AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Foreign exchange reserves in India and China

Professor Irma Glicman Adelman, an Irish Economist working in California University at Berkely, in her research work on ‘Development Over Two Centuries’, which is published in the Journal of Evolutionary Economics, 1995, has identified that India, along with China, would be one of the largest economies in this 21st Century. She has stated that the period 1700 - 1820 is the period of Netherlands, the period 1820 - 1890 is the period of England the period 1890 - 2000 is the period of America and this 21st Century is the century of China and India. World Bank has also identified India as one of the leading players of this century after China. India will be third largest economy after USA and China. India will challenge the Global Economic Order in the next 15 years. India will overtake Italian economy in 2015, England economy in 2020, Japan economy in 2025 and USA economy in 2050 (China will overtake Japan economy in 2016 and USA economy in 2027). India has the following advantages compared with other economies. India is 4th largest GDP in the world in terms of Purchasing Power. India is third fastest growing economy in the world after China and Vietnam. Service sector contributes around 57% of GDP. The share of agriculture is around 17% and Manufacture is 16% in 2005 - 2006. This is a character of a developed country. Expected GDP growth rate is 10% shortly (It has come down from 9.2% in 2006 - 2007 to 6.2% during 2008 - 2009 due to recession. It is only a temporary phenomenon). India has $284 billion as Foreign Exchange Reserve as on today. India had just $1 billion as Foreign Exchange Reserve when it opened its economy in the year 1991. In this research paper an attempt has been made to study the two booming economies of the globe with respect to their foreign exchange reserves. This study mainly based on secondary data published by respective governments and various studies done on this area