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.

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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.

With the proliferation of renewable energy and its integration into the modern power grid, we face some new issues. Aside from the increased switching rate, which results in faster dynamic behavior, realistic models for these Inverter-Based Resources (IBRs) are not widely available. This complicates researching the behavior of this quickly changing system, and without proper models, simulations may not be totally reliable. To address this, it is recommended that measurement data be used, which includes the entire grid and all of its unique characteristics. Signal processing techniques have been employed exclusively to construct spectrograms, which are time-frequency representations of a signal's spectral information. These spectrograms show ridges that represent the system's changing modes. It can be extremely beneficial to track these modes and generate labeled data representing the evolution of modes as the system evolves. This labeled data can aid in the development of correlation and causation hypotheses linking specific abnormal behaviors to proximity to instability. This can also assist analyze these IBRs and identify flaws in their modeling. This thesis describes a step-by-step process for creating spectrograms, reducing them for better visualization, and then estimating mode evolution with a ridge-tracking algorithm based on penalized jump criteria. The results show that the tracker works effectively with both synthetic and real PMU data. / Master of Science / By 2050, the number of electric cars on the road will increase almost tenfold, and renewable energy will make up almost 50% of the global power mix. While this is great news for the environment, it also poses new challenges to the power sector in ensuring the reliable delivery of clean energy. To address this, we need to collect real-time information about the system. A spectrogram is a visual representation of the power grid's dynamic behavior, providing essential information about frequency and power. Despite its extensive use in biomedical data, this tool is not used much in the power system industry. Spectrograms can be used as a forensic tool or preventive measure to detect system instability. Our project aims to track the system's dynamic behavior using ambient data, which is shown to be rich in information. The proposed algorithm suggests a detailed step-by-step methodology to use this tool for system identification and monitoring. The work's novelty lies in the tracking algorithm developed to identify and track the spectral components in a time-frequency representation.

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.

Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.

The development of low-frequency SONAR systems, using a network of autonomous systems in unmanned vehicles, provides a practical means for bistatic measurements (i.e. when the source and receiver are widely separated, thus allowing multiple viewpoints of a target). Furthermore, time-frequency analysis, in particular Wigner-Ville analysis, takes advantage of the evolution of the time dependent echo spectrum to differentiate a man-made target (e.g. an elastic spherical shell, or cylinder) from a natural one of the similar shape (e.g. a rock). Indeed, key energetic features of man-made objects can aid in identification and classification in the presence of clutter and noise. For example, in a fluid-loaded thin spherical shell, an energetic feature is the mid-frequency enhancement echoes (MFE) that result from antisymmetric Lamb waves propagating around the circumference of the shell, which have been shown to be an acoustic feature useful in this pursuit. This research investigates the enhancement and benefits of bistatic measurements using the Wigner-Ville analysis along with acoustic imaging methods. Additionally, the advantage of joint space-time-frequency coherent processing is investigated for optimal array processing to enhance the detection of non-stationary signals across an array. The proposed methodology is tested using both numerical simulations and experimental data for spherical shells and solid cylinders. This research was conducted as part of the Shallow Water Autonomous Mine Sensing Initiative (SWAMSI) sponsored by ONR.

Inverse Synthetic Aperture Radar coherently processes the return signal from the target in order to construct the image of the target. The conventional methodology used for obtaining the image is the Fourier transform which is not capable of suppressing the Doppler change in the return signal. As a result, Range-Doppler image is degraded. A proper time-frequency transform suppresses the degradation due to time varying Doppler shift. In this thesis, high resolution joint-time frequency transformations that can be used in place of the conventional method are evaluated. Wigner-Ville Distribution, Adaptive Gabor Representation with Coarse-to-Fine search algorithm, and Time-Frequency Distribution Series are examined for the target imaging system. The techniques applied to sample signals compared with each other. The computational and memorial complexity of the methods are evaluated and compared to each other and possible improvements are discussed. The application of these techniques in the target imaging system is also performed and resulting images compared to each other.

Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.