An Input-Adaptive Algorithm for High Performance Sparse Fast Fourier Transform

2014 ◽  
pp. 252-271
Author(s):  
Shuo Chen ◽  
Xiaoming Li
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Adaptive Algorithm ◽  
High Performance ◽  
Sparse Fast Fourier Transform

scholarly journals On Performance of Sparse Fast Fourier Transform Algorithms Using the Aliasing Filter

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1117
Author(s):  
Bin Li ◽  
Zhikang Jiang ◽  
Jie Chen
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
High Efficiency ◽  
Theory And Practice ◽  
Long Time ◽  
And Performance ◽  
Binary Tree Search ◽  
The One ◽  
Sparse Fast Fourier Transform ◽  
L1 And L2

Computing the sparse fast Fourier transform (sFFT) has emerged as a critical topic for a long time because of its high efficiency and wide practicability. More than twenty different sFFT algorithms compute discrete Fourier transform (DFT) by their unique methods so far. In order to use them properly, the urgent topic of great concern is how to analyze and evaluate the performance of these algorithms in theory and practice. This paper mainly discusses the technology and performance of sFFT algorithms using the aliasing filter. In the first part, the paper introduces the three frameworks: the one-shot framework based on the compressed sensing (CS) solver, the peeling framework based on the bipartite graph and the iterative framework based on the binary tree search. Then, we obtain the conclusion of the performance of six corresponding algorithms: the sFFT-DT1.0, sFFT-DT2.0, sFFT-DT3.0, FFAST, R-FFAST, and DSFFT algorithms in theory. In the second part, we make two categories of experiments for computing the signals of different SNRs, different lengths, and different sparsities by a standard testing platform and record the run time, the percentage of the signal sampled, and the L0, L1, and L2 errors both in the exactly sparse case and the general sparse case. The results of these performance analyses are our guide to optimize these algorithms and use them selectively.

Efficient Timing/Frequency Synchronization Based on Sparse Fast Fourier Transform

2019 ◽  
Vol 37 (20) ◽  
pp. 5299-5308
Author(s):  
Jianing Lu ◽  
Qiong Wu ◽  
Hexun Jiang ◽  
Songnian Fu ◽  
Ming Tang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Frequency Synchronization ◽  
Sparse Fast Fourier Transform

On Efficient Acquisition and Recovery Methods for Certain Types of Big Data

2016 ◽  
pp. 137-147
Author(s):  
George Avirappattu
Keyword(s):  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Compressive Sensing ◽  
Data Streams ◽  
Volume Velocity ◽  
Almost All ◽  
Sparse Fast Fourier Transform ◽  
Recovery Methods

Big data is characterized in many circles in terms of the three V's – volume, velocity and variety. Although most of us can sense palpable opportunities presented by big data there are overwhelming challenges, at many levels, turning such data into actionable information or building entities that efficiently work together based on it. This chapter discusses ways to potentially reduce the volume and velocity aspects of certain kinds of data (with sparsity and structure), while acquiring itself. Such reduction can alleviate the challenges to some extent at all levels, especially during the storage, retrieval, communication, and analysis phases. In this chapter we will conduct a non-technical survey, bringing together ideas from some recent and current developments. We focus primarily on Compressive Sensing and sparse Fast Fourier Transform or Sparse Fourier Transform. Almost all natural signals or data streams are known to have some level of sparsity and structure that are key for these efficiencies to take place.

Sign in / Sign up

Export Citation Format

Share Document