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Mangayarkarasi P.,Adhiparasakthi Engineering College Melmaruvathur | Saseendran A.,Adhiparasakthi Engineering College | Jayashri S.,Adhiparasakthi Engineering College
International Conference on Communication and Signal Processing, ICCSP 2013 - Proceedings | Year: 2013

Relay Selection (RS) is a strategy that has widely been studied for many protocols. In this paper, relay selection is studied for amplify and forward protocol. RS is used to enhance the performance of cooperative relay networks. The spectral efficiency of relay selection acts better when compared to energy efficiency. Moreover, RS will perform better when the number of hops between the source and the destination is less. There are many methods used at the receiver end such as Maximal Ratio Combining (MRC) selection combining etc. In this paper., selection combining is employed at the receiver since MRC is recently proved to have many drawbacks in cooperative relay networks. After performing relay selection., those users having signal to noise ratio (SNR) greater than the threshold value are selected. © 2013 IEEE. Source

Mahendran R.,Adhiparasakthi Engineering College Melmaruvathur
International Conference on Communication and Signal Processing, ICCSP 2014 - Proceedings | Year: 2014

This paper presents a novel artificial neural network approach to control an intelligent wheelchair using myoelectric signals. The work is divided into six stages out of which feature extraction and classification are the main stages for this research. The type of classification technique used is Multi-Layer Perceptron. The EMG data is collected by placing the electrodes on the forearm muscles. This data is segmented for every 200 milliseconds after which the feature extraction is performed using mean absolute value. The signals are fed to the artificial neural networks and processed to attain parameters that are related to the muscles temporal hand activities. The resulting commands are sent to drive the wheelchair according to the user's intention. The software was tested on the intelligent wheelchair in real-time, which confirm that the system is robust for different gender and environments. © 2014 IEEE. Source

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