Notre but était de mieux comprendre les mécanismes associés à la récupération en mémoire à court terme acoustique à l’aide de mesures électrophysiologiques. La tâche utilisée était une version modifiée de la tâche de Sternberg dans laquelle les participants devaient retenir deux, quatre ou six sons purs hors de la gamme musical bien tempérée, ne pouvant pas être facilement recodés verbalement. Après un intervalle de rétention silencieux, ils entendaient un son et devaient indiquer si celui-ci était présent ou absent dans l’ensemble mémoire. En utilisant plusieurs conditions de charge et en contrôlant pour les durées de stimulation, nous avons comparé les effets de position sérielle, ainsi que les effets de charge, sur les données comportementales et sur les données électrophysiologiques mesurées lors du balayage mnésique. Notamment, nous avons trouvé un effet de récence important peu importe le nombre d’items mémorisés, se traduisant par des temps de réponses courts et des taux de bonne réponse presque parfaits, ainsi que par l’augmentation de l’amplitude de la P3, une composante de potentiels reliés aux évènements (PRE). Les données liées aux autres positions sérielles étaient similaires, indépendamment de la charge mnésique, et montraient des performances moins bonnes et une P3 moins ample. Une méthode de décomposition du signal, l’analyse en composantes indépendantes (ACI) nous a permis d’observer et de décrire les différences électrophysiologiques, dans le temps, entre la récupération d’un son parfaitement retenu (le dernier) et celle d’un son peu retenu. Nos résultats suggèrent l’existence de deux sous-types de mémoire sensorielle. / We aimed to understand better the processes involved in acoustic short-term memory retrieval using electrophysiology. We used a modified Sternberg task in which participants had to encode two, four, or six pure non-musical tones (out of the well-tempered musical scale) that cannot be readily recoded verbally. After a silent retention interval, we presented them with a probe tone and they had to report its presence or absence in the memory set. By using multiple load conditions, and by controlling for stimulation duration, we compared the serial position effects, as well as the load effects, on behavioral and electrophysiological data during memory scanning. In particular, we found a recency effect, similar across loads, where shorter response times, near perfect accuracy, and an increased P3 amplitude in the event-related potential (ERP) data were observed. Serial position data for all other positions were similar regardless of the memory load and showed lower performances (response times and accuracy), as well as smaller P3 components. We also performed a signal decomposition analysis, the independent component analysis (ICA), which allowed us to observe and describe better the time courses of the electrophysiological data for the retrieval of a perfectly memorised tone (the last one), and a lesser memorised one (any other serial position). Our results suggest the existence of two subtypes of sensory memory.

In SharePoint 2013, Microsoft added a social networking function in the personal sites (My Site) of a user. In this version, a personal news feed has been added which shows events regarding subjects the user follows, such as document changes, user updates, tagged posts, and site activities. The purpose of the thesis is to investigate whether or not it is possible to extend the news feed function by adding an independent component as part of My Site, to allow users to follow corporate news by choosing their categories of interests. A prototype of the component was implemented and it met most of the objectives stated in the thesis. It is added to the default page of the user’s My Site as a web part and it is able to retrieve and display news that matches the user’s subscription. Although the web part still needs improvements in both functionality and design, it still confirms that it is possible to extend the current My Site news feed with such a component. Since the students working on this thesis had no prior knowledge of SharePoint or .NET development, the project brought new challenges, as the students needed to learn how to work in a SharePoint environment and to learn to use Microsoft Visual Studio for .NET development. / Microsoft har i SharePoint 2013 förbättrat användarnas personliga sidor (My Site) genom att sammankoppla dem i ett socialt nätverk. I förbättringen har ett personligt nyhetsflöde som visar händelser som användaren följer tillagts. Denna rapport avser att undersöka möjligheten att utöka det personliga nyhetsflödet med att lägga till en oberoende komponent i My Site. Komponenten ska tillåta användarna att prenumerera på företagsnyheter genom att välja bland olika nyhetskategorier. En prototyp av komponenten implementerades och resultatet uppfyllde de flesta kraven som ställdes i början av arbetet. Komponenten har lagts till i användarens My Site som en webb del och hämtar automatiskt de senaste företagsnyheterna som matchar användarens prenumeration. Den utvecklade prototypen kan förbättras både i funktion och design, men har uppfyllt behovet för denna rapport som avser att undersöka möjligheten att utöka det personliga nyhetsflödet i My Site med en sådan komponent. Eftersom projektmedlemmarna saknade förkunskaper i SharePoint och .NET utveckling innebar projektet nya utmaningar. Studenterna lärde sig att arbeta i SharePoint miljö samt i Microsoft Visual Studio för .NET utveckling.

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden. / (Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features.

Tato práce se zabývá vylepšením algoritmu pro sdružování (clustering) ERP signálů pomocí analýzy časových a prostorových vlastností pseudo-signálů získaných za pomocí metody analýzy nezávislých komponent (Independent Component Analysis). Naším zájmem je nalezení nových vlastností, které by zlepšily stávající výsledky. Tato práce se zabývá použitím Fourierovy transformace (Fourier Transform), FIR filtru a krátkodobé Fourierovy transformace ke zkvalitnění informace pro sdružovací algoritmy. Princip a použitelnost metody jsou popsány a demonstrovány ukázkovým algoritmem. Výsledky ukázaly, že pomocí dané metody je možné získat ze vstupních dat zajímavé informace, které mohou být úspěšně použity ke zlepšení výsledků.

[ES] El procesado de señal sobre grafos es un campo emergente de técnicas que combinan conceptos de dos áreas muy consolidadas: el procesado de señal y la teoría de grafos. Desde la perspectiva del procesado de señal puede obtenerse una definición de la señal mucho más general asignando cada valor de la misma a un vértice de un grafo. Las señales convencionales pueden considerarse casos particulares en los que los valores de cada muestra se asignan a una cuadrícula uniforme (temporal o espacial). Desde la perspectiva de la teoría de grafos, se pueden definir nuevas transformaciones del grafo de forma que se extiendan los conceptos clásicos del procesado de la señal como el filtrado, la predicción y el análisis espectral. Además, el procesado de señales sobre grafos está encontrando nuevas aplicaciones en las áreas de detección y clasificación debido a su flexibilidad para modelar dependencias generales entre variables. En esta tesis se realizan nuevas contribuciones al procesado de señales sobre grafos. En primer lugar, se plantea el problema de estimación de la matriz Laplaciana asociada a un grafo, que determina la relación entre nodos. Los métodos convencionales se basan en la matriz de precisión, donde se asume implícitamente Gaussianidad. En esta tesis se proponen nuevos métodos para estimar la matriz Laplaciana a partir de las correlaciones parciales asumiendo respectivamente dos modelos no Gaussianos diferentes en el espacio de las observaciones: mezclas gaussianas y análisis de componentes independientes. Los métodos propuestos han sido probados con datos simulados y con datos reales en algunas aplicaciones biomédicas seleccionadas. Se demuestra que pueden obtenerse mejores estimaciones de la matriz Laplaciana con los nuevos métodos propuestos en los casos en que la Gaussianidad no es una suposición correcta. También se ha considerado la generación de señales sintéticas en escenarios donde la escasez de señales reales puede ser un problema. Los modelos sobre grafos permiten modelos de dependencia por pares más generales entre muestras de señal. Así, se propone un nuevo método basado en la Transformada de Fourier Compleja sobre Grafos y en el concepto de subrogación. Se ha aplicado en el desafiante problema del reconocimiento de gestos con las manos. Se ha demostrado que la extensión del conjunto de entrenamiento original con réplicas sustitutas generadas con los métodos sobre grafos, mejora significativamente la precisión del clasificador de gestos con las manos. / [CAT] El processament de senyal sobre grafs és un camp emergent de tècniques que combinen conceptes de dues àrees molt consolidades: el processament de senyal i la teoria de grafs. Des de la perspectiva del processament de senyal pot obtindre's una definició del senyal molt més general assignant cada valor de la mateixa a un vèrtex d'un graf. Els senyals convencionals poden considerar-se casos particulars en els quals els valors de la mostra s'assignen a una quadrícula uniforme (temporal o espacial). Des de la perspectiva de la teoria de grafs, es poden definir noves transformacions del graf de manera que s'estenguen els conceptes clàssics del processament del senyal com el filtrat, la predicció i l'anàlisi espectral. A més, el processament de senyals sobre grafs està trobant noves aplicacions en les àrees de detecció i classificació a causa de la seua flexibilitat per a modelar dependències generals entre variables. En aquesta tesi es donen noves contribucions al processament de senyals sobre grafs. En primer lloc, es planteja el problema d'estimació de la matriu Laplaciana associada a un graf, que determina la relació entre nodes. Els mètodes convencionals es basen en la matriu de precisió, on s'assumeix implícitament la gaussianitat. En aquesta tesi es proposen nous mètodes per a estimar la matriu Laplaciana a partir de les correlacions parcials assumint respectivament dos models no gaussians diferents en l'espai d'observació: mescles gaussianes i anàlisis de components independents. Els mètodes proposats han sigut provats amb dades simulades i amb dades reals en algunes aplicacions biomèdiques seleccionades. Es demostra que poden obtindre's millors estimacions de la matriu Laplaciana amb els nous mètodes proposats en els casos en què la gaussianitat no és una suposició correcta. També s'ha considerat el problema de generar senyals sintètics en escenaris on l'escassetat de senyals reals pot ser un problema. Els models sobre grafs permeten models de dependència per parells més generals entre mostres de senyal. Així, es proposa un nou mètode basat en la Transformada de Fourier Complexa sobre Grafs i en el concepte de subrogació. S'ha aplicat en el desafiador problema del reconeixement de gestos amb les mans. S'ha demostrat que l'extensió del conjunt d'entrenament original amb rèpliques substitutes generades amb mètodes sobre grafs, millora significativament la precisió del classificador de gestos amb les mans. / [EN] Graph signal processing appears as an emerging field of techniques that combine concepts from two highly consolidated areas: signal processing and graph theory. From the perspective of signal processing, it is possible to achieve a more general signal definition by assigning each value of the signal to a vertex of a graph. Conventional signals can be considered particular cases where the sample values are assigned to a uniform (temporal or spatial) grid. From the perspective of graph theory, new transformations of the graph can be defined in such a way that they extend the classical concepts of signal processing such as filtering, prediction and spectral analysis. Furthermore, graph signal processing is finding new applications in detection and classification areas due to its flexibility to model general dependencies between variables. In this thesis, new contributions are given to graph signal processing. Firstly, it is considered the problem of estimating the Laplacian matrix associated with a graph, which determines the relationship between nodes. Conventional methods are based on the precision matrix, where Gaussianity is implicitly assumed. In this thesis, new methods to estimate the Laplacian matrix from the partial correlations are proposed respectively assuming two different non-Gaussian models in the observation space: Gaussian Mixtures and Independent Component Analysis. The proposed methods have been tested with simulated data and with real data in some selected biomedical applications. It is demonstrate that better estimates of the Laplacian matrix can be obtained with the new proposed methods in cases where Gaussianity is not a correct assumption. The problem of generating synthetic signal in scenarios where real signals scarcity can be an issue has also been considered. Graph models allow more general pairwise dependence models between signal samples. Thus a new method based on the Complex Graph Fourier Transform and on the concept of subrogation is proposed. It has been applied in the challenging problem of hand gesture recognition. It has been demonstrated that extending the original training set with graph surrogate replicas, significantly improves the accuracy of the hand gesture classifier. / Belda Valls, J. (2022). Nuevas contribuciones a la teoría y aplicación del procesado de señal sobre grafos [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191333

This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''.

This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''.

A substantial number of works have aimed at modeling the receptive field properties of the primary visual cortex (V1). Their evaluation criterion is usually the similarity of the model response properties to the recorded responses from biological organisms. However, as several algorithms were able to demonstrate some degree of similarity to biological data based on the existing criteria, we focus on the robustness against loss of information in the form of occlusions as an additional constraint for better understanding the algorithmic level of early vision in the brain. We try to investigate the influence of competition mechanisms on the robustness. Therefore, we compared four methods employing different competition mechanisms, namely, independent component analysis, non-negative matrix factorization with sparseness constraint, predictive coding/biased competition, and a Hebbian neural network with lateral inhibitory connections. Each of those methods is known to be capable of developing receptive fields comparable to those of V1 simple-cells. Since measuring the robustness of methods having simple-cell like receptive fields against occlusion is difficult, we measure the robustness using the classification accuracy on the MNIST hand written digit dataset. For this we trained all methods on the training set of the MNIST hand written digits dataset and tested them on a MNIST test set with different levels of occlusions. We observe that methods which employ competitive mechanisms have higher robustness against loss of information. Also the kind of the competition mechanisms plays an important role in robustness. Global feedback inhibition as employed in predictive coding/biased competition has an advantage compared to local lateral inhibition learned by an anti-Hebb rule.

Ce travail a porté sur les remaniements corticaux précoces des circuits moteurs et extramoteurs dans la SLA grâce à l’étude des activations IRMf issues de tâches motrices et cognitives. La première partie de nos travaux nous a permis de montrer grâce des tâches simples d’activation motrice en IRMf (1) qu’alors que les patients SLA présentaient un déficit moteur discret, une augmentation des activations corticales est apparue dans les aires sensorimotrices bilatérales du cerveau. (2) Ces modifications précoces de l’activité neuronale étaient corrélées à la latéralisation du déficit moteur du membre ou la prédominance manuelle et surtout, (3) au taux de progression de la maladie à un an et à la survie, suggérant que ce remaniement de l’activité qui correspond probablement à de la plasticité cérébrale a des implications fonctionnelles. Enfin, (4) ce phénomène apparaissait actif puisqu’il s’est poursuivi pendant au moins onze mois. Dans la deuxième partie, nous avons montré lors d’une tâche de fluence verbale silencieuse que (1) les SLA présentaient une suractivation initiale des aires dévolues au processus sémantique avec un renforcement de la connectivité fonctionnelle entre les réseaux (CFR). (2) Au bout de onze mois et alors que l’atteinte des fluences était stable, ce phénomène de compensation s’épuisait avec une diminution conjointe de l’activation des réseaux et de la CFR. La tâche de 2-Back, réalisée alors que les patients ne présentaient pas d’atteinte de la mémoire de travail lors des tests psychométriques, nous a permis de montrer en outre que certains circuits non-moteurs se réorganisaient très précocement chez les patients, alors même qu’ils étaient asymptomatiques / In this work we used motor and cognitive tasks in an fMRI study to explore the early cortical reorganizations of the motor and extra-motor circuits in ALS patients. In a first part, using a simple motor task, we demonstrated (1) that increased cortical BOLD signal changes occurred in specific regions of the brain of ALS patients when their motor deficit was still moderate, and that this early signal changes correlated with (2) the lateralisation of the motor deficit or hand predominance and, more importantly, (3) with the rate of disease progression at one year and survival time, suggesting that modulations of cerebral activity in ALS may have functional implications. Furthermore, (4) this brain plasticity was maintained with time and disease progression during at least eleven months. In a second part, we demonstrated during a silent verbal fluency task (1) that ALS patients presented initially an increased cortical activation of areas devolved to the semantic process with an intensification of the functional network connectivity (FNC). (2) After eleven months and while their performance in tests of verbal fluency was stable, this cerebral compensation ran out with a decrease of the previous cerebral activations and the FNC. A N-back working memory paradigm, realized while the patients did not present any deficit of their working memory, allowed us to show that certain non-motor circuits were reorganized prematurely while patients were still asymptomatic

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