In this paper, we discuss a neural network approach for blind signal extraction of temporally correlated sources. Assuming autoregressive models of source signals, we propose a very simple neural network model and an efficient on-line adaptive algorithm that extract, from linear mixtures, a temporally correlated source with an arbitrary distribution, including a colored Gaussian source and a source with extremely low value (or even zero) of kurtosis. We then combine these extraction processing units with deflation processing units to extract such sources sequentially in a cascade fashion. Theory and simulations show that the proposed neural network successfully extracts all arbitrarily distributed, but temporally correlated source signals from linear mixtures.

Using Hopfield-type neural network model, we present an algorithm for reconstructing 3D mesh processor arrays using single-track switches where spare processors are laid on the six surfaces of a 3D array and show its effectiveness in terms of reconstruction rate and computing time by computer simulation. Next, we show how the algorithm can be realized by a digital neural circuit. It consists of subcircuits for finding candidate compensation paths, deciding whether the neural system reaches a stable state and at the time the system energy is minimum, and subcircuits for neurons. The subcircuit for each neuron including the other subcircuits can only be made with 16 gates and two flip-flops. Since the state transitions are done in parallel, the circuit will be able to find a set of compensation paths for a fault pattern very quickly within a time less than 1 µs. Furthermore, the hardware implementation of the algorithm leads to making a self-reconfigurable system without the aid of a host computer.

When we have a singular Cab curve with many rational points, we had better to construct linear codes on its normalization rather than the original curve. The only obstacle to construct linear codes on the normalization is finding a basis of L( Q) having pairwise distinct pole orders at Q, where Q is the unique place of the Cab curve at infinity. We present an algorithm finding such a basis from defining equations of the normalization of the original Cab curve.

A new hardware algorithm for the block matching video motion estimation is presented. The algorithm works in the full-search fashion but unlike the Full-Search Block Matching Algorithm (FSBMA) it adjusts the number of computations dynamically to variable picture contents. Due to incorporated mechanism of data-driven thresholding, the proposed algorithm performs as four times as less operations comparing to the FSBMA while maintaining the same quality of results. Its hardware implementation is simple and compact. A supportive hardware design as well as simulation results on benchmarks are outlined.

This paper shows the design of multi-stage fuzzy inference system with smaller number of rules based upon the optimization of rules by using the genetic algorithm. Since the number of rules of fuzzy inference system increases exponentially in proportion to the number of input variables powered by the number of membership function, it is preferred to divide the inference system into several stages (multi-stage fuzzy inference system) and decrease the number of rules compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and the output of the stage is treated as an input to the next stage. If we use the simplified inference scheme and assume the shape of membership function is given, the same backpropagation algorithm is available to optimize the weight of each rule as is usually used in the single stage inference system. On the other hand, the shape of the membership function is optimized by using the GA (genetic algorithm) where the characteristics of the membership function is represented as a set of string to which the crossover and mutation operation is applied. By combining the backpropagation algorithm and the GA, we have a comprehensive optimization scheme of learning for the multi-stage fuzzy inference system. The inference system is applied to the automatic bond rating based upon the financial ratios obtained from the financial statement by using the prescribed evaluation of rating published by the rating institution. As a result, we have similar performance of the multi-stage fuzzy inference system as the single stage system with remarkably smaller number of rules.

This paper proposes an adaptive noise canceller with low signal-distortion in the presence of crosstalk. The proposed noise canceller has two pairs of cross-coupled adaptive filters, each of which consists of the main filter and a sub filter. The signal-to-noise ratios (SNRs) of the primary and the reference signals are estimated by the sub filters. To reduce signal distortion at the output of the adaptive noise canceller, the step sizes for coefficient adaptation in the main filters are controlled according to the estimated SNRs. Computer simulation results show that the proposed noise canceller reduces signal distortion in the output signal by up to 15 dB compared to the conventional noise canceller.

We propose a query processing method for amalgamated knowledge bases. Our query processing method is an extension of the magic sets technique for query processing in amalgamated knowledge bases, augmented with the capabilities of handling amalgamated atoms. Through rewriting rules in a given amalgamated knowledge base, our method offers the advantages associated with top-down as well as bottom-up evaluation. We discuss how to handle amalgamated atoms, consider how to check whether an amalgamated atom is satisfiable in a fact set and how to extend a fact set by inserting an amalgamated atom. We also give the transformation procedures for amalgamated knowledge databases and show the correctness of our method.

In this paper, a new gradient-based adaptive algorithm for the estimation of discrete Fourier coefficients (DFC) of a noisy sinusoidal signal is proposed based on a summed least mean squared error criterion. This algorithm requires exactly the same number of multiplications as the conventional LMS algorithm, and presents much improved performance in both white and colored noise environments at the expense of some additional memories and additions only. We first analyze the performance of the conventional LMS algorithm in colored additive noise, and point out when its performance deteriorates. Then, a summed least mean squared error criterion is proposed, which leads to the above-mentioned new gradient-based adaptive algorithm. The performance of the proposed algorithm is also analyzed for a single frequency case. Simulation results are provided to support the analytical findings and the superiority of the new algorithm.

In this paper we propose an algorithm for generating maximum weight independent sets in a circle graph, that is, for putting out all maximum weight independent sets one by one without duplication. The time complexity is O(n3 + β ), where n is the number of vertices, β output size, i. e. , the sum of the cardinalities of the output sets. It is shown that the same approach can be applied for spider graphs and for circular-arc overlap graphs.

In this paper, a new feature which is characterized by the extrema density of 2-D wavelet frames estimated at the output of the corresponding filter bank is proposed for texture segmentation. With and without feature selection, the discrimination ability of features based on pyramidal and tree-structured decompositions are comparatively studied using the extrema density, energy, and entropy as features, respectively. These comparisons are demonstrated with separable and non-separable wavelets. With the three-, four-, and five-category textured images from Brodatz album, it is observed that most performances with feature selection improve significantly than those without feature selection. In addition, the experimental results show that the extrema density-based measure performs best among the three types of features investigated. A Min-Min method based on genetic algorithms, which is a novel approach with the spatial separation criterion (SPC) as the evaluation function is presented to evaluate the segmentation performance of each subset of selected features. In this work, the SPC is defined as the Euclidean distance within class divided by the Euclidean distance between classes in the spatial domain. It is shown that with feature selection the tree-structured wavelet decomposition based on non-separable wavelet frames has better performances than the tree-structured wavelet decomposition based on separable wavelet frames and pyramidal decomposition based on separable and non-separable wavelet frames in the experiments. Finally, we compare to the segmentation results evaluated with the templates of the textured images and verify the effectiveness of the proposed criterion. Moreover, it is proved that the discriminatory characteristics of features do spread over all subbands from the feature selection vector.

This paper proposes adaptive line enhancers with new coefficient update algorithms on the basis of least-square-error criteria. Adaptive algorithms by least-squares are known to converge faster than stochastic-gradient ones. However they have high computational complexity due to matrix inversion. To avoid matrix inversion the proposed algorithms adapt only one coefficient to detect one sinusoid. Both FIR and IIR types of adaptive algorithm are presented, and the techniques to reduce the influence of additive noise is described in this paper. The proposed adaptive line enhancers have simple structures and show excellent convergence characteristics. While the convergence of gradient-based algorithms largely depend on their stepsize parameters, the proposed ones are free from them.

In this paper, a new scheme for designing multilevel BTC coding is proposed. Optimal quantization can be obtained by selecting the quantization threshold with an exhaustive search. However, this requires an enormous amount of computation and is, thus impractical when we consider an exhaustive search for the multilevel BTC. In order to find a better threshold so that the average mean square error between the original and reconstructed images is a minimum, the genetic algorithm is applied. Comparison of the results of the proposed method with the exhaustive search reveal that the former method can almost achieve optimal quantization with much less computation than that required in the latter case.

In this paper, an analytical model for evaluating the performance of a packet scheduling algorithm, called lookahead scheduling, is proposed. Using lookahead scheduling, each input port of a switch has B packet buffers. A packet arrives at an input port is scheduled for conflict-free transmission for up to B time slots in advance. If it cannot be scheduled for transmission in the next B slots, the packet is immediately dropped to prevent it from blocking the subsequently arrived packets. To evaluate this scheduling algorithm, we first construct a set of recursive equations for obtaining the buffer occupancy and the probability that a packet cannot be placed into a buffer. Based on that, analytical expressions for switch throughput, packet loss probability and mean packet delay are derived. Analytical results are then compared with the simulations and good agreement is found. A pipeline implementation of the lookahead scheduling is also proposed in this paper.

In this paper, a general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model guarantees the discontinuity at the object boundary region, and the similarity model does the continuity or the high correlation of the disparity distribution. According to the experimental results, the proposed algorithm had good estimation performance. This result showes that the derived formula generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation. Also, the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to O(n()) from O(n()4) of the generalized formula.

Newton based adaptive algorithms are among the algorithms which are known to exhibit a higher convergence speed in comparison to the least mean square (LMS) algorithms. In this paper we propose a simplified Newton based adaptive algorithm for an adaptive infinite impulse response (IIR) filter implemented using cascades of second order allpass filters and a finite impulse response (FIR) filter. The proposed Newton based algorithm avoids the complexity that may arise in the direct differentiation of the mean square error. The analysis and simulation results presented for the algorithm, show that the property of convergence of the poles of the IIR ADF to those of the unknown system will be maintained for both white and colored input signal. Computer simulation results confirm an increase in convergence speed in comparison to the LMS algorithm.

This paper presents a new approach to computer image generation via three proposed methods for translating the evolution of a Petri net into fractal image synthesis. The idea is derived from the concept of fractal iteration principles in the escape-time algorithm and chaos game. The approach uses a Petri net as a powerful abstract modeling tool for fractal image synthesis via its duality, deadlock, inhibitor arc, firing sequence and marking reachability. The objective of this approach is to enhance the analysis technique of a Petri net and use it as a novel technique for fractal image synthesis. Generating fractal images via the dynamics of a Petri net allows an easy and direct proof for the similarity and correspondence between the dynamics of complex quadratic fractals by the recursive procedure of the escape-time algorithm and the state of a Petri net via a reachability problem. The reachability problem will be manipulated in terms of the dynamics of the fractal in order to generate images via three proposed methods. Validation of our approach is given by discussion and an illustration of some experimental results.

This paper addresses the problem of multiple multicast in WDM networks. It presents three efficient algorithms to construct an optimal/sub-optimal multicast tree for each multicast and minimise the network congestion on wavelengths. The first two algorithm achieve an optimal network congestion for a specific class of networks whose all wavelengths are globally accessible and convertible at a unit cost. The third algorithm produces an approximation solution for the general case of WDM networks.

In this paper, by making good use of the parallel-transit-evaluation algorithm and sparsity of the connection between neurons, a pipeline structure is successfully introduced to the sequential Boltzmann machine processor. The novel structure speeds up nine times faster than the previous one, with only the 12% rise in hardware resources under 10,000 neurons. The performance is confirmed by designing it using 1.2 µm CMOS process standard cells and analyzing the probability of state-change.

In this paper, a clustering-based method is proposed for automatically constructing a multi-input Takagi-Sugeno (TS) fuzzy model where only the input-output data of the identified system are available. The TS fuzzy model is automatically generated by the process of structure identification and parameter identification. In the structure identification step, a clustering method is proposed to provide a systematic procedure to partition the input space so that the number of fuzzy rules and the shapes of fuzzy sets in the premise part are determined from the given input-output data. In the parameter identification step, the recursive least-squares algorithm is applied to choose the parameter values in the consequent part from the given input-output data. Finally, two examples are used to illustrate the effectiveness of the proposed method.

A distributed computing system consists of processing elements, communication links, memory units, data files, and programs. These resources are interconnected via a communication network and controlled by a distributed operating system. The distributed program reliability (DPR) in a distributed computing system is the probability that a program which runs on multiple processing elements and needs to retrieve data files from other processing elements will be executed successfully. This reliability varies according to 1) the topology of the distributed computing system, 2) the reliability of the communication edges, 3) the data files and programs distribution among processing elements, and 4) the data files required to execute a program. In this paper, we show that computing the distributed program reliability on a star distributed computing system is #P-complete. A polynomially solvable case is developed for computing the distributed program reliability when some additional file distribution is restricted on the star topology. We also propose a polynomial time algorithm for computing the distributed program reliability with approximate solutions when the star topology has no the additional file distribution.