An improved non-uniform fast Fourier transform method for radio imaging of coronal mass ejections

Radioheliographs can obtain solar images at high temporal and spatial resolution,with a high dynamic range.These are among the most important instruments for studying solar radio bursts,understanding solar eruption events,and conducting space weather forecasting.This study aims to explore the effective use of radioheliographs for solar observations,specifically for imaging coronal mass ejections(CME),to track their evolution and provide space weather warnings.We have developed an imaging simulation program based on the principle of aperture synthesis imaging,covering the entire data processing flow from antenna configuration to dirty map generation.For grid processing,we propose an improved non-uniform fast Fourier transform(NUFFT)method to provide superior image quality.Using simulated imaging of radio coronal mass ejections,we provide practical recommendations for the performance of radioheliographs.This study provides important support for the validation and calibration of radioheliograph data processing,and is expected to profoundly enhance our understanding of solar activities.

Fast GNSS Acquisition Algorithm Based on SFFT with High Noise Immunity

The Global Navigation Satellite System(GNSS)has been widely used in various fields.To achieve positioning,the receiver must first lock the satellite signal.This is a complicated and expensive process that consumes a lot of resources of the receiver.For this reason,this paper proposes a new fast acquisition algorithm with High Signal-tonoise ratio(SNR)performance based on sparse fast Fourier transform(HSFFT).The algorithm first replaces the IFFT process of the traditional parallel code phase capture algorithm with inverse sparse fast Fourier transform(ISFFT)with better computing performance,and then uses linear search combined with code phase discrimination to replace the positioning loop and the estimation loop with poor noise immunity in ISFFT.Theoretical analysis and simulation results show that,compared with the existing SFFT parallel code phase capture algorithm,the calculation amount of this algorithm is reduced by 19%,and the SNR performance is improved by about 5dB.Compared with the classic FFT parallel code phase capture algorithm,the calculation amount of the algorithm in this paper is reduced by 43%,and when the capture probability is greater than 95%,the SNR performance of the two is approximately the same.

基于双窗全相位FFT的变流器阻抗高精度辨识新方法

为了提高变流器阻抗辨识精度,提出将布莱克曼双窗全相位快速傅里叶变换算法应用于并网变流器输出阻抗高精度辨识之中。首先,建立了dq坐标系下的三相并网变流器的dq阻抗模型;其次,基于原理分析论证了所提方法具有优良的频谱泄漏抑制性能以及较强的抗噪能力,特别适合于在施加小扰动信号背景下非线性特性较强的并网变流器输出阻抗的高精度辨识,并给出变流器阻抗辨识流程;最后,通过仿真试验,验证了方法的优越性。基于布莱克曼双窗全相位快速傅里叶变换的阻抗辨识方法,相比传统方法能以更高的精度辨识出变流器输出阻抗。

脉冲相干激光测风FFT和fCWT融合算法的研究

在脉冲相干激光雷达测风中,广泛使用的FFT算法运算简便快速,但测风的距离分辨率难以进一步提高,而连续小波变换(CWT)等时频分析方法具有时频精细分析能力,但计算实时性差,提出了一种结合FFT和快速连续小波变换(fCWT)优势的融合算法,算法继承了CWT精细分析能力,而运算速度显著加快。通过对融合算法以及CWT、FFT算法就雷达测风仿真数据及实测数据进行对比,融合算法及CWT和FFT算法绘制的风速曲线趋势一致,但具有更丰富细节,融合算法计算时间相比CWT算法减少了45%以上。此融合算法为提高脉冲相干激光测风雷达测风性能提供了一种新思路。

一种基于CNN与FFT‑ELM的输电线路故障识别与定位方法

及时、准确地检测输电线路故障类型与位置是提高电力系统可靠性最重要的问题之一,为此提出一种基于卷积神经网络(convolutional neural networks,CNN)与基于快速傅里叶变换(fast Fourier transform,FFT)的极限学习机(extreme learning machine,ELM)分类模型并行的输电线路故障识别及定位方法。首先,以故障电压时序图作为输入,构建CNN;然后,利用FFT将时域故障电压数据分解,提取各频段的电压峰值与相角作为故障特征样本;接着,以提取的故障特征样本集作为输入,构建ELM网络;最后,通过特征融合层将2个神经网络进行融合,输出故障类型和定位结果。实验结果表明,此方法对输电线路故障识别的准确率为99.95%、故障定位误差在500 m以内、平均误差为263.5 m,可靠性优于其他模型。

大点数FFT在“申威26010”上的并行优化

根据“神威·太湖之光”超级计算机所用国产“申威26010”处理器的架构特点和编程规范,提出针对大点数FFT的众核并行优化方案.该方案源自经典的Cooley-Tukey FFT算法,通过将一维大点数数据迭代分解为二维小规模矩阵进行并行加速.为了解决矩阵“列FFT”的读写、转置和计算问题,提出“列均分-行连续”的读写策略,通过对数据进行合理的分配、重排、交换,结合SIMD向量化、旋转因子优化、双缓冲、寄存器通信、跨步传输等优化手段,充分利用了众核处理器的计算资源和传输带宽.实验结果显示,单核组64从核并行程序较主核运行FFTW库,可以达到最高65x、平均48x以上的加速比.

Performance of Continuous Wavelet Transform over Fourier Transform in Features Resolutions

This study presents a comparative analysis of two image enhancement techniques, Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT), in the context of improving the clarity of high-quality 3D seismic data obtained from the Tano Basin in West Africa, Ghana. The research focuses on a comparative analysis of image clarity in seismic attribute analysis to facilitate the identification of reservoir features within the subsurface structures. The findings of the study indicate that CWT has a significant advantage over FFT in terms of image quality and identifying subsurface structures. The results demonstrate the superior performance of CWT in providing a better representation, making it more effective for seismic attribute analysis. The study highlights the importance of choosing the appropriate image enhancement technique based on the specific application needs and the broader context of the study. While CWT provides high-quality images and superior performance in identifying subsurface structures, the selection between these methods should be made judiciously, taking into account the objectives of the study and the characteristics of the signals being analyzed. The research provides valuable insights into the decision-making process for selecting image enhancement techniques in seismic data analysis, helping researchers and practitioners make informed choices that cater to the unique requirements of their studies. Ultimately, this study contributes to the advancement of the field of subsurface imaging and geological feature identification.

A Deepfake Detection Algorithm Based on Fourier Transform of Biological Signal

Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal threats.Although current deepfake generators strive for high realism in visual effects,they do not replicate biometric signals indicative of cardiac activity.Addressing this gap,many researchers have developed detection methods focusing on biometric characteristics.These methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography(rPPG)signal,resulting in high detection accuracy.However,in the spectral analysis,existing approaches often only consider the power spectral density and neglect the amplitude spectrum—both crucial for assessing cardiac activity.We introduce a novel method that extracts rPPG signals from multiple regions of interest through remote photoplethysmography and processes them using Fast Fourier Transform(FFT).The resultant time-frequency domain signal samples are organized into matrices to create Matrix Visualization Heatmaps(MVHM),which are then utilized to train an image classification network.Additionally,we explored various combinations of time-frequency domain representations of rPPG signals and the impact of attention mechanisms.Our experimental results show that our algorithm achieves a remarkable detection accuracy of 99.22%in identifying fake videos,significantly outperforming mainstream algorithms and demonstrating the effectiveness of Fourier Transform and attention mechanisms in detecting fake faces.

可扩展架构的超大点数FFT处理器设计

面向合成孔径雷达、遥感、电子对抗等领域研究了一款高性能的超大点数快速傅里叶变换(FFT)处理器。文中提出了一种可扩展架构,即针对不同的应用场景可以动态实时调整FFT算法的基数以及处理点数;存储器划分为16个存储模块,可以通过产生无冲突地址进行访问,输出与输入数据帧可以共享同一存储器,具备高效存储器特征。FFT运算采用并行流水线排布,当采用高基算法时,可高并行度访问存储器,实现并行计算,从而获得明显的实时性优势。FFT各级运算采用循环移位寄存器产生地址,以保证蝶算单元输入数据的抽取间隔,并在最后一级输出时进行循环移位寄存器反转操作产生地址,实现输入输出共享存储器。所提设计方法规整、高效、适用范围广泛,便于现场可编程逻辑器件以及集成电路实施,并且也将持续受益于存储器工艺的提升。

Enhanced Wideband Frequency Estimation via FFT: Leveraging Polynomial Interpolation and Array Indexing

Accurate frequency estimation in a wideband digital receiver using the FFT algorithm encounters challenges, such as spectral leakage resulting from the FFT’s assumption of signal periodicity. High-resolution FFTs pose computational demands, and estimating non-integer multiples of frequency resolution proves exceptionally challenging. This paper introduces two novel methods for enhanced frequency precision: polynomial interpolation and array indexing, comparing their results with super-resolution and scalloping loss. Simulation results demonstrate the effectiveness of the proposed methods in contemporary radar systems, with array indexing providing the best frequency estimation despite utilizing maximum hardware resources. The paper demonstrates a trade-off between accurate frequency estimation and hardware resources when comparing polynomial interpolation and array indexing.

基于置零点窗函数的电力谐波FFT分析方法

基于快速傅里叶变换(fast Fourier transform,FFT)的电力谐波算法,受到栅栏效应与非同步采样频谱泄露的影响,测量精度有待进一步提高。加窗和插值修正算法是改善FFT谐波参数测量准确度的有效方法。文章在置零点余弦窗的旁瓣特性和多项拟合插值算法基础上,提出置零点余弦窗FFT的电力谐波分析方法。根据谐波特点设计不同的余弦窗函数,由于窗函数的设计具有灵活性,针对某一种窗函数的插值算法不再适用,因此给出了更具一般性的双谱线和三谱线插值修正公式,为基于FFT的谐波分析提供新的方法。对旁瓣特性以及各次谐波的频率、幅度、相位的测量算法进行仿真分析,通过与经典的Hanning窗、Nuttal窗、Rife-Vincent窗的比较验证了所提方法具有更高的谐波参数分析精度。

基于FFT和Masking的实时语音通话降噪算法

针对语音通话质量的提升问题,提出一种基于快速傅里叶变换(Fast Fourier Transform,FFT)和Masking技术的实时语音通话降噪算法。首先,提出一个实时语音通话降噪的基本框架,并研究了帧分割、窗函数处理及FFT的数学原理。其次,阐述了基于人耳听觉特性的Masking方法及其在频域中的应用。最后,通过逆快速傅里叶变换(Inverse Fast Fourier Transform,IFFT)将信号转换回时域,并进行实验分析。实验结果表明,该降噪算法可以有效改善语音的清晰度和整体感知质量。

使用快速傅里叶变换优化周期参数的EMD-FFT-SARIMA光伏发电预测模型

根据分布式能源工业园区的光伏电力单元特点,对园区光伏发电功率预测模型进行优化,为后续的调度策略提供数据支持。针对经验模式分解(EMD)与季节性差分自回归移动平均模型(SARIMA)相组合的EMD-SARIMA预测模型中,原始数据经过EMD分解得到的各固有本征模态函数(IMF)分量周期计算问题,提出加入快速傅里叶变换(FFT)的周期计算方法,建立EMD-FFT-SARIMA光伏发电功率预测模型。再将每个IMF对应的预测结果进行叠加重构得到最终的预测结果。通过预测结果的误差计算可以发现,加入FFT环节后均方根误差(RMSE)从120.6 MW下降到19.3 MW,平均绝对误差(MAE)从52.87 MW下降到12.3 MW。

一种二级分段PMF-FFT卫星信号捕获算法

针对在小点数快速傅里叶变换(Fast Fourier Transform,FFT)中,经典分段匹配滤波(Partial Matched Filtering,PMF)与FFT相结合的PMF-FFT卫星信号捕获算法存在增益损失,导致捕获灵敏度低、频率覆盖范围小和搜索速度慢的问题,提出了一种改进的二级分段PMF-FFT卫星信号捕获算法。截取部分FFT频率分量,缩短算法有效频率覆盖范围,以改善分段求和带来的包络增益衰减;采用在FFT频率分量中间位置追加搜索的方法,以改善FFT带来的扇贝损失;使用从粗略搜索到高精度搜索的策略,以减少搜索次数,提高搜索速度。仿真结果表明,相比经典PMF-FFT算法,在FFT点数不大于64点的情况下,改进算法能有效提高搜索增益和搜索速度,在8点FFT、125 Hz频率精度条件下,平均增益提升了22.19%,搜索次数减少了7.69%。

基于全相位FFT的铁路信号频率检测算法研究

本文主要研究基于全相位快速傅里叶变换(All Phase Fast Fourier Transform,APFFT)的铁路信号频率检测算法。介绍铁路信号的基本概念,并对传统快速傅里叶变换与APFFT的理论基础进行深入分析。通过矩阵分析方法比较传统FFT和APFFT的性能差异。在实验分析部分,通过具体的数据模拟实验验证APFFT在频率检测方面相较于传统FFT的优势。针对铁路CPFSK(Continuous Phase Frequency Shift Keying)信号,提出基于APFFT的低频率和边缘频率检测技术。通过仿真实验验证所提方法的有效性。总结全相位FFT在铁路信号频率检测中的应用前景与研究价值。

基于FFT算法的ADN电能计量研究

随着应用场景的日益复杂化,在智能计算、图像处理和数学等领域中,快速傅里叶变换(fast Fourier transform,FFT)算法的运算能力不断提高。将FFT算法应用于主动配电网(active distribution network,ADN)电能计量具有十分重要的意义和应用价值。首先对ADN进行了叙述;接着为了对ADN进行电能计量提出了一种算法——FFT算法;然后介绍了基于FFT算法的ADN电能计量研究算法流程;最后通过计算仿真实现了ADN电能计量。试验结果表明,所提出的算法在保证ADN电能计量精度的同时,具有计算简便和灵活性高等优点。

基于五项Rife-Vincent(Ⅰ)窗的四谱线插值FFT谐波分析方法

目前,在电力谐波分析中快速傅里叶变换(FFT)应用得最为广泛,但是在非同步采样时,应用该变换容易产生频谱泄漏,出现栅栏效应。为减小非同步采样对FFT的影响,对旁瓣峰值电平小且下降速率快的五项Rife-Vincent(Ⅰ)窗进行了分析,并将它应用于FFT算法中,提出了基于五项Rife-Vincent(Ⅰ)窗的四谱线插值FFT谐波检测算法。经过多项式函数的拟合,得到了简单实用的四谱线插值修正公式,使计算过程更为简单。结果表明,与Hanning窗、Nuttall窗和四项Rife-Vincent(Ⅰ)窗插值FFT相比,相同条件下,五项Rife-Vincent(Ⅰ)窗具有更高的准确度,其幅值相对误差EAh≤6.524×10-5%,相位相对误差Eφh≤7.751×10^(-3)%。

量化噪声对插值fast Fourier transform谐波估计的影响

插值fast Fourier transform(FFT)算法是对谐波频率、幅度和相位三参数进行精确估计的常见方法.信号中的噪声无疑会影响插值FFT算法的谐波估计结果.有文献讨论过信号中的白噪声对插值FFT算法谐波估计结果的影响,尚没有文献讨论过A/D(analog-to-digital)量化噪声对插值FFT算法谐波估计结果的影响.本文首先对插值FFT谐波估计算法做了简单介绍,接着对A/D量化噪声的特点进行了分析研究.在此基础上,仔细研究了量化噪声对插值FFT谐波估计方法的影响程度——给出了计算模型、进行了大量仿真计算、得出了量化噪声对谐波频率、幅度和相位3参数估计结果影响程度的结论.相信所得结论对电路设计中的A/D器件、信号调理器件等的选型有指导意义.

基于插值fast Fourier transform谐波分析的桥梁状态评估方法

桥梁的环境振动信号包含了各种环境噪声成分,如何从噪声影响中提取桥梁的真实自振频率,并进行桥梁状态评估是亟待解决的问题.本文对环境激励下的大跨桥梁响应信号进行了研究,根据环境激励在低频段近于平白噪声以及线性系统在环境激励下,各输出点测量信号之间相关函数与结构的脉冲响应函数具有相同的数学表达式——均是主频正弦函数的叠加这一特点,建立了插值fast Fourier transform(FFT)频谱校正技术分析各测点相关函数的方法.该方法能在白噪声背景下精确提取桥梁振动频率、幅度和相位等模态参数,简单实用,不仅能在线识别参数,而且对桥梁单点激励和多点同时激励均适用.将校正后的主频与三维有限元计算结果相对比,能对桥梁结构整体状态和工作性能进行评估.最后,一座实桥动力试验和计算结果证明了该方法的能有效用于运营条件下桥梁状态的评估.

Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network

Cardiac Arrhythmias shows a condition of abnor-mal electrical activity in the heart which is a threat to humans. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the fea-tures, for the classification of heart beats according to different arrhythmias. Data were obtained from 40 records of the MIT-BIH arrhythmia database (only one lead). Cardiac arrhythmias which are found are Tachycardia, Bradycardia, Supraventricular Tachycardia, Incomplete Bundle Branch Block, Bundle Branch Block, Ventricular Tachycardia. A learning dataset for the neural network was obtained from a twenty records set which were manually classified using MIT-BIH Arrhythmia Database Directory and docu- mentation, taking advantage of the professional experience of a cardiologist. Fast Fourier transforms are used to identify the peaks in the ECG signal and then Neural Networks are applied to identify the diseases. Levenberg Marquardt Back-Propagation algorithm is used to train the network. The results obtained have better efficiency then the previously proposed methods.