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.

Exploring the dicationic gemini surfactant for the generation of mesopores: a step towards the construction of a hierarchical metal–organic framework

Abstract: The present study demonstrates a soft-template-assisted synthesis route to the preparation of a hierarchical microporous–mesoporous Cu-BTC metal–organic framework. The supramolecular assembly of di-cationic quaternary ammonium structure-directing agents cooperatively interacts with Cu-BTC framework coordination layers via a weak electrostatic interaction and self-assemble to form a Cu-BTC framework containing a hierarchical system of mesopores interconnected with micropores. Pore-size distribution measurements revealed that the produced metal–organic framework has a bimodal pore-size distribution consisting of micropores and mesopores. Materials prepared with the di-cationic structure-directing agent and swelling agent 1,3,5-trimethyl benzene showed further improvement in the mesopore diameter. The hierarchical Cu-BTC framework demonstrated higher CO2 uptake capacity and better oxidation activity in reactions involving large molecules, when compared to the conventional Cu-BTC framework.

An End-to-End Network for Image De-Hazing and Beyond

Abstract: Degradation in the quality of images that are captured in the hazy environment is mainlydue to 1) different weather conditions and 2) the attenuation in reflected light. These factors introduce a severe color distortion and low visibility in the captured images. To tackle these problems, we propose an end-to-end trainable image de-hazing network named as LIGHT-Net. The proposed LIGHT-Net comprises of color constancy module and haze reduction module. Among these, the color constancy module removes the color cast added in hazy image due to the weather condition. Whereas, the proposed haze reduction module, which is build using an inception-residual block, is aimed to reduce the effect of haze as well as to improve the visibility in the hazy image. Unlike traditional feature concatenation, in the haze reduction module, we propose a dense feature sharing to effectively share the features learned at initial layers across the network. In general, a major hurdle to train a convolution neural network for haze removal task is the unavailability of large-scale real-world hazy, and corresponding haze-free image (i.e. paired data). Thus, we make use of an unpaired training approach to train the proposed LIGHT-Net for image de-hazing. Extensive analysis has been carried out to validate the necessity and impact of each sub-block of the proposed LIGHT-Net. A large set of real-world hazy images captured in different weather conditions are considered to validate the proposed approach for image de-hazing. Also, the benchmark synthetic hazy image database is considered for a quantitative analysis of the proposed LIGHTNet for image de-hazing. Further, we have shown the usefulness of the proposed LIGHT-Net for underwater image enhancement. Experiments show that the proposed LIGHT-Net outperforms the other existing approaches for both image de-hazing as well as underwater image enhancement.

Thermal analysis of the increasing subcutaneous fat thickness within the human skin—a numerical study

Abstract: This article reports a numerical study on the thermal response of skin with increasing fat thickness. The study considers the Pennes bioheat model for skin to simulate the thermal recovery phase after the removal of undercooled condition. Based on the surface thermal maps of a three-dimensional skin, the change in fat thickness within the skin is characterized. Sensitivity study reveals that the major variation in the thermal pattern at the skin surface is mainly due to the change in fat thickness. Possible random noise associated with background disturbances is also considered to determine the associated signal-to-noise ratio.

A control topology for frequency regulation capability in a grid integrated PV system

Abstract: Photovoltaic (PV) cells are very costly because of the silicon element which is not cheaply available. Usually, PV cells are preferred to be used at maximum efficiency. Therefore, PV plants are emphasized to extract maximum power from PV cells. When inertia free PV plants are integrated into the grid in large numbers, the problem of maintaining system stability subjected to load perturbation is quite difficult. In response to this, a control topology is being an approach to make available the PV cells in maintaining system stability by utilizing the system frequency deviation as feedback to the controller. To implement this, the PVs are operated at Maximum Power Point Tracking (MPPT). This allows the PV to operate at Pseudo Maximum Power Point tracking (PMPPT) which makes it possible to run the PV with reserve power capacity without employing a battery for storage. The control strategy has been implemented over a two-stage power conversion model of the PV system. The simulation results showed that the proposed control PMPPT topology is effective in frequency regulation capability as compared to the MPPT technique.