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 realdata 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 mD (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 siliconbased computing technology and a resourceconstrained environment is assumed and the data is real-valued in nature, hasthus been to seek solutions that best match the actual problem needing to be solved.

Describes solutions to 1-D and m-D versions of the FHT and the real-data FFT targeted at applications Achieves the computational density of the most advanced commercially-available solutions for greatly reduced silicon resources Presents simple design variations that enable one to optimize the use of the available silicon resources

Autorentext
Dr. Keith John Jones received his Ph.D in Computer Science from Birkbeck College, London University and his M.Sc in Applicable Mathematics from Cranfield Institute of Technology. He has an entry in "Who's Who in Science and Engineering" (2008-present) and in "The Dictionary of International Biography" (otherwise known as "The Cambridge Blue Book") (2008-present). He is currently a consultant at Wyke Technologies Ltd., Weymouth, Dorset (2015-present), having previously been employed as a mathematician/algorithmist/programmer & system designer for TRL Technology Ltd., Tewkesbury, Gloucestershire and, prior to that, for QinetiQ, Winfrith, Dorset. In 2010 he published "The Regularized Fast Hartley Transform: Optimal Formulation of Real-Data Fast Fourier Transform for Silicon Based Implementation in Resource-Constrained Environments". He has 10 patents and has published extensively in the field.

Klappentext

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, hasthus been to seek solutions that best match the actual problem needing to be solved.

Inhalt
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. <p