K
Kailash Chandra Ray
Researcher at Indian Institute of Technology Patna
Publications - 70
Citations - 997
Kailash Chandra Ray is an academic researcher from Indian Institute of Technology Patna. The author has contributed to research in topics: FPGA prototype & Field-programmable gate array. The author has an hindex of 14, co-authored 63 publications receiving 681 citations. Previous affiliations of Kailash Chandra Ray include Indian Institute of Information Technology, Allahabad & Indian Institute of Technology Kharagpur.
Papers
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Journal ArticleDOI
ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVM
Sandeep Raj,Kailash Chandra Ray +1 more
TL;DR: D discrete orthogonal stockwell transform using discrete cosine transform is presented for efficient representation of the ECG signal in time–frequency space and particle swarm optimization technique is employed for gradually tuning the learning parameters of the SVM classifier.
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Sparse representation of ECG signals for automated recognition of cardiac arrhythmias
Sandeep Raj,Kailash Chandra Ray +1 more
TL;DR: Experimental results show that the proposed ECG signal representation using sparse decomposition technique with PSO optimized least-square twin SVM (best classifier model among k-NN, PNN and RBFNN) reported higher classification accuracy than the existing methods to the state-of-art diagnosis.
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Cardiac arrhythmia beat classification using DOST and PSO tuned SVM
TL;DR: The proposed feature representation of cardiac signals based on symmetrical features along with PSO based optimization technique for the SVM classifier reported an improved classification accuracy in both the assessment schemes evaluated on the benchmark MIT-BIH arrhythmia database and hence can be utilized for automated computer-aided diagnosis of cardiac arrhythmias.
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Low Latency Hybrid CORDIC Algorithm
Rohit Shukla,Kailash Chandra Ray +1 more
TL;DR: The authors have proposed a new hybrid CORDIC algorithm which reduces the iteration to (3\mbin/8) + 1 for \mbin bit precision including the scale factor calculation and compensation.
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ARM-based arrhythmia beat monitoring system
TL;DR: The proposed methodology for the diagnosis involves the integration of the R-peak detection algorithm, FFT (fast fourier transform) based discrete wavelet transform for feature extraction and feedforward based Neural Network Architecture to classify generic cardiac beat classes into eight categories.