In dieser Doktorarbeit untersuchen wir zeitliche Langsamkeit als Prinzip für die Selbstorganisation des sensorischen Kortex sowie für computer-basierte Mustererkennung. Wir beginnen mit einer Einführung und Diskussion dieses Prinzips und stellen anschliessend den Slow Feature Analysis (SFA) Algorithmus vor, der das matemathisches Problem für diskrete Zeitreihen in einem endlich dimensionalen Funktionenraum löst. Im Hauptteil der Doktorarbeit untersuchen wir zeitliche Langsamkeit als Lernprinzip für rezeptive Felder im visuellen Kortex. Unter Verwendung von SFA werden Transformationsfunktionen gelernt, die, angewendet auf natürliche Bildsequenzen, möglichst langsam variierende Merkmale extrahieren. Die Funktionen können als nichtlineare raum-zeitliche rezeptive Felder interpretiert und mit Neuronen im primären visuellen Kortex (V1) verglichen werden. Wir zeigen, dass sie viele Eigenschaften von komplexen Zellen in V1 besitzen, nicht nur die primären, d.h. Gabor-ähnliche optimale Stimuli und Phaseninvarianz, sondern auch sekundäre, wie Richtungsselektivität, nicht-orthogonale Inhibition sowie End- und Seiteninhibition. Diese Resultate zeigen, dass ein einziges unüberwachtes Lernprinzip eine solche Mannigfaltigkeit an Eigenschaften begründen kann. Für die Analyse der mit SFA gelernten nichtlinearen Funktionen haben wir eine Reihe mathematischer und numerischer Werkzeuge entwickelt, mit denen man die quadratischen Formen als rezeptive Felder charakterisieren kann. Wir erweitern sie im weiteren Verlauf, um sie von allgemeinerem Interesse für theoretische und physiologische Modelle zu machen. Den Abschluss dieser Arbeit bildet die Anwendung des Prinzips der zeitlichen Langsamkeit auf Mustererkennungsprobleme. Die fehlende zeitliche Struktur in dieser Problemklasse erfordert eine Modifikation des SFA-Algorithmus. Wir stellen eine alternative Formulierung vor und wenden diese auf eine Standard-Datenbank von handgeschriebenen Ziffern an. / In this thesis we investigate the relevance of temporal slowness as a principle for the self-organization of the visual cortex and for technical applications. We first introduce and discuss this principle and put it into mathematical terms. We then define the slow feature analysis (SFA) algorithm, which solves the mathematical problem for multidimensional, discrete time series in a finite dimensional function space. In the main part of the thesis we apply temporal slowness as a learning principle of receptive fields in the visual cortex. Using SFA we learn the input-output functions that, when applied to natural image sequences, vary as slowly as possible in time and thus optimize the slowness objective. The resulting functions can be interpreted as nonlinear spatio-temporal receptive fields and compared to neurons in the primary visual cortex (V1). We find that they reproduce (qualitatively and quantitatively) many of the properties of complex cells in V1, not only the two basic ones, namely a Gabor-like optimal stimulus and phase-shift invariance, but also secondary ones like direction selectivity, non-orthogonal inhibition, end-inhibition and side-inhibition. These results show that a single unsupervised learning principle can account for a rich repertoire of receptive field properties. In order to analyze the nonlinear functions learned by SFA in our model, we developed a set of mathematical and numerical tools to characterize quadratic forms as receptive fields. We expand them in a successive chapter to be of more general interest for theoretical and physiological models. We conclude this thesis by showing the application of the temporal slowness principle to pattern recognition. We reformulate the SFA algorithm such that it can be applied to pattern recognition problems that lack of a temporal structure and present the optimal solutions in this case. We then apply the system to a standard handwritten digits database with good performance.

Στο πλαίσιο αυτής της διατριβής μελετάται η επίδοση συστημάτων διαφορισμού MIMO σε γενικευμένα κανάλια διαλείψεων. Αρχικά, εξετάζεται η επίδοση των OSTBC σε περιβάλλοντα διαλείψεων Hoyt. Αποδεικνύεται ότι, στην περίπτωση τέτοιων συστημάτων, ο σηματοθορυβικός λόγος (signal to noise ratio, SNR) εκφράζεται ως μία τετραγωνική μορφή κανονικών τυχαίων μεταβλητών και γίνεται χρήση της συνάρτησης πυκνότητας πιθανότητας και της αθροιστικής συνάρτησης κατανομής αυτής της μορφής για τον υπολογισμό των μετρικών επίδοσης. Επιπλέον, μελετάται η σύγκλιση των σειρών που χρησιμοποιούνται για τον υπολογισμό των δύο αυτών συναρτήσεων και κατασκευάζονται νέα άνω φράγματα για το σφάλμα αποκοπής των σειρών. Τα φράγματα αυτά είναι σαφώς πιο αυστηρά από τα ήδη γνωστά από τη βιβλιογραφία. Στη συνέχεια, εισάγεται ένα γενικευμένο μοντέλο διαλείψεων για την ανάλυση επίδοσης των OSTBC και των δεκτών MRC και υπολογίζονται όλες οι μετρικές επίδοσης των δύο συστημάτων για το συγκεκριμένο μοντέλο διαλείψεων. Το μοντέλο αυτό περιλαμβάνει ως ειδικές περιπτώσεις τα πλέον διαδεδομένα μοντέλα καναλιών διαλείψεων, ενώ επιπλέον, επιτρέπει την ανάλυση επίδοσης σε μικτά περιβάλλοντα διαλείψεων όπου τα πολλαπλά κανάλια μπορούν να ακολουθούν διαφορετικές κατανομές. Στη συνέχεια, μελετάται η επίδοση συστημάτων συνεργατικού διαφορισμού με χρήση αναμεταδοτών ανίχνευσης και προώθησης (Detect and Forward, DaF) σε περιβάλλοντα διαλείψεων Rayleigh. Εξετάζονται τρεις διαφορετικοί δέκτες και υπολογίζεται η πιθανότητα σφάλματος ανά bit γι' αυτούς. Τέλος προτείνεται ένας νέος δέκτης για συνεργατικά συστήματα DaF και αποδεικνύεται η ανωτερότητά του σε σύγκριση με τους υπόλοιπους μελετώμενους δέκτες. Όλα τα θεωρητικά αποτελέσματα που παρουσιάζονται στο πλαίσιο της διατριβής συγκρίνονται με αποτελέσματα προσομοιώσεων Monte Carlo που αποδεικνύουν την ορθότητα της ανάλυσης. / This thesis studies the performance of MIMO diversity systems in generalized fading channels. First, we examine the performance of OSTBC in Hoyt fading channels. It is proven that, for this fading model, and when an OSTBC is employed, the signal-to-noise ratio (SNR) of the OSTBC can be expressed as a quadratic form in normal random variables. Therefore, the performance analysis for OSTBC over Hoyt fading channels is performed using the PDF and the CDF of such quadratic forms. In the statistical literature, these functions are expressed in terms of infinite series. The convergence of the series is thoroughly studied and new expressions for the truncation error bound of these series are proposed. The proposed bounds are much tighter than the bounds that can be found in the literature. The expressions for the PDF and the CDF are then used for the performance analysis of OSTBC over Hoyt fading and several performance metrics are calculated. Then, a generalized fading model for the performance analysis of OSTBC and MRC is proposed and the theoretical performance analysis of both MRC and OSTBC is carried out. The main advantage of this model is the fact that it includes as special cases most of the widely used fading models. Furthermore, the performance of cooperative diversity systems employing Detect and Forward (DaF) relays is studied for Rayleigh fading channels. More specifically, three low complexity detection algorithms for these channels are examined and closed-form expressions of the bit error probability (BEP) for these receivers are derived. Finally, a new low complexity receiver for cooperative systems with DaF relays is proposed. Using Monte Carlo Simulations it is shown that this receiver outperforms the three receivers that have been studied. For the systems studied in the thesis, the performance analysis results that have been derived theoretically are compared with Monte Carlo simulations that prove the validity of the analysis.

Cognitive radio is a technology that aims to maximize the current usage of the licensed frequency spectrum. Cognitive radio aims to provide services for license-exempt users by making use of dynamic spectrum access (DSA) and opportunistic spectrum sharing strategies (OSS). Cognitive radios are defined as intelligent wireless devices capable of adapting their communication parameters in order to operate within underutilized bands while avoiding causing interference to licensed users. An underused band of frequencies in a specific location or time is known as a spectrum hole. Therefore, in order to locate spectrum holes, reliable spectrum sensing algorithms are crucial to facilitate the evolution of cognitive radio networks. Since a large and growing body of literature has mainly focused into the conventional time domain (TD) energy detector, throughout this thesis the problem of spectrum sensing is investigated within the context of a frequency domain (FD) approach. The purpose of this study is to investigate detection based on methods of nonparametric power spectrum estimation. The considered methods are the periodogram, Bartlett's method, Welch overlapped segments averaging (WOSA) and the Multitaper estimator (MTE). Another major motivation is that the MTE is strongly recommended for the application of cognitive radios. This study aims to derive the detector performance measures for each case. Another aim is to investigate and highlight the main differences between the TD and the FD approaches. The performance is addressed for independent and identically distributed (i.i.d.) Rayleigh channels and the general Rician and Nakagami fading channels. For each of the investigated detectors, the analytical models are obtained by studying the characteristics of the Hermitian quadratic form representation of the decision statistic and the matrix of the Hermitian form is identified. The results of the study have revealed the high accuracy of the derived mathematical models. Moreover, it is found that the TD detector differs from the FD detector in a number of aspects. One principal and generalized conclusion is that all the investigated FD methods provide a reduced probability of false alarm when compared with the TD detector. Also, for the case of periodogram, the probability of sensing errors is independent of the length of observations, whereas in time domain the probability of false alarm is increased when the sample size increases. The probability of false alarm is further reduced when diversity reception is employed. Furthermore, compared to the periodogram, both Bartlett method and Welch method provide better performance in terms of lower probability of false alarm but an increased probability of detection for a given probability of false alarm. Also, the performance of both Bartlett's method and WOSA is sensitive to the number of segments, whereas WOSA is also sensitive to the overlapping factor. Finally, the performance of the MTE is dependent on the number of employed discrete prolate spheroidal (Slepian) sequences, and the MTE outperforms the periodogram, Bartlett's method and WOSA, as it provides the minimal probability of false alarm.