Algunas aplicaciones de carretera actuales, tales como los servicios de información al viajero, llamadas de emergencia automáticas, control de flotas o telepeaje eletrónico, requieren una solución de calidad al problema del posicionamiento de un vehículo terrestre, que funcione en cualquier entorno y a un coste razonable. Esta tesis presenta una solución a este problema, fusionando para ello la información procedente principalmente de sensores de navegación por satélite y sensores inerciales. Para ello emplea un nuevo filtro de fusion multisensorial IMM-EKF. El comportamiento del sistema ha sido analizado en entornos reales y controlados, y comparado con otras soluciones propuestas. Finalmente, su aplicabilidad al problema planteado ha sido verificada. / Road applications such as traveller information, automatic emergency calls, freight management or electronic fee, collection require a onboard equipment (OBE) capable to offer a high available accurate position, even in unfriendly environments with low satellite visibility at low cost. Specifically in life critical applications, users demand from the OBEs accurate continuous positioning and information of the reliability of this position. This thesis presents a solution based on the fusion of Global Navigation Satellite Systems (GNSS) and inertial sensors (GNSS/INS), running an Extended Kalman Filter combined with an Interactive Multi-Model method (IMM-EKF). The solution developed in this work supplies continuous positioning in marketable conditions, and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation, as compared with low cost GNSS based solutions, dead reckoning systems and single model extended Kalman filter (SM-EKF) solutions.

This thesis represents Bayesian joint audio-visual tracking for the 3D locations of multiple people and a current speaker in a real conference environment. To achieve this objective, it focuses on several different research interests, such as acoustic-feature detection, visual-feature detection, a non-linear Bayesian framework, data association, and sensor fusion. As acoustic-feature detection, time-delay-of-arrival~(TDOA) estimation is used for multiple source detection. Localization performance using TDOAs is also analyzed according to different configurations of microphones. As a visual-feature detection, Viola-Jones face detection is used to initialize the locations of unknown multiple objects. Then, a corner feature, based on the results from the Viola-Jones face detection, is used for motion detection for robust objects. Simple point-to-line correspondences between multiple cameras using fundamental matrices are used to determine which features are more robust. As a method for data association and sensor fusion, Monte-Carlo JPDAF and a data association with IPPF~(DA-IPPF) are implemented in the framework of particle filtering. Three different tracking scenarios of acoustic source tracking, visual source tracking, and joint acoustic-visual source tracking are represented using the proposed algorithms. Finally the real-time implementation of this joint acoustic-visual tracking system using a PC, four cameras, and six microphones is addressed with two parts of system implementation and real-time processing.

Les capteurs et les biocapteurs sont des moyens d’analyse en plein essor à la fois rapides, sensibles, sélectifs et peu coûteux, applicables à des domaines très variés (agroalimentaire, environnement, biomédical…). Dans ce travail de recherche, nous nous sommes intéressés au développement de trois dispositifs à savoir un nez et une langue électroniques à base de systèmes multicapteurs pour l’analyse des odeurs et des saveurs ainsi que les biocapteurs. Les deux premiers dispositifs ont permis dans un premier temps de caractériser et de détecter les fraudes dans les produits de l’industrie agroalimentaire. Ainsi, nous sommes parvenus à détecter les pratiques frauduleuses dans l’huile d’argan par le nez et par la langue électroniques. Nous avons, par ailleurs, réussi à caractériser des miels de différentes origines géographiques et botaniques et à détecter l’adultération du miel pur par l’utilisation d’une langue électronique voltammétrique. Enfin, nous avons démontré l’efficacité des systèmes de nez et de langue électroniques à discriminer cinq marques de lait pasteurisé marocain. Des limitations du système de nez et de langue électroniques ont été révélées quant à la discrimination du lait pasteurisé (Jawda) en fonction des jours de stockage. Par contre, la fusion des données des deux systèmes moyennant un niveau d'abstraction intermédiaire a permis d’améliorer cette discrimination. Dans une deuxième étape, nous avons développé deux biocapteurs, le premier est basé sur l’utilisation de Polymère à Empreinte Moléculaire (MIP) dédié à la détection de la tétracycline dans le miel. Alors que le second est un immunocapteur conçu pour la détection de l’ochratoxine A. Le MIP, utilisé dans le premier cas, a été synthétisé à la surface d’électrodes en or par électropolymérisation des nanoparticules d'or fonctionnalisées par le p-aminothiophénol en présence de la tétracycline comme molécule empreinte. Dans le deuxième cas, un nouveau biocapteur capacitif basé sur l’utilisation d’un substrat de Nitrure de Silicium (Si3N4) combiné avec une nouvelle structure de nanoparticules magnétiques (MNPs) pour la détection de l’ochratoxine A a été conçu. En effet, Les MNPs possédant une terminaison carboxylique ont été liés de manière covalente à la monocouche auto-assemblée du silane-amine (3-aminopropyl triéthoxysilane APTES). Enfin, les anticorps anti-ochratoxine A ont été immobilisés sur les MNPs par liaison amide. Les performances des deux biocapteurs (limite de détection, sélectivité, reproductibilité) ont ensuite été évaluées / Sensors and Biosensors are rapid, sensitive, selective and low-cost analytical devices of growing interest for a wide range of application fields (e.g. food, environment, health …). This research focused on the development of three devices namely an electronic nose and tongue and electrochemical biosensors with applications in food analysis. The first two devices allowed the characterization and detection of frauds in the food field. Thus, we have been able to detect fraudulent practices in argan oil by using an electronic nose and tongue systems. In addition, the electronic tongue has successfully classified honeys of different geographical and botanical origins and detects the adulteration of pure honey. Finally, we have demonstrated the ability of the electronic nose and tongue systems to classify five brands of Moroccan pasteurized milk and to discriminate against them based on their storage days. In the second stage, we have developed two biosensors. The first one is based on a molecularly imprinted polymer (MIP) for the detection of tetracycline in honey. The second one is based on an immunosensor devoted for the detection of ochratoxin A. For the first biosensor, imprinted gold nanoparticles composites are assembled on Au surfaces by the electropolymerization of p-amino-thiophenol functionalized gold nanoparticles in the presence of the imprint molecule. In the second case, we have developed a novel capacitance electrochemical biosensor based on silicon nitride substrate (Si3N4) combined with a new structure of mangnetic nanoparticles (MNPs). Indeed, The MNPs with terminated carboxylic acid were covalently bonded to Si3N4 through a Self-Assembled Monolayers (SAMs) of the silane-amine (3-Aminopropyl) triethoxysilane (APTES). Finally anti-ochratoxin A antibodies were immobilized on MNPs by amide bonding. The performances of the two biosensors (limit of detection, selectivity, reproducibility) were then evaluated and the results are generally satisfactory

Les avancées technologiques de ces dernières années, combinées au succès avéré et toujours croissant des communications sans fil, ont tout naturellement donné naissance à un nouveau type de réseaux sans fil, communément appelés Body Area networks. A terme, ces réseaux corporels sans fil doivent permettre à un ensemble de senseurs bio-médicaux répartis sur le corps humain de communiquer, soit pour échanger des informations en vue d'un traitement en temps réel du patient, soit pour enregistrer des données physiologiques en vue d'une analyse ultérieure.<p><p>L’objectif de cette Thèse vise la réduction de la consommation énergétique au niveau des senseurs de sorte à leur garantir une autonomie de quelques mois, voire de quelques années. En réponse à cette contrainte énergétique, une association innovante de deux technologies émergentes est proposée, à savoir une combinaison des transmissions à ultra-large bande aux systèmes à multiples antennes. Une nouvelle architecture pour les réseaux corporels sans fil est donc envisagée pour laquelle les performances doivent être évaluées. Notre principale contribution à cet objectif consiste en la proposition d'une modélisation spatio-temporelle complète du canal de transmission dans le cadre de senseurs répartis autour du corps. Cette modélisation fait appel à la définition de nouveaux modèles, l'élaboration d'outils spécifiques d'extraction de paramètres et une compréhension fine des mécanismes de propagation liés à la proximité du corps humain. Ce manuscrit présente les résultats majeurs de nos recherches en cette matière.<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Indiana University-Purdue University Indianapolis (IUPUI) / Multiresolution image fusion is an emerging area of research for use in military and commercial applications. While many methods for image fusion have been developed, improvements can still be made. In many cases, image fusion methods are tailored to specific applications and are limited as a result. In order to make improvements to general image fusion, novel methods have been developed based on the wavelet transform and empirical variance. One particular novelty is the use of directional filtering in conjunction with wavelet transforms. Instead of treating the vertical, horizontal, and diagonal sub-bands of a wavelet transform the same, each sub-band is handled independently by applying custom filter windows. Results of the new methods exhibit better performance across a wide range of images highlighting different situations.

Home-based exercise is a popular physical activity of maintaining fitness, health andwellness in general. However, without proper supervision and basic knowledge of theexercises in the workout plan, there is an increased risk of injury. Considering that noteveryone is willing to attend crowded gyms or schedule professional personal trainingsessions, in this study, a novel feedback system is proposed, in the form of a mobileapplication. Accelerometer and gyroscope data were collected from 10 volunteersperforming 3 exercises, squats, lunges and bridges, with inertial sensors attachedto their back lumbar region, on both shanks and on both thighs. Each participantperformed 5 repetitions of the correct technique and 5 repetitions of 4 mistakes foreach exercise. The accuracies of 3 classifiers, a SVM, a RF and DT were comparedwith the SVM performing the best across all 3 exercises. The best location and numberof sensors was determined by examining the accuracy of a SVM model for 15 uniquemulti-sensor configurations. The best performing setup, being the configuration with 2sensors, one at the lumbar area and one at the shank, was used in exploring the efficacyof different data processing techniques. Time-domain statistical features, sensor angletimeseries and the filtered signal timeseries were evaluated as input to a NN. The timedomainfeatures performed the best achieving the highest accuracy in all 3 exercises,with an accuracy of 67% for the squats, 87% for the lunges and 75% for the hip bridges.Overall, the final model demonstrated promising capabilities of classifying exercisetechnique of basic lower-body exercises, with a real-time feedback implementationbeing a feasible solution for self-efficient fitness. / Hemmaträning är en populär typ av fysisk aktivitet för att upprätthålla kondition,hälsa och välbefinnande. Dock utan övervakning och basal kunskap om hur olikaövningar bör utföras så finns det en ökad risk för skador. Alla människor går intefrivilligt till trånga och fullsatta gym eller bokar in pass med personlig tränare. Därförföreslås i denna studie ett nytt återkopplingssytem vid träning som kan användas via enmobilapp. Data från en accelerometer och ett gyroskop har samlats in från tio frivilligapersoner. De har utfört tre olika styrkeövningar; knäböj, utfallssteg och höftlyft medtröghetssensorer placerade på deras ländrygg, på underbenen och på låren. Varjedeltagare utförde fem repetitioner med korrekt teknik och sedan fem repetitionermed fyra olika typer av felaktig teknik för varje styrkeövning. Noggrannheten förtre klassificerare, SVM, RF och DT jämfördes sedan med det SVM som presteradebäst i alla tre styrkeövningarna. Det optimala antalet sensorer tillsammans med bästplacering av dessa räknades ut genom att undersöka en SVM modell med 15 unikamultisensorkonfigurationer. Det visade sig att kombinationen med två sensorer, envid ländryggen och en på underbenet var den bästa och därför användes den föratt undersöka effektiviteten av olika databehandlingstekniker. Tidsdomänsstatistiskafunktioner, sensorvinkeltidsserier och filtrerade signaltidsserier utvärderades sominmatning till ett NN. Tidsdomänsfunktionerna presterade bäst och uppnådde högstnoggrannhet i alla tre övningarna. Detta med ett korrekt utfall av 67% för knöböj,87% för utfallsteg och 75% för höftlyft. Sammantaget visade den slutliga modellenen lovande förmåga att klassificera träningsteknik för basala styrkeövningar för nedredelen av kroppen. Samtidigt som användaren får feedback i realtid vilket gör detmöjligt att utföra effektiv träning själv hemma.

With the advancement of technology and the development of more sophisticated remote sensing sensor systems, the use of satellite imagery has opened up various fields of exploration and application. There has been an increased interest in analysis of multi-temporal satellite image in the past few years because of the wide variety of possible applications of in both short-term and long-term image analysis. The type of changes that might be of interest can range from short-term phenomena such as flood assessment and crop growth stage, to long-term phenomena such as urban fringe development. This thesis studies flood assessment and land cover mapping of satellite images, and proposes nature inspired algorithms that can be easily implemented in realistic scenarios. Disaster monitoring using space technology is one of the key areas of research with vast potential; particularly flood based disasters are more challenging. Every year floods occur in many regions of the world and cause great losses. In order to monitor and assess such situations, decision-makers need accurate near real-time knowledge of the field situation. How to provide actual information to decision-makers for effective flood monitoring and mitigation is an important task, from the point of view of public welfare. Over-estimation of the flooded area leads to over-compensation to people, while under-estimation results in production loss and negative impacts on the population. Hence it is essential to assess the flood damage accurately, both in qualitative and quantitative terms. In such situations, land cover maps play a very critical role. Updating land cover maps is a time consuming and costlier operation when it is performed using traditional or manual methods. Hence, there is a need to find solutions for such problem through automation. Design of automatic systems dedicated to satellite image processing which involves change detection to discriminate areas of land cover change between imaging dates. The system integrates the spectral and spatial information with the techniques of image registration and pattern classification using nature inspired techniques. In the literature, various works have been carried out for solving the problem of image registration and pattern classification using conventional methods. Many researchers have proved, for different situations, that nature inspired techniques are promising in comparison with that of conventional methods. The main advantage of nature inspired technique over any other conventional methods is its stochastic nature, which converges to optimal solution for any dynamic variation in a given satellite image. Results are given in such terms as to delineate change in multi-date imagery using change-versus-no-change information to guide multi-date data analysis. The main objective of this study is to analyze spatio-temporal satellite data to bring out significant changes in the land cover map through automated image processing methods. In this study, for satellite image analysis of flood assessment and land cover mapping, the study areas and images considered are: Multi-temporal MODerate-resolution Imaging Spectroradiometer (MODIS) image around Krishna river basin in Andhra Pradesh India; Linear Imaging Self Scanning Sensor III (LISS III)and Synthetic Aperture Radar(SAR)image around Kosi river basin in Bihar, India; Landsat7thematicmapperimage from the southern part of India; Quick-Bird image of the central Bangalore, India; Hyperion image around Meerut city, Uttar Pradesh, India; and Indian pines hyperspectral image. In order to develop a flood assessment framework for this study, a database was created from remotely sensed images (optical and/or Synthetic Aperture Radar data), covering a period of time. The nature inspired techniques are used to find solutions to problems of image registration and pattern classification of a multi-sensor and multi-temporal satellite image. Results obtained are used to localize and estimate accurately the flood extent and also to identify the type of the inundated area based on land cover mapping. The nature inspired techniques used for satellite image processing are Artificial Neural Network (ANN), Genetic Algorithm (GA),Particle Swarm Optimization (PSO), Firefly Algorithm(FA),Glowworm Swarm Optimization(GSO)and Artificial Immune System (AIS). From the obtained results, we evaluate the performance of the methods used for image registration and pattern classification to compare the accuracy of satellite image processing using nature inspired techniques. In summary, the main contributions of this thesis include (a) analysis of flood assessment and land cover mapping using satellite images and (b) efficient image registration and pattern classification using nature inspired algorithms, which are more popular than conventional optimization methods because of their simplicity, parallelism and convergence of the population towards the optimal solution in a given search space.

This thesis presents an investigation of the possibility of using the fixed-point arithmetic in the inertial navigation systems, which use the local level navigation frame mechanization equations. Two square root filtering methods, the Potter's square root Kalman filter and UD factorized Kalman filter, are compared with respect to the conventional Kalman filter and its Joseph's stabilized form. The effect of rounding errors to the Kalman filter optimality and the covariance matrix or its factors conditioning is evaluated for a various lengths of the fractional part of the fixed-point computational word. Main contribution of this research lies in an evaluation of the minimal fixed-point arithmetic word length for the Phi-angle error model with noise statistics which correspond to the tactical grade inertial measurements units.

Indiana University-Purdue University Indianapolis (IUPUI) / With the advances in embedded technology and the omnipresence of smartphones, tracking systems do not need to be confined to a specific tracking environment. By introducing mobile devices into a tracking system, we can leverage their mobility and the availability of multiple sensors such as camera, Wi-Fi, Bluetooth and Inertial sensors. This thesis proposes to improve the existing tracking systems, enhanced Distributed Tracking System (e-DTS 2.0) [19] and enhanced Distributed Object Tracking System (eDOTS)[26], in the form of e-DTS 3.0 and provides an empirical analysis of these improvements. The enhancements proposed are to introduce Android-based mobile devices into the tracking system, to use multiple sensors on the mobile devices such as the camera, the Wi-Fi and Bluetooth sensors and inertial sensors and to utilize possible resources that may be available in the environment to make the tracking opportunistic. This thesis empirically validates the proposed enhancements through the experiments carried out on a prototype of e-DTS 3.0.