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Anubha Gupta
Researcher at Indraprastha Institute of Information Technology
Publications - 163
Citations - 1831
Anubha Gupta is an academic researcher from Indraprastha Institute of Information Technology. The author has contributed to research in topics: Wavelet & Wavelet transform. The author has an hindex of 16, co-authored 146 publications receiving 1117 citations. Previous affiliations of Anubha Gupta include Indian Institutes of Information Technology & International Institute of Information Technology, Hyderabad.
Papers
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Journal ArticleDOI
A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals
TL;DR: A novel signal model for EEG data is proposed that best captures the attributes of these signals and hence, allows to boost the classification accuracy of seizure and seizure-free epochs.
Journal ArticleDOI
Fractal and EMD based removal of baseline wander and powerline interference from ECG signals
Sakshi Agrawal,Anubha Gupta +1 more
TL;DR: The proposed methods for baseline wander removal and powerline interference removal from electrocardiogram (ECG) signals have been shown to preserve ECG shapes characteristic of heart abnormalities.
Journal ArticleDOI
A new approach for estimation of statistically matched wavelet
TL;DR: Methods are presented to design a finite impulse response/infinite impulse response (FIR/IIR) biorthogonal perfect reconstruction filterbank, leading to the estimation of a compactly supported/infinitely supported statistically matched wavelet.
Proceedings ArticleDOI
U-Segnet: Fully Convolutional Neural Network Based Automated Brain Tissue Segmentation Tool
TL;DR: In this paper, the authors proposed a hybrid of SegNet and U-Net for improved brain tissue segmentation, which incorporated fine multiscale information for better tissue boundary identification and achieved an average dice ratio of 89.74% on the widely used IBSR dataset.
Book ChapterDOI
SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging
TL;DR: A stain deconvolutional layer affixed at the front of CNN that performs two functions: it transforms the input RGB microscopic images to Optical Density (OD) space and this layer deconvolves OD image with the stain basis learned through backpropagation and provides tissue-specific stain absorption quantities as input to the following CNN layers.