Iterative processing has been shown to be very effective in multiuser space-time block coding (STBC) systems. The complexity and efficiency of an iterative receiver depend heavily on how the log-likelihood ratios (LLRs) of the coded bits are computed and exchanged at the receiver among its three major components, namely the multiuser detector, the maximum a posterior probability (MAP) demodulators and the MAP channel decoders. This thesis first presents a method to quantitatively measure the system complexities with floating-point operations (FLOPS) and a technique to evaluate the iterative receiver's convergence property based on mutual information and extrinsic information transfer (EXIT) charts.<p>Then, an integrated iterative receiver is developed by applying the sigma mappings for M-ary quadrature amplitude modulation (M-QAM) constellations. Due to the linear relationship between the coded bits and the transmitted channel symbol, the multiuser detector can work on the bit-level and hence improves the convergence property of the iterative receiver. It is shown that the integrated iterative receiver is an attractive candidate to replace the conventional receiver when a few receive antennas and a high-order M-QAM constellation are employed.<p> Finally, a more general two-loop iterative receiver is proposed by introducing an inner iteration loop between the MAP demodulators and the MAP convolutional decoders besides the outer iteration loop that involves the multiuser detection (MUD) as in the conventional iterative receiver. The proposed two-loop iterative receiver greatly improves the iteration efficiency. It is demonstrated that the proposed two-loop iterative receiver can achieve the same asymptotic performance as that of the conventional iterative receiver, but with much less outer-loop iterations.

<p>The arrival of microarray technology has produced a lot of expression profiles of genes. The amount of data now available is so huge that new alternate and efficient methods are needed to analyse it. One of the approaches that have been taken is the use of reverse engineering to build up a picture of how the genes are interacting, where one of the obstacles is the amount of calculations needed. Liang et al. (1998) introduced an algorithm called REVEAL, where reverse engineering with entropy and mutual information are used in an attempt to generate the rules of regulation in genetic networks.</p><p>In this dissertation it was investigated if it was possible to compliment the REVEAL algorithm with heuristics. The heuristic approach probed consists of setting a threshold on mutual information values, thereby dismissing combinations of input genes producing values below the threshold value as being non-relevant.</p><p>Four experiments were performed, where each consisted of a different combination of rule complexity, size of network and number of inputs per gene tested.</p><p>The findings of this study are that applying a threshold on mutual information is a realistic option that can reduce the number of calculations and also act as a filter that divides the important information from the irrelevant information. However this method has its limitations; since it is not known in advance where to place the threshold it will always be a chance that true connections fall below the threshold and therefore will be disregarded and not further analysed.</p>

Empowered by linear precoding and decoding, a spatially multiplexed multiple-input multiple-output (MIMO) system becomes a convenient framework to offer high data rate, diversity and interference management. While most of the current precoding/decoding designs have assumed perfect channel state information (CSI) at the receiver, and sometimes even at the transmitter, in this thesis we design the precoder and decoder with imperfect CSI at both the transmit and the receive sides, and investigate the joint impact of channel estimation errors and channel correlation on system structure and performance. The mean-square error (MSE) related performance metrics are used as the design criteria. We begin with the minimum total MSE precoding/decoding design for a single-user MIMO system assuming imperfect CSI at both ends. Here the CSI includes the channel estimate and channel correlation information. The structures of the optimum precoder and decoder are determined. Compared to the perfect CSI case, linear filters are added to the transceiver structure to improve system robustness against imperfect CSI. The effects of channel estimation error and channel correlation are quantified by simulations. With imperfect CSI at both ends, the exact capacity expression for a single-user MIMO channel is difficult to obtain. Instead, a tight capacity lower-bound is used for system design. The optimum structure of the transmit covariance matrix for the lower-bound has not been found in the existing literature. By transforming the transmitter design into a joint precoding/decoding design problem, we derive the expression of the optimum transmit covariance matrix. The close relationship between the maximum mutual information design and the minimum total MSE design is also discovered assuming imperfect CSI. For robust multiuser MIMO communications, minimum average sum MSE transceiver (precoder-decoder pairs) design problems are formulated for both the uplink and the downlink, assuming imperfect channel estimation and channel correlation at the base station (BS). We propose improved iterative algorithms based on the associated Karush-Kuhn-Tucker (KKT) conditions. Under the assumption of imperfect CSI, an uplink--downlink duality in average sum MSE is proved. As an alternative for the uplink optimization, a sequential semidefinite programming (SDP) method is proposed. Simulation results are provided to corroborate the analysis. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2008-07-25 10:53:45.175

Estudos de correlação entre variações de preços de ações e variação de índices de mercado são importantes na compreensão da relação entre o retorno e o risco envolvido na alocação de recursos (investimentos). De acordo com o risco envolvido, deve haver um adequado retorno. Esta questão é abordada pelo modelo CAPM Capital Asset Pricing Model , que parte da premissa de que o risco sistemático de um ativo pode ser mensurado pela sua sensibilidade aos movimentos do mercado, e para isso se supõe que os retornos dos títulos são linearmente relacionados às flutuações de um índice de mercado amplo com um grau conhecido de sensibilidade. No entanto, pode haver relações não lineares entre os retornos dos títulos e as flutuações do índice de mercado. Sendo assim, o presente trabalho analisa uma medida de correlação global vinda da teoria da informação, que mensura qualquer tipo de relação entre duas variáveis, isto é, lineares e não lineares. O objetivo é mostrar a presença de correlações não lineares no mercado de capitais brasileiro. Demonstra-se que a correlação global é expressiva e maior ou igual à correlação linear em toda a amostra constituída de todas as ações que se mantiveram no Índice da Bolsa de Valores de São Paulo (IBOVESPA) de maio de 2001 a abril de 2008, totalizando 84 meses (7 anos). / Correlations among stock price variations and stock market indices variations are important in understanding the relationship between return and risk involved in the allocation of resources (investments). According to the risk involved there exists an appropriate return. This issue is addressed by the CAPM Capital Asset Pricing Model , based on the premise that the systematic risk of an asset can be measured by its sensitivity to market movements and it is assumed that the returns are linearly related to the fluctuations of a market index with a known degree of sensitivity. However, nonlinear relationships may occur. Thus, the present study analyzes a global measure of correlation of information coming from theory, which measures any type of relationship between two variables, i.e. linear and nonlinear. The goal here is to show the presence of nonlinear correlations in the Brazilian capital market. The overall correlation obtained is expressive and greater than the linear correlation across the sample of 33 stock assets from the theoretical portfolio of São Paulo Exchange Index (IBOVESPA), from May 2001 to April 2008, totaling 84 months (7 years).

The arrival of microarray technology has produced a lot of expression profiles of genes. The amount of data now available is so huge that new alternate and efficient methods are needed to analyse it. One of the approaches that have been taken is the use of reverse engineering to build up a picture of how the genes are interacting, where one of the obstacles is the amount of calculations needed. Liang et al. (1998) introduced an algorithm called REVEAL, where reverse engineering with entropy and mutual information are used in an attempt to generate the rules of regulation in genetic networks. In this dissertation it was investigated if it was possible to compliment the REVEAL algorithm with heuristics. The heuristic approach probed consists of setting a threshold on mutual information values, thereby dismissing combinations of input genes producing values below the threshold value as being non-relevant. Four experiments were performed, where each consisted of a different combination of rule complexity, size of network and number of inputs per gene tested. The findings of this study are that applying a threshold on mutual information is a realistic option that can reduce the number of calculations and also act as a filter that divides the important information from the irrelevant information. However this method has its limitations; since it is not known in advance where to place the threshold it will always be a chance that true connections fall below the threshold and therefore will be disregarded and not further analysed.

A multivariate generalization of mutual information, multi-information, is defined in the thermodynamic limit. The definition takes phase coexistence into account by taking the infimum over the translation-invariant Gibbs measures of an interaction potential. It is shown that this infimum is attained in a pure state. An explicit formula can be found for the Ising square lattice, where the quantity is proved to be maximized at the phase-transition point. By this, phase coexis-tence is linked to high model complexity in a rigorous way.

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Modern medicine has become reliant on medical imaging. Multiple modalities, e. g. magnetic resonance imaging (MRI), computed tomography (CT), etc. , are used to provide as much information about the patient as possible. The problem of geometrically aligning the resulting images is called image registration. Mutual information, an information theoretic similarity measure, allows for automated intermodal image registration algorithms. <br /><br /> In applications such as cancer therapy, diagnosticians are more concerned with the alignment of images over a region of interest such as a cancerous lesion, than over an entire image set. Attempts to register only the regions of interest, defined manually by diagnosticians, fail due to inaccurate mutual information estimation over the region of overlap of these small regions. <br /><br /> This thesis examines the region of union as an alternative to the region of overlap. We demonstrate that the region of union improves the accuracy and reliability of mutual information estimation over small regions. <br /><br /> We also present two new mutual information based similarity measures which allow for localized image registration by combining local and global image information. The new similarity measures are based on convex combinations of the information contained in the regions of interest and the information contained in the global images. <br /><br /> Preliminary results indicate that the proposed similarity measures are capable of localizing image registration. Experiments using medical images from computer tomography and positron emission tomography demonstrate the initial success of these measures. <br /><br /> Finally, in other applications, auto-detection of regions of interest may prove useful and would allow for fully automated localized image registration. We examine methods to automatically detect potential regions of interest based on local activity level and present some encouraging results.

An Information Theoretic approach is used for studying the effect of noise on various spiking neural systems. Detailed statistical analyses of neural behaviour under the influence of stochasticity are carried out and their results related to other work and also biological neural networks. The neurocomputational capabilities of the neural systems under study are put on an absolute scale. This approach was also used in order to develop an optimisation framework. A proof-of-concept algorithm is designed, based on information theory and the coding fraction, which optimises noise through maximising information throughput. The algorithm is applied with success to a single neuron and then generalised to an entire neural population with various structural characteristics (feedforward, lateral, recurrent connections). It is shown that there are certain positive and persistent phenomena due to noise in spiking neural networks and that these phenomena can be observed even under simplified conditions and therefore exploited. The transition is made from detailed and computationally expensive tools to efficient approximations. These phenomena are shown to be persistent and exploitable under a variety of circumstances. The results of this work provide evidence that noise can be optimised online in both single neurons and neural populations of varying structures.