Les travaux de recherche présentés dans ce mémoire concernent des contributions à l'identification paramétrique de modèles à temps continu. La première contribution est le développement d'une méthode à erreur de sortie appliquée à des modèles linéaires, en boucle ouverte et en boucle fermée. Les algorithmes sont présentés pour des modèles à temps continu, en utilisant une approche hors ligne ou récursive. La méthode est étendue à l'identification de systèmes linéaires comprenant un retard pur. La méthode développée est appliquée à différents systèmes et comparée aux méthodes d'identification existantes. La deuxième contribution est le développement d'une nouvelle approche d'identification de systèmes à boucles imbriquées. Cette approche est développée pour l'identification de systèmes électromécaniques. Elle se base sur l'utilisation d'un modèle d'identification paramétrique générique d'entraînements électromécaniques en boucle fermée, sur la connaissance du profil des lois de mouvement appliquées appelées excitations, et sur l'analyse temporelle de signaux internes et leurs corrélations avec les paramètres à identifier. L'approche est développée dans le cadre de l'identification d'entraînements à courant continu et synchrone. L'application de cette approche est effectuée au travers de simulations et de tests expérimentaux. Les résultats sont comparés à des méthodes d'identification classiques. / The research works presented in this thesis are about contributions in continuous time model parametric identication. The rst work is the development of an output error method applied on linear models, in open and closed loop. The algorithms are presented for continuous time models, using in-line or oine approaches. The method is extended to the case of the linear systems containing pure time delay. The developed method is applied to several systems and compared to the best existing methods. The second contribution is the development of a new identication approach for cascaded loop systems. This approach is developed for identifying electromechanical systems. It is based on the use of a generic parametric model of electromechanical drives in closed loop, on the knowledge of the movement laws applied and called excitations, and on the analyse of the time internal signals and their correlations with the parameters to identify. This approach is developed for identifying direct current and synchronous drives. The approach is applied with simulations and experimental tests. The obtained results are compared to best identifying known methods.

Cognitive radio is a recent novel approach for the realization of intelligent and sophisticated wireless systems. Although the research and development on cognitive radio is still in the stage of infancy, there are significant interests and efforts towards realization of cognitive radio. Cognitive radio systems are envisioned to support context awareness and related systems. The context can be spectrum, environment, location, waveform, power and other radio resources. Significant amount of the studies related to cognitive radio in the literature focuses on the spectrum awareness since it is one of the most crucial features of cognitive radio systems. However, the rest of the features of cognitive radio such as location and environment awareness have not been investigated thoroughly. For instance, location aware systems are widespread and the demand for more advanced ones are growing. Therefore, the main objective of this dissertation is to develop an underlying location awareness architecture for cognitive radio systems, which is described as location awareness engine, in order to support goal driven and autonomous location aware systems. A cognitive radio conceptual model with location awareness engine and cycle is developed by inspiring from the location awareness features of human being and bat echolocation systems. Additionally, the functionalities of the engine are identified and presented. Upon providing the functionalities of location awareness engine, the focus is given to the development of cognitive positioning systems. Furthermore, range accuracy adaptation, which is a cognitive behavior of bats, is developed for cognitive positioning systems. In what follows, two main approaches are investigated in order to improve the performance of range accuracy adaptation method. The first approach is based on idea of improving the spectrum availability through hybrid underlay and overlay dynamic spectrum access method. On the other hand, the second approach emphasizes on spectrum utilization, where we study performance of range accuracy adaptation from both theoretical and practical perspectives considering whole spectrum utilization approach. Furthermore, we introduced a new spectrum utilization technique that is referred as dispersed spectrum utilization. The performance analysis of dispersed spectrum utilization approach is studied considering time delay estimation problem in cognitive positioning systems. Afterward, the performance of whole and dispersed spectrum utilization approaches are compared in the context of cognitive positioning systems. Finally, some representative advanced location aware systems for cognitive radio networks are presented in order to demonstrate some potential applications of the proposed location awareness engine in cognitive radio systems.

In speech communication systems such as voice-controlled systems, hands-free mobile telephones and hearing aids, the received signals are degraded by room reverberation and background noise. This degradation can reduce the perceived quality and intelligibility of the speech, and decrease the performance of speech enhancement and source localization. These problems are difficult to solve due to the colored and nonstationary nature of the speech signals, and features of the Room Impulse Response (RIR) such as its long duration and non-minimum phase. In this dissertation, we focus on two topics of speech enhancement and speaker localization in noisy reverberant environments. A two-stage speech enhancement method is presented to suppress both early and late reverberation in noisy speech using only one microphone. It is shown that this method works well even in highly reverberant rooms. Experiments under different acoustic conditions confirm that the proposed blind method is superior in terms of reducing early and late reverberation effects and noise compared to other well known single-microphone techniques in the literature. Time Difference Of Arrival (TDOA)-based methods usually provide the most accurate source localization in adverse conditions. The key issue for these methods is to accurately estimate the TDOA using the smallest number of microphones. Two robust Time Delay Estimation (TDE) methods are proposed which use the information from only two microphones. One method is based on adaptive inverse filtering which provides superior performance even in highly reverberant and moderately noisy conditions. It also has negligible failure estimation which makes it a reliable method in realistic environments. This method has high computational complexity due to the estimation in the first stage for the first microphone. As a result, it can not be applied in time-varying environments and real-time applications. Our second method improves this problem by introducing two effective preprocessing stages for the conventional Cross Correlation (CC)-based methods. The results obtained in different noisy reverberant conditions including a real and time-varying environment demonstrate that the proposed methods are superior compared to the conventional TDE methods. / Graduate / 0544 / 0984 / saeed.mosayyebpour@gmail.com

In speech communication systems such as voice-controlled systems, hands-free mobile telephones and hearing aids, the received signals are degraded by room reverberation and background noise. This degradation can reduce the perceived quality and intelligibility of the speech, and decrease the performance of speech enhancement and source localization. These problems are difficult to solve due to the colored and nonstationary nature of the speech signals, and features of the Room Impulse Response (RIR) such as its long duration and non-minimum phase. In this dissertation, we focus on two topics of speech enhancement and speaker localization in noisy reverberant environments. A two-stage speech enhancement method is presented to suppress both early and late reverberation in noisy speech using only one microphone. It is shown that this method works well even in highly reverberant rooms. Experiments under different acoustic conditions confirm that the proposed blind method is superior in terms of reducing early and late reverberation effects and noise compared to other well known single-microphone techniques in the literature. Time Difference Of Arrival (TDOA)-based methods usually provide the most accurate source localization in adverse conditions. The key issue for these methods is to accurately estimate the TDOA using the smallest number of microphones. Two robust Time Delay Estimation (TDE) methods are proposed which use the information from only two microphones. One method is based on adaptive inverse filtering which provides superior performance even in highly reverberant and moderately noisy conditions. It also has negligible failure estimation which makes it a reliable method in realistic environments. This method has high computational complexity due to the estimation in the first stage for the first microphone. As a result, it can not be applied in time-varying environments and real-time applications. Our second method improves this problem by introducing two effective preprocessing stages for the conventional Cross Correlation (CC)-based methods. The results obtained in different noisy reverberant conditions including a real and time-varying environment demonstrate that the proposed methods are superior compared to the conventional TDE methods. / Graduate / 2015-04-23 / 0544 / 0984 / saeed.mosayyebpour@gmail.com