63  Signal Processing Essentials

63.1 Overview

Signal processing bridges the continuous analog world of sensors with the discrete digital world of microcontrollers. This chapter series covers the complete journey from raw sensor signals to clean, usable digital data.

NoteKey Takeaway

In one sentence: Signal processing converts continuous analog sensor signals to discrete digital values through sampling (time discretization) and quantization (amplitude discretization), and getting both right prevents aliasing artifacts and wasted resources.

Remember this rule: Sample at 2.5-5x the highest frequency you care about (not higher), and match ADC resolution to sensor accuracy - thereโ€™s no point having 16-bit precision if your sensor is only accurate to 1%.

63.2 Chapter Contents

This comprehensive topic has been organized into focused chapters:

63.2.1 1. ADC Fundamentals: Sampling and Aliasing

Core concepts for analog-to-digital conversion:

  • Analog vs digital signals and the continuous/discrete divide
  • The Nyquist-Shannon sampling theorem
  • Understanding and preventing aliasing
  • Anti-aliasing filters and their role

Time: ~25 minutes | Level: Foundational to Intermediate

63.2.2 2. Quantization and Digital Filtering

Precision, resolution, and noise reduction:

  • ADC resolution and quantization levels (8-bit to 16-bit)
  • Step size calculations and precision trade-offs
  • Moving average, median, and low-pass filters
  • Choosing the right filter for your application

Time: ~20 minutes | Level: Intermediate

63.2.3 3. Voice Compression for IoT

Audio processing for bandwidth-constrained devices:

  • Toll quality baseline (64 kbps)
  • Companding: ฮผ-law and A-law encoding
  • Linear Predictive Coding (LPC) and source-filter models
  • Codec selection for IoT applications

Time: ~15 minutes | Level: Advanced

63.2.4 4. Sensor Dynamics and Response

Understanding how sensors behave over time:

  • Mass-spring-damper mechanical model
  • Transfer functions and natural frequency
  • Step response: underdamped, critically damped, overdamped
  • Sensor bandwidth and its relationship to sampling

Time: ~15 minutes | Level: Advanced

63.2.5 5. Linearization and Practice

Handling non-linear sensors and hands-on lab:

  • Taylor series linearization
  • Lookup tables and piecewise approximation
  • Knowledge check scenarios
  • ESP32 Wokwi signal processing lab

Time: ~30 minutes | Level: Intermediate to Advanced

63.3 Learning Objectives

By completing this chapter series, you will be able to:

  • Distinguish Analog from Digital: Understand why sensors produce analog signals and why processors need digital data
  • Apply Sampling Concepts: Explain the Nyquist theorem and calculate appropriate sampling rates
  • Understand Quantization: Calculate ADC resolution and its impact on measurement accuracy
  • Recognize Aliasing: Identify when sampling is too slow and how to prevent signal distortion
  • Select Appropriate Filters: Choose between low-pass, high-pass, and moving average filters for noise reduction
  • Configure ADC Parameters: Set up analog-to-digital conversion for real IoT sensor applications
  • Understand Voice Compression: Apply companding and LPC principles to reduce audio bandwidth
  • Analyze Sensor Dynamics: Understand transfer functions and step response behavior
  • Apply Linearization Techniques: Use Taylor series, lookup tables, and piecewise methods for non-linear sensors

63.4 Prerequisites

Before diving into these chapters, you should be familiar with:

63.6 Whatโ€™s Next

Start with ADC Fundamentals: Sampling and Aliasing to understand the core concepts of converting analog sensor signals to digital values.

Begin with ADC Fundamentals โ†’