Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Computer science. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Improved identification of Hammerstein plants using new CPSO and IPSO algorithms

Abstract: Identification of Hammerstein plants finds extensive applications in stability analysis and control design. For identification of such complex plants, the recent trend of research is to employ nonlinear network and to train their weights by evolutionary computing tools. In recent years the area of Artificial Immune System (AIS) has drawn attention of many researchers due to its broad applicability to different fields. In this paper by combining the principles of AIS and PSO, we propose two new but simple hybrid algorithms called Clonal PSO (CPSO) and Immunized PSO (IPSO) which involve less complexity and offers better identification performance. Identification of few benchmark Hammerstein models is carried out through simulation study and the results obtained are compared with those obtained by standard PSO, Clonal and GA based methods. Various simulation results demonstrate that IPSO algorithm offers best identification performance compared to the other algorithms. Out of the two algorithms proposed, the CPSO is computationally simpler but offers identification performance nearly similar to its PSO counterpart.

Identification of polyphenols from Broussonetia papyrifera as SARS CoV-2 main protease inhibitors using in silico docking and molecular dynamics simulation approaches.

Abstract: The current COVID-19 pandemic is caused by SARS CoV-2. To date, ∼463,000 people died worldwide due to this disease. Several attempts have been taken in search of effective drugs to control the spre...

Enhanced Pseudocapacitance of MoO3-Reduced Graphene Oxide Hybrids with Insight from Density Functional Theory Investigations

Abstract: Hydrothermally obtained MoO3/reduced graphene oxide (RGO) hybrid registered a specific capacitance of 724 F g-1 at 1 A g-1, superior to the supercapacitor performance obtained from similar hybrid structures. Density functional theory (DFT) simulations further corroborated our claim in terms of both enhanced quantum capacitance and relevant insight from the electronic density of states (DOS) for MoO3/RGO. Maximum capacitance is achieved for 12 wt % of RGO and then it reduces as observed in the experiment. The appearance of additional density of states from the C pz orbital in the band gap region near the Fermi level on introduction of RGO in MoO3 is responsible for the enhanced capacitance in MoO3/RGO.

Real-time recognition of sign language gestures and air-writing using leap motion

Abstract: A sign language is generally composed of three main parts, namely manual signas that are gestures made by hand or fingers movements, non-manual signs such as facial expressions or body postures, and finger-spelling where words are spelt out using gestures by the signers to convey the meaning. In literature, researchers have proposed various Sign Language Recognition (SLR) systems by focusing only one part of the sign language. However, combination of different parts has not been explored much. In this paper, we present a framework to recognize manual signs and finger spellings using Leap motion sensor. In the first phase, Support Vector Machine (SVM) classifier has been used to differentiate between manual and finger spelling gestures. Next, two BLSTM-NN classifiers are used for the recognition of manual signs and finger-spelling gestures using sequence-classification and sequence-transcription based approaches, respectively. A dataset of 2240 sign gestures consisting of 28 isolated manual signs and 28 finger-spelling words, has been recorded involving 10 users. We have obtained an overall accuracy of 63.57% in real-time recognition of sign gestures.