Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to-noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.

We newly propose a stable algorithm for blind source separation (BSS) combining multistage ICA (MSICA) and linear prediction. The MSICA is the method previously proposed by the authors, in which frequency-domain ICA (FDICA) for a rough separation is followed by time-domain ICA (TDICA) to remove residual crosstalk. For temporally correlated signals, we must use TDICA with a nonholonomic constraint to avoid the decorrelation effect from the holonomic constraint. However, the stability cannot be guaranteed in the nonholonomic case. To solve the problem, the linear predictors estimated from the roughly separated signals by FDICA are inserted before the holonomic TDICA as a prewhitening processing, and the dewhitening is performed after TDICA. The stability of the proposed algorithm can be guaranteed by the holonomic constraint, and the pre/dewhitening processing prevents the decorrelation. The experiments in a reverberant room reveal that the algorithm results in higher stability and separation performance.

A problem of estimating biopotential sources in the brain based on EEG signals observed on the scalp is known as an important inverse problem of electrophysiology. Usually there is no closed-form solution for this problem and it requires iterative techniques such as the Levenberg-Marquardt algorithm. Considering the nonlinear properties of inverse problem, and signal to noise ratio inherent in EEG signals, a back propagation neural network has been recently proposed as a solution. In this paper, we investigated the properties of neural networks and its localization accuracy for single dipole source localization. Based on the results of extensive studies, we concluded the neural networks are highly feasible in single-source localization with a small number of electrodes (18 electrodes), also examined the usefulness of this method for clinical application with a case of epilepsy.

The emerging multimedia applications for future mobile communication systems typically require highly diversified Quality of Service (QoS). However, due to the time and location dependent fluctuating nature of radio resources in the radio link, it is very difficult to maintain a constant level of QoS with the current end-to-end QoS control only. Therefore, wireless-aware QoS is the key issue for achieving better end-end QoS. In this paper, a new wireless QoS scheme for a joint CDMA/NC-PRMA cellular system are proposed considering QoS prioritization mechanism, users' diversified requirements and the harmonization with IP-QoS. Two wireless QoS-aware resource allocation algorithms are proposed to support QoS prioritization while achieving high radio resource utilization. By introducing a set of new QoS resource request parameters (minimum, average and maximum requirements), the algorithms can assign radio resource in a more flexible way than the conventional fixed resource allocation. Computer simulations indicate that the proposed QoS algorithms exhibit superior performance with respect to packet dropping probability for realtime application users, and improve transmission rate for non-realtime application users, which evince the effectiveness of the proposed wireless QoS algorithms.

We propose a new resource management scheme, Distributed Channel Allocation Protocol (DCAP), for multimedia Ad hoc Wireless LANs (AWLANs). This scheme implements a Quality-of-Service (QoS) providing distributed resource management on the Orthogonal Frequency Division Multiplexing-Code Division Multiple Access (OFDM-CDMA) channel architecture. According to the performance evaluation results for MPEG traffic sources, DCAP can be a good choice of resource management scheme for AWLANs supporting multimedia services on the Broadband Wireless Access (BWA)-type physical layer.

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.

The IMT-2000 service launched in 2001 in Japan is expected to popularize multimedia services such as videophone, visual mail, video, and music distribution. With the rapidly increasing demand for high-speed mobile multimedia and the need to support diversified requirements of users, wireless Quality of Service (QoS) resource management has become an important and challenging issue. In order to improve the system capacity and rate of satisfied users, in this paper, a novel wireless QoS resource management scheme is proposed to carry out radio resource cooperative control among base stations. Computer simulations indicate that the proposed QoS resource cooperative control exhibits superior performance over conventional ones, and that a higher rate of satisfied users is achieved.

We propose a coordinated resource allocation (CRA) scheme that can be used to allocate high data-rate users in sectorized cells. This scheme is useful for allocating high data-rate users at cell boundaries. In order to analyze the performance of the proposed scheme, we make an interference model for a sectorized CDMA system and suggest the system load measurement of the forward link. Based on this system load measurement, data throughput for the CDMA system under perfect and imperfect power control is then analyzed. Numerical results show that throughput is significantly increased when the CRA scheme is used.

A type of carbon nanoform (carbon nanowalls: CNWs) has been successfully deposited on both Ni wafers and Ni wires using dc plasma chemical-vapor-deposition (CVD) method. Transmission electron microscopy (TEM) and Raman spectroscopy were used to characterize CNWs' microstructure. It is found that CNWs are well crystallized, and each CNW consists of several pieces of curved graphene sheets, presenting a quasi-two-dimensional geometry. The average length and width of CNWs are about 2-4µm, while their thickness is less than 7nm. Field emission measurement showed that such CNW films exhibited the excellent electron emission efficiency, comparable to the high-grade carbon nanotube (CNT) emitters. The threshold field defined as the field to obtained 1µA/cm2 is less than 1V/µm and the electrical field for 1mA/cm2 current density is only about 1.5V/µm. Moreover, the CNWs have stable emission behaviors, and we have successfully fabricated a kind of high-brightness lamps based on the CNW coated Ni wires.

The achievable rate region related to the problem of generating mutually independent random sequences is determined. Furthermore, it is proved that the output distribution of lossless source encoders with correlated side information is asymptotically independent of the side information. Based on this, we can realize a random number generator that produces mutually asymptotically independent random sequences from random sequences emitted from correlated sources.

A Fair Queueing Algorithm is proposed for data services in an integrated voice/data CDMA system. We introduce short-term and long-term fairness concepts to allocate data users fairly. Using these concepts, we propose a Weighted Fair Queueing with Status Control (WFQS) in the consideration of a Generalized Processor Sharing (GPS) fluid-flow model. This proposed scheme allocates resources using channel status information. The throughput and delay of data users could be improved when this scheme is applied to wireless channels.

The present paper addresses the design of manufacturing systems. A resource planning and task allocation problem is proposed, and a multi-agent system for this problem is discussed. In the multi-agent system, an agent exists for each resource and for each operation. The proposed multi-agent system improves the quality of resulting plans by the learning of these agents.

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.

The proposed medium access protocol deals especially with the timely-transmission of real-time packets in wireless multimedia networks where users of many types of traffic are present. It works based on Time Division Multiple Access/Time Division Duplex (TDMA/TDD) technique and fixed-length packet switching incorporating two different policies to work differently on either non-congestion or congestion periods. In the policy to deal with congestion periods the concept of urgent packet has been introduced as any packet whose transmission deadline is on the next frame. Hence, during periods of congestion users inform to the Base Station the number and average deadline of the urgent packets in their buffers through requirement messages. According to that information the system is able to distribute its resources in a more efficient way during periods of congestion making the real-time packet loss rate decrease considerably. The simulation results show a very good performance of the method in networks where different types of traffic coexist even under high traffic-load conditions. The results also show a good trade-off characteristic between the real-time access delay and the buffer occupancy of non-real time terminals during congestion periods.

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).

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.

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).

In the last three decades of the 20th Century, research in speech recognition has been intensively carried out worldwide, spurred on by advances in signal processing, algorithms, architectures, and hardware. Recognition systems have been developed for a wide variety of applications, ranging from small vocabulary keyword recognition over dial-up telephone lines, to medium size vocabulary voice interactive command and control systems for business automation, to large vocabulary speech dictation, spontaneous speech understanding, and limited-domain speech translation. Although we have witnessed many new technological promises, we have also encountered a number of practical limitations that hinder a widespread deployment of applications and services. On one hand, fast progress was observed in statistical speech and language modeling. On the other hand only spotty successes have been reported in applying knowledge sources in acoustics, speech and language science to improving speech recognition performance and robustness to adverse conditions. In this paper we review some key advances in several areas of speech recognition. A bottom-up detection framework is also proposed to facilitate worldwide research collaboration for incorporating technology advances in both statistical modeling and knowledge integration into going beyond the current speech recognition limitations and benefiting the society in the 21st century.

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.

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.