EARLY ESTIMATION OF DELAY IN BINARY TO BCD CONVERTOR

A novel high speed architecture for fixed bit binary to BCD conversion which is better in terms of delay is presented in this paper. In recent years, decimal data processing applications have grown and thus there is a need to have hardware support for decimal arithmetic. Decimal digit multipliers are having Binary to BCD conversion as the basic building block. This decimal multiplication in turn is an integral part of commercial, internet and financial based applications

Robust Classification of Variable Length Sonar Sequences

. Two types of artificial neural networks are introduced for the robust classification of spatio-temporal sequences. The first network is the Adaptive Spatio-Temporal Recognizer (ASTER), which adaptively estimates the confidence that a (variable length) signal of a known class is present by continuously monitoring a sequence of feature vectors. If the confidence for any class exceeds a threshold value at some moment, the signal is considered to be detected and classified. The nonlinear behavior of ASTER provides more robust performance than the related dynamic time warping algorithm. ASTER is compared with a more common approach wherein a self-organizing feature map is first used to map a sequence of extracted feature vectors onto a lower dimensional trajectory, which is then identified using a variant of the feedforward time delay neural network. The performance of these two networks is compared using artificial sonograms as well as feature vectors strings obtained from short-duration..