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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.
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Autorenporträt
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 intelligent systems integration, mixed-signal embedded electronics, analogue neural networks, edge artificial intelligence and cognitive communication systems. Dr. Vermesan received SINTEF's 2003 award for research excellence for his work on implementing a biometric sensor system. He is currently working on projects addressing nanoelectronics, integrated sensor/actuator systems, communication, cyber-physical systems (CPSs) and the Industrial Internet of Things (IIoT), with applications in green mobility, energy, autonomous systems, and smart cities. He has authored or co-authored over 100 technical articles and conference papers. He is actively involved in the activities of the European partnership for Key Digital Technologies (KDT) Joint Undertaking (JU), now the Chips JU. He has coordinated and managed various national, EU and other international projects related to smart sensor systems, integrated electronics, electromobility and intelligent autonomous systems such as E3Car, POLLUX, CASTOR, IoE, MIRANDELA, IoF2020, AUTOPILOT, AutoDrive, ArchitectECA2030, AI4DI and AI4CSM. Dr. Vermesan actively participates in national, Horizon Europe and other international initiatives by coordinating and managing various projects. He is a member of the Alliance for IoT and Edge Computing Innovation (AIOTI) board. He is currently the coordinator of the Edge AI Technologies for Optimised Performance Embedded Processing (EdgeAI) project.
Dr. Dave Marples is Chief Scientist at Technolution in Gouda, NL where he is responsible for research and early-stage technical activities for the company. His primary research interests are in networked embedded systems in a systems engineering context. He regularly participates in EU Horizon and AENEAS research activities and is a member of the XECS Technical Experts Group. He is a member of the steering board of the IEEE Consumer Communications and Networking Conference (CCNC) and is a Fellow of the OSGi for services rendered as architecture chair and executive director. He contributes to open-source projects and is the founder of orbcode.org. He was previously CS of the Internet Architecture Research laboratory at Telcordia (previously Bellcore) in Morristown, NJ where, amongst other things, he was responsible for the Networked Appliances research program. He was co-founder of the IEEE Communications Magazine Special Topic on Networked Appliances which runs today as the Internet of Things special topic. His Ph.D. in Communications Systems is from Strathclyde University, Scotland and his B.Eng. and M.Eng. degrees in Electronics, Communication and Computer Engineering are from Bradford University, England. He is an Industrial Fellow of the Royal Society for the Exhibition of 1851 and lives in the UK with his partner, son and daughter. His hobbies include anything with an engine and clay pigeon missing.
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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