Part I. Foundations of Sensory AI
- Ch 1. What Is Sensory AI? [F]
- Ch 2. Sensor Physics and Measurement Models [F]
- Ch 3. Signals, Sampling, Time, and Synchronization [F]
- Ch 4. Probability, Estimation, and Uncertainty Primer [F]
- Ch 5. Sensor Data Engineering and Leakage-Safe Datasets [F][C]
Part II. Classical Signal Processing and Feature Engineering
- Ch 6. Filtering and Denoising Sensor Signals [F][C]
- Ch 7. Spectral and Time-Frequency Analysis [C]
- Ch 8. Feature Engineering and Dimensionality Reduction [C]
Part III. State Estimation and Classical Inference
- Ch 9. Bayesian Filtering and the Kalman Family [C]
- Ch 10. Nonlinear and Particle Filtering [C][A]
- Ch 11. Factor Graphs and Smoothing [A]
- Ch 12. Classical Anomaly and Change Detection [C]
Part IV. Deep Learning for Sensor Time Series
- Ch 13. Neural Representations for Sensor Streams [C]
- Ch 14. Recurrent and Temporal Convolutional Models [C]
- Ch 15. Transformers for Sensor Data [C][A]
- Ch 16. State-Space Models: Mamba, S4, and Linear-Time Sequences [A][R]
- Ch 17. Self-Supervised and Contrastive Sensor Learning [A][R]
- Ch 18. Uncertainty, Calibration, and Conformal Prediction [A][R]
Part V. Foundation Models and Agentic Sensing
- Ch 19. Time-Series Foundation Models [R]
- Ch 20. Sensor and Wearable Foundation Models [R]
- Ch 21. Multimodal Sensor-Language Models and Sensor Reasoning [R]
- Ch 22. LLM Agents for Sensing and Operations [R]
Part VI. Motion, Location, and Inertial Intelligence
- Ch 23. Inertial Sensors and Motion Signals [C]
- Ch 24. Orientation Estimation and Dead Reckoning [C][A]
- Ch 25. Localization, GNSS, and RF Positioning [C]
- Ch 26. Human Activity and Behavior Recognition [C]
- Ch 27. Gesture, Pose, and Neuromotor Interfaces [C][R]
Part VII. Health, Biosignals, and Wearable AI
- Ch 28. Biosignal Foundations [C]
- Ch 29. ECG and Cardiac AI [C][A]
- Ch 30. PPG and Wearable Cardiovascular Sensing [C][R]
- Ch 31. EEG, Neural Signals, and Brain-Computer Interfaces [A][R]
- Ch 32. EMG and Neuromuscular AI [C][A]
- Ch 33. Continuous and Contactless Health Monitoring [C][R]
- Ch 34. Clinical Validation, Regulation, and Biometric Privacy [C]
Part VIII. Industrial, Energy, and Infrastructure Sensor AI
- Ch 35. Industrial Sensing Systems [C]
- Ch 36. Predictive Maintenance and Prognostics [C][A]
- Ch 37. Condition Monitoring and Anomaly Detection [C][R]
- Ch 38. Cyber-Physical Anomaly Detection and ICS Security [A][R]
- Ch 39. Energy, Buildings, and Environmental Sensors [C]
Part IX. Radar, Lidar, Depth, Thermal, Event, and RF Sensing
- Ch 40. Depth and 3D Sensing Fundamentals [C]
- Ch 41. Monocular Depth Foundation Models [R]
- Ch 42. Point Clouds and Lidar AI [C][A]
- Ch 43. BEV Perception and 3D Occupancy [A][R]
- Ch 44. Radar AI [C][R]
- Ch 45. Thermal, Infrared, and Multispectral Sensing [C]
- Ch 46. Event-Based and Neuromorphic Sensing [A][R]
- Ch 47. RF and Wireless Sensing [A][R]
Part X. Sensor Fusion, World Models, and Spatial AI
- Ch 48. Foundations of Sensor Fusion [C]
- Ch 49. Probabilistic and Bayesian Fusion [C][A]
- Ch 50. Deep Multimodal Fusion and Missing-Modality Robustness [A][R]
- Ch 51. Neural Fields and Gaussian Splatting for Sensing [A][R]
- Ch 52. SLAM and Spatial AI [A][R]
- Ch 53. World Models and Predictive Sensing [R]
- Ch 54. Graph Neural Networks for Sensor Networks [A]
- Ch 55. Digital Twins, Synthetic Data, and Physics-Informed ML [A][R]
Part XI. Tactile, Embodied, and Robotic Sensing
- Ch 56. Tactile Sensing and Electronic Skin [A][R]
- Ch 57. Proprioception, Exteroception, and Robot Perception [A]
- Ch 58. Vision-Language-Action and Embodied Foundation Models [R]
Part XII. Edge, Embedded, Streaming, and Federated Sensor AI
- Ch 59. Edge AI Fundamentals and Model Optimization [C][A]
- Ch 60. Streaming Inference and Online Learning [C][A]
- Ch 61. TinyML and Microcontroller Sensor AI [A]
- Ch 62. On-Device Continual Learning [A][R]
- Ch 63. Batteryless and Intermittent Sensing [R]
- Ch 64. Federated and Privacy-Preserving Sensor AI [A][R]
Part XIII. Trust, Safety, Evaluation, and Operations
- Ch 65. Evaluation Protocols and Leakage-Safe Benchmarking [C]
- Ch 66. Distribution Shift, OOD, and Test-Time Adaptation [A][R]
- Ch 67. Interpretability and Root-Cause Analysis for Time Series [A]
- Ch 68. Robustness, Sensor Spoofing, and Functional Safety [A][R]
- Ch 69. MLOps for Sensor Fleets [C][A]
- Ch 70. Responsible Sensory AI and Regulation [C]