Conference Objective:

The International Conference on Intelligent Systems and Embedded Design (ISED) serves as a premier platform for researchers, academics, professionals, and industry experts to converge and exchange knowledge, ideas, and experiences in intelligent systems and embedded design. The 12th edition (ISED-2024) aims to foster collaborative endeavors and unveil cutting-edge research that can further drive innovation and development in the interdisciplinary domains of high-performance/low-power circuits, algorithms, electronics, embedded systems, AI chips, and SoC technology, etc. Developments in these domains will have a significant impact on the future electronic system design and advanced technologies focusing on being user-friendly, eco-sensitive, innovative, and energy efficient. The conference would enable fruitful discussions between experts and other delegates leading to concrete contributions towards advancing the state of the art. 

Original, unpublished research papers are solicited from researchers and practitioners from academia, industry, and the government in areas of interest in, but not limited to, the stated conference tracks.

All papers accepted by ISED-2024 will be submitted for inclusion into indexed proceedings, provided at least one of the authors of each paper registers for the conference and presents the paper.
Full Registration (not student registration) is required for each paper to be included in the presentation. In the case of multiple authors, at least one author is expected to register. Registration fee includes Proceedings CD, Conference Kit, Lunch and Tea during Conference, Conference Banquet, and Registration for Tutorials.
Original, unpublished research papers are solicited from industrial and academic researchers in any topics listed below. Topics of interest include but are not limited to the following conference tracks.
Conference Tracks:

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



Proceedings: Accepted papers will be submitted for inclusion in IEEE Xplore subject to meeting IEEE Xplore's scope and quality requirements.
Important Dates:
Full Paper Submission: 
        15th August, 2024
Paper Acceptance Notification: 
16th Oct 2024
Camera-Ready submission: 
16th Nov 2024
20th – 22nd Dec 2024

NIT Rourkela is hosting this 12th edition of the conference.