Computer Vision and Pattern Recognition
• 3D imaging from multi-view and sensors
• 3D imaging from single images
• Adversarial attack and defense mechanisms
• Biometrics and Computational Imaging
• Computer vision for societal good
• Computer vision theory
• Datasets and evaluation
• Machine learning, Deep learning architectures, and techniques
• Document analysis and understanding
• Efficient and scalable vision
• Embodied vision: Active agents, simulation
• Event-based cameras and Explainable computer vision
• Face, body, pose, gesture, and movement detection
• Image and video synthesis and generation, and Low-level vision
• Medical imaging and biological vision, cell microscopy
• Multimodal learning and Optimization methods
• Photogrammetry and remote sensing, Physics-based vision and shape-from-X
• Categorization, detection, retrieval, and Representation learning
• Computer Vision for Robotics
• Understanding of Scene Analysis
• Segmentation, grouping, and shape analysis
• Self-, semi-, meta-, and unsupervised learning
• Transfer learning, low-shot learning, continual, and long-tail learning
• Transparency, fairness, accountability, privacy, and ethics in vision
• Action and event understanding, Low-level analysis, motion, and tracking
• Vision + graphics, Vision, language, and reasoning
• Vision applications, systems, and services
5G and Beyond Wireless Communications Technologies
• 5G and 6G Technologies
• Cell-free Networks
• Cloud-RAN, Programmable RAN
• Ultra Large Cell Technologies for 5G and beyond 5G networks
• 5G and Beyond Small Cell Technologies
• Network Slicing and Multi-service Architectures
• Cloud-based 5G and Beyond Mobile Architectures
• Network Function Virtualization (NFV)
• Software Defined Networking (SDN) for 5G and beyond 5G networks
• Rdio Resource Management
• Millimeter-wave Communications, Massive MIMO Communications, and Adaptive Beamforming Techniques
• Free Space Optical for 5G and beyond 5G networks
• Multicast/Broadcast and Convergence of RAN and Core Network
• Mobility Management and Multi-Connectivity/RAT
• Network, Relay, and Cloud-Computing Resource Management Techniques
• Device-to-Device Communications and Networking
• Energy-Efficient Network Design and Protocols for 5G and Beyond
• QoE/QoS Dynamic QoS Framework for 5G and Beyond
• Disruptive Use Cases
• Network and Protocol Interoperability in 5G and Beyond Wireless Networks
• Machine Learning and Adaptive Techniques for 5G and Beyond
• Ultra-reliability and Low-latency communications
• Terahertz for Future Networks
• Digital Twins of Complex Systems with 5G & Future Networks
• Tactile Internet
Future Network Applications and Services
• Smart Cities, Smart Public Places, Smart Home
• Smart Agriculture and Water Management
• Cyber-physical systems
• Collaborative Applications and Systems
• Service Experiences and Analysis
• Cloud Services with 5G & Future Networks
• Future Generation Consumer Electronics with 5G & Future Networks
• Rural Services and Production
• Wireless Networks for Body Sensors
• Crowd-sensing, Human-centric Sensing, Ambient Intelligence
• Context-aware, Situation-aware, Social-aware 5G and beyond Networks
• Industry of the Future, Semantic Technologies, Collective Intelligence
• Cognitive and Reasoning about Things and Smart Objects
• Open Communities, Open API, and Open Source
Artificial Intelligence in Networking and Communications
• AI/ML-based physical layer technologies for B5G and 6G
• Beamforming in a massive MIMO system based on AI/ML
• AI/ML-based non-orthogonal multiple access (NOMA) techniques
• AI/ML-aided Channel modeling
• AI/ML in network design and planning
• AI/ML for coverage and capacity optimization
• AI/ML-based network load balancing and traffic steering
• Intelligent network slicing
• AI/ML for network deployment automation
• AI/ML for service quality assurance and improvement
• AI/ML self-driving networks
• AI/ML for network energy saving and efficiency improvement
• Reinforce Learning for Autonomous Networks and Federated Learning in Networking
• Artificial intelligence-generated content (AIGC) for wireless security
• Large language model (LLM) for wireless security
• Machine learning/deep learning-driven device identification using radio frequency fingerprint, Physical layer channel features, and network traffic features
• Deep learning enhanced physical layer security
• Deep learning-enhanced RF security
• Adversarial machine learning in wireless communications, including adversarial erosion attacks, poisoning attacks, and Trojan/backdoor attacks
• Defensive and anticipatory aspects of adversarial machine learning in wireless communications
• AI/ML for Security and privacy of deep learning-based wireless sensing
• AI/ML for Intrusion and anomaly detection for wireless networks
Intelligent Transport and Vertical Applications
• Aerospace and Defense Communications
• Smart Grid, Energy, Utilities Management and Operation
• Consumer Electronics and Rural Services
• Mining, Oil & Gas, Digital Oilfield, Agriculture, Hospitality, Retailing
• Large Event Management, Industrial Service Creation, and Management
• Highway, Rail Systems
• Financial Services, Media & Entertainment
• E-Health and Mobile Health over 5G and Beyond Networks, and Assisted Living
• Operation Automation and Building Management
• Environmental Monitoring, Connected Car, Automotive
Internet of Things
• IoT technologies for energy monitoring, efficiency, harvesting, etc.
• IoT Architecture with embedded AI
• AI for IoT edge computing
• Low-power AI for IoT and Distributed AI for IoT
• IoT with SDGs (Sustainable Development Goals)
• Intelligent Transportation Systems
• Big Data and Information Integrity in IoT
• Non-Terrestrial Networks for IoT/AI
• Beyond 5G, 6G technologies for IoT/AI
• Digital Twins in IoT applications
• Cryptography, Key Management, Authentication, and Authorization for IoT
• Biometrics Applications in Enhancing IoT Security and Privacy
• Blockchain for Securing 6G-enabled IoT-based Applications
• Security Awareness and Effective Training Approaches in IoT
• Applying Machine Learning Techniques in IoT Security
• Blockchain and Distributed Ledger Technology for IoT Security and Privacy
• Blockchain-based Security and Privacy in Resilient IoT-enabled 5G and Beyond
• Strategies for Proactive Cybersecurity Incident Prevention and Response in IoT
• Edge Computing and Intelligence in AI and IoT
• Machine Learning for IoT Applications
• Mobile deployment of Large Language Models (LLMs)
• LLMs for AIoT applications
• AI and IoT Solutions for Smart Cities
• Security and Privacy in AI-driven IoT Systems
• 5G and Its Impact on AI and IoT
• Human-Machine Interaction in IoT Environments
• IoT Sensors and Actuators: Innovations and Advances
• AI-driven Predictive Maintenance in IoT
• Energy-Efficient AI Algorithms for IoT Devices
• IoT in Healthcare: Applications and Challenges
• Industrial IoT (IIoT) and AI for Manufacturing
• AI and IoT in Precision Farming
• Ethical Considerations in AI-powered IoT Systems
• IoT Standards and Interoperability
• Robotic Process Automation (RPA) in IoT
• AI-driven Automation in Supply Chain Management
• IoT Analytics and Big Data Processing
• AI in Edge Devices: Challenges and Solutions
• Wireless Sensor Networks in AI and IoT
• IoT for Environmental Monitoring and Sustainability
• AI and IoT in Transportation and Logistics
• Cross-domain Integration of AI and IoT Technologies
Networking and Communications Technologies for Smart Agriculture
• Embedded Systems Solutions and Pervasive Computing for Smart Agriculture.
• Artificial intelligence in Smart Agriculture.
• Communications and Networking Technologies to enable Smart Agriculture.
• Novel systems, Models, Solutions, and Applications to minimize CO2 emissions.
• Technologies and Applications to assist in Agricultural Productivity and Resilience to Climate Change.
• Technologies and Applications for a sustainable Agrifood chain.
• Technologies and Applications to preserve soil, water, and biodiversity and to Sustain Environmental Protection.