Low backscattered dual-polarised metallo-dielectric structure based on sierpinski carpet

A novel technique for backscattering reduction for both TE and TM polarisation, employing a metallo-dielectric structure based on Sierpinski carpet fractal geometry, is reported. A reduction in backscattered power of --30 dB is obtained for normal incidence in the X-band for the structure using the third iterated stage of the fractal geometry

Frequency tunable metallo–dielectric structure for backscattering reduction

Electromagnetic scattering behaviour of a superstrate loaded metallo– dielectric structure based on Sierpinski carpet fractal geometry is reported. The results indicate that the frequency at which backscattering is minimum can be tuned by varying the thickness of the superstrate. A reduction in backscattered power of 44 dB is obtained simultaneously for both TE and TM polarisations of the incident field.

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.