The Regularized Fast Hartley Transform

Low-Complexity Parallel Computation of the FHT in One and Multiple Dimensions

By: Keith John Jones

Write A Review

eText | 3 September 2021 | Edition Number 2

At a Glance

Format
ePUB

eText

$179.00

or 4 interest-free payments of $44.75 with

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

Most real-world spectrum analysis problems involve the computation of the real-data discrete Fourier transform (DFT), a unitary transform that maps elements N of the linear space of real-valued N-tuples, R , to elements of its complex-valued N counterpart, C , and when carried out in hardware it is conventionally achieved via a real-from-complex strategy using a complex-data version of the fast Fourier transform (FFT), the generic name given to the class of fast algorithms used for the ef?cient computation of the DFT. Such algorithms are typically derived by explo- ing the property of symmetry, whether it exists just in the transform kernel or, in certain circumstances, in the input data and/or output data as well. In order to make effective use of a complex-data FFT, however, via the chosen real-from-complex N strategy, the input data to the DFT must ?rst be converted from elements of R to N elements of C . The reason for choosing the computational domain of real-data problems such N N as this to be C , rather than R , is due in part to the fact that computing equ- ment manufacturers have invested so heavily in producing digital signal processing (DSP) devices built around the design of the complex-data fast multiplier and accumulator (MAC), an arithmetic unit ideally suited to the implementation of the complex-data radix-2 butter?y, the computational unit used by the familiar class of recursive radix-2 FFT algorithms.