In many applications of Independent Component Analysis (ICA) and Blind Source Separation (BSS) estimated sources signals and the mixing or separating matrices have some special structure or some constraints are imposed for the matrices such as symmetries, orthogonality, non-negativity, sparseness and specified invariant norm of the separating matrix. In this paper we present several algorithms and overview some known transformations which allows us to preserve several important constraints.

Blind separation of sources from their linear mixtures is a well understood problem. However, if the mixtures are nonlinear, this problem becomes generally very difficult. This is because both the nonlinear mapping and the underlying sources must be learned from the data in a blind manner, and the problem is highly ill-posed without a suitable regularization. In our approach, multilayer perceptrons are used as nonlinear generative models for the data, and variational Bayesian (ensemble) learning is applied for finding the sources. The variational Bayesian technique automatically provides a reasonable regularization of the nonlinear blind separation problem. In this paper, we first consider a static nonlinear mixing model, with a successful application to real-world speech data compression. Then we discuss extraction of sources from nonlinear dynamic processes, and detection of abrupt changes in the process dynamics. In a difficult test problem with chaotic data, our approach clearly outperforms currently available nonlinear prediction and change detection techniques. The proposed methods are computationally demanding, but they can be applied to blind nonlinear problems of higher dimensions than other existing approaches.

The problem of separating blindly independent sources from a convolutive mixture cannot be addressed in its widest generality without resorting to statistics of order higher than two. The core of the problem is in fact to identify the paraunitary part of the mixture, which is addressed in this paper. With this goal, a family of statistical contrast is first defined. Then it is shown that the problem reduces to a Partial Approximate Joint Diagonalization (PAJOD) of several cumulant matrices. Then, a numerical algorithm is devised, which works block-wise, and sweeps all the output pairs. Computer simulations show the good behavior of the algorithm in terms of Symbol Error Rates, even on very short data blocks.

The problem of optimal placement of pilot symbols is considered for single carrier packet-based transmission over time varying channels. Both flat and frequency-selective fading channels are considered, and the time variation of the channel is modeled by Gauss-Markov process. The semi-blind linear minimum mean-square error (LMMSE) channel estimation is used. Two different performance criteria, namely the maximum mean square error (MSE) of the channel tap state over a packet and the cumulative channel MSE over a packet, are used to compare different placement schemes. The pilot symbols are assumed to be placed in clusters of length (2L+1) where L is the channel order, and only one non-zero training symbols is placed at the center of each cluster. It is shown that, at high SNR, either performance metric is minimized by distributing the pilot clusters throughout the packet periodically. It is shown that at low SNR, the placement is in fact not optimal. Finally, the performance under the periodic placement is compared with that obtained with superimposed pilots.

A blind source separation problem in a solicitations context is addressed. The mixture stems from several telecommunication signals, the symbol periods of which are unknown and possibly different. Cost functions are introduced, the optimization of which achieves the equalization for a user, i.e. estimation of the symbol period and the associated sequence of symbols. The method is iterated by implementing a deflation. The theoretical results are validated by simulations.

In this paper we consider blind source separation (BSS) problem of signals which are spatially uncorrelated of order four, but temporally correlated of order four (for instance speech or biomedical signals). For such type of signals we propose a new sufficient condition for separation using fourth order statistics, stating that the separation is possible, if the source signals have distinct normalized cumulant functions (depending on time delay). Using this condition we show that the BSS problem can be converted to a symmetric eigenvalue problem of a generalized cumulant matrix Z⁽⁴⁾(b) depending on L-dimensional parameter b, if this matrix has distinct eigenvalues. We prove that the set of parameters b which produce Z⁽⁴⁾(b) with distinct eigenvalues form an open subset of R^L, whose complement has a measure zero. We propose a new separating algorithm which uses Jacobi's method for joint diagonalization of cumulant matrices depending on time delay. We empasize the following two features of this algorithm: 1) The optimal number of matrices for joint diago- nalization is 100-150 (established experimentally), which for large dimensional problems is much smaller than those of JADE; 2) It works well even if the signals from the above class are, additionally, white (of order two) with zero kurtosis (as shown by an example).

This paper presents novel techniques for blind separation and blind extraction of instantaneously mixed binary sources, which are suitable for the case with less sensors than sources. First, a solvability analysis is presented for a general case. Necessary and sufficient conditions for recoverability of all or some part of sources are derived. A new deterministic blind separation algorithm is then proposed to estimate the mixing matrix and separate all sources efficiently in the noise-free or low noise level case. Next, using the Maximum Likelihood (ML) approach for robust estimation of centers of clusters, we have extended the algorithm for high additive noise case. Moreover, a new sequential blind extraction algorithm has been developed, which enables us not only to extract the potentially separable sources but also estimate their number. The sources can be extracted in a specific order according to their dominance (strength) in the mixtures. At last, simulation results are presented to illustrate the validity and high performance of the algorithms.

This paper discusses a nonlinear function for independent component analysis to process complex-valued signals in frequency-domain blind source separation. Conventionally, nonlinear functions based on the Cartesian coordinates are widely used. However, such functions have a convergence problem. In this paper, we propose a more appropriate nonlinear function that is based on the polar coordinates of a complex number. In addition, we show that the difference between the two types of functions arises from the assumed densities of independent components. Our discussion is supported by several experimental results for separating speech signals, which show that the polar type nonlinear functions behave better than the Cartesian type.

The present paper deals with the blind deconvolution of a Multiple-Input Multiple-Output Finite Impulse Response (MIMO-FIR) system. To deal with the blind deconvolution problem using the second-order statistics (SOS) of the outputs, Hua and Tugnait considered it under the conditions that a) the FIR system is irreducible and b) the input signals are spatially uncorrelated and have distinct power spectra. In the present paper, the problem is considered under a weaker condition than the condition a). Namely, we assume that c) the FIR system is equalizable by means of the SOS of the outputs. Under b) and c), we show that the system can be blindly identified up to a permutation, a scaling, and a delay using the SOS of the outputs. Moreover, based on this identifiability, we show a novel necessary and sufficiently condition for solving the blind deconvolution problem, and then, based on the condition, we propose a new algorithm for finding an equalizer using the SOS of the outputs, while Hua and Tugnait have not proposed any algorithm for solving the blind deconvolution under the conditions a) and b).

This paper considers a link of two problems; multichannel blind deconvolution and multichannel blind identification of linear time-invariant dynamic systems. To solve these problems, cumulant maximization has been proposed for blind deconvolution, while cumulant matching has been utilized for blind identification. They have been independently developed. In this paper, a cumulant maximization criterion for multichannel blind deconvolution is shown to be equivalent to a least-squares cumulant matching criterion after multichannel prewhitening of channel outputs. This equivalence provides us with a new link between a cumulant maximization criterion for blind deconvolution and a cumulant matching criterion for blind identification.

A new blind identification method of transfer functions between variables in feedback systems is introduced for single sweep type of MEG data. The method is based on the viewpoint of stochastic/statistical inverse problems. The required conditions of the model are stationary and linear Gaussian processes. Raw MEG data of the brain activities are heavily contaminated with several noises and artifacts. The elimination of them is a crucial problem especially for the method. Usually, these noises and artifacts are removed by notch and high-pass filters which are preset automatically. In the present paper, we will try two types of more careful preprocessing procedures for the identification method to obtain impulse functions. One is a careful notch filtering and the other is a blind source separation method based on temporal structure. As results, identifiably of transfer functions and their impulse responses are improved in both cases. Transfer functions and impulse responses identified between MEG sensors are obtained by using the method in Appendix A, when eyes are closed with rest state. Some advantages of the blind source separation method are discussed.

Since the beginning of the last two decades, many researchers have been involved in the problem of Blind Source Separation (BSS). Whilst hundreds of algorithms have been proposed to solve BSS. These algorithms are well known as Independent Component Analysis (ICA) algorithms. Nowadays, ICA algorithms have been used to deal with various applications and they are using many performance indices. This paper is dedicated to classify the different algorithms according to their applications and performances.

We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following two parts: frequency-domain ICA with direction-of-arrival (DOA) estimation, and null beamforming based on the estimated DOA. The alternation of learning between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method.

Combining a layered space-time receiver with the OFDM for high-rate transmissions requires the multi-channel estimation process. Therefore, this letter highlights a design of a preamble structure for OFDM-based layered space-time receiver. The new proposed preamble can estimate the multi-channel up to the 8 transmit antennas using two long preambles in the WLAN standards.

New algorithms for the soft-decision and the hard-decision maximum likelihood decoding (MLD) for binary linear block codes are proposed. It has been widely known that both MLD can be regarded as an integer programming with binary arithmetic conditions. Recently, Conti and Traverso have proposed an efficient algorithm which uses Grobner bases to solve integer programming with ordinary integer arithmetic conditions. In this paper, the Conti-Traverso algorithm is extended to solve integer programming with modulo arithmetic conditions. We also show how to transform the soft-decision and the hard-decision MLD to integer programming for which the extended Conti-Traverso algorithm is applicable.

In this paper, we present a polynomial construction based on Jacket and Hadamard matrices over the Galois Field. The construction has two modes, one only includes matrices extension, and the other includes a center-weighted scheme for polynomial representations. Here, an "addition" scheme is used to represent matrices, which can lead to simple operations and convenient implementation of hardware.

This paper derives a set of orthogonal polynomials for a complex random variable that is uniformly distributed in two dimensions (2D). The polynomials are used in a series expansion to approximate memoryless nonlinearities in digital QAM systems. We also study stochastic identification of nonlinearities using the orthogonal polynomials through analysis and simulations.

It is well known that KT method proposed by R. Kumaresan and D. W. Tufts is used as a popular parameter estimation method of exponentially damped signal. It is based on linear backward-prediction method and singular value decomposition (SVD). However, it is difficult to estimate parameters correctly by KT method in the case when high noise exists in the signal. In this paper, we propose a parameter (frequency components and damping factors) estimation method to improve the performance of KT method under high noise. In our proposed method, we find the signal zero groups by calculating zeros with different data record lengths according to the combination of forward-prediction and backward-prediction, the mean value of the zeros in the signal zero groups are calculated to estimate the parameters of the signal. The proposed method can estimate parameters correctly and accurately even when high noise exists in the signal. Simulation results are shown to confirm the effectiveness of the proposed method.

We propose a minimal model of neuronal burst-firing that can be considered as a modification and extention of the Bonhoeffer-van der Pol (BVP) model. By using linear stability analysis we show that one of the equilibrium points of the fast subsystem is a saddle point which divides the phase plane into two regions. In one region all phase trajectories approach a limit cycle and in the other they approach a stable equilibrium point. The slow subsystem describes a slowly varying inward current. Various types of bursting phenomena are presented by using bifurcation analysis. The simplicity of the model and the variety of firing modes are the biggest advantages of our model with obvious applications in understanding underlying mechanisms of generation of neuronal firings and modeling oscillatory neural networks.

This paper proposes two techniques for improving the accuracy of gate-level power analysis for system-on-a-chip (SoC). (1) Creation of custom wire load models for clock nets. (2) Use of layout information (actual net capacitance and input signal transition time). The analysis time is reduced to less than one three-hundredth of the transistor-level power analysis time. Error is within 5% against a real chip, (the same level as that of the transistor-level power analysis), if technique (2) is used, and within 15% if technique (1) is used.

This paper presents a group theoretic approach to the design of Decision diagrams (DDs) with increased functionality of nodes. Basic characteristics of DDs determine their applications, and thus, the optimization of DDs with respect to different characteristics is an important task. Increased functionality of nodes provides for optimization of DDs. In this paper, the methods for optimization of binary DDs by pairing of variables are interpreted as the optimization of DDs by changing the domain group for the represented functions. Then, it is pointed out that, for Abelian groups, the increased functionality of nodes by using larger subgroups may improve some of the characteristics of DDs at the price of other characteristics. With this motivation, we proposed the use of non-Abelian groups for the domain of represented functions by taking advantages from basic features of their group representations. At the same time, the present methods for optimization of DDs, do not offer any criterion or efficient algorithm to choose among a variety of possible different DDs for an assumed domain group. Therefore, we propose Fourier DDs on non-Abelian groups to exploit the reduced cardinality of the Fourier spectrum on these groups.

In certain computer and communication systems, the significant number of byte errors are not hard errors, but a few transient bit errors confined to byte regions. This kind of byte errors are called spotty byte errors, meaning, not all, but only 2 or 3 random bits, are corrupted in a byte. Especially, the codewords of memory systems which use recent high density wide I/O data semiconductor DRAM chips are prone to this kind of spotty byte errors. This is because, the presence of strong electromagnetic waves in the environment or the bombardment of an energetic particle on a DRAM chip is highly likely to upset more than just one bit stored in that chip. Under this situation, codes capable of correcting single spotty byte errors are suitable for application in semiconductor memory systems. This paper defines a spotty byte error as a random t-bit error confined to a b-bit byte and proposes a class of codes called Single t/b-error Correcting (S_t/bEC) codes which are capable of correcting single spotty byte errors occurring in computer and communication systems. For the case where the chip data output is 16 bits, i.e., b=16, the S_3/16EC code proposed in this paper requires only 16 check bits, that is, only one chip is required for check bits at practical information lengths such as 64, 128 and 256 bits. Furthermore, this S_3/16EC code is capable of detecting more than 95% of all single 16-bit byte errors at information length 64 bits.

The wavelet transform (WT) has recently emerged as a powerful tool for image compression. In this paper, a new image compression technique combining the genetic algorithm (GA) and grey-based competitive learning network (GCLN) in the wavelet transform domain is proposed. In the GCLN, the grey theory is applied to a two-layer modified competitive learning network in order to generate optimal solution for VQ. In accordance with the degree of similarity measure between training vectors and codevectors, the grey relational analysis is used to measure the relationship degree among them. The GA is used in an attempt to optimize a specified objective function related to vector quantizer design. The physical processes of competition, selection and reproduction operating in populations are adopted in combination with GCLN to produce a superior genetic grey-based competitive learning network (GGCLN) for codebook design in image compression. The experimental results show that a promising codebook can be obtained using the proposed GGCLN and GGCLN with wavelet decomposition.

The goal of the maximum cut problem is to partition the vertex set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The maximum cut problem has many important applications including the design of VLSI circuits and communication networks. Moreover, many optimization problems can be formulated in terms of finding the maximum cut in a network or a graph. In this paper, we propose an improved Hopfield neural network algorithm for efficiently solving the maximum cut problem. A large number of instances have been simulated. The simulation results show that the proposed algorithm is much better than previous works for solving the maximum cut problem in terms of the computation time and the solution quality.

In this letter, we show that Fan-Chen-Yeh's blind signature scheme and Chien-Jan-Tseng's partially blind signature scheme are vulnerable to the chosen-plaintext attack. We also show that both schemes can be modified so that the chosen-plaintext attack is impossible. But, still Chien-Jan-Tseng's partially blind signature scheme is vulnerable. It fails to satisfy the partial blindness property.