Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

Chapter 1 Convolutional Neural Networks for Efficient Indoor Navigation with Smartphones

Chapter 2 An End-to-End Embedded Neural Architecture Search and Model Compression Framework for Healthcare Applications and Use-Cases

Chapter 3 Robust Machine Learning for Low-Power Wearable Devices: Challenges and Opportunities

Chapter 5 Edge-Centric Optimization of Multi-modal ML-Driven eHealth Applications

Chapter 6 A Survey of Embedded Machine Learning for Smart and Sustainable Healthcare Applications

Chapter 7 Reinforcement Learning for Energy-Efficient Cloud Offloading of Mobile Embedded Applications

Chapter 8 Context-Aware Adaptive Anomaly Detection in IoT Systems

Chapter 9 Machine Learning Components for Autonomous Navigation Systems

Chapter 10 Machine Learning for Efficient Perception in Automotive Cyber-Physical Systems

Chapter 11 Machine Learning for Anomaly Detection in Automotive Cyber-Physical Systems

Chapter 12 MELETI: A Machine-Learning-Based Embedded System Architecture for Infrastructure Inspection with UAVs

Chapter 13 On the Vulnerability of Deep Reinforcement Learning to Backdoor Attacks in Autonomous Vehicles

Chapter 14 Secure Indoor Localization on Embedded Devices with Machine Learning

Chapter 15 Considering the Impact of Noise on Machine Learning Accuracy

Chapter 16 Mitigating Backdoor Attacks on Deep Neural Networks

Chapter 17 Robustness for Embedded Machine Learning Using In-Memory Computing

Chapter 18 Adversarial ML for DNNs, CapsNets, and SNNs at the Edge

Chapter 19 On the Challenge of Hardware Errors, Adversarial Attacks and Privacy Leakage for Embedded Machine Learning

Chapter 20 A Systematic Evaluation of Backdoor Attacks in Various Domains

Chapter 21 Deep Learning Reliability: Towards Mitigating Reliability Threats in Deep Learning Systems by Exploiting Intrinsic Characteristics of DNNs