Blind Search for Optimal Wiener Equalizers Using an Artificial Immune Network Model

This work proposes a framework to determine the optimal Wiener equalizer by using an artificial immune network model together with the constant modulus (CM) cost function. This study was primarily motivated by recent theoretical results concerning the CM criterion and its relation to the Wiener approach. The proposed immune-based technique was tested under different channel models and filter orders, and benchmarked against a procedure using a genetic algorithm with niching. The results demonstrated that the proposed strategy has a clear superiority when compared with the more traditional technique. The proposed algorithm presents interesting features from the perspective of multimodal search, being capable of determining the optimal Wiener equalizer in most runs for all tested channels

Blind search for optimal Wiener equalizers using an artificial immune network model

This work proposes a framework to determine the optimal Wiener equalizer by using an artificial immune network model together with the constant modulus (CM) cost function. This study was primarily motivated by recent theoretical results concerning the CM criterion and its relation to the Wiener approach. The proposed immune-based technique was tested under different channel models and filter orders, and benchmarked against a procedure using a genetic algorithm with niching. The results demonstrated that the proposed strategy has a clear superiority when compared with the more traditional technique. The proposed algorithm presents interesting features from the perspective of multimodal search, being capable of determining the optimal Wiener equalizer in most runs for all tested channels.2003874074

Contributions to the problem of blind source separation, with emphasis on the study of sparse signals

Orientadores: Romis Ribeiro de Faissol Attux, Ricardo SuyamaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Neste trabalho, foi estudado o problema de Separação Cega de Fontes (BSS), com ênfase nos casos chamados de subparametrizados, isto é, em que o número de fontes é maior do que o de misturas. A primeira contribuição proposta foi a de um limitante relacionado ao erro de inversão intrínseco ao problema quando é utilizada uma estrutura linear de separação. As outras contribuições estão relacionadas à hipótese de que as fontes são esparsas: i) uma proposta de metodologia híbrida, que se utiliza de conceitos baseados em independência e esparsidade dos sinais de forma simultânea para estimar tanto o sistema misturador quanto o número de fontes existentes em misturas com dois sensores; ii) a utilização de ferramentas de otimização baseadas na operação do sistema imunológico para a estimação do sistema misturador em problemas intrinsecamente multimodais; por fim, iii) uma proposta de utilização de um critério baseado em esparsidade para separação de fontes, sendo derivado um processo de otimização baseado na norma ?1 para este fimAbstract: In this work, we studied the problem of Blind Source Separation (BSS), with emphasis on cases referred to as underdetermined, which occur when the number of sources is greater than the number of mixtures. The first contribution was a proposal of a bound to the inversion error that is intrinsic to the problem when a linear structure is used to perform separation. The other contributions are related to the hypothesis that the signals of the sources are sparse: i) the proposal of a hybrid methodology that employs concepts based on signal independence and sparsity to simultaneously estimate both the mixing system and the number of existing sources in mixtures with two sensors; ii) the use of optimization tools based on the modus operandi of the immune system to estimate the mixing system in problems that are inherently multimodal; finally, iii) the use of a criterion based on sparsity for source separation, which is derived from an optimization process based on the ?1 normDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

Advanced optimization algorithms for sensor arrays and multi-antenna communications

Optimization problems arise frequently in sensor array and multi-channel signal processing applications. Often, optimization needs to be performed subject to a matrix constraint. In particular, unitary matrices play a crucial role in communications and sensor array signal processing. They are involved in almost all modern multi-antenna transceiver techniques, as well as sensor array applications in biomedicine, machine learning and vision, astronomy and radars. In this thesis, algorithms for optimization under unitary matrix constraint stemming from Riemannian geometry are developed. Steepest descent (SD) and conjugate gradient (CG) algorithms operating on the Lie group of unitary matrices are derived. They have the ability to find the optimal solution in a numerically efficient manner and satisfy the constraint accurately. Novel line search methods specially tailored for this type of optimization are also introduced. The proposed approaches exploit the geometrical properties of the constraint space in order to reduce the computational complexity. Array and multi-channel signal processing techniques are key technologies in wireless communication systems. High capacity and link reliability may be achieved by using multiple transmit and receive antennas. Combining multi-antenna techniques with multicarrier transmission leads to high the spectral efficiency and helps to cope with severe multipath propagation. The problem of channel equalization in MIMO-OFDM systems is also addressed in this thesis. A blind algorithm that optimizes of a combined criterion in order to be cancel both inter-symbol and co-channel interference is proposed. The algorithm local converge properties are established as well

Enabling Technology in Optical Fiber Communications: From Device, System to Networking

This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking

Air Force Institute of Technology Research Report 2007

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

A study on the application of bio-inspired algorithms to the problem of direction of arrival estimation

The classical solution to the problem of estimating the direction of arrival (DOA) of plane waves impinging on a sensor array is based on the application of the maximum likelihood method. This approach leads to the problem of optimizing a cost function which is non-linear, non-quadratic, multimodal and variant with respect to the signal-noise ratio (SNR). The methods proposed in the literature to solve this problem fail for a wide set of SNR values. This work presents the results obtained from a study on the application of natural computing algorithms to the DOA estimation problem. Computational simulations show that four of the analyzed algorithms find the global optimum for a broad range of SNR values with computational efforts lower than that associated with an exaustive search.A solução clássica para o problema de estimação dos ângulos de chegada (DOA) de sinais incidindo em um arranjo de sensores é a aplicação do método de máxima verossimilhança. Este método leva ao problema de otimização de uma função custo não-linear, não-quadrática, multimodal e variante com a relação sinal-ruído (SNR). Os métodos propostos para tal tarefa, presentes na literatura, falham em uma ampla gama de valores de SNR. Este trabalho apresenta os resultados de um estudo sobre a aplicação de ferramentas pertencentes à computação natural ao problema de estimação DOA. Simulações demonstram que quatro dos algoritmos analisados alcançam o ótimo global para uma ampla faixa de valores de SNR, com esforços computacionais inferiores àquele exigido por uma busca exaustiva.60962

"Datum for its own annihilation" : feedback, control, and computing, 1916-1945

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Science, Technology, and Society, 1996.Includes bibliographical references.by David A. Mindell.Ph.D

Novel Estimation and Detection Techniques for 5G Networks

The thesis presents several detection and estimation techniques that can be incorporated into the fifth-generation (5G) networks. First, the thesis presents a novel system for orthogonal frequency division multiplexing (OFDM) to estimate the channel blindly. The system is based on modulating particular pairs of subcarriers using amplitude shift keying (ASK) and phase-shift keying (PSK) adjacent in the frequency domain, which enables the realization of a decision-directed (DD) one-shot blind channel estimator (OSBCE). The performance of the proposed estimator is evaluated in terms of the mean squared error (MSE), where an accurate analytical expression is derived and verified using Monte Carlo simulation under various channel conditions. The system has also extended to exploits the channel correlation over consecutive OFDM symbols to estimate the channel parameters blindly. Furthermore, a reliable and accurate approach has been introduced to evaluate the spectral efficiency of various communications systems. The metric takes into consideration the system dynamics, QoS requirements, and design constraints. Next, a novel efficient receiver design for wireless communication systems that incorporate OFDM transmission has been proposed. The proposed receiver does not require channel estimation or equalization to perform coherent data detection. Instead, channel estimation, equalization, and data detection are combined into a single operation, and hence, the detector performs a direct data detector (D3). The performance of the proposed D3 is thoroughly analyzed theoretically in terms of bit error rate (BER), where closed-form accurate approximations are derived for several cases of interest, and validated by Monte Carlo simulations. The computational complexity of D3 depends on the length of the sequence to be detected. Nevertheless, a significant complexity reduction can be achieved using the Viterbi algorithm (VA). Finally, the thesis proposes a low-complexity algorithm for detecting anomalies in industrial steelmaking furnaces operation. The algorithm utilizes the vibration measurements collected from several built-in sensors to compute the temporal correlation using the autocorrelation function (ACF). Furthermore, the proposed model parameters are tuned by solving multi-objective optimization using a genetic algorithm (GA). The proposed algorithm is tested using a practical dataset provided by an industrial steelmaking plant