Dr. Anand Karuppannan Completed B.E. degree in Electronics and Communication Engineering from Park College of Engineering and Technology (Anna University, Coimbatore in 2011) and M.E. degree in Power Electronics and Drives from Sona College of Technology (Anna University, Chennai in 2014). Completed his Ph.D. degree at Anna University, Chennai in December 2021. Recognized Anna University Supervisor under faculty of Electrical Engineering
EDUCATION
Completed Doctor of Philosophy ( Part time under the faculty of Electrical Engineering at Anna University, Chennai with 9.00 CGPA(Course Work) during July 2016 – December 2021 (Supervisor:
Completed Master of Engineering (M.E.) in Power Electronics and Drives from Sona College of Technology, Salem with 9.07 CGPA during 2012-2014.
Completed Bachelor of Engineering (B.E.) in Electronics and Communication Engineering from Park College of Engineering and Technology, Coimbatore with 7.97 CGPA during 2007-2011.
RESEARCH, TEACHING, or OTHER INTERESTS
Multidisciplinary, Renewable Energy, Sustainability and the Environment, Signal Processing, Electrical and Electronic Engineering
High-sensitivity Kretschmann-con¯gured SPR biosensor based on Cu/WS2/BP/Ni multilayer with machine learning-assisted optimization G. Arunachalam, Anand Karuppannan, U. Arun Kumar, C. Sunitha Ram Modern Physics Letters B, 2026 Early identification and precise quantification of low-concentration protein biomarkers such as C-reactive protein (CRP), prostate-specific antigen (PSA), cardiac troponin, and human epidermal growth factor receptor 2 (HER2) are necessary for the screening and early detection and monitoring of cancer, cardiovascular, and inflammatory diseases. Conventional analytical methods like ELISA, Western blotting, and mass spectrometry typically require long assay times and sophisticated instrumentation, which hinder their use in rapid and portable diagnostic applications. In this paper, we describe a high-sensitivity Kretschmann-configuration surface plasmon resonance (SPR) biosensor, with a Cu/WS 2 /Black phosphorus (BP)/Ni multilayered structure formed on top of a BK7 prism, and illuminated with 633[Formula: see text]nm p-polarized light. The use of a copper layer enhances localized SPR and enables strong electromagnetic field confinement at the sensing interface. A thin nickel (Ni) outer layer serves as the sensing interface, enhancing structural stability and modulating the plasmonic response. The WS 2 /BP heterostructure significantly enhances light–matter interaction and strengthens electromagnetic field confinement at the metal–dielectric interface. Numerical optimizations were performed using both the transfer matrix method (TMM) and FEM simulations within COMSOL Multiphysics to evaluate the effect of varying copper layer thicknesses (40–52[Formula: see text]nm) on the performance of the SPR sensor. The optimized SPR sensor exhibits a peak angular sensitivity of 401.5 ∘ /RIU, a figure of merit of 784.178 RIU[Formula: see text], a signal-to-noise ratio (SNR) of 14.648, and a detection limit of approximately 10[Formula: see text] RIU. Additionally, the full width at half maximum (FWHM) of the optimized SPR sensor remains constant at 0.512 ∘ , indicating good resonance sharpness and stability. Furthermore, an ML-based regression framework was developed to estimate the optimal WS 2 layer thickness, demonstrating [Formula: see text] values [Formula: see text]0.991 and mean absolute percentage error (MAPE) [Formula: see text]1.6% across the evaluated layer thicknesses, which makes the design adaptive and scalable. The proposed multilayer SPR sensor architecture has substantial potential for ultrasensitive screening and early detection of cancer, cardiac proteins, and infectious disease markers, as well as integration into portable lab-on-chip systems.
A Reinforcement Learning Framework for PAPR Minimization in MU-MIMO With GNN-Based CSI Encoding B. Ramesh, Anand Karuppannan, P. Gopinath, A. Mohamedyaseen International Journal of Communication Systems, 2026 In Multiuser Multiple‐Input Multiple‐Output systems, the peak‐to‐average power ratio poses a significant constraint, which impacts power efficiency, signal integrity, and overall system reliability. For reducing peak‐to‐average power ratio, although conventional methods provide varying levels of effectiveness, these techniques frequently encounter issues related to requirements for side information, limited flexibility in adapting to changing channel conditions, and computational complexity. To address these challenges, this paper proposes a novel Proximal Policy Optimization (PPO)‐based Graph Neural Network framework for dynamic precoding optimization, particularly focusing on minimizing the peak‐to‐average power ratio in Multiuser Multiple‐Input Multiple‐Output systems. Because of its effective balance between performance and training reliability, the PPO algorithm is employed, which promotes stable and efficient learning. The model uses the CSI data to build a graph, and then extracts features through a GNN to obtain spatial correlations. These extracted GNN features were used as the state of a PPO reinforcement learning agent. The PPO agent is trained to find optimal precoding policies to minimize PAPR without compromising signal quality. Then, the acquired policy is applied to precode at the transmitter that transmits OFDM signals with low PAPR and high spectral efficiency. Extensive MATLAB simulations confirm the efficacy of the proposed framework, demonstrating a notable peak‐to‐average power ratio reduction of 4.8 dB at a complementary cumulative distribution function of 10 −3 . Even in fluctuating channel conditions and varying user densities, the framework achieves improved signal‐to‐interference‐plus‐noise ratio and bit error rate performance, while keeping computational complexity low. The proposed model eliminates the necessity for side information, guaranteeing seamless compatibility with existing Multiuser Multiple‐Input Multiple‐Output transceiver designs and enabling real‐time applications. These findings emphasize the proposed model's potential as an efficient, robust, and scalable method for future wireless communication systems.
Enhancing the Efficiency and Schedule of Solar Thermal Power Plants by Utilizing Thermal Storage Devices to Minimize Carbon Emissions P. Lingeswaran, Shailendra Kumar Yadav, L. Ganesh Babu, D. Magesh Babu, Anand Karuppannan, et al. E3s Web of Conferences, 2025 The renewable energy method of photo thermal power generation has great promise for future advancements. The core structure and characteristics of energy flow of photo thermal power plants are often overlooked when operating and scheduling these facilities. This paper details the architecture of a Photo Thermal Power Plant (PTPP) with a Thermal Storage System (TSS) and examines the primary energy flow patterns of the plant in order to develop a schedule optimization model for the facility that runs autonomously and generates no carbon emissions. The results of the simulation showed that the photovoltaic power plant’s power output capacity and revenue may be improved by adding a TSS to the self- operating model that was originally developed for planning power generation and peak valley energy pricing. When the capacity of the TSS was more than 6 hours, there was no fine for inadequate power generation in the simulation. A rise of 84.9 % in revenue was achieved by increasing the Thermal Storage (TS) system’s capacity. Carbon emissions dropped from 26.4×103 tons to 22.1×103 tons and the overall operating cost went down from 136531.02 k ₹ to 102247.98 k ₹ when the capacity of the TSS went enhanced from 0 to 8 hours. In comparison to previous research, this study’s exhaustive optimization model and analysis of energy flows yields a more thorough and rigorous response. Improving the long-term viability of renewable energy sources, developing more efficient energy systems, and developing new clean energy technologies are primary goals of this study.
A Vision Transformer Approach to Efficient Brain Tumor Detection and Segmentation in Medical Imaging Ebinesh K, Aashish D, Balaji Sougoumar, Anand Karuppannan 2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025 Brain tumor detection and segmentation are important tasks in the field of medical imaging that have significant potential to enhance treatment planning and accurate diagnosis. The construction of a Vision Transformer (ViT) model for the purpose of identifying brain tumor from medical images specifically MRI scans has been covered in this work. Renowned for its outstanding performance recognition challenges, the Vision Transformer will be utilized to improve brain tumor detection accuracy and efficiency. The proposed system will be evaluated in terms of accuracy, performance, and inference time against conventional deep learning models. This research aims to improve real-time medical image processing with ViT, resulting in faster diagnosis and more efficient tumor identification in clinical practice.
Next-Gen SDN Security: Quantum Keying Meets AI Analytics S. Virushabadoss, Anithaashri T. P, M. Nivedha, S. Hari Krishnan, Anand Karuppannan International Conference on Distributed Systems Computer Networks and Cybersecurity Icdscnc 2024, 2024
CERTAIN INVESTIGATION ON SHELL AND TUBE HEAT EXCHANGER IN SOLAR DESALINATION FOR SUSTAINABLE DEVELOPMENT USING CONTROLLER DESIGN AND OPTIMISATION STUDIES Journal of Environmental Protection and Ecology, 2023
Design and Development of Wearable Medical Devices for Health Monitoring MedapatiSudheer Kumar Reddy, Nellore Manoj Kumar, Anand Karuppannan, Siva Nagaiah Bolleddu, Ashish Verma, et al. 7th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2023 Proceedings, 2023
Optimizing Throughput and Latency in Wormhole Routers for NoCs: A Mathematical Approach to Pipelining and Speculative Virtual Channel Allocation M Hariharan, M Mohan, NS Elakkiya, P Ramu, N Kalliammal, K Anand 2026 International Conference on Next-Gen Quantum and Advanced Computing … , 2026 2026
Machine learning enhanced terahertz metasurface biosensor based on MXene–Graphene–Black phosphorus heterostructure for peptide detection G Arunachalam, A Karuppannan, S Prabhu, UA Kumar Results in Physics 82, 108603 , 2026 2026
Internet of Things and Intelligent Sensors RG Nazrin Salma S., Niyas Ahamed A ., Anand Karuppannan. Advancements in Sensor Technologies , 2026 2026
Intelligent Collision Avoidance for Railways using LoRaWAN Technology SN Salma, A Karuppannan, AN Ahamed 2026 International Conference on Electronics and Renewable Systems (ICEARS … , 2026 2026
A method for developing low cost predictive maintenance system for electrical motors using random forest and IoT sensor technology SM Samy, S Duraisamy, MFB Ali, SR Sangeetha, S Subramani, S Muthu, ... 2026 IEEE International Students' Conference on Electrical, Electronics and … , 2026 2026
A Reinforcement Learning Framework for PAPR Minimization in MU‐MIMO With GNN‐Based CSI Encoding B Ramesh, A Karuppannan, P Gopinath, A Mohamedyaseen International Journal of Communication Systems 39 (1), e70287 , 2026 2026 Citations: 1
INVESTIGATION AND DEVELOPMENT OF A MULTILEVEL INVERTER WITH A 29-LEVEL REDUCED SWITCH COUNT BS Murugesan M, Suganya N, Sivaranjani S, KesavanT, T Bharani Prakash ... Kufa Journal of Engineering 17 (1), 473-488 , 2026 2026
Power Optimization in Wireless Sensor Networks Through Machine Learning-Based Energy Harvesting Algorithms P Kandasamy, R Manimegalai, A Varshney, K Anand, R Arulmozhiyal, ... 2025 3rd International Conference on IoT, Communication and Automation … , 2025 2025
INVESTIGATION OF MODIFIED NEW HYBRID MULTILEVEL INVERTER TOPOLOGY WITH REDUCED SWITCHES BS Murugesan MANIVEL, Kesavan TAMILSELVEN, Lakshmanan PALANI, Brindha ... ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 23 (4), 281-290 , 2025 2025
AI-Enhanced Energy-Efficient Routing for Electric Vehicles Using Real-Time Traffic and Battery Data M Malarvizhi, G Sivakumar, A Karuppannan, R Yuvaraj, ES Kumar, ... 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN … , 2025 2025
Design a Tri-Band Microstrip Patch Antenna and Optimization of Impedance Matching via Coaxial Feed Line Locations J Rangarajan, C Selvi, G Arunachalam, K Anand, N Sneka, K Dinakaran 2025 International Conference on Intelligent Computing, Information and … , 2025 2025
An IoT-Based CNN-LSTM Hybrid Model for Real-Time Water Quality Monitoring in Smart Agriculture A Karuppannan, BH Kumar, S Mineesha, S Garg, PR Dhumal, PS Gokul 2025 International Conference on Information, Implementation, and Innovation … , 2025 2025
A novel channel estimation of MIMO-OFDM using hybrid bionic binary spotted hyena optimization CP Rosy, S Yazhinian, M Therasa, KR Surendra, A Karuppannan, ... International Journal of Cognitive Computing in Engineering , 2025 2025 Citations: 2
ADRAN: Adaptive Deep Residual Aggregation Network-based Cervical Region Segmentation in Cervix Images G Nirmala, P Suresh Kumar, A Karuppannan IETE Journal of Research 71 (6), 1867-1879 , 2025 2025 Citations: 3
Thermal Behavior and Bandgap Tuning of AlGaN Alloys for High Electron Mobility Transistors TS Babu, U Saravanakumar, K Anand, P Rubini, SP Kesavan, ... 2025 International Conference on Advanced Computing Technologies (ICoACT), 1-5 , 2025 2025
IoT-Machine Learning based Hybrid Battery Optimization for Water pumping Using BLDC Motor AK Murugesan M, Bharani Prakash T, Suganya N, Brindha S, Sarathkumar D International Conference on Robotics, Communication and Soft Computing (RCSC … , 2025 2025
Performance Investigation of New Reduced Switch Count Thirty-Three Level Multilevel Inverter M Murugesan, N Suganya, K Anand, D Kesavan, D Sarathkumar, ... Electrotehnica, Electronica, Automatica 73 (2), 15-22 , 2025 2025 Citations: 1
A Vision Transformer Approach to Efficient Brain Tumor Detection and Segmentation in Medical Imaging K Ebinesh, D Aashish 2025 International Conference on Data Science, Agents & Artificial … , 2025 2025
Enhancing the Efficiency and Schedule of Solar Thermal Power Plants by Utilizing Thermal Storage Devices to Minimize Carbon Emissions P Lingeswaran, SK Yadav, L Ganesh Babu, D Magesh Babu, ... E3S Web of Conferences 619, 02004 , 2025 2025
Integration of cloud and edge computing in distributed renewable energy systems S Gobinath, A Karuppannan, V Vijikala, K Radhika, J Gowrishankar Digital innovations for renewable energy and conservation, 195-218 , 2025 2025 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Modelling and evaluation of Li-ion battery performance based on the electric vehicle tiled tests using Kalman filter-GBDT approach N Kumar, SL Kurkute, V Kalpana, A Karuppannan, RVS Praveen, ... 2024 International Conference on Intelligent Algorithms for Computational … , 2024 2024 Citations: 118
Design and analysis of gate stack silicon-on-insulator nanosheet FET for low power applications R Yuvaraj, A Karuppannan, AK Panigrahy, R Swain Silicon 15 (4), 1739-1746 , 2023 2023 Citations: 35
Wavelet neural learning-based type-2 fuzzy PID controller for speed regulation in BLDC motor A Karuppannan, M Muthusamy Neural Computing and Applications 33 (20), 13481-13503 , 2021 2021 Citations: 22
Design and Development of Wearable Medical Devices for Health Monitoring MKC MedapatiSudheer Kumar Reddy, Nellore Manoj Kumar, Anand Karuppannan ... 2023 7th International Conference on I-SMAC (IoT in Social, Mobile … , 2023 2023 Citations: 18
Design of Dynamic Voltage Restorer for Power Quality Improvement A Karuppannan, S Mohanaprasaath Mediterranean Journal of Basic and Applied Sciences 8 (2), 54-62 , 2024 2024 Citations: 5
Analysis of Torque Ripple and Speed Control of Five Phase BLDC Motor K Anand, T Palanisamy, R Loganathan International Scientific Journal on Science Engineering & Technology, 17 (9 … , 2014 2014 Citations: 4
ADRAN: Adaptive Deep Residual Aggregation Network-based Cervical Region Segmentation in Cervix Images G Nirmala, P Suresh Kumar, A Karuppannan IETE Journal of Research 71 (6), 1867-1879 , 2025 2025 Citations: 3
A novel channel estimation of MIMO-OFDM using hybrid bionic binary spotted hyena optimization CP Rosy, S Yazhinian, M Therasa, KR Surendra, A Karuppannan, ... International Journal of Cognitive Computing in Engineering , 2025 2025 Citations: 2
Design of an Area Efficient and High-Performance Adder with a Novel Sum Generator NSS Niyas Ahamed Allavudeen, Madheswaran Muthusamy, Anand Karuppannan Circuits, Systems, and Signal Processing , 2024 2024 Citations: 2
A Reinforcement Learning Framework for PAPR Minimization in MU‐MIMO With GNN‐Based CSI Encoding B Ramesh, A Karuppannan, P Gopinath, A Mohamedyaseen International Journal of Communication Systems 39 (1), e70287 , 2026 2026 Citations: 1
Performance Investigation of New Reduced Switch Count Thirty-Three Level Multilevel Inverter M Murugesan, N Suganya, K Anand, D Kesavan, D Sarathkumar, ... Electrotehnica, Electronica, Automatica 73 (2), 15-22 , 2025 2025 Citations: 1
Integration of cloud and edge computing in distributed renewable energy systems S Gobinath, A Karuppannan, V Vijikala, K Radhika, J Gowrishankar Digital innovations for renewable energy and conservation, 195-218 , 2025 2025 Citations: 1
An IoT-Based Self-Reliable Wheelchair for Enhanced Mobility and Health Monitoring B Samarasam, MP Nallasivam, GD Kulandasamy, M Manivel, ... 2024 Second International Conference on Intelligent Cyber Physical Systems … , 2024 2024 Citations: 1
A Survey on Computational Techniques in WSN Networks A Karuppannan, M Rathinam Irish Interdisciplinary Journal of Science & Research , 2024 2024 Citations: 1
SPECTRAL-SPATIAL DEEP DENSENET LEARNING FOR MULTISPECTRAL IMAGE CLASSIFICATION AND ANALYSIS CMVSA Anand Karuppannan, K. Subba Reddy, Nilesh Madhukar Patil ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING 14 (1), 3073-3078 , 2023 2023 Citations: 1
Optimizing Throughput and Latency in Wormhole Routers for NoCs: A Mathematical Approach to Pipelining and Speculative Virtual Channel Allocation M Hariharan, M Mohan, NS Elakkiya, P Ramu, N Kalliammal, K Anand 2026 International Conference on Next-Gen Quantum and Advanced Computing … , 2026 2026
Machine learning enhanced terahertz metasurface biosensor based on MXene–Graphene–Black phosphorus heterostructure for peptide detection G Arunachalam, A Karuppannan, S Prabhu, UA Kumar Results in Physics 82, 108603 , 2026 2026
Internet of Things and Intelligent Sensors RG Nazrin Salma S., Niyas Ahamed A ., Anand Karuppannan. Advancements in Sensor Technologies , 2026 2026
Intelligent Collision Avoidance for Railways using LoRaWAN Technology SN Salma, A Karuppannan, AN Ahamed 2026 International Conference on Electronics and Renewable Systems (ICEARS … , 2026 2026
A method for developing low cost predictive maintenance system for electrical motors using random forest and IoT sensor technology SM Samy, S Duraisamy, MFB Ali, SR Sangeetha, S Subramani, S Muthu, ... 2026 IEEE International Students' Conference on Electrical, Electronics and … , 2026 2026