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The cover of “Applied Mathematics Data Compression Spectral Methods Fourier Analysis Wavelets And Applications” - Open Library.

"Applied Mathematics Data Compression Spectral Methods Fourier Analysis Wavelets And Applications" was published by Atlantis Press (Zeger Karssen) in 2013 - Paris and it has 551 pages.


“Applied Mathematics Data Compression Spectral Methods Fourier Analysis Wavelets And Applications” Metadata:

  • Title: ➤  Applied Mathematics Data Compression Spectral Methods Fourier Analysis Wavelets And Applications
  • Author:
  • Number of Pages: 551
  • Publisher: Atlantis Press (Zeger Karssen)
  • Publish Date:
  • Publish Location: Paris

“Applied Mathematics Data Compression Spectral Methods Fourier Analysis Wavelets And Applications” Subjects and Themes:

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"Applied Mathematics Data Compression Spectral Methods Fourier Analysis Wavelets And Applications" Description:

The Open Library:

This textbook, apart from introducing the basic aspects of applied mathematics, focuses on recent topics such as information data manipulation, information coding, data approximation, data dimensionality reduction, data compression, time-frequency and time scale bases, image manipulation, and image noise removal. The methods treated in more detail include spectral representation and “frequency” of the data, providing valuable information for, e.g. data compression and noise removal. Furthermore, a special emphasis is also put on the concept of “wavelets” in connection with the “multi-scale” structure of data-sets. The presentation of the book is elementary and easily accessible, requiring only some knowledge of elementary linear algebra and calculus. All important concepts are illustrated with examples, and each section contains between 10 an 25 exercises. A teaching guide, depending on the level and discipline of instructions is included for classroom teaching and self-study.

Open Data:

This textbook, apart from introducing the basic aspects of applied mathematics, focuses on recent topics such as information data manipulation, information coding, data approximation, data dimensionality reduction, data compression, time-frequency and time scale bases, image manipulation, and image noise removal. The methods treated in more detail include spectral representation and “frequency” of the data, providing valuable information for, e.g. data compression and noise removal. Furthermore, a special emphasis is also put on the concept of “wavelets” in connection with the “multi-scale” structure of data-sets. The presentation of the book is elementary and easily accessible, requiring only some knowledge of elementary linear algebra and calculus. All important concepts are illustrated with examples, and each section contains between 10 an 25 exercises. A teaching guide, depending on the level and discipline of instructions is included for classroom teaching and self-study

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