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The control simulation of tactile sensors using constraint modelling techniquesAnsari, Abdul Wahab January 1993 (has links)
No description available.
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Localisation multi-hypothèses pour l'aide à la conduite : conception d'un filtre "réactif-coopératif" / Multi-assumptions localization for driving assistance : design of a "reactive-cooperative" filterAhmed Bacha, Adda Redouane 01 December 2014 (has links)
“ Lorsqu'on utilise des données provenant d'une seule source,C'est du plagiat;Lorsqu'on utilise plusieurs sources,C'est de la fusion de données ”Ces travaux présentent une approche de fusion de données collaborative innovante pour l'égo-localisation de véhicules routiers. Cette approche appelée filtre de Kalman optimisé à essaim de particules (Optimized Kalman Particle Swarm) est une méthode de fusion de données et de filtrage optimisé. La fusion de données est faite en utilisant les données d'un GPS à faible coût, une centrale inertielle, un compteur odométrique et un codeur d'angle au volant. Ce travail montre que cette approche est à la fois plus robuste et plus appropriée que les méthodes plus classiques d'égo-localisation aux situations de conduite urbaine. Cette constatation apparait clairement dans le cas de dégradations des signaux capteurs ou des situations à fortes non linéarités. Les méthodes d'égo-localisation de véhicules les plus utilisées sont les approches bayésiennes représentées par le filtre de Kalman étendu (Extended Kalman Filter) et ses variantes (UKF, DD1, DD2). Les méthodes bayésiennes souffrent de sensibilité aux bruits et d'instabilité pour les cas fortement non linéaires. Proposées pour couvrir les limitations des méthodes bayésiennes, les approches multi-hypothèses (à base de particules) sont aussi utilisées pour la localisation égo-véhiculaire. Inspiré des méthodes de simulation de Monte-Carlo, les performances du filtre à particules (Particle Filter) sont fortement dépendantes des ressources en matière de calcul. Tirant avantage des techniques de localisation existantes et en intégrant les avantages de l'optimisation méta heuristique, l'OKPS est conçu pour faire face aux bruits, aux fortes dynamiques, aux données non linéaires et aux besoins d'exécution en temps réel. Pour l'égo-localisation d'un véhicule, en particulier pour les manœuvres très dynamiques sur route, un filtre doit être robuste et réactif en même temps. Le filtre OKPS est conçu sur un nouvel algorithme de localisation coopérative-réactive et dynamique inspirée par l'Optimisation par Essaim de Particules (Particle Swarm Optimization) qui est une méthode méta heuristique. Cette nouvelle approche combine les avantages de la PSO et des deux autres filtres: Le filtre à particules (PF) et le filtre de Kalman étendu (EKF). L'OKPS est testé en utilisant des données réelles recueillies à l'aide d'un véhicule équipé de capteurs embarqués. Ses performances sont testées en comparaison avec l'EKF, le PF et le filtre par essaim de particules (Swarm Particle Filter). Le filtre SPF est un filtre à particules hybride intéressant combinant les avantages de la PSO et du filtrage à particules; Il représente la première étape de la conception de l'OKPS. Les résultats montrent l'efficacité de l'OKPS pour un scénario de conduite à dynamique élevée avec des données GPS endommagés et/ou de qualité faible. / “ When we use information from one source,it's plagiarism;Wen we use information from many,it's information fusion ”This work presents an innovative collaborative data fusion approach for ego-vehicle localization. This approach called the Optimized Kalman Particle Swarm (OKPS) is a data fusion and an optimized filtering method. Data fusion is made using data from a low cost GPS, INS, Odometer and a Steering wheel angle encoder. This work proved that this approach is both more appropriate and more efficient for vehicle ego-localization in degraded sensors performance and highly nonlinear situations. The most widely used vehicle localization methods are the Bayesian approaches represented by the EKF and its variants (UKF, DD1, DD2). The Bayesian methods suffer from sensitivity to noises and instability for the highly non-linear cases. Proposed for covering the Bayesian methods limitations, the Multi-hypothesis (particle based) approaches are used for ego-vehicle localization. Inspired from monte-carlo simulation methods, the Particle Filter (PF) performances are strongly dependent on computational resources. Taking advantages of existing localization techniques and integrating metaheuristic optimization benefits, the OKPS is designed to deal with vehicles high nonlinear dynamic, data noises and real time requirement. For ego-vehicle localization, especially for highly dynamic on-road maneuvers, a filter needs to be robust and reactive at the same time. The OKPS filter is a new cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO) metaheuristic methods. It combines advantages of the PSO and two other filters: The Particle Filter (PF) and the Extended Kalman filter (EKF). The OKPS is tested using real data collected using a vehicle equipped with embedded sensors. Its performances are tested in comparison with the EKF, the PF and the Swarm Particle Filter (SPF). The SPF is an interesting particle based hybrid filter combining PSO and particle filtering advantages; It represents the first step of the OKPS development. The results show the efficiency of the OKPS for a high dynamic driving scenario with damaged and low quality GPS data.
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The application of cluster analysis to predicting the cellular uptake of foreign compoundsRanade, Sonia January 1997 (has links)
No description available.
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Network management in decentralised sensing systemsUtete, Simukai January 1994 (has links)
No description available.
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Distributed TDOA/AOA Wireless Location for Multi-sensor Data Fusion System with Correlated Measurement NoisesChen, Chien-Wen 22 August 2007 (has links)
In multi-sensor data fusion target tracking system, using information filtering can implement distributed location with uncorrelated measurement noises, but
the measurement noises of different sensors are often correlated. If measurement noises are correlated, the covariance matrix of measurement noises is not a diagonal matrix. We can not use information filtering to implement distributed
location with correlated measurement noises. By using the matrix theory, the covariance matrix of measurement noises can be transformed to a diagonal matrix. The observation models are transformed to new observation models, and
the multi-sensor measurements with correlated measurement noises are transformed to equivalent pseudo ones with uncorrelated measurement noises. There are many methods in the matrix theory, we use Cholesky fatorization in this thesis. Cholesky fatorization is from Gaussian elimination, and there are many advantages in the computation process.However, the observation models need
to be transformed to new observation models, and the measurement datas for the approach need to be separated and recombined. For measurement datas being separated and recombined, every sensor must communicate with each other. In practice, one sensor does not directly communicate with other sensors except its direct neighbors. By formulating the Cholesky factorization process, we present
architectures which are applied in wireless distributed location. Distributed architectures with clustered nodes are proposed to achieve measurement exchange and information sharing for wireless location and target tracking. With limited times
of data exchanges between clustered nodes, the correlated noise components in the measurements are transformed into uncorrelated ones through the Cholesky process, and the resultant information can be directly shared and processed by the derived extended information filters at the nodes in the distributed system. Hybrid TDOA/AOA wireless location systems with the NLOS error effects are
used as examples in investigating the distributed information architecture. Simulation results show that the proposed distributed information processing and data fusion architecture effectively achieve improved location and tracking accuracy.
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Multisensor data fusion /Filippidis, Arthur. January 1993 (has links) (PDF)
Thesis (M. Eng. Sc.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1994? / Includes bibliographical references (leaves 149-152).
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Data fusion of complementary information from parietal and occipital event related potentials for early diagnosis of Alzheimer's disease /Stepenosky, Nicholas. January 2006 (has links)
Thesis (M.S.)--Rowan University, 2006. / Typescript. Includes bibliographical references.
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Traffic management algorithms in wireless sensor networksBougiouklis, Theodoros C. January 2006 (has links) (PDF)
Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2006. / Thesis Advisor(s): Weillian Su. "September 2006." Includes bibliographical references (p. 79-80). Also available in print.
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The management of communications in decentralised Bayesian data fusion systemDeaves, R. H. January 1998 (has links)
No description available.
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Process models for the navigation of high speed land vehiclesJulier, Simon J. January 1997 (has links)
No description available.
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