Implementation of RNS-Based Distributed Arithmetic Discrete Wavelet Transform Architectures Using Field-Programmable Logic

Currently there are design barriers inhibiting the implementation of high-precision digital signal processing (DSP) objects with field programmable logic (FPL) devices. This paper explores overcoming these barriers by fusing together the popular distributed arithmetic (DA) method with the residue number system (RNS) for use in FPL-centric designs. The new design paradigm is studied in the context of a high-performance filter bank and a discrete wavelet transform (DWT). The proposed design paradigm is facilitated by a new RNS accumulator structure based on a carry save adder (CSA). The reported methodology also introduces a polyphase filter structure that results in a reduced look-up table (LUT) budget. The 2C-DA and RNS-DA are compared, in the context of a FPL implementation strategy, using a discrete wavelet transform (DWT) filter bank as a common design theme. The results show that the RNS-DA, compared to a traditional 2C-DA design, enjoys a performance advantage that increases with precision (wordlength).     

A study on flowshop scheduling problem combining Taguchi experimental design and genetic algorithm

As genetic algorithm parameters vary depending on different problem types when applying genetic algorithm to reach global optimum, appropriate design value selection has significant impact on the efficiency of genetic algorithm. However, most users adjust parameters manually based on the reference values of previous literature. Such trial-and-error method is time-consuming, ineffective, and often it could not locate the optimal combination. Therefore, in flowshop scheduling problems, this research anticipates to complete optimal parameter combination design in genetic algorithm using Taguchi experimental design. According to the research results, different ways of producing initial solution have significant influence on this research topic. Consequently, confirmation experiment is conducted using the optimal parameter combination obtained from the research results. It is found that the predicted value of signal-to-noise ratio (S/N ratio) and its actual value exists deviation of 0.238%, indicating repetitiveness and robustness of the obtained parameter combination. Hence, this research method can effectively reduce time spent on parameter design using genetic algorithm and increase efficiency of algorithm.     

Robust decentralized neural networks based LFC in a deregulated power system

In this paper, a decentralized radial basis function neural network (RBFNN) based controller for load frequency control (LFC) in a deregulated power system is presented using the generalized model for LFC scheme according to the possible contracts. To achieve decentralization, the connections between each control area with the rest of system and effects of possible contracted scenarios are treated as a set of input disturbance signals. The idea of mixed H₂/H_∞ control technique is used for the training of the proposed controller. The motivation for using this control strategy for training the RBFNN based controller is to take large modeling uncertainties into account, cover physical constraints on control action and minimize the effects of area load disturbances. This newly developed design strategy combines the advantage of the neural networks and mixed H₂/H_∞ control techniques to provide robust performance and leads to a flexible controller with simple structure that is easy to implement. The effectiveness of the proposed method is demonstrated on a three-area restructured power system. The results of the proposed controllers are compared with the mixed H₂/H_∞ controllers for three scenarios of the possible contracts under large load demands and disturbances. The resulting controller is shown to minimize the effects of area load disturbances and maintain robust performance in the presence of plant parameter changes and system nonlinearities.     

A logical approach to program analysis

This paper describesMicroScope, a framework for developing analysis tools for Lisp programs. MicroScope uses a knowledge-intensive approach for program representation and analysis. The analysis tools share a common object oriented program database, and a common Prolog inference engine. The use of Prolog and a declarative representation for programs permits sharing of information, and provides high bandwidth communication between diverse analysis tools. It also supports program specification and debugging activities in the same framework. Extensions to Prolog to support analysis are described, and two tools, theCritic and theExpector, are presented.This work supported in part by Hewlett-Packard Company, the National Science foundation Under Grant Number MCS81-21750 and the Defense Advanced Research Projects Agency under contract number DAAK11-84-K-0017.     

Markov analysis of qualitative dynamics1

Common sense sometimes predicts events to be likely or unlikely rather than merely possible. We extend methods of qualitative reasoning to predict the relative likelihoods of possible qualitative behaviors by viewing the dynamics of a system as a Markov chain over its transition graph. This involves adding qualitative or quantitative estimates of transition probabilities to each of the transitions and applying the standard theory of Markov chains to distinguish persistent states from transient states and to calculate recurrence times, settling times, and probabilities for ending up in each state. Much of the analysis depends solely on qualitative estimates of transition probabilities, which follow directly from theoretical considerations and which lead to qualitative predictions about entire classes of systems. Quantitative estimates for specific systems are derived empirically and lead to qualitative and quantitative conclusions, most of which are insensitive to small perturbations in the estimated transition probabilities. The algorithms are straightforward and efficient.