News

anirban bose

The Department of Mathematics is glad to announce that Dr Anirban Bose, Assistant Professor, has published an article, ‘Twisted conjugacy in linear algebraic groups II’ in the Q1 journal, Journal of Algebra. The paper was published in collaboration with Sushil Bhunia from Indian Institute of Science Education& Research, Mohali. The present work and its prequel “Twisted conjugacy in linear algebraic groups” are concerned with computing the number of orbits of a twisted conjugacy action of an algebraic group on itself. Dr Bose’s interests mainly lie studying the properties of groups of matrices.

Here’s the link to the article.

 

sunil chinnadurai

Intelligent Transportation System (ITS) is on its way to becoming the biggest player in the coming-of-age transportation system. However, the sheer demand for the enormous amount of data to secure seamless connectivity and functioning with maximum speed and safety tends to increase the power consumption of the ITS. Dr Sunil Chinnadurai and his PhD scholar Mr Shaik Rajak from the Department of Electronics and Communication Engineering present Intelligent Reflecting Surfaces (IRS) as the key enabling technology to provide the data required by the ITS with less power consumption.

Their article “Deep Learning Enabled IRS for 6G Intelligent Transportation Systems: A Comprehensive Study” which makes a comprehensive study on the DL-enabled IRS-aided ITS was published in the esteemed journal ‘IEEE Transactions on Intelligent Transportation Systems’ having an Impact factor of 6.5. The article elucidates the ways and means to overcome the channel estimation, secrecy rate, and energy efficiency optimisation problems.

The research suggests that connecting ITS to wireless networks via IRS will help in reaching the destination within the stipulated time duration with enhanced safety and comfort. Besides highlighting the reduced power consumption and hardware cost of the DL-enabled IRS-aided ITS, the article also projects that IRS usage in 6G-ITS massively helps the traffic control system to precisely send and receive the information of school buses as well as healthcare vehicles like ambulances, fire safety vehicles, etc. Their future research plans also include the experimental analysis of energy efficiency for wireless networks and Intelligent Transportation Systems with IRS.

Abstract of the Research

Intelligent Transportation Systems (ITS) play an increasingly significant role in our life, where safe and effective vehicular networks supported by sixth generation (6G) communication technologies are the essence of ITS. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications need to be studied to implement ITS in a secure, robust, and efficient manner, allowing massive connectivity in vehicular communications networks. Besides, with the rapid growth of different types of autonomous vehicles, it becomes challenging to facilitate the heterogeneous requirements of ITS. To meet the above needs, intelligent reflecting surfaces (IRS) are introduced to vehicular communications and ITS, containing the reflecting elements that can intelligently configure incident signals from and to vehicles. As a novel vehicular communication paradigm at its infancy, it is key to understand the latest research efforts on applying IRS to 6G ITS as well as the fundamental differences with other existing alternatives and the new challenges brought by implementing IRS in 6G ITS. In this paper, we provide a big picture of deep learning enabled IRS for 6G ITS and appraise most of the important literature in this field. By appraising and summarizing the existing literature, we also point out the challenges and worthwhile research directions related to IRS aided 6G ITS.

ranjit thapa

The Department of Physics is proud to announce that Prof Ranjit Thapa and his PhD scholar Mr Samadhan Kapse have published an article titled “Lewis acid-dominated aqueous electrolyte acting as co-catalyst and overcoming N2 activation issues on catalyst surface” in the most prestigious and highly cited multidisciplinary research journal, ‘Proceedings of the National Academy of Sciences’ (PNAS), having an Impact Factor of 11.2. The research was done in collaboration with Ms Ashmita Biswas, Mr Bikram Ghosh, and Dr. Ramendra Sundar Dey from the Institute of Nano Science and Technology (INST), Punjab.

Abstract of the Research

The growing demands for ammonia in agriculture and transportation fuel stimulate researchers to develop sustainable electrochemical methods to synthesize ammonia ambiently, to get past the energy-intensive Haber Bosch process. But the conventionally used aqueous electrolytes limit N2 solubility leading to insufficient reactant molecules in the vicinity of the catalyst during electrochemical nitrogen reduction reaction (NRR). This hampers the yield and production rate of ammonia, irrespective of how efficient the catalyst is. Herein we introduce a new aqueous electrolyte (NaBF4), which not only acts as an N2-carrier in the medium but also works as a full-fledged “co-catalyst” along with our active material MnN4 to deliver high yield of NH3 (328.59 μg h-1 mgcat-1) at 0.0 V vs RHE. BF3-induced charge polarization shifts the metal d-band center of MnN4 unit close to the Fermi level, inviting N2 adsorption facilely. The Lewis acidity of the free BF3 molecules further propagates their importance in polarizing the N≡N bond of the adsorbed N2 and its first protonation. This push-pull electronic interaction has been confirmed from the change in d-band center values of MnN4 site as well as charge density distribution over our active model units, which turned out to be effective enough to lower the energy barrier of the potential determining steps of NRR. Resultantly, a high production rate of NH3 (7.37 × 10-9 mol s-1 cm-2) was achieved, approaching the industrial scale where the source of NH3 was thoroughly studied and confirmed to be chiefly from the electrochemical reduction of the purged N2 gas.

A Brief Summary of the Research

The widely highlighted problem of NRR is that the competitive HER is most likely worked upon with several catalyst development and electrolyte modifications, while the N2 solubility and activation issues in the aqueous medium are generally neglected. This work justifies our aim to contribute towards this troublemaker by using NaBF4 as a working electrolyte, which served as a “full-packaged co-catalyst” along with MnN4, reinforcing the NRR kinetics at the cost of low overpotential. The Lewis-acidic nature of BF3 induced adduct formation with the N2 molecules acted as a carrier of N2 gas into the medium in vicinity of the electrocatalyst. Simultaneously, the charge polarization over MnN4 active site due to BF3 delocalized the metal d-band centre, which triggered N2 adsorption on the catalyst site. Under this condition, free BF3 form the medium interacted with the adsorbed N2 and brought about the facile polarization of the N≡N bond and its first protonation at a much lower energy barrier. This push-pull charge transfer effect enormously helped to overcome the potential determining steps and this BF3 mediated NRR resulted in a huge production rate of NH3, which could be compared to that of industrial scale, which was not achieved so far with any aqueous or ionic liquid electrolytes. In short, this kind of user-friendly aqueous electrolyte is being investigated for the first time for NRR. Since BF3 displayed tremendous potential in triggering the kinetics of NRR, this new finding may encourage researchers to work more on aqueous electrolyte designing towards an even improved NRR performance of the electrocatalysts. Not only that, electrocatalysts could also be functionalized with BF3 derivatives, which could be one entirely new route of study in the field of NRR.

Social Implications

Ammonia is considered as the most abundant and widely used synthetic fertilizer in the world. The sole mean of large-scale ammonia production relies on the century-old Haber-Bosch process, which takes in more energy than it can produce, while the electrochemical nitrogen reduction reaction (NRR) offers a carbon-free and sustainable way of ammonia synthesis. However, electrochemical NH3 synthesis is often arrested by a few factors such as NH3 detection, contaminations from source gases, nitrogen-containing chemicals and the presence of labile nitrogen in the catalysts. In the recent past, several protocols have been proposed to correct the fallacious results. Recently, Choi et el. have concluded that it is difficult to believe from the too-low yield rate of NH3 that the reduction of N2 has actually occurred in the aqueous medium. It is noteworthy that the electrolyte plays a crucial role and offers a suitable environment for any electrochemical reactions to occur. However, the issue with the solubility of N2 in conventional aqueous electrolytes is a real troublemaker to achieve a high yield and production rate of NH3 during electrochemical synthesis. Therefore, it is necessary to solve the most important issue i.e., to solvate a promising concentration of N2 molecules into the electrolyte such that it becomes accessible to the catalyst surface for its subsequent reduction.

The research paper, An under complete autoencoder for denoising computational 3D sectional images from the Department of Electronics and Communication Engineering has been accepted in a prestigious conference called Imaging and Applied Optics Congress to be held in Vancouver, Canada 2022. Assistant Professors; Dr Sunil Chinnadurai, Dr Karthikeyan Elumalai, Dr Inbarasan Muiraj, and the PhD students; Ms Vineela Chandra Dodda and Ms Lakshmi Kuruguntla are the authors who contributed to composing the paper.

Abstract

computational 3D sectional images-research-srmapThis paper proposes to use a deep-stacked under complete autoencoder to denoise the noisy 3D integral (sectional) images with a patch-based approach. In this process, the noisy input 3D sectional image is divided into multiple patches, which are then used to train the neural network. By using the patch-based approach, the time required to prepare the labeled training data is greatly reduced. Results demonstrate the feasibility of our proposed model in terms of the peak-signal-to-noise ratio.

computational 3D sectional images-research-srmapExplanation of the research

Denoising is one of the preliminary processes in image processing that removes noise from an image of interest and restores a clean image. The noise which was generated during the image acquisition process is attenuated using deep learning techniques. The denoised image is further used in various tasks of image processing.

In any image acquisition system, noise is inevitable and needs to be attenuated before further processing for qualitative results. The medical field is an example of this (images acquired through CT, MRI, PET, etc.). The researchers further investigate various techniques in deep learning to improve the denoising performance along with the applicability of deep learning in various tasks such as object recognition etc.

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