Dattaprasad Torse

Work place: Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, India

E-mail: datorse@git.edu


Research Interests: Engineering, Computational Engineering, Computational Science and Engineering


Dr. Dattaprasad Torse has received his Ph.D from the Visvesvaraya Technological University, Belagavi, India in the field of biomedical (EEG) signal analysis for seizure detection/classification applications. He received his master of engineering from Amravati University in Digital Electronics. He has published over 15 research papers on EEG signal analysis in journal and conferences. He is currently Associate Professor in the Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, Karnataka, India. He is a member of IEEE.

Author Articles
An Extensive Review of Feature Extraction Techniques, Challenges and Trends in Automatic Speech Recognition

By Vidyashree Kanabur Sunil S Harakannanavar Dattaprasad Torse

DOI: https://doi.org/10.5815/ijigsp.2019.05.01, Pub. Date: 8 May 2019

Speech is the natural mode of communication between humans. Human-to-machine interaction is gaining importance in the past few decades which demands the machine to be able to analyze, respond and perform tasks at the same speed as performed by human. This task is achieved by Automatic Speech Recognition (ASR) system which is typically a speech-to-text converter. In order to recognize the areas of further research in ASR, one must be aware of the current approaches, challenges faced by each and issues that needs to be addressed. Therefore, in this paper human speech production mechanism is discussed. The various speech recognition techniques and models are addressed in detail. The performance parameters that measure the accuracy of the system in recognizing the speech signal are described. 

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Comprehensive Study of Data Aggregation Models, Challenges and Security Issues in Wireless Sensor Networks

By Veena I Puranikmath Sunil S Harakannanavar Satyendra Kumar Dattaprasad Torse

DOI: https://doi.org/10.5815/ijcnis.2019.03.05, Pub. Date: 8 Mar. 2019

The use of wireless sensor networks has been increasing tremendously in the past decades mainly because of its applications in military, medicine, under water survey and various other fields. Depending on the applications the sensor nodes are placed in different areas and the data sensed will be sent to the base station. The process of transmitting and receiving data sensed by the sensor nodes continues till the sensors have battery life. This leads to generate data redundancy and reduces efficiency of the network. In order to overcome the limitations faced by the wireless sensor networks, the fusion of data known as data aggregation is introduced. In data aggregation, the data sensed by the various nodes are aggregated and sent to the base station as a single data packet. In this paper, a brief review on various data aggregation methods, challenges and issues are addressed. In addition to this, performance parameters of various data fusion methods to measure the efficiency of the network are discussed. The design of single aggregator models are easy compared to the multiple aggregator models. However, the security to most of the data fusion schemes is provided by using message authentication code. It also uses public keys and symmetric to achieve end to end or hop by hop encryptions.

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Performance Analysis of Underwater Acoustic Communication using IDMA-OFDM-MIMO with Reed Solomon and Turbo Code

By Rajashri Khanai Salma S. Shahapur Dattaprasad Torse

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.

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Classification of EEG Signals in a Seizure Detection System Using Dual Tree Complex Wavelet Transform and Least Squares Support Vector Machine

By Dattaprasad Torse Veena Desai Rajashri Khanai

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.  

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