M
Madhur Deo Upadhayay
Researcher at Shiv Nadar University
Publications - 67
Citations - 200
Madhur Deo Upadhayay is an academic researcher from Shiv Nadar University. The author has contributed to research in topics: Antenna (radio) & Patch antenna. The author has an hindex of 5, co-authored 54 publications receiving 97 citations. Previous affiliations of Madhur Deo Upadhayay include Indian Institute of Technology Delhi & Indian Institutes of Technology.
Papers
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Journal ArticleDOI
Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality With Reference to Noise-Free EEG Signal
TL;DR: Two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band of EEG signal are proposed.
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Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal.
TL;DR: The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT).
Proceedings ArticleDOI
Dual port ASA for frequency switchable active antenna
TL;DR: In this paper, a dual-port dual-band annular slot antenna was designed for use in the feed-back path of an oscillator resulting in an active antenna configuration.
Journal ArticleDOI
OAM Wave Generation Using Square-Shaped Patch Antenna as Slot Array Equivalence
TL;DR: In this paper, the use of slot array equivalence of single square-shaped patch antenna for the generation of orbital angular momentum (OAM) wave was described, which can generate +1 or −1 OAM mode.
Journal ArticleDOI
One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram
TL;DR: In this article , a shallow one-dimensional convolutional neural network (1D-CNN) architecture was proposed for cognitive task classification using single/limited channel electroencephalogram (EEG) signals in real-time.