激光超声时频参数对裂纹试件的温度检测

为了实现对高温工作环境下含表面裂纹试件的温度测量,提出了利用激光超声瑞利波的时、频参数实现表面裂纹试件温度测量的新思路。采用稳态解作为瞬态初始值的温度加载方法,建立了293.15~743.15 K温度范围内结构钢的表面裂纹试件模型,分析了激光超声瑞利波与表面裂纹的相互作用。在时域上,利用瑞利反射、透射波的归一化峰值来定义反射率、透射率这两个时域参数,得出反射率和透射率都与温度成线性增长关系,反射率和透射率随温度的变化率分别为1.45×10^(-3)dB·K^(-1)和2.3×10^(-3)dB·K^(-1);在频域上,提出了瑞利波的小波时频分析(TFA)分布下超声能量强度的特征极值点关系法,得出在特征频率为2.4 MHz时,能量强度的特征极值随温度的线性增长率为2.56×10^(-4)dB·K^(-1);实验分析证明了仿真结果的可行性。时、频域的研究结果结合实验分析表明,利用时、频参数与温度的线性关系和变化率,可实现表面含裂纹的试件的温度测量表征。

基于时频参数融合的自适应语音端点检测算法

为了解决低信噪比环境下传统的语音端点检测算法性能较差且不能自适应环境噪声,提出了一种基于时频参数融合的自适应语音端点检测算法。将对数能量与改进的Mel能量进行融合,获得了一种新的时频参数(TF),该参数能有效地区分语音段和噪声段。使用该参数在噪声段对阈值进行更新,采用门限检测法判定出语音端点。仿真实验表明,该算法具有较好的鲁棒性,且能够准确地检测出语音端点。当信噪比(SNR)为0 d B时,端点检测错误率仅为15%左右。

基于信号周期平稳特性的OFDM系统时频参数盲估计算法

该文考虑移动通信信道下的OFDM系统同步参数估计问题,通过对接收信号进行采样,获得具有周期平稳特性的序列,在此基础上提出一种基于OFDM信号周期平稳特性的系统时延和频偏盲估计算法,并对算法进行时频参数估计范围和误差来源分析。理论分析和仿真结果表明,该算法不仅适用于频率选择性衰落信道,而且与同类的方法比较,具有更大的频偏估计范围和更高的估计精度。

时频参数测量中存储容量的压缩方法

介绍一种单片机测量时频参数时的数据处理方法,即线性地址变换法。在存储容量有效压缩的基础上,进一步分析采样数据,用软件实现可控时标,保证测量精度。

Renyi信息和依赖于信号的时频分布参数

本文比较了时频分布中抑制交叉项（Ｃｒｏｓｓ一ｔｅｒｍｓ， ＣＴ）的方法和效果，提出了衡量时频分布的测度的必要性。Ｒｅｎｙｉ信息可作为信号复杂度的测度，本文进一步指出它还可作为时频分布的测度，并且可作为选择依赖于信号的时频分布参数的依据．

波形未知信号时频差参数的最大似然估计算法

提出了波形未知确定信号时频差参数估计的最大似然算法,得出利用互模糊函数可以实现此种信号条件下时频差参数最大似然估计的结论,推导出了闭合形式的克拉美罗下界并对比了不同信号模型下的参数估计精度。仿真实验结果表明了估计算法和性能分析的正确性。

用AE时序参数特征预测岩体失稳的研究

对室内岩体失稳过程的声发射参数时序分布进行分析,提出岩体失稳过程与声发射事件参数时序分布特征之间的相关规律。现场监测结果表明岩体破裂变形过程中的声发射信号是随机的,非周期性的,随着地质环境等改变而改变;岩体声发射事件主频时序参数突变,由小突然增大,或是在连续增加时突然减少,都是临近破坏的征兆,AE参数时序特征可作为预测、预报岩体破裂失稳的依据。

铁矿石价格、国际航运与中国股票市场间的时变溢出效应——来自TVP-VAR-DY模型的证据

采用时频参数向量自回归(TVP-VAR)连通性方法,使用2017年1月4日至2023年3月6日的日度数据探讨铁矿石价格、国际航运与中国股票市场间的时变溢出效应。实证结果表明:国际航运是最强烈的溢出效应;中国股票市场是溢出效应的传导因素;铁矿石价格是主要的接收者。整个溢出效应是时变的,在危机时期溢出效应显著增强,在特殊时期溢出与接收关系会发生短暂的改变。

地震属性提取方法研究及实现

地震属性提取主要是在三维地震体中沿层提取多类地震属性,地震属性提取方法的功能为基于地震记录提取与储层有关的地震属性。其基本思想是将时空域地震记录转换到时频参数域,在时频参数域中定量地提取可以表征各层反射波波形特征的地震属性,再转换到时空地震属性域成图显示,实现时频-时空域地震属性分析。论文对地震属性的定义进行详细理论研究,对地震属性进行分类,编制地震属性提取模块;同时在微机上用C++语言实现全部方法。

L^2(R)中一个Bessel序列界的估计

本文利用傅里叶变换、范数三角不等式及Wirtinger不等式证明了:若g(x),xg(x)∈H1,则{eibnxg(x-an):n∈Z}是L2(R)中的一个Bessel序列,对其界进[1]行了估计,且在时频参数变化下所对应的框架界作了一定的推广.

Joint AVO inversion in the time and frequency domain with Bayesian interference

Amplitude variations with offset or incident angle （AVO/AVA） inversion are typically combined with statistical methods, such as Bayesian inference or deterministic inversion. We propose a joint elastic inversion method in the time and frequency domain based on Bayesian inversion theory to improve the resolution of the estimated P- and S-wave velocities and density. We initially construct the objective function using Bayesian inference by combining seismic data in the time and frequency domain. We use Cauchy and Gaussian probability distribution density functions to obtain the prior information for the model parameters and the likelihood function, respectively. We estimate the elastic parameters by solving the initial objective function with added model constraints to improve the inversion robustness. The results of the synthetic data suggest that the frequency spectra of the estimated parameters are wider than those obtained with conventional elastic inversion in the time domain. In addition, the proposed inversion approach offers stronger antinoising compared to the inversion approach in the frequency domain. Furthermore, results from synthetic examples with added Gaussian noise demonstrate the robustness of the proposed approach. From the real data, we infer that more model parameter details can be reproduced with the proposed joint elastic inversion.

The modulation recognition based on decision-making mechanism and neural network integrated classifier

A neural network integrated classifier(NNIC) designed with a new modulation recognition algorithm based on the decision-making tree is proposed in this paper.Firstly,instantaneous parameters are extracted in the time domain by the coordinated rotation digital computer(CORDIC) algorithm based on the extended convergence domain and feature parameters of frequency spectrum and power spectrum are extracted by the time-frequency analysis method.All pattern identification parameters are calculated under the I/Q orthogonal two-channel structure,and constructed into the feature vector set.Next,the classifier is designed according to the modulation pattern and recognition performance of the feature parameter set,the optimum threshold is selected for each feature parameter based on the decision-making mechanism in a single classifier,multi-source information fusion and modulation recognition are realized based on feature parameter judge process in the NNIC.Simulation results show NNIC is competent for all modulation recognitions,8 kinds of digital modulated signals are effectively identified,which shows the recognition rate and anti-interference capability at low SNR are improved greatly,the overall recognition rate can reach 100%when SNR is12dB.

Effects of Low-Intensity Ultrasound on Cell Proliferation and Reproductivity

Ultrasound has been widely used in clinics. Cellular responses to low-intensity ultrasound are parameter-dependent. Proper parameter setting is vital to its exact use. To get guidelines for parameter setting, lowintensity ultrasound stimulation on the proliferation and reproductivity of Hep G2 and 3T3 cells in vitro was examined with a 1.06 MHz-generator by changing the parameters(including intensity, pulse repetition frequency and duty cycle)in a wide range. Cell viability and reproductivity at different time after sonication were measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide(MTT)and colony formation assay to indicate timerelated proliferation. The results illustrate that ultrasound irradiation at 0.4—0.8 W/cm^2 and high pulse repetition frequency(100 Hz)can facilitate cell proliferation, while above 0.8 W/cm^2 would resist it. The extent of resistance closely correlated with duty cycle and pulse repetition frequency. Resistance effect at low pulse repetition frequency(1 Hz)is greater than that at high pulse repetition frequency(100 Hz)and not time-related. The influence of high pulse repetition frequency is time-accumulated, indicating cellular process involved. These findings would provide valuable guidelines for the application of low-intensity ultrasound in stem cell transformation and tissue engineering.

基于自适应Chirplet分解的偏瘫肌强直症状评估

为对偏瘫患者的痉挛性肌强直症状进行定量评价,设计了一种基于自适应Ch irp let(线性调频小波)分解的表面肌电信号处理方法。采集了15例偏瘫患者在上肢单关节神经康复机器人上做肘关节被动屈伸动作时肱二头肌的表面肌电信号。通过比较不同患者的最优Ch irp let时频参数,量化评价患者肌强直症状。结果表明:通过该方法得到的患者在出现肌强直症状时的肌纤维放电时间与患者相应的简式Fug l-M eyer运动功能评价(上肢)分数存在较好的线性关系,可以从一个方面反映偏瘫患者患侧上肢的运动功能。

Modelling envelope and temporal fine structure components of frequency-following responses in rat inferior colliculus

In studies of auditory perception, a dichotomy between envelope and temporal fine structure(TFS) has been emphasized. It has been shown that frequency-following responses(FFRs) in the rat inferior colliculus can be divided into the envelope component(FFREnv)and the temporal fine structure component(FFRTFS). However, the existing FFR models cannot successfully separate FFREnv and FFRTFS. This study was to develop a new FFR model to effectively distinguish FFREnv from FFRTFS by both combining the advantages of the two existing FFR models and simultaneously adding cellular properties of inferior colliculus neurons. To evaluate the validity of the present model, correlations between simulated FFRs and experimental data from the rat inferior colliculus were calculated. Different model parameters were tested, FFRs were calculated, and the parameters with highest prediction were chosen to establish an ideal FFR model. The results indicate that the new FFR model can provide reliable predictions for experimentally obtained FFREnv and FFRTFS.