Given a Petri net PN=(P, T, E), a siphon is a set S of places such that the set of input transitions to S is included in the set of output transitions from S. Concerning extraction of minimal siphons containing a given specified set Q of places, the paper proposes three algorithms based on branch-and-bound method for enumerating, if any, all minimal siphons containing Q, as well as for extracting such one minimal siphon.

The design of high performance-circuits using Field-Programmable Gate Arrays (FPGAs) requires a balance between the FPGA's architecture and CAD algorithms. Conventional FPGAs and CAD algorithms are developed independently, which makes it difficult to implement application circuits. To solve this problem, we developed a CAD-verified FPGA whose architecture was designed at the same time as the CAD algorithms. This paper shows how a CAD-verified FPGA architecture can simplify a routing algorithm. The algorithm is studied in terms of computational complexity and is simplified using the properties of our FPGA (switch module structure and the number of routing resources). The routing algorithm is almost one hundred times faster than that of the conventional router, and the quality of its circuits is also improved.

A unified algorithm is presented for solving key equations for decoding alternant codes. The algorithm can be applied to various decoding techniques, including bounded distance decoding, generalized minimum distance decoding, Chase decoding, etc.

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.

An evolutionary algorithm is used to evolve a digital circuit which computes a simple hash function mapping a 16-bit address space into an 8-bit one. The target technology is FPGA, where the search space of the algorithm is made of the combinational functions computed by cells and of the interconnections among cells. The evolutionary technique has been applied to five different interconnection topologies, specified by neighbourhood graphs. This circuit is readily applicable to the design of set-associative cache memories. Possible use of the evolutionary approach presented in the paper for on-line tuning of the function during cache operation is also discussed.

Retrieving the unknown parameters of scattering objects from measured field data is the subject of microwave imaging. This is naturally and usually posed as an optimization problem. In this paper, micro genetic algorithm coupled with deterministic method is applied to the shape reconstruction of perfectly conducting cylinders. The combined approach, with a very small population like the micro genetic algorithm, performs much better than the conventional large population genetic algorithms (GA's) in reaching the optimal region. In addition, we propose a criterion for switching the micro GA to the deterministic optimizer. The micro GA is utilized to effectively locate the vicinity of the global optimum, while the deterministic optimizer is employed to efficiently reach the optimum after inside this region. Therefore, the combined approach converges to the optimum much faster than the micro GA. The proposed approach is first tested by a function optimization problem, then applied to reconstruct perfectly conducting cylinders from both synthetic data and real data. Impressive and satisfactory results are obtained for both cases, which demonstrate the validity and effectiveness of the proposed approach.

We present a cascade neural network for blind source extraction. We propose a family of unconstrained optimization criteria, from which we derive a learning rule that can extract a single source signal from a linear mixture of source signals. To prevent the newly extracted source signal from being extracted again in the next processing unit, we propose another unconstrained optimization criterion that uses knowledge of this signal. From this criterion, we then derive a learning rule that deflates from the mixture the newly extracted signal. By virtue of blind extraction and deflation processing, the presented cascade neural network can cope with a practical case where the number of mixed signals is equal to or larger than the number of sources, with the number of sources not known in advance. We prove analytically that the proposed criteria both for blind extraction and deflation processing have no spurious equilibria. In addition, the proposed criteria do not require whitening of mixed signals. We also demonstrate the validity and performance of the presented neural network by computer simulation experiments.

In this paper, the performances of texture classification based on pyramidal and uniform decomposition are comparatively studied with and without feature selection. This comparison using the subband variance as feature explores the dependence among features. It is shown that the main problem when employing 2-D non-separable wavelet transforms for texture classification is the determination of the suitable features that yields the best classification results. A Max-Max algorithm which is a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. It is shown that the performance with feature selection in which only about half of features are selected is comparable to that without feature selection. Moreover, the discriminatory characteristics of texture spread more in low-pass bands and the features extracted from the pyramidal decomposition are more representative than those from the uniform decomposition. Experimental results have verified the selectivity of the proposed approach and its texture capturing characteristics.

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.

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.

The classical max-min policy has been suggested by the ATM Forum to support the available bit rate (ABR) service class. However, there are several drawbacks in adopting the max-min rate allocation policy. In particular, the max-min policy is not able to support the minimum cell rate (MCR) requirement and the peak cell rate (PCR) constraint for each ABR connection. Furthermore, the max-min policy does not offer flexible options for network providers wishing to establish a usage-based pricing criterion. In this paper, we present a generic weight-based rate allocation policy, which generalizes the classical max-min policy by supporting the MCR/PCR for each connection. Our rate allocation policy offers a flexible usage-based pricing strategy to network providers. A centralized algorithm is presented to compute network-wide bandwidth allocation to achieve this policy. Furthermore, a simple switch algorithm using ABR flow control protocol is developed with the aim of achieving our rate allocation policy in a distributed networking environment. The effectiveness of our distributed algorithm in a local area environment is substantiated by simulation results based on the benchmark network configurations suggested by the ATM Forum.

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.

In this paper, a new nonlinear integrator with positive phase shifts is proposed. Results of the digital simulation show that the nonlinear integrator has a better performance than the conventional one in a control system.

In this paper we develop a new family of on-line adaptive learning algorithms for blind separation of time delayed and convolved sources. The algorithms are derived for feedforward and fully connected feedback (recurrent) neural networks on basis of modified natural gradient approach. The proposed algorithms can be considered as generalization and extension of existing algorithms for instantaneous mixture of unknown source signals. Preliminary computer simulations confirm validity and high performance of the proposed algorithms.

A new cost function based on multi-band decomposition of the estimation error and application of a different step-size for each band is used in connection with the least-mean-square criterion to improve the fidelity of estimates as compared to those obtained with conventional least-mean-square adaptive algorithms. The basic new idea is to trade off time and frequency resolutions of the adaptive algorithm along the frequency domain by using different step-sizes in the analysis of distinct frequencies in accordance with the frequency-localized statistical behavior of the imput signal. The mathematical background for a stochatic approach to the multi-band decomposition-based scheme is presented and algorithms with fixed and variable step-sizes are derived. Computer experiments compare the performance of multiband and conventional least-mean-square methods when applied to system identification.

In this paper, we present a new state space-based approach for the two-dimensional (2-D) frequency estimation problem which occurs in various areas of signal processing and communication problems. The proposed method begins with the construction of a state space model associated with the noiseless data which contains a summation of 2-D harmonics. Two auxiliary Hankel-block-Hankel-like matrices are then introduced and from which the two frequency components can be derived via matrix factorizations along with frequency shifting properties. Although the algorithm can render high resolution frequency estimates, it also calls for lots of computations. To alleviate the high computational overhead required, a highly parallelizable implementation of it via the principle subband component (PSC) of some appropriately chosen transforms have been addressed as well. Such a PSC-based transform domain implementation not only reduces the size of data needed to be processed, but it also suppresses the contaminated noise outside the subband of interest. To reduce the computational complexity induced in the transformation process, we also suggest that either the transform of the discrete Fourier transform (DFT) or the Haar wavelet transform (HWT) be employed. As a consequence, such an approach of implementation can achieve substantial computational savings; meanwhile, as demonstrated by the provided simulation results, it still retains roughly the same performance as that of the original algorithm.

In this paper, we investigate inverse problems of the interval query problem in application to data mining. Let I be the set of all intervals on U = {1, 2, , n}. Consider an objective function f(I), conditional functions ui(I) on I, and define an optimization problem of finding the interval I maximizing f(I) subject to ui(I) > Ki for given real numbers Ki (i = 1, 2, , h). We propose efficient alogorithms to solve the above optimization problem if the objective function is either additive or quotient, and the conditional functions are additive, where a function f is additive if f(I) = ΣiIf^(i) extending a function f^ on U, and quotient if it is represented as a quotient of two additive functions. We use computational-geometric methods such as convex hull, range searching, and multidimensional divide-and-conquer.

The maximal linear forest problem is to find, given a graph G = (V, E), a maximal subset of V that induces a linear forest. Three parallel algorithms for this problem are presented. The first one is randomized and runs in O(log n) expected time using n2 processors on a CRCW PRAM. The second one is deterministic and runs in O(log 2n) timeusing n4 processors on an EREW PRAM. The last one is deterministic and runs in O(log 5n) time using n3 processors on an EREW PRAM. The results put the problem in the class NC.

We describe our implementation of the Hypercube variation of the Multiple Polynomial Quadratic Sieve (HMPQS) integer factorization algorithm on a Parsytec GC computer with 128 processors. HMPQS is a variation on the Quadratic Sieve (QS) algorithm which inspects many quadratic polynomials looking for quadratic residues with small prime factors. The polynomials are organized as the nodes of an n-dimensional cube. We report on the performance of our implementations on factoring several large numbers for the Cunningham Project.

In this paper, we consider the following node-to-set disjoint paths problem: given a node s and a set T = {t1,...,tk} of k nodes in a k-connected graph G, find k node-disjoint paths s ti, 1 i k. We give an O(n2) time algorithm for the node-to-set disjoint paths problem in n-dimensional star graphs Gn which are (n - 1)-connected. The algorithm finds the n - 1 node-disjoint paths of length at most d(Gn) + 1 for n 4,6 and at most d(Gn) + 2 for n = 4,6, where d(Gn) = 3(n-1)/2 is the diameter of Gn. d(Gn) + 1 and d(Gn) + 2 are also the lower bounds on the length of the paths for the above problem in Gn for n 4,6 and n = 4,6, respectively.