Generation of Pattern Classifier using LFSRs

Analysis of Pattern Generation and Randomness for LFSRs

The main goal of genetic programming is to get best pattern in terms of randomness. There are various methods for random pattern generation. The patterns generated in this paper are pseudo-random in nature. Different types of linear feedback shift register (LFSR) are used to generate pseudo-random patterns and test their randomness by various testing algorithms. Random patterns are compared acc...

Application of LFSRs for Parallel Sequence Generation in Cryptologic Algorithms

We consider the problem of efficiently generating sequences in hardware for use in certain cryptographic algorithms. The conventional method of doing this is to use a counter. We show that sequences generated by linear feedback shift registers (LFSRs) can be tailored to suit the appropriate algorithms. For hardware implementation, this reduces both time and chip area. As a result, we are able t...

Pattern Detection Using a Maximal Rejection Classifier

In this paper we propose a new classifier the Maximal Rejection Classifier (MRC) for target detection. Unlike pattern recognition, pattern detection problems require a separation between two classes, Target and Clutter, where the probability of the former is substantially smaller, compared to that of the latter. The MRC is a linear classifier, based on successive rejection operations. Each reje...

Nanosecond Pattern Classifier

Abstmcf A CMOS neural network integrated circuit is discussed, which was designed for very high speed applications. This full-custom, mixed analog-digital chip implements a fully connected feedforward neural network with 70 inputs, 6 hidden layer neurons and one output neuron. The neurons perform inner product operation and have sigmoid-like activation function. The 70 network inputs and the ne...

Corpus-based Generation of Numeral Classifier using Phrase Alignment