A modified error correction/detection scheme based on the scheme by Yi and Lee is proposed. Algebraic decoding is used to perform error correction. Error detection is performed by an absolute value test. It is shown that the proposed scheme bridges the performance gap between Yi and Lee's scheme and Forney's optimal scheme.

The concept of a basis matrix is introduced to investigate the trade-off between complexity and storage for multiplication in a finite field. The effect on the storage requirements of using polynomial and normal bases for element representation is also considered.

Genetic algorithms have been shown to be very useful in a variety of search and optimization problems. In this paper we present a genetic algorithm for maximum independent set problem. We adopt a permutation encoding with a greedy decoding to solve the problem. The DIMACS benchmark graphs are used to test our algorithm. For most graphs solutions found by our algorithm are optimal, and there are also a few exceptions that solutions found by the algorithm are almost as large as maximum clique sizes. We also compare our algorithm with a hybrid genetic algorithm, called GMCA, and one of the best existing maximum clique algorithms, called CBH. The exiperimental results show that our algorithm outperformed two of the best approaches by GMCA and CBH in final solutions.

We introduce two probabilistic algorithms to determine the motion parameters of a planar shape without knowing a priori the point-to-point correspondences. If the target is limited to rigid objects, an Euclidean transformation can be expressed as a linear equation with six parameters, i.e. two translational parameters and four rotational parameters (the axis of rotation and the rotational speed about the axis). These parameters can be determined by applying the randomized Hough transform. One remarkable feature of our algorithms is that the calculations of the translation and rotation parameters are performed by using points randomly selected from two image frames that are acquired at different times. The estimation of rotation parameters is done using one of two approaches, which we call the triangle search and the polygon search algorithms respectively. Both methods focus on the intersection points of a boundary of the 2D shape and the circles whose centers are located at the shape's centroid and whose radii are generated randomly. The triangle search algorithm randomly selects three different intersection points in each image, such that they form congruent triangles, and then estimates the rotation parameter using these two triangles. However, the polygon search algorithm employs all the intersection points in each image, i.e. all the intersection points in the two image frames form two polygons, and then estimates the rotation parameter with aid of the vertices of these two polygons.

This paper deals with minimization of ESOPs (exclusive-or sum-of-products) which represent symmetric functions. Se propose an efficient simplification algorithm for symmetric functions, which guarantees the minimality for some subclass of symmetric functions, and present the minimum ESOPs for all 6-variable symmetric functions.

This paper presents algorithms for computing the Minkowski sum of two polygons P and Q for a family of problems. For P being convex and Q being monotone, an algorithm is given with O (nm) time and space complexity. For both P and Q being monotone polygons, an O (nm log nm) time algorithm is presented and it is shown that the complexity of the sum is Θ (nmα(min(n,m))) in the worst case, where α() is the inverse of Ackermann's function. Finally, an O ((nm+k)log nm) time complexity algorithm is given when P is monotone and Q is simple, where k in the worst case could be Θ(n2m). The complexity of P Q is shown to be Θ(n2m) in the worst case. Here, m and n denote the number of edges of P and Q, respectively.

The Virtual Path (VP) concept is one of the versatile features of ATM/B-ISDN. Using the VP concept, a bundle of virtual circuits can be grouped together between any two switching nodes in the network. Further, the VP bandwidth and routing can be dynamic. Building on this idea, a dynamically reconfigurable, dynamic call routing wide area (backbone) broadband network concept is proposed. Specifically, this provides dynamism at two levels: at the VP level and at the connection level. For an incoming connection request, at most two logical virtual path connections (VPCs) are allowed between the origin and the destination; these logical VPCs are defined by setting virtual paths links (VPLs) which are, in turn, physically mapped to the transmission network. Based on the traffic pattern during the day, the bandwidth of such VPCs and their routing, as well as call routing, changes so that the maximum number of connection requests can be granted while maintaining acceptable quality of service (QoS) for various services. Within this framework, we present a mathematical model for network design (dimensioning) taking into account the variation of traffic during the day in a heterogeneous multi-service environment. We present computational results for various cost parameter values to show the effectiveness of such networks compared to static-VP based networks in terms of network cost.

This paper presents a novel approach to a soft-output equalizer, which makes a symbol-by-symbol soft-decision based on a posteriori probabilities (APP's) criterion in the presence of intersymbol interference. The authors propose a soft-output Viterbi equalizer (SOVE) employing expanded memory length in a trellis of the Viterbi algorithm with small arithmetic complexity. The proposed equalizer gives suboptimum soft-decision closer to that of a equalizer with the maximum a posteriori probabilities (MAP) algorithm than the conventional SOVE.

In this paper we discuss a certain constrained optimization problem which is often encountered in the geometrical optimization. Since these kinds of problems occur frequently, constrained genetic optimization becomes very important topic for research. This paper proposes a new methodology to handle constraints using the Genetic Algorithm through a multiprocessor system (FIN) which has a self-similarity network.

A new structure for adaptive AR spectral estimation based on multi-band decomposition of the linear prediction error is introduced and the mathematical background for the soulution of the related adaptive filtering problem is derived. The presented structure gives rise to AR spectral estimates that represent the true underlying spectrum with better fidelity than conventional LS methods by allowing an arbitrary trade-off between variance of spectral estimates and tracking ability of the estimator along the frequency spectrum. The linear prediction error is decomposed through a filter bank and components of each band are analyzed by different window lengths, allowing long windows to track slowly varying signals and short windows to observe fastly varying components. The correlation matrix of the input signal is shown to satisfy both time-update and order-update properties for rectangular windowing functions, and an RLS algorithm based on each property is presented. Adaptive forward and backward relations are used to derive a mathematical framework that serves as a basis for the design of fast RLS alogorithms. Also, computer experiments comparing the performance of conventional and the proposed multi-band methods are depicted and discussed.

We show that the truth of a prenex normal form Presburger sentence bounded only by existential quantifiers (or an EPP-sentence) involving two variables can be decided in deterministic polynomial time. Specifically, an upper bound of the computation for the decision is O(n3u), where n is the number of atoms of the EPP-sentence, and u is the largest absolute value of all coefficients in the EPP-sentence. In the analysis for the upper bound, the random access machine is assumed for the machine model. Additionally, a uniform cost criterion is assumed. Deciding the truth of an EPP-sentence is an NP-complete problem, when the number of variables is not fixed. Furthermore, whether the truth of an EPP-sentence involving two or more variables can be decided in deterministic polynomial time, when the number of variables is fixed, or not has remained an open problem. We previously proposed a procedure for quickly deciding the truth of an EPP-sentence on the basis of a suggestion by D.C.Cooper. We found the upper bound by analyzing the decision procedure. The procedure can be applied to both automated correctness proof of specification in various design fields and detection of infeasible paths in a program. In the procedure, a matrix denoting coefficients of the variables in the EPP-sentence is triangulated.

Recently, Yagisawa proposed a public key cryptosystem which is very similar to the modified Lu-Lee cryptosystem. The differences are the set of messages and the decryption. On the other hand, Brickell and Odlyzko showed that the modified Lu-Lee cryptosystem is completely broken in polynomial time. This paper shows that Yagisawa cryptosystem is completely broken in the same way.

The convergence characteristics of the adaptive beamformer with the RLS algorithm are analyzed in this paper. In case of the RLS adaptive beamformer, the convergence characteristics are significantly affected by the spatial characteristics of the signals/noises in the environment. The purpose of this paper is to show how these physical parameters affect the convergence characteristics. In this paper, a typical environment where a few directional noises are accompanied by background noise is assumed, and the influence of each component of the environment is analyzed separately using rank analysis of the correlation matrix. For directional components, the convergence speed is faster for a smaller number of noise sources since the effective rank of the input correlation matrix is reduced. In the presence of background noise, the convergence speed is slowed down due to the increase of the effective rank. However, the convergence speed can be improved by controlling the initial matrix of the RLS algorithm. The latter section of this paper focuses on the physical interpretation of this initial matrix, in an attempt to elucidate the mechanism of the convergence characterisitics.

In this letter, we propose a blind adaptation method for the decision feedback equalizer (DFE). In the proposed scheme, a DFE is divided into two parts: a front-end linear equalizer (LE), and a prediction error filter (PEF) followed by a feedback part. The coefficients of the filters in each part are updated using constant modulus algorithm and decision feedback prediction algorithm, respectively. The front-end LE removes intersymbol interference ISI, and the PEF with feedback part whitens the noise to reduce noise power enhanced by the LE. Pre-processing by the LE enables the DFE to equalize nonminimum phase channels. Simulation results show that the proposed scheme provides reliable convergence, and the resulting symbol error rate is much less than that of the conventional LE and very close to that of the DFE using a training sequence.

This paper presents a method of multiple fault diagnosis in sequential circuits by input-sequence pairs having sensitizing input pairs. We, first, introduce an input-sequence pair having sensitizing input pairs to diagnose multiple faults in a sequential circuit represented by a combinational array model. We call such input-sequence pair the sensitizing sequence pair in this paper. Next, we describe a diagnostic method for multiple faults in sequential circuits by the sensitizing sequence pair. From a relation between a sensitizing path generated by a sensitizing sequence pair and a subcircuit, the proposed method deduces the suspected faults for the subcircuits, one by one, based on the responses observed at primary outputs without probing any internal line. Experimental results show that our diagnostic method identifies fault locations within small numbers of suspected faults.

A new learning algorithm is proposed to enhance fault tolerance ability of the feedforward neural networks. The algorithm focuses on the links (weights) that may cause errors at the output when they are open faults. The relevances of the synaptic weights to the output error (i.e. the sensitivity of the output error to the weight fault) are estimated in each training cycle of the standard backpropagation using the Taylor expansion of the output around fault-free weights. Then the weight giving the maximum relevance is decreased. The approach taken by the algorithm described in this paper is to prevent the weights from having large relevances. The simulation results indicate that the network trained with the proposed algorithm do have significantly better fault tolerance than the network trained with the standard backpropagation algorithm. The simulation results show that the fault tolerance and the generalization abilities are improved.

A complex fuzzy adaptive decision feedback equalizer based on the RLS algorithm is proposed. The proposed equalizer not only improves the performance but also reduces the computational complexity compared with the conventional complex fuzzy adaptive equalizers under the assumption of perfect knowledge of the linear and nonlinear channels.

In this paper, we introduce a parallel algorithm for parsing context-free languages. Our algorithm can handle arbitrary context-free grammars since it is based on Earley's algorithm. Our algorithm can operate on any loosely coupled multiprocessor which can provide a topology of a one-way ring. Our algorithm uses p processors to parse an input string of length n where 1 p n. It is shown that our algorithm requires O(n3/p) time. The algorithm uses a simple job allocation strategy. However, it achieves high load balancing and uses the processors efficiently.

Recently , a short range millimeter wave or a microwave sensing system has been extensively studied to estimate a target position or a source location. It can be applied to indoor propagation analysis, carborne applications, etc. The application of the superresolution technique has been proposed to obtain a high resolution performance in the time domain or the spatial domain. However, the availability of the polarization synthesis in the receiving antennas has not been considered. In this paper, we use a pair of polarized swept frequency data and propose two modifications of the MUSIC algorithm to enhance the resolution of time delay. One modification is the correlation matrix formulation which relates to the total signal power, and the other is a polarization filtering applied to the correlation matrix. These modifications have advantages such that. 1)Reduction of the estimation problem to the delay time estimation only; 2)Easy implementation. Experimental results are illustrated to show the availability of the methods, and to confirm the high resolution performance compared with the conventional method.

In this paper stochastic aradient adaptive filters using the Sign or Sign-Sign Algorithm are analyzed based upon general assumptions on the reference signal, additive noise and particularly jointly distributed tap errors. A set of difference equations for calculating the convergence process of the mean and covariance of the tap errors is derived with integrals involving characteristic function and its derivative of the tap error distribution. Examples of echo canceller convergence with jointly Gaussian distributed tap errors show an excellent agreement between the empirical results and the theory.