This research concerns analysis of system capacity, development of adaptive transmission schemes with known channel state information at the transmitter (CSIT) and design of new signal detection and channel estimation schemes with low complexity in some multiple antenna systems. We first analyze the sum-rate capacity of the downlink of a cellular system with multiple transmit antennas and multiple receive antennas assuming perfect CSIT. We evaluate the ergodic sum-rate capacity and show how the sum-rate capacity increases as the number of users and the number of receive antennas increases. We develop upper and lower bounds on the sum-rate capacity and study various adaptive MIMO schemes to achieve, or approach, the sum-rate capacity. Next, we study the minimum outage probability transmission schemes in a multiple-input-single-output (MISO) flat fading channel assuming partial CSIT. Considering two special cases: the mean feedback and the covariance feedback, we derive the optimum spatial transmission directions and show that the associated optimum power allocation scheme, which minimizes the outage probability, is closely related to the target rate and the accuracy of the CSIT. Since CSIT is obtained at the cost of feedback bandwidth, we also consider optimal allocation of bandwidth between the data channel and the feedback channel in order to maximize the average throughput of the data channel in MISO, flat fading, frequency division duplex (FDD) systems. We show that beamforming based on feedback CSI can achieve an average rate larger than the capacity without CSIT under a wide range of mobility conditions. We next study a SAGE-aided List-BLAST detection scheme for MIMO systems which can achieve performance close to that of the maximum-likelihood detector with low complexity. Finally, we apply the EM and SAGE algorithms in channel estimation for OFDM systems with multiple transmit antennas and compare them with a recently proposed least-squares based estimation algorithm. The EM and SAGE algorithms partition the problem of estimating a multi-input channel into independent channel estimation for each transmit-receive antenna pair, therefore avoiding the matrix inversion encountered in the joint least-squares estimation.

Signal detection plays a critical role in realizing reliable transmission through communication systems. In this dissertation, by applying information theoretic approach, efficient detection schemes and algorithms are designed for three particular communication systems. First, a computation efficient coding and detection algorithm is developed to decode two dimensional inter-symbol interference (ISI) channels. The detection algorithm significantly reduces the computation complexity and makes the proposed equalization algorithm applicable. A new metric, the post-detection mutual information (PMI), is established to quantify the ultimate information rate between the discrete inputs and the hard detected output. This is the first time that the information rate loss caused by the hard mapping of the detectors is considered. Since the hard mapping step in the detector is irreversible, we expect that the PMI is reduced compared to the MI without hard mapping. The conclusion is confirmed by both the simulation and the theoretic results. Random complex field code is designed to achieve the secrecy capacity of wiretap channel with noiseless main channel and binary erasure eavesdroppers' channel. More importantly, in addition to approaching the secrecy capacity, RCFC is the first code design which provides a platform to tradeoff secrecy performance with the erasure rate of the eavesdropper's channel and the secrecy rate.

In this dissertation, we present the theory and application of polyspectral signal analysis techniques for interharmonics in shipboard power systems. Interharmonics are generated from various kinds of adjustable speed drives (ASD) in such power systems. ASDs are highly nonlinear devices due to the use of rectifiers and inverters. Since interharmonics can seriously hamper the normal operation of electric ships in many different ways (e.g., excitation of undesirable electrical and/or mechanical resonances, misoperation of control devices, and light flicker), the detection and analysis of interharmonic-related events is a critical issue in assessing power quality in an all-electric ship. Standard signal analysis techniques for regular harmonics are not immediately applicable to interharmonics due to their small amplitude and uncertain frequency of occurrence. Hence, we propose the use of alternative polyspectral analysis techniques such as higher-order spectra (the cross bispectrum/bicoherence) for the detection and analysis of the ASD-generated interharmonics. First, we develop the interharmonic application specific definitions of the cross bispectrum and the cross bicoherence. The statistical characteristics and frequency domain symmetries are also investigated. We apply the modified cross bispectrum to interharmonic detection problems. Due to their small amplitudes, the detection of interharmonics is sensitive to many undesirable factors such as spectral leakage and measurement error. Our analysis results demonstrate that the detection performance of the conventional DFT-based method is seriously degraded in the presence of noise. Hence, we develop a constant false alarm rate (CFAR) interharmonic detector based on the modified cross bispectrum. Our analysis and experimental results show that our method can provide more robust detection performance than conventional methods in the presence of noise. We also develop an ASD condition monitoring method based on the cross bicoherence. The key idea is to diagnose the status of the load side of an ASD from observations made at the source side. In this dissertation, we apply our method to detection and analysis of phase imbalance at the load side of the ASD. Our experimental results demonstrate that the proposed method provides a unique interharmonic signature for detection and classification of asymmetric impedance associated with the phase imbalance. Furthermore, the proposed method shows a more sensitive detection performance compared to the conventional imbalance measurement method, which enables prognosis of potential faults. A novel quadratic phase coupling detector for a single data record with coherent interharmonics is developed. The traditional bicoherence definition fails when its ’phase randomization’ assumption is not satisfied. This assumption is not appropriate for certain applications such as continuous monitoring of rotating machines. Therefore, we propose a novel quadratic phase coupling detector and compare it with previous techniques. It is shown that our detector is superior to previous detectors at high SNRs, and can also address partially coherent cases which previous approaches could not properly address. Flicker issues related to interharmonics are also discussed. We present a newly found limitation of the current IEC flickermeter regarding detecting flicker caused by low frequency interharmonics. We also present observation results of flicker responses of various lamps including light-emitting-diode (LED) lamps. Our observation results confirm that compact fluorescent and LED lamps are sensitive to high frequency interharmonics, although the IEC flickmeter can not detect flicker caused by such interharmonics. Hence, we develop an alternative flicker detection method based on down-up sampling. Our experiment results show that our method can detect flicker regardless of the value of the interharmonic frequencies. Independent of interharmonic topics, we also present our additional achievement involving application of wavelet denoising techniques to network congestion monitoring problems. This was a collaboration with researchers at the Department of Computer Sciences in the University of Texas at Austin, and mainly completed before becoming engaged in the electric ship project. By applying wavelet techniques, we could drastically enhance shared congestion detection performance over previously proposed methods. / text

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The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that may be used in the analysis of drug safety data. These methods differ significantly from the statistical methods in that they utilize co variability methods of association to define drug-reaction relationships. Co variability algorithms were developed in collaboration with Musa Mammadov to discover drugs associated with adverse reactions and possible drug-drug interactions. This method uses the system organ class (SOC) classification in the Australian Adverse Drug Reaction Advisory Committee (ADRAC) data to stratify reactions. The text categorization algorithm BoosTexter was found to work with the same drug safety data and its performance and modus operandi was compared to our algorithms. These alternative methods were compared to a standard disproportionality analysis methods for signal detection in drug safety data including the Bayesean mulit-item gamma Poisson shrinker (MGPS), which was found to have a problem with similar reaction terms in a report and innocent by-stander drugs. A classification of drug terms was made using the anatomical-therapeutic-chemical classification (ATC) codes. This reduced the number of drug variables from 5081 drug terms to 14 main drug classes. The ATC classification is structured into a hierarchy of five levels. Exploitation of the ATC hierarchy allows the drug safety data to be stratified in such a way as to make them accessible to powerful existing tools. A data mining method that uses association rules, which groups them on the basis of content, was used as a basis for applying the ATC and SOC ontologies to ADRAC data. This allows different views of these associations (even very rare ones). A signal detection method was developed using these association rules, which also incorporates critical reaction terms. / Doctor of Philosophy

The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that may be used in the analysis of drug safety data. These methods differ significantly from the statistical methods in that they utilize co variability methods of association to define drug-reaction relationships. Co variability algorithms were developed in collaboration with Musa Mammadov to discover drugs associated with adverse reactions and possible drug-drug interactions. This method uses the system organ class (SOC) classification in the Australian Adverse Drug Reaction Advisory Committee (ADRAC) data to stratify reactions. The text categorization algorithm BoosTexter was found to work with the same drug safety data and its performance and modus operandi was compared to our algorithms. These alternative methods were compared to a standard disproportionality analysis methods for signal detection in drug safety data including the Bayesean mulit-item gamma Poisson shrinker (MGPS), which was found to have a problem with similar reaction terms in a report and innocent by-stander drugs. A classification of drug terms was made using the anatomical-therapeutic-chemical classification (ATC) codes. This reduced the number of drug variables from 5081 drug terms to 14 main drug classes. The ATC classification is structured into a hierarchy of five levels. Exploitation of the ATC hierarchy allows the drug safety data to be stratified in such a way as to make them accessible to powerful existing tools. A data mining method that uses association rules, which groups them on the basis of content, was used as a basis for applying the ATC and SOC ontologies to ADRAC data. This allows different views of these associations (even very rare ones). A signal detection method was developed using these association rules, which also incorporates critical reaction terms. / Doctor of Philosophy

Thesis (M.S.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed March 18, 2008) Includes bibliographical references (p. 89-90).

Thesis (Ph.D.)--McMaster University (Canada), 1996. / Source: Dissertation Abstracts International, Volume: 57-10, Section: B, page: 6457. Adviser: S. Haykin.

Thesis (M.S.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on April 7, 2008) Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.