Results 1  10
of
10
Multichannel Blind Deconvolution: Fir Matrix Algebra And Separation Of Multipath Mixtures
, 1996
"... A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and mat ..."
Abstract

Cited by 74 (0 self)
 Add to MetaCart
A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and matrix algorithms for use in multichannel /multipath problems. Using abstract algebra/group theoretic concepts, information theoretic principles, and the Bussgang property, methods of single channel filtering and source separation of multipath mixtures are merged into a general FIR matrix framework. Techniques developed for equalization may be applied to source separation and vice versa. Potential applications of these results lie in neural networks with feedforward memory connections, wideband array processing, and in problems with a multiinput, multioutput network having channels between each source and sensor, such as source separation. Particular applications of FIR polynomial matrix alg...
Eigenvector Algorithm For Blind Equalization
 International Signal Processing Workshop on Higher Order Statistics
, 1993
"... This paper introduces a new algorithm for blind equalization which uses a set of cost functions. Each of them guarantees a closed form solution of the equalization problem and approximates the ideal MSE (mean square error) solution. On the basis of an iterative process the best approximation is sele ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
This paper introduces a new algorithm for blind equalization which uses a set of cost functions. Each of them guarantees a closed form solution of the equalization problem and approximates the ideal MSE (mean square error) solution. On the basis of an iterative process the best approximation is selected. Application of this algorithm is not limited to linear equalizers operating at symbol rate. As a possible generalization to include other areas (such as system identification, decisionfeedback equalization or fractionally spaced equalization), an extension to fractional tap space equalizers is outlined.
Polynomial Matrix Whitening And Application To The Multichannel Blind Deconvolution Problem
 in MILCOM’95
, 1995
"... A method for whitening a polynomial matrix is described, including the calculation of the eigenvalue polynomials and eigenvector polynomials of an FIR polynomial matrix. The multichannel blind deconvolution problem is briefly described and FIR polynomial matrix whitening is applied to the problem. B ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
A method for whitening a polynomial matrix is described, including the calculation of the eigenvalue polynomials and eigenvector polynomials of an FIR polynomial matrix. The multichannel blind deconvolution problem is briefly described and FIR polynomial matrix whitening is applied to the problem. Benefits of the whitening technique are demonstrated through simulation. Data prewhitening or the use of an exact least squares adaptation is necessary in any problem of moderate complexity. The group theoretic aspects of FIR polynomial matrix algebra are discussed. 1. INTRODUCTION AND MOTIVATION A method for whitening a multichannel linear system is presented. Multiple input and multiple output linear systems are considered. A two input and two output system would be written as H = h 11 h 21 h 12 h 22 : (1) The h ij 's are FIR filters which each represent an acoustic multipath transfer function from source i to sensor j. Referring to Figure 1, a twosensor, twosource problem can...
Exploratory spectral analysis of hydrological time series
 Journal of Stochastic Hydrology and Hydraulics
, 1995
"... Current methods of estimation of the univariate spectral density are reviewed and some improvements are suggested. It is suggested that spectral analysis may perhaps be best thought of as another exploratory data analysis (EDA) tool which complements rather than competes with the popular ARIMA model ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Current methods of estimation of the univariate spectral density are reviewed and some improvements are suggested. It is suggested that spectral analysis may perhaps be best thought of as another exploratory data analysis (EDA) tool which complements rather than competes with the popular ARIMA model building approach. A new diagnostic check for ARMA model adequacy based on the nonparametric spectral density is introduced. Two new algorithms for fast computation of the autoregressive spectral density function are presented. A new style of plotting the spectral density function is suggested. Exploratory spectral analysis of a number of hydrological time series is performed and some interesting periodicities are suggested for further investigation. The application of spectral analysis to determine the possible existence of long memory in riverflow time series is discussed with long riverflow, treering and mud varve series. A comparison of the estimated spectral densities suggests the ARMA models fitted previously to these datasets adequately describe the low frequency component. The software and data used in this paper are available by anonymous ftp from fisher.stats.uwo.ca in the directory pub\mhts.
Cynomolgus and Rhesus Monkey Visual Figments Application of Fourier Transform Smoothing and Statistical Techniques to the Determination of Spectral Parameters
"... ABSTRACT Microspectrophotometric measurements were performed on 217 photoreceptors from cynom*us, Macaca fascicularis, and rhesus, M. mulatto, monkeys. The distributions of cell types, for rods and blue, green, and red ..."
Abstract
 Add to MetaCart
ABSTRACT Microspectrophotometric measurements were performed on 217 photoreceptors from cynom*us, Macaca fascicularis, and rhesus, M. mulatto, monkeys. The distributions of cell types, for rods and blue, green, and red
L1 [Spä76, Abd80, RR73b]. ≤ 80 [CM66].
, 2013
"... Version 2.42 Title word crossreference (a, a ′ ) [SR73, SR73]. (a, b) [SMD71, SR73]. (a, bγ) [SR73]. (a, bγ − γ) [SR73]. (a, bσ) [SMD71]. (a, bσ − σ) [SMD71]. (a, γ) [SR73]. (a, γ − γ) [SR73]. + [AI79]. 0 [Fia73, MT78]. 1 [Fia73, MT78, RCL75]. ..."
Abstract
 Add to MetaCart
Version 2.42 Title word crossreference (a, a ′ ) [SR73, SR73]. (a, b) [SMD71, SR73]. (a, bγ) [SR73]. (a, bγ − γ) [SR73]. (a, bσ) [SMD71]. (a, bσ − σ) [SMD71]. (a, γ) [SR73]. (a, γ − γ) [SR73]. + [AI79]. 0 [Fia73, MT78]. 1 [Fia73, MT78, RCL75].
Jn(x) [Col80a]. Jn(x + jy) [Sca71].
, 2012
"... Version 2.40 Title word crossreference (a, a ′ ) [SR73, SR73]. (a, b) [SMD71, SR73]. (a, bγ) [SR73]. (a, bγ − γ) [SR73]. (a, bσ) [SMD71]. (a, bσ − σ) [SMD71]. (a, γ) [SR73]. (a, γ − γ) [SR73]. + [AI79]. 0 [Fia73, MT78]. 1 [Fia73, MT78, RCL75]. ..."
Abstract
 Add to MetaCart
Version 2.40 Title word crossreference (a, a ′ ) [SR73, SR73]. (a, b) [SMD71, SR73]. (a, bγ) [SR73]. (a, bγ − γ) [SR73]. (a, bσ) [SMD71]. (a, bσ − σ) [SMD71]. (a, γ) [SR73]. (a, γ − γ) [SR73]. + [AI79]. 0 [Fia73, MT78]. 1 [Fia73, MT78, RCL75].
Organization of the Documentation.......................................................................................... xi
"... www.roguewave.com ..."
BY
, 2004
"... SERGEY DRAKUNOV Ph.D.To my family and grandparents: I dedicate my work to you, and it is in loving memory of my grandfather Joseph Raymond McMahon, Sr., that I dedicate it. Without your love and support throughout my life, I wouldn’t be who I am today; nor would I have pursued my university studies ..."
Abstract
 Add to MetaCart
SERGEY DRAKUNOV Ph.D.To my family and grandparents: I dedicate my work to you, and it is in loving memory of my grandfather Joseph Raymond McMahon, Sr., that I dedicate it. Without your love and support throughout my life, I wouldn’t be who I am today; nor would I have pursued my university studies this far. Acknowledgement I am grateful to Dr. Martinez for his brilliant advice and support during my work on this thesis. His insight into the domain of speech and signal processing and the problem of voice conversion has been a great asset in finishing this work. His deep knowledge of each model that I have used in the voice conversion system and his ability to quickly provide both philosophical and theoretical answers to my questions has proved invaluable. I thank the other two members of my committee, Dr. Qidwai and Dr. Drakunov for answering questions and taking the time to assist me with my work. In addition, the EECS department at Tulane has supported me financially for the past two years of my Master’s work so I extend my warm appreciation. To my colleagues, Arman, Carr, Monika, Nick, Rafi, and Vipin, it’s been wonderful being your friend for this time, and I hope that we see each other at future academic