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.

In this paper, we propose a new error feedback (EF) structure for 2-D separable-denominator digital filters described by a rational transfer function. In implementing two-dimensional separable-denominator digital filters, the minimum delay elements structures are common. In the proposed structure, the filter feedback-loop corresponding to denominator polynomial is placed at a different location compared to the commonly used structures. The proposed structure can minimize the roundoff noise more than the previous structure though the number of multipliers is less than that of previous one. Finally, we present a numerical example by designing the EF on the proposed structure and demonstrate the effectiveness of the proposed method.

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.

Frequency domain blind source separation has the great advantage that the complicated convolution in time domain becomes multiple efficient multiplications in frequency domain. However, the inherent ambiguity of permutation of ICA becomes an important problem that the separated signals at different frequencies may be permuted in order. Mapping the separated signal at each frequency to a target source remains to be a difficult problem. In this paper, we first discuss the inter-frequency correlation based method, and propose a new method using the continuity in power between adjacent frequency components of same source. The proposed method also implicitly utilizes the information of inter-frequency correlation, as such has better performance than the previous method.

Recently, Das et al. proposed a dynamic ID-based verifier-free password authentication scheme using smart cards. To resist the ID-theft attack, the user's login ID is dynamically generated and one-time used. Herein, we demonstrate that Das et al.'s scheme is vulnerable to an impersonation attack, in which the adversary can easily impersonate any user to login the server at any time. Furthermore, we also show several minor weaknesses of Das et al.'s scheme.

Due to the pruning and joining of members, multicast groups are dynamic. Both the topology and the total number of links change during multicast sessions, and the multicast performance, measured in terms of the bandwidth consumption, will change accordingly. In this paper, we investigate the dynamic performance of multicast communication with homogeneous packet loss probability; indeed, we evaluate the effects of the pruning of receivers and of subnets, after which we find the optimal placements of repair servers. A new 3-phase algorithm for adapting the optimal repair server placements to the dynamic changes of network topologies is presented and analyzed.

In this paper, single-input multiple-output (SIMO)-model-based blind source separation (BSS) is addressed, where unknown mixed source signals are detected at microphones, and can be separated, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. This technique is highly applicable to high-fidelity signal processing such as binaural signal processing. First, we provide an experimental comparison between two kinds of SIMO-model-based BSS methods, namely, conventional frequency-domain ICA with projection-back processing (FDICA-PB), and SIMO-ICA which was recently proposed by the authors. Secondly, we propose a new combination technique of the FDICA-PB and SIMO-ICA, which can achieve a higher separation performance than the two methods. The experimental results reveal that the accuracy of the separated SIMO signals in the simple SIMO-ICA is inferior to that of the signals obtained by FDICA-PB under low-quality initial value conditions, but the proposed combination technique can outperform both simple FDICA-PB and SIMO-ICA.

This paper presents a new method for blind source separation by exploiting phase and frequency redundancy of cyclostationary signals in a complementary way. It requires a weaker separation condition than those methods which only exploit the phase diversity or the frequency diversity of the source signals. The separation criterion is to diagonalize a polynomial matrix whose coefficient matrices consist of the correlation and cyclic correlation matrices, at time delay τ= 0, of multiple measurements. An algorithm is proposed to perform the blind source separation. Computer simulation results illustrate the performance of the new algorithm in comparison with the existing ones.

We present a built-in self-reconfiguring system for a mesh-connected processor array where faulty processor elements are compensated for by spare processing elements located in one row and one column. It has advantages in that the number of spare processing elements is small and additional control circuits and networks for changing interconnections of processing elements is so simple that hardware overhead for reconfiguration is also small. First, to indicate the motivation to the proposed reconfiguration scheme, we briefly describe other schemes with the same number of spares as that of the proposed scheme where faulty processing elements are replaced using straight shifts toward spares, and compare their reconfiguration probabilities to each other. Then, we show that a variant of the proposed scheme has the highest probability. Next, we present a built-in self-reconfiguring system for the scheme and formally prove that it works correctly. It can automatically replace faulty processors by spare processors on detecting faults of processors.

The AODV (Ad Hoc On-Demand Distance Vector) protocol is one of the typical reactive routing protocols in Ad Hoc networks, in that mobile nodes initiate routing activities only in the presence of data packets in need of a route. In this paper, we focus upon the local repair mechanism of AODV. When a link is broken, the upstream node of the broken link repairs the route to the destination by initiating a local route discovery process. The process involves the flooding of AODV control messages in every node within a radius of the length from the initiating node to the destination. In this paper, we propose an efficient local repair scheme for AODV, called AELR (AODV-based Efficient Local Repair). AELR utilizes the existing routing information in the downstream intermediate nodes which have been on the active route to the destination before a link break occurs. AELR can reduce the flooding range of AODV control messages and the route recovery time for route recovery because it can repair a route through the nearby downstream intermediate nodes. The performance results show that AELR can achieve faster route recovery than the local repair mechanism of AODV.

The transformation of a data set using a second-order polynomial mapping to find statistically independent components is considered (quadratic independent component analysis or ICA). Based on overdetermined linear ICA, an algorithm together with separability conditions are given via linearization reduction. The linearization is achieved using a higher dimensional embedding defined by the linear parametrization of the monomials, which can also be applied for higher-order polynomials. The paper finishes with simulations for artificial data and natural images.

We newly propose a novel blind separation framework for Single-Input Multiple-Output (SIMO)-model-based acoustic signals using an extended ICA algorithm, SIMO-ICA. The SIMO-ICA consists of multiple ICAs and a fidelity controller, and each ICA runs in parallel under the fidelity control of the entire separation system. The SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. Thus, the separated signals of SIMO-ICA can maintain the spatial qualities of each sound source. In order to evaluate its effectiveness, separation experiments are carried out under both nonreverberant and reverberant conditions. The experimental results reveal that the signal separation performance of the proposed SIMO-ICA is the same as that of the conventional ICA-based method, and that the spatial quality of the separated sound in SIMO-ICA is remarkably superior to that of the conventional method, particularly for the fidelity of the sound reproduction.

This paper describes a real-time blind source separation (BSS) method for moving speech signals in a room. Our method employs frequency domain independent component analysis (ICA) using a blockwise batch algorithm in the first stage, and the separated signals are refined by postprocessing using crosstalk component estimation and non-stationary spectral subtraction in the second stage. The blockwise batch algorithm achieves better performance than an online algorithm when sources are fixed, and the postprocessing compensates for performance degradation caused by source movement. Experimental results using speech signals recorded in a real room show that the proposed method realizes robust real-time separation for moving sources. Our method is implemented on a standard PC and works in realtime.

We propose a new algorithm for overdetermined blind source separation (BSS) based on multistage independent component analysis (MSICA). To improve the separation performance, we have proposed MSICA in which frequency-domain ICA and time-domain ICA are cascaded. In the original MSICA, the specific mixing model, where the number of microphones is equal to that of sources, was assumed. However, additional microphones are required to achieve an improved separation performance under reverberant environments. This leads to alternative problems, e.g., a complication of the permutation problem. In order to solve them, we propose a new extended MSICA using subarray processing, where the number of microphones and that of sources are set to be the same in every subarray. The experimental results obtained under the real environment reveal that the separation performance of the proposed MSICA is improved as the number of microphones is increased.

An analog neurochip for independent component analysis (ICA) is designed with on-line learning capability. Due to the limited dynamic range of analog device, the nonholonomic ICA algorithm is adopted. In order to accommodate the offsets due to device mismatches, a modified algorithm is developed with 2-quadrant multipliers and self-adjusting biases. Performance of the developed system was demonstrated by Monte-Carlo simulation.

DualFS is a next-generation journaling file system which has the same consistency guaranties as traditional journaling file systems but better performance. This paper introduces three new enhancements which significantly improve DualFS performance during normal operation, and presents different experimental results which compare DualFS and other traditional file systems, namely, Ext2, Ext3, XFS, JFS, and ReiserFS. The experiments carried out prove, for the first time, that a new file system design based on separation of data and metadata can significantly improve file systems' performance without requiring several storage devices.

In distant-talking speech recognition, the recognition accuracy is seriously degraded by reverberation and environmental noise. A robust speech recognition technique in such environments, HMM separation and composition, has been described in. HMM separation estimates the model parameters of the acoustic transfer function using adaptation data uttered from an unknown position in noisy and reverberant environments, and HMM composition builds an HMM of noisy and reverberant speech, using the acoustic transfer function estimated by HMM separation. Previously, HMM separation has been applied to the acoustic transfer function based on a single Gaussian distribution. However the improvement was smaller than expected for the impulse response with long reverberations. This is because the variance of the acoustic transfer function in each frame increases, since the length of the impulse response of the room reverberation is longer than that of the spectral analysis window. In this paper, HMM separation is extended to estimate the acoustic transfer function based on the Gaussian mixture components in order to compensate for the greater variability of the acoustic transfer function, and the re-estimation formulae are derived. In addition, this paper introduces a technique to adapt the noise weight for each mel-spaced frequency in order to improve the performance of the HMM separation in the linear-spectral domain, since the use of the HMM separation in the linear-spectral domain sometimes causes a negative mean output due to the subtraction operation. The extended HMM separation is evaluated on distant-talking speech recognition tasks. The results of the experiments clarify the effectiveness of the proposed method.

This paper analyzes the voice packet dropping probability and the average message transmission delay of a Bluetooth radio link in which talkspurts and messages are simultaneously transmitted as voice packets and data packets over an SCO (Synchronous Connection-Oriented) link and an ACL (Asynchronous ConnectionLess) link, respectively. The behaviors of an SCO link and an ACL link are modeled using Markov processes. Using these two Markov models and EPA (Equilibrium Point Analysis), the voice packet dropping probability and the average message transmission delay are derived analytically in terms of the permission probability of a voice packet and a data packet, the length of a message, the number of slaves, and the arrival rate of the messages. Some numerical examples are given to show how the permission probabilities, the number of slaves and other parameters influence the transmission delay, when both the SCO link and the ACL link are used at the same time.

In this paper a new adaptive multi-input multi-output (MIMO) channel estimation and multiuser detection algorithm based kernel space iterative inversion is proposed. The functions of output signals are mapped from a low dimensional space to a high dimensional reproducing kernel Hilbert space. The function of the output signals is represented as a linear combination of a set of basis functions, and a Mercer kernel function is constructed by the distribution function. In order to avoid finding the function f(.) and g(.), the correlation among the output signals is calculated in the low dimension space by the kernel. Moreover, considering the practical application, the algorithm is extended to online iteration of mixture system. The computer simulation results illustrated that the new algorithm increase the performance of channel estimation, the global convergence, and the system stability.

A novel audio watermarking based on wavelet modulation is presented. The watermark signals are constructed by M-band wavelet modulation that can increase redundancy to improve the detection performance. In order to maximize the watermarking strength within the perceptual constraints, the watermark signals synthesized from different subbands are separately masked using a frequency auditory model. CDMA technique is implemented to achieve watermarking capacity. Experimental results show that this method is very robust.