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Available Research Projects
Showing 164 projects
CBEAEE002
Aerospace Engineering
Coimbatore
Autonomous Systems and Intelligent Control
This research aims to develop a machine learning-based guidance and fault-tolerant control framework for autonomous unmanned systems. The study focuses on intelligent fault diagnosis, adaptive guidance, and robust control strategies using multi-sensor data. The proposed framework will improve system reliability, safety, and autonomous operation under uncertainties, disturbances, and component failures.
Project Description
This research aims to develop a machine learning-based guidance and fault-tolerant control framework for autonomous unmanned systems. The study focuses on intelligent fault diagnosis, adaptive guidance, and robust control strategies using multi-sensor data. The proposed framework will improve system reliability, safety, and autonomous operation under uncertainties, disturbances, and component failures.
multidisciplinary design optimization
Evolving quicker and cheaper design optimization framework for design of aircraft, at different levels. Involves Probability/statistics, mechanics and optimization
Project Description
Evolving quicker and cheaper design optimization framework for design of aircraft, at different levels. Involves Probability/statistics, mechanics and optimization
Physics of Intelligent Machines
This project investigates the principles of swarm intelligence in living systems, exploring how simple interactions among individuals give rise to collective decision-making, adaptation, and resilient behavior. Inspired by bird flocks, fish schools, and biological swarms, the project aims to uncover the mechanisms underlying emergent natural intelligence without centralized control.
Project Description
This project investigates the principles of swarm intelligence in living systems, exploring how simple interactions among individuals give rise to collective decision-making, adaptation, and resilient behavior. Inspired by bird flocks, fish schools, and biological swarms, the project aims to uncover the mechanisms underlying emergent natural intelligence without centralized control.
Multimodal AI for Audio, Speech, Vision, Biomedical & IoT Systems
Research interests include Artificial Intelligence, deep learning, multimodal learning, audio and speech processing, computer vision, biomedical signal processing, IoT, and embedded intelligence. The focus is on developing innovative, explainable, and human-centric AI solutions for healthcare and intelligent real-world applications.
Multimodal AI for Audio, Speech, Vision, Biomedical & IoT Systems
Project Description
Research interests include Artificial Intelligence, deep learning, multimodal learning, audio and speech processing, computer vision, biomedical signal processing, IoT, and embedded intelligence. The focus is on developing innovative, explainable, and human-centric AI solutions for healthcare and intelligent real-world applications.
healthcare and explainability
Computational intelligence methods are employed to analyze biomedical signals, medical images, and multimodal healthcare data for developing trustworthy and interpretable healthcare technologies. Research focuses on explainable AI, brain-computer interfaces, physiological signal analysis, multimodal learning, and intelligent decision-support systems for clinically relevant applications.
Project Description
Computational intelligence methods are employed to analyze biomedical signals, medical images, and multimodal healthcare data for developing trustworthy and interpretable healthcare technologies. Research focuses on explainable AI, brain-computer interfaces, physiological signal analysis, multimodal learning, and intelligent decision-support systems for clinically relevant applications.
Sustainable high-performance hybrid composite
The proposed research is aimed at developing a sustainable, high-performance hybrid composite reinforced with granite nanoparticles, specifically tailored for defence and aerospace applications
Project Description
The proposed research is aimed at developing a sustainable, high-performance hybrid composite reinforced with granite nanoparticles, specifically tailored for defence and aerospace applications
Hybrid Joining Techniques
The project will deliver an optimized progressive notch-based dissimilar weld joint design with reduced stress concentration and residual stress through AI-based optimization of notch geometry and welding parameters.
Project Description
The project will deliver an optimized progressive notch-based dissimilar weld joint design with reduced stress concentration and residual stress through AI-based optimization of notch geometry and welding parameters.
AI Security and Trustworthy AI, Evolutionary Computing, Distributed Intelligent Systems, Adversarial Machine Learning, Federated Learning, Cyber Security
The proposed research seeks to strengthen the security and resilience of federated learning systems against data poisoning attacks. Through the use of evolutionary algorithms to model diverse adversarial behaviors, the work aims to develop robust defense strategies that improve model integrity, trustworthiness, and privacy preservation in distributed AI ecosystems.
AI Security and Trustworthy AI, Evolutionary Computing, Distributed Intelligent Systems, Adversarial Machine Learning, Federated Learning, Cyber Security
Project Description
The proposed research seeks to strengthen the security and resilience of federated learning systems against data poisoning attacks. Through the use of evolutionary algorithms to model diverse adversarial behaviors, the work aims to develop robust defense strategies that improve model integrity, trustworthiness, and privacy preservation in distributed AI ecosystems.
Computer Vision and Intelligent Surveillance
This research explores multimodal AI approaches that combine visual and audio data for anomaly detection. By incorporating explainability into advanced deep learning models, the study seeks to enhance transparency, reliability, and decision-making in intelligent surveillance and monitoring systems operating in complex environments.
Project Description
This research explores multimodal AI approaches that combine visual and audio data for anomaly detection. By incorporating explainability into advanced deep learning models, the study seeks to enhance transparency, reliability, and decision-making in intelligent surveillance and monitoring systems operating in complex environments.
Computational Intelligence for Health and Environmental Systems
Adaptive Intelligence Research focuses on the development of advanced computational intelligence methodologies to address complex challenges in human health, environmental sustainability, and smart systems. The research integrates Machine Learning, Artificial Intelligence, Time Series Analytics, Optimization, Prescriptive Analytics, and Generative AI to build intelligent systems capable of learning, adapting, and making informed decisions in dynamic and uncertain environments. A key objective is to bridge predictive intelligence with actionable decision support, enabling systems not only to forecast future outcomes but also to recommend optimal strategies for achieving desired goals.
Computational Intelligence for Health and Environmental Systems
Project Description
Adaptive Intelligence Research focuses on the development of advanced computational intelligence methodologies to address complex challenges in human health, environmental sustainability, and smart systems. The research integrates Machine Learning, Artificial Intelligence, Time Series Analytics, Optimization, Prescriptive Analytics, and Generative AI to build intelligent systems capable of learning, adapting, and making informed decisions in dynamic and uncertain environments. A key objective is to bridge predictive intelligence with actionable decision support, enabling systems not only to forecast future outcomes but also to recommend optimal strategies for achieving desired goals.
Predictive Analytics, Precision Agriculture
Design and implementation of an AI-driven predictive analytics framework that leverages multi-source agricultural and climatic data to forecast crop-specific climate risks. The system will enable early risk detection, informed decision-making, and targeted interventions to maximize yield and reduce crop losses.
Project Description
Design and implementation of an AI-driven predictive analytics framework that leverages multi-source agricultural and climatic data to forecast crop-specific climate risks. The system will enable early risk detection, informed decision-making, and targeted interventions to maximize yield and reduce crop losses.
Remote Sensing
Research focuses on Machine Learning, Deep Learning, and AI for Multi-Modal and Spatio-Temporal Data Analysis, integrating Explainable AI, Computer Vision, and Forecasting across diverse real-world applications
Project Description
Research focuses on Machine Learning, Deep Learning, and AI for Multi-Modal and Spatio-Temporal Data Analysis, integrating Explainable AI, Computer Vision, and Forecasting across diverse real-world applications
Sustainable Materials in Geotechnical Applications
1. Geotechnical Earthquake Engineering & Disaster Resilience: Research focuses on the seismic performance of geotechnical systems under extreme loading conditions. It involves evaluating the stability of slopes and retaining structures through analytical, numerical, and experimental approaches to develop resilient and innovative design methodologies for infrastructure in disaster-prone regions. 2. Sustainable Ground Improvement & Soil Stabilization: Research addresses the improvement of soft and weak soils using sustainable mechanical and chemical stabilization techniques. It emphasizes eco-friendly materials to enhance soil performance and develop efficient, durable, and sustainable ground improvement solutions for challenging geotechnical conditions.
Sustainable Materials in Geotechnical Applications
Project Description
Area: Sustainable Ground Improvement & Soil Stabilization
Opportunities are also available in soft soil improvement and ground stabilization using sustainable materials. This includes enhancing the engineering behaviour of weak and compressible soils through mechanical and chemical stabilization techniques, along with eco-friendly materials. The objective of this research is to develop efficient and sustainable ground improvement solutions for challenging soil conditions.
Intelligent Decision Support Systems
The proposed framework can assist healthcare organizations, environmental monitoring agencies, and government authorities in outbreak prediction, pollution management, resource allocation, and policy formulation by providing accurate spatial-temporal insights and data-driven forecasting capabilities.
Project Description
The system supports multidimensional data preprocessing, feature extraction, normalization, and correlation analysis for both healthcare and environmental datasets. Temporal and geospatial relationships are modeled to identify high-risk disease zones and pollution hotspots. The integration of clustering and deep learning techniques enables scalable predictive analytics capable of supporting real-time monitoring and intelligent decision support.
Edge AI and IoT for Smart Healthcare Systems
Development of an IoT and embedded system-based healthcare monitoring platform for real-time physiological signal monitoring and edge-level analysis using lightweight AI techniques, wireless communication, and low-power hardware. The research focuses on remote healthcare, anomaly detection, and continuous patient monitoring applications.
Project Description
This research focuses on developing an intelligent healthcare monitoring system using IoT and embedded technologies for continuous monitoring of physiological parameters such as ECG, heart rate, body temperature, and motion activity. The objective is to design a low-power and practical system capable of performing real-time monitoring and preliminary analysis directly at the edge device level.
The work involves sensor interfacing, embedded firmware development, wireless communication, and lightweight machine learning techniques for health condition assessment and anomaly detection. The proposed system can be applied in areas such as remote patient monitoring, elderly care, athlete monitoring, and emergency health assistance.
The research also explores the use of long-range communication technologies such as LoRaWAN for reliable healthcare data transmission in remote areas. Cloud integration and dashboard visualization may be incorporated for long-term data storage and analysis. The overall aim is to build a scalable, energy-efficient, and cost-effective healthcare monitoring platform suitable for real-world deployment.
Artificial Intelligence and Computer Vision for Intelligent Surveillance Systems
Research focused on developing robust Person Re-Identification systems for intelligent surveillance applications. The project aims to improve identity matching across multiple cameras under real-world variations such as accessory changes, occlusion, and viewpoint differences using deep learning and attention-based methods.
Artificial Intelligence and Computer Vision for Intelligent Surveillance Systems
Project Description
“Research project focused on developing robust Person Re-Identification systems for smart surveillance environments. The work aims to improve identity matching across multiple cameras under real-world appearance variations such as accessory changes, occlusions, and viewpoint differences using deep learning and attention-based techniques.”
