In this paper, we propose two new pipelined adaptive digital filter architectures. The architectures are based on an equivalent expression of the least mean square (LMS) algorithm. It is shown that one of the proposed architectures achieves the minimum output latency, or zero without affecting the convergence characteristics. We also show that, by increasing the output latency be one, the other architecture can be obtained which has a shorter critical path.

This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.

Recently, a great deal of effort has been devoted to the design problem of "constrained least squares M-D FIR filter" because a significant improvement of the squared error is expected by a slight relaxation of the minimax error condition. Unfortunately, no design method has been reported, which has some theoretical guarantee of the convergence to the optimal solution. In this paper, we propose a class of novel design methods of "constrained least squares M-D FIR filter. " The most remarkable feature is that all of the proposed methods have theoretical guarantees of convergences to the unique optimal solution under any consistent set of prescribed maximal error conditions. The proposed methods are based on "convex projection techniques" that computes the metric projection onto the intersection of multiple closed convex sets in real Hilbert space. Moreover, some of the proposed methods can still be applied even for the problem with any inconsistent set of maximal error conditions. These lead to the unique optimal solution over the set of all filters that attain the least sum of squared distances to all constraint sets.

In this paper, we propose a novel power distribution method which can be adopted in case of the nonuniform distribution for mobiles in DS/CDMA distributed power cellular system. DS/SS distributed power cellular system has been proposed for achieving RAKE reception in micro-cellular environment. In forward link of this system, optimum power distribution method which can minimize the required total transmitting power has been discussed. The performance of this system has been shown in case of the uniform distribution for mobiles. In this paper, first, we propose a novel method in case of the nonuniform distribution. In the proposed method, replacing the path and its combinations of signals from base stations successively, we can find a new condition of less power distribution which is passed over in a conventional distribution method. We adopt simple distribution models for mobiles and compare the proposed method with the other methods by computing the total transmitting power and the quantity of calculations. As a result, we show that it is possible to almost obtain optimum power distribution by using the proposed method. Next, we adopt a nonuniform distribution model, in which the difference of the number of users exists only in the center cell. Using this model, we compare the proposed method with the other methods by computing the total transmitting power, the quantity of calculations, and a probability of impossible power distribution. Finally, in order to simplify and decrease the quantity of calculations of the proposed method, we propose a modified calculation algorithm which is applicable in case of that a new mobile station has increased. And we show the performance of this algorithm.

Two new algorithms are introduced, respectively called syndrome erasing and double syndrome decoding, which permit to achieve fast error correction with a wide class of cyclic codes.

The matrix decomposition of transformation associated with the Kronecker product not only provides a thoughtful structure in hardware realization but also bestows a skillful tool for complexity evaluation. Hence, there are several fast algorithms developed to achieve efficient computation of two-dimensional (2-D) discrete cosine transform (DCT) with matrix decomposition techniques. However, we found that their derivations associated with their computation structures were not shown formally. In this paper, we propose formal derivations to remedy their deficiencies to achieve more structural 2-D DCT and inverse DCT (IDCT) algorithms. Furthermore, we also show that the remedied algorithms are with less computational complexity and more regular structure for realization.

This paper presents an evolutionary scheduling scheme for solving the job shop scheduling problem (JSSP) and other combinatorial optimization problems. The approach is based on a genetized-knowledge genetic algorithm (gkGA). The basic idea behind the gkGA is that knowledge of heuristics which are used in the GA is also encoded as genes alongside the genetic strings, referred to as chromosomes. Furthermore, during the GA selection, weaker heuristics die out while stronger ones survive for a given problem instance. We evaluate our evolutionary scheduling scheme based on the gkGA approach using well known benchmark instances for the JSSP. We observe that the gkGA based scheme is shown to consistently outperform the scheme based on ordinary GAs. In addition the gkGA-based scheme removes the problem of instance dependency.

Parallel Transaction Processing (TP) systems have great potential to serve the ever-increasing demands for high transaction processing rate. This potential, however, may not be reached due to the data contention and the widely-used two-phase locking (2PL) Concurrency Control (CC) method. In this paper, a distributed locking-based CC policy called LWDC (Local Wait-Depth Control) was proposed for dealing with this problem for the shared-nothing parallel TP system. On the basis of the LWDC policy, an algorithm called LWDCk was designed. Using simulation LWDCk was compared with the 2PL and the base-line Distributed Wait-Depth Limited (DWDL) CC methods. Simulation studies show that the new algorithm offers better system performance than those compared.

In [1], approximate eigenvalues and eigenvectors are defined and algorithms to compute them are described. However, the algorithms require a certain condition: the eigenvalues of M modulo S are all distinct, where M is a given matrix with polynomial entries and S is a maximal ideal generated by the indeterminate in M. In this paper, we deal with the construction of approximate eigenvalues and eigenvectors when the condition is not satisfied. In this case, powers of approximate eigenvalues and eigenvectors become, in general, fractions. In other words, approximate eigenvalues and eigenvectors are expressed in the form of Puiseux series. We focus on a matrix with univariate polynomial entries and give complete algorithms to compute the approximate eigenvalues and eigenvectors of the matrix.

In this paper, we propose a new cooperative search algorithm based on pheromone communication for solving the Vehicle Routing Problems. In this algorithm, multi-agents can partition the problem cooperatively and search partial solutions independently using pheromone communication, which mimics the communication method of real ants. Through some computer experiments the cooperative search of multi-agents is confirmed.

An efficient encoding method of excitation codes using a partial algebraic codebook (PAC) is proposed. Since the conventional algebraic code excited linear prediction (ACELP) encodes the positions and signs of all excitation pulses separately, the bits required for encoding excitation codes take a large portion of the total bit rate. Vector quantization (VQ) of the positions and signs of the excitation pulses results in a PAC. Using PAC instead of the full set of algebraic codes, we can reduce the bits required to encode the excitation codes while maintaining the output speech quality. An iterative training algorithm is proposed to obtain the suboptimal PAC by modifying the Lloyd algorithm. Simulation results show that considerable bit savings can be obtained with only a small amount of degradation in the segmental signal to noise ratio (SEGSNR).

This letter presents a novel variable-rate vector quantizer (VQ) design algorithm, which is a hybrid approach combining a genetic algorithm with the entropy-constrained VQ (ECVQ) algorithm. The proposed technique outperforms the ECVQ algorithm in the sense that it reaches to a nearby global optimum rather than a local one. Simulation results show that, when applied to the image coding, the technique achieves higher PSNR and image quality than those of ECVQ algorithm.

The increasing activity at millimeter wave frequency band and the growing demand for waveguide components to be applied for integrated circuit purpose have promoted the need for applying the field-theory-based approaches to the design procedure. In this paper, genetic algorithms (GA's) are applied to accurately design the iris-coupled waveguide filters based on network-boundary element method (NBEM). GA's model the natural selection and evolve towards the global optimum, thus avoid being trapped in local minima. Network-boundary element method, which combines boundary element method with network analysis method, derives the network parameters of the guided wave structures with less storage location and central processing unit time. Therefore, NBEM is a feasible and efficient field-theory-based approach for the GA optimization of waveguide filters. With NBEM performing the task of evaluating the performance of the filter designs optimized by the GA, rigorous and optimal designs of the waveguide filters are realized. The obtained analysis and optimization results are compared to a number of reference solutions to demonstrate the validity and accuracy of the proposed approach.

We present experimental results for a structural learning method of feed-forward neural-network classifiers using Principal Component Analysis (PCA) network and Species Genetic Algorithm (SGA). PCA network is used as a means for reducing the number of input units. SGA, a modified GA, is employed for selecting the proper number of hidden units and optimizing the connection links. Experimental results show that the proposed method is a useful tool for choosing an appropriate architecture for high dimensions.

In this paper, we propose an efficient and powerful crossover operator in the genetic algorithm for solving the traveling salesman problem (TSP). Our proposed crossover is called the complete subtour exchange crossover (CSEX), and inherits as many good subtours as possible because they are worth preserving for descendants. Before generating the descendants, a prerequisite for the CSEX is that it enumerates all common subtours, which consist of the same set in a pair of subtours on the given two tours of n cities. An algorithm to enumerate all common subtours in the CSEX consumes only O(n) time. In a fundamental experiment, we show the experimental number and length of the common subtours for two randomly generated tours with 5 to 500 thousand elements. In addition, we give the practical behavior of the CSEX and compare the CSEX with a hopeful crossover operator using the benchmark instances for the TSP. Moreover, in another experiment of parallel computing, in order to analyze the performance of the CSEX, we compare the CSEX with hopeful crossovers for 25 benchmarks (48 - 2392 city) using a parallel supercomputer, Paragon. From these results, the CSEX shows an extremely bright performance.

We introduce a concept of regularization into Genetic Algorithms (GAs). Conventional GAs include no explicit regularizing operations. However, the regularization is very effective in solving ill-posed problems. So, we propose a method of regularization to apply GAs to ill-posed problems. This regularization is a kind of consensus operation among neighboring individuals in GAs, and plays the role of `smoothing the solution. ' Our method is based on the evaluation of macroscopic fitness, which is a new fitness criterion. Conventional fitness of an individual in GAs is defined only from the phenotype of the individual, whereas the macroscopic fitness of an individual is evaluated from the phenotypes of the individual and its neighbors. We tested our regularizing operation by means of experiments with an elastic image mapping problem, and showed the effectiveness of the regularization.

This paper investigates the relations between the computational complexity and the restrictions for several problems that determine whether a given graph with edge costs and edge lengths has a spanning subgraph with such restrictions as the diameter, the connectivity, and the NA-distance and the NA-(edge)-connectivity proposed and investigated in [1]-[5]. The NA-distance and the NA-(edge)-connectivity are the measures for the distance and the connectivity between a vertex and a vertex subset (area). In this paper we prove that the minimum diameter spanning subgraph problem considering the restrictions of the diameter and the sum of edge costs is NP-complete even if the following restrictions are satisfied: all edge costs and all edge lengths are equal to one, and the upper bound of the diameter is restricted to four. Next, we prove that the minimum NA-distance spanning subgraph problem considering the restrictions of the NA-distances and the sum of edge costs is NP-complete even if the following conditions are satisfied: all edge costs and all edge lengths are equal to one, the upper bound of the NA-distance is restricted to four, each area is composed of a vertex, and the number of areas is restricted to two. Finally, we investigate the preserving NA-distance and NA-edge-connectivity spanning subgraph problem considering the preservations of the NA-distances and the NA-edge-connectivity and the restrictions of the sum of edge costs, and prove that a sparse spanning subgraph can be constructed in polynomial time if all edge costs are equal to one.

Topological Walk is an algorithm that can sweep an arrangement of n lines in O(n2) time and O(n) space. This paper revisits Topological Walk to give its new interpretation in contrast with Topological Sweep. We also survey applications of Topological Walk to make the distinction clearer.

An algorithm for modular division which is suitable for VLSI implementation is proposed. It is based on the plus-minus algorithm which is a modification of the binary method for calculating the greatest common divisor (GCD). The plus-minus algorithm for calculating GCD is extended for performing modular division. A modular division is carried out through iteration of simple operations, such as shifts and addition/subtractions. A redundant binary representation is employed so that addition/subtractions are performed without carry propagation. A modular divider based on the algorithm has a linear array structure with a bit-slice feature and carries out an n-bit modular division in O(n) clock cycles, where the length of clock cycle is constant independent of n.

As a super class of tournament digraphs, Bang-Jensen, Huang and Prisner defined an in-tournament digraph (in-tournament for short) and investigated a number of its nice properties. The in-tournament is a directed graph in which the set of in-neighbors of every vertex induces a tournament digraph. In other words, the presence of arcs (x,z) and (y,z) implies that exactly one of (x,y) or (y,x) exists. In this paper, we propose, for in-tournaments, parallel algorithms for examining the existence of a Hamiltonian path and a Hamiltonian cycle and for constructing them, if they exist.