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Faculty / Organisational entity
Distributed message-passing systems have become ubiquitous and essential for our daily lives. Hence, designing and implementing them correctly is of utmost importance. This is, however, very challenging at the same time. In fact, it is well-known that verifying such systems is algorithmically undecidable in general due to the interplay of asynchronous communication (messages are buffered) and concurrency. When designing communication in a system, it is natural to start with a global protocol specification of the desired communication behaviour. In such a top-down approach, the implementability problem asks, given such a global protocol, if the specified behaviour can be implemented in a distributed setting without additional synchronisation. This problem has been studied from two perspectives in the literature. On the one hand, there are Multiparty Session Types (MSTs) from process algebra, with global types to specify protocols. Key to the MST approach is a so-called projection operator, which takes a global type and tries to project it onto every participant: if successful, the local specifications are safe to use. This approach is efficient but brittle. On the other hand, High-level Message Sequence Charts (HMSCs) study the implementability problem from an automata-theoretic perspective. They employ very few restrictions on protocol specifications, making the implementability problem for HMSCs undecidable in general. The work in this thesis is the first to formally build a bridge between the world of MSTs and HMSCs. To start, we present a generalised projection operator for sender-driven choice. This allows a sender to send to different receivers when branching, which is crucial to handle common communication patterns from distributed computing. Despite this first step, we also show that the classical MST projection approach is inherently incomplete. We present the first formal encoding from global types to HMSCs. With this, we prove decidability of the implementability problem for global types with sender-driven choice. Furthermore, we develop the first direct and complete projection operator for global types with sender-driven choice, using automata-theoretic techniques, and show its effectiveness with a prototype implementation. We are the first to provide an upper bound for the implementability problem for global types with sender-driven (or directed) choice and show it to be in PSPACE. We also provide a session type system that uses the results from our projection operator. Last, we introduce protocol state machines (PSMs) – an automata-based protocol specification formalism – that subsume both global types from MSTs and HMSCs with regard to expressivity. We use transformations on PSMs to show that many of the syntactic restrictions of global types are not restrictive in terms of protocol expressivity. We prove that the implementability problem for PSMs with mixed choice, which requires no dedicated sender for a branch but solely all labels to be distinct, is undecidable in general. With our results on expressivity, this answers an open question: the implementability problem for mixed-choice global types is undecidable in general.
To increase situational awareness of the crane operator, the aim of this thesis is to develop a vision-based deep learning object detection from crane load-view using an adaptive perception in the construction area. Conventional worker detection methods are based on simple shape or color features from the worker's appearances. Nonetheless, these methods can fail to recognize the workers who do not wear the protective gears. To find out an image representation of the object from the top view manually or handcrafted feature is crucial. We, therefore, employed deep learning methods to automatically learn those features.
To yield optimal results, deep learning methods require mass amount of data.
Due to the data deficit especially in the construction domain, we developed the photorealistic world to create the data in addition to our samples collected from the real construction area. The simulated platform does not benefit only from diverse data types, but also concurrent research development which speeds up the pipeline at a low cost.
Our research findings indicate that the combination of synthetic and real training samples improved the state-of-the-art detector. In line with previous studies to bridge the gap between synthetic and real data, the results of preprocessed synthetic images are substantially better than using the raw data by approximately 10%.
Finding the right deep learning model for load-view detection is challenging.
By investigating our training data, it becomes evident that the majority of bounding box sizes are very small with a complex background.
In addition, we gave the priority to speed over accuracy based on the construction safety criteria. Finally, RetinaNet is chosen out of the three primary object detection models.
Nevertheless, the data-driven detection algorithm can fail to handle scale invariance, especially for detectors whose input size is changed in an extremely wide range.
The adaptive zoom feature can enhance the quality of the worker detection.
To avoid further data gathering and extensive retraining, the proposed automatic zoom method of the load-view crane camera supports the deep learning algorithm, specifically in the high scale variant problem. The finite state machine is employed for control strategies to adapt the zoom level to cope not only with inconsistent detection but also abrupt camera movement during lifting operation. Consequently, the detector is able to detect a small size object by smooth continuous zoom control without additional training.
The adaptive zoom control not only enhances the performance of the top-view object detection but also reduces the interaction of the crane operator with camera system, reducing the risk of fatality during load lifting operation.
In recent years, there has been a growing need for accurate 3D scene reconstruction. Recent developments in the automotive industry have led to the increased use of ADAS where 3D reconstruction techniques are used, for example, as part of a collision detection system. For such applications, scene geometry reconstruction is usually performed in the form of depth estimation, where distances to scene objects are obtained.
In general, depth estimation systems can be divided into active and passive. Both systems have their advantages and disadvantages, but passive systems are usually cheaper to produce and easier to assemble and integrate than active systems. Passive systems can be stereo- or multiple-view based. Up to a certain limit, increasing the number of views in multi-view systems usually results in improved depth estimation accuracy.
One potential problem for ensuring the reliability of multi-view systems is the need to accurately estimate the orientation of their optical sensors. One way to ensure sensor placement for multi-view systems is to rigidly fix the sensors at the manufacturing stage. Unlike arbitrary sensor placement, using of a simplified and known sensor placement geometry further simplifies the depth estimation.
We meet with the concept of light field, which parameterizes all visible light passing through all viewpoints by their intersection with angular and spatial planes. When applied to computer vision, this gives us a 2D set of 2D images, where the physical distances between each image are fixed and proportional to each other.
Existing light field depth estimation methods provide good accuracy, which is suitable for industrial applications. However, the main problems of these methods are related to their running time and resource requirements. Most of the algorithms presented in the literature are typically sharpened for accuracy, can only be run on high-performance machines and often require a significant amount of time to process and obtain results.
Real-world applications often have running time requirements. Also, often there is a power-consumption limitation. In this dissertation, we investigate the problem of building a depth estimation system with an light field camera that satisfies the operating time and power consumption constraints without significant loss of estimation accuracy.
First, an algorithm for calibrating light field cameras is proposed, together with an algorithm for automatic calibration refinement, that works on arbitrary captured scenes. An algorithm for classical geometric depth estimation using light field cameras is proposed. Ways to optimize the algorithm for real-time use without significant loss of accuracy are presented. Finally, the ways how the presented depth estimation methods can be extended using modern deep learning paradigms under the two previously mentioned constraints are shown.
Knowledge workers face an ever increasing flood of information in their daily work. They live in a “multi-tasking craziness”, involving activities like creating, finding, processing, assessing or organizing information while constantly switching from one context to another, each being associated with different tasks, documents, mails, etc. Hence, their personal information sphere consisting of file, mail and bookmark folders as well as their content, calendar entries, etc. is cluttered with information that has become irrelevant. Finding important information thus gets harder and much of previously gained knowledge is practically lost.
This thesis explores new ways of solving this problem by investigating the potential of self-(re)organizing and especially forgetting-enabled personal knowledge assistants in the given scenario. It utilizes so-called Managed Forgetting, which is an escalating set of measures to overcome the binary keep-or-delete paradigm, ranging from temporal hiding, to condensation, to adaptive reorganization, synchronization, archiving and deletion. Managed Forgetting is combined with two other major ideas: First, it uses the Semantic Desktop as an ecosystem, which brings Semantic Web and thus knowledge graph technologies to a user’s desktop, making it possible to capture and represent major parts of a user’s personal mental model in a machine-understandable way and exploit it in many different applications. Second, the system uses explicated context information – so-called Context Spaces: context is seen as an explicit interaction element users can work with (i.e. a “tangible” object similar to a folder) and in (immersion). The thesis is structured according to the basic interaction cycle with such a system, ranging from evidence collection to information extraction and context elicitation, followed by information value assessment and the actual support measures consisting of self-(re)organization decisions (back-end) and user interface updates (front-end). The system’s data foundation are personal or group knowledge graphs as well as native data. This work makes contributions to all of these aspects, whereas several of them have been investigated and developed in interdisciplinary research with cognitive scientists. On a more general level, searching and trust in such highly autonomous assistants have also been investigated.
In summary, a self-(re)organizing and especially forgetting-enabled support system for information management and knowledge work has been realized. Its different features vary in maturity: the most mature ones are already in practical use (also in industry), while the latest are just well elaborated (position papers) or rough ideas. Different evaluation strategies have been applied ranging from mere data-driven experiments to various user studies. Some of them were rather short-term with controlled laboratory conditions, others less controlled but spanning several months. Different benefits of working with such a system could be quantified, e.g. cognitive offloading effects and reduced task switching/resumption time. Other benefits were gathered qualitatively, e.g. tidiness of the information sphere and its better alignment with the user’s mental model. The presented approach has been shown to hold a lot of potential. In some aspects, however, only first steps have been taken towards tapping it, e.g. several support measures can be further refined and automation further increased.
This thesis focuses on the operation of reliability-constrained routes in wireless ad-hoc networks. A complete communication protocol that is capable of guaranteeing a statistical minimum reliability level would have to support several functionalities: first, routes that are capable of supporting the specified Quality of Service requirement have to be discovered. During operation of discovered routes, the current Quality of Service level has to be monitored continuously. Whenever significant deviations are detected and the required level of Quality of Service is endangered, route maintenance has to ensure continuous operation. All four functionalities, route discovery, route operation, route maintenance and collection and distribution of network status information, will be addressed in this thesis.
In the first part of the thesis, we propose a new approach for Quality-of- Service routing in wireless ad-hoc networks called rmin-routing, with the provision of statistical minimum route reliability as main route selection criterion. To achieve specified minimum route reliabilities, we improve the reliability of individual links by well-directed retransmissions, to be applied during the operation of routes. To select among a set of candidate routes, we define and apply route quality criteria concerning network load.
High-quality information about the network status is essential for the discovery and operation of routes and clusters in wireless ad-hoc networks. This requires permanent observation and assessment of nodes, links, and link metrics, and the exchange of gathered status data. In the second part of the thesis, we present cTEx, a configurable topology explorer for wireless ad-hoc networks that efficiently detects and exchanges high-quality network status information during operation.
In the third part, we propose a decentralized algorithm for the discovery and operation of reliability-constrained routes in wireless ad-hoc networks called dRmin-routing. The algorithm uses locally available network status information about network topology and link properties that is collected proactively in order to discover a preliminary route candidate. This is followed by a distributed, reactive search along this preselected route to remove imprecisions of the locally recorded network status before making a final route selection. During route operation, dRmin-routing monitors routes and performs different kinds of route repair actions to maintain route reliability in order to overcome varying link reliabilities.
Modeling and Simulation of Internet of Things Infrastructures for Cyber-Physical Energy Systems
(2024)
This dissertation presents a novel approach to the model-based development and simulation-based validation of Internet of Things (IoT) infrastructures within the context of Cyber-Physical Energy Systems (CPES). CPES represents an evolution in energy management, seamlessly blending physical and cyber components for efficient, secure, and dependable energy distribution. However, the intricate interplay of these components demands innovative modeling and simulation strategies.
The work begins by establishing a robust foundation, exploring essential background elements such as requirements engineering, model-based systems engineering, digitalization approaches, and the intricacies of IoT platforms. It introduces the novel concept of homomorphic encryption, a critical enabler for securing IoT data within CPES.
In the exploration of the state of the art, the dissertation delves into the multifaceted landscape of IoT simulation, emphasizing the significance of versatility, community support, scalability, and synchronization.
The core contribution emerges in the chapter on simulating IoT networks. It introduces a sophisticated framework that encompasses hardware-in-the-loop, software-in-the-loop, and human-in-the-loop simulation. This innovative framework extends the boundaries of conventional simulation, enabling holistic evaluations of IoT systems.
A practical case study on smart energy usage showcases the application of the framework. Detailed SysML models, including requirements, package diagrams, block definition diagrams, internal block diagrams, state machine diagrams, and activity diagrams, are meticulously examined. The performance evaluation encompasses diverse aspects, from hardware and software validation to human interaction.
In conclusion, this dissertation represents a significant leap forward in the integration of IoT infrastructures within CPES. Its contributions extend from a comprehensive understanding of foundational elements to the practical implementation of a holistic simulation framework. This work not only addresses the current challenges but also outlines a path for future research, shaping the landscape of IoT integration within the dynamic realm of CPES. It offers invaluable insights for researchers, engineers, and stakeholders working towards resilient, secure, and energy-efficient infrastructures.
Weak memory consistency models capture the outcomes of concurrent
programs that appear in practice and yet cannot be explained by thread
interleavings. Such outcomes pose two major challenges to formal
methods. First, establishing that a memory model satisfies its
intended properties (e.g., supports a certain compilation scheme) is
extremely error-prone: most proposed language models were initially
broken and required multiple iterations to achieve soundness. Second,
weak memory models make verification of concurrent programs much
harder, as a result of which there are no scalable verification
techniques beyond a few that target very simple models.
This thesis presents solutions to both of these problems.
First, it shows that the relevant metatheory of weak memory
models can be effectively decided (sparing years of manual proof
efforts), and presents Kater, a tool that can answer metatheoretic
queries in a matter of seconds. Second, it presents GenMC, the first
(and only) scalable stateless model checker that is parametric in the
choice of the memory model, often improving the prior state of the art
by orders of magnitude.
In one-dimensional (1-D) Ultrasound (US) measurements, signals are
acquired that form the basis of more sophisticated two-dimensional (2-D) or
three-dimensional (3-D) US imaging. These 1-D signals contain a lot of raw
information about the US wave propagation and interaction with the
medium that is only processed in parts during image generation. While
image representations are easy to interpret for humans, the analysis of US
wave signals is hard to perform without applying algorithms to extract
desired features.
This work investigates reliable and fast 1-D US signal classifications to
distinguish between different stages or states in biomedical US scenarios and
shows how the new field of Machine Learning (ML) on raw US wave data
provides advantages and different applications. To achieve good results, the
input signals are treated as time series, which requires the deployment of
comparatively complex Time Series Classification (TSC) algorithms.
The literature shows that a lot of research efforts have previously only
tackled the classification and segmentation of US Brightness mode (B-Mode)
images, while neglecting approaches to classify 1-D signals to a large extent.
This research contributes by developing, deploying and evaluating
classification approaches for three distinct biomedical US classification tasks
and finds that respective signal classifications for different scenarios are
possible with varying degrees of accuracies. It entails the comparison of
several combinations of data types (e.g. temporal, spectral and statistical
features or raw signals), ML models and pre-processing steps to provide a
strong foundation for robust, binary classifications of 1-D US signals for
scenarios based on low-cost wearable, mobile and stationary devices. This
research addresses scientific questions not answered before by informing on
detailed descriptions of beneficial domain specific knowledge (domain specific
knowledge (DSK)), achieved accuracies and times needed for training and
evaluation of the examined ML models.
The resulting ML pipelines includes solutions based on data acquired from
custom experimental setups or clinical trials. Possible real-world applications
might include muscle contraction trackers, muscle fatigue detectors,
epiphyseal radius bone closure detectors or devices providing information
about advanced liver disease stages.
Automated machine-assisted
classifications requiring as little DSK as possible from the end user enable
application scenarios ranging from fitness or rehabilitation trackers as
consumer devices to solutions providing diagnostic support without requiring
extensive knowledge from professional medical practitioners. For example,
decision support systems for bone age assessments in clinical use or liver
health assessment systems for gastroenterologists.
This work shows that reliable, robust and fast classifications based on 1-D
US signals are possible with high degrees of accuracies depending on the
examined scenario with achieved F 1 -scores ranging from ≈ 70% to ≈ 87%.
These results prove that real-life applications for recreational purposes are
already possible and that critical applications for clinical use are highly likely
to be achieved once the presented approaches are further optimized in the future.
The field of 3D reconstruction is one of the most important areas in computer
vision. It is not only of theoretical importance, but it is also increasingly
used in practical applications, be it in reverse engineering, quality control or
robotics. In practical applications, where high precision reconstructions are
required for a large variety of different objects, structured light reconstruction
is often the method of choice. It allows to achieve accurate and dense
point correspondences over the entire scene, regardless of object texture or
features. Techniques that project phase-shifted sinusoidals are widely used
because, based on the harmonic addition theorem, they theoretically allow
surface encoding in full camera resolution invariant to the object’s coloring.
In this thesis, a fully-automatic reconstruction pipeline based on the sinusoidal
structured light technique is presented. From the projection of the
fringe patterns for encoding the object’s surface, the robust matching of the
point correspondences in sub-pixel accuracy, the auto-calibration of the setup
including the active device, up to the fully-automatic alignment of the partial
reconstructions, all steps will be described and examined in detail. During
that, improvements will be achieved in the area of matching, obtaining highly
accurate and topologically consistent correspondences in sub-pixel precision
between all the devices used. Furthermore, the auto-calibration from point
correspondences, based on the epipolar geometry of the structured light system
is improved. Weaknesses of previous methods in the extraction of focal
lengths from the fundamental matrices are discovered and addressed. The partial
point clouds, reconstructed from the auto-calibrated devices, are finally
pre-aligned using a neural network approach, based on light-resistant optical
flow estimation and subsequently refined using a global approach.
The weaknesses of the structured light method itself will also be addressed
and partially fixed during the course of this work. Since it is an active reconstruction method, certain surface properties can affect the quality of the
reconstruction. It will be shown how these problems can be eliminated or at
least be reduced using an iterative approach that combines fringe patterns with
an inverse texture. Another weakness of the method is its time-consuming acquisition procedure. Typically, a large number of horizontal and vertical fringe
patterns are projected onto the scene to achieve high-precision encoding despite
the limited dynamic range and resolution of the projector. Therefore, a
method will be presented which allows to combine the horizontal and vertical
patterns for a simultaneous two dimensional surface encoding.
During our daily lives, we are confronted with vast amounts of data, the processing of which can dramatically influence our lives, both positively and negatively. The enormous amount of data (images, texts, tables, and time series), its variety and possible applications are not always obvious. Due to advancements in the internet of things (IoT), there exist billions of sensors that produce time series which can be found everywhere, whether in medicine, the financial sector or the agricultural economy. This incredible amount of time series data has many hidden features which are useful for industry as well as for daily use, e.g. improving the cancer prediction can save real human lives. Recently, several deep learning methods have been proposed for analyzing this time series data. However, due to their black box nature, their applicability is limited in critical sectors like medicine, finance, and communication. In addition, it is now a compulsion as per artificial intelligence (AI) Act and per General Data Protection Regulation (GDPR) to protect any sensitive data and provide explanations in safety-critical domains. To enable use of DNNs in a broader domain scope, this thesis presents a framework for privacy-preserved and interpretable time series analysis. TimeFrame consists of four main components, namely, post-hoc interpretability, intrinsic interpretability, direct privacy, and indirect privacy. Interpretability is indispensable to avoid damaging people or the infrastructure. In the past years, the development mostly focused on image data, which prevented the full potential of DNNs in time series processing from being exploited. To overcome this limitation, TimeFrame introduces five (Time to Focus, TSViz, TimeREISE, TSInsight, Data Lens) novel post-hoc and two (PatchX, P2ExNet) novel intrinsic interpretability components. TimeFrame addresses multiple perspectives such as attribution, compression, visualization, influence, prototyping, and hierarchical splitting. Compared to existing methods, the components show better explanations, robustness, and scalability. Another crucial factor is the privacy when dealing with sensitive data and deep learning. In this context, TimeFrame introduces two (PPML, PPML x XAI) components for direct and one (From Private to Public) component for indirect privacy. These components benchmark privacy approaches, their effect on interpretability, and the synthetic generation of data to overcome privacy concerns. TimeFrame offers a large set of interpretability and privacy components that can be combined and consider numerous different aspects. Furthermore, the novel approaches have shown to consistently outperform twenty existing state-of-the-art methods across up to 20 different datasets. To guarantee the fairness, various metrics were used including performance change, Sensitivity, Infidelity, Continuity, runtime, model dependency, compression rate, and others. This broad set of metrics makes it possible to provide guidelines for a more appropriate use of existing state-of-the-art approaches as well as the novel components included in TimeFrame.