Malaya Kumar Hota
Other affiliations: Motilal Nehru National Institute of Technology Allahabad, Indira Gandhi Institute of Technology
Bio: Malaya Kumar Hota is an academic researcher from VIT University. The author has contributed to research in topics: Filter (signal processing) & Discrete Fourier transform. The author has an hindex of 6, co-authored 19 publications receiving 114 citations. Previous affiliations of Malaya Kumar Hota include Motilal Nehru National Institute of Technology Allahabad & Indira Gandhi Institute of Technology.
••01 Nov 2008
TL;DR: It is observed that complex indicator sequence provides strong spectral component compared to EIIP indicator sequence, and windowed DFT taking complex indicator sequences provides better exon prediction compared to windowedDFT taking EIIp indicator sequence and digital filters methods.
Abstract: It has been observed by many researchers that the protein coding regions of DNA exhibit a period-3 behavior due to the codon structure. The period-3 property is not present outside coding regions, and can be exploited to locate coding regions. For eukaryotes (cells with nucleus) this periodicity has mostly been observed within the exons and not within the introns. Previously binary indicator sequence and electron-ion interaction pseudo potentials (EIIP) indicator sequence has been used for the identification of the coding regions. In this paper we observed that complex indicator sequence provides strong spectral component compared to EIIP indicator sequence. We also observed that windowed DFT taking complex indicator sequence provides better exon prediction compared to windowed DFT taking EIIP indicator sequence and digital filters methods. Computational overhead is reduced by 75% in complex indicator sequence compared to binary indicator sequence. There is maximum discrimination between coding and non-coding regions in complex indicator sequence.
••04 Apr 2019
TL;DR: Different filtering techniques comprising of Discrete Wavelet Transform (DWT), Normalized Least Mean Square (NLMS) filter, Finite Impulse Response (FIR) filter and Infinite Impulse response (IIR) filter were used in this paper for denoising the ECG signal which was corrupted by the PLI.
Abstract: ECG signals are often corrupted by 50 Hz noise, the frequency from the power supply. So it becomes quite necessary to remove Power Line Interference (PLI) from the ECG signal. The reference ECG signal data was taken from the MIT-BIH database. Different filtering techniques comprising of Discrete Wavelet Transform (DWT), Normalized Least Mean Square (NLMS) filter, Finite Impulse Response (FIR) filter and Infinite Impulse Response (IIR) filter were used in this paper for denoising the ECG signal which was corrupted by the PLI. Later, the comparison was made among the methods, to find the best methodology to denoise the corrupted ECG signal. The parameters that were used for the comparison are Mean Square Error (MSE), Mean Absolute Error (MAE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR). Higher values of SNR & PSNR and lower values of MSE & MAE define the best denoising algorithm.
TL;DR: Three antinotch filter methods are proposed to improve the identification accuracy of protein coding regions using the period-3 property, and results show that proposed methods outperform the existing method, giving improved identification of theprotein coding regions.
03 Apr 2018
TL;DR: Automatic Speech and Emotion Recognition is a widely researched topic that is a subset of Human Computer Interface (HCI) and has a range of applications.
Abstract: With the advent of digitization of every possible avenue, Automatic Speech and Emotion Recognition is a widely researched topic that is a subset of Human Computer Interface (HCI) and has a range of applications. With machines taking over many menial jobs it has become important for the computer to understand us as we understand it. Features such as MFCC, pitch and amplitude are extracted from a given sample and run across the existing and growing database of training samples. MFCC is being used to detected speaker and utterance, while SVM is used to distinguish the emotion of the sample given. An SVM classifier differentiates between anger, happiness, fear, sadness and updates the database as it goes.
01 Nov 2010
TL;DR: The results show better identification accuracy for Voss, z-curve and tetrahedron mapping technique as compared to other mapping techniques when tapered window based short-time discrete Fourier transform method applied to the GENSCAN test set.
Abstract: Prior to applying the digital signal processing techniques for identification of protein coding regions, mapping of DNA alphabet into numerical sequences is necessary In this paper, the performance of existing DNA to numerical mapping techniques is analyzed at the nucleotide level for the identification of protein coding regions using tapered window based short-time discrete Fourier transform (ST-DFT) method applied to the GENSCAN test set The results show better identification accuracy for Voss, z-curve and tetrahedron mapping technique as compared to other mapping techniques when tapered windows are used for ST-DFT method
TL;DR: Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance and the experimental result showed that the proposed stationary wavelet transform based ECGDenoising technique outperformed the other ECG Denoising techniques as more ECGs signal components are preserved than other denoised algorithms.
Abstract: Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. During ECG signal acquisition, various noises like power line interference, baseline wandering, motion artifacts, and electromyogram noise corrupt the ECG signal. As an ECG signal is non-stationary, removing these noises from the recorded ECG signal is quite tricky. In this paper, along with the proposed denoising technique using stationary wavelet transform, various denoising techniques like lowpass filtering, highpass filtering, empirical mode decomposition, Fourier decomposition method, discrete wavelet transform are studied to denoise an ECG signal corrupted with noise. Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance. The experimental result showed that the proposed stationary wavelet transform based ECG denoising technique outperformed the other ECG denoising techniques as more ECG signal components are preserved than other denoising algorithms.
TL;DR: This work provides an accessible introduction and comparative review of DSP methods for the identification of protein-coding regions by breaking down the approaches into four steps, and suggests new combinations that may be worthy of future study.
Abstract: The identification of regions of DNA sequences that code for proteins is one of the most fundamental applications in bioinformatics. These protein-coding regions are in contrast to other DNA regions that encode functional RNA molecules, provide structural stability of chromosomes, serve as genetic raw materials, represent molecular fossils, or have no known purpose (sometimes called “junk DNA”). A number of approaches have been suggested for differentiating between the protein-coding and non-protein-coding regions of DNA. A selection of these approaches is based on digital signal processing (DSP) techniques. These DSP techniques rely on the phenomenon that protein-coding regions have a prominent power spectrum peak at frequency f = ⅓ arising from the length of codons (three nucleic acids). This article partitions the identification of protein-coding regions into four discrete steps. Based on this partitioning, DSP techniques can be easily described and compared based on their unique implementatio...
01 Jan 1998
TL;DR: This paper presents a coding DNA water marking method in a lifting-based discrete wavelet transform (DWT) domain that focuses on the feasibility of frequency domain watermarking for DNA sequences.
TL;DR: Results demonstrate that epiMiner is effective in detecting and visualizing epistatic interactions and the application of the method on a real Age-related Macular Degeneration data set provides several new clues for the exploration of causative factors of AMD.