This book is large by design: fourteen parts, seventy-two chapters, and more than four hundred sections that carry you from the physics of a sensor to a deployed, monitored perception system. You are not meant to read all of it in order. This page shows you how to take exactly the path you need, whether you have one semester, one project, or a research question.
Level tags and what to skip
Every chapter and every section carries a level tag. Read the tag before you read the section; it tells you whether the material is a prerequisite or an optional side trip.
| Tag | Level | Role |
|---|---|---|
| [F] | Foundational | Prerequisites developed in-book: signals, sampling, probability, estimation, a deep-learning refresher. |
| [C] | Core | The main method line of each chapter, from intuition to method to math. |
| [A] | Advanced | Deeper treatment, alternatives, and edge cases. Self-contained. |
| [R] | Research frontier | Current state-of-the-art and open problems. Self-contained. |
The rule for a one-semester course is simple: read only the [F] and [C] sections. The [A] and [R] sections are written to be skippable, so nothing in a later core section depends on them. Roughly half of all sections are [A]/[R], so this single rule cuts the reading in half without leaving a gap. When you want depth or the newest results, the advanced and frontier sections are waiting exactly where the topic was introduced.
Reading paths
Each track below names the Parts to read, consistent with the fourteen-part plan. Within any track, apply the level tags: a first course stays on [F]/[C]; a graduate or research track adds [A]/[R].
- First course / undergraduate (one semester): Parts I through IV, plus selected chapters from Part VI, Parts VII/VIII, and Part XIII. Follow [F]/[C].
- Graduate deep-learning-for-sensing: Parts III through V, X, and XIII, at [C]/[A]/[R] depth.
- IoT / edge AI: Parts I, II, IV, VIII, XII, and XIII.
- Robotics / autonomous systems: Parts I, III, IX, X, XI, and XIII.
- Health / wearables: Parts I, II, IV, VII, XII (privacy), and XIII.
- Industrial / cyber-physical: Parts I through III, VIII, XII, and XIII.
- Researcher / frontier survey: Part V end to end, plus every research-frontier box and the frontier chapter in Part XIV.
Tiered labs
Every chapter is hands-on, and there are seventy-two labs on public datasets. They are not equal in weight. About thirty are Core Labs: full, graded, dataset-backed, one per key competency. The Core Labs alone form a complete practical course. The remainder are lighter Mini-Labs, each a focused exercise of about an hour. Instructors pick a lab spine to match their calendar. If your term is short, a shorter-course consolidation guide lists chapters that can be merged or assigned as reading, so the same book supports a seven-week module, a one-semester course, and a two-semester sequence.
Callouts: read deep or skim
Colored callout boxes let you move at two speeds. Skimming for the argument, read the key-insight boxes and section headings alone. Working a topic seriously, stop at every callout. The system is consistent throughout the book:
| Callout | What it gives you |
|---|---|
| Key insight | The one idea to remember from the section. |
| Practical example | A concrete, worked case with real numbers or data. |
| Library shortcut | The production tool or API that does this in a few lines. |
| Warning | A failure mode, bias, or pitfall that bites in practice. |
| Exercise | A level-tagged problem to test understanding. |
| Self-check | A quick question to confirm you followed the argument. |
| Research frontier | Current state-of-the-art models and open questions. |
| Lab | A runnable, dataset-backed practical exercise. |
Throughout, sensors are treated as measurement instruments with bias, noise, drift, and failure modes, not as clean arrays. When a measured value \(y\) relates to a true state \(x\) through a model \(y = h(x) + \varepsilon\), the noise term \(\varepsilon\) is a first-class part of the story, and the callouts keep pointing back to it.
Ready to choose a path? Open the Table of Contents, find your track above, and follow the level tags.