We present pipelined simple matching, called PSM, for an input buffered switch to relax the scheduling timing constraint by modifying pipelined maximal-sized matching (PMM). Like the pipelined manner of PMM, to produce the matching results in every time slot, PSM employs multiple subschedulers which take more than one time slot to complete matching. Using only head-of-line information of input buffers, PSM successively sends each request to all subschedulers to provide a better matching opportunity. To obtain better performance, PSM uses unique starting points of scheduling pointers in which the difference between the starting points is equal for any two adjacent subschedulers for a same output. Using computer simulations under a uniform traffic, we show PSM is more appropriate than PMM for pipelined scheduling of an input buffered switch.

Various niching methods have been developed to maintain the population diversity. The feature of these methods is to prevent the proliferation of similar individuals in the niche (subpopulation) based on the similarity measure. This paper demonstrates that they are effective to avoid premature convergence in a case where only one global optimum in multimodal functions is searched. The performance of major niching methods in such a case is investigated and compared by experiments using seven benchmark functions. The niching methods tested in this paper are deterministic crowding, probabilistic crowding, restricted tournament selection, clearing procedure and diversity-control-oriented genetic algorithm (DCGA). According to the experiment, each method shows a fairly good global-optimum-searching capability. However, no method can completely avoid premature convergence in all functions. In addition, no method shows a better searching capability than the other methods in all functions.

This paper discusses encoding of vehicular position information using predictive algorithms in inter-vehicle communications (IVC) from the viewpoints of source coding and noisy channels. Two vehicular driving models are assumed; one is the 15-mode as a suburban rapid transit driving pattern, the other is called calming mode as a street-driving pattern. Three types of schemes are compared; a pulse code modulation (PCM) scheme, a predictive coding (PC) scheme, and the variable interval prediction (VIP) scheme that is proposed here. This paper assumes that precise position information is got from a positioning system, and that all the transmitters and receivers have common predictors. Performance comparisons of the three types of schemes are carried out both of noiseless and noisy channels. Results show that the VIP scheme is superior to any other scheme.

A siphon-trap of a Petri net N is defined as a place set S with S = S, where S = { u| N has an edge from u to a vertex of S} and S = { v| N has an edge from a vertex of S to v}. A minimal siphon-trap is a siphon-trap such that any proper subset is not a siphon-trap. The following polynomial-time algorithms are proposed: (1) FDST for finding, if any, a minimal siphon-trap or even a maximal class of mutually disjoint minimal siphon-traps of a given Petri net; (2) FDSTi that repeats FDST i times in order to extract more minimal siphon-traps than FDST. (3) STFM_T (STFM_Ti, respectively) which is a combination of the Fourier-Motzkin method and FDST (FDSTi) and which has high possibility of finding, if any, at least one minimal-support nonnegative integer invariant.

This paper presents a flexible learning algorithm for the binary neural network that can realize a desired Boolean function. The algorithm determines hidden layer parameters using a genetic algorithm. It can reduce the number of hidden neurons and can suppress parameters dispersion. These advantages are verified by basic numerical experiments.

We describe in this paper our experience of developing a large-scale, highly distributed multi-agent system using wireless-networked sensors. We provide solutions to the problems of localization (position estimation) and dynamic, real-time mobile object tracking, which we call PET problems for short, using wireless sensor networks. We propose system architectures and a set of distributed algorithms for organizing and scheduling cooperative computation in distributed environments, as well as distributed algorithms for localization and real-time object tracking. Based on these distributed algorithms, we develop and implement a hardware system and software simulator for the PET problems. Finally, we present some experimental results on distance measurement accuracy using radio signal strengths of the wireless sensors and discuss future work.

This letter presents text-independent speaker identification results for telephone speech. A speaker identification system based on Karhunen-Loeve transform (KLT) derived from codebook design using genetic algorithm (GA) is proposed. We have combined genetic algorithm (GA) and the vector quantization (VQ) algorithm to avoid typical local minima for speaker data compression. Identification accuracies of 91% were achieved for 100 Mandarin speakers.

An adaptive array antenna should be applied for suppression of CCI in the spatial domain. However, the adaptive array antenna has some problems as follows. Because the adaptive array antenna takes a long time to converge to the optimum antenna weights, it's hard to track in case of quick varying channel. On the other hand, processing burden increases with the number of elements in the array antenna. To solve these problems, we propose an adaptive array antenna using array antenna as element antennas, the so-called "Layered array antenna." At the 1st layer, sector area are defined. We can change the sector areas according to the DOA distribution, because the sector areas are defined by the antenna weights. At the 2nd layer, MMSE is performed. Interference that couldn't be suppressed at the 1st layer is suppressed at the 2nd layer. By the proposed system, we confirmed higher convergence speed while relieving processing complexity.

In this paper, a contention-based reservation MAC protocol is proposed for non-real-time burst traffic class in wireless ATM networks. The proposed protocol is characterized by the contention-based transmission of the reservation request and contention-free transmission of burst traffic. The design objective of the proposed protocol is to reduce contention delay during the contention phase of a connection. In order to reduce collision of reservation requests, the base station calculates the transmission probability based on the estimated load of reservation requests and the number of random access minislots, and broadcasts it over the frame header period of downlink channel. Wireless terminal, which has traffic burst, selects a random access minislot and transmits its reservation request with a received transmission probability. Based on the successfully received reservation, the scheduler allocates the uplink data slots to wireless terminal. Simulation results show that the proposed protocol can provide higher channel utilization, and furthermore, maintains constant delay performance in a heavy traffic environment.

According to the grey data generating techniques in grey system theory, we propose a novel cryptosystem, whose applications can develop a new direction in the field of information security. In this paper, we present the concepts of sum-lock, difference-lock, sum-ladder, and difference-ladder. By using these concepts, we can obtain a cryptosystem with lock generation and sum-difference replacement ladder. In addition, we provide the encryption and decryption algorithms of our cryptosystem and adopt an illustrative example to verify it.

Multiple access interference (MAI) due to many simultaneous users is the main factor that limits the performance of DS-CDMA system. Multiuser detection is a method to avoid performance degradation due to MAI. We propose a blind multiuser detection method based on the algorithm consisting of two-stage decoding process, i.e., linearly constrained constant modulus (LCCM) and iterative least squares (ILS). The computer simulations confirmed that the algorithm is near-far resistant and that the proposed method is effective in the application to the slow fading channels.

A new blind restoration method applying Real-coded genetic algorithm (RcGA) will be proposed, and this method will be proven valid for the blurred image restoration with unidentified degradation in the experiments. In this restoration method, the degraded and blurred image is going to get restricted to the images possible to be expressed in the point spread function (PSF), then the restoration filter for this degraded image, which is also the 2-dimentional inverse filter, will be searched among several points applying RcGA. The method will enable to seek efficiently among vast solution space consists of numeral coefficient filters. And perceiving the essential features of the spectrum in the frequency space, an evaluation function will be proposed. Also, it will be proposed to apply the Rolling-ball transform succeeding an appropriate Gaussian degrade function against the dual degraded image with blur convoluting impulse noise. By above stated features of this restoration method, it will enable to restore the degraded image closer to the original within a practical processing time. Computer simulations verify this method for image restoration problem when the factors causing image distortions are not identified.

In this work we study the performance of a distributed GA that incorporates in its core parallel cooperative-competitive genetic operators. A series of controlled experiments are conducted using various large and difficult 0/1 multiple knapsack problems to test the robustness of the distributed GA. Simulation results verify that the proposed distributed GA compared with a canonical distributed GA significantly gains in search speed and convergence reliability with less communication cost for migration.

Feature subset selection is an important preprocessing task for pattern recognition, machine learning or data mining applications. A Genetic Algorithm (GA) with a fuzzy fitness function has been proposed here for finding out the optimal subset of features from a large set of features. Genetic algorithms are robust but time consuming, specially GA with neural classifiers takes a long time for reasonable solution. To reduce the time, a fuzzy measure for evaluation of the quality of a feature subset is used here as the fitness function instead of classifier error rate. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function. Simulation over two data sets shows that the proposed algorithm is efficient for selection of near optimal solution in practical problems specially in case of large feature set problems.

The input queued (IQ) switching architecture is becoming an attractive alternative for high-speed switches owing to its scalability. In this paper, three new algorithms, referred to as the maximum credit first (MCF), enhanced MCF (EMCF), and iterative MCF (IMCF) algorithms, are introduced. Simulations show that both MCF and IMCF have similar performance as the Birkhoff-von Neumann decomposition (BVND) algorithm, which can provide cell delay bound and 100% throughput, with lower off-line computational and on-line memory complexity. Simulations also show the fairness of MCF is much better than that of BVND. Theoretic analysis shows that the EMCF algorithm has a better performance than MCF in terms of throughput and cell delay with the same complexity level as MCF. Simulation results indicate the EMCF algorithm has much lower average cell delay and delay variance as compared to the BVND algorithm.

This paper presents an efficient graph-based evolutionary optimization technique called Evolutionary Graph Generation (EGG) and its extension to a parallel version. A new version of parallel EGG system is based on a coarse-grained model of parallel processing and can synthesize heterogeneous networks of various different components efficiently. The potential capability of parallel EGG system is demonstrated through the design of current-mode logic circuits.

An image halftoning technique that uses a simple GA has proven to be effective generating bi-level halftone images with quality higher than conventional techniques. Many devices are designed to handle more than two halftone levels and a GA based multi-level halftoning technique is desirable. In this paper we extend the bi-level halftoning technique to generate multi-level halftone images. Also we introduce an improved GA (GA-SRM) into the proposed multi-level halftoning technique. Experimental results show that the proposed technique can effectively generate high quality multi-level halftone images and that the inclusion of GA-SRM substantially contributes reducing memory usage and accelerating image generation.

Adaptive optimization of the notch bandwidth of a lattice-based adaptive infinite impulse response (IIR) notch filter is presented in this paper. The filter is used to improve the performance of a direct sequence spread spectrum (DSSS) binary phase shift keying (BPSK) communication system by suppressing a narrow-band interference at the receiver. A least mean square (LMS) algorithm used to adapt the notch bandwidth coefficient to its optimum value which corresponds to the maximum signal to noise ratio (SNR) improvement factor is derived. Bit error rate (BER) improvement gained by the DSSS communication system using the filter with the optimized notch bandwidth is also shown. Computer simulation results are compared with those obtained analytically to demonstrate the validity of theoretical predictions for various received signal parameters.

In this paper, we investigate the optimal assignment problem of cells in PCS (Personal Communication Service) to switches on a ATM (Asynchronous Transfer Mode) network. Given cells and switches on an ATM network (whose locations are fixed and known), the problem is to group cells into clusters and assign these clusters to switches in an optimum manner. This problem is modeled as a complex integer programming problem. Since finding an optimal solution of this problem is NP-hard, a heuristic solution model consists of three phases (Cell Pre-Partitioning Phase, Cell Exchanging Phase, and Cell Migrating Phase) is proposed. Experimental results show that Cell Exchanging and Cell Migrating Phases can really reduce total cost near 44% on average.

This paper attempts to establish a theory for a general auto-associative memory model. We start by defining a new concept called supporting function to replace the concept of energy function. As known, the energy function relies on the assumption of symmetric interconnection weights, which is used in the conventional Hopfield auto-associative memory, but not evidenced in any biological memories. We then formulate the information retrieving process as a dynamic system by making use of the supporting function and derive the attraction or asymptotic stability condition and the condition for convergence of an arbitrary state to a desired state. The latter represents a key condition for associative memory to have a capability of learning from variant samples. Finally, we develop an algorithm to learn the asymptotic stability condition and an algorithm to train the system to recover desired states from their variant samples. The latter called sample learning algorithm is the first of its kind ever been discovered for associative memories. Both recalling and learning processes are of finite convergence, a must-have feature for associative memories by analogy to normal human memory. The effectiveness of the recalling and learning algorithms is experimentally demonstrated.