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Könyv Applied Topological Signal Processing with Python Helena K. Marwood

Applied Topological Signal Processing with Python

Persistent Homology and Feature Extraction for Temporal Data

Szerző: Helena K. Marwood, Alice Schwartz
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Independently published
Elérhetőség: Várható készletfeltöltés
Küldés 30. 09. 2026
13 759 Ft
Reactive PublishingTraditional signal processing relies heavily on Fourier transforms and time-frequ...

Információk a könyvről

Szerző
Helena K. Marwood, Alice Schwartz
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2026
oldal
446
EAN
9798176996296
Enbook ID
54031320
Kiadó
Independently published
Súly
537
Méretek
152 x 229 x 28

Teljes leírás

Reactive Publishing

Traditional signal processing relies heavily on Fourier transforms and time-frequency methods-tools that often fail when dealing with non-stationary, noisy, or high-dimensional real-time data. Applied Topological Signal Processing with Python bridges the gap between abstract mathematical topology and practical engineering, providing a hands-on guide to analyzing complex temporal data streams.

This book delivers a concrete framework for implementing Topological Data Analysis (TDA) directly in real-world signal workflows. Through complete Python examples, you will learn how to transform raw physical measurements and time-series arrays into persistence diagrams, extract robust structural features, and strip out background noise without losing critical phase information.

Inside, you will explore:

  • Fundamentals of Persistent Homology: Construct Vietoris-Rips and filtration complexes from numerical time-series.

  • Noise Reduction & Filtering: Separate true topological signal signatures from random ambient noise.

  • Feature Vectorization: Convert persistence landscapes and diagrams into ML-ready inputs for Scikit-Learn and PyTorch models.

  • Real-Time Signal Workflows: Implement sliding-window algorithms designed for streaming data pipelines.

  • Python Tooling: Practical implementations using Gudhi, Ripser, SciPy, and NumPy.

Whether you are a data scientist working with sensor networks, a biomedical engineer analyzing ECG/EEG signals, or a quantitative developer processing financial ticks, this text provides the exact code patterns and mathematical foundations needed to deploy topological methods into production.