Research News

The Department of Electronics and Communication Engineering has come out with yet another rewarding publication, “Energy-Efficient Hybrid Relay – IRS aided wireless IoT network for 6G communications”, in the Electronics Journal, with Impact Factor 2.4. The article was published by Mr Rajak Shaik, PhD Scholar, in collaboration with the faculty members; Dr Sunil Chinnadurai, Dr Karthikeyan Elumalai and Dr Inbarasan Muniraj. This research is the first of its kind, which examines and compares the impact of relay-aided, IRS-aided, and novel hybrid relay-IRS-aided wireless IoT networks for 6G communications in terms of Energy Efficiency.

The article examines Energy Efficiency as a function of user distance and various SNR (Signal-to-noise ratio) values. The Energy Efficiency with fixed and varying numbers of IRS elements is analysed for the proposed IoT network. The results show that the proposed hybrid relay-IRS-assisted IoT network outperforms both the conventional relay and IRS-aided wireless IoT networks. The hybrid relay-IRS-aided IoT network can fulfil the requirements of high data rate, reliable data transfer, and large bandwidth needed for 6G communications. The multiple IRS concept can also be used in 6G communications at high SNR values to reduce both the cost and additional power consumption of wireless IoT networks. Their future research plan also includes the real-time implementations to improve the energy efficiency for wireless IoT networks with IRS in 6G communications.

Abstract of the Research

Intelligent Reflecting Surfaces (IRS) have been recognized as presenting a highly energy-efficient and optimal solution for future fast-growing 6G communication systems by reflecting the incident signal towards the receiver. A large number of Internet of Things (IoT) devices are distributed randomly in order to serve users while providing a high data rate, seamless data transfer, and Quality of Service (QoS). The major challenge in satisfying the above requirements is the energy consumed by the IoT network. Hence, in this paper, we examine the energy efficiency (EE) of a hybrid relay-IRS-aided wireless IoT network for 6G communications. In our analysis, we study the EE performance of IRS-aided and DF relay-aided IoT networks separately, as well as a hybrid relay-IRS-aided IoT network. Our numerical results showed that the EE of the hybrid relay-IRS-aided system has better performance than both the conventional relay and the IRS-aided IoT network. Furthermore, we realized that the multiple IRS blocks can beat the relay in a high SNR regime, which results in lower hardware costs and reduced power consumption.

Two research papers from the Department of Computer Science and Engineering were presented at the 4th International Conference on Energy, Power, and Environment held from April 29 to May 1, 2022. Assistance professor V M Manikandan and three BTech students participated in the conference organised by NIT Meghalaya, India. The papers will be published in IEEE Xplore Digital Library (Scopus Indexed).

Third-year BTech CSE student Harshad Dhane presented the paper A Novel High Capacity Reversible Data Hiding through Encryption Scheme by Permuting Encryption Key and Entropy Analysis, co-authored by Palak Agarwal, third-year BTech student, and Dr V M Manikandan. The reversible data hiding scheme proposed by the research can be used in the healthcare sector to transmit electronic patient reports along with medical images. Improving the embedding rate of the reversible data hiding is the further plan of the researchers.

Explanation of the research

A Reversible Data Hiding Through Encryption (RDTE) scheme will consider an original image and a sequence of bits as the input and generate an encrypted image as the output. This encrypted image will be able to transmit through the network securely, and the authorized receiver can take out the hidden details along with the restoration of the actual image. This paper proposes a new RDTE scheme with a good rate of embedding without any issues during the restoration of the original image. The researchers used the well-known RC4 pseudo-random generator for the image encryption and performed data hiding during block-wise image encryption. In the proposed scheme, the original image is considered non-overlapping blocks of size BXB pixels, and these blocks will be encrypted using a sequence of pseudo-random integers. During the RDTE process, all the possible unique permutations of the encryption key, K, will be generated, say (K0, K1,…, KN). Further, the sender will be capable of embedding one integer value from the set {0, 1, …,N} in a selected image block. A selected block will be encrypted using the pseudo-random sequence of integers using the key K_Q to embed the integer Q in the selected block. The proposed scheme prefers to select keys with unique characters with sufficient length to ensure the maximum embedding capacity. The message extraction and image restoration are performed by analysing the entropy measure from each block after attempting the decryption.

The paper presented by second year BTech CSE student Sri Satya Maram is titled A Novel System for Automated Coloring of Neat Sketches and was co-authored by Dr V M Manikandan. The research introduces a new algorithm to colour a given neat sketch. The proposed algorithm can be used to colour drawings to create animated movies or to colour the designs developed by the designers. The researchers plan to develop artistic features on the coloured image for better visual appearance.

Explanation of the research

The process of colouring neat sketches is a significant activity when making animated movies or for better visualization in computer modelling. The colour filling tools are widely available in almost all the image/video editing software, which will help us pick a colour from a colour palette and can be filled in a selected region. This process is known as flat colouring. The flat colouring process has several challenges. One of the significant challenges is that the colour may leak from the selected regions to neighbouring regions if there are some small openings on the contours. The second concern while using flat colouring is that the designated areas will be filled entirely with the same colour, so the drawing will not have an artistic look. The research proposes a new software application that will take a neat sketch as the input, and the system will generate a coloured drawing as the output. In the proposed scheme, the researchers have converted the given sketch to a grayscale or binary image and applied image dilation operation to fill the small open spaces in the contours (if any). Further, the closed regions are identified and coloured with a predefined set of colours or random colour combinations. While colouring the regions, the proposed system will ensure that the adjacent regions will not be coloured with the same colours. A number of sketches have been considered during the experimental study, and the results are validated manually.

Computational and artificial intelligence is enjoying an unparalleled relevance in the modern world. They improve people’s lives and are highly anticipated in the healthcare industry. Research in this domain is hugely appreciated by the contemporary world, considering its potential to create revolutionary changes in the health care system. The Department of Computer Science and Engineering is delighted to inform you that the paper Robust, Reversible Medical Image Watermarking for Transmission of Medical Images over Cloud in Smart IoT Healthcare has been accepted for publishing as a chapter in the book Predictive Analysis in Cloud, Fog and Edge Computing and Practice of Blockchain, IoT and 5G.

The paper was submitted by Assistant Professor Dr Priyanka S, her PhD student Ms Jyothsna Devi, and MTech student Mr Jayant Krishna. The book chapter for the edited book is entitled Predictive Data Security using Ai – Insights and Issues of Blockchain, IoT, and DevOps and is published by Springer Nature. It is a part of the book series, Studies in Computational Intelligence, indexed by SCOPUS.

The book Studies in Computational Intelligence targets to bring together researchers and practitioners in computational intelligence and AI technology, especially those related to the areas of Machine learning, blockchain, multimedia using AI, smart IoT environment and email spam and online surveys, and many more recent emerging fields. The research work mainly provides highly secure, robust medical image transmission over the cloud in a smart IoT healthcare environment to ensure high embedding capacity and integrity.

This book’s target audience comprises professionals and researchers working in the field of computational intelligence and AI for health services in a smart environment. The book will attract Engineers (computer, industrial, software, and others), health care scholars, and information scientists since it caters to their interests.

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

This 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.

Explanation 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.

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.

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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.

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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.

Abstract

“Enhanced Dynamic Performance in DC-DC Converter-PMDC Motor Combination through an Intelligent Nonlinear Adaptive Control Scheme”, is the latest paper published by Dr Tousif Khan, Assistant Professor of EEE Department at SRM University-AP in the reputed IET Power Electronics (Q1 journal) having an Impact Factor of 2.95.

Abstract

A novel neuro-adaptive control scheme is proposed in the context of angular velocity tracking in DC-DC buck converter-driven permanent magnet DC motor system. The controller builds upon the idea of backstepping control. The proposed method guarantees a rapid recovery of nominal angular velocity tracking under parametric and non-parametric uncertainties. In order to verify the performance of the proposed neuro-adaptive speed controller, extensive experimentation has been conducted in the laboratory under various real-time scenarios. Results are obtained for start-up, time-varying angular velocity tracking and under the influence of highly non-linear unknown load torque. The performance metrics such as peak undershoot/overshoot and settling time are computed to quantify the transient response behaviour. The results clearly substantiate theoretical propositions and demonstrate an enhanced dynamic speed tracking under a wide operating regime, thus confirming the suitability of the proposed method for fast industrial applications.

The power of merging art with science is beyond our imagination. This amalgamation can pull off things that may seem insurmountable without the assistance of the other. Professor Hiren Deva Sarma, Guest faculty of the Department of Computer Science and Engineering, has developed a computational technique to find the similarity between the given songs in a pool. His paper titled An Approach to Discover Similar Musical Patterns has been published in IEEE ACCESS, a Q1 journal with an impact factor of 3.36.

Abstract

An algorithm has been developed to find the similarity between given songs. The song pattern similarity has been determined by knowing the note structures and the fundamental frequencies of each note of the two songs under consideration. The statistical concept, Correlation of Coefficient, is used in this work. The correlation of Coefficient is determined by applying the 16 Note-Measure Method. If the Correlation of Coefficient is near 1, it indicates that the patterns of the two songs under consideration are similar. Otherwise, there exists a certain percentage of similarity only. This basic principle is used in a set of Indian Classical Music (ICM) based songs. The proposed algorithm can determine the similarity between songs, so alternative songs in place of some well-known songs can be identified in terms of the embedded raga patterns. A digital music library has been constructed as a part of this work. The library consists of different songs, their raga name, and their corresponding healing capabilities in terms of music therapy. The proposed work may find application in the area of music therapy. Music therapy is an area of research that has been explored significantly in recent times. This work can also be exploited for developing an intelligent multimedia tool applicable in the healthcare domain. A multimedia-based mobile app has been developed encapsulating the abovementioned idea that can recommend alternative or similar songs to the existing ICM-based songs. This mobile app-based music recommendation system may be used for different purposes, including entertainment and healthcare. As a result of the applications of the proposed algorithm, similar songs in terms of raga patterns can be discovered from within the pool of a set of songs. A Music Recommendation System built on this algorithm can retrieve an alternative song from within the pool of songs as a replacement to a well-known song, which otherwise may be used for particular music therapy. Results are reported and analysed thoroughly. The future scope of the work is outlined.

Explanation of the research

A computational technique has been developed to identify a particular song similar to another in terms of its embedded raga pattern. Indian Classical Music (ICM) based songs are considered in this work. As a result of the application of the proposed technique, it is possible to identify similar songs in terms of their raga patterns from within a pool of songs. Subsequently, a similar alternative song can be recommended for different applications, including music therapy. If we consider music therapy, an alternative medicine (note: here, medicine is the song) is possible to recommend due to the proposed technique. This algorithm will find many applications in the domain of music information retrieval (MIR) and music recommendation systems (MRS).

Practical implementation of the research

This algorithm may be applied in recommending music in music recommendation systems. Moreover, music information retrieval based on raga patterns can be an important domain where the proposed algorithm may be exploited. Considering the social implications, music therapy has been the intended area of the research; therefore, this algorithm has been developed considering numerous applications of music therapy based on Indian Classical Music. The music therapy community will be benefited from the proposed algorithm.

In this research project, Professor Hiren Deva Sarma has collaborated with; Assistant Professor Sudipta Chakrabarty, Techno India, Salt Lake at the Department of Master of Computer Application; Mr Ruhul Islam, IT Consultant, Cloud Shine Global LLP; and Emil Pricop, Associate Professor in the Department of Automatic Control, Computers and Electronics, Petroleum-Gas University of Ploiesti, Romania.

In the future, the researchers look forward to exploring the music therapy capabilities of Indian Folk Music (IFM) like Kamrupia Lokgeet, Goalparia Lokgeet, and Baul Geet. Understanding the similarity and dissimilarity of the above-mentioned folk songs with Indian Classical Music (ICM) from computational musicology perspectives is another objective of the proposed research work. The researchers also aim to develop Music Recommendation Systems (i.e., applications) considering the songs mentioned above (ICM + IFM) and the different requirements of the users.

The Electrochemical Society Transactions (ECST) is the official conference proceedings publication of The Electrochemical Society. Recently, a research paper was published in ECST by  Mr Vasudeva Bevara, a PhD scholar of the Department of Electronics and Communication Engineering, under the supervision of Assistant professor Dr Pradyut Kumar Sanki. The paper is titled VLSI Architecture of Decision Based Adaptive Denoising Filter for Removing Salt & Pepper Noise and proposes an innovative concept to restore a highly corrupted digital image.

Abstract

A new Decision Based Adaptive Denoising Filter (DBADF) algorithm and hardware architecture are proposed for restoring the digital image that is highly corrupted with impulse noise. The proposed DBADF detects only the corrupted pixels, and that pixel is restored by the noise-free median value or previous value based upon the noise density in the image. The proposed DBADF uses a 3×3 window initially and adaptively goes up to a 7×7 window based on the noise corruption of more than 50% by impulse noise in the current processing window. The proposed architecture was found to exhibit better visual qualitative and quantitative evaluation based on PSNR, IEF, EKI, SSIM, FOM, and error rate. The DBAMF architecture also preserves the original information of digital image with a high density of salt and pepper noise compared to many standard conventional algorithms. The proposed architecture has been simulated using the VIRTEX7 FPGA device, and the reported maximum post place and route frequency are 149.995MHz, and the dynamic power consumption is 179mW.