05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Low-field chip-based Overhauser dynamic nuclear polarization platforms(2026) Yang, Qing; Anders, Jens (Prof. Dr.)Item Open Access Methodology to qualify batteries for safety-critical vehicle applications(2025) Conradt, Rafael; Birke, Kai Peter (Prof. Dr.-Ing.)Item Open Access Challenges of computational social science analysis with NLP methods(2022) Dayanik, Erenay; Padó, Sebastian (Prof. Dr.)Computational Social Science (CSS) is an emerging research area at the intersection of social science and computer science, where problems of societal relevance can be addressed by novel computational methods. With the recent advances in machine learning and natural language processing as well as the availability of textual data, CSS has opened up to new possibilities, but also methodological challenges. In this thesis, we present a line of work on developing methods and addressing challenges in terms of data annotation and modeling for computational political science and social media analysis, two highly popular and active research areas within CSS. In the first part of the thesis, we focus on a use case from computational political science, namely Discourse Network Analysis (DNA), a framework that aims at analyzing the structures behind complex societal discussions. We investigate how this style of analysis, which is traditionally performed manually, can be automated. We start by providing a requirement analysis outlining a roadmap to decompose the complex DNA task into several conceptually simpler sub-tasks. Then, we introduce NLP models with various configurations to automate two of the sub-tasks given by the requirement analysis, namely claim detection and classification, based on different neural network architectures ranging from unidirectional LSTMs to Transformer based architectures. In the second part of the thesis, we shift our focus to fairness, a central concern in CSS. Our goal in this part of the thesis is to analyze and improve the performances of NLP models used in CSS in terms of fairness and robustness while maintaining their overall performance. With that in mind, we first analyze the above-mentioned claim detection and classification models and propose techniques to improve model fairness and overall performance. After that, we broaden our focus to social media analysis, another highly active subdomain of CSS. Here, we study text classification of the correlated attributes, which pose an important but often overlooked challenge to model fairness. Our last contribution is to discuss the limitations of the current statistical methods applied for bias identification; to propose a multivariate regression based approach; and to show that, through experiments conducted on social media data, it can be used as a complementary method for bias identification and analysis tasks. Overall, our work takes a step towards increasing the understanding of challenges of computational social science. We hope that both political scientists and NLP scholars can make use of the insights from this thesis in their research.Item Open Access A design space for pervasive advertising on public displays(2013) Alt, Florian; Schmidt, Albrecht (Prof. Dr.)Today, people living in cities see up to 5000 ads per day and many of them are presented on public displays. More and more of these public displays are networked and equipped with various types of sensors, making them part of a global infrastructure that is currently emerging. Such networked and interactive public displays provide the opportunity to create a benefit for society in the form of immersive experiences and relevant content. In this way, they can overcome the display blindness that evolved among passersby over the years. We see two main reasons that prevent this vision from coming true: first, public displays are stuck with traditional advertising as the driving business model, making it difficult for novel, interactive applications to enter the scene. Second, no common ground exists for researchers or advertisers that outline important challenges. The provider view and audience view need to be addressed to make open, interactive display networks, successful. The main contribution made by this thesis is presenting a design space for advertising on public displays that identifies important challenges -- mainly from a human-computer interaction perspective. Solutions to these core challenges are presented and evaluated, using empirical methods commonly applied in HCI. First, we look at challenges that arise from the shared use of display space. We conducted an observational study of traditional public notice areas that allowed us to identify different stakeholders, to understand their needs and motivations, to unveil current practices used to exercise control over the display, and to understand the interplay between space, stakeholders, and content. We present a set of design implications for open public display networks that we applied when implementing and evaluating a digital public notice area. Second, we tackle the challenge of making the user interact by taking a closer look at attracting attention, communicating interactivity, and enticing interaction. Attracting attention is crucial for any further action to happen. We present an approach that exploits gaze as a powerful input modality. By adapting content based on gaze, we are able to show a significant increase in attention and an effect on the user's attitude. In order to communicate interactivity, we show that the mirror representation of the user is a powerful interactivity cue. Finally, in order to entice interaction, we show that the user needs to be motivated to interact and to understand how interaction works. Findings from our experiments reveal direct touch and the mobile phone as suitable interaction technologies. In addition, these findings suggest that relevance of content, privacy, and security have a strong influence on user motivation. Third, this thesis makes a set of contributions towards understanding audience behavior, which is particularly important for advertisers in order to choose appropriate content and to select suitable locations for future advertising displays. Our findings provide an in-depth understanding of the honeypot effect as a powerful interactivity cue. Furthermore, we identify a number of interesting effects (e.g., the landing effect) and explain how developers could design for them. We envision the results of this thesis to provide a basis for future research and for practitioners to shape future advertisements on public displays in a positive way.Item Open Access Analyzing code corpora to improve the correctness and reliability of programs(2021) Patra, Jibesh; Pradel, Michael (Prof. Dr.)Bugs in software are commonplace, challenging, and expensive to deal with. One widely used direction is to use program analyses and reason about software to detect bugs in them. In recent years, the growth of areas like web application development and data analysis has produced large amounts of publicly available source code corpora, primarily written in dynamically typed languages, such as Python and JavaScript. It is challenging to reason about programs written in such languages because of the presence of dynamic features and the lack of statically declared types. This dissertation argues that, to build software developer tools for detecting and understanding bugs, it is worthwhile to analyze code corpora, which can uncover code idioms, runtime information, and natural language constructs such as comments. The dissertation is divided into three corpus-based approaches that support our argument. In the first part, we present static analyses over code corpora to generate new programs, to perform mutations on existing programs, and to generate data for effective training of neural models. We provide empirical evidence that the static analyses can scale to thousands of files and the trained models are useful in finding bugs in code. The second part of this dissertation presents dynamic analyses over code corpora. Our evaluations show that the analyses are effective in uncovering unexpected behaviors when multiple JavaScript libraries are included together and to generate data for training bug-finding neural models. Finally, we show that a corpus-based analysis can be useful for input reduction, which can help developers to find a smaller subset of an input that still triggers the required behavior. We envision that the current dissertation motivates future endeavors in corpus-based analysis to alleviate some of the challenges faced while ensuring the reliability and correctness of software. One direction is to combine data obtained by static and dynamic analyses over code corpora for training. Another direction is to use meta-learning approaches, where a model is trained using data extracted from the code corpora of one language and used for another language.Item Open Access Scalable computer network emulation using node virtualization and resource monitoring(2011) Maier, Steffen Dirk; Rothermel, Kurt (Prof. Dr. rer. nat. Dr. h. c.)Ongoing development of computer network technology requires new communication protocols on all layers of the protocol stack to adapt to and to exploit technology specifics. The performance of new protocol implementations has to be evaluated before deployment. Computer network emulation enables the execution of real unmodified protocol implementations within a configurable synthetic environment. Since network properties are reproduced synthetically, emulation supports reproducible measurement results for wired and wireless networks. Meaningful evaluation scenarios typically involve a large number of communicating nodes. Reproducing the network properties of the medium access control layer can be accomplished efficiently on cheap common off the shelf computers and allows to evaluate network protocols, transport protocols, and applications. However, meaningful emulation scenario sizes often require more nodes than affordable computers. To scale the number of nodes in an emulation scenario beyond the available computers, we discuss approaches to virtualization and operating system partitioning. Focusing on the latter, we argue for virtual protocol stacks, which provide an extremely lightweight node virtualization enabling the execution of multiple instances of software to be evaluated on each physical computer. To connect virtual nodes on the same and on different computers, we design and implement a highly efficient software communication switch. A centralized emulation control component distributes dynamic network property updates which result from node mobility for instance. To handle the large number of nodes and thus increased updates, we propose a hierarchical control where the central component delegates updates to sub-components distributed over the computers of an emulation system. Extensive evaluations show the scalability of our virtualized network emulation system. Virtual nodes executed on the same computer share its limited resources. Hosting too many virtual nodes on the same computer may lead to resource contention. This can cause unrealistic measurement results and is thus undesirable. Discussing different approaches to handle resource contention, we argue for detection and recovery. We define quality criteria that allow the detection of resource contention. In order to observe those quality criteria during emulation experiments, we propose a highly lightweight monitoring approach. Our monitoring is based on instrumenting an operating system kernel and observing basic resource scheduling events. This enables the detection of even peak resource usage within a split second. Thorough evaluations demonstrate the effectiveness of quality criteria and monitoring as well as the negligible overhead of our monitoring approach.Item Open Access Stochastic neural networks : components, analysis, limitations(2022) Neugebauer, Florian; Polian, Ilia (Prof. Dr.)Stochastic computing (SC) promises an area and power-efficient alternative to conventional binary implementations of many important arithmetic functions. SC achieves this by employing a stream-based number format called Stochastic numbers (SNs), which enables bit-sequential computations, in contrast to conventional binary computations that are performed on entire words at once. An SN encodes a value probabilistically with equal weight for every bit in the stream. This encoding results in approximate computations, causing a trade-off between power consumption, area and computation accuracy. The prime example for efficient computation in SC is multiplication, which can be performed with only a single gate. SC is therefore an attractive alternative to conventional binary implementations in applications that contain a large number of basic arithmetic operations and are able to tolerate the approximate nature of SC. The most widely considered class of applications in this regard is neural networks (NNs), with convolutional neural networks (CNNs) as the prime target for SC. In recent years, steady advances have been made in the implementation of SC-based CNNs (SCNNs). At the same time however, a number of challenges have been identified as well: SCNNs need to handle large amounts of data, which has to be converted from conventional binary format into SNs. This conversion is hardware intensive and takes up a significant portion of a stochastic circuit's area, especially if the SNs have to be generated independently of each other. Furthermore, some commonly used functions in CNNs, such as max-pooling, have no exact corresponding SC implementation, which reduces the accuracy of SCNNs. The first part of this work proposes solutions to these challenges by introducing new stochastic components: A new stochastic number generator (SNG) that is able to generate a large number of SNs at the same time and a stochastic maximum circuit that enables an accurate implementation of max-pooling operations in SCNNs. In addition, the first part of this work presents a detailed investigation of the behaviour of an SCNN and its components under timing errors. The error tolerance of SC is often quoted as one of its advantages, stemming from the fact that any single bit of an SN contributes only very little to its value. In contrast, bits in conventional binary formats have different weights and can contribute as much as 50\% of a number's value. SC is therefore a candidate for extreme low-power systems, as it could potentially tolerate timing errors that appear in such environments. While the error tolerance of SC image processing systems has been demonstrated before, a detailed investigation into SCNNs in this regard has been missing so far. It will be shown that SC is not error tolerant in general, but rather that SC components behave differently even if they implement the same function, and that error tolerance of an SC system further depends on the error model. In the second part of this work, a theoretical analysis into the accuracy and limitations of SC systems is presented. An existing framework to analyse and manage the accuracy of combinational stochastic circuits is extended to cover sequential circuits. This framework enables a designer to predict the effect of small design changes on the accuracy of a circuit and determine important parameters such as SN length without extensive simulations. It will further be shown that the functions that are possible to implement in SC are limited. Due to the probabilistic nature of SC, some arithmetic functions suffer from a small bias when implemented as a stochastic circuit, including the max-pooling function in SCNNs.Item Open Access Scalable traffic engineering heuristics for time-triggered communication in real-time networks(2026) Geppert, Heiko; Rothermel, Kurt (Prof. Dr. rer. nat. Dr. h. c.)Distributed safety-critical cyber-physical systems require real-time behavior. This means they must respond not just quickly, but in time, to new situations considering both, the task processing and network communication time. From a networking perspective, meticulous, time-driven traffic planning performed at the frame level is necessary to guarantee low end-to-end delay bounds and low latency. This involves carefully planning transmission operations along each time-critical frame's network path are carefully planned, including precise timing, to limit or even eliminate interference from cross-traffic and ensure timely delivery. Since modern real-time systems can consist of hundreds or thousands of devices - for example, large manufacturing plants or continental-sized power grids - the traffic planning must be highly scalable. Although there are many traffic planning approaches in the literature, there is a lack of very fast heuristics that can handle very large stream sets and networks quickly. This thesis investigates traffic planning heuristics and optimization techniques, focusing on different aspects of the traffic planning domain. The traffic planning consists of novel methods for conflict-graph-based scheduling and new heuristics for very large instances of traffic planning problem. The optimizations include multicast partitioning, which combines the benefits of multicast and unicast traffic plans, and load-balanced stream placement, which generates traffic plans that can accommodate additional streams joining the system later. We created prototype implementations and analyzed their performance in solving the traffic planning problem. Our traffic plans yielded a higher accumulated network throughput or admitted more streams while maintaining computation times ranging from sub-seconds to minutes, even for extremely large-scale problem instances. The traffic planning methods and optimization techniques presented in this thesis can be applied to modern real-time networking technologies, such as Time-Sensitive Networking and TTEthernet.Item Open Access Digital pre- and post-equalizers for in-car data transmission over plastic optical fibers(2014) Voigt, Yixuan; Speidel, Joachim (Prof. Dr.-Ing. )Lately, a hot topic in the automobile industry is the development of the in-vehicle infotainment communication network based on the media oriented system transport (MOST) standard, where a cost-effective optical physical layer composed of light emitting diodes (LED), plastic optical fibers (POF) and positive-intrinsic-negative photodiodes (PIN PD) is used by the in-car network. The latest MOST150 standard has specified a transmission speed of 150 Mbit/s, while the next MOST generation is targeted at multi-Gbit/s. Obviously, the very limited bandwidth of the current physical layer will weigh on the future MOST generations. However, it is important to evaluate the potential of the current physical layer, for the reason that the car-manufacturers may continue using the low-cost and easily operable POFs and LEDs. The objective of this dissertation is to increase the data-rate for the next MOST generation from 150 Mbit/s to 2 ∼ 3 Gbit/s, based upon the current MOST150 optical physical layer. The main emphasis lies in investigating electronic signal processing techniques to detect the multi-level pulse-amplitude modulated (MPAM) signal transmitted through the noisy dispersive POF-based optical channel. To be specific, four different transmission schemes are studied respectively: the post-equalization scheme using either linear or decision-feedback equalizer, the joint pre- and post-equalization scheme, the non-linear Tomlinson-Harashima precoding (THP) scheme, and the bidirectional decision feedback equalization (BiDFE) scheme. In the BiDFE scheme, a novel trellis-based BiDFE (TB-BiDFE) equalizer is proposed. Their performances are investigated by means of theoretical analysis and computer simulations. As will be shown, with the help of electronic equalizers and error-correcting code, the final bitrate is able to reach 3 Gbit/s over a 10 m standard step-index POF, despite the use of a low-cost LED transmitter.Item Open Access Novel characterization techniques for the study of the dynamic behavior of silicon carbide power MOSFETs(2022) Salcines, Cristino; Kallfass, Ingmar (Prof. Dr.-Ing.)This dissertation provides insight into the dynamic behavior of SiC power MOSFETs from their inherent static IV and CV characteristics. While conventional dynamic measurements extracted from a DPT or a similar dynamic test-bench yield accurate quantitative data, the static IV and CV characteristics of a power semiconductor device offer more qualitative information to delve into the root mechanisms responsible for its dynamic behavior. Conventional characterization techniques are limited to power levels way below those which the power device withstands in the application. As a result, the static IV and CV characteristics attained by available measurement solutions are reduced to a limited scope of bias conditions insufficient to infer information about the dynamic behavior of the power device. This work tackles this gap and proposes novel measurement techniques that enable the characterization of the static IV and CV characteristics of SiC power MOSFETs at the full range of bias conditions the power device goes through in the application. Iso-thermal IV characteristics of a commercially available SiC power MOSFET are measured up to 40 kW power (instantaneous 50 A and 800 V) at junction temperatures ranging from 25°C to 175 °C. The CV characteristics are mapped at drain-source and gate-source bias combinations of VDS = 0 - 40 V and VGS = 0 - 20 V, respectively, at junction temperatures ranging from 25°C to 150 °C. The results of these measurements reveal unique insights into the electrical characteristics of SiC power MOSFETs which impact their performance in the application and explain unclear phenomena observed in their dynamic behavior. On the one hand, the intrinsic capacitances of the SiC power MOSFET extend their non-linearity, function of both VGS and VDS, to the saturation region of the power device. Moreover, they are also affected by the junction temperature of the power device. The impact of these in the voltage commutation speed of the device under different switching conditions is thoroughly analyzed in the thesis. On the other hand, the IV characteristics of the SiC power MOSFET reveal the existence of short channel effects that drastically affect the transconductance of the power device in its high voltage saturation region. Furthermore, the measurements show a positive temperature coefficient of the drain current in the high voltage saturation region of the SiC power device, attributed to the density of trap energy states in the SiC/SiO2 interface. These effects effectively lower the plateau voltage of the device and lead to faster current commutation speeds in the application than those expected from the datasheet values. The insights revealed by the proposed characterization techniques are intended to help fine-tune semiconductor technology processes and improve the accuracy of simulation models to achieve a higher grade of optimization in the design of future SiC-based energy conversion circuits.