In this paper, our aim is to extract real-time movement-related potentials, especially readiness-potentials, from EEGs with a small number of scalp electrodes. We proposed a method composed of independent component analysis (ICA), dipole tracing (DT) and scalp Laplacian methods. The proposed method shows a good real-time RP extraction capability from a single-trial of movement by means of the selection of EEGs with high reliability based on the DT and the improvement of the spatial resolution of the scalp potentials based on the scalp Laplacian.

A compound error is any combination of burst errors with various burst lengths including random errors. The compound weight of any such error is defined as a kind of combinational metric which is a generalization of Gabidulin's metric. First, we present a fast method for calculating the weight of any word. Based on this method, as an extension of Wadayama's augmenting method in the case of Hamming weight, we propose a method of constructing codes having higher coding rate by augmenting any compound-error-correcting codes. Furthermore, we show some examples of good compound-error-correcting codes obtained by using our augmenting method.

An adaptive blind signal separation filter is proposed using a risk-sensitive criterion framework. This criterion adopts an exponential type function. Hence, the proposed criterion varies the consideration weight of an adaptation quantity depending on errors in the estimates: the adaptation is accelerated when the estimation error is large, and unnecessary acceleration of the adaptation does not occur close to convergence. In addition, since the algorithm derivation process relates to an H filtering, the derived algorithm has robustness to perturbations or estimation errors. Hence, this method converges faster than conventional least squares methods. Such effectiveness of the new algorithm is demonstrated by simulation.

In the problem of determining the major frequency components of a signal disturbed by noise, a model selection criterion has been proposed. In this paper, the criterion has been extended to cover a penalized cost function that yields a componentwise shrinkage estimator, and it exhibited a consistent model selection when the proposed criterion was used. Then, a simple numerical simulation was conducted, and it was found that the proposed criterion with an empirically estimated componentwise shrinkage estimator outperforms the original criterion.

In recent years, sliding electric contacts came to be often used under very severe conditions such as high temperature, extremely low temperature, high vacuum, etc. Conventionally, solid lubricants having excellent properties in lubricating performance are generally used compositely with a metal of high electric conductivity, because of their high electrical resistivity. In the present study, we proved that more excellent sliding electrical contacts can be produced with a design made by controlling the distribution on contact surface of a solid lubricant having excellent lubricating performance and of a metal with high electric conductivity through expansion of Greenwood's theory.

In the study, we build a system called Adaptive Visual Attentive Tracker (AVAT) for the purpose of developing a non-verbal communication channel between the system and an operator who presents intended movements. In the system, we constructed an HMM (Hidden Markov Models)-based TD (Temporal Difference) learning algorithm to track and zoom in on an operator's behavioral sequence which represents his/her intention. AVAT extracts human intended movements from ordinary walking behavior based on the following two algorithms: the first is to model the movements of human body parts using HMMs algorithm, and the second is to learn the model of the tracker's action using a model-based TD learning algorithm. In the paper, we describe the integrated algorithm of the above two methods: whose linkage is established by assigning the state transition probability in HMM as a reward in TD learning. Experimental results of extracting an operator's hand sign action sequence during her natural walking motion are shown which demonstrates the function of AVAT as it is developed within the framework of perceptual organization. Identification of the sign gesture context through wavelet analysis autonomously provides a reward value for optimizing AVAT's action patterns.

A minor component analysis approach based on the generalized sidelobe canceler is presented to realize the blind suppression of multiple-access interference in multicarrier code division multiple access systems. With a rough user-code and timing estimations, this proposed method of less computation performs the same as minimum mean square error detectors and outperforms existing blind detectors. Simulation results illustrate the effectiveness of the blind multiuser detection.

We propose a novel pixel pattern-based approach for texture classification, which is independent of the variance of illumination. Gray scale images are first transformed into pattern maps in which edges and lines, used for characterizing texture information, are classified by pattern matching. We employ principal component analysis (PCA) which is widely applied to feature extraction. We use the basis functions learned through PCA as templates for pattern matching. Using PCA pattern maps, the feature vector is comprised of the numbers of the pixels belonging to a specific pattern. The effectiveness of the new feature is demonstrated by applications to the image retrievals of the Brodatz texture database. Comparisons with multichannel and multiresolution features indicate that the new feature is quite time saving, free of the influence of illumination, and has comparable accuracy.

One of the difficulties in routing problem is in wirability which is to guarantee a physical connection of a given topological route. Wirability often fails since the width of a wire is relatively large compared with the size of modules. As a possible solution, this paper proposes an incremental wiring algorithm which generates wires net-by-net without overlapping other pre-placed circuit elements. The idea is to divide a wire into a series of rectangles and handles them as modules with additional constraints to keep the shape of the wire. The algorithm was implemented and experimented on a small instance to show its promising performance.

We propose a new algorithm for blind source separation (BSS), in which frequency-domain independent component analysis (FDICA) and time-domain ICA (TDICA) are combined to achieve a superior source-separation performance under reverberant conditions. Generally speaking, conventional TDICA fails to separate source signals under heavily reverberant conditions because of the low convergence in the iterative learning of the inverse of the mixing system. On the other hand, the separation performance of conventional FDICA also degrades significantly because the independence assumption of narrow-band signals collapses when the number of subbands increases. In the proposed method, the separated signals of FDICA are regarded as the input signals for TDICA, and we can remove the residual crosstalk components of FDICA by using TDICA. The experimental results obtained under the reverberant condition reveal that the separation performance of the proposed method is superior to those of TDICA- and FDICA-based BSS methods.

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.

Changing vehicle structures and backgrounds makes it very difficult to correctly extract a license plate region from a vehicle image. In this paper, we propose a simple method to extract the license plate region using edge properties of wavelet subband. The High Frequency Subband (HFS) of an image has edge information for each direction. Edge information is concentrated in each direction of the Headlight-Radiator-Headlight (H-R-H) and the license plate region compared to other regions in the vehicle image. This paper shows a license plate region extraction method using these edge properties and our experimental results with various vehicle images.

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 RL, 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 a method of temporal decomposition (TD) for line spectral frequency (LSF) parameters, called "Modified Restricted Temporal Decomposition" (MRTD), and its application to low rate speech coding. The LSF parameters have not been used for TD due to the stability problems in the linear predictive coding (LPC) model. To overcome this deficiency, a refinement process is applied to the event vectors in the proposed TD method to preserve their LSF ordering property. Meanwhile, the restricted second order TD model, where only two adjacent event functions can overlap and all event functions at any time sum up to one, is utilized to reduce the computational cost of TD. In addition, based on the geometric interpretation of TD the MRTD method enforces a new property on the event functions, named the "well-shapedness" property, to model the temporal structure of speech more effectively. This paper also proposes a method for speech coding at rates around 1.2 kbps based on STRAIGHT, a high quality speech analysis-synthesis method, using MRTD. In this speech coding method, MRTD based vector quantization is used for encoding spectral information of speech. Subjective test results indicate that the speech quality of the proposed speech coding method is close to that of the 4.8 kbps FS-1016 CELP coder.

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.

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.

This paper describes truncated and impossible differentials of Feistel block ciphers with round functions of 2-layer SPN (Substitution and Permutation Network) transformation modules such as the 128-bit block cipher Camellia, which was proposed by NTT and Mitsubishi Electric Corporation. Our work improves on the best known truncated and impossible differentials, and has found a nontrivial 9-round truncated differential that may lead to a possible attack against a reduced-round version of Camellia without input/output whitening, FL or FL-1 (Camellia-NFL), in the chosen plain text scenario. Previously, only 6-round differentials were known that may suggest a possible attack of Camellia-NFL reduced to 8-rounds. We also show a nontrivial 7-round impossible differential, whereas only a 5-round impossible differential was previously known. We also consider the truncated differential of a reduced-round version of Camellia (Camellia-DS) whose round functions are composed of D-S (Diffusion and Substitution) transformation modules and without input/output whitening, FL or FL-1 (Camellia-DS-NFL), and show a nontrivial 9-round truncated differential, which may lead to a possible attack in the chosen plain text scenario. This truncated differential is effective for general Feistel structures with round functions composed of S-D (Substitution and Diffusion) or D-S transformation.

This paper addresses a maximum likelihood method for source separation in the case of overdetermined mixtures corrupted by additive white Gaussian noise. We consider an approximate likelihood which is based on the Laplace approximation and develop a natural gradient adaptation algorithm to find a local maximum of the corresponding approximate likelihood. We present a detailed mathematical derivation of the algorithm using the Lie group invariance. Useful behavior of the algorithm is verified by numerical experiments.

OPKFDDs (Ordered Pseudo-Kronecker Functional Decision Diagrams) are a data structure that provides compact representation of Boolean functions. The size of OPKFDDs depends on a variable ordering and on decomposition type choices. Finding an optimal representation is very hard and the size of the search space is n! 32n-1, where n is the number of input variables. To overcome the huge search space of the problem, a genetic algorithm is proposed for the generation of OPKFDDs with minimal number of nodes.