Indian Institute of Technology Ropar

About: Indian Institute of Technology Ropar is a education organization based out in Ropar, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 1014 authors who have published 2878 publications receiving 35715 citations.

Highly Efficient CeO2 Decorated Nano-ZSM-5 Catalyst for Electrochemical Oxidation of Methanol

Abstract: Cerium oxide (CeO2) decorated nanocrystalline zeolite (Nano-ZSM-5) nanocomposites with different weight ratios were prepared by the calcination of a physical mixture of nanocrystalline CeO2 and Nano-ZSM-5. Materials were characterized by the complementary combination of X-ray diffraction, N2-adsorption, transmission electron microscopic, and X-ray photoelectron spectroscopic techniques. The material was investigated as a precious-metal-free electrode catalyst for methanol oxidation. The electrochemical oxidation of methanol was investigated at a CeO2/Nano-ZSM-5 modified glassy-carbon electrode in alkaline medium using electrochemical impedance spectroscopy, cyclic voltammetry, and chronoamperometry. Comparative investigations were made with commercial Pt(20%)/C catalyst with respect to current density, stability, and CO tolerance capacity. CeO2/Nano-ZSM-5 with a weight ratio of 30% exhibited remarkably high electrocatalytic activity in the methanol oxidation in comparison to nanocrystalline CeO2 and comme...

Optical chemosensors for water sample analysis

Abstract: Molecular recognition is an emerging field of Supramolecular Chemistry. The major challenge for supramolecular chemists is detection in an aqueous medium. However, most biological and environmental processes happen in an aqueous medium; therefore, chemosensors that can operate in water are highly demanded. This review mainly focuses on the recognition of analytes (metal, anion and biomolecules) in an aqueous medium (≥90% water) and demonstrates that sensing in an aqueous medium can be achieved without compromising the selectivity and sensitivity. The detail discussion on the design of chemosensors and the mechanism of action for detection of metal ions, anions and biomolecules in an aqueous medium has been provided, reported in the period of 2001–2015.

Slurry Erosion Mechanism of Hydroturbine Steel: Effect of Operating Parameters

Abstract: Performance of hydropower plant is severely affected by the presence of sand particles in river water. Degree of degradation significantly depends on the level of operating parameters (velocity, impingement angle, concentration, particle size and shape), which is further related to erosion mechanism. In this investigation, the effect of some of these operating parameters on erosion mechanism of generally used hydroturbine steel, CA6NM (13Cr4Ni), is reported. Morphology and variation in the martensite and austenite phases of the eroded surfaces were investigated using SEM and XRD. It was observed that velocity and impingement angle affect the erosion mechanism of CA6NM steel. Erosion mechanism was also significantly affected by the radial distance from the impact zone. Primary mechanism responsible for the removal of material at normal impingement angle was the formation and removal of platelets. At acute impingement angle, ploughing was observed to be one of the prime mechanisms responsible for the loss of the material. Other than these two well-known erosion mechanisms, the presence of another two erosion mechanisms was also observed. Models have been proposed for these unfamiliar erosion mechanisms. Interaction amongst different operating parameters was studied using line and contour plots. It was observed that the interaction between velocity and concentration was most significant. Using the experimental results, a statistical model based on regression approach was developed. Validity of this statistical model was checked using the experimental results from the literature and present study.

A Wide-Area Damping Controller Considering Network Input and Output Delays and Packet Drop

Abstract: This paper presents the development of a novel synchrophasor measurement-based wide-area centralized damping controller to improve the stability of a power system in presence of time-varying delay and packet dropout in the communication network. Two different strategies have been adopted to deal with the input and the output delays. In the first strategy, the system output delay has been compensated by predicting the dynamics of the signals. In the second strategy, a wide-area controller, based on delay-range-dependent stability criteria, has been designed for the time-varying delays in the input signals. The network induced delays and the maximum amount of the consecutive packet dropout are assumed to be bounded. The controller parameters are optimally obtained using a trace minimization of certain matrix variables by formulating Linear Matrix Inequalities problem. The system has been identified by applying Prediction-Error-Minimization methodology, and proposing a new type of probing test input-signal. A new methodology has been proposed to obtain the feedback channel latency in real time. The proposed methodology has been simulated on New England 39-bus test system and also validated on a hardware testbed consisting of physical Phasor Measurement Units connected, in feedback loop, to a Real-Time Digital Simulator.

Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization

Abstract: We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS). We hypothesize that manipulation of either modality will lead to dis-harmony between the two modalities, e.g., loss of lip-sync, unnatural facial and lip movements, etc. MDS is computed as the mean aggregate of dissimilarity scores between audio and visual segments in a video. Discriminative features are learnt for the audio and visual channels in a chunk-wise manner, employing the cross-entropy loss for individual modalities, and a contrastive loss that models inter-modality similarity. Extensive experiments on the DFDC and DeepFake-TIMIT Datasets show that our approach outperforms the state-of-the-art by up to 7%. We also demonstrate temporal forgery localization, and show how our technique identifies the manipulated video segments.