An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR regime.

With the recent explosion in the number of wireless communication technologies, the frequency spectrum has become a scarce resource. The need of the hour is an efficient method to utilize the existing spectrum and Cognitive Radio is one such technology that can mitigate the spectrum scarcity. In a cognitive radio system, the unlicensed secondary user accesses the spectrum allotted to licensed primary users when it lies vacant. To implement dynamic or opportunistic access of spectrum, secondary users perform spectrum sensing, which is a quintessential part of a Cognitive radio. From the Cognitive user's point of view, lesser error probability means an increased likelihood of channel reuse when it is vacant, and a higher detection probability signifies better protection to the licensed users. In both cases the decision threshold plays a pivotal role in determining the fate of the unused spectrum. In this paper, we study the difficulty of selecting an appropriate threshold to minimize the error probability in an uncertain low SNR regime. The sensing failure issue is analyzed, and an optimal threshold is computed that yields minimum error rate. An adaptive double threshold concept has been proposed to make the detection robust and a closed-form equation for optimal threshold has been derived to minimize the error. The novel findings through simulation results exhibit improvement in Probability of detection and reduction in probability of error at low SNR in the presence of noise uncertainty factor.

Epileptic EEG detection using neural networks and post-classification.

Electroencephalogram (EEG) has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. In most cases, identification of the epileptic EEG signal is done manually by skilled professionals, who are small in number. In this paper, we try to automate the detection process. We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet coefficients. A feed-forward backpropagating artificial neural network (ANN) is used for the classification. We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. Average specificity of 99.19%, sensitivity of 91.29% and selectivity of 91.14% are obtained.

On modeling genetic algorithms for functions of unitation.

We discuss a novel model for analyzing the working of Genetic Algorithms (GAs), when the objective function is a function of unitation. The model is exact (not approximate), and is valid for infinite populations. Functions of unitation depend only on the number of 1's in any string. Hence, we only need to model the variations in the distribution of strings with respect to the number of 1's in the strings. We introduce the notion of a Binomial Distributed Population (BDP) as the building block of our model, and we show that the effect of uniform crossover on BDPs is to generate two other BDPs. We demonstrate that a population with any general distribution may be decomposed into several BDPs. We also show that a general multipoint crossover may be considered as a composition of several uniform crossovers. Based on these results, the effects of mutation and crossover on the distribution of strings have been characterized, and the model has been defined. GASIM-a Genetic Algorithm Simulator for functions of unitation-has been implemented based on the model, and the exactness of the results obtained from GASIM has been verified using actual Genetic Algorithm runs. The time complexity of the GA simulator derived from the model is O(l(3)) (where l is the string length), a significant improvement over previous models with exponential time complexities.

Mapping of artificial neural networks onto message passing systems.

Various Artificial Neural Networks (ANNs) have been proposed in recent years to mimic the human brain in solving problems involving human-like intelligence. Efficient mapping of ANNs comprising of large number of neurons onto various distributed MIMD architectures is discussed in this paper. The massive interconnection among neurons demands a communication efficient architecture. Issues related to the suitability of MIMD architectures for simulating neural networks are discussed. Performance analysis of ring, torus, binary tree, hypercube, and extended hypercube for simulating artificial neural networks is presented. Our studies reveal that the performance of the extended hypercube is better than those of ring, torus, binary tree, and hypercube topologies.

ADAMIS: a database for medical information systems.

This paper describes the design and implementation of ADAMIS ('A database for medical information systems'). ADAMIS is a relational database management system for a general hospital environment. Apart from the usual database (DB) facilities of data definition and data manipulation, ADAMIS supports a query language called the 'simplified medical query language' (SMQL) which is completely end-user oriented and highly non-procedural. Other features of ADAMIS include provision of facilities for statistics collection and report generation. ADAMIS also provides adequate security and integrity features and has been designed mainly for use on interactive terminals.