abstract: Peptide microarrays have been used in molecular biology to profile immune responses and develop diagnostic tools. When the microarrays are printed with random peptide sequences, they can be used to identify antigen antibody binding patterns or immunosignatures. In this thesis, an advanced signal processing method is proposed to estimate epitope antigen subsequences as well as identify mimotope antigen subsequences that mimic the structure of epitopes from random-sequence peptide microarrays. The method first maps peptide sequences to linear expansions of highly-localized one-dimensional (1-D) time-varying signals and uses a time-frequency processing technique to detect recurring patterns in subsequences. This technique is matched to the aforementioned mapping scheme, and it allows for an inherent analysis on how substitutions in the subsequences can affect antibody binding strength. The performance of the proposed method is demonstrated by estimating epitopes and identifying potential mimotopes for eight monoclonal antibody samples. The proposed mapping is generalized to express information on a protein's sequence location, structure and function onto a highly localized three-dimensional (3-D) Gaussian waveform. In particular, as analysis of protein homology has shown that incorporating different kinds of information into an alignment process can yield more robust alignment results, a pairwise protein structure alignment method is proposed based on a joint similarity measure of multiple mapped protein attributes. The 3-D mapping allocates protein properties into distinct regions in the time-frequency plane in order to simplify the alignment process by including all relevant information into a single, highly customizable waveform. Simulations demonstrate the improved performance of the joint alignment approach to infer relationships between proteins, and they provide information on mutations that cause changes to both the sequence and structure of a protein. In addition to the biology-based signal processing methods, a statistical method is considered that uses a physics-based model to improve processing performance. In particular, an externally developed physics-based model for sea clutter is examined when detecting a low radar cross-section target in heavy sea clutter. This novel model includes a process that generates random dynamic sea clutter based on the governing physics of water gravity and capillary waves and a finite-difference time-domain electromagnetics simulation process based on Maxwell's equations propagating the radar signal. A subspace clutter suppression detector is applied to remove dominant clutter eigenmodes, and its improved performance over matched filtering is demonstrated using simulations. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2014

abstract: A signal with time-varying frequency content can often be expressed more clearly using a time-frequency representation (TFR), which maps the signal into a two-dimensional function of time and frequency, similar to musical notation. The thesis reviews one of the most commonly used TFRs, the Wigner distribution (WD), and discusses its application in Fourier optics: it is shown that the WD is analogous to the spectral dispersion that results from a diffraction grating, and time and frequency are similarly analogous to a one dimensional spatial coordinate and wavenumber. The grating is compared with a simple polychromator, which is a bank of optical filters. Another well-known TFR is the short time Fourier transform (STFT). Its discrete version can be shown to be equivalent to a filter bank, an array of bandpass filters that enable localized processing of the analysis signals in different sub-bands. This work proposes a signal-adaptive method of generating TFRs. In order to minimize distortion in analyzing a signal, the method modifies the filter bank to consist of non-overlapping rectangular bandpass filters generated using the Butterworth filter design process. The information contained in the resulting TFR can be used to reconstruct the signal, and perfect reconstruction techniques involving quadrature mirror filter banks are compared with a simple Fourier synthesis sum. The optimal filter parameters of the rectangular filters are selected adaptively by minimizing the mean-squared error (MSE) from a pseudo-reconstructed version of the analysis signal. The reconstruction MSE is proposed as an error metric for characterizing TFRs; a practical measure of the error requires normalization and cross correlation with the analysis signal. Simulations were performed to demonstrate the the effectiveness of the new adaptive TFR and its relation to swept-tuned spectrum analyzers. / Dissertation/Thesis / M.S. Electrical Engineering 2012

abstract: The use of electromyography (EMG) signals to characterize muscle fatigue has been widely accepted. Initial work on characterizing muscle fatigue during isometric contractions demonstrated that its frequency decreases while its amplitude increases with the onset of fatigue. More recent work concentrated on developing techniques to characterize dynamic contractions for use in clinical and training applications. Studies demonstrated that as fatigue progresses, the EMG signal undergoes a shift in frequency, and different physiological mechanisms on the possible cause of the shift were considered. Time-frequency processing, using the Wigner distribution or spectrogram, is one of the techniques used to estimate the instantaneous mean frequency and instantaneous median frequency of the EMG signal using a variety of techniques. However, these time-frequency methods suffer either from cross-term interference when processing signals with multiple components or time-frequency resolution due to the use of windowing. This study proposes the use of the matching pursuit decomposition (MPD) with a Gaussian dictionary to process EMG signals produced during both isometric and dynamic contractions. In particular, the MPD obtains unique time-frequency features that represent the EMG signal time-frequency dependence without suffering from cross-terms or loss in time-frequency resolution. As the MPD does not depend on an analysis window like the spectrogram, it is more robust in applying the timefrequency features to identify the spectral time-variation of the EGM signal. / Dissertation/Thesis / M.S. Electrical Engineering 2012

Cette thèse s'inscrit dans le contexte de l'analyse/transformation/synthèse des signaux audio utilisant des représentations temps-fréquence, de type transformation de Gabor. Dans ce contexte, la complexité des transformations permettant de relier des sons peut être modélisée au moyen de multiplicateurs de Gabor, opérateurs de signaux linéaires caractérisés par une fonction de transfert temps-fréquence, à valeurs complexes, que l'on appelle masque de Gabor. Les multiplicateurs de Gabor permettent deformaliser le concept de filtrage dans le plan temps-fréquence. En agissant de façon multiplicative dans le plan temps-fréquence, ils sont a priori bien adaptés pour réaliser des transformations sonores telles que des modifications de timbre des sons. Dans un premier temps, ce travail de thèses intéresse à la modélisation du problème d'estimation d'un masque de Gabor entre deux signaux donnés et la mise en place de méthodes de calculs efficaces permettant de résoudre le problème. Le multiplicateur de Gabor entre deux signaux n'est pas défini de manière unique et les techniques d'estimation proposées de construire des multiplicateurs produisant des signaux sonores de qualité satisfaisante. Dans un second temps, nous montrons que les masques de Gabor contiennent une information pertinente capable d'établir une classification des signaux,et proposons des stratégies permettant de localiser automatiquement les régions temps-fréquence impliquées dans la différentiation de deux classes de signaux. Enfin, nous montrons que les multiplicateurs de Gabor constituent tout un panel de transformations sonores entre deux sons, qui, dans certaines situations, peuvent être guidées par des descripteurs de timbre / Analysis/Transformation/Synthesis is a generalparadigm in signal processing, that aims at manipulating or generating signalsfor practical applications. This thesis deals with time-frequencyrepresentations obtained with Gabor atoms. In this context, the complexity of a soundtransformation can be modeled by a Gabor multiplier. Gabormultipliers are linear diagonal operators acting on signals, andare characterized by a time-frequency transfer function of complex values, called theGabor mask. Gabor multipliers allows to formalize the conceptof filtering in the time-frequency domain. As they act by multiplying in the time-frequencydomain, they are "a priori'' well adapted to producesound transformations like timbre transformations. In a first part, this work proposes to model theproblem of Gabor mask estimation between two given signals,and provides algorithms to solve it. The Gabor multiplier between two signals is not uniquely defined and the proposed estimationstrategies are able to generate Gabor multipliers that produce signalswith a satisfied sound quality. In a second part, we show that a Gabor maskcontain a relevant information, as it can be viewed asa time-frequency representation of the difference oftimbre between two given sounds. By averaging the energy contained in a Gabor mask, we obtain a measure of this difference that allows to discriminate different musical instrumentsounds. We also propose strategies to automaticallylocalize the time-frequency regions responsible for such a timbre dissimilarity between musicalinstrument classes. Finally, we show that the Gabor multipliers can beused to construct a lot of sounds morphing trajectories,and propose an extension

No / Word spotting in continuous speech is considered a challenging issue due to dynamic nature of speech. Literature contains a variety of novel techniques for the isolated word recognition and spotting. Most of these techniques are based on pattern recognition and similarity measures. This paper amalgamates the use of different techniques that includes wavelet transform, feature extraction and Euclidean distance. Based on the acoustic features, the proposed system is capable of identifying and localizing a target (test) word in a continuous speech of any length. Wavelet transform is used for the time-frequency representation and filtration of speech signal. Only high intensity frequency components are passed to feature extraction and matching process resulting robust performance in terms of matching as well as computational cost.

No description available.

No description available.

No description available.

Most existing transceivers are Fourier-centric where complex sinusoids play a central role in the internals of the core building blocks. From the channel perspective, complex sinusoids constitute the fundamental effects in the wireless baseband equivalent channel model; exemplified by the time-invariant and time-varying transfer functions in static and time-varying channel conditions respectively. In addition, complex sinusoids are used as signaling waveforms for data transmission through the channel. The dominant mode of transmission in modern communications is in the form of finite time duration blocks having approximately finite bandwidth. As a result, the time-frequency space becomes projected to a time-frequency subspace having essentially limited support where complex sinusoids suffer from leakage effects due to the finite time extent of a block. In addition, Kronecker delta signals (duals of complex sinusoids) suffer from the same vulnerability due to the finite extent bandwidth. Gabor signaling bases using non-rectangular pulse shapes can attain good confinement in the time-frequency space, however, at the expense of completeness which reduces the utilization efficiency of the time-frequency signaling resources. Over a signaling block period, a doubly dispersive (DD) channel is projected onto an essentially limited time-frequency subspace. In this subspace, the Discrete Prolate Spheroidal (DPS) basis matched to the channel parameters is known to be optimally compact in representing the channel using a basis expansion decomposition. Unlike the Discrete Fourier Transform (DFT) basis which lacks compactness due to the leakage effect. Leakage in the expansion coefficients of a particular channel using the DFT basis has a direct correspondence with the Inter-Symbol Interference (ISI) between the DFT signaling components when transmitted through the same channel. For the DPS basis, however, the correspondence is not as obvious. Nevertheless, DPS when used for signaling results in ISI compactness in the form of an exponential decay of distant ISI components. The efficacy of DPS signaling in DD channels in addition to its efficiency in modeling DD channels motivates the investigation of a new transceiver baseband architecture where DFT is supplanted by DPS. / Ph. D.

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.