Degradation of effective carrier-power-to-noise densityratio based on code trackingspectral sensitivitycoefficient for GNSS radio frequencycompatibilityin C band

The frequency band between 5 010 MHz and 5 030 MHz allocated as C band has been used as a candidate in the global navigation satellite systems （GNSS） along with more and more naviga- tion services in L band. The potential benefits and technical requirements of C band for satellite navi- gation have been analyzed before. However the degradation of effective carrier-power-to-noise densi- ty ratio（ A （C/No ）eu） based on code tracking spectral sensitivity coefficient（ CT_SSC ） as a compati- bility assessment methodology for potential GNSS radio frequency compatibility in C-Band has not been discussed clearly. So the compatibility of the signals in the C band between BeiDou （BD） B1 C and GPS L1C, L1C/A, Galileo E1Os as the interoperability or classical signals in L band is analyzed. Simulation results reveal the interference degree between BD III B1C and GPS L1C/A, L1C, Galileo E1OS. The results can also reveal that the multiplexed binary offset carrier （MBOC） and binary phase shift keying （BPSK） modulation is not appropriate for C band.

Coefficients de-noising with wavelet transform for magnetic flux leakage data obtained from oil pipeline

This paper considers the problem of noise cancellation for the magnetic flux leakage (MFL) data obtained from the inspection of oil pipelines. MFL data is contaminated by various sources of noise, and the noise can considerably reduce the detectability of flaw signals in MFL data. This paper presents a new de-noising approach for removing the system noise contained in the MFL data by using the coefficients de-noising with wavelet transform. Experimental results are presented to demonstrate the advantages of this de-noising approach over the conventional wavelet de-noising method.

Drift Mobility, Diffusion Coefficient of Randomly Moving Charge Carriers in Metals and Other Materials with Degenerated Electron Gas

In this short review some aspects of applications of free electron theory on the ground of the Fermi statistics will be analyzed. There it is an intention to attempt somebody’s attention to problems in widespread literature of interpretation of conductivity of metals, superconductor in the normal state and semiconductors with degenerated electron gas. In literature there are many cases when to these materials the classical statistics is applied. It is well known that the electron heat capacity and thermal noise (and as a consequence the electrical conductivity) are determined by randomly moving electrons, which energy is close to the Fermi energy level, and the other part of electrons, which energy is well below the Fermi level can not be scattered and change its energy. Therefore there was tried as simple as possible on the ground of Fermi distribution, and on random motion of charge carriers, and on the well known experimental results to take general expressions for various kinetic parameters which are applicable for materials both without and with degenerated electron gas. It is shown, that drift mobility of randomly moving charge carriers, depending on the degree degeneracy, can considerably exceed the Hall mobility. Also it is shown that the Einstein relation between the diffusion coefficient and the drift mobility of charge carriers is valid even in the case of degeneracy. There also will be presented the main kinetic parameter values for different metals.

Noise level estimation method with application to EMD-based signal denoising

This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition（EMD） based signal denoising method. The proposed estimation method can effectively extract the candidate regions for the noise level estimation by measuring the correlation coefficient between noisy signal and a Gaussian filtered signal. For the improved EMD based method, the situation of decomposed intrinsic mode function（IMFs） which contains noise and signal simultaneously are taken into account. Experimental results from two simulated signals and an X-ray pulsar signal demonstrate that the proposed method can achieve better performance than the conventional EMD and wavelet transform（WT） based denoising methods.

Effects of signal modulation and coloured cross-correlation of coloured noises on the diffusion of a harmonic oscillator

The diffusion in a harmonic oscillator driven by coloured noises ξ（t） and η（t） with coloured cross-correlation in which one of the noises is modulated by a biased periodic signal is investigated. The exact expression of diffusion coefficient d as a function of noise parameter, signal parameter, and oscillator frequency is derived. The findings in this paper are as follows. 1） The curves of d versus noise intensity D and d versus noises cross-correlation time z3 exist as two different phases. The transition between the two phases arises from the change of the cross-correlation coefficient A of the two Ornstein-Uhlenbeck （O-U） noises. 2） Changing the value of T3, the curves of d versus Q, the intensity of colored noise that is modulated by the signal, can transform from a phase having a minimum to a monotonic phase. 3） Changing the value of signal amplitude A, d versus Q curves can transform from a phase having a minimum to a monotonic phase. The above-mentioned results demonstrate that a like noise-induced transition appears in the model.

Robust stability analysis for Markovian jumping stochastic neural networks with mode-dependent time-varying interval delay and multiplicative noise

This paper is concerned with the problem of robust stability for a class of Markovian jumping stochastic neural networks （MJSNNs） subject to mode-dependent time-varying interval delay and state-multiplicative noise. Based on the Lyapunov-Krasovskii functional and a stochastic analysis approach, some new delay-dependent sufficient conditions are obtained in the linear matrix inequality （LMI） format such that delayed MJSNNs are globally asymptotically stable in the mean-square sense for all admissible uncertainties. An important feature of the results is that the stability criteria are dependent on not only the lower bound and upper bound of delay for all modes but also the covariance matrix consisting of the correlation coefficient. Numerical examples are given to illustrate the effectiveness.

Artificial neural network-based determination of denoised optical properties in double integrating spheres measurement

Accurate determination of the optical properties of biological tissues enables quantitative understanding of light propagation in these tissues for optical diagnosis and treatment applications.The absorption(μa)and scattering(μs)coe±cients of biological tissues are inversely analyzed from their diffuse re°ectance(R)and total transmittance(T),which are measured using a double integrating spheres(DIS)system.The inversion algorithms,for example,inverse adding doubling method and inverse Monte Carlo method,are sensitive to noise signals during the DIS measurements,resulting in reduced accuracy during determination.In this study,we propose an arti ficial neural network(ANN)to estimateμa andμs at a target wavelength from the R and T spectra measured via the DIS to reduce noise in the optical properties.Approximate models of the optical properties and Monte Carlo calculations that simulated the DIS measurements were used to generate spectral datasets comprisingμa,μs,R and T.Measurement noise signals were added to R and T,and the ANN model was then trained using the noise-added datasets.Numerical results showed that the trained ANN model reduced the effects of noise inμa andμs estimation.Experimental veri fication indicated noise-reduced estimation from the R and T values measured by the DIS with a small number of scans on average,resulting in measurement time reduction.The results demonstrated the noise robustness of the proposed ANN-based method for optical properties determination and will contribute to shorter DIS measurement times,thus reducing changes in the optical properties due to desiccation of the samples.

Global Pressure of One-Dimensional Polydisperse Granular Gases Driven by Gaussian White Noise

We study the global pressure of a one-dimensional polydisperse granular gases system for the first time, in which the size distribution of particles has the fractal characteristic and the inhomogeneity is described by a fractal dimension D. The particles are driven by Gaussian white noise and subject to inelastic mutual collisions. We define the global pressure P of the system as the impulse transferred across a surface in a unit of time, which has two contributions, one from the translational motion of particles and the other from the collisions. Explicit expression for the global pressure in the steady state is derived. By molecular dynamics simulations, we investigate how the inelasticity of collisions and the inhomogeneity of the particles influence the global pressure. The simulation results indicate that the restitution coefficient e and the fractal dimension D have significant effect on the pressure.

An Improved Image Denoising Method Based on Wavelet Thresholding

VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.

基于随机系数Logit模型的专利价值评估研究——以主动降噪技术为例

[研究目的]从分析专利价值入手,通过经济学研究方法围绕专利侵权损害赔偿的标准进行定量研究,为评估专利价值提供有意义的知识增量。[研究方法]基于实证产业组织理论,采用随机系数Logit模型,以无线蓝牙耳机主动降噪(ANC)技术作为研究对象,构建该技术遭受侵权的反事实情形来估计经济指标变化,由此评估专利价值及侵权赔偿比例。[研究结论]专利侵权企业的行为产生了反竞争效应,提升了企业垄断势力,挤占了其他耳机企业的生存空间,也对潜在进入企业形成了进入威胁。蓝牙耳机价格中约有4.06%源于ANC专利标识。如果发生ANC专利侵权,赔偿额度应是侵权企业利润的2.6~2.8倍。该方法利用市场数据在均衡框架下评估专利价值,为相关研究提供了新方向。

GPR信号去噪的变分模态分解

为了进一步提高探地雷达(ground penetrating radar,GPR)数据的信噪比,压制由随机扰动引起的随机绕射能量,将二维变分模态分解(two-dimensional variational mode decomposition,2D-VMD)引入二维GPR数据的噪声压制处理中。首先,对GPR数据进行2D-VMD处理,并分析各阶本征模态函数(intrinsic mode function,IMF)分量及其对应的频率-波数域谱来确定雷达剖面中的各回波类型。然后,计算IMF分量与原始数据的互相关系数来确定信号模态和噪声模态,并对信号模态进行重构得到降噪后的数据。理论数据和实测数据测试表明,相比于传统的1D-VMD法,2D-VMD滤波后的含噪正演记录峰值信噪比由6.44 dB增加到7.72 dB;经2D-VMD降噪处理后的雷达剖面在保留有效信号的基础上,可以有效压制随机扰动带来的噪声,并且得到的雷达剖面同相轴连续性更好。

贝叶斯优化模糊聚类地级行政区声环境

声环境功能区划是噪声污染管理的重要手段。当前声环境功能区划研究大多是基于某个特定的地级行政区来进行的,难以反映各地级行政区声环境的异同。该文基于134个地级行政区的人口、面积、各声环境功能区面积和及面积占比,进行地级行政区声环境表征和归一化处理。以轮廓系数作为聚类有效性评价指标,基于贝叶斯优化模糊聚类方法对地级行政区声环境表征进行聚类分析。通过与谱聚类、K-medoids聚类、高斯混合模型聚类的聚类性能对比,验证了该方法的有效性。结果表明,我国地级行政区声环境分为9类,城市规模和用地情况发生显著变化导致其声环境表征和归类发生变化后应重新评估当前噪声污染管理政策,并借鉴同类的地级行政区的噪声污染管理政策做出必要的调整。

一种基于高频噪声定量约束函数的地震高分辨率处理方法

系统分析了现有地震数据高分辨率处理方法的适用性及不足,探索了一种基于高频噪声定量约束函数的高分辨率处理方法,有效且保真地提高地震资料信噪比与分辨率。通过分析叠后地震数据有效信号带宽,设计了压制高频噪声的最大概率准则和定量约束函数,通过多次迭代压制噪声,提高全频带信号的信噪比,逐步分级拓宽地震有效频带宽度,循环迭代求取反射系数,将提取的地震子波与反射系数褶积得到高分辨率地震数据体。测试校验对比其它高分辨率地震处理结果,处理后的数据体具有垂向分辨率高、主频高且频带宽的特点,有效视主频由50 Hz提高至100 Hz左右,地质目标视分辨率相应提高近1倍。基于该提频数据与波阻抗反演实现了2~5 m地质目标的定性与定量预测,钻井验证符合率90%以上,有效指导井位部署。该方法弥补了在传统“有效带宽”内提高地震高频信号的不足,实现了全频带内重构高、低频有效信号的目的。

降雨和大气分子环境对引信回波响应的影响

针对大气环境特性数据在引信工程设计中运用不足的问题,基于Monte-Carlo辐射传输模型和数字信号处理模型,提出了一种将大气环境特性数据应用于引信回波响应特性的仿真建模方法,利用ITU建立雨衰模型,验证了本文中的雨衰模型,研究了雨滴、水蒸气和氧气分子综合作用下对非大气窗口毫米波的衰减特性,并将毫米波的衰减特性数据应用于引信回波响应特性的仿真建模中,建立了引信回波响应模型,给出了引信回波响应特性中的一维距离像和距离-多普勒像,分析了暴雨和特大暴雨中引信目标回波功率和信噪比的变化规律。研究发现,中心频率60、118、184 GHz引信目标回波功率在暴雨中分别降低了2.5、1.8、2.9 dB,信噪比分别降低了1.2、0.9、1.3 dB。结果表明:即使是特大暴雨,空气中雨滴的散射与吸收作用对短距离引信目标回波并不能产生质的改变,但会降低目标回波的信噪比,使引信探测系统目标检测概率下降。从无雨到暴雨再到特大暴雨,中心频率60 GHz引信回波信噪比分别为15.7、14.4、12.9 dB,基于Neyman-Pearson准则,当引信探测系统虚警概率为10-7时,引信探测器的目标检测概率分别为0.999 7、0.986 9和0.852 1,结果显示,极端条件下的降雨颗粒和大气分子的衰减特性对引信的目标检测概率影响显著。文中降雨和大气分子对引信探测影响的建模方法可为引信工程设计中的环境适应性评估提供理论与方法支撑。

基于离散系数与Harris角点的微光像增强器闪烁噪声测试方法

为了弥补信噪比无法在二维空间上准确定位分析像增强器闪烁噪声特性的不足,本文针对像增强器闪烁噪声特性设计了一种基于离散系数与Harris角点检测的微光像增强器闪烁噪声测试方法。本方法采用基于Gsense400BSI CMOS图像传感器的高帧频图像采集系统实现与像增强器荧光屏余晖时间相匹配的闪烁噪声图像采集。通过对连续多帧采集到的图像进行像素级离散系数计算,热点图实现可视化,与Harris角点检测算法能够准确分析像增强器荧光屏各区域内的闪烁噪声情况并准确标记荧光屏上的高亮噪点。实验结果表明,该方法能够实现像增强器闪烁噪声的二维分析与定位,从而为像增强器性能优化以及噪声特性测试提供技术支持。

面向城市交通噪声控制的树种筛选与种植策略

植物降噪是一种减碳且低成本的降噪方式,可有效改善城市声环境。然而现存植物降噪影响因素与预测模型主要集中在植物群落层面,忽略了树种间差异在不同频率噪声控制过程中的重要作用,难以满足城市交通噪声有效防控的需求。基于此,该研究以植物树干为切入点,提出一种量化树干与树皮粗糙度的方法,针对交通噪声频谱特征系统测量13种树种的吸收作用,并模拟降噪效果。研究首先测得树干吸声系数的频谱特征,确定了树干主要吸声频率范围与交通噪声频率分布具备较好的一致性;其次,从树种视角分析视觉因素与测试获取因素两个层面对树干吸声效果有影响的主要因素;最后,通过模拟对比分析15 m宽植物带种植不同树种的降噪效果,发现相差∼0.9 dBA,证明了种植吸声系数高的树种对交通噪声控制的有效性。以期从树形、树干吸声效果、树种生长速度以及种植方式等方面,提出应对城市交通噪声污染的树种筛选依据与设计策略。

基于参数优化VMD-小波阈值的轴承振动信号降噪方法

为了解决复杂工况下滚动轴承振动信号存在随机噪声的问题,提出了一种基于参数优化变分模态分解(VMD)-小波阈值的滚动轴承降噪方法。首先,利用以包络熵为适应度函数的天鹰算法对变分模态分解算法的模态分解数K和惩罚因子α进行了自适应选择,代入VMD分解中,得到若干本征模态函数(IMFs);然后,根据峭度-相关系数将IMF分量划分为纯净分量和含噪分量,对含噪分量进行了小波阈值降噪处理;最后,对处理后的分量进行了重构,并用重构信号进行了包络谱分析,实现了滚动轴承的信号降噪目的,并利用仿真信号和美国凯斯西储大学公开的轴承数据集对上述降噪方法的有效性进行了验证。研究结果表明:基于参数优化VMD-小波阈值的降噪方法减少了滚动轴承运行状态下的随机噪声,相对小波阈值降噪方法,所得仿真信号信噪比提升53%,均方误差降低13%;在故障特征频率为162 Hz时,所得实验降噪信号包络谱的前6倍频谱峰值更为明显,且受随机噪声影响较小。该研究方法在滚动轴承等旋转机械信号降噪方面具有一定的参考价值。

开孔泡沫铝无规则入射的吸声性能研究

通过混响室对不同厚度的开孔泡沫铝在不同背腔深度下的吸声性能进行测试,获得了不同厚度的泡沫铝板在不同腔深下的吸声系数。研究表明在频率低于630 Hz时,不同厚度泡沫铝板的吸声系数随着背腔深度的增加逐渐提高。在630~5 000 Hz频率范围内,当泡沫铝材料的厚度大于4 mm,背腔深度大于40 mm时,不同厚度泡沫铝板的吸声系数均大于0.6。计算了不同厚度的开孔泡沫铝材料在不同背腔条件下的降噪系数,为泡沫铝材料的实际工程应用提供参数选择。

泡沫铝材料声学参数计算及其应用

泡沫铝材料曲折因子、热特征长度和黏性特征长度等声学参数对其吸声性能有重要影响。针对泡沫铝声学参数难以在实验室完成检测的问题,本文研究采用Johnson-Champoux-Allard多孔介质模型,运用全局最小二乘优化方法完成对泡沫铝材料的声学参数反求,通过与熔体发泡法制备的泡沫铝吸声性能实测值比较,验证所提方法的可行性;通过数值计算,进一步分析泡沫铝流阻率、黏性特征长度、热特征长度、曲折因子和孔隙率等声学参数与其吸声系数的关系。结果表明:增大流阻率、曲折因子和孔隙率均使泡沫铝吸声性能增强;热特征长度和黏性特征长度的改变对吸声系数影响较小;增大孔隙率,泡沫铝的吸声性能提升最为明显。研究成果可为泡沫铝的研发制备和实际工程应用提供借鉴和指导。

粉煤灰陶粒轻质混凝土声屏障性能试验研究

混凝土是一种应用广泛、可使用多种固废制备的建筑材料。本研究将粉煤灰陶粒作为轻骨料,水泥-粉煤灰-硅灰-煤系偏高岭土作为复合胶凝材料,制备用作声屏障的粉煤灰陶粒轻质混凝土,并对其力学性能、吸声性能和抗碳化性能进行试验研究。结果表明:当粉煤灰-硅灰-煤系偏高岭土总掺量为胶凝材料总质量的10%时,试样的力学性能、抗碳化性能有所提高,吸声性能有所降低;随着粉煤灰-硅灰-煤系偏高岭土掺量进一步增加,试样的力学性能、抗碳化性能有所降低,吸声性能有所提高。1500 Hz及以下频率吸声系数变化幅度较小,2000 Hz时吸声效果最好,试样的平均吸声系数在0.44~0.58,降噪系数在0.74~0.89。