A classification of the objectives and types of parallel processing gives the context of the prinicipal subjects of this paper. A compiler and machine organization which are capable of detecting opportunities for para...
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A classification of the objectives and types of parallel processing gives the context of the prinicipal subjects of this paper. A compiler and machine organization which are capable of detecting opportunities for parallel processing in algebraic expressions and executing these by multiple processing units operating concurrently are described. The compiler algorithm, based on a tree structure of algebraic expressions, translates the expression into an object program containing concurrency groups of instructions. Within each such group. all of the instructions can be executed at the same time. The machine organization (described in a schematic way) includes a simple control for executing the object program by allocating instructions to processors within a group and interlocking advance to the next group until execution of the current group is completed.
This paper describes a set of novel conditions that can be integrated in a computer-aided-testing (CAT) package for repair of redundant RAM's. A new approach is proposed; the innovative feature of this approach is...
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This paper describes a set of novel conditions that can be integrated in a computer-aided-testing (CAT) package for repair of redundant RAM's. A new approach is proposed; the innovative feature of this approach is the independence of analysis on the distribution of faulty bits in memory. This results in better exploitation of redundancy and efficient adaptability of this technique to various testing methods, such as the ones that employ region totalizers and fault counters. algorithms that provide repair solution and earliest detection of unrepairability of a device are presented. The benefits that result by using this approach include a reduction in repair time. Conditions of unrepairability are given as a function of the number of spare resources (columns and rows) in the redundant memory; significant improvement over existing techniques is accomplished. Simulation results are provided to substantiate the validity of the proposed theory.
Learning algorithms for feedforward connectionist systems in a reinforcement learning environment are developed and analyzed in this paper. The connectionist system is made of units of groups of learning automata, The...
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Learning algorithms for feedforward connectionist systems in a reinforcement learning environment are developed and analyzed in this paper. The connectionist system is made of units of groups of learning automata, The learning algorithm used is the L(R-I) and the asymptotic behavior of this algorithm is approximated by an Ordinary Differential Equation (ODE) for low values of the learning parameter, This is done using weak convergence techniques, The reinforcement learning model is used to pose the goal of the system as a constrained optimization problem, It is shown that the ODE, and hence the algorithm exhibits local convergence properties, converging to local solutions of the related optimization problem, The three layer pattern recognition network is used as an example to show that the system does behave as predicted and reasonable rates of convergence are obtained, Simulations also show that the algorithm is robust to noise.
The coordinate rotation digital computer (CORDIC) is a rotation-based arithmetic computing algorithm which has found many important digital signal processing applications. In this paper, a detailed analysis of the qua...
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The coordinate rotation digital computer (CORDIC) is a rotation-based arithmetic computing algorithm which has found many important digital signal processing applications. In this paper, a detailed analysis of the quantization error encountered in the CORDIC algorithm is presented. Two types of quantization error are examined: One is an approximation error due to the quantized representation of rotation angles. The other is a rounding error due to the finite precision representation in both fixed point and floating point arithmetic. Tight error bounds for these two types of error are derived. The rounding error due to a scaling (normalization) operation in the CORDIC algorithm is also discussed. Lastly, an expression for overall quantization error is derived and several simulation examples are presented.
Divide-and-conquer is a natural way to cope with the complexity of automatic testbench generation. The key to developing an effective divide-and-conquer approach is to identify the partitioning boundaries where intera...
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Divide-and-conquer is a natural way to cope with the complexity of automatic testbench generation. The key to developing an effective divide-and-conquer approach is to identify the partitioning boundaries where interactions among divided components are minimized. The authors propose a novel design decomposition scheme and show how it can help improve the performance of constraint solving for test generation.
The authors present a survey and a comparative analysis of various algorithms introduced for the differential protection of three-phase transformers. After a brief review of the concept, the problems associated with t...
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The authors present a survey and a comparative analysis of various algorithms introduced for the differential protection of three-phase transformers. After a brief review of the concept, the problems associated with the differential protection of transformers, and the existing solutions as implemented with conventional (electromagnetic) relays, the algorithms introduced for digital relaying are outlined. Some of these algorithms are derived from conventional protection methods while others represent novel methods. They are analyzed in view of how well they address the two subproblems of differential protection per se and exciting-current detection. Finally, a simple ranking procedure is introduced. Two optimality performance indices, based on time and frequency, are defined and used as a basis for a comparison of the algorithm presented.< >
A new parallel normalized optimized approximate inverse algorithm, based on the concept of the "fish bone" computational approach satisfying an antidiagonal data dependency, for computing classes of explicit...
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A new parallel normalized optimized approximate inverse algorithm, based on the concept of the "fish bone" computational approach satisfying an antidiagonal data dependency, for computing classes of explicit approximate inverses, is introduced for symmetric multiprocessor systems. The parallel normalized explicit approximate inverses are used in conjunction with parallel normalized explicit preconditioned conjugate gradient square schemes, for the efficient solution of finite element sparse linear systems. The parallel design and implementation issues of the new proposed algorithms are discussed and the parallel performance is presented, using OpenMP. (c) 2007 Elsevier Inc. All rights reserved.
A tracking analysis of the adaptive filters equipped with the sign algorithm and operating in nonstationary environments is presented. Under the assumption that the nonstationary can be modeled using a random disturba...
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A tracking analysis of the adaptive filters equipped with the sign algorithm and operating in nonstationary environments is presented. Under the assumption that the nonstationary can be modeled using a random disturbance, it is shown that the long-term time average of the mean-absolute error is bounded, and that there exists an optimal choice of the convergence constant mu which minimizes this quality. Applying the commonly used independence assumption, and under the assumption that the nonstationarity is solely due to the time-varying behavior of the optimal coefficients, it is shown that the distributions of the successive coefficient misalignment vectors converge to a limiting distribution when the adaptive filter is used in the system identification mode. Finally, under the additional assumption that the signals involved are zero mean and Gaussian, a set of nonlinear difference equations is derived that characterizes the mean and mean-squared behavior of the filter coefficients and the mean-squared estimation error during adaptation and tracking. Results of several experiments that show very good correlation with the theoretical analyses are presented.< >
In this paper, we present a technique for designing the optimal (in the mean square error sense) separable 2-D filter for recovering a signal from a noise-corrupted signal when the joint statistics of the signal and n...
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In this paper, we present a technique for designing the optimal (in the mean square error sense) separable 2-D filter for recovering a signal from a noise-corrupted signal when the joint statistics of the signal and noise are known. A set of nonlinear equations in the design parameters is derived, and an iterative algorithm to solve them is presented, The algorithm is shown to be nondivergent in theory and rapidly convergent in practice. The results of applying the separable filter designalgorithm to several typical image recovery problems are given, In most cases, the performance of the resulting separable filter is similar to that of the optimal nonseparable filter with the same region of support.
An analysis of a beamspace version of the MUSIC algorithm applicable to two closely spaced emitters in diverse scenarios is presented. Specifically, the analysis is applicable to uncorrelated far-field emitters of any...
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An analysis of a beamspace version of the MUSIC algorithm applicable to two closely spaced emitters in diverse scenarios is presented. Specifically, the analysis is applicable to uncorrelated far-field emitters of any relative power level, confined to a known plane, and observed by an arbitrary array of omnidirectional sensors. An expression for the threshold array signal-to-noise ratio at which beamspace MUSIC is able to resolve the emitters is obtained. The preprocessor that minimizes the resolution threshold is identified. It is demonstrated that the resolution threshold is proportional to the dimension of the noise subspace; therefore, the threshold can be reduced substantially by utilizing an appropriate beamformer to reduce the dimension of the noise subspace. It is also demonstrated that MUSIC in conjunction with a suitable preprocessor can provide a resolution threshold lower than conventional (sensor-space) MinNorm.< >
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