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{ "item_title" : "Advanced Signal Processing", "item_author" : [" Yuning Zhang", "Chenxin Yang", "Peng Luo "], "item_description" : "This brief explores advanced signal processing techniques, focusing on signal decomposition, entropy analysis, and machine learning, with applications in energy-related fields such as hydroturbines, wind turbines, and power grids. It provides a detailed overview of methods for signal denoising and pattern recognition, covering techniques like wavelet transform, empirical mode decomposition, permutation entropy, and deep learning models. Through real-world engineering case studies, the book demonstrates how these methods enhance data analysis, improve fault detection, and optimize system performance, making it a valuable resource for researchers, engineers, and students in signal processing and mechanical engineering. ", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/03/211/853/3032118530_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Advanced Signal Processing|Yuning Zhang

Advanced Signal Processing : Decomposition, Entropy, and Machine Learning

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Overview

This brief explores advanced signal processing techniques, focusing on signal decomposition, entropy analysis, and machine learning, with applications in energy-related fields such as hydroturbines, wind turbines, and power grids. It provides a detailed overview of methods for signal denoising and pattern recognition, covering techniques like wavelet transform, empirical mode decomposition, permutation entropy, and deep learning models. Through real-world engineering case studies, the book demonstrates how these methods enhance data analysis, improve fault detection, and optimize system performance, making it a valuable resource for researchers, engineers, and students in signal processing and mechanical engineering.

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Details

  • ISBN-13: 9783032118530
  • ISBN-10: 3032118530
  • Publisher: Springer
  • Publish Date: January 2026
  • Dimensions: 9.24 x 6.15 x 0.2 inches
  • Shipping Weight: 0.35 pounds
  • Page Count: 83

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