Adaptive Short-Time Fractional Fourier Transform Based on Minimum Information Entropy

Traditional short-time fractional Fourier transform(STFrFT)has a single and fixed window function,which can not be adjusted adaptively according to the characteristics of fre-quency and frequency change rate.In order to overcome the shortcomings,the STFrFT method with adaptive window function is proposed.In this method,the window function of STFrFT is ad-aptively adjusted by establishing a library containing multiple window functions and taking the minimum information entropy as the criterion,so as to obtain a time-frequency distribution that better matches the desired signal.This method takes into account the time-frequency resolution characteristics of STFrFT and the excellent characteristics of adaptive adjustment to window func-tion,improves the time-frequency aggregation on the basis of eliminating cross term interference,and provides a new tool for improving the time-frequency analysis ability of complex modulated sig-nals.

Application of short-time Fourier transform to high-rise frame structural-health monitoring based on change of inherent frequency over time

The high-rise frame structure has become more and more widespread, like its damage from the complication of the environment. The traditional method of damage detection, which is only suitable for the stationary signal, does not apply to a high-rise frame structure because its damage signal is non-stationary. Thus, this paper presents an application of the short-time Fourier transform(STFT) to damage detection of high-rise frame structures. Compared with the fast Fourier transform, STFT is found to be able to express the frequency spectrum property of the time interval using the signal within this interval. Application of STFT to analyzing a Matlab model and the shaking table test with a twelve-story frame-structure model reveals that there is a positive correlation between the slope of the frequency versus time and the damage level. If the slope is equal to or greater than zero, the structure is not damaged. If the slope is smaller than zero, the structure is damaged, and the less the slope is, the more serious the damage is. The damage results from calculation based on the Matlab model are consistent with those from the shaking table test, demonstrating that STFT can be a reliable tool for the damage detection of high-rise frame structures.

Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN

The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.

Radar Signal Intra-Pulse Feature Extraction Based on Improved Wavelet Transform Algorithm

With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applicable to the modern radar signal processing, and it is necessary to seek new methods in the two-dimensional transformation domain. The time-frequency analysis method is the most widely used method in the two-dimensional transformation domain. In this paper, two typical time-frequency analysis methods of short-time Fourier transform and Wigner-Ville distribution are studied by analyzing the time-frequency transform of typical radar reconnaissance linear frequency modulation signal, aiming at the problem of low accuracy and sen-sitivity to the signal noise of common methods, the improved wavelet transform algorithm was proposed.

Rolling bearing fault diagnostics based on improved data augmentation and ConvNet

Convolutional neural networks(CNNs)are well suited to bearing fault classification due to their ability to learn discriminative spectro-temporal patterns.However,gathering sufficient cases of faulty conditions in real-world engineering scenarios to train an intelligent diagnosis system is challenging.This paper proposes a fault diagnosis method combining several augmentation schemes to alleviate the problem of limited fault data.We begin by identifying relevant parameters that influence the construction of a spectrogram.We leverage the uncertainty principle in processing time-frequency domain signals,making it impossible to simultaneously achieve good time and frequency resolutions.A key determinant of this phenomenon is the window function's choice and length used in implementing the shorttime Fourier transform.The Gaussian,Kaiser,and rectangular windows are selected in the experimentation due to their diverse characteristics.The overlap parameter's size also influences the outcome and resolution of the spectrogram.A 50%overlap is used in the original data transformation,and±25%is used in implementing an effective augmentation policy to which two-stage regular CNN can be applied to achieve improved performance.The best model reaches an accuracy of 99.98%and a cross-domain accuracy of 92.54%.When combined with data augmentation,the proposed model yields cutting-edge results.

Wind turbine clutter mitigation using morphological component analysis with group sparsity

To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.

基于温度调制的气体传感器动态信号识别研究

利用4只掺杂Pt的Sn O2微热板气体传感器组成传感器阵列来识别三种不同的工业气体(CO,CH4和C2H5OH),传感器加热分别采用了三角波、正弦波、锯齿波和脉冲波四种温度调制模式。然后将得到的传感器动态响应信号进行短时傅里叶变换(STFT)提取特征信息,并结合主成分分析(PCA)方法将特征信息进行降维处理,发现调制周期对气体种类影响较小。最后利用决策树识别三种不同的气体,识别准确率较高。实验结果证明了该算法的正确性和可行性。

基于短时分数阶傅里叶变换的时频分析方法

本文研究了短时分数阶傅里叶变换(STFRFT)时频分析方法的分辨率精度和算法性能.首先,文中给出了一种STFRFT时频分辨率的数学计算表达式,其有利于时频分辨率的量化比较,仿真结果表明该理论量化值与观察值基本吻合;其次,针对算法运算量大的问题,提出了一种STFRFT的快速计算方法,它较传统的穷举搜索方法运算量约降低1个数量级;最后,给出了算法估计误差的理论分析并运用该方法对多目标信号进行了分析,仿真表明该方法可有效抑制交叉项和解决多分量时频信号的分离问题.

FM interference suppression for PRC-CW radar based on adaptive STFT and time-varying filtering

The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deteriorates greatly when the FM inter- ference power exceeds the anti-jamming limit of the radar. Accord- ing to the fact that the PRC-CW radar echo is a wideband pseudo random signal occupying the whole TF plane, while the FM in- terference only concentrates in a small portion, a new method is proposed based on adaptive short-time Fourier transform （STFT） and time-varying filtering for FM interference suppression. This method filters the received signal by using a binary mask to excise only the portion of the TF plane corrupted by the interference. Two types of interference, linear FM （LFM） and sinusoidal FM （SFM）, under different signal-to-jamming ratio （S JR） are studied. It is shown that the proposed method can effectively suppress the FM interference and improve the performance of target detection.

Feature Extraction Techniques of Non-Stationary Signals for Fault Diagnosis in Machinery Systems

Previously, fault diagnosis of fixed or steady state mechanical failures (e.g., pumps in nuclear power plant turbines, engines or other key equipment) applied spectrum analysis (e.g., fast Fourier transform, FFT) to extract the frequency features as the basis for identifying the causes of failure types. However, mechanical equipment for increasingly instant speed variations (e.g., wind turbine transmissions or the mechanical arms used in 3C assemblies, etc.) mostly generate non-stationary signals, and the signal features must be averaged with analysis time which makes it difficult to identify the causes of failures. This study proposes a time frequency order spectrum method combining the short-time Fourier transform (STFT) and speed frequency order method to capture the order features of non-stationary signals. Such signal features do not change with speed, and are thus effective in identifying faults in mechanical components under non-stationary conditions. In this study, back propagation neural networks (BPNN) and time frequency order spectrum methods were used to verify faults diagnosis and obtained superior diagnosis results in non-stationary signals of gear-rotor systems.

指纹图像增强技术综述

通过对指纹识别技术的应用现状和目前存在问题的分析,介绍了指纹识别中常用的图像增强算法。并重点介绍两种主流低质量指纹增强算法的原理,最后总结了该方向上的难点问题和一些新的研究方向。

A WT-STFT combining Algorithm

A fast wavelet packet (WP) algorithm is presented, in which the wavelet transform (WT) and the short-time Fourier transform (STFT) are combined. As WT produces multiresolution of frequency and time, and STFT has a fast algorithm, the combining algorithm is suitable for fast signal analysis.

Non-Stationary Signal Segmentation and Separation from Joint Time-Frequency Plane

Multi-components sinusoidal engineering signals who are non-stationary signals were considered in this study since their separation and segmentations are of great interests in many engineering fields. In most cases, the segmentation of non-stationary or multi-component signals is conducted in time domain. In this paper, we explore the advantages of applying joint time-frequency (TF) distribution of the multi-component signals to identify their segments. The Spectrogram that is known as Short-Time Fourier Transform (STFT) will be used for obtaining the time-frequency kernel. Time marginal of the computed kernel is optimally used for the signal segmentation. In order to obtain the desirable segmentation, it requires first to improve time marginal of the kernel by using two-dimensional Wiener mask filter applied to the TF kernel to mitigate and suppress non-stationary noise or interference. Additionally, a proper choice of the sliding window and its overlaying has enhanced our scheme to capture the discontinuities corresponding to the boundaries of the candidate segments.

Time-extracting S-transform algorithm and its application in rolling bearing fault diagnosis

Time-frequency(TF)analysis(TFA)is one of the effective methods to deal with non-stationary signals.Due to their advantages,many experts and scholars have recently developed post-processing algorithms based on traditional TFA.Among them,shorttime Fourier transform(STFT)based post-processing algorithms have developed the fastest.However,these methods rely heavily on the window length selected in STFT,which has great influence on the post-processing algorithm.In this paper,a postprocessing algorithm for effectively processing pulse signals was proposed and called time-extracting S-transform(TEST).The time-domain extraction method based on S-transform avoids the influence of uncertain parameters.After comparing the performance of various TFA methods when processing analog signals,the proposed TEST can clearly show the pulse occurrence time under the premise of ensuring high TF aggregation.The actual signal proves that the method can be used for fault diagnosis of rolling bearings.

Enhancing non-profiled side-channel attacks by time-frequency analysis

Side-channel analysis(SCA)has become an increasing important method to assess the physical security of cryptographic systems.In the process of SCA,the number of attack data directly determines the performance of SCA.With sufficient attack data,the adversary can achieve a successful SCA.However,in reality,the cryptographic device may be protected with some countermeasures to limit the number of encryptions using the same key.In this case,the adversary cannot use casual numbers of data to perform SCA.The performance of SCA will be severely dropped if the attack traces are insufficient.In this paper,we introduce wavelet scatter transform(WST)and short-time fourier transform(STFT)to non-profiled side-channel analysis domains,to improve the performance of side-channel attacks in the context of insufficient data.We design a practical framework to provide suitable parameters for WST/STFT-based SCA.Using the proposed method,the WST/STFT-based SCA method can significantly enhance the performance and robustness of non-profiled SCA.The practical attacks against four public datasets show that the proposed method is able to achieve more robust performance.Compared with the original correlation power analysis(CPA),the number of attack data can be reduced by 50–95%.

A New Symbol Synchronization Scheme for Cyclic Prefix Based Systems

This contribution proposes a new symbol synchronization scheme for cyclicprefix based modulation systems, which is disclosed in Ref.. The proposed algorithm involves twosteps . By using short-time Fourier transform, ISI-free intervals are estimated from time-frequencyspectrum of the received signal, and then an optimum symbol start time is obtained. Computersimulation results show that the algorithm is very robust, and outperforms those based upontime-domain correlations.

Comparison of different techniques for time-frequency analysis of internal combustion engine vibration signals

In this study,we report an analysis of cylinder head vibration signals at a steady engine speed using short-time Fourier transform(STFT).Three popular time-frequency analysis techniques,i.e.,STFT,analytic wavelet transform(AWT) and S transform(ST),have been examined.AWT and ST are often applied in engine signal analyses.In particular,an AWT expression in terms of the quality factor Q and an analytical relationship between ST and AWT have been derived.The time-frequency resolution of a Gaussian function windowed STFT was studied via numerical simulation.Based on the simulation,the empirical limits for the lowest distinguishable frequency as well as the time and frequency resolutions were determined.These can provide insights for window width selection,spectrogram interpretation and artifact identification.Gaussian function windowed STFTs were applied to some cylinder head vibration signals.The spectrograms of the same signals from ST and AWT were also determined for comparison.The results indicate that the uniform resolution feature of STFT is not necessarily a disadvantage for time-frequency analysis of vibration signals when the engine is in stationary state because it can more accurately localize the frequency components excited by transient excitations without much loss of time resolution.

Performance Analysis of STFT Based Timing Approach to OFDM Systems

This paper mainly focuses on performance analysis of the previously proposedSTFT based 2-D timing approach to OFDM systems and presents simulations results of its performancein AWGN and multipath fading environment and its robustness against the duration of Channel ImpulseResponse (CIR) and frequency offset. Simulation results suggest that a revised version of Short-TimeFourier Transform (STFT) can be used to greatly reduce computational complexity, especially athigher SNR.

FLOW NOISE MEASUREMENT OF SURFACE SHIP WITH TOWED MODEL

In this article, a new acoustic test technique using towed model was introduced to study flow noise caused by a surface ship. The project of model test was be properly designed for acoustic signal collecting and with the help of appropriate data processing method different kinds of acoustic sources could be successfully identified. A lot of work about fuid noise could be carried on with the towed model, and the noise corresponding to low frequency which is especially interested for its long distance radiating with small attenuation could also be studied in this way.

Application of signal processing techniques to the detection of tip vortex cavitation noise in marine propeller

The tip vortex cavitation and its relevant noise has been the subject of extensive researches up to now. In most cases of experimental approaches, the accurate and objective decision of cavitation inception is primary, which is the main topic of this paper. Although the conventional power spectrum is normally adopted as a signal processing tool for the analysis of cavitation noise, a faithful exploration cannot be made especially for the cavitation inception. Alternatively, the periodic occurrence of bursting noise induced from tip vortex cavitation gives a diagnostic proof that the repeating frequency of the bursting contents can be exploited as an indication of the inception. This study, hence, employed the Short-Time Fourier Transform （STFT） analysis and the Detection of Envelope Modulation On Noise （DEMON） specmma analysis, both which are appropriate for finding such a repeating frequency. Through the acoustical measurement in a water tunnel, the two signal processing techniques show a satisfactory result in detecting the inception of tip vortex cavitation.