小波时-频变换的高强钢丝弹性波传播模态分析

超声导波是近年来桥梁拉索无损检测研究的重要方法之一。针对弹性波在高强钢丝介质中传播的多模态频散问题,采用单点时域波形的小波时频变换进行混叠信号的模态识别分离。通过数值求解Pochhammer频率超越方程,计算得到0~1.5 MHz范围内纵向导波模态理论频散曲线;采用有限元模拟半波正弦脉冲激励导波在钢丝中传播过程,由小波时-频变换得到导波模态分布,并进行了不同腐蚀程度钢丝实验对比分析。结果表明,经小波时-频变换得到的第1、2、3阶纵向导波模态与理论值对应吻合,单点时域波形的小波时-频变换结果能够有效识别高强钢丝中的导波模态;钢丝在无腐蚀状态下,一阶纵向导波模态能量占比达57.74%,随腐蚀程度增加,能量更为集中到一阶纵波模态,二阶模态能量逐渐减小。

基于线性时-频变换的风廓线雷达间歇性杂波抑制

风廓线雷达大气湍流回波信号的识别常受到间歇性杂波的严重干扰。为了能够有效抑制并消除该类杂波,本文提出了基于线性时-频变换的间歇性杂波消除抑制算法。该算法采用具有线性时-频分布特性的短时傅里叶变换,获取雷达回波中杂波成分的调制频率参数,然后估计出相应分数阶傅里叶变换(FRFT)的最优阶数,最后再利用FRFT检测出杂波并对其抑制,通过信号重构回到时域,从而获取有效回波信号频谱。本文提出的方法与常规的FRFT最优阶数扫描法相比,将杂波检测二维搜索问题简化为一维峰值搜索问题,计算量大大降低。理论及计算机仿真表明,该方法效果明显,具有良好的工程应用前景。

煤岩破裂过程声发射时-频信号特征与演化机制

掌握声发射信号时-频域特征及其与煤岩力学性质间的本质联系是利用该方法预测、预警煤岩失稳的基础。以具有不同夹矸和原始裂隙煤岩压缩破坏声发射监测试验为基础,引入小波变换方法,结合数字信号分析、岩石力学等相关理论深入分析煤岩破坏过程声发射时-频信号演化规律,构建了裂纹扩展释放弹性能引起应力波的振幅、频率力学表达。结果表明:受所含弱夹矸或裂隙增加影响煤岩强度、弹性模量降低,峰后软化特征明显,声发射存在由低幅振荡向高幅脉冲转化的信号激增点,强度越高,能量信号幅值显著提高、累积总能量越多,波形幅值增加,信号波形两相邻波峰间隔时间增长,夹杂的小幅振荡波越少;db5和sym2小波基函数分别与激增点、峰值点时域波形相似度最高,更适用于煤岩声发射信号研究;试验所用煤岩声发射信号主频带为0~70 kHz,煤岩强度越低信号频率分布越宽泛,随所受应力升高信号频带分布范围逐渐向主频移动。弹性模量和裂纹扩展速率共同确定了应力波振幅的变化范围,裂纹尺寸决定了振幅和频率的变化趋势,裂纹扩展速率是决定应力波频率的关键参量,进而3个参量共同影响声发射信号时-频特征。试验结果建立了裂纹表征参量与声发射信号频率、幅值的定性描述,为提高声发射信号监测准确性提供了理论基础,开展该理论的定量化应用是后续研究工作的重点。

地震分倒频处理技术

为刻画地震信号的局部层次特征,提高复杂地质体的地震分辨率,本文利用倒谱变换能够提高分辨率的特点,结合短时傅里叶变换,构造了倒时-频变换算法,并提出地震分倒频处理技术.文中对地震分倒频处理技术的公式推导、算法设计、分倒频处理技术提高地震纵向分辨率的机理进行了详细研究.设计出了适合三维地震资料处理的软件,并从理论上阐述了地震分倒频处理技术能够提高地震分辨率.利用倒时-频变换对多分量线性调谐信号模型做倒时-频分析,可以得到良好的时频分辨率.实际三维地震资料计算表明,该方法能够提高地震纵向分辨率,且计算效率高,适合大规模三维地震资料的处理.

基于Zak变换风廓线雷达间歇性杂波抑制

间歇性强杂波严重干扰了风廓线雷达信号检测及风谱的识别,为了抑制该类杂波,文中提出了基于Zak线性时-频变换检测方法,该方法依据杂波的瞬变特性,能够有效地分离强间歇性杂波,且对信号分离后的时-频分布重构得到时域大气湍流回波信号,进而达到抑制杂波的目的,为后续数据处理提供高质量的数据资料。理论及计算机仿真表明该方法效果明显,具有良好的工程应用前景。

两种抗时间片的攻击方法

针对插入时间片造成功耗曲线不能对齐,最终造成CPA攻击失败的情况,提出了两种解决方法:POC和时-频转换。POC方法利用相位计算功耗曲线间的波峰位置,得到功耗曲线间的偏移量,根据偏移量将功耗曲线进行对齐。时-频转换方法将时域的实测功耗通过频谱变换,换转成频域的功耗。以SIC90C58AD微控制器为硬件仿真平台,SM4算法为研究对象,对加入了随机时间片的功耗曲线进行POC和时-频转换处理,并进行相关功耗分析攻击,结果显示:使用POC方法,197条功耗曲线可以成功获取SM4密码算法的第一轮轮密钥;使用时-频转换方法,439条功耗曲线可以获取密钥。证明POC和时-频转换方法可以对抗时间片。

Intelligibility evaluation of enhanced whisper in joint time-frequency domain

Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyzed. Experimental results show that for a spectrum of some density, the joint time-frequency gain-modification based speech enhancement algorithm achieves significant improvement in intelligibility. Additionally, the spectrum region where the estimated spectrum is smaller than the clean spectrum, is the most important region contributing to intelligibility improvement for the enhanced whisper. The spectrum region where the estimated spectrum is larger than twice the size of the clean spectrum is detrimental to speech intelligibility perception within the whisper context.

Fractional S-transform-part 2:Application to reservoir prediction and fluid identification

The fractional S-transform （FRST） has good time-frequency focusing ability. The FRST can identify geological features by rotating the fractional Fourier transform frequency （FRFTfr） axis. Different seismic signals have different optimal fractional parameters which is not conducive to multichannel seismic data processing. Thus, we first decompose the common-frequency sections by the FRST and then we analyze the low-frequency shadow. Second, the combination of the FRST and blind-source separation is used to obtain the independent spectra of the various geological features. The seismic data interpretation improves without requiring to estimating the optimal fractional parameters. The top and bottom of a limestone reservoir can be clearly recognized on the common-frequency section, thus enhancing the vertical resolution of the analysis of the low-frequency shadows compared with traditional ST. Simulations suggest that the proposed method separates the independent frequency information in the time-fractional-frequency domain. We used field seismic and well data to verify the proposed method.

Improving the resolution of seismic traces based on the secondary time-frequency spectrum

The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.

Ultrasonic attenuation estimation based on time-frequency analysis

The quality factor(or Q value)is an important parameter for characterizing the inelastic properties of rock.Achieving a Q value estimation with high accuracy and stability is still challenging.In this study,a new method for estimating ultrasonic attenuation using a spectral ratio based on an S transform(SR-ST)is presented to improve the stability and accuracy of Q estimation.The variable window of ST is used to solve the time window problem.We add two window factors to the Gaussian window function in the ST.The window factors can adjust the scale of the Gaussian window function to the ultrasonic signal,which reduces the calculation error attributed to the conventional Gaussian window function.Meanwhile,the frequency bandwidth selection rules for the linear regression of the amplitude ratio are given to further improve stability and accuracy.First,the feasibility and influencing factors of the SR-ST method are studied through numerical testing and standard sample experiments.Second,artificial samples with different Q values are used to study the adaptability and stability of the SR-ST method.Finally,a further comparison between the new method and the conventional spectral ratio method(SR)is conducted using rock field samples,again addressing stability and accuracy.The experimental results show that this method will yield an error of approximately 36%using the conventional Gaussian window function.This problem can be solved by adding the time window factors to the Gaussian window function.The frequency bandwidth selection rules and mean slope value of the amplitude ratio used in the SR-ST method can ensure that the maximum error of different Q values estimation(Q>15)is less than 10%.

Continuous time-varying Q-factor estimation method in the time-frequency domain

The Q-factor is an important physical parameter for characterizing the absorption and attenuation of seismic waves propagating in underground media,which is of great signifi cance for improving the resolution of seismic data,oil and gas detection,and reservoir description.In this paper,the local centroid frequency is defi ned using shaping regularization and used to estimate the Q values of the formation.We propose a continuous time-varying Q-estimation method in the time-frequency domain according to the local centroid frequency,namely,the local centroid frequency shift(LCFS)method.This method can reasonably reduce the calculation error caused by the low accuracy of the time picking of the target formation in the traditional methods.The theoretical and real seismic data processing results show that the time-varying Q values can be accurately estimated using the LCFS method.Compared with the traditional Q-estimation methods,this method does not need to extract the top and bottom interfaces of the target formation;it can also obtain relatively reasonable Q values when there is no eff ective frequency spectrum information.Simultaneously,a reasonable inverse Q fi ltering result can be obtained using the continuous time-varying Q values.

High-frequency compensation for seismic data based on adaptive generalized S transform

The low-pass fi ltering eff ect of the Earth results in the absorption and attenuation of the high-frequency components of seismic signals by the stratum during propagation.Hence,seismic data have low resolution.Considering the limitations of traditional high-frequency compensation methods,this paper presents a new method based on adaptive generalized S transform.This method is based on the study of frequency spectrum attenuation law of seismic signals,and the Gauss window function of adaptive generalized S transform is used to fi t the attenuation trend of seismic signals to seek the optimal Gauss window function.The amplitude spectrum compensation function constructed using the optimal Gauss window function is used to modify the time-frequency spectrum of the adaptive generalized S transform of seismic signals and reconstruct seismic signals to compensate for high-frequency attenuation.Practical data processing results show that the method can compensate for the high-frequency components that are absorbed and attenuated by the stratum,thereby eff ectively improving the resolution and quality of seismic data.

The Time-Frequency Energy Attenuation Factor and Its Application on the Basis of Gauss Linear Frequency-Modulated Continuous Wavelet Transform

Based on the Gauss linear frequency modulated wavelet transform, a new characteristic index is presented, namely time frequency energy attenuation factor which can reflect the difference features of waveform in earthquake focus mechanism, wave traveling path and its attenuation characteristics in focal area or near field. In order to test its validity, we select the natural earthquakes and explosion or collapse events whose focus mechanisms vary obviously,and some natural earthquakes located at the same site or in a very small area. The study indicates that the time frequency energy attenuation factors of the natural earthquakes are obviously different with that of explosion or collapse events, and the change of the time frequency energy attenuation factors is relatively stable for the earthquakes under the normal seismicity background. Using the above mentioned method, it is expected to offer a useful criterion for strong earthquake prediction by continuous earthquake observation.

Suppression to the cross-channel interference based on the short time Fourier transform

A new cross-channel interference suppression method is proposed to decrease the cross-channel interference in beat signals based on the short time Fourier transform （STY3＂） and the inverse short time Fourier transform （ISTFT） when the dual-orthogonal polarimetric frequency-modulated continu- ous wave （FMCW） radar adopts the opposite-slope linear frequency modulation signal pair in the simultaneous measurement mode. The STFT is applied only on the signals in the cross-interference intervals in the four polarimetric channels to decrease the computation complexity. A mask matrix for suppressing the interference is constructed using the constant false alarm ratio （CFAR） detection on the spectrograms by the STFY. The simulative results show that the cross-channel interference is effi- ciently suppressed by the proposed method. The comparison between the proposed method and the rejection method verifies the improved performance of the proposed method.

Estimation of structural modal parameters by fourier transform with an optimal window

An adaptive Fourier Transform （FT） with an optimal window has been proposed for the time-frequency analysis of nonstationary time series. The method allows for a good estimation of both frequency and amplitude of the spectrum and can be easily applied to the general case of time-varying signals. The evaluation of the proposed approach has been performed on measured time-varying signals from a suspension bridge model and a steel frame model whose data have the typical non-stationary characteristics. The numerical results show that the proposed approach can overcome some of the difficulties encountered in the classic Fourier transform technique and can achieve higher computation accuracy.

Joint Timing and Frequency Synchronization for OFDM based on Fractional Fourier Transform

In this paper a joint timing and frequency synchronization method based on Fractional Fourier Transform （FIFT） is proposed for Orthogonal Frequency-Division Multiplexing （OFDM） system. The combination of two chirp signals with opposite chirp rates are used as the training signal, the received training signal with timing and frequency offset is transformed by FrFT and the two peaks representing two chirps in FrFT domain are detected, then the position coordinates of the two peaks are precisely corrected and substituted into an equation group to calculate timing and frequency offset simultaneously. This method only needs one FrFT calculation to implement synchronization, the computational complexity is equal to that of FFT and less than that of correlation or maximum likelihood calculation of existing methods, and estimation range of frequency offset is Large, greater than half the signal bandwidth, while the simulation results show that even at low SNR it can accurately estimate timing and frequency offset and the estimation error is less than that of existing methods.

A Parametric Autoregressive Model for the Extraction of Electric Network Frequency Fluctuations in Audio Forensic Authentication

This paper proposes a new method for extracting ENF （electric network frequency） fluctuations from digital audio recordings for the purpose of forensic authentication. It is shown that the extraction of ENF components from audio recordings is realizable by applying a parametric approach based on an AR （autoregressive） model. The proposed method is compared to the existing STFT （short-time Fourier transform） based ENF extraction method. Experimental results from recorded electrical grid signals and recorded audio signals show that the proposed approach can improve the time resolution in the extracted ENF fluctuations and improve the detection of tampering with short alterations in longer audio recordings.