NAGARAJAN. S

@srmrmp.edu.in

Assistant Professor and Electronics and communication engineering Department /ECE
SRM Institute of Science and Technology, Ramapuram campus,CHENNAI

NAGARAJAN. S


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https://researchid.co/notoutnaga

EDUCATION

Nano devices)

RESEARCH INTERESTS

VLSI,NANO DEVICES, COMMUNICATION ENGINEERING
14

Scopus Publications

94

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Enhancing VLSI circuit performance prediction through qualitative data augmentation using Mixed-decomposed convolutional network
    S. Nagarajan, J. Jeba Johannah, E. Malarvizhi, S. Lakshmi
    Analog Integrated Circuits and Signal Processing, 2026
  • Development of Hydrogel-Embedded Protective Layer EGG Shell for Wound Healing
    S. Nagarajan, C. Rohith Bhat, M. Sujatha, M. Moorthi, Deepan, A. Shanmugapragash, K. Mohan Gandh
    Ccic 2026 Contemporary Computing Innovations Conference 2026, 2026
  • Machine learning-driven fast carry look ahead adder architecture using simple carry checker
    M. Shunmugathammal, G. Ramya, M. Roopa, S. Nagarajan, K. Lalitha, M. Shagar Banu
    Machine Learning Predictive Analytics and Optimization in Complex Systems, 2025
    In VLSI design, adders and multipliers are vital in performing summing and multiplying operations in different ways. Advances in VLSI technology have led to the emergence of designer adders with increased efficiency and cost-efficiency. Carry Look Ahead Adder (CLA Adder) is used in various operations where the system must add many bits with at least three inputs. CLA Adder is mainly used because of its ability to predict the carry before actually summing the inputs, reducing the overall execution time of the entire process. Still, there can be errors when the carry has been predicted. This paper proposes a new algorithm called 'Simple Carry Checker' based on approximate computing, which is used to reduce the frequency of errors in CLA adder while generating the carry of the inputs with a reduced execution time.
  • Safe Tracks with LoRa: Employing Long-Range Technology for Preventing Major Train Crash
    Nagarajan S, Ramya G, R. Karthiga, Shalini. D, Baargavee. V, Kashika Ramesh
    International Conference on Emerging Technologies in Electronics and Green Energy Iceteg 2025, 2025
    Railway accidents caused by track cracks, signal failures, and human error remain a critical safety concern. Existing monitoring approaches are either costly or limited in range, making real-time detection and communication difficult. This paper proposes a cost-effective smart railway safety framework that integrates multi-sensor monitoring with LoRabased long-range communication. The system employs ultrasonic, infrared, electrical connectivity, and GPS sensors to detect cracks, obstacles, and abnormal track conditions. Data are transmitted through a LoRa mesh topology, enabling reliable train-to-train and train-to-station alerts even in remote regions. A prototype implementation demonstrates the system's capability to identify rail damage, monitor train speed and position, and provide timely notifications for collision prevention and automated level-crossing control. Experimental results confirm that LoRa offers wide-area coverage with low power consumption, ensuring efficient and scalable deployment. This work highlights a novel, IoT-driven approach that enhances railway safety, reduces operational risks, and supports the development of intelligent transportation infrastructure.
  • Smart Clinic: A New Innovation in Telemedicine
    Nivedha.D, S. Nagarajan, C.Rohith Bhat, M.Sujatha, M.Moorthi, M.Madhu
    Proceedings of the 6th International Conference on Smart Electronics and Communication Icosec 2025, 2025
    Telemedicine is revolutionizing healthcare by enabling remote consultations through interactive audiovisual media. The proposed system provides basic counseling to patients and automatically dispenses medication for minor ailments, following consultation with a doctor via network connections. Unfortunately, many rural areas face a shortage of medical professionals, leading to concerns about healthcare quality and staffing. To address this, our system checks patients' vital signs, such as temperature and heartbeat, and offers voice-based counseling in local languages. The computer then analyzes the data and provides a diagnosis, which is sent to a doctor for review. Based on the doctor's prescription, the machine either dispenses medication or advises the patient to visit a hospital.
  • Multi agent deep reinforcement learning for resource allocation in container-based clouds environments
    S. Nagarajan, P. Shobha Rani, M. S. Vinmathi, V. Subba Reddy, Angel Latha Mary Saleth, D. Abdus Subhahan
    Expert Systems, 2025
    Virtualization enables the deployment of several virtual servers on the same physical layer, critical component of the cloud. As cloud services advance, more apps that use repositories are developed, which adds to the overburden. Containers have evolved into the most reliable and lightweight virtualization technology for cloud services thanks to their flexible sorting, mobility, and scalability. In container‐based clouds, containers can potentially cut data centre energy usage more than virtual machines (VMs) do. Containers are less energy intensive than VMs. Resource allocation is the most prevalent method in cloud systems. However, resource allocation in container‐based clouds (RAC) is innovative and complicated due to its two‐level architecture. This includes the pairing of virtual machines and physical computers with containers. In cloud container services, planner components are essential. This lowers expenses while improving the performance and variety of workloads using cloud resources. The cloud infrastructure resource allocation framework is gaining popularity since it is energy‐efficient and focuses on cloud data management to maximize income and minimize costs. In this paper, we proposed a deep learning‐based architecture capable of achieving high data centre energy efficiency and preventing Service Level Agreement (SLA) violations from deploying green cloud resources. This research describes a hybrid optimum and multi‐agent deep reinforcement learning (MADRL) technique for dynamic task scheduling (DTS) in a container cloud environment. The MADRL‐DTS model for the RAC problem considers VM overheads, VM types, and an affinity restriction. Then, to address the RAC issue, we develop a DTS hyper‐heuristic technique. MADRL‐RAC may give allocation rules by recognizing workload trends and VM types from previous workload traces. Compared to modern procedures, the results demonstrate a significant reduction in energy consumption. The evaluation for energy‐efficient resource allocation is tested in several virtualized environments to get a high power usage effectiveness and CPU usage.
  • Hierarchical Taylor quantized kernel least mean square filter for data aggregation in wireless sensor network
    Poonguzhali Ilango, Anitha Ravichandran, Nagarajan Sivarajan, Asha Aiyappan
    International Journal of Communication Systems, 2024
    SummaryThe advanced technology in recent years that has achieved more attention among researchers and the social community is the wireless sensor network (WSN) that includes a number of nodes that are commonly distributed in remote zones. While deploying the WSN in huge areas, WSNs produce a massive amount of data. Thus, there is a significant need to process the data through efficient models. The data aggregation technique is the common solution widely employed to obstruct congestion on large‐scale WSNs. However, the demanding part of the data aggregation scheme is to mitigate the network overhead without affecting the system efficiency. Most of the data transmitted by sensor nodes are repetitious and thus result in high power consumption. Therefore, sensor nodes should utilize an efficient data aggregation model for data transmission that minimizes duplicate data. In order to maintain such complications, this article proposes a hierarchical Taylor quantized kernel least mean square (HTQKLMS) filter for aggregating data in WSN. For this purpose, WSN is initially simulated, and then data aggregation is accomplished using developed HTQKLMS filter. Additionally, the HTQKLMS is derived by amalgamating the hierarchical fractional quantized kernel least mean square (HFQKLMS) filter with the Taylor series. Here, the data prediction mechanism is done by employing HFQKLMS model that is an integration of quantized kernel least mean square (QKLMS) and hierarchical fractional bidirectional least mean square (HFBLMS). Apart from this, data redundancy is achieved by broadcasting needed data utilizing data detected at the destination. Furthermore, HTQKLMS approach has delivered a minimum energy consumption of 0.0333 J and less prediction error of 0.0326.
  • PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi-constraint load balancing and routing framework in FANET
    Poonguzhali Ilango, Nagarajan Sivarajan, Anitha RaviChandran, Rajesh Arunachalam
    International Journal of Communication Systems, 2024
    SummaryFlying ad hoc network (FANET) is a technology that has seen tremendous growth in the past years due to its application in various military and civil developments. Conventional topology‐aided routing schemes are not suitable for large‐scale FANETs. This is because of the higher mobility rate in UAVs with the architecture differences. Hence, several path entries in routing become invalid and the neighboring nodes can be occupied before the interruption. Hence, it is very important to solve the afore‐explained issues in FANET. Initially, the task is assigned to the UAVs. These tasks are performed by satisfying the demands such as timing, congestion, and energy to attain the effective load‐balancing performance. Then, the optimal clustering and cluster head (CH) selection are performed using the developed Percentage of Circle Search and Spotted Hyena Optimizer (PCSSHO) in the communication path. Here, the feature selection is done based on constraints like position, speed, moving direction, height variation, link quality, and inter‐ and intra‐cluster distance. Then, the developed PCSSHO model performs routing by considering different constraints such as “end‐to‐end delay, delivery ratio, power consumption, and link quality.” Thus, the recommenced load balancing model in FANET secures an enhanced performance rate than the conventional load balancing frameworks.
  • A Cloud based architecture for hosting ecg arrhythmia data classification service
    S. Nagarajan, V. Nivaskumar, M. Vanitha Lakshmi, C. Senthilkumar, S. Sathish Kumar, M. Moorthi
    Aip Conference Proceedings, 2023
  • Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks
    Nidhi Agarwal, M. Gokilavani, S. Nagarajan, S. Saranya, Hadeel Alsolai, Sami Dhahbi, Amira Sayed Abdelaziz
    Computers Materials and Continua, 2023
    In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.
  • Wavelet transform based multiple image watermarking technique
    R Nanmaran, S Nagarajan, R Sindhuja, Garudadri Venkata Sree Charan, Venkata Sai Kumar Pokala, S Srimathi, G Gulothungan, A. S Vickram, S Thanigaivel
    Iop Conference Series Materials Science and Engineering, 2020
  • Impact of gatelength on the performance of InGaAs/InAs/InGaAs composite channel dual material double gate-high electron mobility transistor devices for high-frequency applications
    S. Nagarajan, Reeba Korah, G. Maria Kalavathy
    Journal of Nanoelectronics and Optoelectronics, 2017
  • Relaxation rate and polarization charge density model for AlN/AlxGa1−xN/AlN heterostructures
    Nagarajan SIVARAJAN, Reeba KORAH, Maria Kalavathy GNANAMANI
    Turkish Journal of Electrical Engineering and Computer Sciences, 2017
  • Analytical model of symmetric halo doped DG-Tunnel FET
    S. Nagarajan, , Reeba korah, N. Mohankumar, C.K. Sarkar, , , and
    Journal of Engineering Science and Technology Review, 2015

RECENT SCHOLAR PUBLICATIONS

  • Enhancing VLSI circuit performance prediction through qualitative data augmentation using Mixed-decomposed convolutional network
    S Nagarajan, JJ Johannah, E Malarvizhi, S Lakshmi
    Analog Integrated Circuits and Signal Processing 126 (2), 33 , 2026
    2026.0
  • Safe Tracks with LoRa: Employing Long-Range Technology for Preventing Major Train Crash
    S Nagarajan, G Ramya
    2025 International Conference on Emerging Technologies in Electronics and … , 2025
    2025.0
  • Smart Clinic: A New Innovation in Telemedicine
    S Nagarajan, CR Bhat, M Sujatha, M Moorthi, M Madhu
    2025 6th International Conference on Smart Electronics and Communication … , 2025
    2025.0
  • Hierarchical Taylor quantized kernel least mean square filter for data aggregation in wireless sensor network
    P Ilango, A Ravichandran, N Sivarajan, A Aiyappan
    International Journal of Communication Systems 37 (18), e5952 , 2024
    2024.0
  • PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET
    P Ilango, N Sivarajan, A RaviChandran, R Arunachalam
    International Journal of Communication Systems 37 (9), e5758 , 2024
    2024.0
    Citations: 4
  • A cloud based architecture for hosting ECG arrhythmia data classification service
    S Nagarajan, V Nivaskumar, MV Lakshmi, C Senthilkumar, SS Kumar, ...
    AIP Conference Proceedings 2764 (1), 060012 , 2023
    2023.0
  • Multi agent deep reinforcement learning for resource allocation in container-based clouds environments
    Nagarajan, S., Rani, P. S., Vinmathi, M. S., Subba Reddy, V., Saleth, A. L ...
    Expert systems, 1– 22 , 2023
    2023.0
    Citations: 25
  • Early Detection of Exercise - Associated Muscle cramp
    NM Sinthia P, M. Malathi , S. Nagarajan , Anitha Juiette
    YMER 21 (1), 251-260 , 2022
    2022.0
  • Iot based automatic smart parking system with ev-charging point in crowd sensing area
    R Gandhi, S Nagarajan, J Chandramohan, A Parimala, VS Arulmurugan
    Annals of the Romanian Society for Cell Biology 25 (6), 6398-6409 , 2021
    2021.0
    Citations: 5
  • Wavelet transform based multiple image watermarking technique
    R Nanmaran, S Nagarajan, R Sindhuja, GVS Charan, VSK Pokala, ...
    IOP Conference Series: Materials Science and Engineering 993 (1), 012167 , 2020
    2020.0
    Citations: 37
  • Impact of Gatelength on the Performance of InGaAs/InAs/InGaAs Composite Channel Dual Material Double Gate-High Electron Mobility Transistor Devices for High-Frequency Applications
    S Nagarajan, R Korah, GM Kalavathy
    Journal of Nanoelectronics and Optoelectronics 12 (12), 1314-1320 , 2017
    2017.0
    Citations: 1
  • Relaxation rate and polarization charge density model for AlN/Al Ga N/AlN heterostructures
    N Sivarajan, R Korah, MK Ganamani
    Turkish Journal of Electrical Engineering and Computer Sciences 25 (4), 3468 … , 2017
    2017.0
  • Analytical Model of Symmetric Halo Doped DG-Tunnel FET.
    S Nagarajan, N Mohankumar, CK Sarkar
    Journal of Engineering Science & Technology Review 8 (4) , 2015
    2015.0
  • Tunnel Field Effect Transistors for Ultra Low Power Applications
    KM S.Nagarajan , R.B.Revathy
    International Journal of Advanced Research in Electrical, Electronics and … , 2014
    2014.0
  • Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks
    ASA Nidhi Agarwal, M. Gokilavani, S. Nagarajan, S. Saranya, Hadeel Alsolai ...
    Computers, Materials & Continua 74 (1), 141-152 , 0
    Citations: 22

MOST CITED SCHOLAR PUBLICATIONS

  • Wavelet transform based multiple image watermarking technique
    R Nanmaran, S Nagarajan, R Sindhuja, GVS Charan, VSK Pokala, ...
    IOP Conference Series: Materials Science and Engineering 993 (1), 012167 , 2020
    2020.0
    Citations: 37
  • Multi agent deep reinforcement learning for resource allocation in container-based clouds environments
    Nagarajan, S., Rani, P. S., Vinmathi, M. S., Subba Reddy, V., Saleth, A. L ...
    Expert systems, 1– 22 , 2023
    2023.0
    Citations: 25
  • Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks
    ASA Nidhi Agarwal, M. Gokilavani, S. Nagarajan, S. Saranya, Hadeel Alsolai ...
    Computers, Materials & Continua 74 (1), 141-152 , 0
    Citations: 22
  • Iot based automatic smart parking system with ev-charging point in crowd sensing area
    R Gandhi, S Nagarajan, J Chandramohan, A Parimala, VS Arulmurugan
    Annals of the Romanian Society for Cell Biology 25 (6), 6398-6409 , 2021
    2021.0
    Citations: 5
  • PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET
    P Ilango, N Sivarajan, A RaviChandran, R Arunachalam
    International Journal of Communication Systems 37 (9), e5758 , 2024
    2024.0
    Citations: 4
  • Impact of Gatelength on the Performance of InGaAs/InAs/InGaAs Composite Channel Dual Material Double Gate-High Electron Mobility Transistor Devices for High-Frequency Applications
    S Nagarajan, R Korah, GM Kalavathy
    Journal of Nanoelectronics and Optoelectronics 12 (12), 1314-1320 , 2017
    2017.0
    Citations: 1
  • Enhancing VLSI circuit performance prediction through qualitative data augmentation using Mixed-decomposed convolutional network
    S Nagarajan, JJ Johannah, E Malarvizhi, S Lakshmi
    Analog Integrated Circuits and Signal Processing 126 (2), 33 , 2026
    2026.0
  • Safe Tracks with LoRa: Employing Long-Range Technology for Preventing Major Train Crash
    S Nagarajan, G Ramya
    2025 International Conference on Emerging Technologies in Electronics and … , 2025
    2025.0
  • Smart Clinic: A New Innovation in Telemedicine
    S Nagarajan, CR Bhat, M Sujatha, M Moorthi, M Madhu
    2025 6th International Conference on Smart Electronics and Communication … , 2025
    2025.0
  • Hierarchical Taylor quantized kernel least mean square filter for data aggregation in wireless sensor network
    P Ilango, A Ravichandran, N Sivarajan, A Aiyappan
    International Journal of Communication Systems 37 (18), e5952 , 2024
    2024.0
  • A cloud based architecture for hosting ECG arrhythmia data classification service
    S Nagarajan, V Nivaskumar, MV Lakshmi, C Senthilkumar, SS Kumar, ...
    AIP Conference Proceedings 2764 (1), 060012 , 2023
    2023.0
  • Early Detection of Exercise - Associated Muscle cramp
    NM Sinthia P, M. Malathi , S. Nagarajan , Anitha Juiette
    YMER 21 (1), 251-260 , 2022
    2022.0
  • Relaxation rate and polarization charge density model for AlN/Al Ga N/AlN heterostructures
    N Sivarajan, R Korah, MK Ganamani
    Turkish Journal of Electrical Engineering and Computer Sciences 25 (4), 3468 … , 2017
    2017.0
  • Analytical Model of Symmetric Halo Doped DG-Tunnel FET.
    S Nagarajan, N Mohankumar, CK Sarkar
    Journal of Engineering Science & Technology Review 8 (4) , 2015
    2015.0
  • Tunnel Field Effect Transistors for Ultra Low Power Applications
    KM S.Nagarajan , R.B.Revathy
    International Journal of Advanced Research in Electrical, Electronics and … , 2014
    2014.0