Performance analysis of bio-signal processing in ocean environment using soft computing techniques

Abstract

Wireless communication has become an essential technology in our day-to-day life both in air and water medium. To monitor the health parameter of human begins, advancement techniques like internet of things is evolved. But to analyze underwater living organisms health parameters, researchers finding difficulties to do so. The reason behind is underwater channels has drawbacks like signal degradation due to multipath propagation, severe ambient noise and Attenuation by bottom and surface loss. In this paper Artificial Neural Networks (ANN) is used to perform data transfer in water medium. A sample EEG signal is generated and trained with 2 and 20 hidden layers. Simulation result showed that error free communication is achieved with 20 hidden layers at 10th iteration. The proposed algorithm is validated using a real time watermark toolbox. Two different modulation scheme was applied along with ANN. In the first scenario, the EEG signal is modulated using convolution code and decoded by Viterbi Algorithm. Multiplexing technique is applied in the second scenario. It is observed that energy level in the order of 40 dB is required for least error rate. It is also evident from simulation result that maximum of 5% CP can be maintained to attain the least Mean Square Error.