L’imagerie par résonance magnétique (IRM) est un outil important pour l’investigation qualitative et quantitative de la physiologie du cerveau. L’investigation de l’activité neuronale à l’aide de cette modalité est possible grâce à la détection de changements hémodynamiques qui surviennent de manière concomitante aux activités de signalisation des neurones, tels l’augmentation régionale du débit sanguin cérébral (CBF) ou encore la variation de la concentration de désoxyhémoglobine dans les vaisseaux veineux. Pour étudier la formation de contrastes fonctionnels qui découlent de ces phénomènes, deux séquences de pulses ont été développées en vue d’expériences en IRM fonctionnelle (IRMf) visant l’imagerie du signal oxygéno-dépendant BOLD ainsi que de la perfusion. Le premier objectif de cette thèse fut le développement d’une séquence de type écho-planar (EPI) permettant l’acquisition entrelacée d’images en mode échos de gradient (GRE-EPI) ainsi qu’en mode échos de spins (SE-EPI) pour l’évaluation de la performance de ces deux méthodes d’imagerie au cours d’une expérience en IRMf BOLD impliquant l’utilisation d’un stimulus visuel chez 4 sujets adultes sains. Le deuxième objectif principal de cette thèse fut le développement d’une séquence de marquage de spins artériels employant un module de marquage fonctionnant en mode pseudo-continu (pCASL) pour la quantification du CBF au repos. Cette séquence fut testée chez 3 sujets adultes en bonne santé et sa performance fut comparée à celle d’une séquence similaire développée par un groupe de recherche extérieur. Les résultats de l’expérience portant sur le contraste BOLD indiquent une supériorité de la performance du mode GRE-EPI vis-à-vis celle du mode SE-EPI en termes des valeurs moyennes du pourcentage de l’ampleur d’effet et du score t associés à l’activité neuronale en réponse au stimulus. L’expérience visant la quantification du CBF démontra la capacité de la séquence pCASL développée au cours de ce projet de calculer des valeurs de la perfusion de la matière grise ainsi que du cerveau entier se retrouvant dans une plage de valeurs qui sont physiologiquement acceptables, mais qui demeurent inférieures à celles obtenues par la séquence pCASL développée par le groupe de recherche extérieur. Des expériences futures seront effectuées pour optimiser le fonctionnement des séquences présentées dans ce mémoire en plus de quantifier l’efficacité d’inversion de la séquence pCASL. / Magnetic resonance imaging (MRI) is an important tool for the qualitative and quantitative investigation of brain physiology. The investigation of neuronal activation using this modality is made possible by the detection of concomitantly-arising hemodynamic changes in the brain’s vasculature, such as localized increases of the cerebral blood flow (CBF) or the variation of the concentration of paramagnetic deoxyhemoglobin in venous vessels. To study the formation of functional contrasts that stem from these changes in MRI, two pulse sequences were developed in this thesis to carry out experiments in blood oxygenation level dependent (BOLD) and perfusion functional MRI (fMRI). The first objective laid out in this work was the development of an echo planar imaging (EPI) sequence permitting the interleaved acquisition of images using gradient-echo EPI and spin-echo EPI to assess the performances of these imaging techniques in a BOLD fMRI experiment involving a visual stimulation paradigm in 4 healthy adult subjects. The second main objective of this thesis was the development of a pseudo-continuous arterial spin labelling (pCASL) sequence for the quantification of cerebral blood flow (CBF) at rest. This sequence was tested on 3 healthy adult subjects and compared to an externally-developed pCASL sequence to assess its performance. The results of the BOLD fMRI experiment indicated that the performance of GRE-EPI was superior to that of SE-EPI in terms of the average percent effect size and t-score associated with stimulus-driven neuronal activation. The CBF quantification experiment demonstrated the ability of the in-house pCASL sequence to compute values of CBF that are within a range of physiologically-acceptable values while remaining inferior to those computed using the externally-developed pCASL sequence. Future experiments will focus on the optimization of the sequences presented in this thesis as well as on the quantification of the pCASL sequence’s labelling efficiency.

Sedimentary processes are critical geological phenomena in the evolution of continental margins. Environmental forces from various temporal and spatial scales control these processes. Due to the fast increase of anthropogenic activities in marine environments, these studies on sedimentary processes and their products are becoming increasingly necessary. In this context, geophysical data, such as high-resolution 2D seismic reflection profiles, are efficient tools to characterize sedimentary processes and deposits. This study aimed to map and interpret different types of echo character on the continental shelf of the São Paulo Bight to increase the knowledge of the spatial variability of sedimentary processes in current-dominated shelf settings. With this aim, 120 seismic lines, almost 3200 km in length, have been analyzed in conjunction with 451 surficial sediment samples. Based on the broad-scale seafloor morphology, microtopography and seismic characteristics (e.g., sharpness, amplitude, continuity) of sub-bottom reflections, seven echo types distributed in three echo classes were identified in the study area. Type 1-1 is a flat seafloor with either no or low-amplitude sub-bottom reflectors, mainly observed in the middle shelf; type 1-2 is a flat sea floor with medium to high-amplitude sub-bottom reflectors also found in middle shelf settings; type 1-3 is defined by a transparent acoustic response below the seafloor evolving downsection into a highly reflective pattern that covers most of the inner shelf; type 1-4 presents a flat seafloor molded by regularly spaced undulations, and it is restricted to the outer shelf; type 2-1 consists of mounds with no internal reflectors also located on the outer shelf; type 3-1 is an irregular echo with prolonged internal reflectors found around the São Sebastião Island; finally, type 3-2 is a broad irregular echo with high relief variations mainly found in the outer shelf. The analysis of the seismic survey allowed recognizing that the distribution of the echoes reflects not only the composition of the seabed but also the sedimentary processes that acted during the deposition of the material. In the inner shelf and middle shelves, the Brazilian Coastal Current and the action of waves are the main processes responsible for the echo types found, while the outer shelf echo types are strongly influenced by the Brazil Current. / Processos sedimentares são fenômenos importantes na evolução das margens continentais. Esses processos são controlados por forçantes de várias escalas espaciais e temporais. Devido ao rápido crescimento das atividades antropogênicas em ambientes marinhos, estudos sobre processos sedimentares e seus produtos tem se tornado cada vez mais necessários. Nesse contexto, dados geofísicos, como perfis de sísmica de reflexão de alta resolução, são ferramentas importantes na caracterização desses processos e depósitos. O objetivo do estudo foi mapear e interpretar os tipos de caráter de eco na plataforma continental do estado de São Paulo visando aumentar o conhecimento a respeito da variabilidade espacial dos processos sedimentares em plataformas dominadas por correntes. Para alcançar esse objetivo 120 linhas sísmicas totalizando quase 3200 km em extensão foram analisadas em conjunto com 451 amostras sedimentares. Baseados na morfologia de macro e microescala e características sísmicas do sinal acústico de fundo como nitidez, amplitude e continuidade dos refletores, foram identificados 7 tipos de caráter de eco distribuídos em 3 classes (fundo plano, monticular ou irregular). O tipo 1-1 apresenta fundo plano com refletores de sub fundo ausentes ou de baixa amplitude localizado principalmente na plataforma média; o tipo 1-2 refere-se a um fundo plano com refletores internos com amplitudes médias a altas também presente na plataforma média; o tipo 1-3 é definido por uma camada superficial transparente que é substituída em profundidade por uma camada altamente refletiva que cobre grande parte da plataforma interna; o eco 1-4 apresenta fundo moldado por ondulações com localização restrita na plataforma externa; o tipo 2-1 consiste em montes sem refletores internos localizado próximo a quebra da plataforma; o tipo 3-1 é irregular com refletores internos prolongados e é encontrado ao sul da Ilha de São Sebastião; finalmente, o eco 3-2 é um eco irregular que apresenta grandes variações de relevo presente em grande parte da plataforma externa. A análise dos dados sísmicos permitiu o reconhecimento de que os ecos respondem não somente a composição sedimentar superficial, mas também os processos sedimentares que atuaram durante a deposição do material. Nas plataformas interna e média, a Corrente Costeira Brasileira e a ação de ondas geradas pelos ventos são os principais processos responsáveis pela geração dos ecos encontrados, enquanto na plataforma externa, os tipos de eco são fortemente influenciados pela Corrente do Brasil.

[pt] A humanidade está vivenciando a Quarta Revolução Industrial, caracterizada pela implementação global da internet, utilização de inteligência artificial e automatização dos processos. Este último é de grande importância para indústria química, uma vez que seu desenvolvimento possibilitou um aumento significativo da quantidade de dados armazenados diariamente, o que gerou uma demanda para análise desses dados. Este enorme fluxo de informações tornou o sistema cada vez mais complexo com uma aleatoriedade de falhas no processo que se identificadas poderiam ajudar a melhorar o processo e evitar acidentes. Uma solução ainda pouco comum na indústria, porém com grande potencial para identificar estas falhas de processo com excelência, é a emergente inteligência artificial. Para lidar com esta questão, o presente trabalho realiza a detecção e identificação de falhas em processos industriais através da modelagem de redes neurais artificias. O banco de dados foi obtido através do uso do benchmark de processo Tennessee Eastman, implementado no Software Matlab 2017b, o qual foi projetado para simular uma planta química completa. A enorme quantidade de dados gerados pelo processo tornou possível a simulação em um contexto de Big Data. Para modelagem dos dados, foram tanto aplicadas redes neurais tradicionais feedforward, quanto redes recorrentes: Rede de Elman e Echo State Network. Os resultados apontaram que as redes feedforward e de Elman obtiveram melhores desempenhos analisados pelo coeficiente de determinação (R2). Assim, o primeiro modelo obteve melhor topologia com 37x60x70x1, algoritmo de treinamento trainlm, funções de ativação tansig para as duas camadas intermediárias e camada de saída ativada pela purelin com R2 de 88,69 por cento. O modelo da rede de Elman apresentou sua melhor topologia com 37x45x55x1, algoritmo de treinamento trainlm, funções de ativação tansig para as duas camadas intermediárias e camada de saída ativada pela função purelin com R2 de 83,63 por cento. Foi concluido que as redes analisadas podem ser usadas em controle preditivo de falhas em processos industriais, podendo ser aplicadas em plantas químicas no futuro. / [en] Humanity is experiencing the 4th Industrial Revolution, characterized by the global implementation of the internet, use of artificial intelligence and automation of processes. The last one is of great importance for the chemical industry, since its development allowed a significant increase in the amount of data stored daily, which generated a demand for the analysis of this data. This enormous flow of information made the system more and more complex with a randomness of process faults that if identified could help improve the process and prevent accidents. A solution not yet common in industry, but with great potential to identify these process faults with excellence, is the emergent artificial intelligence. To deal with this issue, the present work performs fault detection and diagnosis in industrial processes through artificial neural networks modeling. The database was obtained using the benchmark of processes Tennessee Eastman, implemented in Matlab 2017b Software, which is designed to simulate a complete chemical plant. The huge amount of data generated by the process made it possible to simulate in a Big Data context. For data modeling, were applied both traditional feedforward neural networks as well as recurrent networks: Elman Network and Echo State Network. The results indicated that the feedforward and Elman networks obtained better performances analyzed by the determination coefficient (R2). Thus, the first model obtained the best topology with 37x60x70x1, trainlm as training algorithm, tansig as activation functions for the two intermediate layers and output layer activated by the purelin function with R2 of 88.69 percent. The Elman network model presented its best topology with 37x45x55x1, trainlm as training algorithm, tansig as activation functions for the two intermediate layers and output layer activated by purelin function with R2 of 83.63 percent. It was concluded that the analyzed networks can be used in predictive control of fault in industrial processes and can be applied in chemical plants in the future.

Sedimentary processes are critical geological phenomena in the evolution of continental margins. Environmental forces from various temporal and spatial scales control these processes. Due to the fast increase of anthropogenic activities in marine environments, these studies on sedimentary processes and their products are becoming increasingly necessary. In this context, geophysical data, such as high-resolution 2D seismic reflection profiles, are efficient tools to characterize sedimentary processes and deposits. This study aimed to map and interpret different types of echo character on the continental shelf of the São Paulo Bight to increase the knowledge of the spatial variability of sedimentary processes in current-dominated shelf settings. With this aim, 120 seismic lines, almost 3200 km in length, have been analyzed in conjunction with 451 surficial sediment samples. Based on the broad-scale seafloor morphology, microtopography and seismic characteristics (e.g., sharpness, amplitude, continuity) of sub-bottom reflections, seven echo types distributed in three echo classes were identified in the study area. Type 1-1 is a flat seafloor with either no or low-amplitude sub-bottom reflectors, mainly observed in the middle shelf; type 1-2 is a flat sea floor with medium to high-amplitude sub-bottom reflectors also found in middle shelf settings; type 1-3 is defined by a transparent acoustic response below the seafloor evolving downsection into a highly reflective pattern that covers most of the inner shelf; type 1-4 presents a flat seafloor molded by regularly spaced undulations, and it is restricted to the outer shelf; type 2-1 consists of mounds with no internal reflectors also located on the outer shelf; type 3-1 is an irregular echo with prolonged internal reflectors found around the São Sebastião Island; finally, type 3-2 is a broad irregular echo with high relief variations mainly found in the outer shelf. The analysis of the seismic survey allowed recognizing that the distribution of the echoes reflects not only the composition of the seabed but also the sedimentary processes that acted during the deposition of the material. In the inner shelf and middle shelves, the Brazilian Coastal Current and the action of waves are the main processes responsible for the echo types found, while the outer shelf echo types are strongly influenced by the Brazil Current. / Processos sedimentares são fenômenos importantes na evolução das margens continentais. Esses processos são controlados por forçantes de várias escalas espaciais e temporais. Devido ao rápido crescimento das atividades antropogênicas em ambientes marinhos, estudos sobre processos sedimentares e seus produtos tem se tornado cada vez mais necessários. Nesse contexto, dados geofísicos, como perfis de sísmica de reflexão de alta resolução, são ferramentas importantes na caracterização desses processos e depósitos. O objetivo do estudo foi mapear e interpretar os tipos de caráter de eco na plataforma continental do estado de São Paulo visando aumentar o conhecimento a respeito da variabilidade espacial dos processos sedimentares em plataformas dominadas por correntes. Para alcançar esse objetivo 120 linhas sísmicas totalizando quase 3200 km em extensão foram analisadas em conjunto com 451 amostras sedimentares. Baseados na morfologia de macro e microescala e características sísmicas do sinal acústico de fundo como nitidez, amplitude e continuidade dos refletores, foram identificados 7 tipos de caráter de eco distribuídos em 3 classes (fundo plano, monticular ou irregular). O tipo 1-1 apresenta fundo plano com refletores de sub fundo ausentes ou de baixa amplitude localizado principalmente na plataforma média; o tipo 1-2 refere-se a um fundo plano com refletores internos com amplitudes médias a altas também presente na plataforma média; o tipo 1-3 é definido por uma camada superficial transparente que é substituída em profundidade por uma camada altamente refletiva que cobre grande parte da plataforma interna; o eco 1-4 apresenta fundo moldado por ondulações com localização restrita na plataforma externa; o tipo 2-1 consiste em montes sem refletores internos localizado próximo a quebra da plataforma; o tipo 3-1 é irregular com refletores internos prolongados e é encontrado ao sul da Ilha de São Sebastião; finalmente, o eco 3-2 é um eco irregular que apresenta grandes variações de relevo presente em grande parte da plataforma externa. A análise dos dados sísmicos permitiu o reconhecimento de que os ecos respondem não somente a composição sedimentar superficial, mas também os processos sedimentares que atuaram durante a deposição do material. Nas plataformas interna e média, a Corrente Costeira Brasileira e a ação de ondas geradas pelos ventos são os principais processos responsáveis pela geração dos ecos encontrados, enquanto na plataforma externa, os tipos de eco são fortemente influenciados pela Corrente do Brasil.

In system identification scenario, the linear approximation of the system modelled by its impulse response, is estimated in real time by gradient type Least Mean Square (LMS) or Recursive Least Squares (RLS) algorithms. In recent applications like acoustic echo cancellation, the order of the impulse response to be estimated is very high, and these traditional approaches are inefficient and real time implementation becomes difficult. Alternatively, the system is modelled by a set of shorter adaptive filters operating in parallel on subsampled signals. This approach, referred to as subband adaptive filtering, is expected to reduce not only the computational complexity but also to improve the convergence rate of the adaptive algorithm. But in practice, different subband adaptive algorithms have to be used to enhance the performance with respect to complexity, convergence rate and processing delay. A single subband adaptive filtering algorithm which outperforms the full band scheme in all applications is yet to be realized. This thesis is intended to study the subband adaptive filtering techniques and explore the possibilities of better algorithms for performance improvement. Three different subband adaptive algorithms have been proposed and their performance have been verified through simulations. These algorithms have been applied to acoustic echo cancellation and EEG artefact minimization problems. Details of the work To start with, the fast FIR filtering scheme introduced by Mou and Duhamel has been generalized. The Perfect Reconstruction Filter Bank (PRFB) is used to model the linear FIR system. The structure offers efficient implementation with reduced arithmetic complexity. By using a PRFB with non adjacent filters non overlapping, many channel filters can be eliminated from the structure. This helps in reducing the complexity of the structure further, but introduces approximation in the model. The modelling error depends on the stop band attenuation of the filters of the PRFB. The error introduced due to approximation is tolerable for applications like acoustic echo cancellation. The filtered output of the modified generalized fast filtering structure is given by (formula) where, Pk(z) is the main channel output, Pk,, k+1 (z) is the output of auxiliary channel filters at the reduced rate, Gk (z) is the kth synthesis filter and M the number of channels in the PRFB. An adaptation scheme is developed for adapting the main channel filters. Auxiliary channel filters are derived from main channel filters. Secondly, the aliasing problem of the classical structure is reduced without using the cross filters. Aliasing components in the estimated signal results in very poor steady state performance in the classical structure. Attempts to eliminate the aliasing have reduced the computation gain margin and the convergence rate. Any attempt to estimate the subband reference signals from the aliased subband input signals results in aliasing. The analysis filter Hk(z) having the following antialiasing property (formula) can avoid aliasing in the input subband signal. The asymmetry of the frequency response prevents the use of real analysis filters. In the investigation presented in this thesis, complex analysis filters and real'synthesis filters are used in the classical structure, to reduce the aliasing errors and to achieve superior convergence rate. PRFB is traditionally used in implementing Interpolated FIR (IFIR) structure. These filters may not be ideal for processing an input signal for an adaptive algorithm. As third contribution, the IFIR structure is modified using discrete finite frames. The model of an FIR filter s is given by Fc, with c = Hs. The columns of the matrix F forms a frame with rows of H as its dual frame. The matrix elements can be arbitrary except that the transformation should be implementable as a filter bank. This freedom is used to optimize the filter bank, with the knowledge of the input statistics, for initial convergence rate enhancement . Next, the proposed subband adaptive algorithms are applied to acoustic echo cancellation problem with realistic parameters. Speech input and sufficiently long Room Impulse Response (RIR) are used in the simulations. The Echo Return Loss Enhancement (ERLE)and the steady state error spectrum are used as performance measures to compare these algorithms with the full band scheme and other representative subband implementations. Finally, Subband adaptive algorithm is used in minimization of EOG (Electrooculogram) artefacts from measured EEG (Electroencephalogram) signal. An IIR filterbank providing sufficient isolation between the frequency bands is used in the modified IFIR structure and this structure has been employed in the artefact minimization scheme. The estimation error in the high frequency range has been reduced and the output signal to noise ratio has been increased by a couple of dB over that of the fullband scheme. Conclusions Efforts to find elegant Subband adaptive filtering algorithms will continue in the future. However, in this thesis, the generalized filtering algorithm could offer gain in filtering complexity of the order of M/2 and reduced misadjustment . The complex classical scheme offered improved convergence rate, reduced misadjustment and computational gains of the order of M/4 . The modifications of the IFIR structure using discrete finite frames made it possible to eliminate the processing delay and enhance the convergence rate. Typical performance of the complex classical case for speech input in a realistic scenario (8 channel case), offers ERLE of more than 45dB. The subband approach to EOG artefact minimization in EEG signal was found to be superior to their fullband counterpart. (Refer PDF file for Formulas)

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[pt] Através das técnicas utilizadas em Identificação de Sistemas é possível obter um modelo matemático para um sistema dinâmico somente a partir de dados medidos de suas entradas e saídas. Por possuírem comportamento naturalmente dinâmico e um procedimento de treinamento simples e rápido, o uso de redes neurais do tipo Echo State Networks (ESNs) é vantajoso nesta área. Entretanto, as ESNs possuem hiperparâmetros que devem ser ajustados para que obtenham um bom desempenho em uma dada tarefa, além do fato de que a inicialização aleatória de pesos da camada interna destas redes (reservatório) nem sempre ser a ideal em termos de desempenho. Por teoricamente conseguirem obter boas soluções com poucas avaliações, o AEIQ-R (Algoritmo Evolutivo com Inspiração Quântica e Representação Real) e a estratégia evolucionária com adaptação da matriz de covariâncias (CMA-ES) representam alternativas de algoritmos evolutivos que permitem lidar de maneira eficiente com a otimização de hiperparâmetros e/ou pesos desta rede. Sendo assim, este trabalho propõe um modelo neuroevolucionário que define automaticamente uma ESN para aplicações de Identificação de Sistemas. O modelo inicialmente foca na otimização dos hiperparâmetros da ESN utilizando o AEIQ-R ou o CMA-ES, e, num segundo momento, seleciona o reservatório mais adequado para esta rede, o que pode ser feito através de uma segunda otimização focada no ajuste de alguns pesos do reservatório ou por uma escolha simples baseando-se em redes com reservatórios aleatórios. O método proposto foi aplicado a 9 problemas benchmark da área de Identificação de Sistemas, apresentando bons resultados quando comparados com modelos tradicionais. / [en] Through System Identification techniques is possible to obtain a mathematical model for a dynamic system from its input/output data. Due to their intrinsic dynamic behavior and simple and fast training procedure, the use of Echo State Networks, which are a kind of neural networks, for System Identification is advantageous. However, ESNs have global parameters that should be tuned in order to improve their performance in a determined task. Besides, a random reservoir may not be ideal in terms of performance. Due to their theoretical ability of obtaining good solutions with few evaluations, the Real Coded Quantum-Inspired Evolutionary Algorithm (QIEA-R) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) represent efficient alternatives of evolutionary algorithms for optimizing ESN global parameters and/or weights. Thus, this work proposes a neuro-evolutionary method that automatically defines an ESN for System Identification problems. The method initially focuses in finding the best ESN global parameters by using the QIEA-R or the CMA-ES, then, in a second moment, in selecting its best reservoir, which can be done by a second optimization focused on some reservoir weights or by doing a simple choice based on networks with random reservoirs. The method was applied to 9 benchmark problems in System Identification, showing good results when compared to traditional methods.

Cette thèse porte sur l'utilisation de plusieurs techniques de diffusion de neutrons polarisés pour la conduite d'expériences de diffraction et de spectroscopie inélastique à haute résolution. Nous décrivons de façon exhaustive l'option à écho de spin neutronique résonant ZETA, installée sur le spectromètre triple axe thermique CRG IN22 à l'Institut Laue Langevin. Grâce à elle, nous étudions la structure nucléaire et la dynamique de spin de plusieurs systèmes modèles. Dans un premier temps, nous nous intéressons à la série BaM2(XO4)2 (M = Co, Ni; X = As, P) dont les membres sont de bons exemples de systèmes magnétiques quasi-bidimensionnels. L'effet de la mise en ordre magnétique sur leurs paramètres de maille est révélé par diffraction de Larmor. De plus, nous montrons que l'évolution thermique de la durée de vie du mode de magnon optique dans BaNi2(PO4)2 est fortement affecté par la présence de défauts dans sa structure. Ensuite, nous abordons le composé à chaînes et échelles de spin 1/2 Sr14Cu24O41. Nous nous focalisons d'abord sur l'étude du pic inélastique associé au gap de spin des échelles et présentons une méthode capable de montrer de façon directe la dégénérescence de la transition concernée. Ensuite, nous évaluons sa largeur énergétique intrinsèque et observons l'effet des différentes mises en ordre de charge sur la structure cristallographique du matériau. Finalement, nous adaptons l'instrumentation disponible pour mener des expériences de réflectométrie résolues en temps, par le biais de la méthode MIEZE, sur une multicouche magnétique pouvant posséder des propriétés intéressante pour des applications en spintronique / This thesis is mainly concerned with the use of several polarized neutron scattering techniques for carrying high resolution diffraction and inelastic spectroscopy experiments. We describe exhaustively our neutron resonant spin-echo option ZETA, installed on the thermal triple-axis spectrometer CRG IN22 at Institut Laue Langevin. Through it, we study the nuclear structure and spin dynamics of several model systems. First, we are interested in the BaM2(XO4)2 (M = Co, Ni; X = As, P)-family which members are good prototypes of quasi-2D magnetic systems. The effect of magnetic ordering on lattice constants is revealed thanks to Larmor diffraction. Moreover, we show that the thermal evolution of optic magnon lifetime in BaNi2(PO4)2 is strongly affected by the presence of defects in its structure. Then, we address the spin-chain and -ladder compound Sr14Cu24O41. We first focus on the study of the inelastic peak associated with the spin gap in the ladders spectrum and introduce a method capable of showing directly the degeneracy of the associated spin transition. We also evaluate its intrinsic linewidth and observe the effect of different charge ordering process on the material crystallographic structure. Ultimately, we adapt our instrumentation to perform time-resolved reflectometry experiments on a magnetic multilayer which can possess interesting properties for spintronics applications, through the so-called MIEZE technique.

Le Reservoir Computing (RC) est un paradigme s’inspirant du cerveau humain, apparu récemment au début des années2000. Il s'agit d'un calculateur neuromorphique habituellement décomposé en trois parties dont la plus importanteappelée "réservoir" est très proche d'un réseau de neurones récurrent. Il se démarque des autres réseaux de neuronesartificiels notamment grâce aux traditionnelles phases d'apprentissage et d’entraînement qui ne sont plus appliquées surla totalité du réseau de neurones mais uniquement sur la lecture du réservoir, ce qui simplifie le fonctionnement etfacilite une réalisation physique. C'est précisément dans ce contexte qu’ont été réalisés les travaux de recherche de cettethèse, durant laquelle nous avons réalisé une première implémentation physique opto-électronique de système RC.Notre approche des systèmes physiques RC repose sur l'utilisation de dynamiques non-linéaires à retards multiples dansl'objectif de reproduire le comportement complexe d'un réservoir. L'utilisation d'un système dynamique purementtemporel pour reproduire la dimension spatio-temporelle d'un réseau de neurones traditionnel, nécessite une mise enforme particulière des signaux d'entrée et de sortie, appelée multiplexage temporel ou encore étape de masquage. Troisannées auront été nécessaires pour étudier et construire expérimentalement nos démonstrateurs physiques basés sur desdynamiques non-linéaires à retards multiples opto-électroniques, en longueur d'onde et en intensité. La validationexpérimentale de nos systèmes RC a été réalisée en utilisant deux tests de calcul standards. Le test NARMA10 (test deprédiction de séries temporelles) et la reconnaissance vocale de chiffres prononcés (test de classification de données) ontpermis de quantifier la puissance de calcul de nos systèmes RC et d'atteindre pour certaines configurations l'état del'art. / Reservoir Computing (RC) is a currently emerging new brain-inspired computational paradigm, which appeared in theearly 2000s. It is similar to conventional recurrent neural network (RNN) computing concepts, exhibiting essentiallythree parts: (i) an input layer to inject the information in the computing system; (ii) a central computational layercalled the Reservoir; (iii) and an output layer which is extracting the computed result though a so-called Read-Outprocedure, the latter being determined after a learning and training step. The main originality compared to RNNconsists in the last part, which is the only one concerned by the training step, the input layer and the Reservoir beingoriginally randomly determined and fixed. This specificity brings attractive features to RC compared to RNN, in termsof simplification, efficiency, rapidity, and feasibility of the learning, as well as in terms of dedicated hardwareimplementation of the RC scheme. This thesis is indeed concerned by one of the first a hardware implementation of RC,moreover with an optoelectronic architecture.Our approach to physical RC implementation is based on the use of a sepcial class of complex system for the Reservoir,a nonlinear delay dynamics involving multiple delayed feedback paths. The Reservoir appears thus as a spatio-temporalemulation of a purely temporal dynamics, the delay dynamics. Specific design of the input and output layer are shownto be possible, e.g. through time division multiplexing techniques, and amplitude modulation for the realization of aninput mask to address the virtual nodes in the delay dynamics. Two optoelectronic setups are explored, one involving awavelength nonlinear dynamics with a tunable laser, and another one involving an intensity nonlinear dynamics with anintegrated optics Mach-Zehnder modulator. Experimental validation of the computational efficiency is performedthrough two standard benchmark tasks: the NARMA10 test (prediction task), and a spoken digit recognition test(classification task), the latter showing results very close to state of the art performances, even compared with purenumerical simulation approaches.

The work analyses formation of deformations of RF magnetic field within the environment of electrically and magnetically conductive materials. An analytic solution of the problem is created and the problems of numerical modeling are solved. In theoretical way, the work also familiarize with the use of magnetic resonance imaging method - the spin echo method (SE), with its features and possibilities of its use within the aim of the project. This method is used for experimental measurements of electrically conductive materials in the NMR laboratory in ISI AV Czech Republic. Results of the simulation are compared with results of the experimental measurement.