Digital Signal Processing using Arm Cortex-M based Microcontrollers: Theory and Practice
This textbook introduces readers to digital signal processing fundamentals using low-cost, high-performance Arm Cortex-M based microcontrollers as demonstrator platforms. It covers foundational concepts, principles and techniques in digital signal processing, such as signals and systems, sampling, reconstruction and anti-aliasing, FIR and IIR filter design, transforms, and adaptive signal processing. Key features include a set of hands-on labs that highlight the practical side of digital signal processing, end of chapter exercises that reinforce the theoretical concepts presented, with answers available online, and online instructor resources.
The textbook is suitable for use in ECE, EE and CS university departments. The labs in this textbook edition target the low-cost Arm Cortex-M4-based STM32F4 Discovery microcontroller board.
Why Read This Book
You will get a compact bridge from DSP theory to practical embedded implementation, learning how to run real DSP algorithms on low‑cost Arm Cortex‑M microcontrollers using hands‑on labs and worked examples. The book emphasizes both the mathematical foundations (filters, transforms, sampling) and the implementation details (fixed‑point issues, CMSIS‑DSP, optimization) that matter when moving algorithms into resource‑constrained devices.
Who Will Benefit
ECE/EE/CS undergraduates, graduate students, and embedded engineers who want to design, implement, and optimize DSP algorithms on Arm Cortex‑M microcontrollers for real‑world sensor, audio, and IoT applications.
Level: Intermediate — Prerequisites: Basic signals and systems concepts (or introductory DSP), undergraduate calculus and linear algebra, and familiarity with C programming and basic microcontroller concepts (interrupts, memory, toolchain).
Key Takeaways
- Implement common DSP algorithms (FIR, IIR, FFT) on Arm Cortex‑M microcontrollers using C and CMSIS‑DSP
- Design and analyze filters and sampling systems, including anti‑aliasing and reconstruction considerations
- Manage fixed‑point arithmetic and quantization effects to preserve numerical accuracy on constrained MCUs
- Optimize real‑time DSP code for Cortex‑M cores (use of SIMD/DSP instructions, memory and performance tradeoffs)
- Build and validate hands‑on lab projects that process sensor and audio data in embedded environments
- Apply adaptive filtering techniques and basic spectral analysis for practical signal‑processing tasks
Topics Covered
- 1. Introduction to DSP and Embedded Platforms
- 2. Signals, Systems, and Discrete‑Time Models
- 3. Sampling, Quantization, and Anti‑Aliasing
- 4. Discrete‑Time FIR Filters: Theory and Design
- 5. IIR Filters and Filter Realizations
- 6. Fourier Analysis, DFT and FFT Algorithms
- 7. Fixed‑Point Arithmetic, Quantization and Numerical Issues
- 8. Arm Cortex‑M Architecture and CMSIS‑DSP Overview
- 9. Implementing DSP on Cortex‑M: Coding and Optimization
- 10. Adaptive Signal Processing and LMS Filters
- 11. Practical Labs and Projects (sensor, audio, IoT demos)
- 12. Testing, Validation, and Performance Measurement
- Appendices: Toolchains, MATLAB/Octave Examples, Answer Key and Instructor Resources
Languages, Platforms & Tools
How It Compares
Compared with classic theory texts like Lyons' Understanding Digital Signal Processing or Smith's DSP Guide, Unsalan's book is more application‑focused—pairing DSP fundamentals with concrete Arm Cortex‑M implementation guidance and labs rather than deep theoretical proofs.
