Anand Karuppannan

@citchennai.edu.in

Associate Professor, Department of Electronics and Communication Engineering
Chennai Institute of Technology

Anand Karuppannan


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https://researchid.co/anandkaruppannan
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
24

Scopus Publications

215

Scholar Citations

5

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • A novel channel estimation of MIMO-OFDM using hybrid bionic binary spotted hyena optimization
    C. Premila Rosy, S. Yazhinian, M. Therasa, K.R. Surendra, Anand Karuppannan, A. Manikandan
    International Journal of Cognitive Computing in Engineering, 2026
    A promising generalized inverse discrete Fourier transform non-orthogonal frequency division multiplexing (GIDFT-OFDM) system can satisfy the requirement of supporting higher data rates in fifth-generation (5G) technology. However, this system has a high peak-to-average power ratio (PAPR) because many subcarrier signals are transmitted. The inverse discrete Fourier transform (IDFT) is used in an orthogonal frequency-division multiplexing (OFDM) modulator to convert symbols from the frequency domain to the time domain and add a cyclic prefix before sending them through the channel. In pilot-based channel estimation, pilots are inserted into the transmitter and detected at the receiver, along with the OFDM symbols. In this study, we searched for local and global optimal solutions of the Bionic Binary Spotted Hyena Optimization (BBSHO) algorithm with position coordinate vectors (PCVs) of social behavior. It also introduces the BBSHO algorithm to improve the local search capability within the search space. Optimized pilots provided better performance than orthogonal and randomly placed pilots. The stochastic, quadrature, and whale swarm algorithms detect the position of the pilot. To improve the data quality and reduce the BER, MSE, and SER, we introduced several optimization algorithms on the channels of MIMO-OFDM devices. The performance of the two optimization algorithms proposed above contrasts with that of the current simple algorithms and shows improved results in MIMO-OFDM networks. The proposed optimization algorithm was implemented using the MATLAB 2021(a) software. For channel optimization, metaheuristic algorithms such as the Whale Swarm Algorithm (WSA) and the Hybrid Bionic Binary Spotted Hyena Optimization (BBSHO) algorithm are used.
  • 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.
  • Machine learning enhanced terahertz metasurface biosensor based on MXene–Graphene–Black phosphorus heterostructure for peptide detection
    G. Arunachalam, Anand Karuppannan, Sandeep Prabhu, U. Arun Kumar
    Results in Physics, 2026
    Terahertz metasurface biosensors offer a promising route for label-free and non-invasive biomolecular detection; however, many existing designs rely on limited material platforms and operate over narrow refractive-index ranges, restricting their practical applicability in complex biological environments. In this work, a hybrid MXene–graphene–black phosphorus (BP) based terahertz metasurface biosensor is proposed for the quantitative detection of peptide biomolecules. The sensor is systematically analyzed using finite element method simulations in COMSOL Multiphysics to optimize the resonator geometry and investigate its electromagnetic response. The influence of key parameters, including graphene Fermi level, incident wave angle, and structural dimensions, on the transmission spectra is comprehensively examined. The optimized design achieves a high refractive-index sensitivity of 500 GHz/RIU over a broad operating range of 1.606–1.706 RIU, which is well suited for peptide sensing applications. To accelerate performance evaluation, a machine-learning framework based on Bayesian Ridge Regression is employed to predict resonance frequency shifts and transmission characteristics, yielding a strong predictive accuracy with R 2 = 0.94. Comparative analysis confirms the competitive sensitivity and wide dynamic range of the proposed sensor. Overall, the results demonstrate the strong potential of the proposed metasurface platform for rapid, label-free, and quantitative peptide biomarker detection in terahertz diagnostic applications.
  • 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.
  • Intelligent Collision Avoidance for Railways using LoRaWAN Technology
    S Nazrin Salma, Anand Karuppannan, A Niyas Ahamed, Sandhiya R, Ravibabu V, Nafeez Ahmed L
    Proceedings of 4th 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
    Suresh Muthu Samy, Sarathkumar Duraisamy, Mohamed Fahim Barkath Ali, Santhosh Ragunathan Sangeetha, Saravanakumar Subramani, Sundaram Muthu, Anand Karuppannan, Prabakaran Murugesan
    2026 IEEE International Students Conference on Electrical Electronics and Computer Science Sceecs 2026, 2026
    In modern industrial environments, maintaining the equipment is essential since continuous maintenance leads to higher maintenance costs, which is not an economical thing. Although uneven maintenance results in unconditional downtimes, which reduce the reliability of the motor. This project examines the development of an AI-based predictive maintenance system for electric motors, utilizing Arduino Uno, various sensors, and machine learning techniques. By using affordable hardware components, including a vibration sensor, a current sensor, and a speed sensor. This system monitors physical motor parameters. These parameters are collected in real-time by the Arduino and transmitted to a system, where a machine learning AI model will process the data. The Random Forest classifier is employed for its high accuracy and suitability for binary classification tasks, distinguishing between healthy and faulty motor conditions. To implement predictive maintenance, the system continuously tracks motor health using sensors by which it allows us to predict early faults, allowing us to reduce the maintenance cost and unexpected downtimes, which increases the reliability of the motor which resulting in making the system a user-friendly environment. To predict the fault, we have to record the physical parameters of the motor by using various sensors. Data collection involves recording motor current, vibration, and speed under both normal and faulty conditions, creating a labeled dataset for testing. The trained Random Forest model is then applied to real-time data, enabling the system to detect the motor when it is fault state. When the model identifies a potential issue, it gives a timely alert and reduces the risk of failure. The result highlights the potential for affordable, effective predictive maintenance solutions in industrial settings.
  • INVESTIGATION OF MODIFIED NEW HYBRID MULTILEVEL INVERTER TOPOLOGY WITH REDUCED SWITCHES
    Murugesan MANIVEL, Kesavan TAMILSELVEN, Suganya NAGULSAMY, Lakshmanan PALANI, Brindha SAMARASAM, Anand KARUPPANNAN, Jayanthi RAJADURAIPANDI, Balambigai SUBRAMANIAN
    Advances in Electrical and Electronic Engineering, 2025
  • 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, Beporam Iftekhar Hussain, S. Nanthakumar
    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.
  • ADRAN: Adaptive Deep Residual Aggregation Network-based Cervical Region Segmentation in Cervix Images
    G. Nirmala, P. Suresh Kumar, Anand Karuppannan
    IETE Journal of Research, 2025
  • 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.
  • Thermal Behavior and Bandgap Tuning of AlGaN Alloys for High Electron Mobility Transistors
    T Suresh Babu, U Saravanakumar, K Anand, P Rubini, S P Kesavan, S Bhoopalan
    International Conference on Advanced Computing Technologies Icoact 2025, 2025
  • AI-Enhanced Energy-Efficient Routing for Electric Vehicles Using Real-Time Traffic and Battery Data
    M.Malarvizhi, G.Sivakumar, Anand Karuppannan, R Yuvaraj, E.Sathish Kumar, A.Venkatesh
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking Gcwcn 2025, 2025
  • An IoT-Based CNN-LSTM Hybrid Model for Real-Time Water Quality Monitoring in Smart Agriculture
    Anand Karuppannan, Budhavarapu Hari Kumar, Sama Mineesha, Shobhit Garg, Priyanka Ravindra Dhumal, Pran. S Gokul
    2025 International Conference on Information Implementation and Innovation in Technology I2itcon 2025, 2025
  • Performance Investigation of New Reduced Switch Count Thirty-Three Level Multilevel Inverter
    , Manivel MURUGESAN, N. SUGANYA, , Karuppannan ANAND, , Tamilselven KESAVAN, , Duraisamy SARATHKUMAR, , R. JAYANTHI, , S. BALAMBIGAI, , V.J. VIJAYALAKSHMI, and
    Eea Electrotehnica Electronica Automatica, 2025
  • Integration of cloud and edge computing in distributed renewable energy systems
    Digital Innovations for Renewable Energy and Conservation, 2024
  • Design of an Area Efficient and High-Performance Adder with a Novel Sum Generator
    Niyas Ahamed Allavudeen, M. Muthusamy, Anand Karuppannan, Nazrin Salma Sheriff
    Circuits Systems and Signal Processing, 2024
  • 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
  • Enhancing the Wear Resistance of AA2024 Alloys Through Ni and MgO Composite Reinforcements
    M. Manikandan, S. Seenivasan, Prasanth Ponnusamy, R. Girimurugan, S. Sakthi, et al.
    Advances in Science Technology and Innovation, 2024
  • Modelling and Evaluation of Li-ion Battery Performance Based on the Electric Vehicle Tiled Tests using Kalman Filter-GBDT Approach
    Neeraj Kumar, Sanjay Laxmanrao Kurkute, V. Kalpana, Anand Karuppannan, RVS Praveen, Soumya Mishra
    International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2024, 2024
  • An IoT-Based Self-Reliable Wheelchair for Enhanced Mobility and Health Monitoring
    Brindha Samarasam, Manikanda Prabu Nallasivam, Gayathri Devi Kulandasamy, Murugesan Manivel, Anand Karuppannan, Charmila Duraisamy
    2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2024 Proceedings, 2024
  • Design and Analysis of Gate Stack Silicon-on-Insulator Nanosheet FET for Low Power Applications
    R. Yuvaraj, Anand Karuppannan, Asisa Kumar Panigrahy, Raghunandan Swain
    Silicon, 2023
  • 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, M. Kalyan Chakravarthi
    7th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2023 Proceedings, 2023
  • Wavelet neural learning-based type-2 fuzzy PID controller for speed regulation in BLDC motor
    Anand Karuppannan, Madheswaran Muthusamy
    Neural Computing and Applications, 2021

RECENT SCHOLAR PUBLICATIONS

  • 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