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008			210903s2022 sz | s |||| 0|eng d
020			_a9783030682453
024	7		_a10.1007/978-3-030-68245-3 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aTK5102.9
072		7	_aTJF _2bicssc
072		7	_aUYS _2bicssc
072		7	_aTEC008000 _2bisacsh
072		7	_aTJF _2thema
072		7	_aUYS _2thema
090			_aTK5102.9EBK
100	1		_aJones, Keith John. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	4	_aThe Regularized Fast Hartley Transform _h[electronic resource] : _bLow-Complexity Parallel Computation of the FHT in One and Multiple Dimensions / _cby Keith John Jones.
250			_a2nd ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aPart 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.
520			_aThis 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.
650		0	_aSignal processing.
650		0	_aComputer science.
650		0	_aTelecommunication.
650	1	4	_aSignal, Speech and Image Processing .
650	2	4	_aTheory and Algorithms for Application Domains.
650	2	4	_aCommunications Engineering, Networks.
653		0	_aHartley transforms
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-68245-3 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK