Work place: Department of Electronics and Communication Engineering, KLE’s Dr. M.S. Sheshgiri College of Engineering and Technology, Belagavi, India
Research Interests: Computational Learning Theory, Process Control System, Error Control, Analysis of Algorithms, Control Theory
Rajashri Khanai received her PhD in Error Correction Coding and Cryptography for wireless communication from the Visvesvaraya Technological University, Belagavi, India. Her research interests include error control codes, cryptography, and machine learning applications to signal analysis. She is currently working as Professor in the Department of Electronics and Communication Engineering, KLE’s Dr. M. S. Sheshgiri College of Engineering and Technology, Belagavi, Karnataka, India. She has published over 25 research papers. Dr. Rajashri is a member of IEEE.
DOI: https://doi.org/10.5815/ijcnis.2018.12.05, Pub. Date: 8 Dec. 2018
The underwater acoustic environment is a promising technology which explores the real-time data collection for various applications. However, these channels are prone to errors, and characterized by propagation delay, half duplex communication. At low frequencies, the fading phenomenon extensively affect the behavior of the channel and hence the effect the design of reliable communication system. The underwater acoustic channels to perform appreciably reliable communication, an attempt are made by various modulation and coding techniques. Simulation results for the combination of BPSK modulation with Reed Solomon code (BPSK-RS) having various interleavers Random Interleaver, Matrix Interleaver, have been investigated. To improve the Bit Error Rate performance various modulation techniques such as BPSK, QPSK, and QAM were combined with coding algorithms like RS code, Turbo code and different Interleavers. The investigation of the above combination reveals that IDMA-OFDM-MIMO with BPSK modulation, Turbo code with Random Interleaver technique improves significantly Bit Error Rate performance.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2018.01.07, Pub. Date: 8 Jan. 2018
Epilepsy is a chronic brain disorder which affects normal neuronal activity of the brain. It results in sudden repeated episodes of higher electrical activity due to sensory disturbance. Electroencephalogram (EEG) plays an important role in the diagnosis of epilepsy. Currently, manual observation of EEG is done by experienced neurologist to diagnose epilepsy and related disorders. However, automated system is a promising method for seizure detection and diagnosis. The EEG signals recorded from the patient’s scalp are preprocessed, and classified as seizure and non-seizure based on the extracted signal features. The procedure significantly eliminates the error involved in manual observation. Due to non-linear nature of EEG, joint time-frequency methods are used to analyse the EEG signals. This paper proposes a EEG feature extraction technique using Dual Tree Complex Wavelet Transform (DTℂWT) to overcome the problem of shift variance in DWT. The estimation of improved multi-scale Permutation Entropy (IMPmEn) is done for the level-3 subband of DTℂWT. The performance of the Least Squares Support Vector Machine (LS-SVM) classifier is tested using these features and highest classification accuracy of 99.87 % is obtained on the real time EEG database.[...] Read more.
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