This thesis presents both a control scheme for naturalistic control of an exoskeleton glove and a glove design. Exoskeleton development has been focused primarily on design, improving soft actuator and cable-driven systems, with only limited focus on intelligent control. There is a need for control that is not limited to position or force reference signals and is user-driven. By implementing a motion amplification controller to increase weak movements of an impaired individual, a finger joint trajectory can be observed and used to predict their grasping intention. The motion amplification functions off of a virtual dynamical system that safely enforces the range of motion of the finger joints and ensures stability. Three grasp prediction algorithms are developed with improved levels of accuracy: regression, trajectory, and deep learning based. These algorithms were tested on published finger joint trajectories. The fusion of the amplification and prediction could be used to achieve naturalistic, user-guided control of an exoskeleton glove. The key to accomplishing this is series elastic actuators to move the finger joints, thereby allowing the wearer to deflect against the glove and inform the controller of their intention. These actuators are used to move the fingers in a nine degree of freedom exoskeleton that is capable of achieving all the grasps used most frequently in daily life. The controllers and exoskeleton presented here are the basis for improved exoskeleton glove control that can be used to assist or rehabilitate impaired individuals. / Master of Science / Millions of Americans report difficulty holding small or even lightweight objects. In many of these cases, their difficulty stems from a condition such as a stroke or arthritis, requiring either rehabilitation or assistance. For both treatments, exoskeleton gloves are a potential solution; however, widespread deployment of exoskeletons in the treatment of hand conditions requires significant advancement. Towards that end, the research community has devoted itself to improving the design of exoskeletons. Systems that use soft actuation or are driven by artificial tendons have merit in that they are comfortable to the wearer, but lack the rigidity required for monitoring the state of the hand and controlling it. Electromyography sensors are also a commonly explored technology for determining motion intention; however, only primitive conclusions can be drawn when using these sensors on the muscles that control the human hand. This thesis proposes a system that does not rely on soft actuation but rather a deflectable exoskeleton that can be used in rehabilitation or assistance. By using series elastic actuators to move the exoskeleton, the wearer of the glove can exert their influence over the machine. Additionally, more intelligent control is needed in the exoskeleton. The approach taken here is twofold. First, a motion amplification controller increases the finger movements of the wearer. Second, the amplified motion is processed using machine learning algorithms to predict what type of grasp the user is attempting. The controller would then be able to fuse the two, the amplification and prediction, to control the glove naturalistically.

Over the next decade, 5G networks will become more and more prevalent in everyday life. This will provide solutions to current limitations by allowing access to bands previously unavailable to civilian communication networks. However, this also provides new challenges primarily for the military operations. Radar bands have traditionally operated primarily in the sub-6 GHz region. In the past, these bands were off limits to civilian communications. However, that changed when they were opened up in the 2010's. With these bands now being forced to co-exist with commercial users, military operators need systems to identify the signals within a spectrum environment. In this thesis, we extend current research in the area of signal identification by using previous work in the area to construct a deep learning-based classifier that is able to classify a signal as either as a communication waveform (Single-Carrier (SC), Single-Carrier Frequency Division Multiple Access (SC-FDMA), Orthogonal Frequency Division Multiplexing (OFDM), Amplitude Modulation (AM), Frequency Modulation (FM)) or a radar waveform (Linear Frequency Modulation (LFM) or Phase-coded). However, the downside to this method is that the classifier is based on the assumption that all possible signals within the spectrum environment are within the training dataset. To account for this, we have proposed a novel classifier design for detection of unknown signals outside of the training dataset. This two-classifier system forms an open-set recognition (OSR) system that is used to provide more situational awareness for operators. / M.S. / Over the next decade, next-generation communications will become prevalent in everyday life providing solutions to limitation previously experienced by older networks. However, this also brings about new challenges. Bands in the electromagnetic spectrum that were reserved for military use are now being opened up to commercial users. This means that military and civilian networks now have a challenge of co-existence that must be addressed. One way to address this is being aware of what signals are operating in the bands such as either communication signals, radar signals, or both. In this thesis, we will developed a system that can do that task of identifying a signal as one of five communication waveforms or two radar waveforms by using machine learning techniques. We also develop a new technique for identifying unknown signals that might be operating within these bands to further help military and civilian operators monitor the spectrum.

This thesis presents a novel method for online Multi-Object Tracking (MOT) using Graph Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature matching for object association. The Graph based approach incorporates both appearance and geometry of objects at past frames as well as the current frame into the task of feature learning. This new paradigm enables the network to leverage the "contextual" information of the geometry of objects and allows us to model the interactions among the features of multiple objects. Another central innovation of the proposed framework is the use of the Sinkhorn algorithm for end-to-end learning of the associations among objects during model training. The network is trained to predict object associations by taking into account constraints specific to the MOT task. Additionally, in order to increase the sensitivity of the object detector, a new approach is presented that propagates previous frame detections into each new frame using optical flow. These are treated as added object proposals which are then classified as objects. A new traffic monitoring dataset is also provided, which includes naturalistic video footage from current infrastructure cameras in Virginia Beach City with a variety of vehicle density and environment conditions. Experimental evaluation demonstrates the efficacy of the proposed approaches on the provided dataset and the popular MOT Challenge Benchmark. / Master of Science / This thesis presents a novel method for Multi-Object Tracking (MOT) in videos, with the main goal of associating objects between frames. The proposed method is based on a Deep Neural Network Architecture operating on a Graph Structure. The Graph based approach makes it possible to use both appearance and geometry of detected objects to retrieve high level information about their characteristics and interaction. The framework includes the Sinkhorn algorithm, which can be embedded in the training phase to satisfy MOT constraints, such as the 1 to 1 matching between previous and new objects. Another approach is also proposed to improve the sensitivity of the object detector by using previous frame detections as a guide to detect objects in each new frame, resulting in less missed objects. Alongside the new methods, a new dataset is also provided which includes naturalistic video footage from current infrastructure cameras in Virginia Beach City with a variety of vehicle density and environment conditions. Experimental evaluation demonstrates the efficacy of the proposed approaches on the provided dataset and the popular MOT Challenge Benchmark.

A laboratory testing program was conducted on cement-treated soil mixtures fabricated to represent materials produced by the wet method of deep mixing. The testing program focused on investigating the influences that variations in laboratory testing conditions and in the mix design have on measured property values. A base soil was fabricated from commercially available soil components to produce a very soft lean clay that is relatively easy to mix and can be replicated for future research. The mix designs included a range of water-to-cement ratios of the slurries and a range of cement factors to produce a range of mixture consistencies and a range of unconfined compressive strengths after curing. Unconfined compressive strength (UCS) tests and unconsolidated-undrained (UU) triaxial compression tests were conducted. Secant modulus of elasticity were determined from bottom platen displacements, deformations between bottom platen and cross bar, and from LVDT's placed directly on the cement-treated soil specimens. Five end-face treatment methods were used for the specimens: sawing-and-hand-trimming, machine grinding, sulfur capping, neoprene pads, and gypsum capping. Key findings of this research include the following: (1) The end-face treatment method does not have a significant effect on the unconfined compressive strength and secant modulus; (2) a relationship of UCS with curing time, total-water-to-cement ratio, and dry density of the mixture; (3) the secant modulus determined by bottom platen displacements is significantly affected by slack and deformations in the load frame; (4) the secant modulus determined by local strain measurements was about 630 time the UCS; (5) typical values of Poisson's ratio range from about 0.05 to 0.25 for stress levels equal to half the UCS and about 0.15 to 0.35 at the UCS; (6) Confinement increased the strength at high strains from less than 20% the UCS to about 60% the UCS. In addition to testing the cured mixtures, the consistency of the mixtures were measured right after mixing using a laboratory miniature vane. A combination of the UCS relationship along with the mixture consistency may provide useful information for deep mixing contractors. / MS

In Indian Classical Music (ICM), the Raga is a musical piece's melodic framework. It encompasses the characteristics of a scale, a mode, and a tune, with none of them fully describing it, rendering the Raga a unique concept in ICM. The Raga provides musicians with a melodic fabric, within which all compositions and improvisations must take place. Identifying and categorizing the Raga is challenging due to its dynamism and complex structure as well as the polyphonic nature of ICM. Hence, Raga recognition—identify the constituent Raga in an audio file—has become an important problem in music informatics with several known prior approaches. Advancing the state of the art in Raga recognition paves the way to improving other Music Information Retrieval tasks in ICM, including transcribing notes automatically, recommending music, and organizing large databases. This thesis presents a novel melodic pattern-based approach to recognizing Ragas by representing this task as a document classification problem, solved by applying a deep learning technique. A digital audio excerpt is hierarchically processed and split into subsequences and gamaka sequences to mimic a textual document structure, so our model can learn the resulting tonal and temporal sequence patterns using a Recurrent Neural Network. Although training and testing on these smaller sequences, we predict the Raga for the entire audio excerpt, with the accuracy of 90.3% for the Carnatic Music Dataset and 95.6% for the Hindustani Music Dataset, thus outperforming prior approaches in Raga recognition. / Master of Science / In Indian Classical Music (ICM), the Raga is a musical piece's melodic framework. The Raga is a unique concept in ICM, not fully described by any of the fundamental concepts of Western classical music. The Raga provides musicians with a melodic fabric, within which all compositions and improvisations must take place. Raga recognition refers to identifying the constituent Raga in an audio file, a challenging and important problem with several known prior approaches and applications in Music Information Retrieval. This thesis presents a novel approach to recognizing Ragas by representing this task as a document classification problem, solved by applying a deep learning technique. A digital audio excerpt is processed into a textual document structure, from which the constituent Raga is learned. Based on the evaluation with third-party datasets, our recognition approach achieves high accuracy, thus outperforming prior approaches.

Recently, disaster scene description and indexing challenges attract the attention of researchers. In this dissertation, we solve a disaster-related multi-labeling task using a newly developed Low Altitude Disaster Imagery dataset. In the first task, we realize video content by selecting a set of summary key frames to represent the video sequence. Through inter-frame differences, the key frames are generated. The key frame extraction of disaster-related video clips is a powerful tool that can efficiently convert video data into image-level data, reduce the requirements for the extraction environment and improve the applicable environment. In the second, we propose a novel application of using deep learning methods on low altitude disaster video feature recognition. Supervised learning-based deep-learning approaches are effective in disaster-related features recognition via foreground object detection and background classification. Performed dataset validation, our model generalized well and improved performance by optimizing the YOLOv3 model and combining it with Resnet50. The comprehensive models showed more efficient and effective than those in prior published works. In the third task, we optimize the whole scene labeling classification by pruning the lightweight model MobileNetV3, which shows superior generalizability and can disaster features recognition from a disaster-related dataset be accomplished efficiently to assist disaster recovery.

The analysis and understanding of physiological and brain signals is critical in order to decode user’s behavioral/neural outcome measures in different domain scenarios. Personal Health-Care agents have been proposed recently in order to monitor and acquire reliable data from daily activities to enhance control participants’ wellbeing, and the quality of life of multiple non-neurotypical participants in clinical lab-controlled studies. The inclusion of new wearable devices with increased and more compact memory requirements,and the possibility to include long-size datasets on the cloud and network-based applications agile the implementation of new improved computational health-care agents. These new enhanced agents are able to provide services including real time health-care,medical monitoring, and multiple biological outcome measures-based alarms for medicaldoctor diagnosis. In this dissertation we will focus on multiple Signal Processing (SP), Machine Learning (ML), Saliency Relevance Maps (SRM) techniques and classifiers with the purpose to enhance the Personal Health-care agents in a multimodal clinical environment. Therefore, we propose the evaluation of current state-of-the-art methods to evaluate the incidence of successful hypertension detection, categorical and emotion stimuli decoding using biosignals. To evaluate the performance of ML, SP, and SRM techniques proposed in this study, wedivide this thesis document in two main implementations: 1) Four different initial pipelines where we evaluate the SP, and ML methodologies included here for an enhanced a) Hypertension detection based on Blood-Volume-Pulse signal (BVP) and Photoplethysmography (PPG) wearable sensors, b) Heart-Rate (HR) and Inter-beat-interval (IBI) prediction using light adaptive filtering for physical exercise/real environments, c) Object Category stimuli decoding using EEG features and features subspace transformations, and d) Emotion recognition using EEG features from recognized datasets. And 2) A complete performance and robust SRM evaluation of a neural-based Emotion Decoding/Recognition pipeline using EEG features from Autism Spectrum Disorder (ASD) groups. This pipeline is presented as a novel assistive system for lab-controlled Face Emotion Recognition (FER) intervention ASD subjects. In this pipeline we include a Deep ConvNet asthe Deep classifier to extract the correct neural information and decode emotions successfully.

The quality of clinical pathology is a critical index for evaluating a nation's healthcare level. Recently developed digital pathology techniques have the capability to transform pathological slides into digital whole slide images (WSI). This transformation facilitates data storage, online transmission, real-time viewing, and remote consultations, significantly elevating clinical diagnosis. The effectiveness and efficiency of digital pathology imaging often hinge on the precision and speed of autofocusing. However, achieving autofocusing of pathological images presents challenges under constraints including uneven focus distribution and limited Depth of Field (DoF). Current autofocusing methods, such as those relying on image stacks, need to use more time and resources for capturing and processing images. Moreover, autofocusing based on reflective hardware systems, despite its efficiency, incurs significant hardware costs and suffers from a lack of system compatibility. Finally, machine learning-based autofocusing can circumvent repetitive mechanical movements and camera shots. However, a simplistic end-to-end implementation that does not account for the imaging process falls short of delivering satisfactory focus prediction and in-focus image restoration. In this thesis, we present three distinct autofocusing techniques for defocus pathology images: (1) Aberration-aware Focal Distance Prediction leverages the asymmetric effects of optical aberrations, making it ideal for focus prediction within focus map scenarios; (2) Dual-shot Deep Autofocusing with a Fixed Offset Prior is designed to merge two images taken at different defocus distances with fixed positions, ensuring heightened accuracy in in-focus image restoration for fast offline situations; (3) Semi-blind Deep Restoration of Defocus Images utilizes multi-task joint prediction guided by PSF, enabling high-efficiency, single-pass scanning for offline in-focus image restoration. / Thesis / Doctor of Philosophy (PhD)

Deep Learning (DL) models are trained on a downstream task by feeding (potentially preprocessed) input data through a trainable Neural Network (NN) and updating its parameters to minimize the loss function between the predicted and the desired output. While this general framework has mainly remained unchanged over the years, the architectures of the trainable models have greatly evolved. Even though it is undoubtedly important to choose the right architecture, we argue that it is also beneficial to develop methods that address other components of the training process. We hypothesize that utilizing domain knowledge can be helpful to improve DL models in terms of performance and/or efficiency. Such model-agnostic methods can be applied to any existing or future architecture. Furthermore, the black box nature of DL models motivates the development of techniques to understand their inner workings. Considering the rapid advancement of DL architectures, it is again crucial to develop model-agnostic methods. In this thesis, we explore six principles that incorporate domain knowledge to understand or improve models. They are applied either on the input or output side of the trainable model. Each principle is applied to at least two DL tasks, leading to task-specific implementations. To understand DL models, we propose to use Generated Input Data coming from a controllable generation process requiring knowledge about the data properties. This way, we can understand the model’s behavior by analyzing how it changes when one specific high-level input feature changes in the generated data. On the output side, Gradient-Based Attribution methods create a gradient at the end of the NN and then propagate it back to the input, indicating which low-level input features have a large influence on the model’s prediction. The resulting input features can be interpreted by humans using domain knowledge. To improve the trainable model in terms of downstream performance, data and compute efficiency, or robustness to unwanted features, we explore principles that each address one of the training components besides the trainable model. Input Masking and Augmentation directly modifies the training input data, integrating knowledge about the data and its impact on the model’s output. We also explore the use of Feature Extraction using Pretrained Multimodal Models which can be seen as a beneficial preprocessing step to extract useful features. When no training data is available for the downstream task, using such features and domain knowledge expressed in other modalities can result in a Zero-Shot Learning (ZSL) setting, completely eliminating the trainable model. The Weak Label Generation principle produces new desired outputs using knowledge about the labels, giving either a good pretraining or even exclusive training dataset to solve the downstream task. Finally, improving and choosing the right Loss Function is another principle we explore in this thesis. Here, we enrich existing loss functions with knowledge about label interactions or utilize and combine multiple task-specific loss functions in a multitask setting. We apply the principles to classification, regression, and representation tasks as well as to image and text modalities. We propose, apply, and evaluate existing and novel methods to understand and improve the model. Overall, this thesis introduces and evaluates methods that complement the development and choice of DL model architectures. / Deep-Learning-Modelle (DL-Modelle) werden trainiert, indem potenziell vorverarbeitete Eingangsdaten durch ein trainierbares Neuronales Netz (NN) geleitet und dessen Parameter aktualisiert werden, um die Verlustfunktion zwischen der Vorhersage und der gewünschten Ausgabe zu minimieren. Während sich dieser allgemeine Ablauf kaum geändert hat, haben sich die verwendeten NN-Architekturen erheblich weiterentwickelt. Auch wenn die Wahl der Architektur für die Aufgabe zweifellos wichtig ist, schlagen wir in dieser Arbeit vor, Methoden für andere Komponenten des Trainingsprozesses zu entwickeln. Wir vermuten, dass die Verwendung von Domänenwissen hilfreich bei der Verbesserung von DL-Modellen bezüglich ihrer Leistung und/oder Effizienz sein kann. Solche modellagnostischen Methoden sind dann bei jeder bestehenden oder zukünftigen NN-Architektur anwendbar. Die Black-Box-Natur von DL-Modellen motiviert zudem die Entwicklung von Methoden, die zum Verständnis der Funktionsweise dieser Modelle beitragen. Angesichts der schnellen Architektur-Entwicklung ist es wichtig, modellagnostische Methoden zu entwickeln. In dieser Arbeit untersuchen wir sechs Prinzipien, die Domänenwissen verwenden, um Modelle zu verstehen oder zu verbessern. Sie werden auf Trainingskomponenten im Eingang oder Ausgang des Modells angewendet. Jedes Prinzip wird dann auf mindestens zwei DL-Aufgaben angewandt, was zu aufgabenspezifischen Implementierungen führt. Um DL-Modelle zu verstehen, verwenden wir kontrolliert generierte Eingangsdaten, was Wissen über die Dateneigenschaften benötigt. So können wir das Verhalten des Modells verstehen, indem wir die Ausgabeänderung bei der Änderung von abstrahierten Eingabefeatures beobachten. Wir untersuchen zudem gradienten-basierte Attribution-Methoden, die am Ausgang des NN einen Gradienten anlegen und zur Eingabe zurückführen. Eingabefeatures mit großem Einfluss auf die Modellvorhersage können so identifiziert und von Menschen mit Domänenwissen interpretiert werden. Um Modelle zu verbessern (in Bezug auf die Ergebnisgüte, Daten- und Recheneffizienz oder Robustheit gegenüber ungewollten Eingaben), untersuchen wir Prinzipien, die jeweils eine Trainingskomponente neben dem trainierbaren Modell betreffen. Das Maskieren und Augmentieren von Eingangsdaten modifiziert direkt die Trainingsdaten und integriert dabei Wissen über ihren Einfluss auf die Modellausgabe. Die Verwendung von vortrainierten multimodalen Modellen zur Featureextraktion kann als ein Vorverarbeitungsschritt angesehen werden. Bei fehlenden Trainingsdaten können die Features und Domänenwissen in anderen Modalitäten als Zero-Shot Setting das trainierbare Modell gänzlich eliminieren. Das Weak-Label-Generierungs-Prinzip erzeugt neue gewünschte Ausgaben anhand von Wissen über die Labels, was zu einem Pretrainings- oder exklusiven Trainigsdatensatz führt. Schließlich ist die Verbesserung und Auswahl der Verlustfunktion ein weiteres untersuchtes Prinzip. Hier reichern wir bestehende Verlustfunktionen mit Wissen über Label-Interaktionen an oder kombinieren mehrere aufgabenspezifische Verlustfunktionen als Multi-Task-Ansatz. Wir wenden die Prinzipien auf Klassifikations-, Regressions- und Repräsentationsaufgaben sowie Bild- und Textmodalitäten an. Wir stellen bestehende und neue Methoden vor, wenden sie an und evaluieren sie für das Verstehen und Verbessern von DL-Modellen, was die Entwicklung und Auswahl von DL-Modellarchitekturen ergänzt.

Neural networks (NN) have demonstrated considerable capabilities in tackling tasks in a diverse set of fields, including natural language processing, image classification, and regression. In recent years, the amount of available data to train Deep Learning (DL) models has increased tremendously, thus requiring larger and larger models to learn the underlying patterns in the data. Inference time, communication cost in the distributed case, required storage resources, and computational capabilities have increased proportional to the model's size, thus making NNs less suitable for two cases: i) tasks requiring low inference time (e.g., real-time monitoring) and ii) training on low powered devices. These two cases, which have become crucial in the last decade due to the pervasiveness of low-powered devices and NN models, are addressed in this licentiate thesis. As the first contribution, we analyze the distributed case with multiple low-powered devices in a federated scenario. Cross-device Federated Learning (FL) is a branch of Machine Learning (ML) where multiple participants train a common global model without sharing data in a centralized location. In this thesis, a novel technique named Coded Federated Dropout (CFD) is proposed to carefully split the global model into sub-models, thus increasing communication efficiency and reducing the burden on the devices with only a slight increase in training time. We showcase our results for an example image classification task. As the second contribution, we consider the anomaly detection task on Electrocardiogram (ECG) recordings and show that including prior knowledge in NNs models drastically reduces model size, inference time, and storage resources for multiple state-of-the-art NNs. In particular, this thesis focuses on AEs, a subclass of NNs, which is suitable for anomaly detection. I propose a novel approach, called FMM-Head, which incorporates basic knowledge of the ECG waveform shape into an AE. The evaluation shows that we improve the AUROC of baseline models while guaranteeing under-100ms inference time, thus enabling real-time monitoring of ECG recordings from hospitalized patients. Finally, several potential future works are presented. The inclusion of prior knowledge can be further exploited in the ECG Imaging (ECGI) case, where hundreds of ECG sensors are used to reconstruct the 3D electrical activity of the heart. For ECGI, the reduction in the number of sensors employed (i.e., the input space) is also beneficial in terms of reducing model size. Moreover, this thesis advocates additional techniques to integrate ECG anomaly detection in a distributed and federated case. / Neurala nätverk (NN) har visat god förmåga att tackla uppgifter inom en mängd olika områden, inklusive Natural Language Processing (NLP), bildklassificering och regression. Under de senaste åren har mängden tillgänglig data för att träna Deep Learning (DL)-modeller ökat enormt, vilket kräver större och större modeller för att lära sig de underliggande mönstren i datan. Inferens tid och kommunikationskostnad i det distribuerade fallet, nödvändiga lagringsresurser och beräkningskapacitet har ökat proportionerligt mot modellens storlek vilket gör NN mindre lämpliga använda i två fall: (i) uppgifter som kräver snabba slutledningar (t.ex. realtidsövervakning) och (ii) användning på mindre kraftfulla enheter. De här två fallen, som har blivit mer förekommande under det senaste decenniet på grund av omfattningen av mindre kraftfulla enheter och NN-modeller, behandlas i denna licentiatuppsats. Som det första bidraget analyserar vi det distribuerade fallet med flera lättdrivna enheter i ett federerat scenario. Cross-device Federated Learning (FL) är en gren av Machine Learning (ML) där flera deltagare tränar en gemensam global modell utan att dela data på en centraliserad plats. I denna avhandling föreslås en nyteknik, Coded Federated Dropout (CFD), som delar upp den globala modellen i undermodeller, vilket ökar kommunikationseffektiviteten och samtidigt minskar belastningen på enheterna. Detta erhålls med endast en liten förlängning av träningstiden. Vi delger våra resultat för en exempeluppgift för bildklassificering. Som det andra bidraget betraktar vi anomalidetekteringsuppgiften Elektrokardiogram (EKG)-registrering och visar att inklusionen av förkunskaper i NN-modeller drastiskt minskar modellstorlek, inferenstider och lagringsresurser för flera moderna NN. Speciellt fokuserar denna avhandling på Autoencoders (AEs), en delmängd av NN, lämplig för avvikelsedetektering. En ny metod, kallad FMM-Head, föreslås. vilken  omformar grundläggande kunskaper om EKG-vågformen till en AE. Utvärderingen visar att vi förbättrar arean under kurvan (AUROC) för baslinjemodeller samtidigt som vi garanterar under 100 ms inferenstid, vilket möjliggör realtidsövervakning av EKG-inspelningar från inlagda patienter.  Slutligen presenteras flera potentiella framtida utvidgningar. Införandet av tidigare kunskap kan utnyttjas ytterligare i fallet med EKG Imaging (ECGI), där hundratals EKG-sensorer används för att rekonstruera den elektriska 3D-aktiviteten hos hjärtat. För ECGI är minskningen av antalet använda sensorer (dvs inmatningsutrymme) också fördelaktig när det gäller att minska modellstorleken. Dessutom förespråkas i denna avhandling ytterligare tekniker för att integrera EKG-avvikelsedetektering i distribuerade och federerade fall. / <p>This research leading to this thesis is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Award No. ORA-CRG2021-4699</p>