The regularized fast Hartley transform low-complexity parallel computation of the FHT in one and multiple dimensions / [electronic resource] :

• 作者： Jones, Keith John.
• 其他作者：
  • SpringerLink (Online service)
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 版本：Second edition.
• 主題： Hartley transforms. , Signal, Image and Speech Processing. , Theory of Computation. , Communications Engineering, Networks.
• ISBN： 9783030682453 (electronic bk.) 、 9783030682446 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Part 1: The Discrete Fourier and Hartley Transforms -- Background to Research -- The Real-Data Discrete Fourier Transform -- The Discrete Hartley Transform -- Part 2: The Regularized Fast Hartley Transform -- Derivation of Regularized Formulation of Fast Hartley Transform -- Design Strategy for Silicon-Based Implementation of Regularized Fast Hartley Transform -- Architecture for Silicon-Based Implementation of Regularized Fast Hartley Transform -- Design of CORDIC-Based Processing Element for Regularized Fast Hartley Transform -- Part 3: Applications of Regularized Fast Hartley Transform -- Derivation of Radix-2 Real-Data Fast Fourier Transform Algorithms using Regularized Fast Hartley Transform -- Computation of Common DSP-Based Functions using Regularized Fast Hartley Transform -- Part 4: The Multi-Dimensional Discrete Hartley Transform -- Parallel Reordering and Transfer of Data between Partitioned Memories of Discrete Hartley Transform for 1-D and m-D Cases -- Architectures for Silicon-Based Implementation of m-D Discrete Hartley Transform using Regularized Fast Hartley Transform -- Part 5: Results of Research -- Summary and Conclusions.
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• 摘要註: 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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