Improved motional Stark effect signal processing using fast Fourier transform spectral analysis

This Article is on the discrete Fourier transform (DFT) and the fast Fourier transform (FFT). As we shall see, FFT is a slight misnomer, causing confusion to beginners. The idiosyncratic title will be clarified in §4.Computing machines are highly efficient nowadays, and much of the efficiency is based on the use of the FFT to speed up calculations in ultrahigh precision arithmetic. The algorithm is now an indispensable tool for solving problems that involve a large amount of computation, resulting in many useful and important applications: for example, in signal processing, data compression and photo-images in general, and WiFi, mobile phones, CT scanners and MR imaging in particular.

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An Approximate Fast Fourier Transform Technique for Vernier Spectral Analysis

10.21236/ad0785850 ◽

1974 ◽

Cited By ~ 3

Author(s):

Albert H. Nuttall

Keyword(s):

Spectral Analysis ◽

Fourier Transform ◽

Fast Fourier Transform

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The fast Fourier transform

Parallel Scientific Computation ◽

10.1093/oso/9780198788348.003.0003 ◽

2020 ◽

pp. 134-189

Author(s):

Rob H. Bisseling

Keyword(s):

Signal Processing ◽

Fourier Transform ◽

Fast Fourier Transform ◽

Weather Forecasting ◽

Data Access ◽

Regular Pattern ◽

Matrix Products ◽

Data Redistribution ◽

Fft Algorithms ◽

Matrix Vector

This chapter demonstrates the use of different data distributions in different phases of a parallel fast Fourier transform (FFT), which is a regular computation with a predictable but challenging data access pattern. Both the block and cyclic distributions are used and also intermediates between them. Each required data redistribution is a permutation that involves communication. By making careful choices, the number of such redistributions can be kept to a minimum. FFT algorithms can be concisely expressed using matrix/vector notation and Kronecker matrix products. This notation is also used here. The chapter then shows how permutations with a regular pattern can be implemented more efficiently by packing the data. The parallelization techniques discussed for the specific case of the FFT are also applicable to other related computations, for instance in signal processing and weather forecasting.

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Comparison of fast Fourier transform and autoregressive spectral analysis for the study of heart rate variability in diabetic patients

International Journal of Cardiology ◽

10.1016/j.ijcard.2004.12.018 ◽

2005 ◽

Vol 104 (3) ◽

pp. 307-313 ◽

Cited By ~ 42

Author(s):

Denis Chemla ◽

Jacques Young ◽

Fabio Badilini ◽

Pierre Maison-Blanche ◽

Hélène Affres ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Spectral Analysis ◽

Fourier Transform ◽

Fast Fourier Transform ◽

Diabetic Patients

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Application of Chirp-z Transform in Doppler Weather Radar

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.4265 ◽

2014 ◽

Vol 513-517 ◽

pp. 4265-4268

Author(s):

Jun Zhu ◽

Xiao Jia Lu ◽

Xiang Liu

Keyword(s):

Signal Processing ◽

Fourier Transform ◽

Fast Fourier Transform ◽

Measurement Accuracy ◽

Weather Radar ◽

Doppler Weather Radar ◽

Transform Method ◽

Fast Fourier Transform Method ◽

Sampling Points ◽

Signal Processing Methods

Among the signal processing methods of Doppler weather radar, the FFT (Fast Fourier Transform) method is widely used. If the measurement accuracy needs to be improved, the number of FFT points also needs to be increased. As a result, the amount of computation increases exponentially. Chirp-z transform can directly refine certain spectrum in the spectrum of weather echoes. In the case that the sampling points and the amount of computation increase fewer, the measurement accuracy can be greatly advanced.

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