Search

The design of thinned planar microstrip arrays under specific constraints concerning the impedance-matching condition of the array elements and the radiation pattern is presented. The radiation characteristics of the structure are extracted by applying the method-of-moments. The array design is based on a novel optimization method, which is a modified version of the boolean particle swarm optimization that employs velocity mutation (BPSO-vm). Apart from the optimization of the array geometry, the proposed method is applicable to other discrete-variable optimization problems. Moreover, the planar array design is coped with by means of other techniques, namely, a binary coded Genetic Algorithm, the binary Particle Swarm Optimization, and the Boolean PSO. The comparison of the above methods and the BPSO-vm shows the efficiency of the proposed technique. Index Terms--Artificial immune system, particle swarm optimization (PSO), planar arrays, thinned arrays.

Several analyses are conducted to explain the impact of the function perturbations on the steady-state distribution of genetic regulatory networks, like the Boolean networks (BNs). A single function perturbation is shown to affect only the network dynamics by changing the long-run distribution.

A report on network decomposition method using Binary Decision Diagrams and manipulate Boolean functions for computing the reliability of networks such as computer, communications or power and its effectiveness is shown by several examples and experiments is presented.

A common problem encountered in disciplines such as statistics, data analysis, signal processing, textual data representation, and neural network research, is finding a suitable representation of the data in the lower dimension space. One of the principles used for this reason is a factor analysis. In this paper, we show that Hebbian learning and a Hopfield-like neural network could be used for a natural procedure for Boolean factor analysis. To ensure efficient Boolean factor analysis, we propose our original modification not only of Hopfield network architecture but also its dynamics as well. In this paper, we describe neural network implementation of the Boolean factor analysis method. We show the advantages of our Hopfield-like network modification step by step on artificially generated data. At the end, we show the efficiency of the method on artificial data containing a known list of factors. Our approach has the advantage of being able to analyze very large data sets while preserving the nature of the data. Index Terms--Associative memory, Boolean factor analysis, concepts search, dimensionality reduction, features clustering, Hopfield-like neural network, information retrieval, neural network application, neural network architecture, recurrent neural network, statistics, unsupervised learning.

A new algorithm is introduced to evaluate (non) coherent fault trees, obtaining exact lower and upper bounds on system unavailability, with a prespecified maximum error. The algorithm is based on the canonical normal form of the Boolean function, but overcomes the large number of terms needed, by using an intrinsic order criterion (IOC) to select the elementary states to evaluate.

The generation of a set of rules underlying a classification problem is performed by applying a new algorithm called Hamming Clustering (HC). It reconstructs the AND-OR expression associated with any Boolean function from a training set of samples. The basic kernel of the method is the generation of clusters of input patterns that belong to the same class and are close to each other according to the Hamming distance. Inputs which do not influence the final output are identified, thus automatically reducing the complexity of the final set of rules. The performance of HC has been evaluated through a variety of artificial and real-world benchmarks. In particular, its application in the diagnosis of breast cancer has led to the derivation of a reduced set of rules solving the associated classification problem. Index Terms--Rule generation, Hamming clustering, knowledge discovery, Boolean function approximation, generalization.

As the number of Internet servers increases rapidly, it becomes difficult to determine the relevant servers when searching for information. We develop a new method to rank Internet servers for Boolean queries. Our method reduces time and space complexity from exponential to polynomial in the number of Boolean terms. We contrast it with other known methods and describe its implementation. Index Terms - Boolean query, information retrieval, ranking, resource discovery, similarity measure.

The calculation of mean system-failure frequency (MSFF) with prescribed accuracy for good engineering designs using Bonferroni-type inequalities was analyzed. The shortening sum-of-products forms of the fault-tree Boolean-function was then theorized to avoid higher-order Bonferroni inequalities for approximate analysis. Upper and lower bounds for mincuts approximation were found through MSFF with the approximated values of system mean time to failure and mean time to repair.

The theory of Boolean derivatives, the activities of a Boolean function (BF) and Chow parameters allow the statistical analysis of a class of nonlinear filters-stack filters. The partial derivatives of a positive BF (PBF) are related to the selection probabilities of stack filters. The joint selection probability matrix calculation involves the weighted activities of the variables of PBF and weighted Chow parameters. A study discusses the computation of selection probabilities of stack filters by spectral algorithms.

A training framework which designs optimal nonlinear filters, such as the Boolean and stack filters, for different signal and image processing tasks is devoid of constraining models. A representative training set provides fast procedures for designing optimal or close to optimal filters. A case study reveals low sensitivity and a good generalization power of the filters for restoration of natural images. The Boolean-based filters are robust and capable of building libraries of optimal filters.

In this paper we present a new approach to solving the false path problem. The method is based on our previous work on Timed Boolean Algebra [13]. Given a logic circuit, we first derive timed Boolean expressions to model its dynamic behavior. Then, for each term in the expressions, we compute its corresponding sensitizability function, expressed in conjunction normal form; and use an expression in product form to approximate the function. Finally we remove the redundant terms whose sensitizability functions are not satisfiable and determine the maximal delays from the terms remained. Complexity analysis shows that our method identifies false paths and computes delays for sensitizable paths in polynomial time, while experimental results on ISCAS benchmark circuits prove its better efficiency and effectiveness.

Two methods are presented in this paper for removing single variable static hazards in Boolean Functions. The first method is a hand calculation method that uses a single variable static hazard cover algorithm. The hand calculation method can be used in the classroom and does not require computer assistance. The second method involves using a single variable static hazard cover software synthesis tool. The second method is more convenient to use outside the classroom on a PC that is available for student use or perhaps on personal computers. To illustrate the process of obtaining single variable static hazard covers, the standard Karnaugh map approach is also used. A commercially available design synthesis tool and schematic capture/simulation timing tool are also used in this paper.

Several characteristics have been demonstrated and/or reproved on the basis of characterizations of nondominated (ND) coteries, ND bicoteries and ND wr-coteries by using Boolean algebra, in efforts to prove that Boolean functions offer a facile route to modeling coteries, bicoteries and write-read coteries. Functions signifying an ND coterie can be decomposed into copies of the three-majority function, and a binary tree can represent this decomposition.

The sum of disjoint products (SDP) approach for network reliability evaluation is examined with respect to implementing paths/cuts preprocessing for decreasing the computation time and the amount of generated SDP terms. The paths/cuts ordering is carried out via cardinality, lexicographic and Hamming distance techniques and their generalizations on 19 benchmark networks with paths ranging from four to 780 and cuts from four to 7376 using the Computer Aided Relilability Evaluator.

Stack filters, a class of nonlinear filters based on a Boolean function, are graded into decreasing, increasing and mixed classes. Their associative memory capability, which refers to the filter's set of root signals, is evaluated. Learning schemes are developed for designing minimal decreasing and increasing stack filters and proof is given of the existence of a minimal mixed stack filter for any pattern set.

Stack filters are a type of rank-order based filters which treat binary signals via positive Boolean functions. The latter's sum-of-product or product-of-sum forms are proven to be a representation of itson-set or off-set elements and may be formulated as a Boolean equation without complementing the input variables. An algorithm for obtaining a stack filter representation by the described method is also included.

The paper describes a new, logic-based methodology for analyzing observations. The key features of the Logical Analysis of Data (LAD) are the discovery of minimal sets of features necessary for explaining all observations and the detection of hidden patterns in the data capable of distinguishing observations describing 'positive' outcome events from 'negative' outcome events. Combinations of such patterns are used for developing general classification procedures. An implementation of this methodology is described in the paper along with the results of numerical experiments demonstrating the classification performance of LAD in comparison with the reported results of other procedures. In the final section, we describe three pilot studies on applications of LAD to oil exploration, psychometric testing, and the analysis of developments in the Chinese transitional economy. These pilot studies demonstrate not only the classification power of LAD, but also its flexibility and capability to provide solutions to various case-dependent problems. Index Terms--Data mining, knowledge discovery, machine learning, classification, Boolean functions, patterns, decision support.

In this paper a novel method of generating canonic sinusoidal oscillators with two operational transconductance amplifiers (OTA's), two grounded capacitors (GC's), and a small number of resistors is introduced. Noninteracting control of the frequency of oscillation (FO) and the condition of oscillation (CO) are realized with the transconductance parameters of the two OTA's respectively. Bandpass (BP) and lowpass (LP) filter functions can also be realized with the same topologies. The independent tuning of the natural frequency [[Omega].sub.o] and [[Omega].sub.o]/Q of most filters can be realized without changing the topologies. Index Terms--Analog, Boolean method, filter, oscillator, OTA.

The problem of finding all cycles in the exponentially growing state space of synchronous Boolean networks was studied in the paper by C. Farrow, J. Heidel, J. Maloney, and J. R. Scalar, 'Equations for synchronous Boolean networks with biological applications,' IEEE Trans. Neural Networks, vol. 15, no. 2, pp. 348-354 Mar. 2004. No efficient algorithm was given to solve the problem. We show that even the determination of the number of fixed points (cycles of length 1) for monotone Boolean networks and the determination of the existence of fixed points for general Boolean networks are both strong NP-complete. Index Terms--Cycles, fixed points, synchronous Boolean networks.

Manipulations in the Boolean expression in the m-array algorithm simplify the VLSI architecture, as described by Murthy and Swamy. The number of logical AND operators for the 8-bit order statistic filter is reduced significantly. The mathematical derivations are given, and comparisons are made between the required operators for an existing method and the proposed simplification.

Mirrored threshold decomposition and binary positive Boolean functions are used to define a richer group of stack filters.