The book presents some key topics and early thinking from the EdgeAI project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs.
The book presents some key topics and early thinking from the EdgeAI project, covering data backhaul technologies, lifecycle management, mechanisms for developing AIs at the edge and techniques for interacting with those AIs.
Dr. Ovidiu Vermesan holds a Ph.D. degree in microelectronics and a Master of International Business (MIB) degree. He is Chief Scientist at SINTEF Digital, Oslo, Norway. His research interests are in the area of mixed-signal embedded electronics and cognitive communication systems. Dr. Vermesan received SINTEF's 2003 award for research excellence for his work on the implementation of a biometric sensor system. He is currently working on projects addressing nano-electronics, integrated sensor/actuator systems, communication, cyber-physical systems and the IoT, with applications in green mobility, energy, autonomous systems and smart cities. He has authored or co-authored over 85 technical articles and conference papers. He is actively involved in the activities of the Electronic Components and Systems for European Leadership (ECSEL) Joint Technology Initiative (JTI). He has coordinated and managed various national, EU and other international projects related to integrated electronics. Dr. Vermesan actively participates in national, H2020 EU and other international initiatives by coordinating and managing various projects. He is the coordinator of the IoT European Research Cluster (IERC) and a member of the board of the Alliance for Internet of Things Innovation (AIOTI).
Inhaltsangabe
1. Edge AI LoRa Mesh Technologies 2. Edge AI Lifecycle Management 3. Federated Learning: Privacy, Security and Hardware Perspectives 4. Inside the AI Accelerators: From High Performance to Energy Efficiency 5. Designing Lightweight CNN for Images: Architectural Components and Techniques 6. Natural Language Conditioned Planning of Complex Robotics Tasks 7. An Overview of the Automated Optical Inspection Edge AI Inference System Solutions 8. Efficient AI-based Attack Detection Methods for Sensitive Edge Devices and Systems 9. Explainability and Interpretability Concepts for Edge AI Systems
1. Edge AI LoRa Mesh Technologies 2. Edge AI Lifecycle Management 3. Federated Learning: Privacy, Security and Hardware Perspectives 4. Inside the AI Accelerators: From High Performance to Energy Efficiency 5. Designing Lightweight CNN for Images: Architectural Components and Techniques 6. Natural Language Conditioned Planning of Complex Robotics Tasks 7. An Overview of the Automated Optical Inspection Edge AI Inference System Solutions 8. Efficient AI-based Attack Detection Methods for Sensitive Edge Devices and Systems 9. Explainability and Interpretability Concepts for Edge AI Systems
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