De nombreux signaux physiques incluant des signaux audio (musique, parole), médicaux (ECG, PCG), de mammifères marins ou d'ondes gravitationnelles peuvent être modélisés comme une superposition d'ondes modulées en amplitude et en fréquence (modes AM-FM), appelés signaux multicomposantes (SMCs). L'analyse temps-fréquence (TF) joue un rôle central pour la caractérisation de tels signaux et, dans ce cadre, diverses méthodes ont été développées au cours de la dernière décennie. Néanmoins, ces méthodes souffrent d'une limitation intrinsèque appelée le principe d'incertitude. Dans ce contexte, la méthode de réallocation (MR) a été développée visant à améliorer les représentations TF (RTFs) données respectivement par la transformée de Fourier à court terme (TFCT) et la transformée en ondelette continue (TOC), en les concentrant autour des lignes de crête correspondant aux fréquences instantanées. Malheureusement, elle ne permet pas de reconstruction des modes, contrairement à sa variante récente connue sous le nom de transformée synchrosqueezée (TSS). Toutefois, de nombreux problèmes associés à cette dernière restent encore à traiter tels que le traitement des fortes modulations en fréquence, la reconstruction des modes d'un SMC à partir de sa TFCT sous-échantillonnée or l'estimation des signatures TF de modes irréguliers et discontinus. Cette thèse traite principalement de tels problèmes afin de construire des nouvelles méthodes TF inversibles plus puissantes et précises pour l'analyse des SMCs.Cette thèse offre six nouvelles contributions précieuses. La première contribution introduit une extension de TSS d'ordre deux appliqué à la TOC ainsi qu'une discussion sur son analyse théorique et sa mise en œuvre pratique. La seconde contribution propose une généralisation des techniques de synchrosqueezing construites sur la TFCT, connue sous le nom de transformée synchrosqueezée d'ordre supérieur (FTSSn), qui permet de mieux traiter une large gamme de SMCs. La troisième contribution propose une nouvelle technique utilisant sur la transformée synchrosqueezée appliquée à la TFCT de second ordre (FTSS2) et une procédure de démodulation, appelée DTSS2, conduisant à une meilleure performance de la reconstruction des modes. La quatrième contribution est celle d'une nouvelle approche permettant la récupération des modes d'un SMC à partir de sa TFCT sous-échantillonnée. La cinquième contribution présente une technique améliorée, appelée calcul de représentation des contours adaptatifs (CRCA), utilisée pour une estimation efficace des signatures TF d'une plus grande classe de SMCs. La dernière contribution est celle d'une analyse conjointe entre l'CRCA et la factorisation matricielle non-négative (FMN) pour un débruitage performant des signaux phonocardiogrammes (PCG). / Many physical signals including audio (music, speech), medical data (ECG, PCG), marine mammals or gravitational-waves can be accurately modeled as a superposition of amplitude and frequency-modulated waves (AM-FM modes), called multicomponent signals (MCSs). Time-frequency (TF) analysis plays a central role in characterizing such signals and in that framework, numerous methods have been proposed over the last decade. However, these methods suffer from an intrinsic limitation known as the uncertainty principle. In this regard, reassignment method (RM) was developed with the purpose of sharpening TF representations (TFRs) given respectively by the short-time Fourier transform (STFT) or the continuous wavelet transform (CWT). Unfortunately, it did not allow for mode reconstruction, in opposition to its recent variant known as synchrosqueezing transforms (SST). Nevertheless, many critical problems associated with the latter still remain to be addressed such as the weak frequency modulation condition, the mode retrieval of an MCS from its downsampled STFT or the TF signature estimation of irregular and discontinuous signals. This dissertation mainly deals with such problems in order to provide more powerful and accurate invertible TF methods for analyzing MCSs.This dissertation gives six valuable contributions. The first one introduces a second-order extension of wavelet-based SST along with a discussion on its theoretical analysis and practical implementation. The second one puts forward a generalization of existing STFT-based synchrosqueezing techniques known as the high-order STFT-based SST (FSSTn) that enables to better handle a wide range of MCSs. The third one proposes a new technique established on the second-order STFT-based SST (FSST2) and demodulation procedure, called demodulation-FSST2-based technique (DSST2), enabling a better performance of mode reconstruction. The fourth contribution is that of a novel approach allowing for the retrieval of modes of an MCS from its downsampled STFT. The fifth one presents an improved method developed in the reassignment framework, called adaptive contour representation computation (ACRC), for an efficient estimation of TF signatures of a larger class of MCSs. The last contribution is that of a joint analysis of ACRC with non-negative matrix factorization (NMF) to enable an effective denoising of phonocardiogram (PCG) signals.

Le corps humain possède une grande redondance musculo-squelettique, se traduisant par une infinité de coordinations musculaires possibles pour produire un effort résultant. Lors d'un mouvement, le système nerveux central est confronté à la gestion de cette redondance. A travers l’analyse de cohérence entre les signaux électromyographiques, ce travail de thèse étudie le rôle fonctionnel du couplage intermusculaire et explore la contribution des mécanismes nerveux impliqués dans la régulation de la redondance musculaire en termes de contrôle de l’activité des muscles agonistes, et antagonistes impliqués dans le phénomène de co-contraction. Nos résultats ont révélé que le couplage intermusculaire entre deux muscles agonistes est modulé en présence de fatigue et en fonction de l’expertise sportive. De plus, le couplage entre muscles agonistes et antagonistes dépend des contraintes mécaniques et du rôle fonctionnel des muscles, et semble directement lié au niveau de co-contraction. La cohérence intermusculaire est modulée dans plusieurs bandes de fréquence, témoignant de l’implication de différentes commandes centrales communes d’origines spinales et supra-spinales. Nos conclusions amènent à penser que la coordination musculaire est en partie contrôlée par des commandes nerveuses communes dont la contribution est modulée suivant les propriétés fonctionnelles des muscles concernées, pour s’adapter de manière optimale aux contraintes internes ou externes de la tâche. Les travaux déjà engagés proposent de contribuer à une meilleure compréhension des mécanismes sous-jacents l’altération de la fonction motrice chez des patients cérébro-lésés. / The human motor system is characterized by high musculoskeletal redundancy, implying that a given resultant effort can result from infinity of feasible muscle coordinations. During a movement, the central nervous system has to manage such redundancy. Through coherence analysis between electromyographic signals, this thesis work aims at investigating the functional role of intermuscular coupling and at better understanding the contribution of central nervous mechanisms responsible for the regulation of muscle redundancy, in terms of agonist muscle activity and also antagonist muscles activity involved in co-contraction. Our results revealed that intermuscular coupling between agonist muscles is modulated according to both the fatigue level and the training status. We also showed that the coupling between agonist and antagonist muscles depends on the mechanical configuration and functional role of muscle pairs, and seems directly related to co-contraction. The modulation of intermuscular coherence occurs in several frequency bands, suggesting the involvement of different common central drives of spinal and supra-spinal origins according to task constraints. Taken together, our results lead us to conclude that common neural drives take part in the control of muscular coordination, with different relative contribution according to the functional properties of recruited muscles, in order to optimally adapt to both internal and external task contraints. Work already undertaken proposes to provide a better understanding of the mechanisms underlying impairment of motor function in brain-injured patients.

Afin de satisfaire les exigences de sécurité requises dans l’aviation civile, la stratégie adoptée consiste à multiplier les chaînes de mesure pour une même information. Il est aujourd’hui recommandé d’introduire une chaîne de mesure dissemblable (reposant sur un principe physique différent) afin d’augmenter le niveau de sécurité. Dans cette optique, Thales mène des travaux sur le développement d’un anémomètre laser Doppler embarqué sur aéronef. Ce capteur, composé de quatre axes LiDAR (Light Detection And Ranging) répartis autour de l’avion, permet d’estimer la vitesse air par l’analyse de la réflexion de l’onde laser émise sur les particules présentes dans l’air.L’objectif de ces travaux est de concevoir une chaîne de traitement du signal LiDAR adaptée à un capteur sur avion. Cette chaîne, basée sur une représentation temps-fréquence, inclut des étapes de détection du signal utile optimisée pour les conditions de faible ensemencement en particules, de sélection des aérosols utiles dans un nuage et d’estimation robuste afin de contrôler la qualité de la mesure. Cette chaîne de traitement, évaluée lors d’une campagne d’essais réalisée à l’observatoire du Pic du Midi, apporte un gain de performances élevé dans les situations critiques. L’architecture du système a été le second axe d’étude. Une méthode d’estimation du vecteur vitesse à partir des estimations effectuées sur chaque axe LiDAR et d’un modèle aérodynamique de l’avion permet de compenser les perturbations observées à proximité de ce dernier. Puis, une procédure d’optimisation de l’architecture est proposée afin d’améliorer les performances du capteur. Les performances de la chaîne de traitement présentée devront être évaluées en conditions réelles, lors d’essais en vol, afin de sonder une grande variété de conditions atmosphériques et d’évaluer le gain apporté et les faiblesses éventuelles du traitement proposé en fonction de ces conditions. / The method use to respect security requirements in civil aviation consists in multiplying measuring chains for the same information. Now, it is recommended to add a dissimilar measuring chain, based on a different physical principle, in order to improve security level. Thus, Thales works on the development of a laser Doppler anemometer embedded on aircraft. This sensor is composed by four LiDAR (Light Detection And Ranging) axis distributed around the aircraft and air speed is estimated by the analysis of the reflection of the emitted laser wave on particles.This thesis objective is to design a LiDAR signal processing chain adapted to an aircraft sensor. The process is based on a time-frequency representation and it includes methods for signal detection in low concentrated air mass, useful particles selection in clouds and robust estimation to control measure reliability. The process has been evaluated during a test campaign realized at the Pic du Midi observatory. Its performances are greatly improved, especially in critical situations.The system architecture has also been studied. An estimation method designed from estimations performed on different LiDAR axis and an aerodynamic model of an aircraft is proposed in order to compensate for air mass perturbations close to the aircraft. Then, an optimization process is presented to improve sensor performances.The signal processing chain will have to be evaluated by flight tests, to explore a large atmospheric conditions variety and to quantify its strengths and weaknesses depending on conditions.

Cette thèse dans le domaine du traitement des signaux non-stationnaires, appliqué aux bruits du cœur mesurés avec un stéthoscope numérique, vise à concevoir un outil automatisé et « intelligent », permettant aux médecins de disposer d’une source d’information supplémentaire à celle du stéthoscope traditionnel. Une première étape dans l’analyse des signaux du cœur, consiste à localiser le premier et le deuxième son cardiaque (S1 et S2) afin de le segmenter en quatre parties : S1, systole, S2 et diastole. Plusieurs méthodes de localisation des sons cardiaques existent déjà dans la littérature. Une étude comparative entre les méthodes les plus pertinentes est réalisée et deux nouvelles méthodes basées sur la transformation temps-fréquence de Stockwell sont proposées. La première méthode, nommée SRBF, utilise des descripteurs issus du domaine temps-fréquence comme vecteur d’entré au réseau de neurones RBF qui génère l’enveloppe d’amplitude du signal cardiaque, la deuxième méthode, nommée SSE, calcule l’énergie de Shannon du spectre local obtenu par la transformée en S. Ensuite, une phase de détection des extrémités (onset, ending) est nécessaire. Une méthode d’extraction des signaux S1 et S2, basée sur la transformée en S optimisée, est discutée et comparée avec les différentes approches qui existent dans la littérature. Concernant la classification des signaux cardiaques, les méthodes décrites dans la littérature pour classifier S1 et S2, se basent sur des critères temporels (durée de systole et diastole) qui ne seront plus valables dans plusieurs cas pathologiques comme par exemple la tachycardie sévère. Un nouveau descripteur issu du domaine temps-fréquence est évalué et validé pour discriminer S1 de S2. Ensuite, une nouvelle méthode de génération des attributs, basée sur la décomposition modale empirique (EMD) est proposée.Des descripteurs non-linéaires sont également testés, dans le but de classifier des sons cardiaques normaux et sons pathologiques en présence des souffles systoliques. Des outils de traitement et de reconnaissance des signaux non-stationnaires basés sur des caractéristiques morphologique, temps-fréquences et non linéaire du signal, ont été explorés au cours de ce projet de thèse afin de proposer un module d’aide au diagnostic, qui ne nécessite pas d’information à priori sur le sujet traité, robuste vis à vis du bruit et applicable dans des conditions cliniques. / This thesis in the field of biomedical signal processing, applied to the heart sounds, aims to develop an automated and intelligent module, allowing medical doctors to have an additional source of information than the traditional stethoscope. A first step in the analysis of heart sounds is the segmentation process. The heart sounds segmentation process segments the PCG (PhonoCardioGram) signal into four parts: S1 (first heart sound), systole, S2 (second heart sound) and diastole. It can be considered one of the most important phases in the auto-analysis of PCG signals. The proposed segmentation module in this thesis can be divided into three main blocks: localization of heart sounds, boundaries detection of the localized heart sounds and classification block to distinguish between S1and S2. Several methods of heart sound localization exist in the literature. A comparative study between the most relevant methods is performed and two new localization methods of heart sounds are proposed in this study. Both of them are based on the S-transform, the first method uses Radial Basis Functions (RBF) neural network to extract the envelope of the heart sound signal after a feature extraction process that operates on the S-matrix. The second method named SSE calculates the Shannon Energy of the local spectrum calculated by the S-transform for each sample of the heart sound signal. The second block contains a novel approach for the boundaries detection of S1 and S2 (onset & ending). The energy concentrations of the S-transform of localized sounds are optimized by using a window width optimization algorithm. Then the SSE envelope is recalculated and a local adaptive threshold is applied to refine the estimated boundaries. For the classification block, most of the existing methods in the literature use the systole and diastole duration (systole regularity) as a criterion to discriminate between S1 and S2. These methods do not perform well for all types of heart sounds, especially in the presence of high heart rate or in the presence of arrhythmic pathologies. To deal with this problem, two feature extraction methods based on Singular Value Decomposition (SVD) technique are examined. The first method uses the S-Transform and the second method uses the Intrinsic Mode Functions (IMF) calculated by the Empirical Mode Decomposition (EMD) technique. The features are applied to a KNN classifier to estimate the performance of each feature extraction method. Nonlinear features are also tested in order to classify the normal and pathological heart sounds in the presence of systolic murmurs. Processing and recognition signal processing tools based on morphological, time-frequency and nonlinear signal features, were explored in this thesis in order to propose an auto-diagnosis module, robust against noise and applicable in clinical conditions.

Brushless Direct Current (BLDC) motors are one of the motor types rapidly gaining popularity. BLDC motors are being increasingly used in critical high performance industries such as appliances, automotive, aerospace, consumer, medical, industrial automation equipment and instrumentation. Fault detection and condition monitoring of BLDC machines is therefore assuming a new importance. The objective of this research is to advance the field of rotor and load fault diagnosis in BLDC machines operating in a variety of operating conditions ranging from constant speed to continuous transient operation. This objective is addressed as three parts in this research. The first part experimentally characterizes the effects of rotor faults in the stator current and voltage of the BLDC motor. This helps in better understanding the behavior of rotor defects in BLDC motors. The second part develops methods to detect faults in loads coupled to BLDC motors by monitoring the stator current. As most BLDC applications involve non-stationary operating conditions, the diagnosis of rotor faults in non-stationary conditions forms the third and most important part of this research. Several signal processing techniques are reviewed to analyze non-stationary signals. Three new algorithms are proposed that can track and detect rotor faults in non-stationary or transient current signals.

In Inverse Synthetic Aperture Radar (ISAR) systems the motion of the target can be classified in two main categories: Translational Motion and Rotational Motion. A small degree of rotational motion is required in order to generate the synthetic aperture of the ISAR systems. On the other hand, the remaining part of the target&rsquo / s motion, that is any degree of translational motion and the large degree of rotational motion, degrades ISAR image quality. Motion compensation techniques focus on eliminating the effect of the targets&rsquo / motion on the ISAR images. In this thesis, ISAR image generation is discussed using both Conventional Fourier Based and Time-Frequency Based techniques. Standard translational motion compensation steps, Range and Doppler Tracking, are examined. Cross-correlation method and Dominant Scatterer Algorithm are employed for Range and Doppler tracking purposes, respectively. Finally, Time-Frequency based motion compensation is studied and compared with the conventional techniques. All of the motion compensation steps are examined using the simulated data. Stepped frequency waveforms are used in order to generate the required data of the simulations. Not only successful results, but also worst case examinations and lack of algorithms are also discussed with the examples.

The capability to detect, track and monitor human activities behind building walls and other non-line-of-sight environments is an important component of security and surveillance operations. Over the years, both ultrawideband and Doppler based radar techniques have been researched and developed for tracking humans behind walls. In particular, Doppler radars capture some interesting features of the human radar returns called microDopplers that arise from the dynamic movements of the different body parts. All the current research efforts have focused on building hardware sensors with very specific capabilities. This dissertation focuses on developing a physics based Doppler radar simulator to generate the dynamic signatures of complex human motions in nonline-of-sight environments. The simulation model incorporates dynamic human motion, electromagnetic scattering mechanisms, channel propagation effects and radar sensor parameters. Detailed, feature-by-feature analyses of the resulting radar signatures are carried out to enhance our fundamental understanding of human sensing using radar. First, a methodology for simulating the radar returns from complex human motions in free space is presented. For this purpose, computer animation data from motion capture technologies are exploited to describe the human movements. Next, a fast, simple, primitive-based electromagnetic model is used to simulate the human body. The microDopplers of several human motions such as walking, running, crawling and jumping are generated by integrating the animation models of humans with the electromagnetic model of the human body. Next, a methodology for generating the microDoppler radar signatures of humans moving behind walls is presented. This involves combining wall propagation functions derived from the finite-difference time-domain (FDTD) simulation with the free space radar simulations of humans. The resulting hybrid simulator of the human and wall is used to investigate the effects of both homogeneous and inhomogeneous walls on human microDopplers. The results are further corroborated by basic point-scatterer analysis of different wall effects. The wall studies are followed by an analysis of the effects of flat grounds on human radar signatures. The ground effect is modeled using the method of images and a ground reflection coefficient. A suitable Doppler radar testbed is developed in the laboratory for simulation validation. Measured data of different human activities are collected in both line-of-sight and through-wall environments and the resulting microDoppler signatures are compared with the simulation results. The human microDopplers are best observed in the joint timefrequency space. Hence, suitable joint time-frequency transforms are investigated for improving the display and the readability of both simulated and measured spectrograms. Finally, two new Doppler radar paradigms are considered. First, a scenario is considered where multiple, spatially distributed Doppler radars are used to measure the microDopplers of a moving human from different viewing angles. The possibility of using these microDoppler data for estimating the positions of different point scatterers on the human body is investigated. Second, a scenario is considered where multiple Doppler radars are collocated in a two-dimensional (2-D) array configuration. The possibility of generating frontal images of human movements using joint Doppler and 2-D spatial beamforming is considered. The performance of this concept is compared with that of conventional 2-D array processing without Doppler processing. / text

Harmonic distortions are inevitably caused by a rectifier and an inverter due to their inherent nonlinearities. An AC-DC-AC converter, configured by the series connection of a rectifier, DC link, and an inverter, induces harmonic distortions at both AC sides and at the DC link. These harmonics can nonlinearly interact or modulate the fundamental frequencies at the AC sides to cause interharmonic distortions. Harmonic and interharmonic distortions can seriously hamper the normal operation of the power system by means of side effects such as excitation of undesirable electrical and/or mechanical resonances, misoperation of control devices, and so forth. This dissertation presents effective methodologies to mitigate harmonic and interharmonic distortions by applying dithered pulse-width modulated (PWM) signals to a voltage-sourced inverter (VSI) type adjustable speed drive (ASD). The proposed methods are also efficient because the dithering applications are performed on control signals without the need for additional devices. By the help of dithering, the rejection bandwidth of a harmonic filter can be relaxed, which enables a lower-order configuration of harmonic filters. First, this dissertation provides a dithering application on gating signals of a sinusoidal PWM (SPWM) inverter in the simulated VSI-ASD model. The dithering is implemented by adding intentional noise into the SPWM process to randomize rising and falling edges of each pulse in a PWM waveform. As a result of the randomized edges, the periodicity of each pulse is varied, which result in mitigated harmonic tones. This mitigation of PWM harmonics also reduces associated interharmonic distortions at the source side of the ASD. The spectral densities at harmonic and interharmonic frequencies are quanti fied by Fourier analysis. It demonstrates approximately up to 10 dB mitigation of harmonic and interharmonic distortions. The nonlinear relationship between the mitigated interharmonics and harmonics is confirmed by cross bicoherence analysis of source- and DC-side current signals. Second, this dissertation proposes a dithered sigma-delta modulation (SDM) technique as an alternative to the PWM method. The dithering method spreads harmonic tones of the SD M bitstream into the noise level. The noise-shaping property of SDM induces lower noise density near the fundamental frequency. The SDM bitstream is then converted into SDM waveform after zero-order interpolation by which the noise-shaping property repeats at every sampling frequency of the bitstream. The advantages of SDM are assessed by comparing harmonic densities and the number of switching events with those of SPWMs. The dithered SD M waveform bounds harmonic and noise densities below approximately -30 dB with respect to the fundamental spectral density without increasing the number of switching events. Third, this dissertation provides additional validity of the proposed method via hardware experiments. For harmonic assessment, a commercial three-phase inverter module is supplied by a DC voltage source. Simulated PWM signals are converted into voltage waveforms to control the inverter. To evaluate interharmonic distortions, the experimental configuration is extended to a VSI-ASD model by connecting a three-phase rectifier to the inverter module via a DC link. The measured voltage and current waveforms are analyzed to demonstrate coincident properties with the simulation results in mitigating harmonics and interharmonics. The experimental results also provide the efficacy of the proposed methods; the dithered SPWM method effectively mitigates the fundamental frequency harmonics and associated interharmonics, and the dithered SDM reduces harmonics with the desired noise-shaping property. / text

Η επεξεργασία του εγκεφαλογραφικού σήματος με τη χρήση νέων υπολογιστικών τεχνικών δίνει τεράστια ώθηση στη μελέτη νευροφυσιολογικών ερωτημάτων. Η χρήση αυτών των μεθόδων από ερευνητές με ελάχιστες γνώσεις προγραμματισμού απαιτεί την ανάπτυξη ενός εύχρηστου γραφικού περιβάλλοντος που να περιλαμβάνει εργαλεία για την αυτοματοποιημένη εφαρμογή των υπολογιστικών τεχνικών. Στην παρούσα εργασία παρουσιάζεται το γραφικό περιβάλλον που αναπτύχθηκε στη Μονάδα Νευροφυσιολογίας στο Εργαστήριο Φυσιολογίας της Ιατρικής Σχολής του Πανεπιστημίου Πατρών για την υποστήριξη των εγκεφαλογραφικών μελετών. Επεξηγούνται οι δυσκολίες της επεξεργασίας εγκεφαλογραφικού σήματος, οι λόγοι που καθιστούν τη χρήση ήδη υπαρχόντων εργαλείων αδύνατη ή ασύμφορη και δικαιολογείται η επιλογή της πλατφόρμας του MATLAB για την ανάπτυξη του περιβάλλοντος. Δίνεται αναλυτικά η πορεία υλοποίησης του προγράμματος και οι οδηγίες χρήσης του. Το περιβάλλον περιλαμβάνει μεθόδους για εισαγωγή δεδομένων από το πρόγραμμα καταγραφής Neuroscan, επιλογή τμημάτων για επεξεργασία, απεικονίσεις στα πεδία του χρόνου, του χώρου και της συχνότητας, εφαρμογή φίλτρων, ανάλυση προκλητών δυναμικών με παρουσίαση μέσης κυματομορφής και μέσου φασματογραφήματος, δημιουργία εικονικών καναλιών και συνεργασία με άλλα προγράμματα όπως τη χρήση της μεθόδου ανάλυσης ανεξαρτήτων συνιστωσών του EEGLAB. Παρουσιάζονται τα αποτελέσματα από τη χρήση του προγράμματος σε δυο μελέτες του εργαστηρίου. Καταρχάς, σε φυσιολογικό ύπνο για τη μελέτη της σχέσης δυο κυματομορφών του δεύτερου σταδίου του ύπνου, των συμπλεγμάτων Κ και των ατράκτων του ύπνου, όπου διαπιστώθηκε ότι η εμφάνιση του συμπλέγματος Κ επηρεάζει τη συχνότητα των ατράκτων όταν συμπίπτουν χρονικά. Έπειτα, σε παθολογικό ύπνο για τη διερεύνηση μεταβολών του θαλαμοφλοιικού κυκλώματος και των ατράκτων του ύπνου σε ένα παιδί με ιστορικό επιληψίας αφαιρέσεων παιδικής ηλικίας. Σε αυτή την περίπτωση διαπιστώθηκε η ύπαρξη ενός ρυθμού με χαρακτηριστικά παρόμοια των ατράκτων του ύπνου, αλλά σε διαφορετική συχνότητα, ενώ παράλληλα, σημαντικά μειωμένη ήταν η εμφάνιση φυσιολογικών ατράκτων. Τέλος, αναδεικνύονται τα πλεονεκτήματα της χρήσης του περιβάλλοντος και συζητείται η εκπλήρωση των στόχων και αναγκών του εργαστηρίου μέσα από το πρόγραμμα καθώς και οι πιθανές μελλοντικές επεκτάσεις. / The use of novel computational techniques in the analysis of encephalographic signals has given a huge boost to the study of neurophysiological questions. The use of such methods by researchers who have little knowledge of computer programming requires the development of a user-friendly graphical interface that includes tools for the automated application of these computational techniques. The present work presents the graphical interface developed at the Neurophysiology Unit of the University of Patras' Medical School for the support of EEG studies. The difficulties of the processing of EEG signals and the reasons that render the use of existing tools impossible or unfit are explained and I justify the choice of the MATLAB platform for the development of the environment. The course of the realization of the program and directions for its use are given in detail. The environment includes methods that import data from the Neuroscan recording system, select portions for processing, plot data over time, space and frequency, apply filters, analyze event-related potentials using average waveform and average spectrogram views, create virtual channels and cooperate with other programs, like using EEGLAB's technique of independent component analysis. The results of using the program in two laboratory studies are presented. First, it helped analyze normal sleep data, for the study of the relationship between two graphoelements of the second NREM sleep stage, the K complex and the sleep spindle. It was shown that the occurrence of a K complex affects the frequency of a spindle when they coincide. Next, in abnormal sleep data, for the study of possible changes of the thalamocortical pathway and sleep spindles on a child with medical history of childhood absence. In this case, the appearance of a rhythmic wave with attributes similar of a sleep spindle but different frequency of oscillation was shown, while at the same time, the incidence of normal spindles was significantly lower. Finally, the advantages of using this environment are shown and the fulfillment of the lab's goals and needs by the program, as well as possible future expansions, are discussed.

The problem studied in this thesis, is the classification of the small-scale aircraft targets by using a natural resonance based electromagnetic feature extraction technique. The aircraft targets are modeled by perfectly conducting, thin wire structures. The electromagnetic back-scattered data used in the classification process, are numerically generated for five aircraft models. A contemporary signal processing tool, the Wigner-Ville distribution is employed in this study in addition to using the principal components analysis technique to extract target features mainly from late-time target responses. The Wigner-Ville distribution (WD) is applied to the electromagnetic back-scattered responses from different aspects. Then, feature vectors are extracted from suitably chosen late-time portions of the WD outputs, which include natural resonance related v information, for every target and aspect to decrease aspect dependency. The database of the classifier is constructed by the feature vectors extracted at only a few reference aspects. Principal components analysis is also used to fuse the feature vectors and/or late-time aircraft responses extracted from reference aspects of a given target into a single characteristic feature vector of that target to further reduce aspect dependency. Consequently, an almost aspect independent classifier is designed for small-scale aircraft targets reaching high correct classification rate.