Utvidet returrett til 31. januar 2025

The Regularized Fast Hartley Transform

Om The Regularized Fast Hartley Transform

This book describes how a key signal/image processing algorithm ¿ that of the fast Hartley transform (FHT) or, via a simple conversion routine between their outputs, of the real¿data version of the ubiquitous fast Fourier transform (FFT) ¿ might best be formulated to facilitate computationally-efficient solutions. The author discusses this for both 1-D (such as required, for example, for the spectrum analysis of audio signals) and m¿D (such as required, for example, for the compression of noisy 2-D images or the watermarking of 3-D video signals) cases, but requiring few computing resources (i.e. low arithmetic/memory/power requirements, etc.). This is particularly relevant for those application areas, such as mobile communications, where the available silicon resources (as well as the battery-life) are expected to be limited. The aim of this monograph, where silicon¿based computing technology and a resource¿constrained environment is assumed and the data is real-valued in nature, has thus been to seek solutions that best match the actual problem needing to be solved.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9783030682446
  • Bindende:
  • Hardback
  • Sider:
  • 320
  • Utgitt:
  • 3. september 2021
  • Utgave:
  • 22022
  • Dimensjoner:
  • 155x235x0 mm.
  • Vekt:
  • 676 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 22. desember 2024
Utvidet returrett til 31. januar 2025

Beskrivelse av The Regularized Fast Hartley Transform

This book describes how a key signal/image processing algorithm ¿ that of the fast Hartley transform (FHT) or, via a simple conversion routine between their outputs, of the real¿data version of the ubiquitous fast Fourier transform (FFT) ¿ might best be formulated to facilitate computationally-efficient solutions. The author discusses this for both 1-D (such as required, for example, for the spectrum analysis of audio signals) and m¿D (such as required, for example, for the compression of noisy 2-D images or the watermarking of 3-D video signals) cases, but requiring few computing resources (i.e. low arithmetic/memory/power requirements, etc.). This is particularly relevant for those application areas, such as mobile communications, where the available silicon resources (as well as the battery-life) are expected to be limited. The aim of this monograph, where silicon¿based computing technology and a resource¿constrained environment is assumed and the data is real-valued in nature, has thus been to seek solutions that best match the actual problem needing to be solved.

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