A parallel-prefix adder gives the best performance in VLSI design. However, performance of Ladner-Fischer adder through black cell takes huge memory. So, gray cell can be replaced instead of black cell which gives the Efficiency in Ladner-Fischer Adder. The proposed system consists of three stages of operations they are pre-processing stage, carry generation stage, post-processing stage. The pre-processing stage focuses on propagate and generate, carry generation stage focuses on carry generation and post-processing stage focuses on final result. In ripple carry adder each bit of addition operation is waited for the previous bit addition operation. In efficient Ladner -Fischer adder, addition operation does not wait for previous bit addition operation and modification is done at gate level to improve the speed and to decreases the memory used. General TermsRipple carry adder, Efficient Ladner-Fischer adder, Black cell, Gray cell KeywordsEfficient Ladner-Fischer adder-(ELF).

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Pattern classification is a system for classifying patterns into dissimilar potential categories. The classifier that is used for classification is granular neural network. A granular neural network called granular reflex fuzzy min-max neural network (GrRFMN). GrRFMN uses hyperbox fuzzy set to signify grainy information. Using known data the neural network will be trained, and using this trained neural network data can be classified. Its structural design consists of a spontaneous effect system motivated from human brain to handle group overlies.The GFMN cannot hold data granules of dissimilar sizes professionally. It can be practically done that a convinced quantity of such preprocessing can assist to recover the presentation of a classifier. The GrRFMN is skilled of managing grainy information capably by the training algorithm. The experimental outcomes on valid datasets confirm a good presentation of GRFMN. Experimental results on valid data sets confirm that the GrRFMN can categorize granules of dissimilar granularity further acceptably.

The main aim of the project is to design and Development of data acquisition card and Process parameters of explosive materials in ARM Microcontroller to capture, store and analyze the data and Display characteristic waveform on LCD Screen. The data Acquisition card integrated into ARM9263 processor board. This can be widely used in Defense and army to detect the bombs and RDXs and to protect soldiers and people from harmful substances and to take remedy actions from explosives. The software coding of the total project is realized by using EMBEDDED C language in the KEIL software and the total hardware is realized by using DAC card, ADC with the processor.