Python for Signal Processing

Theory, Computation, and Experiment
2. Auflage 2027
ISBN: 978-3-032-47624-1
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

This new edition connects mathematical theory with reproducible numerical experiments, helping readers understand not only how to compute a result, but how to interpret it. This computation-first introduction develops sampling, convolution, Fourier analysis, spectral estimation, and digital filter design. It extends these foundations to image and multirate processing, stochastic signals, linear estimation, adaptive filters, Kalman filtering, and time-frequency analysis with wavelets.

Using NumPy, SciPy, Matplotlib, and Jupyter notebooks, readers explore how signal-processing ideas become working computations. Examples and exercises illustrate aliasing, spectral leakage, boundary effects, numerical conditioning, and model assumptions. The emphasis throughout is on building practical Python skills together with the engineering judgment needed to evaluate computational results. Designed for students, engineers, and researchers, the book assumes basic familiarity with Python, linear algebra, elementary probability, and undergraduate signals and systems.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Unpingco, José

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Inhaltsverzeichnis

Introduction.- Part I: Computational and signal-processing foundations.- Modern scientific Python for signal processing.- Signals, systems, and numerical representation.- Part II: Frequency-domain foundations.- Sampling, aliasing, and reconstruction.- The DFT, FFT, and frequency-domain thinking.- Spectral analysis of deterministic and random signals.- Part III: Digital filtering and rate conversion.- FIR filter design.- IIR filters and the z-transform.- Multirate signal processing.- Part IV: Random signals, estimation, and dynamic models.- Stochastic signals and noise.- Linear estimation and adaptive filters.- State-space models and Kalman filtering.- Part V: Modern signal representations and applications.- Time-frequency analysis and wavelets.- Sparse and compressed signal representations.- Signal processing and machine learning.- Graph signal processing.- Conclusion.

Über Autor(innen)

Dr. José Unpingco earned his PhD in Electrical Engineering from the University of California, San Diego (UCSD) and has worked across industry, government, and academia as an engineer, technical leader, and educator. He is currently a Senior Staff AI/ML Engineer at ServiceNow, where he leads work on production large language model agents and AI evaluation systems. Previously, he served as Lead Cognitive/ML Engineer at Humana and Vice President of Data Science and Machine Learning at the Gary and Mary West Health Institute. Earlier in his career, he directed large-scale signal and image processing initiatives for the Department of Defense (DoD), spearheaded the adoption of scientific Python, and trained more than 600 scientists and engineers. Dr. Unpingco also serves as a Senior Lecturer at UCSD, teaching undergraduate and graduate courses in data analysis and computational data science. He received the 2020–21 UCSD ECE Lecturer of the Year award and is the author of three Springer textbooks on Python, statistics, machine learning, and signal processing.