Efficient simulation of spatially correlated non-stationary ground motions by wavelet-packet algorithm and spectral representation method

Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice.The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function.Another difficulty is the estimation of evolutionary power spectral density(EPSD)with quite a few samples.To tackle these problems,the wavelet packet transform(WPT)algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain.The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process.For the simulation of spatially varying ground motions,the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models.Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques.In addition to a good match with the target coherency model,two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording,including waveform,temporal/spectral non-stationarity,normalized energy buildup,and significant duration.

基于滑动窗宽优化的LMSSGST识别非平稳信号瞬时频率

为提高非平稳响应信号瞬时频率的识别效果,提出基于滑动窗宽优化的局部最大同步挤压广义S变换(local maximum synchrosqueezing generalized S-transform,LMSSGST)。该方法首先对非平稳响应信号进行广义S变换获得相应的时频系数;其次,利用该响应信号的功率谱密度特征曲线确定局部最大同步挤压算子中滑动窗的宽度;再次,通过局部最大同步挤压算子进行时频重排;最后,采用模极大值改进算法提取瞬时频率曲线。通过两个数值算例、一个滑动窗宽参数分析和一个时变拉索试验验证了所提方法的有效性,研究结果表明:利用功率谱密度特征曲线能够有效确定滑动窗的窗宽和模极大值算法的提取范围。相比局部最大同步挤压变换算法,基于滑动窗宽优化的LMSSGST具有更佳的瞬时频率识别效果。

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.

基于自适应快速S变换和XGBoost的心电信号精确快速分类方法

针对心电信号(ECG)传统分类方法效率较低的问题,该文提出一种基于自适应快速S变换(AFST)和XGBoost的心电信号精确快速分类方法。该方法首先通过快速定位算法确定心电信号特征频率点,再根据特征频率点自适应调节S变换窗宽因子,增强S变换的时频分辨率的同时避免迭代计算,大大减少运行时间。其次,基于自适应快速S变换的时频矩阵提取12个特征量来表征5种心电信号的特征信息,特征向量维数低,识别能力强。最后,利用XGBoost算法对特征向量进行识别。MIT-BIH心律失常数据库和患者实测数据验证表明,该方法显著地缩短了分类时间,对5种心电信号的分类准确率分别为99.59%和97.32%,适用于实际检测系统中心律失常疾病的快速诊断。

Q-factor estimation in CMP gather and the continuous spectral ratio slope method

The attenuation factor or quality factor(Q-factor or Q) has been used to measure the energy attenuation of seismic waves propagating in underground media. Many methods are used to estimate the Q-factor. We propose a method to calculate the Q-factor based on the prestack Q-factor inversion and the generalized S-transform. The proposed method specifies a standard primary wavelet and calculates the cumulative Q-factors; then, it finds the interlaminar Q-factors using the relation between Q and offset(QVO) and the Dix formula. The proposed method is alternative to methods that calculate interlaminar Q-factors after horizon picking. Because the frequency spectrum of each horizon can be extracted continuously on a 2D time–frequency spectrum, the method is called the continuous spectral ratio slope(CSRS) method. Compared with the other Q-inversion methods, the method offers nearly effortless computations and stability, and has mathematical and physical significance. We use numerical modeling to verify the feasibility of the method and apply it to real data from an oilfield in Ahdeb, Iraq. The results suggest that the resolution and spatial stability of the Q-profile are optimal and contain abundant interlaminar information that is extremely helpful in making lithology and fluid predictions.

Application of Wavelet Algorithm to Spectral Analysis of Oceanic Waves and Offshore Structure Responses

Fourier transform （FF） is a commonly used method in spectral analysis of ocean wave and offshore structure responses, but it is not suitable for records of short length. In this paper another method, wavelet transform （WT）, is applied to ＇analyze the data of short length. The Morlet wavelet is employed to calculate the spectra density functions for wave records and simulated Floating Production Storage and Offloading （FPSO） vessels＇ responses. Computed wave data include simulated wave data based on JONSWAP spectrum and the recorded data of Storm 149 from North Alwyn. Wavelet method is validated by comparing the statistical characteristics by WF method and those by fast Fourier transform （FFT） method with those of target spectra. The spectral density fnnctions＇ shapes calculated by WT are less malformed and have less error of statistical characteristics compared with those by FT especially when the record lengths decrease.

Constant envelope FrFT OFDM: spectral and energy efficiency analysis

Constant envelope with a fractional Fourier transformorthogonal frequency division multiplexing(CE-FrFT-OFDM)is a special case of a constant envelope OFDM(CE-OFDM),both being energy efficient wireless communication techniques with a 0 dB peak to average power ratio(PAPR).However,with the proper selection of fractional order,the first technique has a high bit error rate(BER)performance in the frequency-time selective channels.This paper performs further analysis of CE-FrFT-OFDM by examining its spectral efficiency(SE)and energy efficiency(EE)and compare to the famous OFDM and FrFT-OFDM techniques.Analytical and comprehensive simulations conducted show that,the CE-FrFT-OFDM has five times the EE of OFDM and FrFT-OFDM systems with a slightly less SE.Increasing CE-FrFT-OFDM’s transmission power by increasing its amplitude to 1.7 increases its SE to match that of the OFDM and FrFT-OFDM systems while slightly reducing its EE by 20%to be four times that of OFDM and FrFTOFDM systems.OFDM and FrFT-OFDM’s amplitude fluctuations cause rapid changing output back-off(OBO)power requirements and further reduce power amplifier(PA)efficiency while CE-FrFTOFDM stable operational linear range makes it a better candidate and outperforms the other techniques when their OBO exceeds 1.7.Higher EE and low BER in time-frequency selective channel are attracting features for CE-FrFT-OFDM deployment in mobile devices.

Hyper-spectral characteristics of rolled-leaf desert vegetation in the Hexi Corridor, China

Desert plants survive harsh environment using a variety of drought-resistant structural modifications and physio-ecological systems.Rolled-leaf plants roll up their leaves during periods of drought,making it difficult to distinguish between the external structures of various types of plants,it is therefore necessary to carry out spectral characteristics analysis for species identification of these rolled-leaf plants.Based on hyper-spectral data measured in the field,we analyzed the spectral characteristics of seven types of typical temperate zone rolled-leaf desert plants in the Hexi Corridor,China using a variety of mathematical transformation methods.The results show that:(1)during the vigorous growth period in July and August,the locations of the red valleys,green peaks,and three-edge parameters,namely,the red edge,the blue edge,and the yellow edge of well-developed rolled-leaf desert plants are essentially consistent with those of the majority of terrestrial vegetation types;(2)the absorption regions of liquid water,i.e.,1400-1500 and 1600-1700 nm,are the optimal bands for distinguishing various types of rolled-leaf desert plants;(3)in the leaf reflectance regions of 700-1250 nm,which is controlled by cellular structure,it is difficult to select the characteristic bands for differentiation rolled-leaf desert vegetation;and(4)after processing the spectral reflectance curves using a first-order differential,the envelope removal method,and the normalized differential ratio,we identify the other characteristic bands and parameters that can be used for identifying various types of temperate zone rolled-leaf desert plants,i.e.,the 510-560,650-700 and 1330-1380 nm regions,and the red edge amplitude.In general,the mathematical transformation methods in the study are effective tools to capture useful spectral information for species identification of rolled-leaf plants in the Hexi Corridor.

Spectral transformation-based technique for reducing effect of limited pre-correlation bandwidth in the GNSS receiver filter in presence of noise and multipath

This paper proposes an efficient scheme to reduce the pre-correlation bandwidth effect in the global navigation satellite system(GNSS)receiver filtering process.It is mainly based on the application of a spectral transformation to the satellite-emitted signal that effectively reduces its band.At the receiver's end,this operation causes the spreading of noise over a much wider band than that used by the radio frequency stage.Consequently,the resulting auto-correlation function in the acquisition process acquires properties that enhance considerably the performance of the receiver in the presence of the multipath and noise disturbing phenomena.The simulation results demonstrate that the proposed method is a plausible solution for both multipath and noise problems in the GNSS applications for any limited value of the pre-correlation bandwidth in the receiver filter.

WAVELET BASED SPECTRAL CORRELATION METHOD FOR DPSK CHIP RATE ESTIMATION

A wavelet-based spectral correlation algorithm to detect and estimate BPSK signal chip rate is proposed. Simulation results show that the proposed method can correctly estimate the BPSK signal chip rate, which may be corrupted by the quadratic characteristics of the spectral correlation function, in a low SNR environment.

Condition Evaluation in Steel Truss Bridge with Fused Hilbert Transform,Spectral Kurtosis,and Bandpass Filter

This study is concerned with the diagnosis of discrepancies in a steel truss bridge by identifying dynamic properties from the vibration response signals of the bridges.The vibration response signals collected at bridges under three different vehicular speeds of 10 km/hr,20 km/hr,and 30 km/hr are analyzed using statistical features such as kurtosis,magnitude of peak-to-peak,root mean square,crest factor as well as impulse factor in time domain,and Stockwell transform in the time-frequency domain.The considered statistical features except for kurtosis show uncertain behavior.The Stockwell transform showed low-resolution outcomes when the presence of noise in the recorded vibration responses.The elimination of noise and extraction of meaningful dynamic properties from the vibration responses is done by applying a new method which comes from the fusion of Hilbert transform with Spectral kurtosis and bandpass filtering.The outcomes obtained from Hilbert transform processed residual signals which are further filtered using bandpass filter show more robustness and accuracy in characterizing bridge modal frequencies from the noisy vibration responses.The proposed method produces a high-resolution frequency response which can unveil the joint discrepancy in the bridge structure.

TGF-β_(1)联合aPTT对急性ST段抬高型心肌梗死患者高血栓负荷的预测价值

目的分析转化生长因子-β_(1)(TGF-β_(1))联合活化部分凝血活酶时间(aPTT)对急性ST段抬高型心肌梗死(STEMI)患者高血栓负荷(HTB)的预测价值。方法选取140例STEMI患者,视血栓负荷情况分为HTB组66例和低血栓负荷(LTB)组74例,采用酶联免疫吸附法与凝固法检测血清TGF-β_(1)水平与aPTT。通过多因素Logistic回归分析STEMI患者HTB的影响因素,受试者工作特征(ROC)曲线分析血清TGF-β_(1)水平和aPTT对STEMI患者HTB的预测价值。结果HTB组男性、吸烟比例和年龄、白细胞计数、TGF-β_(1)水平高于LTB组,aPTT短于LTB组(P均<0.05)。多因素Logistic回归分析显示,年龄增加(OR=1.033,95%CI:1.005~1.063)、吸烟(OR=1.880,95%CI:1.102~3.208)、TGF-β_(1)升高(OR=1.924,95%CI:1.047~3.536)为STEMI患者HTB的独立危险因素,aPTT延长(OR=0.893,95%CI:0.820~0.972)为独立保护因素(P均<0.05)。ROC曲线分析显示,血清TGF-β_(1)水平和aPTT单独与联合预测STEMI患者HTB的ROC曲线下面积(AUC)分别为0.786、0.711、0.840,灵敏度分别为64.89%、59.54%、83.21%,特异度分别为81.88%、71.81%、70.47%。血清TGF-β_(1)水平和aPTT联合预测STEMI患者HTB的AUC大于二者单独预测(P均<0.05)。结论血清TGF-β_(1)水平升高和aPTT缩短与STEMI患者HTB密切相关,可作为STEMI患者HTB的辅助预测指标,二者联合对STEMI患者HTB的辅助预测价值较高。

Analysis of Spectral Characteristics Based on Optical Remote Sensing and SAR Image Fusion

Because of cloudy and rainy weather in south China, optical remote sens-ing images often can＇t be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model and effect of ENVISAT/SAR and HJ-1A satel ite multispectral remote sensing images. Based on the ARSIS strat-egy, using the wavelet transform and the Interaction between the Band Structure Model （IBSM）, the research progressed the ENVISAT satel ite SAR and the HJ-1A satel ite CCD images wavelet decomposition, and low/high frequency coefficient re-construction, and obtained the fusion images through the inverse wavelet transform. In the light of low and high-frequency images have different characteristics in differ-ent areas, different fusion rules which can enhance the integration process of self-adaptive were taken, with comparisons with the PCA transformation, IHS transfor-mation and other traditional methods by subjective and the corresponding quantita-tive evaluation. Furthermore, the research extracted the bands and NDVI values around the fusion with GPS samples, analyzed and explained the fusion effect. The results showed that the spectral distortion of wavelet fusion, IHS transform, PCA transform images was 0.101 6, 0.326 1 and 1.277 2, respectively and entropy was 14.701 5, 11.899 3 and 13.229 3, respectively, the wavelet fusion is the highest. The method of wavelet maintained good spectral capability, and visual effects while improved the spatial resolution, the information interpretation effect was much better than other two methods.

EVALUATION OF POWER SPECTRAL DENSITY OF PASSIVE INTERMODULATION DISTORTION IN HIGH-POWER COMMUNICATION SATELLITE SYSTEMS

In order to analyze the deleterious effects of Passive InterModulation (PIM) on high power communication satellite systems, the basic concept of PIM is introduced, and an equation for the power spectral density of the n-th order PIM distortion insuch systems is derived by applying flat signal-power spectrum assumption and Fourier transform method. It is indicated that PIM level generally decreases with order and the lowest frequency receive channel in the receive band is the channel of most affected by PIM interference.

A SPECTRAL ESTIMATION ALGORITHM USING THE HOUSEHOLDER TRANSFORM

Householder transform is used to triangularize the data matrix, which is basedon the near prediction error equation. It is proved that the sum of squared residuals for eachAR order can be obtained by the main diagonal elements of upper triangular matrix, so thecolumn by column procedure can be used to develop a recursive algorithm for AR modeling andspectral estimation. In most cases, the present algorithm yields the same results as the covariancemethod or modified covariance method does. But in some special cases where the numerical ill-conditioned problems are so serious that the covariance method and modified covariance methodfail to estimate AR spectrum, the presented algorithm still tends to keep good performance. Thetypical computational results are presented finally.

Quantum-Classical Algorithm for an Instantaneous Spectral Analysis of Signals:A Complement to Fourier Theory

A quantum time-dependent spectrum analysis, or simply, quantum spectral analysis (QSA) is presented in this work, and it’s based on Schr&#246;dinger’s equation. In the classical world, it is named frequency in time (FIT), which is used here as a complement of the traditional frequency-dependent spectral analysis based on Fourier theory. Besides, FIT is a metric which assesses the impact of the flanks of a signal on its frequency spectrum, not taken into account by Fourier theory and lets alone in real time. Even more, and unlike all derived tools from Fourier Theory (i.e., continuous, discrete, fast, short-time, fractional and quantum Fourier Transform, as well as, Gabor) FIT has the following advantages, among others: 1) compact support with excellent energy output treatment, 2) low computational cost, O(N) for signals and O(N2) for images, 3) it does not have phase uncertainties (i.e., indeterminate phase for a magnitude = 0) as in the case of Discrete and Fast Fourier Transform (DFT, FFT, respectively). Finally, we can apply QSA to a quantum signal, that is, to a qubit stream in order to analyze it spectrally.

A Short Note on a Differential-Difference Gauge Transformation and a New Spectral Problem

We show that a class of spectral problems are related to the spectral problem of the Volterra lattice through a gauge transformation. The transformation is given. We hope that our discussion can draw attention to the study of gauge transformation theory of differential-difference integrable systems.

High impedance fault detection in distribution network based on S-transform and average singular entropy

When a high impedance fault(HIF)occurs in a distribution network,the detection efficiency of traditional protection devices is strongly limited by the weak fault information.In this study,a method based on S-transform(ST)and average singular entropy(ASE)is proposed to identify HIFs.First,a wavelet packet transform(WPT)was applied to extract the feature frequency band.Thereafter,the ST was investigated in each half cycle.Afterwards,the obtained time-frequency matrix was denoised by singular value decomposition(SVD),followed by the calculation of the ASE index.Finally,an appropriate threshold was selected to detect the HIFs.The advantages of this method are the ability of fine band division,adaptive time-frequency transformation,and quantitative expression of signal complexity.The performance of the proposed method was verified by simulated and field data,and further analysis revealed that it could still achieve good results under different conditions.

基于小波变换的Stark展宽算法的优化研究

为了提升Stark展宽计算等离子体电子密度的准确性,基于小波阈值去噪处理方法,对EAST氘原子光谱信号进行了处理,以信噪比(SNR)和均方根误差(RMSE)作为滤波效果的评价依据,通过对比确定了最优小波基db4,最优小波分解4层。根据噪声估计值计算适合的阈值参数进行信号重构,并将经过去噪处理后的数据应用到后期Stark展宽算法计算等离子体密度的分析过程中。结果表明,小波硬阈值去噪能够有效提高光谱信号信噪比、降低均方根误差,在消除光谱信号中噪声的同时,最大程度保留了有用的光谱细节特征信息,进而有利于光谱数据的建模效果,获得更为准确的等离子体电子密度。

A Noise-Resistant Superpixel Segmentation Algorithm for Hyperspectral Images

The superpixel segmentation has been widely applied in many computer vision and image process applications.In recent years,amount of superpixel segmentation algorithms have been proposed.However,most of the current algorithms are designed for natural images with little noise corrupted.In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise,we propose a noiseresistant superpixel segmentation(NRSS)algorithm in this paper.In the proposed NRSS,the spectral signatures are first transformed into frequency domain to enhance the noise robustness;then the two widely spectral similarity measures-spectral angle mapper(SAM)and spectral information divergence(SID)are combined to enhance the discriminability of the spectral similarity;finally,the superpixels are generated with the proposed frequency-based spectral similarity.Both qualitative and quantitative experimental results demonstrate the effectiveness of the proposed superpixel segmentation algorithm when dealing with hyperspectral images with various noise levels.Moreover,the proposed NRSS is compared with the most widely used superpixel segmentation algorithm-simple linear iterative clustering(SLIC),where the comparison results prove the superiority of the proposed superpixel segmentation algorithm.