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.

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.

A fast acquisition method of DSSS signals using differential decoding and fast Fourier transform

In low earth orbit (LEO) satellite or missile communication scenarios, signals may experience extremely large Doppler shifts and have short visual time. Thus, direct sequence spread spectrum (DSSS) systems should be able to achieve acquisition in a very short time in spite of large Doppler frequencies. However, the traditional methods cannot solve it well. This work describes a new method that uses a differential decoding technique for Doppler mitigation and a batch process of FFT (fast Fourier transform) and IFFT (invert FFT) for the purpose of parallel code phase search by frequency domain correlation. After the code phase is estimated, another FFT process is carried out to search the Doppler frequency. Since both code phase and Doppler frequency domains are searched in parallel, this architecture can provide acquisition fifty times faster than conventional FFT methods. The performance in terms of the probability of detection and false alarm are also analyzed and simulated, showing that a signal-to-noise ratio (SNR) loss of 3 dB is introduced by the differential decoding. The proposed method is an efficient way to shorten the acquisition time with slightly hardware increasing.

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.

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.

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。

A high performance fast-Fourier-transform spectrum analyzer for measuring spin noise spectrums

A high performance fast-Fourier-transform (FFT) spectrum analyzer, which is developed for measure spin noise spectrums, is presented in this paper. The analyzer is implemented with a field-programmable-gate-arrays (FPGA) chip for data and command management. An analog-to-digital-convertor chip is integrated for analog signal acquisition. In order to meet the various requirements of measuring different types of spin noise spectrums, multiple operating modes are designed and realized using the reprogrammable FPGA logic resources. The FFT function is fully managed by the programmable resource inside the FPGA chip. A 1 GSa/s sampling rate and a 100 percent data coverage ratio with non-dead-time are obtained. 30534 FFT spectrums can be acquired per second, and the spectrums can be on-board accumulated and averaged. Digital filters, multi-stage reconfigurable data reconstruction modules, and frequency down conversion modules are also implemented in the FPGA to provide flexible real-time data processing capacity, thus the noise floor and signals aliasing can be suppressed effectively. An efficiency comparison between the FPGA-based FFT spectrum analyzer and the software-based FFT is demonstrated, and the high performance FFT spectrum analyzer has a significant advantage in obtaining high resolution spin noise spectrums with enhanced efficiency.

Comparison of computation time for estimation of dominant frequency of atrial electrograms: Fast fourier transform, blackman tukey, autoregressive and multiple signal classification

Dominant frequency (DF) of electrophysiological data is an effective approach to estimate the activation rate during Atrial Fibrillation (AF) and it is important to understand the pathophysiology of AF and to help select candidate sites for ablation. Frequency analysis is used to find and track DF. It is important to minimize the catheter insertion time in the atria as it contributes to the risk for the patients during this procedure, so DF estimation needs to be obtained as quickly as possible. A comparison of computation tim- es taken for spectrum estimation analysis is presented in this paper. Fast Fourier Transform (FFT), Blackman-Tukey (BT), Autoregressive (AR) and Multiple Signal Classification (MUSIC) methods are used to obtain the frequency spectrum of the signals. The time to produce DF was measured for each method. The method which takes the shortest time for analysis is selected for real time application purpose.

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.

Fast Fourier Transform of Multi-Assets Options under Economic Recession Induced Uncertainties

A Fast Fourier transform approach has been presented by Carr & Madan (2009) on a single underlying asset. In this current research paper, we present fast Fourier transform algorithm for the valuation of Multi-asset Options under Economic Recession Induced Uncertainties. The issue of multi-dimension in both finite and infinite case of Options is part of the focus of this research. The notion of economic recession was incorporated. An intuition behind the introduction of recession induced volatility uncertainty is revealed by huge volatility variation during the period of economic recession compared to the period of recession-free. Nigeria economic recession outbreak in 2016 and its effects on the uncertainty of the payoffs of Nigeria Stocks Exchange (NSE) among other investments was among the motivating factors for proposing economic recession induced volatility in options pricing. The application of the proposed Fast Fourier Transform algorithm in handling multi-assets options was shown. A new result on options pricing was achieved and capable of yielding efficient option prices during and out of recession. Numerical results were presented on assets in 3-dimensions as an illustration taking Black Scholes prices as a bench mark for method effectiveness comparison. The key findings of this research paper among other crucial contributions could be seen in computational procedure of options valuation in multi-dimensions and uncertainties in options payoffs under the exposure of economic recession.

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.

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。

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。

Fast Fourier Transform Approximation of Foreign Currency Option Pricing Based on Exponential Lévy Model

To study the approximation of foreign currency option prices when the underlying assets' price dynamics are described by exponential Lévy processes, the convolution representations for option pricing formulas were given, and then the fast Fourier transform (FFT) algorithm was used to get the approximate values of option prices. Finally, a numerical example was given to demonstrate the calculate steps to the option price by FFT.

FAST FOURIER TRANSFORMS ON BINARY FIELDS

We give a unified treatment of Fast Fourier Transforms for UDMD systems which contains , as special cases, Fast Fourier algorithms for character groups of many subgroups associated with binary fields.

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)%。

面向5G终端基带处理的高量化信噪比FFT加速器设计

针对第5代移动通信系统(5G)终端基带处理芯片的设计要求,本文提出一种基于新型块浮点(BFP)技术的快速傅里叶变换(FFT)加速器。为了降低FFT计算过程中的量化误差,本文实现了一种逐级迭代、动态调整共享指数的块浮点技术,并在此基础上,引入“银行家舍入规则”处理BFP尾数缩放过程中的舍入误差,进一步提高该加速器的整体精度。此外,为了实现5G基带连续FFT处理需求,本文还实现了一种无地址冲突的顺序访存机制。实验结果表明,该加速器从128点至4096点FFT处理的量化信噪比(SQNR)都超过75 dB,比未采用块浮点的方案高8~15 dB;与其他较优秀的设计相比,在高点数(2048点、4096点)上也有3 dB的优势。本文FFT加速器在TSMC 28 nm工艺库下综合显示,总面积为0.193 mm^(2),最高支持600 MHz时钟频率,计算4096点FFT平均功耗为16.3 mW。