Le problème de la restauration est rencontré dans domaines très variés notamment en traitement de signal et de l'image. Il correspond à la récupération des données originales à partir de données observées. Dans le cas de données multidimensionnelles, la résolution de ce problème peut se faire par différentes approches selon la nature des données, l'opérateur de transformation et la présence ou non de bruit. Dans ce travail, nous avons traité ce problème, d'une part, dans le cas des données discrètes en présence de bruit. Dans ce cas, le problème de restauration est analogue à celui de la segmentation. Nous avons alors exploité les modélisations dites chaînes de Markov couples et triplets qui généralisent les chaînes de Markov cachées. L'intérêt de ces modèles réside en la possibilité de généraliser la méthode de calcul de la probabilité à posteriori, ce qui permet une segmentation bayésienne. Nous avons considéré ces méthodes pour des observations bi-dimensionnelles et nous avons appliqué les algorithmes pour une séparation sur des documents issus de manuscrits scannés dans lesquels les textes des deux faces d'une feuille se mélangeaient. D'autre part, nous avons attaqué le problème de la restauration dans un contexte de séparation aveugle de sources. Une méthode classique en séparation aveugle de sources, connue sous l'appellation "Analyse en Composantes Indépendantes" (ACI), nécessite l'hypothèse d'indépendance statistique des sources. Dans des situations réelles, cette hypothèse n'est pas toujours vérifiée. Par conséquent, nous avons étudié une extension du modèle ACI dans le cas où les sources peuvent être statistiquement dépendantes. Pour ce faire, nous avons introduit un processus latent qui gouverne la dépendance et/ou l'indépendance des sources. Le modèle que nous proposons combine un modèle de mélange linéaire instantané tel que celui donné par ACI et un modèle probabiliste sur les sources avec variables cachées. Dans ce cadre, nous montrons comment la technique d'Estimation Conditionnelle Itérative permet d'affaiblir l'hypothèse usuelle d'indépendance en une hypothèse d'indépendance conditionnelle / The restoration problem is usually encountered in various domains and in particular in signal and image processing. It consists in retrieving original data from a set of observed ones. For multidimensional data, the problem can be solved using different approaches depending on the data structure, the transformation system and the noise. In this work, we have first tackled the problem in the case of discrete data and noisy model. In this context, the problem is similar to a segmentation problem. We have exploited Pairwise and Triplet Markov chain models, which generalize Hidden Markov chain models. The interest of these models consist in the possibility to generalize the computation procedure of the posterior probability, allowing one to perform bayesian segmentation. We have considered these methods for two-dimensional signals and we have applied the algorithms to retrieve of old hand-written document which have been scanned and are subject to show through effect. In the second part of this work, we have considered the restoration problem as a blind source separation problem. The well-known "Independent Component Analysis" (ICA) method requires the assumption that the sources be statistically independent. In practice, this condition is not always verified. Consequently, we have studied an extension of the ICA model in the case where the sources are not necessarily independent. We have introduced a latent process which controls the dependence and/or independence of the sources. The model that we propose combines a linear instantaneous mixing model similar to the one of ICA model and a probabilistic model on the sources with hidden variables. In this context, we show how the usual independence assumption can be weakened using the technique of Iterative Conditional Estimation to a conditional independence assumption

Functional brain connectivity is a marker of the brain state. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during EEG data analysis may leave part of the neural activity unrecognized. A proposed approach blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. The blind decomposition of EEG spectrogram by Parallel Factor Analysis has been shown to be a useful technique for uncovering patterns of neural activity where each pattern contains three signatures (spatial, temporal, and spectral). The decomposition takes into account the trilinear structure of EEG data, as compared to the standard approaches of electrode averaging, electrode subset selection or using standard frequency bands. The simultaneously acquired BOLD fMRI data were decomposed by Independent Component Analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and functional connectivity network states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of functional connectivity network states and the fluctuations of EEG spectral patterns. Three patterns related to the dynamics of functional connectivity network states were found. Previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. This work suggests that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

Tato práce se zabývá vývojem nezávislého elektrického zdroje pro moderní nízkopříkonové elektrické aplikace. Protože tradiční řešení napájení drobných spotřebičů s využitím baterií či akumulátorů snižuje uživatelský komfort kvůli potřebě pravidelné údržby, navrhovaný zdroj využívá principu energy harvesting. Tento princip spočívá v získávání energie přímo z okolního prostředí napájené aplikace a její přeměně na energii elektrickou, která je dále využita pro na-pájení moderních MEMS (mikroelektromechanických) zařízení. Potenciální aplikací vyvíjeného zdroje je především moderní nositelná elektronika a biomedicínské senzory. Tato oblast využití ovšem klade zvýšené nároky na parametry generátoru, který musí zajistit dostatečný generovaný výkon z energie, dostupné v okolí lidského těla, a to při zachování prakticky využitelné velikosti a hmotnosti. Po stanovení předběžných požadavků a provedení analýz vhodnosti dostupných zdrojů energie ke konverzi byla k využití vybrána kinetická energie lidských aktivit. Byla provedena série měření zrychlení na lidském těle, především v místě předpokládaného umístění generátoru, aby bylo možno analyzovat a generalizovat hodnoty energie dostupné ke konverzi v daném umístění. V návaznosti na tato měření a analýzy byl vyvinut inovativní kinetický energy harvester, který byl následně vyroben jako funkční vzorek. Tento vzorek byl pak testován v reálných podmínkách pro verifikaci simulačního modelu a vyhodnocení reálné použitelnosti takového zařízení. Kromě samotného vývoje generátoru je v práci popsán i originální způsob zvýšení generovaného výkonu pro kinetické energy harvestery a jsou prezentována statistická data a modely pro predikci využitelnosti kinetických harvesterů pro získávání energie z lidské aktivity.

Since the introduction of economic reforms in the late 1970s, China has experienced phenomenal economic growth along with rapid urbanization. The accelerated urbanization coincides with remarkable social-economic transformations and urban landscape changes. A city, as an urban system, is composed of social and physical subsystems that interact with each other. Equally assessing each component is necessary for a comprehensive understanding of the urbanization process. The goal of this thesis research is to deconstruct the urbanization process through a social-ecological perspective. More specifically, this study examines social transformations, physical evolutions, and their relationships. Four research questions are proposed as (1) How does urban social landscape transform in time and space? (2) What trends are apparent in the urban land growth process and spatial heterogeneity? (3) How does social transformation relate to urban land growth, within a spatio-temporal perspective? and (4) How do social-demographic features relate to residents’ use and perception of urban green open spaces, focusing on the ecological services provided by and the need to improve those spaces? Given the lack of research on second-tier cities’ growth processes, this study selects Wuhan, a megacity in central China, as a case study, with a focus on its urban development zone (UDZ). A social-ecological approach is applied to study the multi-dimensional features of an urban system. The thesis is in paper format, containing five chapters. Besides the Introduction (Chapter 1) and Conclusion (Chapter 5), the main body consists of three articles. These three articles correspond to the four research questions proposed. Chapter 2 responds to the first research question by addressing how the urban social landscape transforms. Chapter 3 seeks to answer the second and third questions by evaluating urban land growth and its links with social factors from a spatio-temporal perspective. Chapter 4 matches the fourth question by seeking to understand residents’ preferences and feelings toward the urban green open space. Chapter 1 introduces the research context, reviews the urban ecology theory and relevant empirical research, as well as assesses the social-ecological approach related to studying the urban system. In this chapter, we also propose an improved social-ecological system (ISES) framework which guides the equally weighted study of both social and physical subsystems in an urban area. Chapter 2 (the first paper) seeks to investigate progressive transformations in the social dimensions of Wuhan UDZ while also focusing on their spatial transformations, using national census data in 1990, 2000, and 2010. We used varimax rotated principal component analysis (PCA) for the extraction of social dimensions and ArcMap for spatial visualization. This allows us to further analyze the spatial distribution of social clusters. The results suggest that industrial relocation, educational attainment increase, population aging, and migration are the main characteristics of social transformation during 1990 and 2010. Industrial relocation along with the spatial separation appeared as principal social dimensions in the 1990s but became more prominent in the 2000s, accompanied by the improvement of workers’ education levels. Aging population presented spatial movement outward from the city center. Population mobility increased significantly, and immigration became an important social dimension and presented spatial expansion in the 2000s. The socio-spatial patterns transform with a combination of concentric rings and sectoral clusters in different stages. These transformations are formed by the regional push-pull forces and the centripetal-centrifugal forces inside the city. We conclude that the social landscape transforms in a way with diversity and inclusion. Government dominates socio-spatial transformations in the initial stages, while market plays an increasing role in the later stages. To build a more inclusive society requires continuous and systematic improvement of relevant policies. Chapter 3 (the second paper) discusses urban land growth patterns and answers how social factors are associated with the evolution patterns between 1990 and 2010. We extract land cover information based on Landsat images with the vegetation area – impervious surface –water area (V-I-W) model and examine the urban growth patterns during various stages using landscape metrics of the area, aggregation, and shape. Then, we apply geographically weighted regression (GWR) to depict the link between urban land metrics and social factors. The results show that urban land coalescence and diffusion simultaneously exist; the city center is dominated by redevelopment, infilling, edge expansion; and the peripheral areas by outlying expansion. GWR coefficient surfaces show little differences in the models between social factors and urban land area metrics PLAND while remarkable differences are present in the coefficients of GWR models for the urban land patch shape irregularities and social factors. Urban land growth patterns relate to the government-led land supply system, the functional zoning of urban space planning, and the agglomeration and dispersion of social space under the market orientation. The authors conclude that urban management should consider the coexistence of different spatial growth modes and introduce factors such as social preferences in the urban land layout. This may apply to rapidly urbanizing areas. Chapter 4 (the third paper) aims to understand social-natural relationships, with a focus on how socio-demographic features can shape residents’ preference toward green open spaces and their perceptions of ecological services and improvements. Data is collected through online questionnaire surveys and interviews. The results indicate that preferences toward green open spaces vary among different social groups. Demands for improvement to green open spaces are rooted in residents’ appreciation for daily relaxation and health benefits, and link with their preference for visiting. However, how residents perceive green open spaces’ benefits does not rely only on an in-person visit. Interaction experience with nature and knowledge of natural development affect perception of daily use and health-related services. Residents’ perceptions of green open space’s ecological functions are associated with the changes in nature reported by those respondents. Responses to improving green open space reflect the residents’ pursuit of the aesthetics and practicality of such spaces. Though respondents are commonly aware of the ecological importance of green open space, there are differences in their willingness to voluntarily participate in its management. We conclude that to encourage the public to participate in configuration and improvement of green open spaces through a variety of ways, including considering residents’ opinions, is an efficient way in order to better social-ecological relationships. Chapter 5 reviews the main findings and conclusions, research limitations as well as future possibilities. This study establishes a dialogue between urban social and physical subsystems, with an integrated quantitative study of the urbanization process, emphasizing the relationships between two urban subsystems. It provides a comprehensive social-ecological view on a second-tier city based on the social and physical transformations that occurred in Wuhan during a transitional period of a socialist market economy. We conclude that the development of China's second-tier cities between 1990 and 2010 is characterized by the transformations of social dimensions and landscape, the coexistence of multiple urban spatial development modes, and the spatial differentiation between the center and the periphery of the city. The GWR models present spatial non-stationary relationships between social factors and the urban patch shape regularities. The further examination of social-natural relationships finds that residents’ social-demographic features and environmental experience affect their perceptions toward green open space, especially ecological services and improvement necessity. The evolution of urban social and physical systems and their relationships has brought increased attention to inclusive urban social management, public participatory planning, and people-centered social and ecological interactions. This research provides a constructive rethinking of second-tier cities’ growth in China and may serve as a reference for other rapidly urbanizing areas.

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The separation of independent sources from mixed observed data is a fundamental problem in many practical situations. A typical example is speech recordings made in an acoustic environment in the presence of background noise or other speakers. Problems of signal separation are explored by a group of methods called Blind Source Separation. Blind Source Separation (BSS) consists on estimating a set of N unknown sources from P observations resulting from the mixture of these sources and unknown background. Some existing solutions for instantaneous mixtures are reviewed and in Matlab implemented , i.e Independent Componnent Analysis (ICA) and Time-Frequency Analysis (TF). The acoustic signals recorded in real environment are not instantaneous, but convolutive mixtures. In this case, an ICA algorithm for separation of convolutive mixtures in frequency domain is introduced and in Matlab implemented. This diploma thesis examines the useability and comparisn of proposed separation algorithms.

This thesis considers the problematic of the object recognition in a digital picture, particularly about the human face recognition and its components. There are described the basics of the computer vision, the object detector Viola-Jones, its computer realization with help of the OpenCV libraries and the test results. This thesis also describes the accurate system of the facial features detection per the algorithm of the Active Shape Models and also related mechanism of the classifier training, including the software implementation.

This thesis deals with the study of contactless and non-invasive methods for estimating heart and respiratory rate. Non-contact measurement involves sensing persons by using camera and the values of the physiological parameters are then assessed from the sets of image sequences by using suitable approaches. The theoretical part is devoted to description of the various methods and their implementation. The practical part describes the design and realization of the experiment for contactless detection of heart and respiratory rate. The experiment was carried out on 10 volunteers with a known heart and respiratory rate, which was covered by using of a sophisticated system BIOPAC. Processing and analysis of the measured data was conducted in software environment Matlab. Finally, results from contactless detection were compared with the reference from measurement system BIOPAC. Experiment results are statistically evaluated and discussed.

This master‘s thesis deals with biometric fingerprint liveness detection. The theoretical part of the work describes fingerprint recognition biometric systems, fingerprint liveness detection issues and methods for fingerprint liveness detection. The practical part of the work describes proposed set of discriminant features and preprocessing of fingerprint image. Proposed approach using neural network to detect a liveness. The algorithm is tested on LivDet database comprising real and fake images acquired with tree sensors. Classification performance approximately 93% was obtained.