Track 1: Embedded AI and Machine Learning

• Deep learning applications for embedded systems
• Real-time machine learning on edge devices
• Efficient algorithms for embedded AI
• Neural network optimization for resource-constrained devices
• Edge computing and distributed learning in embedded systems
• Hardware accelerators for AI inference on embedded platforms
• Explainable AI for embedded systems
• Federated learning on edge devices
• Transfer learning in resource-constrained environments
• Adaptive learning algorithms for dynamic embedded systems
• Energy-efficient training strategies for embedded AI models
• Benchmarking and performance evaluation of embedded ML models

Track 2: IoT and Sensor Networks

• Integration of intelligent sensors in embedded systems
• Energy-efficient communication protocols for IoT devices
• Security and privacy in embedded IoT networks
• Edge computing for IoT data processing
• Sensor fusion and data analytics in embedded systems
• Wireless sensor networks for smart environments
• Edge-based anomaly detection in IoT networks
• Low-power communication protocols for sensor networks
• Cognitive IoT: Learning and adapting IoT devices
• Edge-based data aggregation and compression techniques
• Blockchain for securing IoT transactions
• Energy harvesting techniques for IoT devices

Track-3: Embedded Systems Security:

• Secure boot and firmware update mechanisms
• Hardware-based security for embedded devices
• Intrusion detection and prevention in embedded systems
• Cryptographic techniques for securing embedded communications
• Trustworthy computing in resource-constrained environments
• Security challenges in the Internet of Things (IoT) devices
• Side-channel attack mitigation in embedded systems
• Post-quantum cryptography for embedded security
• Hardware Trojan detection and prevention
• Secure bootstrapping and attestation in IoT devices
• Biometric authentication in embedded systems
• Security-aware design methodologies for embedded systems

Track 4: Robotics and Autonomous Systems

• Intelligent control systems for robots
• Embedded vision and perception for autonomous robots
• Swarm robotics and collaborative embedded systems
• Human-robot interaction in embedded environments
• Navigation and mapping algorithms for autonomous systems
• Safety and reliability in embedded robotic systems
• Explainable decision-making in autonomous systems
• Human-aware navigation for robots
• Swarm intelligence and optimization in robotic networks
• Embedded systems for medical and healthcare robotics
• Adaptive learning for robotic task optimization
• Ethical considerations in the deployment of autonomous systems

Track 5: Edge Computing and Fog Computing

• Architecture and design of edge/fog computing systems
• Edge-based analytics and decision-making
• Resource management and optimization in edge/fog computing
• Latency-aware applications for edge devices
• Edge intelligence for real-time data processing
• Case studies of successful edge computing implementations
• Edge-based machine learning model deployment strategies
• Adaptive resource allocation in fog computing
• Edge-based data preprocessing and filtering techniques
• Edge/fog computing for real-time video analytics
• Integration of edge computing with cloud services
• Energy-efficient algorithms for edge device communication

Track 6: Emerging Technologies in Embedded Systems

• Quantum computing for embedded applications
• Neuromorphic computing in embedded systems
• Bio-inspired algorithms for embedded devices
• 5G and beyond for embedded communication
• Augmented reality and embedded systems
• Integration of blockchain in intelligent embedded systems
• Edge quantum computing applications
• Neuromorphic hardware design for embedded AI
• Bio-inspired sensor networks and algorithms
• Integration of AI and 5G technologies in embedded systems
• Edge-based augmented reality applications
• Robustness and security considerations in blockchain-enabled embedded systems

Track 7: Drone Technologies and Applications:

• Autonomous navigation and obstacle avoidance for drones
• Real-time embedded vision processing in drone applications
• Energy-efficient algorithms for drone flight control
• Swarming and collaborative behaviors in drone networks
• Embedded systems for aerial mapping and surveying
• Security and privacy considerations in drone communication
• Edge computing for on-board data processing in drones
• AI-based decision-making in autonomous drone missions
• Integration of sensors and actuators for enhanced drone capabilities
• Emerging trends in drone hardware design and miniaturization

Track 8: Medical and Healthcare Embedded Devices

• Embedded systems for remote patient monitoring
• Wearable healthcare devices and biosensors
• Real-time processing of medical imaging data on embedded platforms
• Edge computing for healthcare analytics and decision support
• Secure communication in medical IoT networks
• Assistive technologies and smart prosthetics
• Embedded systems for drug delivery and dosage control
• Biomedical signal processing and analysis in embedded devices
• Patient-centric healthcare applications of embedded technology
• Ethical and regulatory considerations in medical embedded systems

Track 9: Pollution and Environmental Monitoring

• IoT-based air quality monitoring systems
• Water quality sensing and monitoring with embedded devices
• Soil contamination detection using embedded sensors
• Noise pollution monitoring and control strategies
• Integration of drones for environmental surveillance
• Smart city solutions for pollution management
• Real-time data analytics for pollution prediction and control
• Low-power embedded systems for long-term environmental monitoring
• Crowdsourced data collection for pollution mapping
• Climate change and sustainability through embedded technologies

Track 10: Green Technology and Sustainable Embedded Systems

• Energy-efficient embedded systems design and optimization
• Renewable energy sources for powering embedded devices
• Green computing strategies for resource conservation
• Eco-friendly materials and manufacturing processes in embedded systems
• Life cycle assessment of embedded devices and technologies
• Smart grids and energy-aware communication protocols
• Sustainable practices in hardware and software development
• Carbon footprint reduction through intelligent embedded solutions
• Integration of green technology in smart cities
• Environmental and social responsibility in embedded system design