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Fourier transform

About: Fourier transform is a research topic. Over the lifetime, 50467 publications have been published within this topic receiving 990947 citations. The topic is also known as: FT.


Papers
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Journal ArticleDOI
TL;DR: To the knowledge, this is the first use of this technique on opaque and nonperiodic objects and experimental data showing enhancement of defects as small as 0.14 microm is presented.
Abstract: We present real-time optical preprocessor intended to enhance small defects in a smooth surface. The heart of the system is a photorefractive Bi12SiO20 phase-conjugate mirror which reflects only the low intensity features of the object’s optical Fourier transform. By adjusting the beam ratios so that only the low intensity, high spatial frequency image components are phase conjugated and inverse transformed, an image consisting of just the defects is produced. To our knowledge, this is the first use of this technique on opaque and nonperiodic objects. We present experimental data showing enhancement of defects as small as 0.14 μm.

20 citations

Journal ArticleDOI
TL;DR: A novel coarse-grained simulation method for modelling the dynamics of globular macromolecules, such as proteins, is described and it is demonstrated that the overall deformation of the biomolecule is consistent with the results obtained for proteins in general from atomistic molecular dynamics simulations.

20 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: A novel approach for music/voice separation that uses the 2D Fourier Transform (2DFT) that is connected to research in biological auditory systems as well as image processing and competitive with existing unsupervised source separation approaches that leverage similar assumptions.
Abstract: Audio source separation is the act of isolating sound sources in an audio scene. One application of source separation is singing voice extraction. In this work, we present a novel approach for music/voice separation that uses the 2D Fourier Transform (2DFT). Our approach leverages how periodic patterns manifest in the 2D Fourier Transform and is connected to research in biological auditory systems as well as image processing. We find that our system is very simple to describe and implement and competitive with existing unsupervised source separation approaches that leverage similar assumptions.

20 citations

Journal ArticleDOI
29 Mar 2021
TL;DR: In this article, Fourier decomposition of non-stationary EEG signals has been used for the diagnosis of epilepsy using fast Fourier transform (FFT) algorithm and support vector machine (SVM).
Abstract: Epilepsy is a disease recognized as the chronic neurological dysfunction of the human brain which is described by the sudden and excessive electrical discharges of the brain cells Electroencephalogram (EEG) is a prime tool applied for the diagnosis of epilepsy In this study, a novel and effective approach is introduced to decompose the non-stationary EEG signals using the Fourier decomposition method The concept of position, velocity, and acceleration has been employed on the EEG signals for feature extraction using $$L^p$$ norms computed from Fourier intrinsic band functions (FIBFs) The proposed scheme comprises three main sections In the first section, the EEG signal is decomposed into a finite number of FIBFs In the second stage, the features are extracted from FIBFs and relevant features are selected by using the Kruskal–Wallis test In the last stage, the significant features are passed on to the support vector machine (SVM) classifier By applying 10-fold cross-validation, the proposed method provides better results in comparison to the state-of-the-art methods discussed in the literature, with an average classification accuracy of 9996% and 9994% for classification of EEG signals from the BONN dataset and the CHB-MIT dataset, respectively It can be implemented using the computationally efficient fast Fourier transform (FFT) algorithm

20 citations

Journal ArticleDOI
01 Nov 2021-Optik
TL;DR: In this article, the authors proposed a short-time quadratic-phase Fourier transform (QPFT) which can effectively localize the quadratically-phase spectrum of non-transient signals.

20 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,190
20222,572
20211,430
20201,724
20191,863
20181,883