A time-domain equalization (TEQ) algorithm is presented to shorten the effective channel impulse response to increase the transmission efficiency of the 54 Mbps IEEE 802.11a orthogonal frequency division multiplexing (OFDM) system. In solving the linear equation Aw = B for the optimum TEQ coefficients, A is shown to be Hermitian and positive definite. The LDLT and LU decompositions are used to factorize A to reduce the computational complexity. Simulation results show high performance gains at a data rate of 54 Mbps with moderate orders of TEQ finite impulse response (FIR) filter. The design and implementation of the algorithm in field programmable gate array (FPGA) are also presented. The regularities among the elements of A are exploited to reduce hardware complexity. The LDLT and LU decompositions are combined in hardware design to find the TEQ coefficients in less than 4 µs. To compensate the effective channel impulse response, a radix-4 pipeline fast Fourier transform (FFT) is implemented in performing zero forcing equalization. The hardware implementation information is provided and simulation results are compared to mathematical values to verify the functionalities of the chips running at 54 Mbps.

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.

This paper deals with the statistical modeling of a Time-Frequency Series of Speech (TFSS), obtained by Short-Time Fourier Transform (STFT) analysis of the speech signal picked up by a linear microphone array with two elements. We have attempted to find closer match between the distribution of the TFSS and theoretical distributions like Laplacian Distribution (LD), Gaussian Distribution (GD) and Generalized Gaussian Distribution (GGD) with parameters estimated from the TFSS data. It has been found that GGD provides the best models for real part, imaginary part and polar magnitudes of the time-series of the spectral components. The distribution of the polar magnitude is closer to LD than that of the real and imaginary parts. The distributions of the real and imaginary parts of TFSS correspond to strongly LD. The phase of the TFSS has been found uniformly distributed. The use of GGD based model as PDF in the fixed-point Frequency Domain Independent Component Analysis (FDICA) provides better separation performance and improves convergence speed significantly.

We propose Optimal Temporal Decomposition (OTD) of speech for voice morphing preserving Δ cepstrum. OTD is an optimal modification of the original Temporal Decomposition (TD) by B. Atal. It is theoretically shown that OTD can achieve minimal spectral distortion for the TD-based approximation of time-varying LPC parameters. Moreover, by applying OTD to preserving Δ cepstrum, it is also theoretically shown that Δ cepstrum of a target speaker can be reflected to that of a source speaker. In frequency domain interpolation, the Laplacian Spectral Distortion (LSD) measure is introduced to improve the Inverse Function of Integrated Spectrum (IFIS) based non-uniform frequency warping. Experimental results indicate that Δ cepstrum of the OTD-based morphing spectra of a source speaker is mostly equal to that of a target speaker except for a piecewise constant factor and subjective listening tests show that the speech intelligibility of the proposed morphing method is superior to the conventional method.

Self-stabilization is a theoretical framework of non-masking fault-tolerant distributed algorithms. In this paper, we investigate the Steiner tree problem in distributed systems, and propose a self-stabilizing heuristic solution to the problem. Our algorithm is constructed by four layered modules (sub-algorithms): construction of a shortest path forest, transformation of the network, construction of a minimum spanning tree, and pruning unnecessary links and processes. Competitiveness is 2(1-1/l), where l is the number of leaves of optimal solution.

Non-repudiation is a basic security requirement for electronic business applications to protect against a sender's false denial of having created and sent a message. Typically non-repudiation protocols are constructed based on digital signatures. However, there has been no theoretical treatment of such non-repudiation protocols. In this paper, we provide a formal security definition of non-repudiation protocols and analyze the security of a signature-based protocol. Our security definition and analysis are based on Canetti's framework of universally composable security.

Marginal reliability importance (MRI) of a component in a system is defined as the rate at which the system reliability changes over changes of the component reliability. MRI helps network designers to construct a reliable network layout. We consider a problem to compute MRI of all components in a network system considering all-terminal reliability in order to rank the components with respect to MRI. The problem is time-consuming since computing network reliability is #P-complete. This paper improves the traditional approach for the problem to proposes an efficient algorithm. The algorithm applies some network transformations, three network reductions and one network decomposition. We have proved lemmas with respect to the relationship between the transformations and MRI, which compute MRI for an original network by using MRI and reliability for transformed networks. Additionally, we have derived a deformed formula to compute MRI, which can also reduce computational task. Numerical experiments revealed that the proposed algorithm reduced computational time considerably compared to the traditional approach.

We propose a novel method for Web page grouping based only on hyperlink information. Because of the explosive growth of the World Wide Web, page grouping is expected to provide a general grasp of the Web for effective information search and netsurfing. The Web can be regarded as a gigantic digraph where pages are vertices and links are arcs. Our grouping method is a generalization of decomposition into strongly connected components, in which each group is constructed as a subset of a strongly connected component. Moreover, group sizes can be controlled by adjusting a parameter, called the threshold parameter. We call the resulting groups parameterized connected components (PCCs). The algorithm is simple and admits parallelization. Notably, we apply Dijkstra's shortest path algorithm in our grouping method. This paper also includes experimental results for 15 million Web pages, which show the contribution of our method to efficient Web surfer navigation.

Concurrency in the computing systems using a deadlock avoidance strategy varies largely according to the way that resource usage plan of process is used in testing the possibility of deadlocks. If a detailed resource usage plan of process is to be taken into account, the deadlock avoidance problem is known to be NP-complete. A decomposition model to manage resources is proposed, where process is logically partitioned into a number of segments each of which uses at least one resource. It is shown that one of our deadlock avoidance algorithms achieves the best concurrency among the polynomial-bounded algorithms. We also present a heuristic algorithm that achieves concurrency close to the optimal one. Finally, we analyze concurrency of various algorithms.

This letter presents a lower bound and approximation for the coverage probability of the pilot channel that can be used for a CDMA downlink design. The approximation of a compound truncated Poisson distribution is used to obtain a closed form equation for the coverage probability of the pilot channel. Computer simulations show that our lower bound curve is truly less than the empirical curve, and our proposed approximation agrees well with the empirical result.

Temporal locality in IP destination address sequences can be captured by the addresses' reuse distance distribution. Based on measurements from data for a wide range of networks, we propose an accurate empirical model in contrast to results derived from the stationarity assumption of address generation processes.

This paper presents a framework for building mobile agent-based components. The framework introduces the notion of agent hierarchy and inter-agent migration and thus allows a group of mobile agents to be dynamically assembled into a single mobile agent. It provides a powerful method of constructing complex, large-scale mobile agent-based applications. To demonstrate how to exploit our framework, we construct an extensible and portable mobile agent system for executing and migrating hierarchical mobile agents. The system itself is also implemented as a collection of mobile agents and can thus dynamically change and adapt its functions through migrating agents that offer the functions. Mobile agent-based applications running on the system can naturally inherit the extensibility and adaptability of the system.

We propose a normalization scheme of syntactic structures using a binary phrase structure grammar with composite labels. The normalization adopts binary rules so that the dependency between two sub-trees can be represented in the label of the tree. The label of a tree is composed of two attributes, each of which is extracted from each sub-tree, so that it can represent the compositional information of the tree. The composite label is generated from part-of-speech tags using an automatic labelling algorithm. Since the proposed normalization scheme is binary and uses only part-of-speech information, it can readily be used to compare the results of different syntactic analyses independently of their syntactic description and can be applied to other languages as well. It can also be used for syntactic analysis, which performs higher than the previous syntactic description for Korean corpus. We implement a tool that transforms a syntactic description into normalized one based on this proposed scheme. It can help construct a unified syntactic corpus and extract syntactic information from various types of syntactic corpus in a uniform way.

The radio wave intensity time series of the quasar is observed with the radio wave interferometer on the earth. External noise may superimpose with the radio wave on the path of wave propagation over the cosmological distance. In this paper, the effects of the superimposed noise on the radio wave intensity time series are discussed assuming nonlinear dynamics to apply on the time series. A convolution method is applied to the original observed radio wave intensity time series. Both the original and the convolution time series are analyzed by the Grassberger-Procaccia (GP) method with correlation integration and compared the results to estimate the presence and the effects of superimposed subtle noise. In addition, surrogate and Judd methods are applied to the radio wave intensity time series to increase the credibility of the results of the GP method. The effects of added random noise in Lorenz model are also analyzed with the GP method to estimate the above results.

In this paper, we present our investigation of Latent Semantic Indexing (LSI) on the local query regions for solving the computation restrictions of the LSI on the global information space. Through the experiments with different SVD dimensionality on the local query regions, the results show that low-dimensional LSI can achieve much better precision than VSM and similar precision to global LSI. Such small SVD factors indicate that there is an almost linear surface in the local query regions. The largest or the two largest singular vectors have the ability to capture such a linear surface and benefit the particular query. In spite of the fact that Local LSI analysis needs to perform the Singular Value Decomposition (SVD) computation for each query, the surprisingly small requirements of the SVD dimension resolve the computation restrictions of LSI for large scale IR tasks. Moreover, on the condition that several relevant sample documents are available, application of low dimensional LSI for these documents can obtain comparable precision with the Local RF in a different manner.

We show that the necessary and sufficient condition for the existence of a balanced bowtie decomposition of the complete multigraph λKn is n 5 and λ(n-1) 0 (mod 12). Decomposition algorithms are also given.

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.

Cochannel interference and multipath propagation reduce the performance of mobile communication systems. Multi-input MLSE with whitening processing can mitigate the influence of the interference and provide path diversity gain. In conventional considerations, however, the required complexity rapidly rises with the number of array elements. In this paper, we propose multi-input MLSE that whitens error signals in the signal space by using a multibeam adaptive array. This scheme can reduce the computational load of multi-input MLSE than the conventional type when using a large-element array. The results of an analysis show that the proposed type is equivalent to conventional one in the sense of the metric and provides less computational complexity.

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.

In this paper, we consider the problem of estimating the time-varying directions-of-arrival (DOAs) of coherent narrowband cyclostationary signals impinging on a uniform linear array (ULA). By exploiting the cyclostationarity of most communication signals, we investigate a new computationally efficient subspace-based direction estimation method without eigendecomposition and spatial smoothing (SS) processes. The proposed method uses the inherently temporal property of incident signals and a subarray scheme to decorrelate the signal coherency and to suppress the noise and interfering signals, while the null subspace is obtained from the resulting cyclic correlation matrix through a linear operation. Then an on-line implementation of this method is presented for tracking the DOAs of slowly moving coherent signals. The proposed algorithm is computationally simple and has a good tracking performance. The effectiveness of the proposed method is verified through numerical examples.