On optimal design and applications of linear transforms

Linear transforms are encountered in many fields of applied science and engineering. In the past, conventional block transforms provided acceptable answers to different practical problems. But now, under increasing competitive pressures, with the growing reservoir of theory and a corresponding development of computing facilities, a real demand has been created for methods that systematically improve performance. As a result the past two decades have seen the explosive growth of a class of linear transform theory known as multiresolution signal decomposition. The goal of this work is to design and apply these advanced signal processing techniques to several different problems. The optimal design of subband filter banks is considered first. Several design examples are presented for M-band filter banks. Conventional design approaches are found to present problems when the number of constraints increases. A novel optimization method is proposed using a step-by-step design of a hierarchical subband tree. This method is shown to possess performance improvements in applications such as subband image coding. The subband tree structuring is then discussed and generalized algorithms are presented. Next, the attention is focused on the interference excision problem in direct sequence spread spectrum (DSSS) communications. The analytical and experimental performance of the DSSS receiver employing excision are presented. Different excision techniques are evaluated and ranked along with the proposed adaptive subband transform-based excises. The robustness of the considered methods is investigated for either time-localized or frequency-localized interferers. A domain switchable excision algorithm is also presented. Finally, sonic of the ideas associated with the interference excision problem are utilized in the spectral shaping of a particular biological signal, namely heart rate variability. The improvements for the spectral shaping process are shown for time-frequency analysis. In general, this dissertation demonstrates the proliferation of new tools for digital signal processing

Real-time fMRI neurofeedback and smartphone-based interventions to modulate mental functions

Our brains are constantly changing on a molecular level depending on the demands thrown at them by our environments, behavior, and thoughts. This neuronal plasticity allows us to voluntarily influence mental functions. Taking conscious control over mental functions goes potentially back millenia, but it was psychotherapy since the early 20th century which moulded this concept into a concrete form to target specific mental disorders. Mental disorders constitute a large burden on modern societies. Stress-related disorders like anxiety and depression particularly make up a large part of this burden and new ways to treat or prevent them are highly desirable, since traditional approaches are not equally helpful to every person affected. This might be because the infrastructure is not available where the person lives, their schedules and obligations or financial means do not enable them to seek help or they simply do not respond to traditional forms of treatment. Technological advances bring forth new potential approaches to modulate mental functions and allow using additional information to tailor an intervention better to an individual patient. The focus of this dissertation lies on two promising approaches to cognitively intervene and modulate mental functions: real-time functional magnetic resonance imaging neurofeedback (rtfMRInf) on one hand and smartphone-based interventions (SBIs) on the other. To investigate various aspects of both these methods in the context of stress and in relation to personalized interventions, we designed and conducted two experiments with a main rtfMRInf intervention, and also with ambulatory training of mental strategies, which participants accessed on their mobile phones. The four publication this thesis entails, are related to this topic as follows: The first publication focuses on rtfMRInf effects on the physiological stress response, exploring whether neurofeedback could reduce stress-related changes in brain activity and blood pressure. The second publication focuses on rtfMRInf effects on psychological measures related to the stress response, namely on arousal and mood, based on data from self-report by the participants. The third publication focuses on rtfMRInf methodology itself, looking at complex connectivity data between major neural networks. Finally, the fourth publication focuses on personalized prediction of intervention success of an SBI using data from previous training days

Sensor Path Planning for Emitter Localization

The localization of a radio frequency (RF) emitter is relevant in many military and civilian applications. The recent decade has seen a rapid progress in the development of small and mobile unmanned aerial vehicles (UAVs), which offer a way to perform emitter localization autonomously. The path a UAV travels influences the localization significantly, making path planning an important part of a mobile emitter localization system. The topic of this thesis is path planning for a UAV that uses bearing measurements to localize a stationary emitter. Using a directional antenna, the direction towards the target can be determined by the UAV rotating around its own vertical axis. During this rotation the UAV is required to remain at the same position, which induces a trade-off between movement and measurement that influences the optimal trajectories. This thesis derives a novel path planning algorithm for localizing an emitter with a UAV. It improves the current state of the art by providing a localization with defined accuracy in a shorter amount of time compared to other algorithms in simulations. The algorithm uses the policy rollout principle to perform a nonmyopic planning and to incorporate the uncertainty of the estimation process into its decision. The concept of an action selection algorithm for policy rollout is introduced, which allows the use of existing optimization algorithms to effectively search the action space. Multiple action selection algorithms are compared to optimize the speed of the path planning algorithm. Similarly, to reduce computational demand, an adaptive grid-based localizer has been developed. To evaluate the algorithm an experimental system has been built and the algorithm was tested on this system. Based on initial experiments, the path planning algorithm has been modified, including a minimal distance to the emitter and an outlier detection step. The resulting algorithm shows promising results in experimental flights

Automatic vision based fault detection on electricity transmission components using very highresolution

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations. Fault identification is one of the most significant bottlenecks faced by Electricity transmission and distribution utilities in developing countries to deliver credible services to customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial resolution to monitor four major Electric power transmission network (EPTN) components condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks (CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage object detection model on the electric transmission power line imagery to localize, classify and inspect faults present. The components fault considered include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision of 89.61%. All the developed SSD based models achieve a high precision rate and low recall rate in detecting the faulty components, thus achieving acceptable balance levels F1-score and representation. Finally, comparable to other works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood

Massivel y parallel declarative computational models

Current computer archictectures are parallel, with an increasing number of processors. Parallel programming is an error-prone task and declarative models such as those based on constraints relieve the programmer from some of its difficult aspects, because they abstract control away. In this work we study and develop techniques for declarative computational models based on constraints using GPI, aiming at large scale parallel execution. The main contributions of this work are: A GPI implementation of a scalable dynamic load balancing scheme based on work stealing, suitable for tree shaped computations and effective for systems with thousands of threads. A parallel constraint solver, MaCS, implemented to take advantage of the GPI programming model. Experimental evaluation shows very good scalability results on systems with hundreds of cores. A GPI parallel version of the Adaptive Search algorithm, including different variants. The study on different problems advances the understanding of scalability issues known to exist with large numbers of cores; ### SUMÁRIO: Actualmente as arquitecturas de computadores são paralelas, com um crescente número de processadores. A programação paralela é uma tarefa propensa a erros e modelos declarativos baseados em restrições aliviam o programador de aspectos difíceis dado que abstraem o controlo. Neste trabalho estudamos e desenvolvemos técnicas para modelos de computação declarativos baseados em restrições usando o GPI, uma ferramenta e modelo de programação recente. O Objectivo é a execução paralela em larga escala. As contribuições deste trabalho são as seguintes: a implementação de um esquema dinâmico para balanceamento da computação baseado no GPI. O esquema é adequado para computações em árvores e efectiva em sistemas compostos por milhares de unidades de computação. Uma abordagem à resolução paralela de restrições denominadas de MaCS, que tira partido do modelo de programação do GPI. A Avaliação experimental revelou boa escalabilidade num sistema com centenas de processadores. Uma versão paralela do algoritmo Adaptive Search baseada no GPI, que inclui diferentes variantes. O estudo de diversos problemas aumenta a compreensão de aspectos relacionados com a escalabilidade e presentes na execução deste tipo de algoritmos num grande número de processadores

Aeronautical Engineering: a Continuing Bibliography with Indexes (Supplement 243)

This bibliography lists 423 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1989. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics