Springer The Regularized Fast Hartley Transform: Low-Complexity Parallel Computation of the FHT in One and Multiple Dimensions

Product Description 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. From the Back Cover 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. About the Author 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.