Atrial fibrillation is an arrhythmia commonly detected from ECG using its specific characteristics. An early detection of this arrhythmia is a key to prevention of more serious conditions. Nowadays, atrial fibrillation detection is being implemented more often using deep learning. This work presents detection of atrial fibrillation from 12lead ECG using deep convolutional network. In the first section, there is a theoretical context of this work, then there is a description of proposed algorithm. Detection is implemented by a program in Python in two variations and their accuracy is rated by Accuracy and F1 measure. Results of the work are being discussed, mutually compared and compared to other similar publications.

This thesis attempts to find an alternative method for biometric identification using retinal images. First part is focused on the introduction to biometrics, human eye anatomy and methods used for retinal biometry. The essence of neural networks and deep learning methods is described as it will be used practically. In the last part of the thesis a chosen identification algorithm and its implementation is described and the results are presented.

The clue of learning to recognize objects using neural network lies in imitation of animal neural network's behavior. In spite the details of how brain works is not known yet, the teams consisting of scientists from various medical or technical professions are trying to search for them. Thanks to giants like Geoffrey Hinton science made a big progress in this domain. The convolutional networks which are based on animal model of optical system can be advantageously used for image segmentation and therefore they ware chosen for segmentation of tumor and edema from images of magnetic resonance. The models of artificial neural networks used in this work had achieved the 41\% of success in edema segmentation and 79\% in segmentation of tumor from brain issue.

Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data characteristic of realistic knowledge graphs for node property classification tasks. We propose a deep and improved variant, Deep-RGCNs, with dense and residual skip connections between layers. These skip connections are known to be very successful with popular deep CNN-architectures such as ResNet and DenseNet. In our experiments, we investigate and compare the performance of Deep-RGCN with different baselines on multi-relational graph benchmark datasets, AIFB and MUTAG, and show how the deep architecture boosts the performance in the task of node property classification. We also study the training performance of Deep-RGCNs (with N layers) and discuss the gradient vanishing and over-smoothing problems common to deeper GCN architectures.

This report analyzes the difference between discriminative and generative image classifiers when tested on noise. The generative classifier was a maximum-likelihood based classifier using a normalizing flow as the generative model. In this work, a coupling flow such as RealNVP was used. For the discriminative classifier a convolutional network was implemented. A detailed description of how these classifiers were implemented is given in the report. The report shows how this generative classifier outperforms the discriminative classifier when tested on adversarial noise. However, tests are also conducted on salt and pepper noise and Gaussian noise, here the results show that the generative classifier gets outperformed by the discriminative classifier. Tests were also conducted on Gaussian noise once both classifiers had been trained on Gaussian noise, the results from these tests show that the discriminative classifier performs significantly better once trained on Gaussian noise. However, the generative classifier does only show marginal increases in performance and performs worse on clean data once trained on Gaussian noise. / Den här rapporten analyserar skillnaden mellan diskriminativa och generativa modellklasser för bildigenkänning när de testas på brus. Den generativa modellklassen var en maximum-likelihood baserad generativ klassifikationsmodell. Inom detta arbete användes kopplingsflödet RealNVP. För den diskriminativa bildigenkänningsmodellen så implementerades ett faltningsnätverk. En detaljerad beskrivning för hur dessa bildigenkänningsmodeller genomfördes är given i rapporten. Rapporten visar hur den generativa modellklassen överträffar den diskriminativa modellklassen när de testas på adversarialt brus. Testerna utförs emellertid med salt och peppar brus och Gaussiskt brus, för dessa visar resultaten att den generativa modellklassen överträffas av den diskriminativa modellklassen. Den generativa modellklassen visar emellertid endast marginella ökningar i prestanda, och har en sämre prestanda på ren data efter att den tränats på Gaussiskt brus. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Instruction Scheduling is a back-end compiler optimisation technique that can provide significant performance gains. It refers to ordering instructions in a particular order to reduce latency for processors with instruction-level parallelism. At the present typical compilers use heuristics to perform instruction scheduling and solve other related non-polynomial complete problems. This thesis aims to present a machine learning-based approach to challenge heuristic methods concerning performance. In this thesis, a novel reinforcement learning (RL) based model for the instruction scheduling problem is developed including modelling features of processors such as forwarding, resource utilisation and treatment of the action space. An efficient optimal scheduler is presented to be used for an optimal schedule length based reward function, however, this is not used in the final results as a heuristic based reward function was deemed to be sufficient and faster to compute. Furthermore, an RL agent that interacts with the model of the problem is presented using three different types of graph neural networks for the state processing: graph conventional networks, graph attention networks, and graph attention based on the work of Lee et al. A simple two-layer neural network is also used for generating embeddings for the resource utilisation stages. The proposed solution is validated against the modelled environment and favourable but not significant improvements were found compared to the most common heuristic method. Furthermore, it was found that having embeddings relating to resource utilisation was very important for the explained variance of the RL models. Additionally, a trained model was tested in an actual compiler, however, no informative results were found likely due to register allocation or other compiler stages that occur after instruction scheduling. Future work should include improving the scalability of the proposed solution. / Instruktionsschemaläggning är en optimeringsteknik för kompilatorer som kan ge betydande prestandavinster. Det handlar om att ordna instruktioner i en viss ordning för att minska latenstiden för processorer med parallellitet på instruktionsnivå. För närvarande använder vanliga kompilatorer heuristiker för att utföra schemaläggning av instruktioner och lösa andra relaterade ickepolynomiala kompletta problem. Denna avhandling syftar till att presentera en maskininlärningsbaserad metod för att utmana heuristiska metoder när det gäller prestanda. I denna avhandling utvecklas en ny förstärkningsinlärningsbaserad (RL) modell för schemaläggning av instruktioner, inklusive modellering av processorns egenskaper såsom vidarebefordran, resursutnyttjande och behandling av handlingsutrymmet. En effektiv optimal schemaläggare presenteras för att eventuellt användas för belöningsfunktionen, men denna används inte i de slutliga resultaten. Dessutom presenteras en RL-agent som interagerar med problemmodellen och använder tre olika typer av grafneurala nätverk för tillståndsprocessering: grafkonventionella nätverk, grafuppmärksamhetsnätverk och grafuppmärksamhet baserat på arbetet av Lee et al. Ett enkelt neuralt nätverk med två lager används också för att generera inbäddningar för resursanvändningsstegen. Den föreslagna lösningen valideras mot den modellerade miljön och gynnsamma men inte signifikanta förbättringar hittades jämfört med den vanligaste heuristiska metoden. Dessutom visade det sig att det var mycket viktigt för den förklarade variansen i RL-modellerna att ha inbäddningar relaterade till resursutnyttjande. Dessutom testades en tränad modell i en verklig kompilator, men inga informativa resultat hittades, sannolikt på grund av registerallokering eller andra kompilatorsteg som inträffar efter schemaläggning av instruktioner. Framtida arbete bör inkludera att förbättra skalbarheten hos den föreslagna lösningen.

[pt] Este trabalho propõe e avalia arquiteturas profundas para o reconhecimento de culturas agrícolas a partir de seqüências de imagens multitemporais de sensoriamento remoto. Essas arquiteturas combinam a capacidade de modelar contexto espacial prórpia de redes totalmente convolucionais com a capacidade de modelr o contexto temporal de redes recorrentes para a previsão prever culturas agrícolas em cada data de uma seqüência de imagens multitemporais. O desempenho destes métodos é avaliado em dois conjuntos de dados públicos. Ambas as áreas apresentam alta dinâmica espaçotemporal devido ao clima tropical/subtropical e a práticas agrícolas locais, como a rotação de culturas. Nos experimentos verificou-se que as arquiteturas propostas superaram os métodos recentes baseados em redes recorrentes em termos de Overall Accuracy (OA) e F1-score médio por classe. / [en] This work proposes and evaluates deep learning architectures for multi-date agricultural crop recognition from remote sensing image sequences. These architectures combine the spatial modelling capabilities of fully convolutional networks and the sequential modelling capabilities of recurrent networks into end-to-end architectures so-called fully convolutional recurrent networks, configured to predict crop type at multiple dates from a multitemporal image sequence. Their performance is assessed over two publicly available datasets. Both datasets present highly spatio-temporal dynamics due to their tropical/sub-tropical climate and local agricultural practices such as crop rotation. The experiments indicated that the proposed architectures outperformed state of the art methods based on recurrent networks in terms of Overall Accuracy (OA) and per-class average F1 score.

Le dévracage robotisé est une tâche industrielle en forte croissance visant à automatiser le déchargement par unité d’une pile d’instances d'objet en vrac pour faciliter des traitements ultérieurs tels que la formation de kits ou l’assemblage de composants. Cependant, le modèle explicite des objets est souvent indisponible dans de nombreux secteurs industriels, notamment alimentaire et automobile, et les instances d'objet peuvent présenter des variations intra-classe, par exemple en raison de déformations élastiques.Les techniques d’estimation de pose, qui nécessitent un modèle explicite et supposent des transformations rigides, ne sont donc pas applicables dans de tels contextes. L'approche alternative consiste à détecter des prises sans notion explicite d’objet, ce qui pénalise fortement le dévracage lorsque l’enchevêtrement des instances est important. Ces approches s’appuient aussi sur une reconstruction multi-vues de la scène, difficile par exemple avec des emballages alimentaires brillants ou transparents, ou réduisant de manière critique le temps de cycle restant dans le cadre d’applications à haute cadence.En collaboration avec Siléane, une entreprise française de robotique industrielle, l’objectif de ce travail est donc de développer une solution par apprentissage pour la localisation des instances les plus prenables d’un vrac à partir d’une seule image, en boucle ouverte, sans modèles d'objet explicites. Dans le contexte du dévracage industriel, notre contribution est double.Premièrement, nous proposons un nouveau réseau pleinement convolutionnel (FCN) pour délinéer les instances et inférer un ordre spatial à leurs frontières. En effet, les méthodes état de l'art pour cette tâche reposent sur deux flux indépendants, respectivement pour les frontières et les occultations, alors que les occultations sont souvent sources de frontières. Plus précisément, l'approche courante, qui consiste à isoler les instances dans des boîtes avant de détecter les frontières et les occultations, se montre inadaptée aux scénarios de dévracage dans la mesure où une région rectangulaire inclut souvent plusieurs instances. A contrario, notre architecture sans détection préalable de régions détecte finement les frontières entre instances, ainsi que le bord occultant correspondant, à partir d'une représentation unifiée de la scène.Deuxièmement, comme les FCNs nécessitent de grands ensembles d'apprentissage qui ne sont pas disponibles dans les applications de dévracage, nous proposons une procédure par simulation pour générer des images d'apprentissage à partir de moteurs physique et de rendu. Plus précisément, des vracs d'instances sont simulés et rendus avec les annotations correspondantes à partir d'ensembles d'images de texture et de maillages auxquels sont appliquées de multiples déformations aléatoires. Nous montrons que les données synthétiques proposées sont vraisemblables pour des applications réelles au sens où elles permettent l'apprentissage de représentations profondes transférables à des données réelles. A travers de nombreuses expériences sur une maquette réelle avec robot, notre réseau entraîné sur données synthétiques surpasse la méthode industrielle de référence, tout en obtenant des performances temps réel. L'approche proposée établit ainsi une nouvelle référence pour le dévracage orienté-objet sans modèle d'objet explicite. / Referred to as robotic random bin-picking, a fast-expanding industrial task consists in robotizing the unloading of many object instances piled up in bulk, one at a time, for further processing such as kitting or part assembling. However, explicit object models are not always available in many bin-picking applications, especially in the food and automotive industries. Furthermore, object instances are often subject to intra-class variations, for example due to elastic deformations.Object pose estimation techniques, which require an explicit model and assume rigid transformations, are therefore not suitable in such contexts. The alternative approach, which consists in detecting grasps without an explicit notion of object, proves hardly efficient when the object geometry makes bulk instances prone to occlusion and entanglement. These approaches also typically rely on a multi-view scene reconstruction that may be unfeasible due to transparent and shiny textures, or that reduces critically the time frame for image processing in high-throughput robotic applications.In collaboration with Siléane, a French company in industrial robotics, we thus aim at developing a learning-based solution for localizing the most affordable instance of a pile from a single image, in open loop, without explicit object models. In the context of industrial bin-picking, our contribution is two-fold.First, we propose a novel fully convolutional network (FCN) for jointly delineating instances and inferring the spatial layout at their boundaries. Indeed, the state-of-the-art methods for such a task rely on two independent streams for boundaries and occlusions respectively, whereas occlusions often cause boundaries. Specifically, the mainstream approach, which consists in isolating instances in boxes before detecting boundaries and occlusions, fails in bin-picking scenarios as a rectangle region often includes several instances. By contrast, our box proposal-free architecture recovers fine instance boundaries, augmented with their occluding side, from a unified scene representation. As a result, the proposed network outperforms the two-stream baselines on synthetic data and public real-world datasets.Second, as FCNs require large training datasets that are not available in bin-picking applications, we propose a simulation-based pipeline for generating training images using physics and rendering engines. Specifically, piles of instances are simulated and rendered with their ground-truth annotations from sets of texture images and meshes to which multiple random deformations are applied. We show that the proposed synthetic data is plausible for real-world applications in the sense that it enables the learning of deep representations transferable to real data. Through extensive experiments on a real-world robotic setup, our synthetically trained network outperforms the industrial baseline while achieving real-time performances. The proposed approach thus establishes a new baseline for model-free object-oriented bin-picking.

Structural health monitoring and building assessment are crucial to acquire structures’ states and maintain their conditions. Besides human-labor surveys that are subjective, time-consuming, and expensive, autonomous image and video analysis is a faster, more efficient, and non-destructive way. This thesis focuses on crack detection from videos, crack segmentation from images, and building assessment from street view images. For crack detection from videos, three approaches are proposed based on local binary pattern (LBP) and support vector machine (SVM), deep convolution neural network (DCNN), and fully-connected network (FCN). A parametric Naïve Bayes data fusion scheme is introduced that registers video frames in a spatiotemporal coordinate system and fuses information based on Bayesian probability to increase detection precision. For crack segmentation from images, the rotation-invariant property of crack is utilized to enhance the segmentation accuracy. The architectures of several approximately rotation-invariant DCNNs are discussed and compared using several crack datasets. For building assessment from street view images, a framework of multiple DCNNs is proposed to detect buildings and predict their attributes that are crucial for flood risk estimation, including founding heights, foundation types (pier, slab, mobile home, or others), building types (commercial, residential, or mobile home), and building stories. A feature fusion scheme is proposed that combines image feature with meta information to improve the predictions, and a task relation encoding network (TREncNet) is introduced that encodes task relations as network connections to enhance multi-task learning.

This paper is focus on flowers recognition in an image and class classification. Theoretical part is focus on problematics of deep convolutional neural networks. The practical part if focuse on created flowers database, with which it is further worked on. The database conteins it total 13000 plant pictures of 26 spicies as cornflower, violet, gerbera, cha- momile, cornflower, liverwort, hawkweed, clover, carnation, lily of the valley, marguerite daisy, pansy, poppy, marigold, daffodil, dandelion, teasel, forget-me-not, rose, anemone, daisy, sunflower, snowdrop, ragwort, tulip and celandine. Next is in the paper described used neural network model Inception v3 for class classification. The resulting accuracy has been achieved 92%.