The thesis deals with an image processing problem called image matting. The problem involves detection of a foreground and background in an image with minimal user interaction using trimaps. Foreground detection is used in image composition. The goal of the thesis is to apply already known algorithms, in this case A Global sampling matting, in an Android application. The most important result is an intuitive application that can be used for making creative viral photos. Agile methodology is applied throughout the whole application development cycle. From the very beginning, the application is publicly available as a minimum viable product on Google play. The work’s contribution is in optimization of the mentioned algorithm for use in mobile devices and parallelization on a GPU, together with a publicly available application.

The aim of master thesis is to describe different calculation of global illumination methods including Light Propagation Volumes. All three steps of LPV calculation are widely described: injection, propagation and rendering. It is also proposed several custom extensions improving graphics quality of this method. Two parts of design and implementation are focused on scene description, rendering system, shadow rendering, implementation of LPV method and proposed extensions. As conclusion, measurement and several images of application are presented, followed by comparison in environment with diffenent parameters, thesis summary with evaluation of achieved results and suggestions of further improvements.

We consider the problem of secure communications in a Gaussian two-way relay network where two nodes exchange confidential messages only via an untrusted relay. The relay is assumed to be honest but curious, i.e., an eavesdropper that conforms to the system rules and applies the intended relaying scheme. We analyze the achievable secrecy rates by applying network coding on the physical layer or the network layer and compare the results in terms of complexity, overhead, and efficiency. Further, we discuss the advantages and disadvantages of the respective approaches.

La pathologie numérique s’est développée ces dernières années grâce à l’avancée récente des algorithmes d’analyse d’images et de la puissance de calcul. Notamment, elle se base de plus en plus sur les images histologiques. Ce format de données a la particularité de révéler les objets biologiques recherchés par les experts en utilisant des marqueurs spécifiques tout en conservant la plus intacte possible l’architecture du tissu. De nombreuses méthodes d’aide au diagnostic à partir de ces images se sont récemment développées afin de guider les pathologistes avec des mesures quantitatives dans l’établissement d’un diagnostic. Les travaux présentés dans cette thèse visent à adresser les défis liés à l’analyse d’images histologiques, et à développer un modèle d’aide au diagnostic se basant principalement sur les relations spatiales, une information que les méthodes existantes n’exploitent que rarement. Une technique d’analyse de la texture à plusieurs échelles est tout d’abord proposée afin de détecter la présence de tissu malades dans les images. Un descripteur d’objets, baptisé Force Histogram Decomposition (FHD), est ensuite introduit dans le but d’extraire les formes et l’organisation spatiale des régions définissant un objet. Finalement, les images histologiques sont décrites par les FHD mesurées à partir de leurs différents types de tissus et des objets biologiques marqués qu’ils contiennent. Les expérimentations intermédiaires ont montré que les FHD parviennent à correctement reconnaitre des objets sur fonds uniformes y compris dans les cas où les relations spatiales ne contiennent à priori pas d’informations pertinentes. De même, la méthode d’analyse de la texture s’avère satisfaisante dans deux types d’applications médicales différents, les images histologiques et celles de fond d’œil, et ses performances sont mises en évidence au travers d’une comparaison avec les méthodes similaires classiquement utilisées pour l’aide au diagnostic. Enfin, la méthode dans son ensemble a été appliquée à l’aide au diagnostic pour établir la sévérité d’un cancer via deux ensembles d’images histologiques, un de foies métastasés de souris dans le contexte du projet ANR SPIRIT, et l’autre de seins humains dans le cadre du challenge CPR 2014 : Nuclear Atypia. L’analyse des relations spatiales et des formes à deux échelles parvient à correctement reconnaitre les grades du cancer métastasé dans 87, 0 % des cas et fourni des indications quant au degré d’atypie nucléaire. Ce qui prouve de fait l’efficacité de la méthode et l’intérêt d’encoder l’organisation spatiale dans ce type d’images particulier. / During the last decade, digital pathology has been improved thanks to the advance of image analysis algorithms and calculus power. Particularly, it is more and more based on histology images. This modality of images presents the advantage of showing only the biological objects targeted by the pathologists using specific stains while preserving as unharmed as possible the tissue structure. Numerous computer-aided diagnosis methods using these images have been developed this past few years in order to assist the medical experts with quantitative measurements. The studies presented in this thesis aim at adressing the challenges related to histology image analysis, as well as at developing an assisted diagnosis model mainly based on spatial relations, an information that currently used methods rarely use. A multiscale texture analysis is first proposed and applied to detect the presence of diseased tissue. A descriptor named Force Histogram Decomposition (FHD) is then introduced in order to extract the shapes and spatial organisation of regions within an object. Finally, histology images are described by the FHD measured on their different types of tissue and also on the stained biological objects inside every types of tissue. Preliminary studies showed that the FHD are able to accurately recognise objects on uniform backgrounds, including when spatial relations are supposed to hold no relevant information. Besides, the texture analysis method proved to be satisfactory in two different medical applications, namely histology images and fundus photographies. The performance of these methods are highlighted by a comparison with the usual approaches in their respectives fields. Finally, the complete method has been applied to assess the severity of cancers on two sets of histology images. The first one is given as part of the ANR project SPIRIT and presents metastatic mice livers. The other one comes from the challenge ICPR 2014 : Nuclear Atypia and contains human breast tissues. The analysis of spatial relations and shapes at two different scales achieves a correct recognition of metastatic cancer grades of 87.0 % and gives insight about the nuclear atypia grade. This proves the efficiency of the method as well as the relevance of measuring the spatial organisation in this particular type of images.

<div>The ‘Internet of Things’ has increased the demand for artificial intelligence (AI)-based edge computing in applications ranging from healthcare monitoring systems to autonomous vehicles. However, the growing complexity of machine learning workloads requires rethinking to make AI amenable to resource constrained environments such as edge devices. To that effect, the entire stack of machine learning, from algorithms to hardware primitives, have been explored to enable energy-efficient intelligence at the edge. </div><div><br></div><div>From the algorithmic aspect, model compression techniques such as quantization are powerful tools to address the growing computational cost of ML workloads. However, quantization, particularly, can result in substantial loss of performance for complex image classification tasks. To address this, a principal component analysis (PCA)-driven methodology to identify the important layers of a binary network, and design mixed-precision networks. The proposed Hybrid-Net achieves a significant improvement in classification accuracy over binary networks such as XNOR-Net for ResNet and VGG architectures on CIFAR-100 and ImageNet datasets, while still achieving up remarkable energy-efficiency. </div><div><br></div><div>Having explored compressed neural networks, there is a need to investigate suitable computing systems to further the energy efficiency. Memristive crossbars have been extensively explored as an alternative to traditional CMOS based systems for deep learning accelerators due to their high on-chip storage density and efficient Matrix Vector Multiplication (MVM) compared to digital CMOS. However, the analog nature of computing poses significant issues due to various non-idealities such as: parasitic resistances, non-linear I-V characteristics of the memristor device etc. To address this, a simplified equation-based modelling of the non-ideal behavior of crossbars is performed and correspondingly, a modified technology aware training algorithm is proposed. Building on the drawbacks of equation-based modeling, a Generalized Approach to Emulating Non-Ideality in Memristive Crossbars using Neural Networks (GENIEx) is proposed where a neural network is trained on HSPICE simulation data to learn the transfer characteristics of the non-ideal crossbar. Next, a functional simulator was developed which includes key architectural facets such as tiling, and bit-slicing to analyze the impact of non-idealities on the classification accuracy of large-scale neural networks.</div><div><br></div><div>To truly realize the benefits of hardware primitives and the algorithms on top of the stack, it is necessary to build efficient devices that mimic the behavior of the fundamental units of a neural network, namely, neurons and synapses. However, efforts have largely been invested in implementations in the electrical domain with potential limitations of switching speed, functional errors due to analog computing, etc. As an alternative, a purely photonic operation of an Integrate-and-Fire Spiking neuron is proposed, based on the phase change dynamics of Ge2Sb2Te5 (GST) embedded on top of a microring resonator, which alleviates the energy constraints of PCMs in electrical domain. Further, the inherent parallelism of wavelength-division multiplexing (WDM) was leveraged to propose a photonic dot-product engine. The proposed computing platform was used to emulate a SNN inferencing engine for image-classification tasks. These explorations at different levels of the stack can enable energy-efficient machine learning for edge intelligence. </div><div><br></div><div>Having explored various domains to design efficient DNN models and studying various hardware primitives based on emerging technologies, we focus on Silicon implementation of compute-in-memory (CIM) primitives for machine learning acceleration based on the more available CMOS technology. CIM primitives enable efficient matrix-vector multiplications (MVM) through parallelized multiply-and-accumulate operations inside the memory array itself. As CIM primitives deploy bit-serial computing, the computations are exposed bit-level sparsity of inputs and weights in a ML model. To that effect, we present an energy-efficient sparsity-aware reconfigurable-precision compute-in-memory (CIM) 8T-SRAM macro for machine learning (ML) applications. Standard 8T-SRAM arrays are re-purposed to enable MAC operations using selective current flow through the read-port transistors. The proposed macro dynamically leverages workload sparsity by reconfiguring the output precision in the peripheral circuitry without degrading application accuracy. Specifically, we propose a new energy-efficient reconfigurable-precision SAR ADC design with the ability to form (n+m)-bit precision using n-bit and m-bit ADCs. Additionally, the transimpedance amplifier (TIA) –required to convert the summed current into voltage before conversion—is reconfigured based on sparsity to improve sense margin at lower output precision. The proposed macro, fabricated in 65 nm technology, provides 35.5-127.2 TOPS/W as the ADC precision varies from 6-bit to 2-bit, respectively. Building on top of the fabricated macro, we next design a hierarchical CIM core micro-architecture that addresses the existing CIM scaling challenges. The proposed CIM core micro-architecture consists of 32 proposed sparsity-aware CIM macros. The 32 macros are divided into 4 matrix-vector multiplication units (MVMUs) consisting of 8 macros each. The core has three unique features: i) it can adaptively reconfigure ADC precision to achieve energy-efficiency and lower latency based on input and weight sparsity, determined by a sparsity controller, ii) it deploys row-gating feature to maintain SNR requirements for accurate DNN computations, and iii) hardware support for load balancing to balance latency mismatches occurring due to different ADC precisions in different compute units. Besides the CIM macros, the core micro-architecture consists of input, weight, and output memories, along with instruction memory and control circuits. The instruction set architecture allows for flexible dataflows and mapping in the proposed core micro-architecture. The sparsity-aware processing core is scheduled to be taped out next month. The proposed CIM demonstrations complemented by our previous analysis on analog CIM systems progressed our understanding of this emerging paradigm in pertinence to ML acceleration.</div>

In telecommunication, messages sent through a wireless medium often experience noise interfering with the signal in a way that corrupts the messages. As the demand for high throughput in the mobile network is increasing, algorithms that can detectand correct these corrupted messages quickly and accurately are of interest to the industry. Polar codes have been chosen by the Third Generation Partnership Project as the error correction code for 5G New Radio control channels. This thesis work aimed to investigate whether the polar code Successive Cancellation (SC) could be parallelized and if a graphics processing unit (GPU) can be utilized to optimize the execution time of the algorithm. The polar code Successive Cancellation was enhanced by implementing tree pruning and support for GPUs to leverage their parallelization. The difference in execution time between the concurrent and sequential versions of the SC algorithm with and without tree pruning was evaluated. The tree pruning SC algorithm almost always offered shorter execution times than the SC algorithm that did not employ treepruning. However, the support for GPUs did not reduce the execution time in these tests. Thus, the GPU is not certain to be able to improve this type of enhanced SC algorithm based on these results. / Meddelanden som överförs över ett mobilt nät utsätts ofta för brus som distorterar dem. I takt med att intresset ökat för hög genomströmning i mobilnätet har också intresset för algoritmer som snabbt och tillförlitligt kan upptäcka och korrigera distorderade meddelanden ökat. Polarkoder har valts av "Third Generation Partnership Project" som den klass av felkorrigeringskoder som ska användas för 5G:s radiokontrollkanaler. Detta examensarbete hade som syfte att undersöka om polarkoden "Successive Cancellation" (SC) skulle kunna parallelliseras och om en grafisk bearbetningsenhet (GPU) kan användas för att optimera exekveringstiden för algoritmen. SC utökades med stöd för trädbeskärning och parallellisering med hjälp av GPU:er. Skillnaden i exekveringstid mellan de parallella och sekventiella versionerna av SC-algoritmen med och utan trädbeskärning utvärderades. SC-algoritmen för trädbeskärning erbjöd nästan alltid kortare exekveringstider än SC-algoritmen som inte använde trädbeskärning. Stödet för GPU:er minskade dock inte exekveringstiden. Således kan man med dessa resultat inte med säkerhet säga att GPU-stöd skulle gynna SC-algoritmen.

Low-Density Parity-Check (LDPC) codes are linear error-correcting codes used to establish reliable communication between units on a noisy transmission channel in mobile telecommunications. LDPC algorithms detect and recover altered or corrupted message bits using sparse parity-check matrices in order to decipher messages correctly. LDPC codes have been shown to be fitting coding schemes for the fifth generation (5G) New Radio (NR), according to the third generation partnership project (3GPP).  TietoEvry, a consultant in telecom, has discovered that optimizations of LDPC decoding algorithms can be achieved/obtained with the use of a parallel computing platform called Compute Unified Device Architecture (CUDA), developed by NVIDIA. This platform utilizes the capabilities of a graphics processing unit (GPU) rather than a central processing unit (CPU), which in turn provides parallel computing. An optimized version of an LDPC decoding algorithm, the Min-Sum Algorithm (MSA), is implemented in CUDA and in C++ for comparison in terms of CPU execution time, to explore the capabilities that CUDA offers. The testing is done with a set of 12 sparse parity-check matrices and input-channel messages with different sizes. As a result, the CUDA implementation executes approximately 55% faster than a standard, unoptimized C++ implementation.

Au cours de la dernière décennie, la pathologie numérique a été améliorée grâce aux avancées des algorithmes d'analyse d'images et de la puissance de calcul. Néanmoins, le diagnostic par un expert à partir d'images histopathologiques reste le gold standard pour un nombre considérable de maladies notamment le cancer. Ce type d'images préserve la structure des tissus aussi proches que possible de leur état vivant. Ainsi, cela permet de quantifier les objets biologiques et de décrire leur organisation spatiale afin de fournir une description plus précise des tissus malades. L'analyse automatique des images histopathologiques peut avoir trois objectifs: le diagnostic assisté par ordinateur, l'évaluation de la sévérité des maladies et enfin l'étude et l'interprétation des mécanismes sous-jacents des maladies et leurs impacts sur les objets biologiques. L'objectif principal de cette thèse est en premier lieu de comprendre et relever les défis associés à l'analyse automatisée des images histologiques. Ensuite, ces travaux visent à décrire les populations d'objets biologiques présents dans les images et leurs relations et interactions à l'aide des statistiques spatiales et également à évaluer la significativité de leurs différences en fonction de la maladie par des tests statistiques. Après une étape de séparation des populations d'objets biologiques basée sur la couleur des marqueurs, une extraction automatique de leurs emplacements est effectuée en fonction de leur type, qui peut être ponctuel ou surfacique. Les statistiques spatiales, basées sur la distance pour les données ponctuelles, sont étudiées et une fonction originale afin de mesurer les interactions entre deux types de données est proposée. Puisqu'il a été montré dans la littérature que la texture d'un tissu est altérée par la présence d'une maladie, les méthodes fondées sur les motifs binaires locaux sont discutées et une approche basée sur une modification de la résolution de l'image afin d'améliorer leur description est introduite. Enfin, les statistiques descriptives et déductives sont appliquées afin d'interpréter les caractéristiques extraites et d'étudier leur pouvoir discriminant dans le cadre de l'étude des modèles animaux de cancer colorectal. Ce travail préconise la mesure des associations entre différents types d'objets biologiques pour mieux comprendre et comparer les mécanismes sous-jacents des maladies et leurs impacts sur la structure des tissus. En outre, nos expériences confirment que l'information de texture joue un rôle important dans la différenciation des deux modèles d'implantation d'une même maladie. / During the last decade, digital pathology has been improved thanks to the advance of image analysis algorithms and calculus power. However, the diagnosis from histopathology images by an expert remains the gold standard in a considerable number of diseases especially cancer. This type of images preserves the tissue structures as close as possible to their living state. Thus, it allows to quantify the biological objects and to describe their spatial organization in order to provide a more specific characterization of diseased tissues. The automated analysis of histopathological images can have three objectives: computer-aided diagnosis, disease grading, and the study and interpretation of the underlying disease mechanisms and their impact on biological objects. The main goal of this dissertation is first to understand and address the challenges associated with the automated analysis of histology images. Then it aims at describing the populations of biological objects present in histology images and their relationships using spatial statistics and also at assessing the significance of their differences according to the disease through statistical tests. After a color-based separation of the biological object populations, an automated extraction of their locations is performed according to their types, which can be point or areal data. Distance-based spatial statistics for point data are reviewed and an original function to measure the interactions between point and areal data is proposed. Since it has been shown that the tissue texture is altered by the presence of a disease, local binary patterns methods are discussed and an approach based on a modification of the image resolution to enhance their description is introduced. Finally, descriptive and inferential statistics are applied in order to interpret the extracted features and to study their discriminative power in the application context of animal models of colorectal cancer. This work advocates the measure of associations between different types of biological objects to better understand and compare the underlying mechanisms of diseases and their impact on the tissue structure. Besides, our experiments confirm that the texture information plays an important part in the differentiation of two implemented models of the same disease.

Au cours de la dernière décennie, la pathologie numérique a été améliorée grâce aux avancées des algorithmes d'analyse d'images et de la puissance de calcul. Néanmoins, le diagnostic par un expert à partir d'images histopathologiques reste le gold standard pour un nombre considérable de maladies notamment le cancer. Ce type d'images préserve la structure des tissus aussi proches que possible de leur état vivant. Ainsi, cela permet de quantifier les objets biologiques et de décrire leur organisation spatiale afin de fournir une description plus précise des tissus malades. L'analyse automatique des images histopathologiques peut avoir trois objectifs: le diagnostic assisté par ordinateur, l'évaluation de la sévérité des maladies et enfin l'étude et l'interprétation des mécanismes sous-jacents des maladies et leurs impacts sur les objets biologiques. L'objectif principal de cette thèse est en premier lieu de comprendre et relever les défis associés à l'analyse automatisée des images histologiques. Ensuite, ces travaux visent à décrire les populations d'objets biologiques présents dans les images et leurs relations et interactions à l'aide des statistiques spatiales et également à évaluer la significativité de leurs différences en fonction de la maladie par des tests statistiques. Après une étape de séparation des populations d'objets biologiques basée sur la couleur des marqueurs, une extraction automatique de leurs emplacements est effectuée en fonction de leur type, qui peut être ponctuel ou surfacique. Les statistiques spatiales, basées sur la distance pour les données ponctuelles, sont étudiées et une fonction originale afin de mesurer les interactions entre deux types de données est proposée. Puisqu'il a été montré dans la littérature que la texture d'un tissu est altérée par la présence d'une maladie, les méthodes fondées sur les motifs binaires locaux sont discutées et une approche basée sur une modification de la résolution de l'image afin d'améliorer leur description est introduite. Enfin, les statistiques descriptives et déductives sont appliquées afin d'interpréter les caractéristiques extraites et d'étudier leur pouvoir discriminant dans le cadre de l'étude des modèles animaux de cancer colorectal. Ce travail préconise la mesure des associations entre différents types d'objets biologiques pour mieux comprendre et comparer les mécanismes sous-jacents des maladies et leurs impacts sur la structure des tissus. En outre, nos expériences confirment que l'information de texture joue un rôle important dans la différenciation des deux modèles d'implantation d'une même maladie. / During the last decade, digital pathology has been improved thanks to the advance of image analysis algorithms and calculus power. However, the diagnosis from histopathology images by an expert remains the gold standard in a considerable number of diseases especially cancer. This type of images preserves the tissue structures as close as possible to their living state. Thus, it allows to quantify the biological objects and to describe their spatial organization in order to provide a more specific characterization of diseased tissues. The automated analysis of histopathological images can have three objectives: computer-aided diagnosis, disease grading, and the study and interpretation of the underlying disease mechanisms and their impact on biological objects. The main goal of this dissertation is first to understand and address the challenges associated with the automated analysis of histology images. Then it aims at describing the populations of biological objects present in histology images and their relationships using spatial statistics and also at assessing the significance of their differences according to the disease through statistical tests. After a color-based separation of the biological object populations, an automated extraction of their locations is performed according to their types, which can be point or areal data. Distance-based spatial statistics for point data are reviewed and an original function to measure the interactions between point and areal data is proposed. Since it has been shown that the tissue texture is altered by the presence of a disease, local binary patterns methods are discussed and an approach based on a modification of the image resolution to enhance their description is introduced. Finally, descriptive and inferential statistics are applied in order to interpret the extracted features and to study their discriminative power in the application context of animal models of colorectal cancer. This work advocates the measure of associations between different types of biological objects to better understand and compare the underlying mechanisms of diseases and their impact on the tissue structure. Besides, our experiments confirm that the texture information plays an important part in the differentiation of two implemented models of the same disease.

A 2T2C ferroelectric memory cell consisting of a select transistor, a read transistor and two ferroelectric capacitors that can be operated either in FeRAM mode or in memristive ferroelectric tunnel junction mode is proposed. The two memory devices can be programmed individually. By performing a combined readout operation, the two stored bits of the memory cells can be combined to perform in-memory logic operation. Moreover, additional input logic signals that are applied as external readout voltage pulses can be used to perform logic operation together with the stored logic states of the ferroelectric capacitors. Electrical characterization results of the logic-in-memory (LiM) functionality is presented.