Showing papers on "Fast Fourier transform published in 2022"

Multisignal Modulation Classification Using Sliding Window Detection and Complex Convolutional Network in Frequency Domain

Abstract: With the development of the Internet of Things (IoT), the IoT devices are increasing day by day, resulting in increasingly scarce spectrum resources. At the same time, many IoT devices are facing inevitable malicious attacks. The cognitive Radio-enabled IoT (CR-IoT) is proposed as an effective method for spectrum resource allocation and risk monitoring in the IoT. The signal detection and modulation recognition are the key technologies for CR-IoT, addressing the problem of multisignal detection and automatic modulation classification (AMC) is one of the prerequisites for realizing secure dynamic spectrum access. Based on sliding window and deep learning (DL), this study proposes a multisignal frequency domain detection and recognition method. The frequency spectrum of the time-domain overlapping signal is obtained through the fast Fourier transform (FFT), and the frequency spectrum is segmented based on the signal energy detection method. Finally a complex convolutional neural network (CNN) is constructed for the identification of signal spectrum information. The proposed method can recognize 264 time-domain aliasing and frequency-closed signals with an accuracy of 97.3% under the influence of −2 dB corresponding to the noise of the calibration signal. In addition, the proposed method eliminates the influence of bandwidth, which can effectively detect and recognize the signal types of each component in the frequency band. This method has wide applicability and provides an effective scheme for the IoT cognitive technology.

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection.

Abstract: Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical effects. Moreover, hormones, physiological effects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones' instability. Therefore, it is crucial to study stress using different mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is applied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands' power density for the frontal lobe. After that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications.

A Fast Impedance Calculation-Based Battery State-of-Health Estimation Method

Abstract: State-of-health (SOH) is crucial to the maintenance of various kinds of energy storage systems, including power batteries. Relevant research articles are mostly based on battery external information, such as current, voltage, and temperature, which are susceptible to fluctuation and ultimately affects the SOH estimation accuracy. In this article, to solve these problems, a fast impedance calculation-based battery SOH estimation method for lithium-ion battery is proposed from the perspective of electrochemical impedance spectroscopy (EIS). The relationship between EIS and state of charge and that between EIS and degraded capacity is first studied by experimental tests. Some impedance features called health factors effectively indicating battery aging states are selected. Second, an improved fast Fourier transform (FFT) utilizing the conversion relationship between the real and complex signals is proposed to realize online fast EIS acquisition. Compared with ordinary FFT, such treatments reduce computational complexity. Then, the SOH evaluation model is built by the extreme learning machine with regularization mechanism, further reducing the computational burden. The relationship between the health factors and aging capacity of batteries is established. Finally, an experimental bench is established. The results indicate that the estimated SOH can be obtained within 35 s for a four-cell series-connected battery pack and the estimation errors are less than 2%.

Fast Processing Approach for Near-Field Terahertz Imaging With Linear Sparse Periodic Array

Abstract: The benefits of terahertz (THz) radiation have increased its use, especially in imaging systems. Recently, the use of a linear sparse periodic array (SPA) has been proposed as an effective solution for two-dimensional (2D) scanning in THz imaging systems. However, the special multistatic structure of the SPA is such that it is not possible to apply fast Fourier transform-based techniques directly in the near-field (NF). Therefore, in this paper, a fast processing approach based on two Fourier techniques compatible with linear SPA is presented for NF THz imaging. In this approach, we first employ a multistatic-to-monostatic conversion to reduce phase errors due to NF multistatic imaging. Then, to improve the quality of the results, we mathematically derive an interpolation formula to counteract the non-uniform spacing of the virtual array. The modified data is then processed by three rapid techniques (fast Fourier transform (FFT)-inverse fast Fourier transform, matched filtering and a novel 1D FFT-based technique with low computational complexity) to obtain reconstructed images of the scene. Numerical and experimental results confirm the satisfactory performance of the proposed approach in terms of both the computational time and the quality of the reconstructed images.

Spectral envelope-based adaptive empirical Fourier decomposition method and its application to rolling bearing fault diagnosis.

Abstract: Adaptive empirical Fourier decomposition (AEFD) is a recently developed approach of nonstationary signal mode separation. However, it requires to set the spectrum segmentation boundary relying on the users' professional experience ahead of time. In this paper, a novel spectral envelope-based adaptive empirical Fourier decomposition (SEAEFD) method is proposed to improve the performance of AEFD for rolling bearing vibration signal analysis. In the proposed SEAEFD approach, fast Fourier transform (FFT) of the raw signal is calculated to obtain the frequency spectrum at first. Then, the spectral envelope processing is implemented on the spectrum signal obtained by FFT to achieve an adaptive segmentation. In the traditional segmentation method, generally, the minima and midpoints between adjacent extreme points are taken as the spectrum segmentation boundary, in which the obtained frequency band contains more interference components. To achieve the effect of denoising and restrain the noise that existed in the collected vibration signal, SEAEFD is proposed to optimize the spectrum segmentation boundary so that the obtained frequency band contains the least noise components. Lastly, the inverse FFT is used to reconstruct the component signal within each frequency band and the gained signals are termed as Fourier intrinsic mode functions (FIMFs). Therefore, SEAEFD enables a nonstationary signal to be decomposed into several single-component signals with instantaneous frequencies of physical significance. The proposed SEAEFD method is compared with recently developed methods, including EAEFD, AEFD, EWT, VMD and EMD methods, by analyzing the simulation signals and the measured data of rolling bearing. The results indicate that SEAEFD is valid in diagnosing rolling bearing faults and gets a better diagnosis performance than the compared methods.

Computationally Efficient Implementation of Joint Detection and Parameters Estimation of Signals with Dispersive Distortions on a GPU

Abstract: The detector is an integral part of the device for receiving and processing radio signals. Signals that have passed through the ionospheric channel acquire an unknown Doppler shift and are subject to dispersion distortions. It is necessary to carry out joint detection and parameter estimation to improve reception quality and detection accuracy. Modern hardware base developing makes it possible to implement a device for joint detection and evaluation of signals based on standard processors (CPU) and graphic processors (GPU). The article discusses the implementation of a signal detector that allows for real-time operation. A comparison of implementations of algorithms for estimating the Doppler frequency shift through multiplication by a complex exponent and the fast Fourier transform (FFT) is performed. A comparison of computational costs and execution speed on the CPU and GPU is considered.

Multifeature Fusion-Based Hand Gesture Sensing and Recognition System

Abstract: With the development of the radar sensing technology, hand gesture sensing and recognition has attracted much attention. This letter adopts a frequency-modulated continuous wave (FMCW) radar to achieve short-range hand gesture sensing and recognition. Specifically, the range, Doppler, and angle parameters of hand gestures are measured by fast Fourier transformation (FFT) and multiple signal classification (MUSIC) algorithm, respectively. The mixup (MP) algorithm combined with augmentation (AU) algorithm using a weight factor is applied to expand the hand gesture data. Then, a complementary multidimensional feature fusion network-based hand gesture recognition (CMFF-HGR) is designed to extract the features and achieve HGR. Finally, a series of experiments are carried out to verify the effectiveness of the proposed approach, and the results show that the recognition accuracy is higher than the existing alternatives with low computational complexity.

FFT-PEEC: A Fast Tool From CAD to Power Electronics Simulations

Abstract: A fast and general partial element equivalent circuit (PEEC) method based on the fast Fourier transform (FFT) is proposed for the first time. The numerical tool only requires common CAD data input files (e.g., $.\text{stl}$ format), then the discretization process is performed automatically by means of a fast voxelization technique based on ray intersection, thus, drastically reducing the human effort required to setup the model. The method allows for considering at the same time inductive and capacitive effects, and is focused on power electronics applications where propagation effects can be neglected, whereas all the other electromagnetic phenomena are considered. Specifically, the proposed method is particularly suited for problems where both electric and magnetic fields are equally important and, therefore, quasistatic approximations do not apply. An ad-hoc preconditioner which significantly speeds-up the solver is also proposed and, thanks to the FFT, both memory and computation time are significantly reduced, without the need of applying data compression. Both linear and nonlinear materials are considered by the proposed FFT-PEEC method. Sample implementation of the method is made publicly available.

Performance Analysis of Massive MIMO-OFDM System Incorporated with Various Transforms for Image Communication in 5G Systems

Abstract: Modern-day applications of fifth-generation (5G) and sixth-generation (6G) systems require fast, efficient, and robust transmission of multimedia information over wireless communication medium for both mobile and fixed users. The hybrid amalgamation of massive multiple input multiple output (mMIMO) and orthogonal frequency division multiplexing (OFDM) proves to be an impressive methodology for fulfilling the needs of 5G and 6G users. In this paper, the performance of the hybrid combination of massive MIMO and OFDM schemes augmented with fast Fourier transform (FFT), fractional Fourier transform (FrFT) or discrete wavelet transform (DWT) is evaluated to study their potential for reliable image communication. The analysis is carried over the Rayleigh fading channels and M-ary phase-shift keying (M-PSK) modulation schemes. The parameters used in our analysis to assess the outcome of proposed versions of OFDM-mMIMO include signal-to-noise ratio (SNR) vs. peak signal-to-noise ratio (PSNR) and SNR vs. structural similarity index measure (SSIM) at the receiver. Our results indicate that massive MIMO systems incorporating FrFT and DWT can lead to higher PSNR and SSIM values for a given SNR and number of users, when compared with in contrast to FFT-based massive MIMO-OFDM systems under the same conditions.

A Fast Diffraction Tomography Algorithm for 3-D Through-the-Wall Radar Imaging Using Nonuniform Fast Fourier Transform

Abstract: This letter proposes the application of the nonuniform fast Fourier transform to the diffraction tomography (DT) image reconstruction method for 3-D through-the-wall radar imaging which leads to a significant speed-up of the image reconstruction procedure. This speed-up is of critical importance in real-time imaging systems since during a long time of imaging, the target displacement may cause errors in detection. Through simulation and experimental examples, the method is validated. In addition, the improved speed of the proposed algorithm is demonstrated by comparing its CPU-time with that of the conventional DT method.

A Review of FE-FFT-Based Two-Scale Methods for Computational Modeling of Microstructure Evolution and Macroscopic Material Behavior

Abstract: Abstract The overall, macroscopic constitutive behavior of most materials of technological importance such as fiber-reinforced composites or polycrystals is very much influenced by the underlying microstructure. The latter is usually complex and heterogeneous in nature, where each phase constituent is governed by non-linear constitutive relations. In order to capture such micro-structural characteristics, numerical two-scale methods are often used. The purpose of the current work is to provide an overview of state-of-the-art finite element (FE) and FFT-based two-scale computational modeling of microstructure evolution and macroscopic material behavior. Spahn et al. (Comput Methods Appl Mech Eng 268:871–883, 2014) were the first to introduce this kind of FE-FFT-based methodology, which has emerged as an efficient and accurate tool to model complex materials across the scales in the recent years.

On the Existence of Fast Modes in Compressible Magnetohydrodynamic Turbulence

Abstract: We study the existence and property of Fast magnetosonic modes in 3D compressible MHD turbulence by carrying out a number of simulations with compressible and incompressible driving conditions. We use two approaches to determine the presence of Fast modes: mode decomposition based on spatial variations only and spatio-temporal 4D-FFT analysis of all fluctuations. The latter method enables us to quantify fluctuations that satisfy the dispersion relation of Fast modes with finite frequency. Overall, we find that the fraction of Fast modes identified via spatio-temporal 4D FFT approach in total fluctuation power is either tiny with nearly incompressible driving or ~2% with highly compressible driving. We discuss the implications of our results for understanding the compressible fluctuations in space and astrophysics plasmas.

A Fractional Fourier Based Medical Image Authentication Approach

Abstract: Patient medical information in all forms is crucial to keep private and secure, particularly when medical data communication occurs through insecure channels. Therefore, there is a bad need for protecting and securing the color medical images against impostors and invaders. In this paper, an optical medical image security approach is introduced. It is based on the optical bit-plane Jigsaw Transform (JT) and Fractional Fourier Transform (FFT). Different kernels with a lone lens and a single arbitrary phase code are exploited in this security approach. A preceding bit-plane scrambling process is conducted on the input color medical images prior to the JT and FFT processes to accomplish a tremendous level of robustness and security. To confirm the efficiency of the suggested security approach for secure color medical image communication, various assessments on different color medical images are examined based on different statistical security metrics. Furthermore, a comparative analysis is introduced between the suggested security approach and other conventional cryptography protocols. The simulation outcomes acquired for performance assessment demonstrate that the suggested security approach is highly secure. It has excellent encryption/decryption performance and superior security results compared to conventional cryptography approaches with achieving recommended values of average entropy and correlation coefficient of 7.63 and 0.0103 for encrypted images.

Development of Intelligent Fault Diagnosis Technique of Rotary Machine Element Bearing: A Machine Learning Approach

Abstract: The bearing is an essential component of a rotating machine. Sudden failure of the bearing may cause an unwanted breakdown of the manufacturing plant. In this paper, an intelligent fault diagnosis technique was developed to diagnose various faults that occur in a deep groove ball bearing. An experimental setup was designed and developed to generate faulty data in various conditions, such as inner race fault, outer race fault, and cage fault, along with the healthy condition. The time waveform of raw vibration data generated from the system was transformed into a frequency spectrum using the fast Fourier transform (FFT) method. These FFT signals were analyzed to detect the defective bearing. Another significant contribution of this paper is the application of a machine learning (ML) algorithm to diagnose bearing faults. The support vector machine (SVM) was used as the primary algorithm. As the efficiency of SVM heavily depends on hyperparameter tuning and optimum feature selection, the particle swarm optimization (PSO) technique was used to improve the model performance. The classification accuracy obtained using SVM with a traditional grid search cross-validation (CV) optimizer was 92%, whereas the improved accuracy using the PSO-based SVM was found to be 93.9%. The developed model was also compared with other traditional ML techniques such as k-nearest neighbor (KNN), decision tree (DT), and linear discriminant analysis (LDA). In every case, the proposed model outperformed the existing algorithms.

A structured review of sparse fast Fourier transform algorithms

Abstract: Discrete Fourier transform (DFT) implementation requires high computational resources and time; a computational complexity of order O(N2) for a signal of size N. Fast Fourier transform (FFT) algorithm, that uses butterfly structures, has a computational complexity of O(Nlog(N)), a value much less than O(N2). However, in recent years by introducing big data in many applications, FFT calculations still impose serious challenges in terms of computational complexity, time requirement, and energy consumption. Involved data in many of these applications are sparse in the spectral domain. For these data by using Sparse Fast Fourier Transform (SFFT) algorithms with a sub-linear computational and sampling complexity, the problem of computational complexity of Fourier transform has been reduced substantially. Different algorithms and hardware implementations have been introduced and developed for SFFT calculations. This paper presents a categorized review of SFFT, highlights the differences of its various algorithms and implementations, and also reviews the current use of SFFT in different applications.

FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area

Abstract: We propose a frequency-modulated continuous wave (FMCW) radar estimation algorithm with high resolution and low complexity. The fast Fourier transform (FFT)-based algorithms and multiple signal classification (MUSIC) algorithms are used as algorithms for estimating target parameters in the FMCW radar systems. FFT-based and MUSIC algorithms have tradeoff characteristics between resolution performance and complexity. While FFT-based algorithms have the advantage of very low complexity, they have the disadvantage of a low-resolution performance; that is, estimating multiple targets with similar parameters as a single target. On the other hand, subspace-based algorithms have the advantage of a high-resolution performance, but have a problem of very high complexity. In this paper, we propose an algorithm with reduced complexity, while achieving the high-resolution performance of the subspace-based algorithm by utilizing the advantages of the two algorithms; namely, the low-complexity advantage of FFT-based algorithms and the high-resolution performance of the MUSIC algorithms. The proposed algorithm first reduces the amount of data used as input to the subspace-based algorithm by using the estimation results obtained by FFT. Secondly, it significantly reduces the range of search regions considered for pseudo-spectrum calculations in the subspace-based algorithm. The simulation and experiment results show that the proposed algorithm achieves a similar performance compared with the conventional and low complexity MUSIC algorithms, despite its considerably lower complexity.