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Adaptive Kernel Canonical Correlation Analysis Algorithms for Nonparametric Identification of Wiener and Hammerstein Systems  [cached]
Steven Van Vaerenbergh,Javier Vía,Ignacio Santamaría
EURASIP Journal on Advances in Signal Processing , 2008, DOI: 10.1155/2008/875351
Abstract: This paper treats the identification of nonlinear systems that consist of a cascade of a linear channel and a nonlinearity, such as the well-known Wiener and Hammerstein systems. In particular, we follow a supervised identification approach that simultaneously identifies both parts of the nonlinear system. Given the correct restrictions on the identification problem, we show how kernel canonical correlation analysis (KCCA) emerges as the logical solution to this problem. We then extend the proposed identification algorithm to an adaptive version allowing to deal with time-varying systems. In order to avoid overfitting problems, we discuss and compare three possible regularization techniques for both the batch and the adaptive versions of the proposed algorithm. Simulations are included to demonstrate the effectiveness of the presented algorithm.
Nonlinear Blind Source Separation Using Kernel Multi-set Canonical Correlation Analysis  [cached]
Hua-Gang Yu,Gao-Ming Huang,Jun Gao
International Journal of Computer Network and Information Security , 2010,
Abstract: To solve the problem of nonlinear blind source separation (BSS), a novel algorithm based on kernel multi-set canonical correlation analysis (MCCA) is presented. Combining complementary research fields of kernel feature spaces and BSS using MCCA, the proposed approach yields a highly efficient and elegant algorithm for nonlinear BSS with invertible nonlinearity. The algorithm works as follows: First, the input data is mapped to a high-dimensional feature space and perform dimension reduction to extract the effective reduced feature space, translate the nonlinear problem in the input space to a linear problem in reduced feature space. In the second step, the MCCA algorithm was used to obtain the original signals.
Blind Joint Maximum Likelihood Channel Estimation and Data Detection for SIMO Systems
Sheng Chen,Xiao-Chen Yang,Lei Chen,Lajos Hanzo,
Sheng
,Chen,Xiao-Chen,Yang,Lei,Chen,Lajos,Hanzo

国际自动化与计算杂志 , 2007,
Abstract: A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single- input multiple-output (SIMO) systems.The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop.An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model,and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence.A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.
Blind Separation of Acoustic Signals Combining SIMO-Model-Based Independent Component Analysis and Binary Masking  [cached]
Mori Yoshimitsu,Saruwatari Hiroshi,Takatani Tomoya,Ukai Satoshi
EURASIP Journal on Advances in Signal Processing , 2006,
Abstract: A new two-stage blind source separation (BSS) method for convolutive mixtures of speech is proposed, in which a single-input multiple-output (SIMO)-model-based independent component analysis (ICA) and a new SIMO-model-based binary masking are combined. SIMO-model-based ICA enables us to separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources in their original form at the microphones. Thus, the separated signals of SIMO-model-based ICA can maintain the spatial qualities of each sound source. Owing to this attractive property, our novel SIMO-model-based binary masking can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. The experimental results reveal that the separation performance can be considerably improved by the proposed method compared with that achieved by conventional BSS methods. In addition, the real-time implementation of the proposed BSS is illustrated.
Blind CFR Estimation for SIMO SC-FDE Systems
SIMO SC-FDE系统的CFR盲估计

LI Meng-xing,HUANG Long-yang,CHENG En,LIU Ze-min,
李梦醒
,黄龙杨,程恩,刘泽民

计算机科学 , 2009,
Abstract: A blind scheme to estimate frequency-domain channel response(is also called channel frequency response,CFR) in single-input multiple-output(SIMO) single-carrier frequency-domain equalization(SC-FDE) systems based on linear prediction algorithm(LPA) was presented.Compared with conventional LPA based time-domain channel estimation approach,this method obtains the closed-form solution for channel estimation in frequency-domain directly from tap weights of the prediction filter,rather than Cross-correlation of ...
A kernel method for canonical correlation analysis  [PDF]
Shotaro Akaho
Computer Science , 2006,
Abstract: Canonical correlation analysis is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features because of its linearity. On the other hand, kernel method used in support vector machine is an efficient approach to improve such a linear method. In this paper, we investigate the effectiveness of applying kernel method to canonical correlation analysis.
Diagonal Kernel Point Estimation of nth-Order Discrete Volterra-Wiener Systems  [cached]
Claudio Turchetti,Simone Orcioni,Massimiliano Pirani
EURASIP Journal on Advances in Signal Processing , 2004, DOI: 10.1155/s1687617204403011
Abstract: The estimation of diagonal elements of a Wiener model kernel is a well-known problem. The new operators and notations proposed here aim at the implementation of efficient and accurate nonparametric algorithms for the identification of diagonal points. The formulas presented here allow a direct implementation of Wiener kernel identification up to the nth order. Their efficiency is demonstrated by simulations conducted on discrete Volterra systems up to fifth order.
Kernel Optimization for Blind Motion Deblurring with Image Edge Prior
Jing Wang,Ke Lu,Qian Wang,Jie Jia
Mathematical Problems in Engineering , 2012, DOI: 10.1155/2012/639824
Abstract: Image motion deblurring with unknown blur kernel is an ill-posed problem. This paper proposes a blind motion deblurring approach that solves blur kernel and the latent image robustly. For kernel optimization, an edge mask is used as an image prior to improve kernel update, then an edge selection mask is adopted to improve image update. In addition, an alternative iterative method is introduced to perform kernel optimization under a multiscale scheme. Moreover, for image restoration, a total-variation-(TV-) based algorithm is proposed to recover the latent image via nonblind deconvolution. Experimental results demonstrate that our method obtains accurate blur kernel and achieves better deblurring results than previous works.
Wiener-Hopf Analysis of Planar Canonical Structures Loaded with Longitudinally Magnetized Plasma Biased Normally to the Extra-Ordinary Wave Propagation
George A. Kyriacou
PIER B , 2008, DOI: 10.2528/PIERB07121907
Abstract: The canonical problem of an extra-ordinary Transverse Electromagnetic wave propagating in a parallel plane waveguide with a semi-infinite upper conductor and loaded with magnetized plasma is considered. The homogeneous biasing constant magnetic field is assumed parallel to the substrate and normal to the wave propagation, which incidents normally on the truncated edge. The Wiener-Hopf technique is employed and the corresponding equations are formulated for the open-radiating structure as well as for a closed one resulting from the placement of a metallic shield parallel to the waveguide planes. Closed form field expressions are obtained for the shielded geometry, while the open geometry Kernel factorization is left for future extensions. Important non-reciprocal wave propagation phenomena are involved, which lend non-even function properties to the involved Kernels. Hence, their factorization becomes non-trivial requiring new mathematical approaches. Finally, a review of the involved non-reciprocal and/or unidirectional surface waves is given, which is related to the involved mathematical complexities.
Canonical Correlated Kernel PCA Method for Face Recognition  [PDF]
N.NagaMounica,Ch.Ganapathy Reddy
International Journal on Computer Science and Engineering , 2012,
Abstract: Practical face recognition systems are sometimes confronted with low-resolution face images. To address this problem, a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image is presented. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analyses (PCA) based features of high-resolution (HR) and LR face images. The obtained features from PCA are not good enough for dimensionality reduction and computational complexity when large set of databases are taken into consideration. To overcome that problem Kernel PCA is introduced. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the KPCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the Yale database show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.
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