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portada Applied Topological Signal Processing with Python: Persistent Homology and Feature Extraction for Temporal Data
Type
Physical Book
Publisher
Author
Language
English
Pages
444
Format
Paperback
ISBN13
9798176996296

Applied Topological Signal Processing with Python: Persistent Homology and Feature Extraction for Temporal Data

Marwood, Helena K. (Author) · Independently published · Paperback

Applied Topological Signal Processing with Python: Persistent Homology and Feature Extraction for Temporal Data - Marwood, Helena K.

New Book Imported to Taiwan
Delivery: 09 Nov - 17 Nov Shipping: 6 to 7 business days.
NT$ 1,445
NT$ 1,445

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Synopsis "Applied Topological Signal Processing with Python: Persistent Homology and Feature Extraction for Temporal Data"

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.

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