Academic literature on the topic 'MRAS TECHNIQUE'

This thesis presents different state estimation techniques for speed sensorlees field oriented control of induction motors. The theoretical basis of each algorithm is explained in detail and its performance is tested with simulations and experiments individually. First, a stochastical nonlinear state estimator, Extended Kalman Filter (EKF) is presented. The motor model designed for EKF application involves rotor speed, dq-axis rotor fluxes and dq-axis stator currents. Thus, using this observer the rotor speed and rotor fluxes are estimated simultaneously. Different from the widely accepted use of EKF, in which it is optimized for either steady-state or transient operations, here using adjustable noise level process algorithm the optimization of EKF has been done for both states<br>the steady-state and the transient-state of operations. Additionally, the measurement noise immunity of EKF is also investigated. Second, Unscented Kalman Filter (UKF), which is an updated version of EKF, is proposed as a state estimator for speed sensorless field oriented control of induction motors. UKF state update computations, different from EKF, are derivative free and they do not involve costly calculation of Jacobian matrices. Moreover, variance of each state is not assumed Gaussian, therefore a more realistic approach is provided by UKF. In this work, the superiority of UKF is shown in the state estimation of induction motor. Third, Model Reference Adaptive System is studied as a state estimator. Two different methods, back emf scheme and reactive power scheme, are applied to MRAS algorithm to estimate rotor speed. Finally, a flux estimator and an open-loop speed estimator combination is employed to observe stator-rotor fluxes, rotor-flux angle and rotor speed. In flux estimator, voltage model is assisted by current model via a closed-loop to compensate voltage model&rsquo<br>s disadvantages.

Nous présentons dans cette thèse un nouveau modèle statistique de forme et l'utilisons pour la segmentation d'images avec a priori. Ce modèle est représenté par un champ de Markov. Les noeuds du graphe correspondent aux points de contrôle situés sur le contour de la forme géométrique, et les arêtes du graphe représentent les dépendances entre les points de contrôle. La structure du champ de Markov est déterminée à partir d'un ensemble de formes, en utilisant des techniques d'apprentissage de variétés et de groupement non-supervisé. Les contraintes entre les points sont assurées par l'estimation des fonctions de densité de probabilité des longueurs de cordes normalisées. Dans une deuxième étape, nous construisons un algorithme de segmentation qui intègre le modèle statistique de forme, et qui le relie à l'image grâce à un terme région, à travers l'utilisation de diagrammes de Voronoi. Dans cette approche, un contour de forme déformable évolue vers l'objet à segmenter. Nous formulons aussi un algorithme de segmentation basé sur des détecteurs de points d'intérêt, où le terme de régularisation est lié à l'apriori de forme. Dans ce cas, on cherche à faire correspondre le modèle aux meilleurs points candidats extraits de l'image par le détecteur. L'optimisation pour les deux algorithmes est faite en utilisant des méthodes récentes et efficaces. Nous validons notre approche à travers plusieurs jeux de données en 2D et en 3D, pour des applications de vision par ordinateur ainsi que l'analyse d'images médicales.

Seventy-four methicillin-resistant Staphylococcus aureus (MRSA) from two Malaysian hospitals were characterised by both phenotypic and genotypic techniques. These isolates were collected over an 18 year time period in the years, 1982, 1989, 1994 and 2000. All of the Malaysian MRSA isolates were found to be multiresistant and resistant to at least five different antimicrobial agents. Over 30% of them were non-typable by the International Basic Set of bacteriophages. The majority of the typable isolates were susceptible to the group III phages, especially phage 85. The majority of the isolates carried one to six plasmids. Only two isolates were plasmid free. The plasmid profiles of these isolates, other than the 1982 isolates, were very similar to each other. Contour-clamped homogeneous electric field (CHEF) gel electrophoresis was used to examine the genetic relatedness of the isolates. Twenty-six CHEF patterns were found among the isolates. These CHEF patterns were closely related to each other. The predominant CHEF pattern A was found in the 1982, 1989 and 1994 isolates. The CHEF patterns of the year 2000 isolates were different to CHEF pattern A, but still closely related. All of the isolates were found to carry the Allotype III SCCmec and have coagulase-gene type 24. Multilocus sequence typing was preformed on the isolates with CHEF pattern A collected in different years. These isolates were found to have either sequence type 239 (ST239), or its single locus variant. The predominant Malaysian clone belongs to the pandemic clone ST239-MRSA-III that is pandemic in Asian countries. (Enright, 2003, Ko et al., 2005).A 1.5 kb cryptic plasmid found in Malaysian isolates was indistinguishable from a cryptic plasmid found in an Australian isolate. A 3.0 kb cryptic plasmid found in Malaysian isolates was undistinguishable from a 3.0 kb plasmid found in Singaporean isolates. Class II multiresistance plasmids of 28, 30.5 and 35 kb were commonly found together in many Malaysian MRSA isolates. Both the 28 and 30.5 kb plasmids encode resistance to the heavy-metals and nucleic acid-binding (NAB) compounds. The 35 kb plasmid carries heavy-metal and NAB resistance but also encodes β-lactamase. Structurally these three plasmids are almost identical and probably have the same origin. The differences observed between these plasmids is probably due to excision or partial deletion of the β-lactamase transposon of the original plasmid. The 28 kb plasmid is identical to the 28 kb plasmid of Singaporean and some Australian isolates. A 20 kb plasmid in Indonesian isolates was found to be closely related to these three plasmids. A conjugative plasmid, pWBG707, conferring trimethoprim-resistance was found in Malaysian isolates. It did not carry either of the two staphylococcal trimethoprim-resistance genes, dfrA and dfrD. (Lyon and Skurray, 1987, Dale et al., 1995b) It either encodes a novel resistance gene or the recently discovered dfrG gene. (Sekiguchi et al., 2005) pWBG707 was also found to mobilise a small 3.0 kb kanamycin-resistance plasmid during conjugation.The mecR1 and mecI genes regulating the transcription of the methicillin-resistance gene, mecA, were also examined in the isolates. The Malaysian isolate, WBG7422, with the predominant CHEF pattern A has a nonsense mutation in its mecI gene that disables it. However, its mecR1 gene is intact. The eastern Australia MRSA (EA MRSA), WBG525, has a CHEF pattern that is closely related to the Malaysian predominant CHEF pattern A and its mecI gene has a mutation identical to the Malaysian isolate. Unlike the Malaysian isolate however, its mecR1 gene has a 166 bp deletion. Both WBG7422 and WBG525 express Class III heterogeneous methicillin resistance. However, WBG525 has more highly resistant cell in its population than WBG7422. The loss of aminoglycoside resistance, together with c. 114 kb of chromosomal DNA, was observed in some Malaysian isolates. The deleted segment was found to carry the aacA-aphD gene that encodes a bifunctional aminoglycoside-modifying enzyme conferring resistance to many of the aminoglycosides. The Malaysian isolates were compared with MRSA from different countries. These MRSA included 18 epidemic MRSA (EMRSA) from the United Kingdom, 15 Australian nosocomial MRSA, five classical MRSA, 22 community-acquired MRSA (CMRSA) from Australia and New Zealand and 46 nosocomial MRSAs from eight Asian-Pacific countries and South Africa. These Asian-Pacific countries were Australia, PR China, Hong Kong, Indonesia, Japan, Philippines, Singapore and Taiwan.The CHEF patterns of most of the Asian-Pacific and South African isolates were closely related to the Malaysian isolates. Isolates from Singapore, Indonesia and Philippines were found to have an identical CHEF pattern to the Malaysian CHEF patterns A5. The Asian-Pacific and South African isolates, including the Malaysian isolates, were found to be closely related to EMRSA-1, -4 and -7. These EMRSA belong to the ST239-MRSA-III clone and are coagulase-gene type 24. The isolates from Japan were the only Asian-Pacific isolates not related to the other Asian-Pacific isolates and EMRSAs. EMRSA-1 and EA MRSA have the same 166 bp deletion in their mecR1 gene. Both of these strains have closely related CHEF patterns, the same sequence type, coagulase-gene type and SCCmec. These results indicate that these two strains belongs to the same clone and confirms the international spread of this clone in the early 1980s. However, the Malaysian isolates have CHEF patterns that are more closely related to EMRSA-4 than to EMRSA-1. Similar to the Malaysian isolates EMRSA-4 has an intact mecR1 gene. The CMRSA isolates were not related to any of the nosocomial MRSA. They also have very diverse genetic backgrounds but carry less diverse SCCmec allotypes. Most of the CMRSA carry either Allotype IV or V SCCmec These results show that the spread of Malaysian MRSA is due to a single clonal expansion. Infection control measures would have to have been more efficient if this clone was to have been contained. The Malaysian epidemic clone is the Asian pandemic clone, ST239-MRSA-III. The Malaysian isolates and EMRSA-4 probably share the same ancestor.The presence of the same MRSA strain in Malaysian hospitals and in the hospitals of neighbouring countries indicates that the inter-hospital spread of an epidemic MRSA has occurred. This observation also suggests that the infection control measures in Malaysian hospitals have not been totally effective. The ineffectiveness of infection control has left Malaysian hospitals vulnerable to the future importation of new pandemic clones and/or highly virulent or resistant clones.

Seventy-four methicillin-resistant Staphylococcus aureus (MRSA) from two Malaysian hospitals were characterised by both phenotypic and genotypic techniques. These isolates were collected over an 18 year time period in the years, 1982, 1989, 1994 and 2000. All of the Malaysian MRSA isolates were found to be multiresistant and resistant to at least five different antimicrobial agents. Over 30% of them were non-typable by the International Basic Set of bacteriophages. The majority of the typable isolates were susceptible to the group III phages, especially phage 85. The majority of the isolates carried one to six plasmids. Only two isolates were plasmid free. The plasmid profiles of these isolates, other than the 1982 isolates, were very similar to each other. Contour-clamped homogeneous electric field (CHEF) gel electrophoresis was used to examine the genetic relatedness of the isolates. Twenty-six CHEF patterns were found among the isolates. These CHEF patterns were closely related to each other. The predominant CHEF pattern A was found in the 1982, 1989 and 1994 isolates. The CHEF patterns of the year 2000 isolates were different to CHEF pattern A, but still closely related. All of the isolates were found to carry the Allotype III SCCmec and have coagulase-gene type 24. Multilocus sequence typing was preformed on the isolates with CHEF pattern A collected in different years. These isolates were found to have either sequence type 239 (ST239), or its single locus variant. The predominant Malaysian clone belongs to the pandemic clone ST239-MRSA-III that is pandemic in Asian countries. (Enright, 2003, Ko et al., 2005).<br>A 1.5 kb cryptic plasmid found in Malaysian isolates was indistinguishable from a cryptic plasmid found in an Australian isolate. A 3.0 kb cryptic plasmid found in Malaysian isolates was undistinguishable from a 3.0 kb plasmid found in Singaporean isolates. Class II multiresistance plasmids of 28, 30.5 and 35 kb were commonly found together in many Malaysian MRSA isolates. Both the 28 and 30.5 kb plasmids encode resistance to the heavy-metals and nucleic acid-binding (NAB) compounds. The 35 kb plasmid carries heavy-metal and NAB resistance but also encodes β-lactamase. Structurally these three plasmids are almost identical and probably have the same origin. The differences observed between these plasmids is probably due to excision or partial deletion of the β-lactamase transposon of the original plasmid. The 28 kb plasmid is identical to the 28 kb plasmid of Singaporean and some Australian isolates. A 20 kb plasmid in Indonesian isolates was found to be closely related to these three plasmids. A conjugative plasmid, pWBG707, conferring trimethoprim-resistance was found in Malaysian isolates. It did not carry either of the two staphylococcal trimethoprim-resistance genes, dfrA and dfrD. (Lyon and Skurray, 1987, Dale et al., 1995b) It either encodes a novel resistance gene or the recently discovered dfrG gene. (Sekiguchi et al., 2005) pWBG707 was also found to mobilise a small 3.0 kb kanamycin-resistance plasmid during conjugation.<br>The mecR1 and mecI genes regulating the transcription of the methicillin-resistance gene, mecA, were also examined in the isolates. The Malaysian isolate, WBG7422, with the predominant CHEF pattern A has a nonsense mutation in its mecI gene that disables it. However, its mecR1 gene is intact. The eastern Australia MRSA (EA MRSA), WBG525, has a CHEF pattern that is closely related to the Malaysian predominant CHEF pattern A and its mecI gene has a mutation identical to the Malaysian isolate. Unlike the Malaysian isolate however, its mecR1 gene has a 166 bp deletion. Both WBG7422 and WBG525 express Class III heterogeneous methicillin resistance. However, WBG525 has more highly resistant cell in its population than WBG7422. The loss of aminoglycoside resistance, together with c. 114 kb of chromosomal DNA, was observed in some Malaysian isolates. The deleted segment was found to carry the aacA-aphD gene that encodes a bifunctional aminoglycoside-modifying enzyme conferring resistance to many of the aminoglycosides. The Malaysian isolates were compared with MRSA from different countries. These MRSA included 18 epidemic MRSA (EMRSA) from the United Kingdom, 15 Australian nosocomial MRSA, five classical MRSA, 22 community-acquired MRSA (CMRSA) from Australia and New Zealand and 46 nosocomial MRSAs from eight Asian-Pacific countries and South Africa. These Asian-Pacific countries were Australia, PR China, Hong Kong, Indonesia, Japan, Philippines, Singapore and Taiwan.<br>The CHEF patterns of most of the Asian-Pacific and South African isolates were closely related to the Malaysian isolates. Isolates from Singapore, Indonesia and Philippines were found to have an identical CHEF pattern to the Malaysian CHEF patterns A5. The Asian-Pacific and South African isolates, including the Malaysian isolates, were found to be closely related to EMRSA-1, -4 and -7. These EMRSA belong to the ST239-MRSA-III clone and are coagulase-gene type 24. The isolates from Japan were the only Asian-Pacific isolates not related to the other Asian-Pacific isolates and EMRSAs. EMRSA-1 and EA MRSA have the same 166 bp deletion in their mecR1 gene. Both of these strains have closely related CHEF patterns, the same sequence type, coagulase-gene type and SCCmec. These results indicate that these two strains belongs to the same clone and confirms the international spread of this clone in the early 1980s. However, the Malaysian isolates have CHEF patterns that are more closely related to EMRSA-4 than to EMRSA-1. Similar to the Malaysian isolates EMRSA-4 has an intact mecR1 gene. The CMRSA isolates were not related to any of the nosocomial MRSA. They also have very diverse genetic backgrounds but carry less diverse SCCmec allotypes. Most of the CMRSA carry either Allotype IV or V SCCmec These results show that the spread of Malaysian MRSA is due to a single clonal expansion. Infection control measures would have to have been more efficient if this clone was to have been contained. The Malaysian epidemic clone is the Asian pandemic clone, ST239-MRSA-III. The Malaysian isolates and EMRSA-4 probably share the same ancestor.<br>The presence of the same MRSA strain in Malaysian hospitals and in the hospitals of neighbouring countries indicates that the inter-hospital spread of an epidemic MRSA has occurred. This observation also suggests that the infection control measures in Malaysian hospitals have not been totally effective. The ineffectiveness of infection control has left Malaysian hospitals vulnerable to the future importation of new pandemic clones and/or highly virulent or resistant clones.

<p>The objective of this essay is to approach a larger comprehension of the drama of George Bernard Shaw. The essay studies the combination of ideas and aesthetics in the play Mrs Warren’s Profession; how theatrical and mainly literary aesthetics interplay with political ideas and what the consequence of this combination is. The study illustrates that the dramatic method consists of using ideas as effective theatrical tools to move the reader/viewer by thought and not by sentiment. The study also illustrates that a key to understanding Shaw’s drama can be found in the construction of operas and symphonies; musical theoretic constructions are an integrated dramatic technique in Mrs Warren’s Profession. The study shows that it is a play with a political and social purpose; to raise awareness of the mechanisms of prostitution. The play does not use simplifications in terms of good and evil. It questions conventionality, unveils social hypocrisy and attempts to disillusion the reader/viewer. The antithesis between realism and idealism is an important source of dynamics and constitutes one of the principal aesthetical constructions.</p>

El cáncer es una de las principales causas de muerte en el mundo. Los tumores cerebrales tienen una incidencia relativamente baja en comparación con otras patologías cancerígenas más generalizadas, pero la prognosis de algunos es muy pobre, contribuyendo significativamente a su morbilidad. La gestión clínica de una masa anormal en el cerebro es materia delicada y difícil, por lo que los expertos han de basarse en mediciones indirectas no invasivas de las características del tumor y de su crecimiento. En la práctica radiológica actual, estas mediciones se realizan a menudo mediante técnicas de resonancia magnética (MR), como la imagen (MRI) y la espectroscopia (MRS). La vasta información contenida en las señales de MR les hace ideales para la aplicación de técnicas de reconocimiento de patrones (PR). Durante las dos últimas décadas, estas técnicas se han aplicado con éxito al problema de la extracción de conocimiento a partir de datos de tumores cerebrales humanos, para su diagnóstico y pronóstico. No obstante, la discriminación de algunos tipos y subtipos de tumores, así como la delimitación precisa del área tumoral, continúan siendo un reto para los investigadores. En esta tesis, abordamos tales retos mediante la aplicación de un conjunto de técnicas avanzadas de PR. En primera instancia, se implementaron una variedad de técnicas comunes y bien conocidas en una herramienta integrada de software. Esta fue utilizada en tareas de reducción de dimensionalidad (DR), clasificación y evaluación de modelos, para el desarrollo de clasificadores apropiados para analizar datos de MRS. Posteriormente, se profundizó en el desarrollo de métodos de extracción de características (FE), para proponer un método que proporciona prototipos de señal interpretables a partir de los datos de MRS. En una siguiente fase, dos técnicas de descomposición espectral fueron utilizadas para extraer fuentes de MRS e identificar la que proporciona mejores resultados en el contexto de problemas de neuro-oncología, utilizando datos de MRS de un solo vóxel (SV). El mejor y más adecuado método de extracción de fuentes resultante se utilizó posteriormente para derivar fuentes correlacionadas con los espectros promedio de los tipos de tejidos estudiados. El primer enfoque se aplicó también en el contexto de datos de múltiples vóxeles (MV), donde se propone un mecanismo para delimitar la zona patológica del tumor. Las contribuciones se pueden resumir de la siguiente manera. En primer lugar, el desarrollo de una herramienta de software que ha permitido reproducir clasificadores previamente publicados, así como probar nuevas hipótesis. También se ha contribuido con un método de FE, cuyo rendimiento es comparable a su contraparte más comúnmente utilizada en el análisis de datos de MRS, al tiempo que mejora su interpretación. Por otra parte, hemos identificado la variante de descomposición espectral que mejor se adapta al análisis de datos de SV MRS, llamada Convex Non-negative Matrix Factorisation (NMF), mostrando su capacidad para discriminar entre tejido sano, necrosis y tumor en proliferación activa, con resultados que son comparables a los obtenidos en modo supervisado. Con los datos de MV, los resultados obtenidos evidencian que es posible conseguir una delimitación precisa de la zona patológica mediante la aplicación de Convex-NMF. Con esta tesis, ofrecemos una herramienta a radiólogos espectroscopistas para facilitar el desarrollo de clasificadores para el análisis de datos de MRS, en un amplio grupo de tipos tumorales. También ofrecemos una alternativa no supervisada para mejorar la discriminación entre tipos y subtipos tumorales, colocando este enfoque un paso por delante de los métodos supervisados clásicos. Esto nos ha permitido hacer frente a una de las principales fuentes de incertidumbre en el manejo clínico de tumores cerebrales: la dificultad de delimitar adecuadamente el área patológica.<br>Cancer is a leading cause of death worldwide. Tumours of the Central Nervous System and, among them, brain tumours have a relatively low incidence as compared to other more widespread cancer pathologies, but the prognosis of some of them is very poor, contributing significantly to morbidity. The clinical management of an abnormal mass in the brain is sensitive and difficult, making experts to rely on non-invasive indirect measurements of the tumour characteristics and growth. In current radiological practice, these data measurements are often provided by magnetic resonance (MR) techniques, such as imaging (MRI) and spectroscopy (MRS). The rich information contained in MR signals makes them ideally suited to the application of pattern recognition (PR) techniques. Over the last two decades, these techniques have been successfully used to address the problem of knowledge extraction from human brain tumour data, for their diagnosis and prognosis. Nevertheless, the discrimination of some tumour types and subtypes, along with the accurate delimitation of the tumour area, remained challenging. In this thesis, we approach these challenges using a set of advanced PR techniques. A variety of common and well-known dimensionality reduction (DR), classification, and evaluation methods are first gathered in a software tool, used for the development of classifiers that are suitable for the analysis of MRS data. We then delve into the feature extraction (FE) family of DR methods to propose a method that is robust in the presence of noise, not prone to overfitting, and which also provides interpretation of the extracted MRS signal prototypes. Two spectral decomposition techniques, in different algorithmic variants, are subsequently used to extract the sources of the MRS signals and identify the one that provides better results in the context of neuro-oncology, using single-voxel (SV) MRS data. The best and most adequate source extraction method is then used to derive sources correlated with the mean spectra of known tissue types. The former, an unsupervised approach, is also applied in this thesis in the multi-voxel (MV) context, where we propose a mechanism for delimiting the pathological area of the tumour. The contributions of this thesis can be summarised as follows. First, the development of a software tool allowed us to reproduce previously published MRS-based classifiers, and test new hypotheses that led to new publications. We also contributed a FE method, whose performance is comparable to its most commonly used counterpart in MRS data analysis, while improving on the interpretability. Moreover, we identified the spectral decomposition variant that best suits the analysis of SV MRS data, namely Convex Non-negative Matrix Factorisation (NMF), and showed its ability to discriminate between healthy tissue, necrosis, and actively proliferating tumour, with results that are comparable to those obtained in fully supervised mode. For MV data, we successfully benchmarked alternative spectral decomposition methods, and provided evidence supporting that very accurate delimitation can be achieved through the application of Convex-NMF. With this thesis, we provide spectroscopists with a tool that facilitates the development of classifiers for the analysis of MRS data, for a large group of tumour types; allowing them to concentrate on the interpretation of the results, without requiring a specialised mathematical expertise for testing their hypotheses. We also provide an unsupervised alternative to improve the discrimination between tumour types and subtypes, placing this approach one step ahead of classical label-requiring supervised methods for detection of the increasingly recognised molecular subtype heterogeneity within human brain tumours. This also allowed us to accurately tackle one of the main sources of uncertainty in the clinical management of brain tumours, which is the difficulty of appropriately delimiting the pathological area.

Nous présentons dans cette thèse une approche nouvelle de la segmentation d’images, avec des descripteurs a priori utilisant des champs de Markov d’ordre supérieur. Nous représentons le modèle de forme par un graphe de distribution de points qui décrit les informations a priori des invariants de pose grâce à des cliques L1 discrètes d’ordre supérieur. Chaque clique de triplet décrit les variations statistiques locales de forme par des mesures d’angle,ce qui assure l’invariance aux transformations globales (translation, rotation et échelle). L’apprentissage d’une structure de graphe discret d’ordre supérieur est réalisé grâce à l’apprentissage d’un champ de Markov aléatoire utilisant une décomposition duale, ce qui renforce son efficacité tout en préservant sa capacité à rendre compte des variations.Nous introduisons la connaissance a priori d’une manière innovante pour la segmentation basée sur un modèle. Le problème de la segmentation est ici traité par estimation statistique d’un maximum a posteriori (MAP). L’optimisation des paramètres de la modélisation- c’est à dire de la position des points de contrôle - est réalisée par le calcul d’une fonction d’énergie globale de champs de Markov (MRF). On combine ainsi les calculs statistiques régionaux et le suivi des frontières avec la connaissance a priori de la forme.Les descripteurs invariants sont estimés par des potentiels de Markov d’ordre 2, tandis que les caractéristiques régionales sont transposées dans un espace de caractéristiques et calculées grâce au théorème de la Divergence.De plus, nous proposons une nouvelle approche pour la segmentation conjointe de l’image et de sa modélisation ; cette méthode permet d’obtenir une segmentation plus fine lorsque la délimitation précise d’un objet est recherchée. Un modèle graphique combinant l’information a priori et les informations de pixel est développé pour réaliser l’unité des modules "top-down" et "bottom-up". La cohérence entre l’image et sa modélisation est assurée par une décomposition qui associe les parties du modèle avec la labellisation de chaque pixel.Les deux champs de Markov d’ordre supérieur considérés sont optimisés par les algorithmes de l’état de l’art. Les résultats prometteurs dans les domaines de la vision par ordinateur et de l’imagerie médicale montrent le potentiel de cette méthode appliquée à la segmentation<br>In this thesis, we propose a novel framework for knowledge-based segmentation using high-order Markov Random Fields (MRFs). We represent the shape model as a point distribution graphical model which encodes pose invariant shape priors through L1 sparse higher order cliques. Each triplet clique encodes the local shape variation statistics on the angle measurements which inherit invariance to global transformations (i.e. translation,rotation and scale). A sparse higher-order graph structure is learned through MRF training using dual decomposition, producing boosting efficiency while preserving its ability to represent the shape variation.We incorporate the prior knowledge in a novel framework for model-based segmentation.We address the segmentation problem as a maximum a posteriori (MAP) estimation in a probabilistic framework. A global MRF energy function is defined to jointly combine regional statistics, boundary support as well as shape prior knowledge for estimating the optimal model parameters (i.e. the positions of the control points). The pose-invariant priors are encoded in second-order MRF potentials, while regional statistics acting on a derived image feature space can be exactly factorized using Divergence theorem. Furthermore, we propose a novel framework for joint model-pixel segmentation towardsa more refined segmentation when exact boundary delineation is of interest. Aunified model-based and pixel-driven integrated graphical model is developed to combine both top-down and bottom-up modules simultaneously. The consistency between the model and the image space is introduced by a model decomposition which associates the model parts with pixels labeling. Both of the considered higher-order MRFs are optimized efficiently using state-of the-art MRF optimization algorithms. Promising results on computer vision and medical image applications demonstrate the potential of the proposed segmentation methods

Cette thèse explore le développement et la mise en œuvre d’algorithmes d’optimisation pour la surveillance de zones industrielles à l’aide d’une équipe de robots mobiles autonomes. Le travail de recherche se concentre sur l’allocation de tâches multi-robots (MRTA), où un plan de mission quasi-optimal doit être généré. Un nouveau modèle prenant en compte des robots et des tâches hétérogènes est proposé, utilisant des algorithmes génétiques (GA) et une méthode de recherche locale 2-Opt pour résoudre le problème. La thèse intègre également des stratégies d’évitement des collisions, qui deviennent nécessaires lorsqu’il y a beaucoup de robots et de tâches. Une solution locale de bas niveau gère de nombreuses situations de conflit pendant la mission, ce qui peut entraîner des retards. Par conséquent, une solution pour ce cas a été proposée en utilisant le clustering. En outre, nous évaluons les solutions proposées à l’aide d’expériences réelles qui incluent un algorithme basé sur la navigation pour résoudre les problèmes de collision. Les résultats démontrent la valeur de ces algorithmes dans l’optimisation de la répartition des tâches et de la planification des chemins pour les robots mobiles autonomes dans les environnements industriels, ouvrant la voie à une planification de mission plus efficace et à une sécurité accrue dans les environnements industriels<br>This thesis explores the development and implementation of optimisation algorithms for monitoring industrial areas using a team of autonomous mobile robots. The research focuses on Multi-Robot Task Allocation (MRTA), where a near-optimal mission plan must be generated. A novel model considering heterogeneous robots and tasks is proposed, using Genetic Algorithms (GA) and 2-Opt local search methods to solve the problem. The thesis also integrates collision avoidance strategies, which become necessary when there are many robots and tasks. A low-level local solution handles many conflict situations during the mission, which can cause delays. Therefore, a solution for this case was proposed using clustering. Furthermore, we evaluate the proposed solutions through real-world experiments including a navigation-based algorithm that addresses collision issues. The results demonstrate the value of these algorithms in optimising task allocation and path planning for autonomous mobile robots in industrial settings, paving the way for more efficient mission planning and enhanced safety in industrial environments