Arun Chakravarthy R

@kgkite.ac.in

Professor & Electronics and Communication Engineering
KGiSL Institute of Technology

Arun Chakravarthy R


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https://researchid.co/arunchakravarthy

RESEARCH, TEACHING, or OTHER INTERESTS

Information Systems, Computer Networks and Communications, Signal Processing
15

Scopus Publications

58

Scholar Citations

4

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Advancements in intelligent traffic monitoring system design for smart urban infrastructure
    R. Arun Chakravarthy, C. Sureshkumar, M. Arun, R. S. Ranjani Shree
    Building A Green Future Through Essential Decision Making Competencies, 2025
    The rise of urbanization drives the demand for intelligent traffic monitoring systems, integrating computer vision, machine learning, and real-time data processing for smart urban infrastructure. By leveraging sensors, MEMS (Microelectromechanical systems), and wireless communication networks, modern traffic systems are capable of dynamically adapting to varying traffic conditions, optimizing traffic flow, and improving overall road safety. The intelligent traffic monitoring system (ITMS) integrates vehicle detection, real-time traffic data analysis, and adaptive control mechanisms to minimize congestion and reduce emissions. Here, it explores the use of machine learning algorithms to predict traffic patterns and pedestrian behavior, contributing to a more sustainable and efficient urban mobility network. These advancements offer scalable, cost-effective solutions for cities aiming to reduce traffic bottlenecks and enhance public safety, ultimately supporting the vision of smart and sustainable urban environments.
  • Prediction of Ultimate Tensile Strength of Dissimilar Substances Welding using Random Forest Regression
    S. Rajesh, Gobikrishnan Udhayakumar, M. Bhuvaneswari, R. Arun Chakravarthy, M. Arun, C. Jayabalan
    Aip Conference Proceedings, 2025
  • Deep Koopman Operator Learning for High-Dimensional Stability Control in Autonomous Electric Vehicles
    Kirubhakaran M, N. Nasiya Niwaz Banu, M. Arun, R Arun Chakravarthy, Akshya. J, M Sundarrajan, Mani Deepak Choudhry
    7th International Conference on Energy Power and Environment Icepe 2025, 2025
    Modern transportation has reached the era of Autonomous Electric Vehicles (AEVs) that are becoming the solution for the future. Hence, we need to keep them safe and efficient by combining stability control. Trajectory deviations are minimized using ‘stability control’ mechanisms while the energy consumption is optimised. Nevertheless, control methods such as Proportional-Integral-Derivative (PID), Model Predictive Control (MPC), and Linear Quadratic Regulator (LQR) suffer from handling nonlinearities of vehicle dynamics, consequently providing suboptimal performance in the dynamic and uncertain environment. Furthermore, because of their high computational complexity and instability during the training of Reinforcement Learning (RL)-based methods such as Deep Networks (DQN), adaptations of adaptive control using them are often fooled. Under these limitations, a need for an advanced control strategy that strikes the right balance in terms of trajectory tracking accuracy, energy efficiency, and computational feasibility is necessary. To tackle such challenges, in this paper, we propose a Koopman Optimal Stability Control framework that makes the highly nonlinear dynamics of vehicles stable via the Koopman operator. It allows for state predictions that are more accurate and permits linear control to be used in a data-driven manner. It is shown that the RMSE is reduced by 57% over PID controllers (0.12m vs 0.28m) and by 13.6% over MPC (950J vs 1100J). The Koopman-based stability control system addresses key problems of existing methods that will pave the way for safer, more energy-efficient autonomous electric vehicles in real-world deployments.
  • Dynamic Obstacle Avoidance for UAV Swarms in Dense Traffic Corridors Using Double Dueling DQN
    N. Celin, Chithra A S, M. Arun, R. Arun Chakravarthy, Akshya. J, M Sundarrajan, Mani Deepak Choudhry
    2025 International Conference on Electrical Communication and Computing Technologies Iconecct 2025, 2025
  • Generative Diffusion-Based Augmentation for Robust UAV Object Detection in Low-Visibility Conditions
    M. Arun, J Biju, Keren Lois Daniel, R. Arun Chakravarthy, Akshya. J, M Sundarrajan, Mani Deepak Choudhry
    2025 International Conference on Electrical Communication and Computing Technologies Iconecct 2025, 2025
    Unmanned Aerial Vehicles (UAVs) are widely used in surveillance, traffic control, and disaster response, where real-time object detection is vital. However, existing CNN-based models like YOLO, SSD, and Faster R-CNN perform poorly in low-visibility conditions (fog, rain, haze, low light) due to training on clear-weather datasets with limited generalization. Traditional enhancement and augmentation techniques (e.g., histogram equalization, blurring, noise addition) fail to capture the complex variability of degraded scenes. To overcome this, the proposed research introduces a Generative Diffusion-Based Augmentation Framework for Robust UAV Object Detection in Low-Visibility Conditions, leveraging diffusion models' noise addition and denoising processes to generate realistic synthetic images simulating fog, rain, and lighting variations. These high-fidelity samples, integrated into a YOLOv8-based training pipeline, enhance feature generalization and robustness, achieving over 30 FPS in real-time UAV inference. Unlike conventional methods, this diffusion-based approach preserves semantic realism, texture, and spatial accuracy, ensuring superior detection stability and localization confidence in adverse environments.
  • Neuro-Symbolic Swarm Intelligence for Autonomous UAV Coordination in Adversarial Environments
    Gowtham T, S Manoranjitham, M Arun, R Arun Chakravarthy, Akshya. J, M Sundarrajan, Mani Deepak Choudhry
    2025 IEEE 4th International Conference for Advancement in Technology Iconat 2025, 2025
    Swarming of Autonomous Unmanned Aerial Vehicle (UAV) is now central in the present-day aerial operations, such as in surveillance, search-and-rescue, disaster management, and military defense operations. Nevertheless, some of the most critical challenges to real-world deployments include the presence of adversarial interference, dynamic obstacle patterns, communication losses, and the need for energy-efficient and understandable decision-making in a multi-agent coordination task. Current methods, such as multiagent reinforcement learning, classical flocking algorithms, and centralized neural controllers, offer partial solutions to the problem. However, these methods are computationally expensive, poorly scalable to complex environments, and lack interpretability, making them vulnerable to adversarial attacks. Swarm intelligence using neural networks can deliver rapid decision-making at the cost of having minimal structured reasoning under uncertainty. At the same time, symbolic planners can guarantee logical consistency at the expense of being rigid in various random environments. In solving these problems, we propose a Neuro-Symbolic Swarm Intelligence (NSSI) architecture that integrates event-based real-time processing of spiking neural networks with logic and high-level decision-making capabilities, represented by symbolic reasoning modules. The proposed framework, tested with typical simulations on adversary terrain, provides a 1518 percentile boost in trajectory precision, a 22 percent energy decrease, and a 30 percent reduction in threat-reconciliation time compared to the state-of-the-art base regimes. Future work will focus on the addition of meta-learning, enabling the adaptation to unknown threats, wave on-edge low-power computation using neuromorphic hardware, and the investigation of quantum attention for symbolic reasoning over multi-agent optimization, ultimately leading to the deployment of robust neuro-symbolic aerial intelligence systems in realworld settings.
  • Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments
    Kirubhakaran, D. Karthikeswaran, M. Arun, R. Arun Chakravarthy, J. Akshya, M. Sundarrajan, Mani Deepak Choudhry
    Proceedings of the 2024 13th International Conference on System Modeling and Advancement in Research Trends Smart 2024, 2024
    Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A* algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry.
  • Real-time implementation of an implantable antenna using chicken swarm optimization for IoT-based wearable healthcare applications
    Internet of Things and Fog Computing Enabled Solutions for Real Life Challenges, 2022
  • Resource Allocation by Fuzzy Based Cluster Using Greedy Algorithm for Secure Communication
    M. Bhuvaneswari, S. Sasi Priya, R. Arun Chakravarthy
    Proceedings of the 5th International Conference on Electronics Communication and Aerospace Technology Iceca 2021, 2021
    In cognitive Wireless Radio networks, information transfer and power transfer are given special consideration in next-generation wireless networking devices like Simultaneous Wireless Information and Power Transfer. The benefit of integrating cognitive network concepts is that it provides greater efficiency on energy and spectrum to address interference, fading, and path loss, among many other issues, in certain critical applications. Energy harvesting (EH) is a secure and cost-effective method of powering up cluster heads when needed. The proposed method considers two phases. The first phase is energy harvesting of SUs transferred in power signals send by the hybrid access point during wireless power transfer (WPT). While in the second phrase, Wireless Information Transfer (WIT) phase, the captured energy is used while simultaneously acquiring transmission possibilities. In addition, a fuzzy-based cluster using a greedy algorithm is used to reduce PU secrecy outages and to provide the best ideal result. These may be identified using a convolutional neural network (CNN) to detect and classify distinct attack types. This paper presents an efficient resource allocation using fuzzy logic-based clustering and greedy algorithms, which can provide better performance than other technologies. The subsystem for cognitive radio networks is configured, and the primary nodes’ secrecy is maintained and free of three attacks, and the results are simulated using MATLAB tools.
  • Fuzzy based clustering in CWPSN using machine learning model
    Indian Journal of Radio and Space Physics, 2021
  • IoT based environmental weather monitoring and farm information tracking system
    Journal of Critical Reviews, 2020
  • Adaptive heart monitoring system using IoT
    International Journal of Recent Technology and Engineering, 2019
  • Cluster header revolving technique to prolong network lifespan in wireless sensor network
    R. Arun Chakravarthy, S Palaniswami, R Sabitha
    Journal of Computational and Theoretical Nanoscience, 2017
  • Effective power based stable path routing for energy efficiency in wireless sensor networks
    R. Arun Chakravarthy, S Palaniswami
    Journal of Computational and Theoretical Nanoscience, 2016
  • Recent investigation on cluster based energy efficient scheduling scheme for WSN
    International Journal of Applied Engineering Research, 2014

RECENT SCHOLAR PUBLICATIONS

  • Substrate handling system, method, and apparatus
    A Chowdhury, NB RAO, EC Suarez, H SATHYANARAYANA, ...
    US Patent 12,553,133 , 2026
    2026
    Citations: 2
  • Dynamic Obstacle Avoidance for UAV Swarms in Dense Traffic Corridors Using Double Dueling DQN
    N Celin, AS Chithra, M Arun, RA Chakravarthy, M Sundarrajan, ...
    2025 International Conference on Electrical, Communication, and Computing … , 2025
    2025
  • Generative Diffusion-Based Augmentation for Robust UAV Object Detection in Low-Visibility Conditions
    M Arun, J Biju, KL Daniel, RA Chakravarthy, M Sundarrajan, ...
    2025 International Conference on Electrical, Communication, and Computing … , 2025
    2025
  • High conductance divert line architecture
    AA Kangude, E NEVILLE, AC Chakravarthy
    US Patent 12,444,621 , 2025
    2025
  • AI-Driven Energy-Efficient Data Aggregation and Routing Protocol Modeling to Maximize Network Lifetime in Wireless Sensor Networks
    RA Chakravarthy, C Sureshkumar, M Arun, M Bhuvaneswari
    NDT 3 (4), 22 , 2025
    2025
    Citations: 4
  • Semiconductor processing system with a manifold for equal splitting and common divert architecture
    AC Chakravarthy, VK PRABHAKAR, DR Srichurnam, H Rezvantalab
    US Patent 12,424,414 , 2025
    2025
    Citations: 5
  • Neuro-Symbolic Swarm Intelligence for Autonomous UAV Coordination in Adversarial Environments
    T Gowtham, S Manoranjitham, M Arun, RA Chakravarthy, M Sundarrajan, ...
    2025 IEEE 4th International Conference for Advancement in Technology (ICONAT … , 2025
    2025
  • Deep Koopman Operator Learning for High-Dimensional Stability Control in Autonomous Electric Vehicles
    M Kirubhakaran, NNN Banu, M Arun, RA Chakravarthy, M Sundarrajan, ...
    2025 7th International Conference on Energy, Power and Environment (ICEPE), 1-6 , 2025
    2025
  • Grounding ring of a process kit for semiconductor substrate processing
    A Chowdhury, NB RAO, EC Suarez, H SATHYANARAYANA, ...
    US Patent App. 29/848,745 , 2025
    2025
  • Prediction of ultimate tensile strength of dissimilar substances welding using random forest regression
    S Rajesh, G Udhayakumar, M Bhuvaneswari, RA Chakravarthy, M Arun, ...
    AIP Conference Proceedings 3204 (1), 050021 , 2025
    2025
    Citations: 2
  • Deposition ring of a process kit for semiconductor substrate processing
    A Chowdhury, NB RAO, EC Suarez, H SATHYANARAYANA, ...
    US Patent App. 29/848,747 , 2025
    2025
  • Advancements in Intelligent Traffic Monitoring System Design for Smart Urban Infrastructure
    RA Chakravarthy, C Sureshkumar, M Arun, RSR Shree
    Building a Green Future Through Essential Decision-Making Competencies, 27-62 , 2025
    2025
    Citations: 2
  • Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments
    D Karthikeswaran, M Arun, RA Chakravarthy, J Akshya, M Sundarrajan, ...
    2024 13th International Conference on System Modeling & Advancement in … , 2024
    2024
  • Process chamber having shutter cover for substrate uniformity and prevention of unintended deposition
    A Chowdhury, EC Suarez, X Che, AC Chakravarthy, ...
    US Patent App. 18/096,533 , 2024
    2024
  • Mass flow controller based fast gas exchange
    A Chowdhury, R PATIL, AC Chakravarthy, JC FARR, S Chandrabalu, ...
    US Patent 11,940,819 , 2024
    2024
    Citations: 1
  • Single process gas feed line architecture
    AA Kangude, AC Chakravarthy
    US Patent App. 17/880,310 , 2024
    2024
  • Shared rps clean and bypass delivery architecture
    AA Kangude, BN RAMAMURTHI, AC Chakravarthy, VK PRABHAKAR, ...
    US Patent App. 17/880,335 , 2024
    2024
  • Gas delivery system for a shared gas delivery architecture
    AC Chakravarthy, C Neema, AA Kangude, E NEVILLE, VS Jamakhandi, ...
    US Patent 11,881,416 , 2024
    2024
    Citations: 5
  • Educational Paradigms: A Exploration of Teaching Strategies
    AP Dean
    GEC2023 2023, 55 , 2023
    2023
  • Autonomous Landing of UAVs in GNSS Denied Environments Using Pan Tilt Visual Servoing
    JAE Arun Chakravarthy R, Arun M, Sathish R, Sureshkumar C, Rajasekaran S
    Interdisciplinary Research for Sustainable Development , 2023
    2023

MOST CITED SCHOLAR PUBLICATIONS

  • IOT BASED ENVIRONMENTAL WEATHER MONITORING AND FARM INFORMATION TRACKING SYSTEM
    AC Bhuvaneswari M, Arun M
    Journal of Critical Reviews 7 (7), 307-310 , 2020
    2020
    Citations: 11
  • LI-FI BASED SMART SHOPPING
    RA Chakravarthy
    International Journal of Technical Innovation in Modern Engineering … , 2019
    2019
    Citations: 6
  • Semiconductor processing system with a manifold for equal splitting and common divert architecture
    AC Chakravarthy, VK PRABHAKAR, DR Srichurnam, H Rezvantalab
    US Patent 12,424,414 , 2025
    2025
    Citations: 5
  • Gas delivery system for a shared gas delivery architecture
    AC Chakravarthy, C Neema, AA Kangude, E NEVILLE, VS Jamakhandi, ...
    US Patent 11,881,416 , 2024
    2024
    Citations: 5
  • AI-Driven Energy-Efficient Data Aggregation and Routing Protocol Modeling to Maximize Network Lifetime in Wireless Sensor Networks
    RA Chakravarthy, C Sureshkumar, M Arun, M Bhuvaneswari
    NDT 3 (4), 22 , 2025
    2025
    Citations: 4
  • Low cost design automated adhesive dispenser for industry
    M Bhuvaneswari, R Arun Chakravarthy, M Arun, T Naveenkumar, ...
    J. Crit. Rev 7 (12), 248-251 , 2020
    2020
    Citations: 4
  • Effective Power Based Stable Path Routing for Energy Efficiency in Wireless Sensor Networks
    RA Chakravarthy, S Palaniswami
    Journal of Computational and Theoretical Nanoscience 13 (7), 4797-4806 , 2016
    2016
    Citations: 3
  • Substrate handling system, method, and apparatus
    A Chowdhury, NB RAO, EC Suarez, H SATHYANARAYANA, ...
    US Patent 12,553,133 , 2026
    2026
    Citations: 2
  • Prediction of ultimate tensile strength of dissimilar substances welding using random forest regression
    S Rajesh, G Udhayakumar, M Bhuvaneswari, RA Chakravarthy, M Arun, ...
    AIP Conference Proceedings 3204 (1), 050021 , 2025
    2025
    Citations: 2
  • Advancements in Intelligent Traffic Monitoring System Design for Smart Urban Infrastructure
    RA Chakravarthy, C Sureshkumar, M Arun, RSR Shree
    Building a Green Future Through Essential Decision-Making Competencies, 27-62 , 2025
    2025
    Citations: 2
  • Fuzzy based clustering in CWPSN using machine learning model
    B Ma, SP Sb, AC Rc
    Indian Journal of Radio & Space Physics 50 (1), 90-94 , 2021
    2021
    Citations: 2
  • „Waste Management Solution for Smart City using Internet of Things‟ International Journal of Creative Research Thoughts
    R Arun Chakravarthy, M Arun, M Bhuvaneswari
    2021
    Citations: 2
  • Design of Solar Powered Air Purifier with Air Quality Monitoring
    SC Arun Chakravarthy R, Bhuvaneswari M, Arun M
    Turkish Online Journal of Qualitative Inquiry 12 (8), 298-306 , 2021
    2021
    Citations: 2
  • Cluster header revolving technique to prolong network lifespan in wireless sensor network
    RA Chakravarthy, S Palaniswami, R Sabitha
    Journal of Computational and Theoretical Nanoscience 14 (12), 5863-5871 , 2017
    2017
    Citations: 2
  • Mass flow controller based fast gas exchange
    A Chowdhury, R PATIL, AC Chakravarthy, JC FARR, S Chandrabalu, ...
    US Patent 11,940,819 , 2024
    2024
    Citations: 1
  • Real-time implementation of an implantable antenna using chicken swarm optimization for IoT-based wearable healthcare applications
    M Bhuvaneswari, S Sasipriya, RA Chakravarthy
    Internet of Things and Fog Computing-Enabled Solutions for Real-Life … , 2022
    2022
    Citations: 1
  • Resource allocation by fuzzy based cluster using greedy algorithm for secure communication
    M Bhuvaneswari, SS Priya, RA Chakravarthy
    2021 5th International Conference on Electronics, Communication and … , 2021
    2021
    Citations: 1
  • System and method for auto configuration of application settings using data source administration scripts
    A Khan, A Ramachandran, A Behera, D Nagabushanam, A Kumar, ...
    US Patent App. 16/672,279 , 2021
    2021
    Citations: 1
  • Waste Management Solution for Smart City using Internet of Things
    RA Chakravarthy, M Arun, M Bhuvaneswari
    International Journal of Creative Research Thoughts , 2021
    2021
    Citations: 1
  • Adaptive Heart Monitoring System Using Iot
    N Kaleeswari, RA Chakravarthy, M Arun
    2019
    Citations: 1

GRANT DETAILS

Grant of Rs. 18,93,333/- for conduct of Project under AICTE - Research Promotion Scheme (RPS) during the financial year 2021-22
Principal Investigator: Chakravarthy R

Grant of ATAL FDP titled “Unveiling New Avenues in the Design of Next Generation Semiconductor Devices” during 2023-24

Grant of from TNSCST under Popularization of Science Activities 2020-21 to Conduct “Raising Awareness of Plastic Hazards”
Coordinator: Dr. Arun Chakravarthy R

Grant of from CSIR to conduct “ICT Academy National Conference on Emerging Evolutions in Information and Communication Technology (NCEEICT’22) during May 20, 2022 at KGiSL Institute of Technology, Coimbatore.
Organising Secretary: Dr. Arun Chakravarthy R