warschawski on the asymptotic behaviour of the Riemann map and its derivatives at an analytic corner are presented as well as the much stronger result of Lehman that the mapping function can be developed in a certain generalised power series which in turn enables to examine the o-minimal content of the Riemann Mapping Theorem. To obtain a similar statement for domains with analytic cusps, it is necessary to investigate the asymptotic behaviour of a Riemann map at the cusp and based on this result to determine the asymptotic power series expansion. Therefore, the aim of the second part of this work is to investigate the asymptotic behaviour of a Riemann map at an analytic cusp. A simply connected domain has an analytic cusp if the boundary is locally given by two analytic arcs such that the interior angle vanishes. Besides the asymptotic behaviour of the mapping function, the behaviour of its derivatives, its inverse, and the derivatives of the inverse are analysed. Finally, we present a conjecture on the asymptotic power series expansion of the mapping function at an analytic cusp.