scholarly journals Approximate string searching with fast fourier transforms and simplexes

2019 ◽  
Author(s):  
Daniel Liu
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Fourier Transforms ◽  
Hamming Distance ◽  
Dimensional Space ◽  
Approximate String Matching ◽  
String Searching ◽  
Novel Approach ◽  
Sigma 1 ◽  
Distance Problem

Previous algorithms for solving the approximate string matching with Hamming distance problem with wildcard ("don't care") characters have been shown to take \(O(|\Sigma| N \log M)\) time, where \(N\) is the length of the text, \(M\) is the length of the pattern, and \(|\Sigma|\) is the size of the alphabet. They make use of the Fast Fourier Transform for efficiently calculating convolutions. We describe a novel approach of the problem, which makes use of special encoding schemes that depend on \((|\Sigma| - 1)\)-simplexes in \((|\Sigma| - 1)\)-dimensional space.

scholarly journals Approximate string searching with fast fourier transforms and simplexes

2019 ◽  
Author(s):  
Daniel Liu
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Fourier Transforms ◽  
Hamming Distance ◽  
Dimensional Space ◽  
Approximate String Matching ◽  
String Searching ◽  
Novel Approach ◽  
Sigma 1 ◽  
Distance Problem

Previous algorithms for solving the approximate string matching with Hamming distance problem with wildcard ("don't care") characters have been shown to take \(O(|\Sigma| N \log M)\) time, where \(N\) is the length of the text, \(M\) is the length of the pattern, and \(|\Sigma|\) is the size of the alphabet. They make use of the Fast Fourier Transform for efficiently calculating convolutions. We describe a novel approach of the problem, which makes use of special encoding schemes that depend on \((|\Sigma| - 1)\)-simplexes in \((|\Sigma| - 1)\)-dimensional space.

IMPLEMENTING FAST FOURIER TRANSFORMS ON DISTRIBUTED-MEMORY MULTIPROCESSSORS USING DATA REDISTRIBUTIONS

1994 ◽  
Vol 04 (04) ◽  
pp. 477-488 ◽  
Author(s):  
S.K.S. GUPTA ◽  
C.-H. HUANG ◽  
P. SADAYAPPAN ◽  
R.W. JOHNSON
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Distributed Memory ◽  
Fourier Transforms ◽  
Experimental Performance ◽  
Minimum Number ◽  
Data Redistribution ◽  
Using Data ◽  
Performance Results ◽  
Fft Algorithms

Implementations of various fast Fourier transform (FFT) algorithms are presented for distributed-memory multiprocessors. These algorithms use data redistribution to localize the computation. The goal is to optimize communication cost by using a minimum number of redistribution steps. Both analytical and experimental performance results on the Intel iPSC/860 system are presented.

SNOCRAFT: A Learning-Oriented Shorthand Notation for Creating, Representing, and Analyzing Fast Fourier Transform Algorithms

1980 ◽  
Vol 17 (3) ◽  
pp. 284-284
Author(s):  
Robert J. Meir ◽  
Sathyanarayan S. Rao
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Fourier Transforms ◽  
Reduced Form ◽  
Fast Fourier Transform Algorithm ◽  
Signal Flow Graph ◽  
Shorthand Notation ◽  
The Matrix ◽  
Fourier Algorithm ◽  
Fft Analysis

This paper presents a full and well-developed view of the Fast Fourier Transform (FFT). It is intended for the reader who wishes to learn and develop his own fast Fourier algorithm. The approach presented here utilizes the matrix description of fast Fourier transforms. This approach leads to a systematic method for greatly reducing the complexity and the space required by variety of signal flow graph descriptions. This reduced form is called SNOCRAFT. From this representation, it is then shown how one can derive all possible fast Fourier transform algorithms, including the Weinograd Fourier transform algorithm. It is also shown from the SNOCRAFT representation that one can easily compute the number of multiplications and additions required to perform a specified fast Fourier transform algorithm. After an elementary introduction to matrix representation of fast Fourier transform algorithm, the method of generating all possible fast Fourier transform algorithms is presented in detail and is given in three sections. The first section discusses the Generation of SNOCRAFT and the second section illustrates how Operations on SNOCRAFT are made. These operations include inversion and rotation. The last section deals with the FFT Analysis. In this section, examples are provided to illustrate how one counts the number of multiplications and additions involved in performing the transform that one has developed.

scholarly journals Programmable FFT Processor using Dual RAM and ROM Technologies for Future 5G Communications

2020 ◽  
Vol 9 (6) ◽  
pp. 1116-1122
Keyword(s):  
Fourier Transform ◽  
Power Consumption ◽  
Fast Fourier Transform ◽  
Fourier Transforms ◽  
Single Port ◽  
Local Networks ◽  
Parallel Pipelined ◽  
4G Wireless ◽  
Derived Design ◽  
Point Fast Fourier Transform

The high-throughput programmable Fast Fourier transform processor supports the usage of 2-stream 1024/2048/4096-point Fast Fourier Transforms and 1-to 4- stream 64/128-point Fast Fourier Transform for 4G,wireless local networks and for 5G.The proposed architecture which was designed is a well-intentionedfour-bank single-port SRAM which is being working in four-word data width, the design which is proposed gives us sixteen memory pathways . where the data is accessed up to this extent where it can be used in upcoming 5G. The radix-16 butterfly process element comprises of 2 cascaded parallel, pipelined radix-4 butterfly units which is specified. The projected memory-addressing methodology will effectively wear down single-port, merged-bank memory with high-radix process components. Comparing with typical memory based Fast Fourier Transform styles, the derived design has higher performance in expressions of area and power consumption. The architecture which is projected occupies the tiniest area of around1.21mm2 .The processor supports 1966MS/s 4096-point FFT and frequency of 1GHz.The Electronic design automation synthesis results show the power consumption is 32.16mW.The SQNR performance analysis is 42.14 dB.

scholarly journals SIMPLIFIED BLUESTEIN NUMERICAL FAST FOURIER TRANSFORMS ALGORITHM FOR DSP AND ASP

2015 ◽  
Vol 3 (11) ◽  
pp. 153-163
Author(s):  
Amannah ◽  
Bakpo
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Numerical Algorithm ◽  
Execution Time ◽  
Fourier Transforms ◽  
Fast Fourier Transforms ◽  
Digital Signals ◽  
Incremental Development ◽  
The One

This research was designed to develop a simplified Bluestein numerical FFT algorithm necessary for the processing of digital signals. The simplified numerical algorithm developed in this study is abbreviated with SBNADSP. The methodology adopted in this work was iterative and incremental development design. The major technology used in this work is the Bluestein numerical FFT algorithm. The study set the pace for its goal by re-indexing, decomposing, and simplifying the default Fast Fourier Transform Algorithms (the Bluestein FFT Algorithm). The improved efficiency of the Bluestein FFT algorithm is accounted for by the obvious reduction in the number of operations and operators in the simplified Bluestein algorithms. The SBTNADSP is designed to have four products, and three exponentiations against the default Bluestein FFT algorithm which has six exponentiations and eight products. Since the increase in the number of operators increases the length of operation, it is therefore reasonable to infer that the algorithm with the less number of operators will run shorter execution time than the one with greater operators. In line with this, we conclude that SBNADSP is of greater efficiency than the Bluestein numerical algorithm.The result of this study showed that a faster numerical algorithm other than the Bluestein fftalgorithms is possible for the processing of digital signals.

An fast Fourier transform–based correlation coefficient approach for structural damage diagnosis

2020 ◽  
pp. 147592172094956
Author(s):  
Nhi K Ngo ◽  
Thanh Q Nguyen ◽  
Thu V Vu ◽  
H Nguyen-Xuan
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Correlation Coefficient ◽  
Structural Damage ◽  
Correlation Coefficients ◽  
Experimental Models ◽  
Damage Diagnosis ◽  
Fast Fourier Transform Analysis ◽  
Novel Approach ◽  
Bridge Structures

We present a novel approach to evaluating mechanical features of structures using correlation coefficients and fast Fourier transform analysis. Although correlation coefficient is always a sensitive parameter to changes of mechanical properties of real structures, it is rarely used due to high complication in data collection. To overcome this drawback, we propose fast Fourier transform analysis to increase the sensitivity of correlation coefficient, simplify calculation, and retain information from the original signal. Numerical results show that the present method not only detects relation between changes in structure with progression of defects but also locates their position. An fast Fourier transform–based correlation coefficient approach provides evaluations in both real bridge structures and experimental models. This study can serve as reference for analyzing, evaluating, and identifying working status of real structures.

scholarly journals A novel approach for clustering proteomics data using Bayesian fast Fourier transform

2005 ◽  
Vol 21 (10) ◽  
pp. 2210-2224 ◽  
Author(s):  
H. Bensmail ◽  
J. Golek ◽  
M. M. Moody ◽  
J. O. Semmes ◽  
A. Haoudi
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Proteomics Data ◽  
Novel Approach

scholarly journals An Approach on MCSA-Based Fault Detection Using Discrete Wavelet Transform and Fault Classification Based on Deep Neural Networks

2021 ◽  
Vol 10 (3) ◽  
pp. 2256-2259
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Fast Fourier Transform ◽  
Discrete Wavelet Transform ◽  
Three Dimensional ◽  
Fault Classification ◽  
Signal Frequency ◽  
Discrete Wavelet ◽  
Novel Approach ◽  
Motor Current Signature Analysis

This paper presents a novel approach on motor current signature analysis (MCSA) forbroken Rotor Bar fault and High Contact Resistance fault using stator current signals as an input from the three phases of Induction motors. Discrete Wavelet Transform is preferred over the Fast Fourier Transform (FFT). Fast Fourier Transform (FFT) converts signals from time domain to frequency domain on the other hand Discrete Wavelet Transform (DWT) gives complete three-dimensional information of the signal, frequency, amplitude, and the time where the frequency components exist. In wavelet analysis, thesignal is converted into scaled and translated version of mother wavelet, which is very irregular so cannot be predicted. Hence, mother wavelets are more appropriate for predicting the local behavior of the signal including irregularities and spikes. In this research features are extracted using DWT and then features are trained in Deep NN sequential model for the purpose of classification of the faults. In this research, MATLAB software has been used for building the motor model in Simulink environment and PyCharm software is used to implement Deep NN for getting accuracy and classification results. This research helps in early detection of the faults that assists in prevention from unscheduled downtimes in industry, economy loss and production loss as well.

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