When solving combinatorial optimization problems with a binary Hopfield-type neural network, the updating process in neural network is an important step in achieving a solution. In this letter, we propose a new updating procedure in binary Hopfield-type neural network for efficiently solving combinatorial optimization problems. In the new updating procedure, once the neuron is in excitatory state, then its input potential is in positive saturation where the input potential can only be reduced but cannot be increased, and once the neuron is in inhibitory state, then its input potential is in negative saturation where the input potential can only be increased but cannot be reduced. The new updating procedure is evaluated and compared with the original procedure and other improved methods through simulations based on N-Queens problem. The results show that the new updating procedure improves the searching capability of neural networks with shorter computation time. Particularly, the simulation results show that the performance of proposed method surpasses the exiting methods for N-queens problem in synchronous parallel computation model.

The EB tester line delay fault localization algorithm for combinational circuits is proposed where line delay fault probabilities are utilized to narrow fault candidates down to one efficiently. Probabilities for two main causes of line delay faults, defects of contact/vias along interconnections and crosstalk, are estimated through layout analysis. The algorithm was applied to 8 kinds of ISCAS'85 benchmark circuits to evaluate its performance where the guided probe (GP) diagnosis was used as the reference method. The proposed method can cut the number of probed lines to about 30% in average compared with those for the GP method. The total fault localization time was 31% of the time for the GP method and was 6% less than that of our previous method where the fault list generated in concurrent fault simulation is utilized.

Recently, neural networks (NNs) have been extensively applied to many signal processing problem due to their robust abilities to form complex decision regions. In particular, neural networks add flexibility to the design of equalizers for digital communication systems. Recurrent neural network (RNN) is a kind of neural network with one or more feedback loops, whereas self-organizing map (SOM) is characterized by the formation of a topographic map of the input patterns in which the spatial locations (i.e., coordinates) of the neurons in the lattice are indicative of intrinsic statistical features contained in the input patterns. In this paper, we propose a novel receiver structure by combining adaptive RNN equalizer with a SOM detector under serious ISI and nonlinear distortion in QAM system. According to the theoretical analysis and computer simulation results, the performance of the proposed scheme is shown to be quite effective in channel equalization under nonlinear distortion.

In this paper, we propose an equalizer for indoor infrared wireless systems using on-off keying code-division multiple-access (OOK-CDMA). The proposed equalizer has a decision-feedforward filter to mitigate the effects of inter symbol interference (ISI) at the previous chip position of the sampling instant. We evaluate the performance of indoor infrared wireless systems using OOK-CDMA with three kinds of equalizers: the decision-feedback qualizer (DFE), the feedforward equalizer (FE), and the proposed equalizer. To estimate the impulse response, we use the training sequence that alternates '1' and '0' sequentially. Among three kinds of equalizers, we show that the system with the proposed equalizer can achieve the best bit error rate (BER) performance at high bit rate, while the system with the FE achieves the best BER performance when the bit rate is low.

In this paper, we propose an efficient requantization method for INTRA-frames in MPEG-1/MPEG-4 transcoding. The quantizer for an MPEG-1 INTRA block usually uses a quantization weighting matrix, while the quantizer for an MPEG-4 simple profile does not. As a result, the quantization step sizes of the two quantizers may not be the same even for the same quantization parameter. Due to this mismatch in the quantization step size, a transcoded MPEG-4 sequence can suffer from serious quality degradation and the number of bits produced by transcoding increases from the original MPEG-1 video sequence. To solve these problems, an efficient method is proposed to identify a near-optimum reconstruction level in the transcoder. In addition, a Laplacian-model based PDF (probability distribution function) estimation for the original DCT coefficients from an input MPEG-1 bitstream is presented, which is required for the proposed requantization. Experimental results show that the proposed method provides a 0.3-0.7 dB improvement in the PSNR over the conventional method, even at a reduced bit-rate of 3-7%.

Low-profile inverter power supplies are increasingly required for backlight systems of liquid crystal displays (LCDs). A great deal of attention has been focused on the application of piezoelectric transformers (PTs) to such power supplies. To miniaturize PT inverters still further, PTs need to have sufficient high voltage-step-up-ratio, which can be achieved by a multilayered PT. First, this paper describes a method for simulating such performance using a distributed constant equivalent circuit model. The results of the simulation for a multilayered PT operated in the third order longitudinal vibration mode show that the resistance of internal electrodes causes the dominant loss factor. Next, a power inverter incorporating the multilayered PT was fabricated. This power inverter can be operated over a wide input DC voltage range from 7-20 V. Regarding a conventional inverter drive circuit, when input DC voltage range was extended, the inverter efficiency remarkably decreased. For the reason, we developed a new inverter circuit, which is equipped with an automatic drive voltage control circuit to maintain the drive voltage to the PT at a constant value. As a result, the fabricated power inverter exhibited more than 90% overall efficiency and 3.5 W output power, which is enough to light up a 12.1-inch color LCD. The maximum luminance efficiency on a light transmission plate of the backlight was as high as 30 cd/m2/W.

In conventional Web Geographic Information System (GIS), there are serious drawbacks of high waiting time and low accessibility because the subsequent query has to wait until all the spatial data has been completely transmitted. In addressing these problems, this paper proposes a progressive transmission method which can reduce waiting time and increase accessibility. The proposed method has the following steps. First, significant vertices of geographic objects are selected into based on an algorithm, Priority_Order_Estimation (POE). Second, the selected vertices are inserted the data structure, Priority_Order_Queue (POQ). This allows a client to view, to request and occasionally to reject spatial data, rather than waiting for the whole map to arrive. Third, the transmission steps are processed progressively, where significant vertices of POQ are sent from server to client one by one. In the final step, if necessary, the entire spatial data set of SENDOBJSP, which is a transmission structure, is transmitted so that the original map is accurately displayed. The performance of the system proposed here has been evaluated according to the factors such as system response time and client storage space. For the comparisons, the total transmission method, layer transmission method and the proposed four-step transmission method were used. Compared with the conventional Web-based GIS, the system response time is twenty-two percent shorter and client storage space is reduced by about twenty-one percent. As a result, performance improvement, fast response time and less client storage usage, is achieved. Therefore, the proposed method can make a significant contribution in support of Web-based Vector GIS applications such as Environmental Management, Map On Demand Service and Assessment and Planning System.

As two simple relaxation oscillators are coupled by periodical and instantaneous switching, the system exhibits rich superstable synchronous phenomena. In order to analyze the phenomena, we derive a hybrid return map of real and binary variables; and give theoretical results for (1) superstability of the synchronous phenomena and (2) period of the synchronous phenomena as a function of the parameters. Using a simple test circuit, typical phenomena are verified in the laboratory.

A fast capture algorithm of the initial attitude is proposed for the spacecraft using reaction wheels. This algorithm is composed of two steps. The first step deals with absorption of the initial angular momentum of the spacecraft into the reaction wheels and the second step a rest-to-reset large angle maneuver for sun tracking. A criterion of the initial attitude capture and a controller for the fast large angle maneuver method are presented under considering constraints of the reaction wheels. Simulation results show that the proposed algorithm has several advantages of the initial attitude capture and fast large angle maneuver. This algorithm can be applied to the near minimum time rotation control of the spacecraft.

We present an efficiency improvement on an existing unlinkable divisible e-cash system. In the based e-cash system, an e-coin can be divided to spent, and thus the exact payments are available. Furthermore, to protect customer's privacy, the system also satisfies the unlinkability in all the payments, which is not satisfied in other existing divisible e-cash systems. The unlinkability means the infeasibility of determining whether two payments are made by the same customer. However, in the unlinkable divisible e-cash system, the payment protocol needs O(N) computations, and thus inefficient, where N indicates the divisibility precision. For example, in case of N=100,000, about 200,000 exponentiations are needed for the worst. We improve the payment protocol using the tree approach. In case of N=100,000, the protocol with our improvement needs only about 600 exponentiations for the worst. This good result can be obtained for other N which is more than about 100.

The starting point of this paper is the definition of a nonlinear model of the read out process in high density optical discs. Under high density condition, the signal read out is not a linear process, and suffers also from cross talk. To cope with these problems, the identification of a suitable nonlinear model is required. A physical model based on the optical scalar theory is used to identify the kernels of a nonlinear model based on the Volterra series. Both analysis and simulations show that a second order bidimensional model accurately describes the read out process. Once equipped with the Volterra channel model, we evaluate the performance of various nonlinear receivers. First we consider Nonlinear Adaptive Volterra Equalization (NAVE). Simulations show that the performance of classical structures for linear channels is significantly affected by the nonlinear response. The nonlinear NAVE receiver can achieve better performance than Maximum Likelihood Sequence Estimator (MLSE), with lower complexity. An innovative Nonlinear Maximum Likelihood Sequence Estimator (NMLSE), based on the combination of MLSE and nonlinear Inter-Symbol Interference (ISI) cancellation, is presented. NMLSE offers significant advantages with respect to traditional MLSE, and performs better than traditional equalization for nonlinear channels (like NAVE). Finally, the paper deals with cancellation of cross talk from adjacent tracks. We propose and analyze an adaptive nonlinear cross talk canceller based on a three spot detection system. For the sake of simplicity, all the performance comparisons presented in this paper are based on the assumption that noise is Additive, White, and Gaussian (AWGN model).

A number of schemes have been proposed for communication using chaos over the past years. Regardless of the exact modulation method used, the transmitted signal must go through a physical channel which undesirably introduces distortion to the signal and adds noise to it. The problem is particularly serious when coherent-based demodulation is used because the necessary process of chaos synchronization is difficult to implement in practice. This paper addresses the channel distortion problem and proposes a technique for channel equalization in chaos-based communication systems. The proposed equalization is realized by a modified recurrent neural network (RNN) incorporating a specific training (equalizing) algorithm. Computer simulations are used to demonstrate the performance of the proposed equalizer in chaos-based communication systems. The Henon map and Chua's circuit are used to generate chaotic signals. It is shown that the proposed RNN-based equalizer outperforms conventional equalizers.

Fuzzy value is a fuzzy set on interval [0,1], whose α-cuts are all closed intervals for α [0,1], i.e., fuzzy value is a fuzzy number on [0,1]. In this note, we introduce three kinds of metrics di (i=1,2,3) into fuzzy-valued space m[0,1] and consider continuity of fuzzy-valued operations on metric spaces (m[0,1], di) (i=1,2,3). The obtained results will provide some theoretical bases for numeral calculation of fuzzy-valued operations.

Vector quantization (VQ) is an attractive image compression technique. VQ utilizes the high correlation between neighboring pixels in a block, but disregards the high correlation between the adjacent blocks. Unlike VQ, side-match VQ (SMVQ) exploits codeword information of two encoded adjacent blocks, the upper and left blocks, to encode the current input vector. However, SMVQ is a fixed bit rate compression technique and doesn't make full use of the edge characteristics to predict the input vector. Classified side-match vector quantization (CSMVQ) is an effective image compression technique with low bit rate and relatively high reconstruction quality. It exploits a block classifier to decide which class the input vector belongs to using the variances of neighboring blocks' codewords. As an alternative, this paper proposes three algorithms using gradient values of neighboring blocks' codewords to predict the input block. The first one employs a basic gradient-based classifier that is similar to CSMVQ. To achieve lower bit rates, the second one exploits a refined two-level classifier structure. To reduce the encoding time further, the last one employs a more efficient classifier, in which adaptive class codebooks are defined within a gradient-ordered master codebook according to various prediction results. Experimental results prove the effectiveness of the proposed algorithms.

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.

We have demonstrated synchronization of chaos in a pair of one-way coupled Colpitts oscillators by both experiment and numerical simulation. We have investigated parameter regions for achieving chaos-synchronization when one of the internal parameters is mismatched between the master and slave oscillators, and clarify the tolerance of parameter regions for synchronization against parameter mismatching.

The Belousov-Zhabotinsky (BZ) reaction provides us important clues in controlling 2D phase-lagged stable synchronous patterns in an excitable medium. Because of the difficulty in computing reaction-diffusion systems in large systems using conventional digital processors, we here propose a cellular-automaton (CA) circuit that emulates the BZ reaction. In the circuit, a two-dimensional array of parallel processing cells is responsible for fast emulation, and its operation rate is independent of the system size. The operations of the proposed CA circuit were demonstrated by using a simulation program with integrated circuit emphasis (SPICE).

In this paper we treat a novel adaptation strength according to neighborhood ranking of self-organizing neural networks with the objective of avoiding the initial dependency of reference vectors, which is related to the strength in the neural-gas network suggested by Martinetz et al. The present approach exhibits the effectiveness in the average distortion compared to the conventional technique through numerical experiments. Furthermore the present approach is applied to image data and the validity in employing as an image coding system is examined.

In previous work we have proposed a steganographic technique for gray scale images called BPCS-Steganography. We also apply this technique to full color images by decomposing the image into its three color component images and treating each as a gray scale image. This paper proposes a method to apply BPCS-Steganography to palette-based images. In palette-based images, the image data can be decomposed into color component images similar to those of full color images. We can then embed into one or more of the color component images. However, even if only one of the color component images is used for embedding, the number of colors in the palette after embedding can be over the maximum number allowed. In order to represent the image data in palette-based format, color quantization is therefore needed. We cannot change the pixel values of the color component image that contains the embedded information, but can only change the pixel values of the other color component images. We assume that the degrading of the color component2 image with information embedded is smaller than that of the color component images that are used for color reduction. We therefore embed secret information into the G component image, because the human visual system is more sensitive to changes the luminance of a color, and G has the largest contribution to luminance of the three color components. In order to reduce the number of colors, the R and B component images are then changed in a way that minimizes the square error.

In many practical situations in NN learning, training examples tend to be supplied one by one. In such situations, incremental learning seems more natural than batch learning in view of the learning methods of human beings. In this paper, we propose an incremental learning method in neural networks under the projection learning criterion. Although projection learning is a linear learning method, achieving the above goal is not straightforward since it involves redundant expressions of functions with over-complete bases, which is essentially related to pseudo biorthogonal bases (or frames). The proposed method provides exactly the same learning result as that obtained by batch learning. It is theoretically shown that the proposed method is more efficient in computation than batch learning.