A review on sparse fast fourier transform applications in image processing

Ghani, Hadhrami Ab.; Malek, Mohamad Razwan Abdul; Azmi, Muhammad Fadzli Kamarul; Muril, Muhammad Jefri; Azizan, Azızul

doi:10.11591/ijece.v10i2.pp1346-1351

Cited by 15 publications

(11 citation statements)

References 26 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Afterwards, removal of fours edges in each image reduces the dimension from 28 × 28 into 8 × 8 but preserves most of frequency features. The reasons for using FFT include not only dimensionality reduction but also the feasibility of FFT in integrated photonics 46,47 .…”

Section: Discussionmentioning

confidence: 99%

Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch

Tang

Zhang

et al. 2022

Light Sci Appl

View full text Add to dashboard Cite

Photonic neural network has been sought as an alternative solution to surpass the efficiency and speed bottlenecks of electronic neural network. Despite that the integrated Mach–Zehnder Interferometer (MZI) mesh can perform vector-matrix multiplication in photonic neural network, a programmable in-situ nonlinear activation function has not been proposed to date, suppressing further advancement of photonic neural network. Here, we demonstrate an efficient in-situ nonlinear accelerator comprising a unique solution-processed two-dimensional (2D) MoS2 Opto-Resistive RAM Switch (ORS), which exhibits tunable nonlinear resistance switching that allow us to introduce nonlinearity to the photonic neuron which overcomes the linear voltage-power relationship of typical photonic components. Our reconfigurable scheme enables implementation of a wide variety of nonlinear responses. Furthermore, we confirm its feasibility and capability for MNIST handwritten digit recognition, achieving a high accuracy of 91.6%. Our accelerator constitutes a major step towards the realization of in-situ photonic neural network and pave the way for the integration of photonic integrated circuits (PIC).

show abstract

Section: Discussionmentioning

confidence: 99%

Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch

Tang

Zhang

et al. 2022

Light Sci Appl

View full text Add to dashboard Cite

show abstract

“…FFT can efficiently convert the signal to obtain information on each frequency component of the signal in the frequency domain. In practical engineering applications, FFT takes advantage of its speed to process signals in the engineering field in real time, which has great practical value [24].…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

Online diagnosis for rolling bearings based on multi-channel convolution and transfer learning

Meng¹,

zhao²,

Bo³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

In recent years, the fault diagnosis methods based on deep learning have been widely applied. In practical engineering, there are great distribution differences between the training and testing data in the network, which leading to the low diagnosis reliability. And transfer learning can solve such problems by learning domain invariant features. In this paper, a multi-channel convolutional online transfer network (MC-OTN) model for rolling bearing fault diagnosis is proposed. In the model, the offline stage merges the time domain and frequency domain features of the original data. A three-channel dataset is constructed as input of the network. And the domain invariant features can be learnt by fully training the offline stage network model. The online model is initialized by the parameters transferred from the offline network. The model also designs an online update strategy according to the prediction error. So that the model can adapt to new data, and finally realize the online diagnosis of the rolling bearing fault state. The validity and accuracy of the model are verified by the different laboratory measurement of rolling bearing operating datasets.

show abstract

“…Elements of measurement matrix representing RS can then be obtained as = { 1 0 if = otherwise (10) and the information signal elements become…”

Section: Elaboration Of Shortcomingsmentioning

confidence: 99%

“…It can only be applied on certain signals and the entire framework-sampling and reconstruction-has to be tailored to each individual application. Despite this disadvantage CS has found its way into applications such as medical imaging [5][6][7][8], audio [9] and video [10][11][12] processing, vibration sensing [13,14] data gathering [15] etc.…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing with continuous parametric reconstruction

Andráš

Michaeli

Šaliga

2021

IJECE

View full text Add to dashboard Cite

This work presents a novel unconventional method of signal reconstruction after compressive sensing. Instead of usual matrices, continuous models are used to describe both the sampling process and acquired signal. Reconstruction is performed by finding suitable values of model parameters in order to obtain the most probable fit. A continuous approach allows more precise modelling of physical sampling circuitry and signal reconstruction at arbitrary sampling rate. Application of this method is demonstrated using a wireless sensor network used for freshwater quality monitoring. Results show that the proposed method is more robust and offers stable performance when the samples are noisy or otherwise distorted.

show abstract

A review on sparse fast fourier transform applications in image processing

Cited by 15 publications

References 26 publications

Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch

Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch

Online diagnosis for rolling bearings based on multi-channel convolution and transfer learning

Compressed sensing with continuous parametric reconstruction

Contact Info

Product

Resources

About