Le travail produit s'inscrit dans un cadre industriel piloté par la société 2MoRO Solutions. La réalisation présentée dans cette thèse doit servir à l'élaboration d'un service à haute valeur, permettant aux exploitants aéronautiques d'optimiser leurs actions de maintenance. Les résultats obtenus permettent d'intégrer et de regrouper les tâches de maintenance en vue de minimiser la durée d'immobilisation des aéronefs et d'en réduire les risques de panne.La méthode que nous proposons comporte trois étapes : (i) une étape de rationalisation des séquences afin de pouvoir les combiner [...] / The present work is part of an industrial project driven by 2MoRO Solutions company.It aims to develop a high value service enabling aircraft operators to optimize their maintenance actions.Given the large amount of data available around aircraft exploitation, we aim to analyse the historical events recorded with each aircraft in order to extract maintenance forecasting. Theresults are used to integrate and consolidate maintenance tasks in order to minimize aircraft downtime and risk of failure. The proposed method involves three steps : (i) streamlining information in order to combinethem, (ii) organizing this data for easy analysis and (iii) an extraction step of useful knowledgein the form of interesting sequences. [...]

Random Linear Network Coding (RLNC) is a new coding technique which can provide higher reliability and efficiency in wireless networks. Applying it on the fifth generation of cellular networks (5G) is now possible due to the softwarization approach of the 5G architecture. However, the complex computations necessary to encode and decode symbols in RLNC are limiting the achievable throughput and energy efficiency on todays mobile computers. Most computers, phones, TVs, or network equipment nowadays come with multiple, possibly heteregoneous (i.e. slow low-power and fast high-power) processing cores. Previous multi core research focused on RLNC optimization for big data chunks which are useful for storage, however network operations tend to use smaller packets (e.g. Ethernet MTUs of 1500 byte) and code over smaller generations of packets. Also latency is an increasingly important performance aspect in the upcoming Tactile Internet, however latency has received only small attention in RLNC optimization so far. The primary research question of my thesis is therefore how to optimize throughput and delay of RLNC on todays most common computing architectures. By fully leveraging the resources of todays consumer electronics hardware, RLNC can be practically adopted in todays wireless systems with just a software update and improve the network efficiency and user experience. I am generally following a constructive approach by introducing algorithms and methods, and then demonstrating their performance by benchmarking actual implementations on common consumer electronics hardware against the state of the art. Inspired by linear algebra parallelization methods used in high performance computers (HPC), I’ve developed a RLNC encoder/decoder which schedules matrix block tasks for multiple cores using a directed acyclic graph (DAG) based on data dependencies between the tasks. A non-progressive variant works with pre-computed DAG schedules which can be re-used to push throughput even higher. I’ve also developed a progressive variant which can be used to minimize latency. Both variants are achieving higher throughput performance than the fastest currently known RLNC decoder, with up to three times the throughput for small generation size and short packets. Unlike previous approaches, they can utilize all cores also on heterogeneous architectures. The progressive decoder greatly reduces latency while allowing to keep a high throughput, reducing the latency up to a factor ten compared to the non-progressive variant. Progressive decoders need special low-delay codes to release packets early instead of waiting for more dependent packets from the network. I'm introducing Caterpillar RLNC (CRLNC), a sliding window code using a fixed sliding window over a stream of packets. CRLNC can be implemented on top of a conventional generation based RLNC decoder. CRLNC combines the resilience against packet loss and fixed resource boundaries (number of computations and memory) of conventional generation based RLNC decoders with the low delay of an infinite sliding window decoder. The DAG RLNC coders and the Caterpillar RLNC method together provide a powerful toolset to practically enable RLNC in 5G or other wireless systems while achieving high throughput and low delay as required by upcoming immersive and machine control applications.:1 Introduction 2 Background and Related Work 2.1 Network Delay 2.2 Network Coding Basics 2.3 RLNC Optimization for Throughput 2.3.1 SIMD Optimization 2.3.2 Block Operation Increasing Cache Efficieny with Subblocking 2.3.3 Optimizing Matrix Computations 2.4 Progressive RLNC Decoders 2.5 Sliding Window RLNC 3 Optimized RLNC Parallelization with Scheduling Graphs 3.1 Offline Directed Acyclic Graph (DAG) Scheduling 3.1.1 Blocked LU Matrix Inversion 3.1.2 Scheduling on a DAG 3.1.3 Phase 1: DAG Recording 3.1.4 Phase 2: DAG Schedule Execution 3.1.5 DAG Scheduling vs. Conventional Multithreading 3.1.6 Task Size Considerations 3.1.7 Scheduling Strategies First Task Strategy Task Dependency Strategy Data Locality Strategy Combined Task Dependency and Data Locality Strategy 3.2 Online DAG Scheduling 3.2.1 Online DAG Operation Forward Elimination Backward Substitution Row Swapping 3.2.2 Scheduling on an Online DAG Data Dependency Traversal Online DAG Creating and Task Delegation 3.2.3 Optimizations Stripe Optimization Full Rows Optimization 3.3 Evaluation Setup 3.3.1 Multicore Boards ODROID-XU3 ODROID-XU4 ODROID-XU+E Cubieboard 4 Raspberry Pi 2 Model B 3.3.2 Evaluation Parameters Parameter Settings Matrix Types 3.3.3 Performance Metrics Throughput Delay Energy 3.3.4 Evaluation Methodology 3.4 Evaluation Results 3.4.1 Block Size b 3.4.2 Comparison of Scheduling Strategies 3.4.3 Single Thread Throughput 3.4.4 Multi Thread Throughput 3.4.5 Comparison of Multicore Boards 3.4.6 Energy Consumption 3.4.7 Online DAG vs. Offline DAG Throughput 3.4.8 DAG vs Progressive CD 3.4.9 Delay 3.4.10 Trading Throughput with Delay 3.4.11 Sparse Coefficient Matrices in Online DAG 4 Sliding Window - Caterpillar RLNC (CRLNC) 4.1 CRLNC Overview 4.2 CRLNC Packet Format And Encoding 4.3 CRLNC Decoding 4.3.1 Shifting the Row Echelon Form Same sequence number: s_p = s_d New Packet: s_p > s_d Old Packet: s_p < s_d 4.3.2 Larger Decoding Windows: w_d > w_e 4.3.3 CRLNC Decoding Storage and Computing Requirements 4.4 CRLNC Evaluation 4.4.1 Performance Metrics Packet Loss Probability In-Order Packet Delay 4.4.2 Evaluation Methodology 4.5 Evaluation Results 4.5.1 Packet Loss Probability 4.5.2 In-Order Packet Delay 4.5.3 Tradeoffs for Larger Decoding Windows 4.5.4 Computation Complexity 5 Summary and Conclusion List of Publications Bibliography

At present, there is no sliding window latch designed and developed for the marine market. Onmar is a company that develops and produces quality products for premium boats. They were asked to supply a latch that is exclusive and is of quality. This request became the basis for the thesis. The aim of the project was to gain an understanding of the concepts of exclusivity and quality to implement them in the design of a latch for premium boats. To ensure that the aim is achieved during the work, two research questions were formulated to be answered through the implementation of the project, which are as follows: Research question 1: How are exclusivity and quality defined and how can these definitions influence the design of an exclusive and quality product for the marine market? Research question 2: What value will a specially designed and developed latch for premium boats add compared to existing latch? The project combined two product development processes. These included concept generation, concept development and prototype creation and further development. Data collection was carried out using both literature and empirical data. The triangulation method was used to increase the reliability of the project. The results show that the investigated concepts are difficult todefine and no previous research regarding the concepts in productdevelopment has been done. The findings show that what indicates product quality and exclusivity within the marine market, is subjective and therefore difficult to define. No previous research on the meanings of these terms within product development were found. Nevertheless, definitions of exclusivity and quality have been defined and were applied as guidelines during the development of the latch for the marine market. A survey carried out to evaluate the final product concept of a latch for the marine market, suggests that the final concept gives a visual impression of exclusivity and quality. Whether a product is perceived as exclusive and whether it gives the impression of quality may depend on more factors than just design. The choice of materials and physical feedback during use are two other factors that can influence a user's impression of exclusivity and quality. The latch developed in this project can be used as a basis for further product development within the exclusive marine market. / I dagsläget finns det inget snäpplås till skjutbara fönster som är utformad och utvecklad för den marina marknaden. Onmar är ett företag som utvecklar och producerar kvalitativa produkter för premiumbåtar. Onmar fick förfrågan om att producera ett exklusivt snäpplås av hög kvalitet. Denna förfrågan blev grunden för detta examensarbete. Syftet med projektet var att presentera ett produktkoncept av ett exklusivt snäpplås för premiumbåtar av hög kvalitet. För att säkerställa att projektets syfte kunde uppnås, skapades två forskningsfrågor som stöd: Forskningsfråga 1: Hur kan exklusivitet och kvalitet definieras inom området utformning av snäpplås för den marina marknaden? Forskningsfråga 2: Hur kan ett exklusivt snäpplås för premiumbåtar tillföra värde till den marina marknaden, jämförtmed befintliga snäpplås? Produktutvecklingsprocessen som tillämpats baserades på enkombination av processerna från Ulrich &amp; Eppinger (2014), samtUllman (2012). Processen inleddes med konceptgenerering och resulterade i ett slutgiltigt konceptval. Datainsamling har genomförts både efter litteratur och empirisk data. För att främja studiens reliabilitet användes metoden triangulering. Resultatet visar att de undersökta begreppen, kvalitet och exklusivitet inom den marina marknaden, är subjektiva och därmed svårdefinierade. Ingen tidigare forskningsresultat om begreppens betydelser inom produktutveckling påträffades. Begreppen har hursomhelst definierats och använts under utvecklingen av snäpplåset för den marina marknaden. En genomförd enkätundersökning om det slutgiltiga konceptet av ett snäpplås för den marina marknaden tyder på att slutkonceptet inger ett visuellt intryck av exklusivitet och kvalitet. Huruvida en produkt upplevs som exklusiv och om den ger intryck av kvalitet kan bero på flera faktorer än bara utformning. Materialval och fysisk feedback vid användning är ytterligare två faktorer som kan påverka en användares intryck av exklusivitet och kvalitet. Snäpplåset som utvecklades i projektet kan användas som underlag för vidare produktutvecklingsarbete inom den exklusivamarina marknaden.

This thesis aims at proposing vehicle license plate detection and recognition algorithms, suitable for vehicle re-identification. Simple urban traffic analysis system is also proposed. Multiple stages of this system was developed and tested. Specifically - vehicle detection, license plate detection and recognition. Vehicle detection is based on background substraction method, which results in an average hit rate of ~92%. License plate detection is done by cascade classifiers and achieves an average hit rate of 81.92% and precision rate of 94.42%. License plate recognition based on Template matching results in an average precission rate of 60.55%. Therefore the new license plate recognition method based on license plate scanning using the sliding window principle and neural network recognition was introduced. Neural network achieves a precision rate of 64.47% for five input features. Low precision rate of neural network is caused by small amount of training sample for some specific license plate characters.

Motion artefacts due to cardiac and respiratory motion present a daily challenge in cardiac Magnetic Resonance Imaging (MRI), and many different motion correction procedures are used in clinical routine imaging. To reduce motion artefacts further, patients are required to hold their breath during parts of the data acquisition, which is physically straining – especially when done repetitively. Self-Gating (SG) is a method that extracts cardiac and respiratory motion information from the MRI data in the form of signals, called SG signals, and uses them to divide the data into the specific cardiac and respiratory phases it was acquired from. This method both avoids motion artefacts and allow for free-breathing acquisition. This project’s goal was to find a method for extracting cardiac and respiratory SG signals from MRI data. The data was acquired with a golden angle radial acquisition method for 3-dimensional (3D) scans. Extraction of the raw signal was tested for both raw k-space data and high temporal resolution image series, where the images were reconstructed using a sliding window reconstruction. Filters were then applied to isolate the cardiac and respiratory information, to create separate cardiac and respiratory SG signals. Thereafter trigger points marking the beginning of the cardiac and respiratory cycles were generated. The trigger points were compared against ECG and respiratory trigger points provided by the MR scanner. The conclusion was that the SG signals based on k-space data was functional on the scans from the evaluated subjects and the most effective choice of the two options, but image based SG signals may prove to be functional after further studies. / Rörelseartefakter på grund av hjärt- och respirationsrörelser är idag vardagliga utmaningar inom magnetresonanstomografi (MR) av hjärtat, och många olika metoder används för att eliminera rörelseartefakterna. Patienterna behöver dessutom hålla andan under delar av dataupptagningen, vilket är fysiskt ansträngande – speciellt när det sker upprepade gånger. Self-Gating (SG) är en metod som extraherar information hjärt- och respirationsrytm från MR-datan i form av signaler, kallade SG signaler, och använder dem för att dela in datan i de specifika hjärt- respektive respirationsfaser som var när datan upptogs. Denna metod både undviker rörelseartefakter och tillåter fri andning under dataupptagningen. Målet med det här projektet var att hitta en metod för att extrahera SG signaler för hjärt- och respirationsrytm från MR-data. Datan samlades in med en golden angle radial-upptagning för 3- dimensionella (3D) scanningar. Extraheringen av den råa signalen testades på både rå k-space data och på bildserier av 3D-bilder med hög tidsupplösning, där bilderna var rekonstruerade med en sliding window rekonstruktion. Därefter applicerades filter för att isolera hjärt- och respirationsinformationen, för att få separata SG signaler med endast hjärt- respektive respirationsrytmer. Till slut genererades triggerpunkter för att markera början av hjärt- respektive respirationscyklerna. Dessa jämfördes med triggerpunkter uppmätta med EKG och andningskudde i magnetkameran. Slutsatsen för projektet var att SG signalerna som baserades på k-space data var funktionell för de scanningar som testades och det mest effektiva alternativet, men SG signalerna som baserades på bilder kan visa sig fungera efter mer studier.

Hybrid stepper motors are a common type of electric motor used throughout industry thanks to its low-cost, high torque at low speed and open loop positioning capabilities. However, a closed loop control is often required for industrial applications with high precision requirements. The closed loop control can also be used to lower the power consumption of the motor and ensure that stalls are avoided. It is quite common to utilise a large and costly position encoder or resolver to feedback the position signal to the control logic. This thesis has explored the possibility of using a low-cost position sensor based on Hall elements. Additionally, a sensorless estimation algorithm, using only stator winding measurements, has been investigated both as a competitive alternative and as a possible complement to the position sensor. The thesis work summarises and discusses previous research attempts to adequately measure or estimate and control the hybrid stepper motors position and load angle without using a typical encoder or resolver. Qualitative results have been produced through simulations prior to implementation and experimental testing. The readings from the position sensor is subject to noise, owing to its resolution and construction. The position signal has been successfully filtered, improving its accuracy from 0.56° to 0.25°. The output from the sensorless estimation algorithm is subject to non-linear errors caused by errors in phase voltage measurements and processing of velocity changes. However, the dynamics are reliable at constant speeds and could be used for position control.

Resting state functional magnetic resonance imaging (fMRI) is currently at the forefront of research on cognition and the brain’s large-scale organization. Patterns of hemodynamic activity that it records have been strongly linked to certain behaviors and cognitive pathologies. These signals are widely assumed to reflect local neuronal activity but our understanding of the exact relationship between them remains incomplete. Researchers often address this using multimodal approaches, pairing fMRI signals with known measures of neuronal activity such as electroencephalography (EEG). It has long been thought that infraslow (< 0.1 Hz) fMRI signals, which have become so important to the study of brain function, might have a direct electrophysiological counterpart. If true, EEG could be positioned as a low-cost alternative to fMRI when fMRI is impractical and therefore could also become much more influential in the study of functional brain networks. Previous works have produced indirect support for the fMRI-EEG relationship, but until recently the hypothesized link between them had not been tested in resting humans. The objective of this study was to investigate and characterize their relationship by simultaneously recording infraslow fMRI and EEG signals in resting human adults. We present evidence strongly supporting their link by demonstrating significant stationary and dynamic correlations between the two signal types. Moreover, functional brain networks appear to be a fundamental unit of this coupling. We conclude that infraslow electrophysiology is likely playing an important role in the dynamic configuration of the resting state brain networks that are well-known to fMRI research. Our results provide new insights into the neuronal underpinnings of hemodynamic activity and a foundational point on which the use of infraslow EEG in functional connectivity studies can be based.

Submitted by Daniele Amaral (daniee_ni@hotmail.com) on 2016-10-20T18:13:56Z No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:42:36Z (GMT) No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:42:42Z (GMT) No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5) / Made available in DSpace on 2016-11-08T18:42:49Z (GMT). No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5) Previous issue date: 2016-06-06 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / The growing amount of data produced daily, by both businesses and individuals in the web, increased the demand for analysis and extraction of knowledge of this data. While the last two decades the solution was to store and perform data mining algorithms, currently it has become unviable even to supercomputers. In addition, the requirements of the Big Data age go far beyond the large amount of data to analyze. Response time requirements and complexity of the data acquire more weight in many areas in the real world. New models have been researched and developed, often proposing distributed computing or different ways to handle the data stream mining. Current researches shows that an alternative in the data stream mining is to join a real-time event handling mechanism with a classic mining association rules or sequential patterns algorithms. In this work is shown a data stream mining approach to meet the Big Data response time requirement, linking the event handling mechanism in real time Esper and Incremental Miner of Stretchy Time Sequences (IncMSTS) algorithm. The results show that is possible to take a static data mining algorithm for data stream environment and keep tendency in the patterns, although not possible to continuously read all data coming into the data stream. / O crescimento da quantidade de dados produzidos diariamente, tanto por empresas como por indivíduos na web, aumentou a exigência para a análise e extração de conhecimento sobre esses dados. Enquanto nas duas últimas décadas a solução era armazenar e executar algoritmos de mineração de dados, atualmente isso se tornou inviável mesmo em super computadores. Além disso, os requisitos da chamada era do Big Data vão muito além da grande quantidade de dados a se analisar. Requisitos de tempo de resposta e complexidade dos dados adquirem maior peso em muitos domínios no mundo real. Novos modelos têm sido pesquisados e desenvolvidos, muitas vezes propondo computação distribuída ou diferentes formas de se tratar a mineração de fluxo de dados. Pesquisas atuais mostram que uma alternativa na mineração de fluxo de dados é unir um mecanismo de tratamento de eventos em tempo real com algoritmos clássicos de mineração de regras de associação ou padrões sequenciais. Neste trabalho é mostrada uma abordagem de mineração de fluxo de dados (data stream) para atender ao requisito de tempo de resposta do Big Data, que une o mecanismo de manipulação de eventos em tempo real Esper e o algoritmo Incremental Miner of Stretchy Time Sequences (IncMSTS). Os resultados mostram ser possível levar um algoritmo de mineração de dados estático para o ambiente de fluxo de dados e manter as tendências de padrões encontrados, mesmo não sendo possível ler todos os dados vindos continuamente no fluxo de dados.

With the release of the Kinect device new possibilities appeared, allowing a simple use of image depth in image processing. The aim of this thesis is to propose a method for object detection and recognition in a depth map. Well known method Bag of Words and a descriptor based on Spin Image method are used for the object recognition. The Spin Image method is one of several existing approaches to depth map which are described in this thesis. Detection of object in picture is ensured by the sliding window technique. That is improved and speeded up by utilization of the depth information.

Le contrôle de santé structural ou Structural Health Monitoring (SHM) des matériaux constitue une démarche fondamentale pour la maîtrise de la durabilité et de la fiabilité des structures en service. Au-delà des enjeux industriels et humains qui ne cessent de s’accroître en termes de sécurité et de fiabilité, le contrôle de santé doit faire face à des exigences de plus en plus élaborées. Les nouvelles stratégies de contrôle de santé doivent non seulement détecter et identifier l’endommagement mais aussi quantifier les différents phénomènes qui en sont responsables. Pour atteindre cet objectif, il est nécessaire d’accéder à une meilleure connaissance des processus d’endommagement. Par ailleurs, ceux-ci surviennent fréquemment sous l’effet de sollicitations mécaniques et environnementales. Ainsi, il est indispensable, d’une part, d’élaborer des méthodes de traitement des signaux permettant d’estimer les effets des conditions environnementales et opérationnelles, dans un contexte de l’analyse des événements précurseurs des mécanismes d’endommagement, et, d’autre part, de définir les descripteurs d’endommagement les plus adaptés à cette analyse. Cette étude propose donc des méthodes de traitement du signal permettant d’atteindre cet objectif, dans un premier temps, pour l’estimation des effets externes sur les ondes multidiffusées dans un contexte de contrôle de santé actif et, dans un second temps, pour l’extraction d’un indicateur d’endommagement à partir de l’analyse des signaux d’émission acoustique dans un contexte de contrôle de santé passif. Dans la première partie de ce travail, quatre méthodes de traitement du signal sont proposées. Celles-ci permettent de prendre en compte les variations des conditions environnementales dans la structure, qui dans le cadre de cette thèse, se sont limitées au cas particulier du changement de la température. En effet, les variations de température ont pour effet de modifier les propriétés mécaniques du matériau et par conséquent la vitesse de propagation des ondes ultrasonores. Ce phénomène entraîne alors une dilatation temporelle des signaux acoustiques qu’il convient d’estimer afin de suivre les variations de température. Quatre estimateurs de coefficients de dilatation sont alors étudiés : Il s’agit de l’intercorrélation à fenêtre glissante, utilisée comme méthode de référence, la méthode du stretching, l’estimateur à variance minimale et la transformée exponentielle. Les deux premières méthodes ont été déjà validées dans la littérature alors que les deux dernières ont été développées spécifiquement dans le cadre de cette étude. Par la suite, une évaluation statistique de la qualité des estimations est menée grâce à des simulations de Monte-Carlo utilisant des signaux de synthèse. Ces signaux sont basés sur un modèle de signal multidiffusé prenant en compte l’influence de la température. Une estimation sommaire de la complexité algorithmique des méthodes de traitement du signal complète également cette phase d’évaluation. Enfin, la validation expérimentale des méthodes d’estimation est réalisée sur deux types de matériaux : Tout d’abord, dans une plaque d’aluminium, milieu homogène dont les caractéristiques sont connues, puis, dans un second temps dans un milieu fortement hétérogène prenant la forme d’une plaque composite en verre/epoxy. Dans ces expériences, les plaques sont soumises à différentes températures dans un environnement thermique contrôlé. Les estimations de température sont alors confrontées à un modèle analytique décrivant le comportement du matériau. La seconde partie de ce travail concerne la caractérisation in situ des mécanismes d’endommagement par émission acoustique dans des matériaux hétérogènes. Les sources d’émission acoustique génèrent des signaux non stationnaires... / Structural health monitoring (SHM) of materials is a fundamental measure to master thedurability and the reliability of structures in service. Beyond the industrial and human issuesever increasing in terms of safety and reliability, health monitoring must cope with demandsincreasingly sophisticated. New health monitoring strategies must not only detect and identifydamage but also quantify the various phenomena involved in it. To achieve this objective, itis necessary to reach a better understanding of the damage process. Moreover, they frequentlyoccur as a result of mechanical and environmental stresses. Thus, it is essential, first, to developsignal processing methods for estimating the effects of environmental and operational conditions,in the context of the analysis of precursor events of damage mechanisms, and on theother hand, to define the damage descriptors that are the most suitable to this analysis. Thisstudy proposes signal processing methods to achieve this goal. At first, to the estimation ofexternal effects on the scattered waves in an active health control context, in a second step, tothe extraction of a damage indicator from the signals analysis of acoustic emission in a passivehealth monitoring context.In the first part of this work, four signal processing methods are proposed. These allow takinginto account the variation of environmental conditions in the structure, which in this thesis,were limited to the particular case of temperature change. Indeed, temperature changes have theeffect of altering the mechanical properties of the material and therefore the propagation velocityof ultrasonic waves. This phenomenon then causes a dilation of the acoustic signals that shouldbe estimated in order to monitor changes in temperature. Four estimators of dilation coefficientsare then studied: the intercorrelation sliding window, used as reference method, the stretchingmethod, the minimum variance estimator and the exponential transform. The first two methodshave already been validated in the literature while the latter two were developed specificallyin the context of this study. Thereafter, a statistical evaluation of the quality of estimates isconducted through Monte Carlo simulations using synthetic signals. These signals are basedon a scattered signal model taking into account the influence of temperature. A raw estimateof the computational complexity of signal processing methods also completes this evaluationphase. Finally, the experimental validation of estimation methods is performed on two types ofmaterial: First, in an aluminum plate, homogeneous medium whose characteristics are known,then, in a second step in a highly heterogeneous environment in the form of a compositeglass/epoxy plate. In these experiments, the plates are subjected to different temperatures in acontrolled thermal environment. The temperature estimates are then faced with an analyticalmodel describing the material behavior.The second part of this work concerns in situ characterization of damage mechanisms byacoustic emission in heterogeneous materials. Acoustic emission sources generate non-stationarysignals. The Hilbert-Huang transform is thus proposed for the discrimination of signals representativeof four typical sources of acoustic emission in composites: matrix cracking, debondingfiber/matrix, fiber breakage and delamination. A new time-frequency descriptor is then definedfrom the Hilbert-Huang transform and is introduced into an online classification algorithm. Amethod of unsupervised classification, based on the k-means method, is then used to discriminatethe sources of acoustic emission and the data segmentation quality is evaluated. Thesignals are recorded from blank samples, using piezoelectric sensors stuck to the surface of thematerial and sensitive samples (sensors integrated within the material)...