In a significant academic achievement, Dr Anirban Ghosh, Assistant Professor from the Department of Electronics and Communication Engineering along with Mr Naga Srinivasarao Chilamkurthy, PhD Scholar, and Mr Shaik Abdul Hakeem, an undergraduate student, have made a remarkable contribution to the field of communication engineering. Their paper, titled “Optimal Routing Protocol in LPWAN Using SWC: A Novel Reinforcement Learning Framework,” has been published in the esteemed IEEE Sensors Journal, with an impressive impact factor of 4.3.

This publication marks a milestone for the university and highlights the innovative research being conducted by its faculty and students. The paper delves into the development of an optimal routing protocol for Low-Power Wide-Area Network (LPWAN) using State-Wise Communication (SWC), employing a novel reinforcement learning framework to enhance network efficiency and performance.
This work will pave the way for advancements in LPWAN technologies, which are crucial for the Internet of Things (IoT) ecosystem. The university community celebrates this achievement and looks forward to the positive impact it will have on technology and society.

Abstract:
Low Power Wide Area Network (LPWAN) has emerged as a dominating communication technology that offers low-power and wide coverage for the Internet of Things (IoT) applications. However, the direct data transmission approach has a limited network lifetime. Even multi-hop data transmission experiences several difficulties including high data latency, poor bandwidth utilization, and reduced data throughput. To overcome these challenges, in this paper, a recent breakthrough in social networks known as Small-World Characteristics (SWC) is incorporated into LPWANs.

In particular, in this work, Small-World LPWANs (SW-LPWANs) are developed by using the Reinforcement Learning (RL) technique and using different node centrality measures like degree, betweenness, and closeness centrality. Further, the performance of the developed SW-LPWANs is evaluated in terms of energy efficiency (alive/dead devices, and network residual energy) and Quality-of-Service (average data latency, data throughput, and bandwidth utilization), and is compared with that of conventional multi-hop LPWAN. Finally, to validate the simulation results, similar analyses are performed on the real-field LPWAN testbed.

The obtained simulation results confirm that SW-LPWAN developed by the RL method performs better than other techniques, with 11% more alive devices, 5.5% higher residual energy, 2.4% improved data throughput, and 14% efficient bandwidth utilization compared to the next best method. A similar trend is observed with real-field LPWAN testbed data also.

Explanation of the Research in Layperson’s Terms

Social networks primarily revolve around establishing human connections, whereas LPWANs are designed for connecting IoT devices that have limited battery-driven power. In this context, the smart devices must communicate in an IoT setting to conserve the limited energy available to them. To achieve this, the concept at the core of social networking also known as small world characteristic is incorporated into LPWAN using the Q-learning technique.

Practical Implementation or the Social Implications of the Research

IoT applications such as remote healthcare, smart environmental monitoring, asset tracking, and smart traffic systems require low transmission delay and high network lifetime. The proposed research helps in achieving the above parameters.

Collaborations
Dr Om Jee Pandey, Assistant professor Department of Electronics Engineering, Indian Institute of Technology, (BHU), Varanasi. e-mail: omjee.ece@iitbhu.ac.in

Dr Linga Reddy Cenkeramaddi, Professor, Department of Information and Communication Technology, University of Agder, Norway. e-mail:linga.cenkeramaddi@uia.no

Future Research Plan
In the next phase of research, we will be interested in investigating how the energy efficiency and other quality of service of smart devices in an IoT setting can be improved if they are partially or completely mobile.

Link to the Article

Dr M Durga Prakash, Assistant Professor in the Department of Electronics and Communication Engineering, and his PhD scholar, Ms U Gowthami, have published a research paper titled “Performance Improvement of Spacer-engineered N-type Tree Shaped NSFET towards Advanced Technology nodes” in the Q1 journal, IEEE Access. The paper has an impact factor of 3.9 and will pave the way for significant advancements in the field.

Here’s an abstract of their research paper

Abstract:

Scaling gate lengths deep is most reliable with tree-shaped Nanosheet FETS (NSFET). This paper uses TCAD simulations to study the 12nm gate length (LG) n-type Tree-shaped NSFET with a stack of high-k dielectric (HfO2) and (SiO2) spacers. The Tree-shaped NFET device features high on-current (ION) and low off-current (IOFF) with T(NS) = 5 nm, W(NS) = 25 nm, WIB=5nm, and HIB = 25 nm. Comparison of single- and dual-k spacer 3D devices and DC properties are shown. Because fringing fields with spacer dielectric prolong the effective gate length, the dual-k device has the highest ION / IOFF ratio, 109, compared to 107. This research also examines where work function, inter bridge height, breadth, gate lengths, temperature, and analog/RF and DC metrics affect the device. The suggested device has good electrical properties at 12 nm LG, with DIBL = 23 mV/V, SS = 62 mV/dec, and switching ratio (ION / IOFF) = 109. The device’s performance proves Moore’s law applies to lower technological nodes, enabling scalability.

The link to the article- https://ieeexplore.ieee.org/document/10499264 DOI: 10.1109/ACCESS.2024.3388504

In the groundbreaking research paper titled “Innovative Web Application Revolutionizing Disease Detection: Empowering Users and Ensuring Accurate Diagnoses,” Dr Pradyut Sanki, Associate Professor at the Department of Electronics and Communication Engineering, along with doctoral scholars Mr P N S B S V Prasad, and Mr Syed Ali Hussain and BTech students Ms Pragya Gupta and Ms Swikriti Khadke introduce a cutting-edge web application that aims to revolutionise disease detection and empower users to understand their health conditions.Their research paper published in the Journal of Electronic Materials has an impact factor of 2.1.

Abstract:

This paper presents an innovative enhancement aimed at revolutionizing disease detection and providing users with a reliable source of information for accurate diagnoses of their symptoms. Our open-source initiative combines a user-friendly interface design with advanced machine learning models, establishing a new benchmark for accuracy and enabling integration with even higher-performing models. We address the pervasive challenges of misinformation and misdiagnosis associated with online symptom searches, presenting a significant advancement in disease detection. Leveraging cutting-edge machine learning techniques.

Practical and Social Implications:

The practical implementation of our research means that people can use our smart tool to get better advice about their symptoms. This could lead to quicker and more accurate diagnoses, helping people get the right treatment sooner. Socially, our research could reduce the spread of false information online about health issues, leading to better-informed decisions and potentially improving overall public health.

Future Research Plans:

As a future research plan the students and faculty together plan to refine and expand their smart tool to make it even more accurate and helpful. They aim to incorporate feedback from users and collaborate with other experts to continually improve the technology. Additionally, they plan to explore ways to make the tool more accessible to a wider range of people and to address any potential biases in the data or algorithms. Overall, they are committed to advancing healthcare technology for the benefit of society.

It is a matter of immense pleasure for the Department of Electronics and Communication Engineering to announce the publication of Dr Duga Prakash, Associate Professor at SRM University-AP. His research paper titled “Analysis of GAA Junctionless NS FET towards Analog and RF Applications at 30 nm Regime”, published in IEEE Open Journal of Nanotechnology, studies how the device can be manufactured with ease and minimal doping, eliminating the need for high-temperature doping processes. The enhanced performance metrics suggest that the device’s potential for faster analog/RF switching circuits paves the way for more efficient analog and RF applications at the 30 nm scale.

Abstract:

A new nanosheet FET is used to generate a quantum model in this research. A Gate-all-around (GAA) Junction-less (JL) nanosheet device with a 1 nm gate dielectric of SiO2 and HfO2 performs according to the standard model. The visual TCAD tool examines ION, IOFF, ION/ IOFF, threshold voltage, DIBL, gain parameters (gm, gd, Av), gate capacitance, and cut-off frequency to evaluate the classical and quantum models of the GAA nanosheet device. Simulation results show that the device’s low gate capacitance of 10–18 makes it suitable for rapid switching applications. Device research reveals a transconductance (gm) value of 21 μS and a remarkable cut-off frequency of 9.03 GHz. Its P-type device response has also been extensively studied. Finally, the inverter model uses the proposed GAA nanosheet device. Despite having larger gate capacitance, the NSFET-based inverter offers the smallest propagation delay helps apply knowledge to real-world situations.

In a remarkable academic achievement, Dr Banee Bandana Das, Assistant Professor in Department of Computer Science and Engineering and Dr. Saswat Kumar Ram, Assistant Professor in Department of Electronics and Communication Engineering, have made significant contributions to the field of biometric security. Their paper, titled “Person Identification using Autoencoder-CNN Approach with Multitask-based EEG Biometric,” has been published in the esteemed ‘Multimedia Tools and Applications journal, which is recognised as a Q1 journal with an impressive impact factor of 3.6.

This pioneering work showcases a novel approach to person identification using electroencephalogram (EEG) data. The research leverages the power of Autoencoder-CNN models combined with multitask learning techniques to enhance the accuracy and reliability of EEG-based biometric systems.

The publication of this paper not only underscores the high-quality research conducted at SRM University-AP but also places the institution at the forefront of innovative developments in biometric technology. It is a testament to the university’s commitment to advancing scientific knowledge and providing its faculty with a platform to impact the global research community positively.

Abstract

In this research paper, we propose an unsupervised framework for feature learning based on an autoencoder to learn sparse feature representations for EEG-based person identification. Autoencoder and CNN do the person identification task for signal reconstruction and recognition. Electroencephalography (EEG) based biometric system is vesting humans to recognise, identify and communicate with the outer world using brain signals for interactions. EEG-based biometrics are putting forward solutions because of their high-safety capabilities and handy transportable instruments. Motor imagery EEG (MI-EEG) is a maximum broadly centered EEG signal that exhibits a subject’s motion intentions without real actions. The Proposed framework proved to be a practical approach to managing the massive volume of EEG data and identifying the person based on their different task with resting states.

The title of Research Paper in the Citation Format

Person identification using autoencoder-CNN approach with multitask-based EEG biometric. Multimedia Tools Appl (2024).

Practical implementation/social implications of the research

1. To develop a personal identification system using MI-EEG data.
2. This work is about an Autoencoder-CNN-based biometric system with EEG motor imagery inputs for dimensionality reduction and denoising (extracting original input from noisy data).
3. The designed Autoencoder-CNN-based biometric architecture to model MI-EEG signals is efficient for cybersecurity applications.

Collaborations

1. IIITDM, Kurnool, India
2. National Institute of Technology, Rourkela, India
3. University of North Texas, Denton, USA

Future Research Plan

In the future, different deep learning and machine learning methods can be merged to explore better performance in this EEG-based security field and other signal processing areas. We will investigate the robustness deep learning architectures to design a multi-session EEG biometric system.

Link to The Article

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