Selected peer-reviewed research publications.
We propose a two-stream action recognition technique for recognizing human actions from dark videos. The proposed action recognition network consists of an image enhancement network with a Self-Calibrated Illumination (SCI) module, followed by a two-stream action recognition network. We have used R(2 + 1)D as a feature extractor for both streams with shared weights. Graph Convolutional Network (GCN), a temporal graph encoder is utilized to enhance the obtained features which are then further fed to a classification head to recognize the actions in a video. The experimental results are presented on the recent benchmark “ARID” dark-video dataset.
We propose a novel structure of a hardware security primitive namely the True Random Number Generator (TRNG) is proposed using Quantum Cellular Automata (QCA) technology. The AND gate, XOR gate and a gate with irregular behavior are used to generate random output depending upon the AQ1 metastability of the QCA structure. Furthermore, the structure is cross-looped and asymmetrically inverted to induce additive randomness.