Automated Receiving Set Connectivity Unmanned Aircraft Charging Station R. Dhanasekar, L. Vijayaraja, S. Rajarajan, T.M. Pragadeesh, M. Yogeshwaran, V. Vijayaselvam Proceedings of 8th International Conference on Intelligent Sustainable Systems Iciss 2026, 2026 The rapid growth of the drone industry is driving a surge in available research and development across various sectors. Scalability is one crucial problem that requires consideration in addition to energy efficiency. Furthermore, even if some charging stations seem promising, there are limitations or legal regulations that make them unsuitable for general use. However, if these solutions prove successful, drones may find new applications in various fields, including delivery and monitoring. Despite of obstacles like battery life problems and practical implementation issues with charging stations, which might alter drone operations in the future with better recharging techniques and greater performances, work is still ongoing. Over time, these recharging station technologies improved. Intelligent features cut down on downtime. These stations also provide maintenance during non-use periods for batteries. It is a significant advancement for UAVs. Initial costs increase, but there may be benefits. Another major advantage is the charging station coil will be charged through solar power.
A Smart Irrigation System for A New Era using Satellite Weather Data and IOT (Internet of Things) Technology Divyaprasath R, Jithendar Singh S, Raghavendra G, Rajarajan S 2025 International Conference on Computing and Communication Technologies Iccct 2025, 2025 The Smart Irrigation System aligns to optimize water use in agrarian fields, grounded on the use of IoT technology along with real-time data relating to the terrain and algorithmic machine literacy. Hence, it uses the data from soil, temperature, moisture, and rain detectors in real-time to stoutly decide the schedules of irrigation for crops, therefore reducing cases of water destruction and adding crop yield. This design uses the Support Vector Regression model to prognosticate the exact quantum of water shops will need with this operation into rainfall conditions and soil, and its armature contains major factors similar as a microcontroller, detectors, Wi-Fi module, pall garcon, and selectors controlling water inflow, allowing remote irrigation for the planter with an app while conserving coffers. The effectiveness of water use and crop product has shown enhancement in tests, therefore validating its scalability to different agrarian operations.
A Comparative Analysis of Various Mppt Techniques Coupled With DC-DC Converters Feed With BLDC Motor in Photovoltaic Systems S. Rajarajan, R. Dhanasekar, S. Dhanushkumar, D. Somesh, G. Aswin, S. Santhoshkumar Proceedings of 2025 International Conference on Signal Processing Computation Electronics Power and Telecommunication Iconscept 2025, 2025 As energy needs grow steadily worldwide, the demand for non-polluting and sustainable energy resources is becoming more important. Solar power is an effective renewable solution since it is plentiful, pollution -free, and sustainable over the long run. Improving system efficiency requires efficient power conversion techniques and suitable DC-DC converter units. This paper performs a comparative performance study of the Perturb and Observe (P&O) and Incremental Conductance (INC) MPPT algorithms applied to two DC-DC converter topologies, Quadratic Boost Converter (QBC) and Conventional Boost Converter in a PV based system supplying a BLDC motor. MATLAB/Simulink is used to simulate the proposed system with a BLDC motor load, and performance parameters such as efficiency, voltage ripple, MPPT response time and motor speed stability are analyzed.
IoT-Powered Predictive Analytics for Effective Asthma Exacerbation Management Using Linear Regression Models S. Rajarajan, Jaya Krishna A. P, Arun R, Radhika M, Meenakshi R, C. Srinivasan Proceedings of the 2025 11th International Conference on Communication and Signal Processing Iccsp 2025, 2025 To improve patient outcomes and protect healthcare costs, it is essential to treat asthma exacerbations effectively. This research aims to understand better how to treat asthma exacerbation using the Internet of Things (IoT) and predictive analytics. It presents a system that uses IoT sensors to track vital signs like heart rate, breathing rate, temperature, humidity, and air quality in real-time. Linear regression (LR) models process the data to foretell when an exacerbation might happen. The prediction model's ability to recognize patterns and connections between asthma symptoms and environmental factors makes personalized treatment and prompt interventions possible. The findings show that exacerbation predictions are much more accurate when IoT and predictive analytics are combined, which allows for proactive management techniques. Healthcare practitioners are equipped with actionable information for improved asthma control, and this strategy also enhances patient safety. According to the research, IoT predictive analytics can revolutionize asthma treatment by facilitating data-driven decisions.
Design and Analysis of Metamaterial Absorber Featuring Split Ring Resonators for Multiband Absorption S Rajarajan, S Anbu, R Vijayram, K Gokul 2024 International Conference on Communication Computing and Internet of Things Ic3iot 2024 Proceedings, 2024 An absorber designed using metamaterial with wideband absorption is presented in this paper. The absorber unit cell comprises two circle-shaped split ring resonators enclosed within a square-shaped Split Ring Resonator (SRR). Multiband absorbance is present in the proposed structure with peak values of 98.5, 99.7, 98.9, 98.5, and 99.1% at 5.3, 7.9, 10.4, 16.2, and 17.7 GHz respectively. In addition to the current density at the resonator surface, the current density in the dielectric is also calculated.
IoT in Brain-Computer Interfaces for Enabling Communication and Control for the Disabled S. Rajarajan, T. Kowsalya, Nukala Sujata Gupta, P M Suresh, P Ilampiray, S. Murugan Proceedings of the 2024 10th International Conference on Communication and Signal Processing Iccsp 2024, 2024 The proposed system integrates Internet of Things (IoT) technologies with Brain-computer interfaces to improve disability-related communication and control. BCIs may directly communicate between the brain and external equipment, providing a lifeline for persons with severe physical restrictions. Incorporating IoT concepts may boost BCI efficacy. Integration of BCI with IoT technology demonstrates unique advantages. BCIs may be connected to the IoT framework to provide a more flexible and comprehensive communication and control environment. Thanks to IoT connection, BCIs can seamlessly interface with assistive devices, home automation systems, wearables, and digital platforms. Interconnectedness expands BCIs’ reach, improves user experiences, and allows creative applications. It shows how IoT-enabled BCIs may help disabled people connect with their surroundings, enhance their quality of life, and recover independence. The study discusses data security, privacy, latency, and device compatibility difficulties while integrating various technologies. This combination promises immediate practicality and future progress in both sectors, creating a more comprehensive and accessible digital world.
IoT-Enabled Respiratory Pattern Monitoring in Critical Care: A Real-Time Recurrent Neural Network Approach S. Rajarajan, R. Kalaivani, N. Kaliammal, S.T. Saravanan, P Ilampiray, C. Srinivasan Proceedings of the 2024 10th International Conference on Communication and Signal Processing Iccsp 2024, 2024 Critical in intensive care units (ICUs), monitoring respiratory patterns is essential for diagnosing respiratory distress early and treating patients with severe illnesses properly. Conventional monitoring techniques may not be able to capture all aspects of respiratory dynamics in real-time. This research presents a real-time recurrent neural network (RNN) respiratory pattern monitoring system that can be integrated with the Internet of Things (IoT). Tidal volume, respiratory rate, and inspiratory and expiratory flow patterns are just a few of the respiratory characteristics that may be remotely monitored using our system IoT sensors that continuously gather data. An RNN model learns patterns in the time series of respiratory signals and gives instantaneous feedback on the patient’s condition based on the processed data. It demonstrates that our approach successfully detects aberrant breathing patterns by evaluating its performance using data obtained from critical care patients. Compared to more conventional ways of monitoring, the proposed system has several benefits, such as the ability to identify respiratory problems early on, analyze data in real time, and provide continuous monitoring. Improving patient outcomes and quality of treatment in critical care settings may potentially provide immediate insights into respiratory dynamics.
Cloud-Based Machine Learning for Voltage and Frequency Control in Transactive Energy Markets P. Sathyanathan, S. Manikandan, D. Chandrakala, V R Rajan, Bhuvaneswari Arunagiri, S. Rajarajan Proceedings of the 2024 10th International Conference on Communication and Signal Processing Iccsp 2024, 2024 To improve transactive energy market voltage and frequency control management using Cloud-Based Machine Learning methods. Because of its superior capacity to learn complicated relationships over time, the Long Short-Term Memory (LSTM) algorithm was chosen to represent the predicted dynamics of the energy market. To better manage the massive volumes of data in energy markets, the research proposes migrating to a cloud-based architecture, tapping into the processing power of distributed systems. The suggested method solves today's problems with voltage and frequency regulation and looks forward to what will be required by tomorrow's advanced energy infrastructures. To contribute to the establishment of smarter and more adaptable energy networks. To enhance grid stability and responsiveness by combining innovative machine learning techniques and cloud computing. It provides useful information on how Cloud-Based machine learning may help the long-term development of transactive energy markets. It may benefit from improved voltage and frequency management using LSTM in the cloud, which leads to a 20% decrease in deviations and increased grid stability, efficiency, and dependability, contributing to more environmentally friendly power distribution.
Enhancing Cloud Security: A Deep Cryptographic Analysis Ashish Govindrao Deshpande, C. Srinivasan, Ramakrishnan Raman, S. Rajarajan, Rachit Adhvaryu 2023 International Conference on Advances in Computation Communication and Information Technology Icaiccit 2023, 2023
On board communication subsystem for Sathyabama University nano-satellite B. Sheela Rani, E. Logashanmugam, S. Rajarajan, M. Sugadev, G. Jegan, N. Jagadhish Kumar, N. Jeevan Kumar Proceedings of the International Conference on Recent Advances in Space Technology Services and Climate Change 2010 Rsts and Cc 2010, 2010
