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An Introduction to the Discrete Fourier Transform and Its Applications in Signal Processing

MATLAB and Python Edition

A practical and pedagogical introduction to the Discrete Fourier Transform (DFT), combining theory, numerical examples, and MATLAB/Python implementations for students, engineers, and educators.

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About

About

About the Book

This course monograph provides a progressive introduction to the Discrete Fourier Transform (DFT) and its applications in signal processing. It combines theoretical explanations, numerical examples, and practical implementations in MATLAB and Python/NumPy.

Topics include:

  • sampling and aliasing;
  • spectral leakage and windowing;
  • frequency resolution;
  • one-sided and two-sided spectra;
  • phase spectra and fftshift;
  • FFT implementation and normalization;
  • practical signal-processing applications.

The material is intended for undergraduate and graduate students, instructors, and engineers who need a clear and operational understanding of spectral analysis and numerical signal processing.

Included with the book:

  • Complete PDF course monograph;
  • Executable Jupyter notebook (.ipynb);
  • HTML version of the course;
  • High-resolution figures.

The notebook contains all Python/NumPy examples presented throughout the text and allows readers to reproduce, modify, and extend the computations discussed in the monograph.

Companion materials available upon request at (please attach your Leanpub purchase receipt):

signal.processing.course@gmail.com

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Author

About the Authors

Laurent Nony

Laurent Nony is Associate Professor (Maître de Conférences Hors Classe) in Physics at Aix-Marseille Université (France) and a member of the Institute of Materials, Microelectronics and Nanosciences of Provence (IM2NP, CNRS UMR 7334, France), where he is currently leading the "Nanostructuration" research group.

He received his Ph.D. in Physics from the University of Bordeaux (France) in 2000 and subsequently held post-doctoral research positions at the IBM Zurich Research Laboratory (Switzerland) and the University of Basel (Switzerland), before joining Aix-Marseille Université in 2005. He obtained his Habilitation (HDR) in 2013.

His research activities focus on experimental and computational nanophysics, scanning probe microscopy, signal acquisition, instrumentation, and data analysis. Throughout his academic career, he has supervised numerous student projects and developed original scientific instrumentation as well as numerical simulation tools.

For more than twenty years, he has taught physics, instrumentation, numerical methods, signal processing, sensors, and scientific computing at undergraduate and graduate levels. He has also created a wide range of pedagogical resources, including lecture notes, laboratory projects, online courses, and educational videos.

His teaching philosophy emphasizes the connection between theory, experimentation, and practical implementation. This course monograph reflects that approach by combining conceptual explanations with concrete MATLAB and Python/NumPy examples, enabling students to develop both intuition and practical skills in signal processing.

He is the author, or co-author, of more than 50 peer-reviewed research articles, books chapters and patents, and developer of several digital pedagogical resources dedicated to signal processing, scientific computing, and experimental physics.

Jean-Marc Themlin is Professor of Physics at Aix-Marseille Université (France), affiliated with the Institute of Materials, Microelectronics and Nanosciences of Provence (IM2NP, CNRS UMR 7334, France), where he is currently leading the PHaNO scientific department.

As an experimental physicist specialized in the electronic structure of solids, surfaces and nanomaterials, he benefits from over 35 years of experience in higher education and research. His research interests focuses on Angle-Resolved Inverse Photoemission Spectroscopy (ARIPES) to probe unoccupied electronic states. His work has led to significant advances in understanding pristine and N-doped epitaxial graphene on SiC, two-dimensional Mott insulators, monolayers of pi-conjugated molecules (fullerenes,...) adsorbed on surfaces,...

With more than 60 top-tier publications, his career is driven by a profound commitment to pedagogy : the conviction that training the next generation of physicists requires an active, experimental, and inclusive approach. Head of the Experimental Physics Service of the Physics Department of AMU since 2002, he leads this multi-site service, ensuring the quality and modernization of practical labs for all physics students through resource sharing and equipment upgrades.

Adept of "Teaching Through Practice", his teaching philosophy centers on reforming traditional methods to prioritize "well-structured minds over well-filled ones" through providing more active learning opportunities to students. To this aim, he restructured numerous courses (Signal theory, Signal & image Processing, Electronics,...) to reduce formal lecture time in favor of hands-on labs, experimental projects, and case studies, utilizing open-source tools and collaborative platforms. Applying these recipes, he created an interdisciplinary "Digital Physics" course tailored for first year B.Sc. students with a computer science background.

More recently, he designed and launched the enhanced "Initiation to Scientific Research" track for Bachelor’s students in their second and third year, allowing them to immerse themselves in "close-to-real" research. In 2025, he initiated the PhysLab project, funded by the University fundation AMidex, to create dedicated spaces for undergraduate experimental research, bridging the gap between standard lab sessions and authentic laboratory research.

Beyond the lab and the classroom, he also held structural leadership roles within the faculty, especially as Head of the Physics Department of AMU. Serving two terms (2018–2024), he led the largest department in the Faculty of Sciences, overseeing a complete redesign of the degree programs (Bachelor’s and Master’s).

Contents

Table of Contents

  • 1. Introduction – Focus of this document
  • 2. Foundations of the Fourier Transform for continuous-time signals
  • 3. The discrete-time Fourier Transform (DTFT)
  • 4. The Discrete Fourier Transform (DFT)
  • 5. DTFT and DFT: synopsis
  • 6. Computation of the DFT
  • 7. Applications
  • 8. DFT implementation: summary
  • 9. Conclusion and key takeaways
  • 10. References

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