Edge Analytics Lab Book

Interactive Companion Resources

Author

Charith Perera (Editor)

Published

January 1, 2026

Download the Complete Book

Download PDF Book (35 MB)

The complete Edge Analytics Lab Book is available as a downloadable PDF with all 18 lab chapters, detailed theory, code examples, and exercises.

Welcome

This companion website provides interactive resources to supplement the Edge Analytics Lab Book. Here you’ll find:

  • Interactive Notebooks: Run code examples directly in your browser
  • Simulations: Visualize key concepts like gradient descent, quantization, and object detection
  • Three-Tier Exercises: Structured hands-on activities for every skill level
  • Reference Materials: Appendices on math, signal processing, and cryptography

Quick Navigation

Resource Description
Lab Notebooks Jupyter notebooks for all 18 labs
Simulations Interactive visualizations
Level 1 Exercises Notebook-based exercises
Level 2 Exercises Simulator exercises
Level 3 Exercises Device deployment exercises

Book Contents at a Glance

flowchart LR
    subgraph Part1["Part I: Foundations"]
        LAB01[LAB01: Introduction]
        LAB02[LAB02: ML Foundations]
        LAB03[LAB03: Quantization]
    end

    subgraph Part2["Part II: Core Skills"]
        LAB04[LAB04: Keyword Spotting]
        LAB05[LAB05: Deployment]
        LAB06[LAB06: Security]
        LAB07[LAB07: CNNs]
    end

    subgraph Part3["Part III: Hardware"]
        LAB08[LAB08: Arduino]
        LAB09[LAB09: ESP32]
        LAB10[LAB10: EMG]
    end

    subgraph Part4["Part IV: Systems"]
        LAB11[LAB11: Profiling]
        LAB12[LAB12: Streaming]
        LAB13[LAB13: Distributed]
    end

    subgraph Part5["Part V: Advanced"]
        LAB14[LAB14: Anomaly]
        LAB15[LAB15: Energy]
        LAB16[LAB16: YOLO]
        LAB17[LAB17: Federated]
        LAB18[LAB18: On-Device]
    end

    Part1 --> Part2 --> Part3 --> Part4 --> Part5

Three-Tier Learning Model

Every lab supports three levels of hands-on experience:

Run code in Google Colab or Jupyter without any hardware. Perfect for learning concepts.

Requirements: Web browser, Google account (for Colab)

Use Wokwi, TinkerCAD, or custom web simulations to practice with virtual hardware.

Requirements: Web browser

Deploy models to real hardware: Arduino Nano 33 BLE Sense, ESP32, Raspberry Pi.

Requirements: Physical hardware (see Hardware Guide)

Getting Started

  1. Download the PDF Book for comprehensive theory and code
  2. Browse Lab Notebooks to run examples
  3. Try Interactive Simulations to visualize concepts
  4. Work through Exercises at your skill level

Prerequisites

Before starting, ensure you have:

  • Basic Python programming knowledge
  • Familiarity with machine learning concepts (helpful but not required)
  • Access to Google Colab (free) or local Jupyter installation

For device-level exercises, see the Hardware Guide.

Acknowledgements

This lab book is a joint collaboration between Cardiff University (UK), Indian Institute of Technology, Ropar (India), and Indian Institute of Information Technology, Kottayam (India), funded by the British Council Going Global India Exploratory Grants Program.

     

Version: 1.0.0 | Release Date: January 2026 | Editor: Charith Perera