The author prepared new composition of Cu-Sn based composite materials containing lamellar solid lubricants, and measured their performance with focus on contact resistance and the coefficient of friction using a low-speed tribo-meter. Among three kinds of composite materials, the composite material containing 26wt.% of total solid lubricants was lower in both of contact resistance and the coefficient of friction and showed stable characteristics compared with those containing 25wt.% and 35wt.% respectively. The author analyzed the characteristics of these materials using several techniques including BSE image, element analysis through EPMA, and mapping analysis, and examined why the composite material containing 26wt.% of total solid lubricants showed higher performance.

Information transmission among biological neurons is carried out by a complex series of spike signals. The input inter-spike arrival times at a neuron are believed to carry information which the neurons utilize to carry out a task. In this paper, a new scheme which utilizes the input inter-spike intervals (ISI) for decoding an input spike train is proposed. A spike train consists of a sequence on input spikes with various inter-spike times. This decoding scheme can also be used for neurons which have multiple synaptic inputs but for which each synapse receives a single spike within one input time window. The ISI decoding neural network requires only a few neurons. Example applications show the usefulness of the decoding scheme.

Recently, DOAS (differential optical absorption spectroscopy) has been used for nondestructive air monitoring, in which the LS (least squares) method is used to calculate trace gas concentrations due to its computational simplicity. This paper applies the ICA (independent component analysis) method to the DOAS system of air monitoring, since the LS method is insufficient to recover the desired spectra perfectly due to sparsity characteristic. If the sparsity of reference spectra in the DOAS system imposes the assumption of independence, the ICA algorithm can be used. The proposed method is used to regress the observed spectrum on the estimates of the reference spectra. The ICA algorithm can be seen as a preprocessing method where the ICs of the references are used as the input in the regression. The performance of the proposed method is evaluated in simulation studies using synthetic data.

This paper proposes hardware-efficient VLSI architectures for 2-channel signal word decomposed filters (2-ch SWDFs) and their design method in consideration of the implemented circuit size. By consideration of the circuit size in design method, 2-ch SWDFs with a minimum output error among SWDFs whose size is equal to or smaller than a specification can be designed. Canonical Signed Digit expressions are used to effectively represent the filter coefficients of the SWDFs in order to make its circuit size small. Through precise analysis of the internal structures, circuit size can be accurately estimated. Some design examples show that the proposed method can design filters whose output error is about -12 dB lower than that of the linear FIR filters. Compared to an exhaustive search method, our method is much faster and can design filters whose output errors are only about 2 dB more.

In adaptive motion estimation, spatial-temporal correlation based motion type inference has been recognized as an effective way to guide the motion estimation strategy adjustment according to video contents. However, the complexity and the reliability of those methods remain two crucial problems. In this paper, a motion vector field model is introduced as the basis for a new spatial-temporal correlation based motion type inference method. For each block, Full Search with Adaptive Search Window (ASW) and Three Step Search (TSS), as two search strategy candidates, can be employed alternatively. Simulation results show that the proposed method can constantly reduce the dynamic computational cost to as low as 3% to 4% of that of Full Search (FS), while remaining a closer approximation to FS in terms of visual quality than other fast algorithms for various video sequences. Due to its efficiency and reliability, this method is expected to be a favorable contribution to the mobile video communication where low power real-time video coding is necessary.

This paper describes a method for separating a target sound from other noise arriving in a single direction when the target cannot, therefore, be separated by directivity control. Microphones are arranged in a line toward the sources to form null sensitivity points at given distances from the microphones. The null points exclude non-target sound sources on the basis of weighting coefficients for microphone outputs determined by blind source separation. The separation problem is thereby simplified to instantaneous separation by adjustment of the time-delays for microphone outputs. The system uses a direct (i.e. non-iterative) algorithm for blind separation based on second-order statistics, assuming that all sources are non-stationary signals. Simulations show that the 2-microphone system can separate a target sound with separability of more than 40 dB for the 2-source problem, and 25 dB for the 3-source problem when the other sources are adjacent.

An algorithm for the sound projection using multiple sources is presented. The source strength vector is obtained by using a fast estimation approach instead of the conventional eigenvalue decomposition (EVD) method. The computation load is therefore greatly reduced, which makes the algorithm more efficient in practical applications.

This paper overviews a total solution for frequency-domain blind source separation (BSS) of convolutive mixtures of audio signals, especially speech. Frequency-domain BSS performs independent component analysis (ICA) in each frequency bin, and this is more efficient than time-domain BSS. We describe a sophisticated total solution for frequency-domain BSS, including permutation, scaling, circularity, and complex activation function solutions. Experimental results of 22, 33, 44, 68, and 22 (moving sources), (#sources#microphones) in a room are promising.

We have proposed in [5] a practical blind channel identification algorithm for the white observation noise. In this paper, we examine the effectiveness of the algorithm given in [5] for the colored observation noise. The proposed algorithm utilizes Gram-Schmidt orthogonalization procedure and estimates (1) the channel order, (2) the noise variance and then (3) the channel impulse response with less computational complexity compared to the conventional algorithms using eigenvalue decomposition. It can be shown through numerical examples that the algorithm proposed in [5] is quite effective in the colored noise case.

In this paper, we address the blind separation problem of binaural mixed signals, and we propose a novel blind separation method, in which a self-generator for initial filters of Single-Input-Multiple-Output-model-based independent component analysis (SIMO-ICA) is implemented. The original SIMO-ICA which has been proposed by the authors can separate mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. Although this attractive feature of SIMO-ICA is beneficial to the binaural sound separation, the current SIMO-ICA has a serious drawback in its high sensitivity to the initial settings of the separation filter. In the proposed method, the self-generator for the initial filter functions as the preprocessor of SIMO-ICA, and thus it can provide a valid initial filter for SIMO-ICA. The self-generator is still a blind process because it mainly consists of a frequency-domain ICA (FDICA) part and the direction of arrival estimation part which is driven by the separated outputs of the FDICA. To evaluate its effectiveness, binaural sound separation experiments are carried out under a reverberant condition. The experimental results reveal that the separation performance of the proposed method is superior to those of conventional methods.

This paper deals with the decomposition of surface data into several fractal signal based on the parameter estimation by the Mean Likelihood and Importance Sampling (IS) based on the Monte Carlo simulations. The method is applied to the feature extraction of surface data. Assuming the stochastic models for generating the surface, the likelihood function is defined by using wavelet coefficients and the parameter are estimated based on the mean likelihood by using the IS. The approximation of the wavelet coefficients is used for estimation as well as the statistics defined for the variances of wavelet coefficients, and the likelihood function is modified by the approximation. After completing the decomposition of underlying surface data into several fractal surface, the prediction method for the fractal signal is employed based on the scale expansion based on the self-similarity of fractal geometry. After discussing the effect of additive noise, the method is applied to the feature extraction of real distribution of surface data such as the cloud and earthquakes.

This paper presents a study on the blind separation of a convoluted mixture of speech signals using Frequency Domain Independent Component Analysis (FDICA) algorithm based on the negentropy maximization of Time Frequency Series of Speech (TFSS). The comparative studies on the negentropy approximation of TFSS using generalized Higher Order Statistics (HOS) of different nonquadratic, nonlinear functions are presented. A new nonlinear function based on the statistical modeling of TFSS by exponential power functions has also been proposed. The estimation of standard error and bias, obtained using the sequential delete-one jackknifing method, in the approximation of negentropy of TFSS by different nonlinear functions along with their signal separation performance indicate the superlative power of the exponential-power-based nonlinear function. The proposed nonlinear function has been found to speed-up convergence with slight improvement in the separation quality under reverberant conditions.

This paper proposes a genetic programming method to synthesize passive filter circuits. This method allows both the circuit topology and the component values to be evolved simultaneously. Experiments show that this method is fast and capable of generating circuits which are more economical than those generated by traditional design approaches. In addition, we take into account practical design considerations at high-frequency applications, where the component values are frequency-dependent and restricted to some discrete values. Experimental results show that our method can effectively generate not only compliant but also economical circuits for practical design tasks.

The increasing complexity and shorter life cycle of software have made it necessary to reuse software. Object-oriented development has not facilitated extensive reuse of software and it has become difficult to manage and understand modern object-oriented systems which have become very extensive and complex. However, components, compared with objects, provide more advanced means of structuring, describing and developing systems, because they are more coarse grained and have more domain-specific aspects than objects. In addition, they are also suited to a current distributed environment due to their reusability, maintainability and granularity. In this paper, we present a process of extracting components from object-oriented systems. We define some static metrics and guidelines that can be applied to transform object-oriented systems into component-based systems. Our process consists of two parts. First, basic components are created based on composition and inheritance relationships between classes. Second, the intermediate system is refined into a component-based system with our proposed static metrics and guidelines.

This paper provides an overview of research in channel modeling for multiple-input multiple-output (MIMO) data transmission focusing on a radio wave propagation. A MIMO channel is expressed as an equivalent circuit with a limited number of eigenpaths according to the singular-value decomposition (SVD). Each eigenpath amplitude depends on the propagation structure not only of the path direction profiles for both transmission and reception points but also of intermediate regions. Inherent in adaptive control is the problem of instability as a hidden difficulty. In this paper these issues are addressed and research topics on MIMO from a radio wave propagation viewpoint are identified.

We show that the necessary and sufficient condition for the existence of a balanced C4-bowtie decomposition of the complete multi-graph λKn is λ(n - 1) 0 (mod 16) and n 7. Decomposition algorithms are also given.

We proposed a novel structure that improved the linear characteristics of electroabsorption modulator (EAM) with composite quantum-wells as an absorption core layer. We fabricated three types of EAM's whose active cores were 8 nm thick, 12 nm thick and a composite core with 8 nm thick and 12 nm thick quantum-well (QW), respectively. The transfer functions of EAM's were investigated and their third-order inter-modulation distortion (IMD3) was obtained by calculation. The spurious free dynamic range (SFDR) was measured and compared with three types of QW. The linearity of the device with composite quantum-well showed a large enhancement in SFDR by 9.3 dBHz2/3 in TE mode and 7.0 dBHz2/3 in TM mode compared with the conventional EAM.

The linear operation of a HBT with a GaAs/InGaP composite collector structure is demonstrated. The composite collector structure allows for a thin collector design that is suitable for the linear operation of a HBT without critical degradation of the breakdown voltage. The load pull measurements under a 1.95 GHz WCDMA signal have shown that a composite-collector HBT with a 400-nm thick collector layer operates with power-added-efficiency (PAE) as high as 53% at VCE = 3.5 V as a result of improved distortion characteristics. Despite the thin collector design, collector-emitter breakdown voltage of 11 V was achieved even at current density of 10 kA/cm2. The composite-collector HBT has even greater advantage for future low voltage (< 3 V) applications where maintaining PAE and linearity becomes one of the critical issues.

Penumbral imaging is a technique which exploits the fact that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. Since the technique is based on linear deconvolution, it is sensitive to noise. In this paper, a two-step method is proposed for decoding penumbral images: first, a noise-reduction algorithm based on ICA-domain (independent component analysis-domain) shrinkage is applied to smooth the given noise; second, the conventional linear deconvolution follows. The simulation results show that the reconstructed image is dramatically improved in comparison to that without the noise-removing filters, and the proposed method is successfully applied to real experimental X-ray imaging.

A new hybrid method for characterizing the irregular power/ground plane pair is developed in this paper by combining the conventional eigen-mode expansion method with the new-presented inverted composition method and a simple model order reduction. By the approach, the eigen-mode expansion method can be extended to the characteristics research of the power/ground plane pair with holes. In this gridless method, ports and decoupling capacitors can be arbitrarily placed on the plane pair. The numerical example demonstrates its good validity.