Start-ups are the centers of innovation. Ideas may seem easy, but their implementation is not. Dr Udaya Shankar from the Department of Electronics and Communication Engineering is ready to confront the challenges of innovation by registering his new start-up, OMACS.

OMACS envisions becoming the best research lab and product company within the next five years. The company’s motto is based on the 3P’s, that is, patents, papers, and product prototypes. The mission is to collaborate with world-class researchers experienced in both academy and industry. This will bring together the best advisors who have expertise in research and industry in the areas of AI and Game theory applications to Visual Inspection systems, 5G wireless communication networks, NFTs for Telecom, and Agriculture Robotics.

This company will be focused on developing products based on AI/ML-based visual inspection systems, Advanced wireless communications, NFTs for telecom, etc. Currently, five students from the university are interning with him and helping him develop the products.

Nine members are already being trained in the respected areas by enjoying their exposure to the industry environment. Four more interns are to be recruited for the ongoing projects. OMACS is in discussion with other start-up companies to offer research support to them. After six months of its implementation, the start-up plan to provide employment to some of the students in the university and recruit world-class people.

Mr Udayan Bakshi, Associate Director of Entrepreneurship, has helped him to initiate the start-up under the Hatchlab Incubation Centre of the university. “In every step of the journey of OMACS, we got all the support from Hatchlab. We are thankful to Hatchlab for their constant support and encouragement,” said Dr Udaya Shankar as he recollected the dynamics of Hatchlab and OMACS.

Click to visit the company website

By publishing two papers in well-acclaimed research journals, Assistant Professor Dr M Durga Prakash of the Department of Electronics and Communication Engineering is expanding the possibilities of his research domain through innovative ideas. The first paper was published in the International Journal of Electronics Letters, an internationally renowned peer-reviewed rapid communication journal. It is titled Design of approximate reverse carry select adder using RCPA and has an impact factor of 1.5.

Abstract

An approximate carry select adder (CSLA) with reverse carry propagation (RCSLA) is shown in this work. This RCSLA was designed with a reverse carry propagate full adder (RCPFA). In the RCPFA structure, the carry signal propagates in the reverse direction, that is, from MSB part to LSB part, then the carry input has greater importance compared to the output carry. Three types of implementations were designed in RCPFA based on the design parameters. This method was applied to RCA & CSLA to design other types of approximate adders. These designs and simulations were done in CADENCE Software tool with 45 nm COMS technology. The design parameters of the three CSLA implementations with RCPFA are compared with the existing CSLA adders.

The other paper, A highly sensitive graphene-based field-effect transistor for detection of myoglobin, has been published in the Silicon Journal, an international and interdisciplinary journal, with an impact factor of 2.67.

Abstract

Biomedical applications adapt Nanotechnology-based transistors as a key component in the biosensors for diagnosing life-threatening diseases like Covid-19, Acute Myocardial Infarction (AMI), etc. The proposed work introduces a new biosensor, based on the Graphene Field Effect Transistor (GFET), which is used in the diagnosis of Myoglobin (Mb) in human blood. Graphene-based biosensors are faster, more precise, stronger, and more trustworthy. A GFET is created in this study for the detection of myoglobin biomarker at various low concentrations. Because graphene is sensitive to a variety of biomarker materials, it can be employed as a gate material. When constructed Graphene FET is applied to myoglobin antigens, it has a significant response. The detection level for myoglobin is roughly 30 fg/ml, which is quite high. The electrical behaviour of the GFET-based biosensor in detecting myoglobin marker is ideal for Lab-on-Chip platforms and Cardiac Point-of-Care Diagnosis.

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.

Continue reading →

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.

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.