On Cooley-Tukey FFT method for zero padded signals

Aamir, Khalid Mahmood; Maud, M.A.; Loan, Asim

doi:10.1109/icet.2005.1558852

Cited by 9 publications

(8 citation statements)

References 12 publications

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“…The second one is the frequency resolution, which is the definition of the distance between frequency bins. Whereas the spectral resolution can only be increased by increasing the time window of the signal, the frequency resolution is determined by the number of input data points in the sequence given to the DFT [27][28][29][30]. A longer data sequence is usually obtained by using the zero-padding method, which is described below.…”

Section: Zero-padded Fftmentioning

confidence: 99%

Area-Efficient Pipelined FFT Processor for Zero-Padded Signals

et al. 2019

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This paper proposes an area-efficient fast Fourier transform (FFT) processor for zero-padded signals based on the radix-2 2 and the radix-2 3 single-path delay feedback pipeline architectures. The delay elements for aligning the data in the pipeline stage are one of the most complex units and that of stage 1 is the biggest. By exploiting the fact that the input data sequence is zero-padded and that the twiddle factor multiplication in stage 1 is trivial, the proposed FFT processor can dramatically reduce the required number of delay elements. Moreover, the 256-point FFT processors were designed using hardware description language (HDL) and were synthesized to gate-level circuits using a standard cell library for 65 nm CMOS process. The proposed architecture results in a logic gate count of 40,396, which can be efficient and suitable for zero-padded FFT processors.

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Section: Zero-padded Fftmentioning

confidence: 99%

Area-Efficient Pipelined FFT Processor for Zero-Padded Signals

et al. 2019

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“…Coming back to FFT with zero padding at the end of the signal, it increases the resolution but also requires an additional length of the signal to transform. Moreover, in the case of two‐dimensional signals, it necessitates storage and manipulation of large matrices (the original image is bordered with zeros), which may lead to on‐board memory concerns.…”

Section: Zoom Fft Algorithmsmentioning

confidence: 99%

Strain measurements on compliant knitted mesh used in space antennas, by means of 2D Fourier analysis

Salvador

Marotta

Salvini

2017

Strain

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The paper presents a strain measurement technique to be applied on repetitive knitted nets. The application of the procedure is focused on large deployable reflectors used in space applications. Manufacturing of these structures involves knitting of thin metallic wires. Besides space antennas, the method herein developed is advantageous for many other types of knitted tissues such as elastic coatings, elastic bandages for medical applications, or any other woven fabric requiring a defined elastic behaviour. All of them display an almost regular repetitive pattern whose variations due to imposed loads are the core of the method. Deformation of such kind of structural elements must be analysed in two dimensions, because the knitting causes an accentuated orthotropic behaviour. Strain measurement is tricky and requires special computational techniques. Indeed, the application of standard experimental methods and indirect strain determination, through well‐established image analysis algorithm, can be totally unsuccessful or unsuitable because of the lack of continuity of these structures. The paper presents a method based on two‐dimensional Fourier analysis. Numerical examples, supported by some experimental evidences, confirm the feasibility of the technique. Furthermore, a newly proposed frequency zooming algorithm, based on fast Fourier transform, makes the computational costs of the indirect strain measurement affordable.

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“…In addition, we can further simplify the proposed algorithm since the large number of samples in S(v n , m) is set to be zero. Then, it can be applied to some of the FFT algorithms that are using information about zero samples [31,32]. For a radar image of dimension M Â N within Q non-zero samples, calculation complexity of these algorithms is O(MN log 2 Q).…”

Section: Algorithm For Imaging Of Moving Targetsmentioning

confidence: 99%

“…However, in the next step, well-concentrated objects are removed from the PFT and we can calculate the PFT for the remaining radar image by removing from the PFT of the radar image obtained in the previous step, the PFT of the radar image of highly concentrated objects. These highly concentrated objects occupy a very small part of the image and again we can use the simplified procedure for the evaluation of the PFT from [31,32]. In order to illustrate the calculation complexity, consider the following setup: size of radar image M Â N ¼ 256 Â 256, number of iterations in the search for optimal chirp rate r ¼ 10, p ¼ 25% of chirps with significant energy, 5 objects of 16 pixels each with different chirp rates (motion parameters).…”

Section: Algorithm For Imaging Of Moving Targetsmentioning

confidence: 99%