Recent News

  • Dr Sujith Kalluri elected as the Honorary Secretary of IETE, Vijayawada Chapter July 1, 2022

    faculty achievement SRMAP

    Dr Sujith Kalluri, Assistant Professor of the Department of Electronics and Communication Engineering, has been elected as the Honorary Secretary of the Institution of Electronics and Telecommunication Engineers (IETE), Vijayawada Chapter for the period, 2022-24. Dr Kalluri is one of the young and spirited faces of SRM University-AP who has already borne out his charisma and capacity as an influential teacher and passionate researcher. He is also the Assistant Director of Alumni Affairs, a forum that oversees and follows up on the activities of students graduating from the University.

    SRM University-AP is proud and privileged to celebrate this achievement as Dr Kalluri is on a roll to make greater strides in his professional career. Being the youngest officer to assume the role of secretary at the office of IETE Vijayawada makes this even more special an accomplishment. “I am indeed privileged to assume the role of Honorary Secretary of the Institution of Electronics and Telecommunication Engineers (IETE) Vijayawada Chapter. This is an incredible opportunity to collaborate with various academic and industrial experts in relevant domains” he exclaimed.

    IETE is India’s leading recognised professional society devoted to the advancement of Science and Technology in electronics, telecommunications and IT. The institution provides leadership in scientific and technical areas of direct importance to the national development and economy. The government of India has recognised IETE as a Scientific and Industrial Research Organisation (SIRO). Dr Kalluri intends to utilise this opportunity to conduct technical events, such as conferences, symposia, and exhibitions, that would benefit the student community to be industry ready and acquaint with different professional networking circles.

    Dr Kalluri is an active member of the World Academic-Industry Research Collaboration Organization (WAIRCO), the Institute of Electrical and Electronics Engineers (IEEE), and the Australian Nanotechnology Network (ANN) among many others. “It is my passion to be associated with professional bodies. I take this as an exciting opportunity to build my leadership and organising skills that could facilitate my professional growth” remarked Dr Kalluri. “I would also like to convey my gratitude to the management and leadership teams at SRM University-AP who have always supported me in terms of availing such opportunities” he maintained.

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  • Hybrid relay – IRS-aided wireless IoT network for 6G communications July 1, 2022

    research news SRMAP

    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.

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  • Paper accepted in the prestigious conference to be held in Caneda June 27, 2022

    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.


    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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  • Deep learning enabled IRS for 6G intelligent transportation systems June 24, 2022


    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.

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  • Restoring the highly corrupted digital image June 21, 2022

    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.


    Paper publicationA 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.

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