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La nécessité d'une transmission sans fils des données à des débits élevés, à la fois fiables et avec de faible latence a donné lieu à ces dernières années à une succession de normes sans fil, allant de 3G-4G, WLAN à la cinquième génération (5G) des réseaux mobiles. Dans ce contexte, les équipementiers, ainsi que les opérateurs, doivent élaborer des méthodes d'essai standard précises et efficaces pour évaluer les performances des systèmes et des terminaux. Les méthodologies de test en direct par voie aérienne ("Over-The-Air") (OTA) visent à reproduire des environnements multi-trajets radio en laboratoire de manière répétable et contrôlable, en évitant les coûteuses mesures in-situ. L'objectif de cette thèse est de proposer une nouvelle méthodologie d'essai OTA, afin de reproduire la propagation des canaux radio, sur une large bande et d'évaluer les performances des systèmes sans fil dans des environnements réels. La thèse débute en présentant les bases de la chaîne radio et de certains modèles de chaînes présentés dans la littérature. Ensuite, un examen critique des méthodologies OTA existantes dans la littérature est fourni. Parmi les différentes méthodologies, nous avons opté pour l'approche de la chambre anéchoïde multi-sonde, qui consiste à déployer un certain nombre de sondes autour d'un équipement radio sous test et à les alimenter avec un émulateur d’évanouissements (fading). Cette méthodologie fournit une reproduction précise des caractéristiques des canaux spatiaux, qui sont nécessaires pour évaluer la performance des terminaux multi-antennes dans des environnements réels. L'avantage le plus important de cette méthodologie est la capacité d'imiter différents modèles de canaux en termes de résolution spatiale, d’évanouissements angulaire et temporel. Un outil de simulation a été développé pour étudier et déterminer les caractéristiques de l'installation OTA pour différents types de canaux d’intérêt. En particulier, le nombre et la mise en place des antennes nécessaires et la taille de l'installation ont été étudiés en fonction de la taille électrique du dispositif testé. Sur la base des études de dimensionnement, une configuration OTA expérimentale a été réalisée pour reproduire les caractéristiques des canaux dans l'espace tridimensionnel pour une plage de fréquences de 2 à 6 GHz. / The need for high data-rate, reliable and low latency transmission in wireless communication systems motivated a multitude of wireless standards, spanning from 3G-4G, WLAN to the upcoming fifth generation (5G) of mobile networks. In this context, technology providers, as well as operators, need to develop accurate and cost effective standard test methods, to evaluate devices performance. Over-The-Air (OTA) test methodologies aim to reproduce radio multipath environments in laboratory in repeatable and controllable manner, avoiding costly field test. The focus of this thesis is to propose a new OTA test methodology, in order to emulate radio channel propagation, over a wide band, and to evaluate the performance of the wireless systems in real environments. We start our study by introducing the basics of radio channel and some channel models presented in literature. Then a critical review of existing OTA methodologies in literature is provided. Among the different methodologies we opted for the multi-probe anechoic chamber approach, which consists into deploying a number of probes around a device, and feed them with fading emulator. This methodology provides an accurate reproduction of spatial channel characteristics, which are needed to assess the performance of multi-antenna terminals in real environments. The most important advantage of this methodology is the capability to emulate different channel model in term of spatial resolution, angular and temporal fading. A simulation tool was developed to investigate and determine the OTA setup under different channel condition. In particular the number and emplacement of antennas needed and the size of the setup were investigated as a function of the electrical size of the device under test. Based on the dimensioning studies, an experimental OTA setup was realized to reproduce the channel characteristics in the three dimensional space for a frequency range from 2 to 6 GHz.

Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing. The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG. Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis. A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG. Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes. The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit. An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%). In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.