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Statistical parametric speech synthesis (SPSS) has seen improvements over recent years, especially in terms of intelligibility. Synthetic speech is often clear and understandable, but it can also be bland and monotonous. Proper generation of natural speech prosody is still a largely unsolved problem. This is relevant especially in the context of expressive audiobook speech synthesis, where speech is expected to be fluid and captivating. In general, prosody can be seen as a layer that is superimposed on the segmental (phone) sequence. Listeners can perceive the same melody or rhythm in different utterances, and the same segmental sequence can be uttered with a different prosodic layer to convey a different message. For this reason, prosody is commonly accepted to be inherently suprasegmental. It is governed by longer units within the utterance (e.g. syllables, words, phrases) and beyond the utterance (e.g. discourse). However, common techniques for the modeling of speech prosody - and speech in general - operate mainly on very short intervals, either at the state or frame level, in both hidden Markov model (HMM) and deep neural network (DNN) based speech synthesis. This thesis presents contributions supporting the claim that stronger representations of suprasegmental variation are essential for the natural generation of fundamental frequency for statistical parametric speech synthesis. We conceptualize the problem by dividing it into three sub-problems: (1) representations of acoustic signals, (2) representations of linguistic contexts, and (3) the mapping of one representation to another. The contributions of this thesis provide novel methods and insights relating to these three sub-problems. In terms of sub-problem 1, we propose a multi-level representation of f0 using the continuous wavelet transform and the discrete cosine transform, as well as a wavelet-based decomposition strategy that is linguistically and perceptually motivated. In terms of sub-problem 2, we investigate additional linguistic features such as text-derived word embeddings and syllable bag-of-phones and we propose a novel method for learning word vector representations based on acoustic counts. Finally, considering sub-problem 3, insights are given regarding hierarchical models such as parallel and cascaded deep neural networks.

Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the vertex is close to zero compared to the largest Fiedler coefficient of the graph. We propose a vertex-weighted spectral clustering algorithm which incorporates a vector of weights for each vertex of a given graph to form a vertex-weighted graph. The proposed algorithm predicts association of equidistant or nearly equidistant data points from both clusters while the unweighted clustering does not provide association. Finally, we implemented both the unweighted and the vertex-weighted spectral clustering algorithms on several data sets to show that the proposed algorithm works in general.

Of the many existing eukaryotic gene finding software programs, none are able to guarantee accurate identification of genomic protein coding regions and other biological signals central to pathway from DNA to the protein. Eukaryotic gene finding is difficult mainly due to noncontiguous and non-continuous nature of genes. Existing approaches are heavily dependent on the compositional statistics of the sequences they learn from and are not equally suitable for all types of sequences. This thesis firstly develops efficient digital signal processing-based methods for the identification of genomic protein coding regions, and then combines the optimum signal processing-based non-data-driven technique with an existing data-driven statistical method in a novel system demonstrating improved identification of acceptor splice sites. Most existing well-known DNA symbolic-to-numeric representations map the DNA information into three or four numerical sequences, potentially increasing the computational requirement of the sequence analyzer. Proposed mapping schemes, to be used for signal processing-based gene and exon prediction, incorporate DNA structural properties in the representation, in addition to reducing complexity in subsequent processing. A detailed comparison of all DNA representations, in terms of computational complexity and relative accuracy for the gene and exon prediction problem, reveals the newly proposed ?paired numeric? to be the best DNA representation. Existing signal processing-based techniques rely mostly on the period-3 behaviour of exons to obtain one dimensional gene and exon prediction features, and are not well equipped to capture the complementary properties of exonic / intronic regions and deal with the background noise in detection of exons at their nucleotide levels. These issues have been addressed in this thesis, by proposing six one-dimensional and three multi-dimensional signal processing-based gene and exon prediction features. All one-dimensional and multi-dimensional features have been evaluated using standard datasets such as Burset/Guigo1996, HMR195, and the GENSCAN test set. This is the first time that different gene and exon prediction features have been compared using substantial databases and using nucleotide-level metrics. Furthermore, the first investigation of the suitability of different window sizes for period-3 exon detection is performed. Finally, the optimum signal processing-based gene and exon prediction scheme from our evaluations is combined with a data-driven statistical technique for the recognition of acceptor splice sites. The proposed DSP-statistical hybrid is shown to achieve 43% reduction in false positives over WWAM, as used in GENSCAN.

<p>The increasing data rates in digital television networks increase the demands on data capacity of the current transmission channels. Through new standards, the capacity of exisiting channels is increased with new methods of error correction coding and modulation.</p><p>This thesis presents the design and implementation of a modulator for transmission of digital terrestrial television according to the Chinese DTMB standard.</p><p>The system is written in VHDL and is intended for implementation on an FPGA.</p>

This thesis details the development of expertise and tools required for the study of amino acids deposited on Au(111), with a primary focus on the design and testing of a Knudsen cell for controlled deposition of L-cysteine and L-methionine. An ultra-high vacuum preparation chamber designed by Dr. Katie Mitchell and built by Torrovap Industries Inc. was installed. This chamber is connected to the existing scanning tunneling microscopy chamber via a gate valve, and both chambers can operate independently. Various instruments such as a mass spectrometer, quartz crystal microbalance, ion source, and sample manipulator were installed on the preparation chamber. Scanning tunneling microscopy was performed on both homemade and commercial Au(111) thin films. High resolution images of "herringbone" reconstruction and individual atoms were obtained on the commercial thin films, and optimal tunneling conditions were determined. A Knudsen cell was designed to be mounted on the preparation chamber. The Knudsen cell operates over the temperature range 300-400K, with temperatures reproducible to ±0.5K, and stable to ±0.1K over a five minute period. Reproducible deposition rates of less than 0.2Ǻ/s were obtained for both L-cysteine and L-methionine. Electron impact mass spectrometry and heat of sublimation measurements were performed to characterize the effusion of L-cysteine and L-methionine from the Knudsen cell. The mass spectrometry results suggest that L-cysteine was decomposing at 403K while L-methionine was stable during effusion. Heats of sublimation of 168.3±33.2kJ/mol and 156.5±10.1kJ/mol were obtained for L-cysteine and L-methionine respectively.

The increasing data rates in digital television networks increase the demands on data capacity of the current transmission channels. Through new standards, the capacity of exisiting channels is increased with new methods of error correction coding and modulation. This thesis presents the design and implementation of a modulator for transmission of digital terrestrial television according to the Chinese DTMB standard. The system is written in VHDL and is intended for implementation on an FPGA.

This thesis details the development of expertise and tools required for the study of amino acids deposited on Au(111), with a primary focus on the design and testing of a Knudsen cell for controlled deposition of L-cysteine and L-methionine. An ultra-high vacuum preparation chamber designed by Dr. Katie Mitchell and built by Torrovap Industries Inc. was installed. This chamber is connected to the existing scanning tunneling microscopy chamber via a gate valve, and both chambers can operate independently. Various instruments such as a mass spectrometer, quartz crystal microbalance, ion source, and sample manipulator were installed on the preparation chamber. Scanning tunneling microscopy was performed on both homemade and commercial Au(111) thin films. High resolution images of "herringbone" reconstruction and individual atoms were obtained on the commercial thin films, and optimal tunneling conditions were determined. A Knudsen cell was designed to be mounted on the preparation chamber. The Knudsen cell operates over the temperature range 300-400K, with temperatures reproducible to ±0.5K, and stable to ±0.1K over a five minute period. Reproducible deposition rates of less than 0.2Ǻ/s were obtained for both L-cysteine and L-methionine. Electron impact mass spectrometry and heat of sublimation measurements were performed to characterize the effusion of L-cysteine and L-methionine from the Knudsen cell. The mass spectrometry results suggest that L-cysteine was decomposing at 403K while L-methionine was stable during effusion. Heats of sublimation of 168.3±33.2kJ/mol and 156.5±10.1kJ/mol were obtained for L-cysteine and L-methionine respectively.

This thesis investigates several design problems concerning two-channel conjugate quadrature (CQ) filter banks and orthogonal wavelets, as well as orthogonal cosine-modulated (OCM) filter banks. It is well known that optimal design of CQ filters and wavelets and optimal design of prototype filters (PFs) of OCM filter banks in the least squares (LS) or minimax sense are nonconvex problems and to date only local solutions can be claimed. In this thesis, we first make some improvements over several direct design techniques for local design problems in terms of convergence and solution accuracy. By virtue of the recent progress in global polynomial optimization and the improved local design methods mentioned above, we describe an attempt at developing several design strategies that may be viewed as our endeavors towards global solutions for LS CQ filter banks, minimax CQ filter banks, and OCM filter banks. In brief terms, the proposed design strategies are based on several observations made among globally optimal impulse responses of low-order filter banks, and are essentially order-recursive algorithms in terms of filter length combined with some techniques in identifying a desirable initial point in each round of iteration. This main idea is applied to three design scenarios in this thesis, namely, LS design of orthogonal filter banks and wavelets, minimax design of orthogonal filter banks and wavelets, and design of orthogonal cosine-modulated filter banks. Simulation studies are presented to evaluate and compare the performance of the proposed design methods with several well established algorithms in the literature.

We considered a Bayesian approach to nonlinear inverse problems in which the unknown quantity is a high dimension spatial field. The Bayesian approach contains a natural mechanism for regularization in the form of prior information, can incorporate information from heterogeneous sources and provides a quantitative assessment of uncertainty in the inverse solution. The Bayesian setting casts the inverse solution as a posterior probability distribution over the model parameters. Karhunen-Lo'eve expansion and Discrete Cosine transform were used for dimension reduction of the random spatial field. Furthermore, we used a hierarchical Bayes model to inject multiscale data in the modeling framework. In this Bayesian framework, we have shown that this inverse problem is well-posed by proving that the posterior measure is Lipschitz continuous with respect to the data in total variation norm. The need for multiple evaluations of the forward model on a high dimension spatial field (e.g. in the context of MCMC) together with the high dimensionality of the posterior, results in many computation challenges. We developed two-stage reversible jump MCMC method which has the ability to screen the bad proposals in the first inexpensive stage. Channelized spatial fields were represented by facies boundaries and variogram-based spatial fields within each facies. Using level-set based approach, the shape of the channel boundaries was updated with dynamic data using a Bayesian hierarchical model where the number of points representing the channel boundaries is assumed to be unknown. Statistical emulators on a large scale spatial field were introduced to avoid the expensive likelihood calculation, which contains the forward simulator, at each iteration of the MCMC step. To build the emulator, the original spatial field was represented by a low dimensional parameterization using Discrete Cosine Transform (DCT), then the Bayesian approach to multivariate adaptive regression spline (BMARS) was used to emulate the simulator. Various numerical results were presented by analyzing simulated as well as real data.