非高斯噪声激励下含周期信号FitzHugh-Nagumo系统的响应特征

研究了非高斯噪声激励下含周期信号的FHN模型的动力学行为. 通过计算神经元的平均响应时间、观察神经元的共振活化和噪声增强稳定现象,分析了非高斯噪声对神经元动力学行为的影响. 发现通过改变非高斯噪声的相关时间可以有效地改变共振活化和噪声增强稳定现象. 观察到在强相关噪声下不同强度的非高斯噪声抑制了神经元的噪声增强稳定现象而共振活化现象几乎不变,也就是非高斯噪声有效地增强了神经响应的效率. 观察了平均响应时间与非高斯噪声参数q之间的关系,当q为一个有限的小于1的值时,平均响应时间取得最小值. 最后表明在一定条件下,非高斯噪声出现重尺度现象,即非高斯噪声产生的效果可以由高斯白噪声来估计. 关键词： FHN神经系统 非高斯噪声 平均响应时间 共振活化现象     

基于信道估计的自适应连续变量量子密钥分发方法

为使连续变量量子密钥分发协议获得稳定的密钥生成率,需要根据信道变化动态调整发送光脉冲的强度.将光纤量子信道看作加性玻色量子高斯信道,给出高斯态通过玻色量子高斯信道仍得到高斯态的证明过程.通过平衡零差检测后,采用最大似然估计法得到了信道参数,进而根据估计的噪声大小自适应调整Alice发送的光脉冲的强度,从而获得稳定的密钥生成率. 关键词： 量子密钥分发 连续变量 玻色量子高斯信道 信道估计     

n型金属氧化物半导体场效应晶体管噪声非高斯性研究

基于n型金属氧化物半导体场效应晶体管（nMOSFET）噪声的数涨落模型,采用高阶统计量双相干系数平方和研究了nMOSFET噪声的非高斯性.通过对nMOSFET实际测试噪声的分析,发现nMOSFET器件噪声存在非高斯性;小尺寸器件噪声的非高斯性强于大尺寸器件;在器件的强反型线性区,其非高斯性随着漏压的增加而增加.文中还通过蒙特卡罗模拟和中心极限定理理论对nMOSFET噪声的非高斯性作了深入的探讨. 关键词： 噪声 非高斯性 n型金属氧化物半导体场效应晶体管 氧化层陷阱     

非高斯噪声驱动下非对称双稳系统的平均首次穿越时间与随机共振研究

研究了乘性非高斯噪声和加性高斯白噪声共同激励下非对称双稳系统的平均首次穿越时间和随机共振问题. 利用路径积分法和两态模型理论,推导出平均首次穿越时间和信噪比的表达式. 研究结果表明:势阱非对称性对两个不同方向的平均首次穿越时间的影响是不同的. 信噪比是加性噪声强度和势阱非对称性的非单调函数,系统出现了随机共振现象;信噪比是乘性噪声强度的单调函数,没有共振峰出现. 这说明该系统中乘性噪声强度和加性噪声强度对信噪比的影响是不同的. 关键词： 非高斯噪声 非对称双稳系统 平均首次穿越时间 随机共振     

压缩真空态与压缩单光子态的相干叠加态的非经典性质

通过对量子比特态进行压缩操作,获得压缩真空态与压缩单光子态按一定的权重比例叠加而成的相干叠加态,此相干叠加态与压缩参数和权重因子有关,研究其非经典性和非高斯性.研究结果表明,通过压缩使得量子比特态的光子数分布范围变宽了.特别是从亚泊松分布和Wigner函数负定性情况分析了该量子态的非经典属性,从Wigner函数的边缘分布角度观察了该量子态的非高斯性.发现调节权重因子和压缩参数可以改善该态的非经典性和非高斯性.     

光源相位噪声对高斯玻色采样的影响

高斯玻色采样是实现量子计算优势的主要途径之一,同时也有望应用于加速稠密子图、量子化学等问题.然而,实验中必不可少的噪声却可能阻碍高斯玻色采样的量子优势.此前的研究主要关注于光子损失和光子非全同噪声.本文通过数值模拟研究了另一种噪声——光源相位噪声对高斯玻色采样的影响.采用蒙特卡罗方法近似计算相位噪声下高斯玻色采样的输出概率分布,发现随着探测光子数的增加,相位噪声带来的误差逐渐加大.同时,相位噪声会导致采样出大概率样本的能力,即HOG (heavy output generation)值显著降低.最后发现,在输入平均光子数相同时,有光子损失的高斯玻色采样相比无损失情形对于相位噪声有更大的容忍性.本文的研究有助于大规模高斯玻色采样中更好地抑制相位噪声.     

公路交通噪声频率特征及等效频率研究

为了确定不同车型在不同行车速度下的噪声频率分布特性及等效频率值,更加准确的对交通噪声的影响进行评估,本文对单车稳态辐射噪声的频率特性进行了现场测定,并对不同类型车辆的频率特性数据进行了分析。结果表明,不同类型车辆车速与频带声压级之间的相关性随频带中心频率的变化而变化。FHWA模型对公路交通噪声中介于500 Hz～2000 Hz频段的噪声有较好的预测效果,而对低于500 Hz和高于2000 Hz的交通噪声的预测精度较差。车辆类型不同,车辆产生噪声的中心频率、声能量集中分布的频率范围以及等效频率均不相同。     

二维映射神经元模型中频率依赖的随机共振

通过数值模拟方法, 研究了在具有稳定次阈值振荡特性的二维映射神经元体系中, 噪声对体系非线性动力学的调控作用. 通过计算发现了噪声诱导的动作电位和随机共振现象. 另外,还研究了体系的控制参数及输入信号的频率对体系动力学的影响, 发现了该体系中频率依赖的随机共振现象. 关键词： 二维映射神经元模型 次阈值振荡 高斯白噪声 随机共振     

色关联的色噪声驱动的分段非线性模型的平均首次穿越时间

研究了色关联的乘性高斯色噪声和加性高斯色噪声驱动的分段非线性系统中, 噪声强度和相关时间对平均首次穿越时间的影响. 利用一致有色噪声近似方法和最速下降方法, 推导出系统平均首次穿越时间的表达式. 研究结果表明: 系统的平均首次穿越时间随着乘性噪声的增加会出现单峰结构, 即“共振”现象, 峰值会随着加性噪声强度和噪声之间关联强度的增加而减小. 而平均首次穿越时间作为加性噪声的函数呈单调曲线, 说明乘性噪声和加性噪声对平均首次穿越时间的影响不同. 此外, 乘性和加性噪声关联时间以及互关联时间在正关联时和负关联时 对系统平均首次穿越时间的影响是不同的. 关键词： 色噪声 分段非线性系统 平均首次穿越时间     

双数态输入型M-Z干涉仪中量子相位的最大似然估计和贝叶斯分析

本文采用最大似然估计和贝叶斯分析两种方法，对双数态输入型马赫-曾德尔干涉仪中的量子相位估计进行研究.通过对干涉仪输出端粒子数差和宇称进行理论计算和数值模拟，发现采用贝叶斯分析和粒子数差测量的组合，可以在全相位空间实现最优测量，即待估相位精度达到由量子克拉美罗下界所给定的测量极限，同时，利用贝叶斯分析进行相位估计所需样本数较最大似然估计更少.在通过宇称测量进行相位估计的研究中，发现无法利用贝叶斯分析实现相位估计，但可采用最大似然估计进行研究.借助蒙特卡洛数值模拟分析，我们验证了理论计算结果，即相位估计精度会随待估相位θ₀的变化而改变，将其与粒子数差测量结果对比，进一步确认了粒子数差测量方案的优越性.     

21-cm background anisotropies can discern primordial non-Gaussianity

The non-Gaussianity of initial perturbations provides information on the mechanism that generated primordial density fluctuations. We find that 21-cm background anisotropies due to inhomogeneous neutral hydrogen distribution prior to reionization captures information on primordial non-Gaussianity better than a high-resolution cosmic microwave background anisotropy map. An all-sky 21-cm experiment over the frequency range from 14 to 40 MHz with angular information out to a multipole of 10(5) can limit the primordial non-Gaussianity parameter f(NL) <, similar 0.01.     

Decimative subspace-based parameter estimation methods of magnetic resonance spectroscopy based on prior knowledge

The method Hankel Total Least Squares (HTLS)-PK, which successfully incorporates prior knowledge of known signal poles into the method HTLS, has been proven to greatly improve the performance for parameter estimation of overlapping peaks of magnetic resonance spectroscopy (MRS) signal. In addition, decimation is also proposed as a way to increase the performance of subspace-based parameter estimation methods in the case of oversampling. Taking advantage of decimation in combination with prior knowledge to estimate the MRS signal parameters, two novel subspace-based parameter estimation methods, HTLSDSumPK and HTLSDStackPK, are presented in this paper. The experimental results and relevant analysis show that the methods HTLSDSumPK, HTLSDStackPK and HTLS-PK are slightly better than the method HTLS at low noise levels; however, the three prior-knowledge-incorporating methods, especially the method HTLSDSumPK, have much better performance than the method HTLS at high noise levels in the terms of robustness, estimated accuracy and computational complexity. Even if some inaccuracy of prior knowledge is considered, the method HTLSDSumPK also shows some advantages.     

A Metropolis Monte Carlo Implementation of Bayesian Time-Domain Parameter Estimation: Application to Coupling Constant Estimation from Antiphase Multiplets

The Bayesian perspective on statistics asserts that it makes sense to speak of a probability of an unknown parameter having a particular value. Given a model for an observed, noise-corrupted signal, we may use Bayesian methods to estimate not only the most probable value for each parameter but also their distributions. We present an implementation of the Bayesian parameter estimation formalism developed by G. L. Bretthorst (1990,J. Magn. Reson.88, 533) using the Metropolis Monte Carlo sampling algorithm to perform the parameter and error estimation. This allows us to make very few assumptions about the shape of the posterior distribution, and allows the easy introduction of prior knowledge about constraints among the model parameters. We present evidence that the error estimates obtained in this manner are realistic, and that the Monte Carlo approach can be used to accurately estimate coupling constants from antiphase doublets in synthetic and experimental data.     

Subspace-based MRS data quantitation of multiplets using prior knowledge

Accurate quantitation of Magnetic Resonance Spectroscopy (MRS) signals is an essential step before converting the estimated signal parameters, such as frequencies, damping factors, and amplitudes, into biochemical quantities (concentration, pH). Several subspace-based parameter estimators have been developed for this task, which are efficient and accurate time-domain algorithms. However, they suffer from a serious drawback: they allow only a limited inclusion of prior knowledge which is important for accuracy and resolution. In this paper, a new method is presented: KNOB-SVD and its improved variant KNOB-TLS. KNOB-SVD is a recently proposed method, based on the Singular Value Decomposition (SVD), which allows the use of more prior knowledge about the signal parameters than previously published subspace-based methods. We compare its performance in terms of robustness and accuracy with the performance of three commonly used methods for signal parameter estimation: HTLS, a subspace-based method which does not allow any inclusion of prior knowledge, except for the model order; HTLSPK(Delta fd(eq)), a subspace-based method obtained by incorporating in HTLS the prior information that the frequency differences between doublet components are known and the damping factors are equal; and AMARES, an interactive maximum likelihood method that allows the inclusion of a variety of prior knowledge. Extensive simulation and in vivo studies, using (31)P as well as proton MRS signals, show that the new method outperforms HTLS and HTLSPK(Delta fd(eq)) in robustness, accuracy, and resolution, and that it provides parameter estimates comparable to the AMARES ones.     

一种新的卫星钟差Kalman滤波噪声协方差估计方法

采用Kalman滤波方法进行钟差参数计算和预报时, 需确定Kalman滤波噪声协方差矩阵. 针对这一问题, 提出了一种新的卫星钟差Kalman滤波噪声协方差估计方法, 通过建立新息的相关函数序列与未知的噪声参数间的线性函数模型, 采用最小二乘法进行噪声参数估计. 采用精密钟差数据进行钟差参数估计和预报分析, 结果表明, 该方法具有较好的收敛性, 并与顾及随机噪声模型的开窗分类因子自适应抗差估计方法进行对比分析, 验证了新方法的正确性和有效性.     

Effects of incoherent and coherent source spectral information in geoacoustic inversion

This paper examines the effect on matched-field geoacoustic inversion of including source spectral information, as can be available in controlled-source acoustic surveys. Source information can consist of relative or absolute knowledge of the source amplitude and/or phase spectra, and can allow frequency-coherent processing of spatial acoustic-field data. A number of multi-frequency acoustic processors, appropriate for specific types of source information, are defined based on the likelihood function for complex acoustic-field data with Gaussian noise. The information content of the various processors is quantified in terms of marginal probability distributions and highest-probability density intervals for the unknown geoacoustic and geometric parameters, which define the accuracy expected in inversion. Marginal distributions are estimated using a fast Gibbs sampler approach to Bayesian inversion, which provides an efficient, unbiased sampling of the multi-dimensional posterior probability density. The analysis is illustrated for incoherent and coherent processors corresponding to several types of source knowledge ranging from complete information to no information, and the results are considered as a function of the spatial and frequency sampling of the acoustic fields.     

一种α稳定分布参数估计方法及其在冰源噪声统计建模中的应用

为研究冰源噪声的统计特性,扩展了一种用于合成孔径雷达图像信号参数估计的α稳定分布参数估计方法的适用范围,解决了该方法在特征指数α接近2时,偏斜参数β的估计精度严重下降的问题,并将其应用于北极冰源噪声的统计建模中。该方法利用两个特征函数点进行参数估计,第二个特征函数点ω₀的取值范围受尺度参数γ值影响。数值仿真表明:当γ∈[0.00006,0.4]时,ω₀> 1时的参数估计性能更好;当γ∈(0.4,2]时, 0 <ω₀ <0.5时的参数估计性能更好。北极噪声数据的验证结果表明,ω₀> 1时的α稳定分布对冰源噪声表现出良好的建模效果。最后给出了不同海冰状态下α值的统计规律。     

Blind parameter estimation of pseudo-random binary code-linear frequency modulation signal based on Duffing oscillator at low SNR

Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBCLFM) signals require prior knowledge, are computationally complex, and exhibit poor performance at low signal-to-noise ratios(SNRs). To overcome these problems, a blind parameter estimation method based on a Duffing oscillator array is proposed. A new relationship formula among the state of the Duffing oscillator, the pseudo-random sequence of the PRBC-LFM signal, and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived, providing the theoretical basis for blind parameter estimation. Methods based on amplitude method, short-time Fourier transform method, and power spectrum entropy method are used to binarize the output of the Duffing oscillator array, and their performance is compared. The pseudo-random sequence is estimated using Duffing oscillator array synchronization, and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency. Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of-35 d B.     

基于CCF-TLS-ESPRIT算法的低频振荡在线辨识

低频振荡在线辨识需用到广域测量系统(WAMS)的采集信号,而WAMS采集的信号中常伴有高斯白噪声,经过常规的低通滤波处理后会产生高斯色噪声,从而影响辨识的精度.针对在线辨识中的色噪声问题,提出以互相关函数(CCF)来代替实测信号,从而抑制色噪声,并结合总体最小二乘-旋转不变技术参数估计(TLS-ESPRIT)算法进行模态辨识.仿真结果表明,CCF-TLS-ESPRIT算法能够有效辨识出色噪声环境中的系统低频振荡模态,并具有一定的效率.     

A parameter estimator based on adaptive noise canceller

I.Introductionwith'thewendevelopmentofadaptivesignalprocessingtheoryandtheraPiddevelopmentofD.S.Pdevices,adaptivenoisecancellerl1'2]iswidelyusedininformationprocessingfieldssuchasradar,sonartspeechsignalprocessingandcommunication.Butadaptivecancellerisseldomusedasaparameterestimatortoestimateaprocessorparameterssuchasbearing,rangetvelocityandpositionofamaneuveringtarget,becausetheparameters(i.e.thesrycalledsignaltobeestimatied)tobeestimatedareusuallynonstationaryandhavethenonzeromean,thatwill…