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.     

辅助判决式最大似然式相干光接收机的冷启动(英文)

在辅助判决式最大似然式相干接收机启动策略基础上,提出了另一种冷启动方案:利用M次幂算法获得启动序列,再过渡至辅助判决最大似然性相位估计.仿真结果验证了该方案的可行性.进一步优化表明:当M次幂算法与辅助判决最大似然性算法参数均取最优值时,冷启动方案性能达到最佳.与传统启动方案相比,这种冷启动方案能够应用于多接收机系统中,且更加适用于未来突发模式相干光传输系统中.     

相干光通信中概率整形信号的偏振解复用算法

为应对概率整形场景下相干光通信系统中的偏振解复用问题,提出了一种基于独立成分分析和极大似然估计的偏振解复用算法.由于各个信号之间相互独立,因此可以对信号采用独立成分分析的手段进行偏振解复用.通过基于最大似然估计的迭代更新寻找最佳的分离矩阵,即偏振解复用矩阵.对所提算法在不同信噪比下的性能及整形强度的容忍度进行了仿真分析...     

带宽受限QPSK相干光侦听接收机最佳性能分析

在带宽受限情况下建立了相干光侦听接收机模型并给出了带宽受限的等效滤波器模型.给出了带宽受限下的接收信号数学模型,利用星座图详细阐述并总结了其它符号对当前符号的干扰规律.重新定义了符号信噪比,推导了基于辅助判决式最大似然式相位估计的带宽受限的侦听接收机的误码率性能,并分析了接收机的最佳采样判决时刻和最佳带宽.仿真结果表明,不同带宽下定义的最大信噪比对应系统的最小误码率;不同滤波器类型对应的最佳采样判决时刻和最佳带宽是不同的,但都可以找到一个最佳点;随着信噪比的增加,带宽受限会降低辅助判决式最大似然式相位估计算法的性能.     

期望最大化算法近场被动定位技术研究

本文介绍了一种对近场声源的距离和方位参数的确定性最大似然估计方法。直接的对近场声源参数的最大似然估计产生了复杂的多参数优化问题,我们在实际采样数据(非完全数据)和假设数据(完全数据)等方面重新构建这个问题,最后提出运用期望最大化迭代方法获得最大似然估计。期望最大化算法将观测数据分解,然后对于最优化问题,运用有效的计算措施单独估计每个信号成分的参数。这种算法的应用性和有效性通过一定的仿真得到了验证。     

采用非局部主成分分析的极大似然估计图像去噪

本文提出一种采用非局部主成分分析的极大似然估计去噪方法.首先采用非局部主成分分析算法来计算像素邻域间的灰度值和纹理结构相似性,然后通过极大似然估计方法估计最优复原图像.本方法使用非局部主成分分析克服现有局部性去噪方法模糊边界等缺陷,引入极大似然估计方法来改进现有非局部均值的简单加权均值去噪处理,从而提高对图像细节信息的...     

采用非局部主成分分析的极大似然估计图像去噪

本文提出一种采用非局部主成分分析的极大似然估计去噪方法.首先采用非局部主成分分析算法来计算像素邻域间的灰度值和纹理结构相似性,然后通过极大似然估计方法估计最优复原图像.本方法使用非局部主成分分析克服现有局部性去噪方法模糊边界等缺陷,引入极大似然估计方法来改进现有非局部均值的简单加权均值去噪处理,从而提高对图像细节信息的复原能力.最后分别使用本文方法、非局部均值和局部极大似然估计三种去噪方法,在不同噪音大小和不同几何纹理复杂度的图像中进行定性和定量的去噪实验.结果表明,本文方法可在保持图像细节和纹理信息的情况下有效去噪,较之现有方法效果更好.     

多基阵联合的无源纯方位目标运动分析研究

研究基于两声呐基阵的方位数据融合的目标运动分析方法,给出了伪线性处理、扩展卡尔曼滤波、最大似然估计等算法的模型,进行了仿真实验,比较和分析了仿真结果。计算结果表明,所给出的多基阵联合的无源纯方位目标运动分析方法,不仅克服了单基阵纯方位目标运动分析需要本舰机动的限制,而且提高了参数估计算法的稳定性和全局性。     

基于均值似然估计的激光探测微动特征提取和分离

最大似然估计是提取目标微动特征参数的最佳估计方法,但直接用网格法求解计算量巨大,且激光探测微多普勒回波信号对应的代价函数具有高度非线性,存在多个局部最大值。为此,提出均值似然估计与蒙特卡罗结合的估计方法,给出了最大似然参数估计的闭合表达式,再通过设计压缩似然函数获得全局最大值,通过蒙特卡罗法抽样并计算循环均值估计出参数。该方法避免了传统方法中对高精度初始值和复杂迭代算法的依赖,能够实现参数的联合估计。对于多分量微多普勒信号,该方法可在参数估计的同时实现各微动分量分离,且不增加算法的复杂性。对仿真和实验数据进行估计,结果表明,该方法在达到近似于最大似然估计性能的同时可有效降低计算复杂度并确保了全局收敛,实现信号的分离和参数估计。     

多谐波脉冲星信号时延估计方法

针对脉冲星导航技术中延时估计这一关键问题, 提出了频域上直接使用脉冲星信号测量到达时间集合进行时延估计的方法——多谐波脉冲星信号时延估计(MHSPE)方法. 该方法建立在频域上相位时延的极大似然估计的基础上, 通过高次谐波对脉冲星观测信号提取出各谐波相位的极大似然估计, 然后取频谱上各谐波的幅值进行归一化作为各谐波相位的权值, 最后取各谐波相位的加权平均作为该时刻的相位估计. 理论上证得MHSPE算法对相位的估计是无偏、一致的, 相比于频域上一次谐波的极大似然估计, MHSPE方法的信噪比随谐波数m的增加而增加, 当各谐波幅值相同时, 信噪比可提高m_1/2倍; 与脉冲星信号时延的克拉美罗界比较, 脉冲星信号时域的导数在频域上的反映就是各谐波分量的数量, 因此随着谐波次数的增加脉冲星信号时延估计可极大趋近克拉美罗界. 采用RXTE航天器对Crab脉冲星的实测数据检验MHSPE方法的性能, 实验结果表明, 针对低信噪比的脉冲星信号, MHSPE可获得高精度的相位估计, 随观测时间增加, 估计精度快速收敛于克拉美罗界.     

Independent component analysis in the presence of noise in fMRI

A noisy version of independent component analysis (noisy ICA) is applied to simulated and real functional magnetic resonance imaging (fMRI) data. The noise covariance is explicitly modeled by an autoregressive (AR) model of order 1. The unmixing matrix of the data is determined using a variant of the FastICA algorithm based on Gaussian moments. The sources are estimated using the principle of maximum likelihood by modeling the source densities as asymmetric exponential functions. Effect of dimensionality reduction on the effective noise covariance used, accuracy of the obtained mixing matrix and degree of improvement in estimating fMRI sources are investigated. The primary conclusions after using this method of evaluation are as follows: (a) weighting matrix estimates are similar for noisy and conventional ICA in the realm of typical fMRI data, and (b) source estimates are improved by 5% (as measured by the correlation coefficient) in realistic simulated data by explicitly modeling the source densities and the noise, even when just a simple white noise model is used.     

A variant of logistic transfer function in Infomax and a postprocessing procedure for independent component analysis applied to fMRI data

Independent component analysis with Infomax algorithm can separate functional magnetic resonance imaging (fMRI) data into independent spatial components (brain activation maps) and their associated time courses. In the current study, we propose a variant of the logistic transfer function in Infomax, referred to as a-logistic Infomax, and a postprocessing procedure to combine a consistently task-related (CTR) component with transiently task-related (TTR) components for a better definition of brain functional localization. This a-logistic Infomax introduced parameter a into the standard logistic transfer function of conventional Infomax algorithm. For postprocessing method, we suggest the use of a stepwise linear regression of CTR and TTR components to fit reference function and then to sum up with different weights only those with significant contributions to the reference function in order to obtain a task component activation map. The effectiveness of both approaches on separating components and functional localization was evaluated with simulated and real fMRI data.     

Synchronization of chaotic oscillator time scales

We consider chaotic oscillator synchronization and propose a new approach for detecting the synchronized behavior of chaotic oscillators. This approach is based on analysis of different time scales in the time series generated by coupled chaotic oscillators. We show that complete synchronization, phase synchronization, lag synchronization, and generalized synchronization are particular cases of the synchronized behavior called time-scale synchronization. A quantitative measure of chaotic oscillator synchronous behavior is proposed. This approach is applied to coupled Rössler systems.     

Improved steered response power method for sound source localization based on principal eigenvector

Sound source localization is essential in many microphone arrays application, ranging from teleconferencing systems to artificial perception in a reverberant noisy environment. The steered response power (SRP) using the phase transform (SRP-PHAT) source localization algorithm has been proved robust, however, the performance of the SRP-PHAT algorithm degrades in highly reverberant noisy environment. Though the SRP-based maximum likelihood localizers are more robust than SRP-PHAT, they have the drawback of requiring noise variance to be estimated in a silent room. This paper presents an improved SRP-PHAT algorithm based on principal eigenvector. Sound source location is estimated from the principal eigenvector computed from the frequency-domain correlation matrix. Using both simulated and real data, we show that the proposed algorithm achieves higher source localization accuracy compared to the SRP-PHAT algorithm.     

联合InSAR与PCA的北京平原地面沉降时空分析

20世纪70年代以来,由于地下水超采、可压缩层厚度不均等引起的不均匀地面沉降已逐渐发展成为北京平原最严重的地质灾害之一。目前,北京平原最新时段的地面沉降时空分析的研究报道较少。根据主动微波获取的39景Sentinel-1A光谱影像,使用SBAS-InSAR技术,获取了北京平原2017年5月—2020年5月的地面形变数据;利用主成分分析法对北京平原的地面沉降时空特征进行了分析。在SBAS-InSAR技术处理中设置时间基线120天、空间基线的阈值设置为最大临界基线的45%,生成154个干涉对;对所有干涉对进行配准、干涉,对干涉后的结果进行去平、使用Goldstein算法进行滤波、生成相干系数图并使用最小费用流算法进行相位解缠,筛选73个优质的干涉对进行轨道精炼与重去平,以估算和去除残余相位;通过时间高通滤波、空间低通滤波去除大气相位;最后采用最小二乘法和奇异值分解获取北京平原2017年5月—2020年5月的地面形变数据。在监测时间段内平均沉降速率最大为114.9 mm·yr^-1,最大累积沉降量为345.9 mm·yr^-1,均位于朝阳金盏。2019...     

An empirical investigation of motion effects in eMRI of interictal epileptiform spikes

We recently developed a functional neuroimaging technique called encephalographic magnetic resonance imaging (eMRI). Our method acquires rapid single-shot gradient-echo echo-planar MRI (repetition time=47 ms); it attempts to measure an MR signal more directly linked to neuronal electromagnetic activity than existing methods. To increase the likelihood of detecting such an MR signal, we recorded concurrent MRI and scalp electroencephalography (EEG) during fast (20-200 ms), localized, high-amplitude (>50 μV on EEG) cortical discharges in a cohort of focal epilepsy patients. Seen on EEG as interictal spikes, these discharges occur in between seizures and induced easily detectable MR magnitude and phase changes concurrent with the spikes with a lag of milliseconds to tens of milliseconds. Due to the time scale of the responses, localized changes in blood flow or hemoglobin oxygenation are unlikely to cause the MR signal changes that we observed. While the precise underlying mechanisms are unclear, in this study, we empirically investigate one potentially important confounding variable — motion. Head motion in the scanner affects both EEG and MR recording. It can produce brief “spike-like” artifacts on EEG and induce large MR signal changes similar to our interictal spike-related signal changes. In order to explore the possibility that interictal spikes were associated with head motions (although such an association had never been reported), we had previously tracked head position in epilepsy patients during interictal spikes and explicitly demonstrated a lack of associated head motion. However, that study was performed outside the MR scanner, and the root-mean-square error in the head position measurement was 0.7 mm. The large inaccuracy in this measurement therefore did not definitively rule out motion as a possible signal generator. In this study, we instructed healthy subjects to make deliberate brief (<500 ms) head motions inside the MR scanner and imaged these head motions with concurrent EEG and MRI. We compared these artifactual MR and EEG data to genuine interictal spikes. While per-voxel MR and per-electrode EEG time courses for the motion case can mimic the corresponding time courses associated with a genuine interictal spike, head motion can be unambiguously differentiated from interictal spikes via scalp EEG potential maps. Motion induces widespread changes in scalp potential, whereas interictal spikes are localized and have a regional fall-off in amplitude. These findings make bulk head motion an unlikely generator of the large spike-related MR signal changes that we had observed. Further work is required to precisely identify the underlying mechanisms.     

Evidence from human scalp electroencephalograms of global chaotic itinerancy

My objective of this study was to find evidence of chaotic itinerancy in human brains by means of noninvasive recording of the electroencephalogram (EEG) from the scalp of normal subjects. My premise was that chaotic itinerancy occurs in sequences of cortical states marked by state transitions that appear as temporal discontinuities in neural activity patterns. I based my study on unprecedented advances in spatial and temporal resolution of the phase of oscillations in scalp EEG. The spatial resolution was enhanced by use of a high-density curvilinear array of 64 electrodes, 189 mm in length, with 3 mm spacing. The temporal resolution was advanced to the limit provided by the digitizing step, here 5 ms, by use of the Hilbert transform. The numerical derivative of the analytic phase revealed plateaus in phase that lasted on the order of 0.1 s and repeated at rates in the theta (3-7 Hz) or alpha (7-12 Hz) ranges. The plateaus were bracketed by sudden jumps in phase that usually took place within 1 to 2 digitizing steps. The jumps were commonly synchronized in each cerebral hemisphere over distances of up to 189 mm, irrespective of the orientation of the array. The jumps were usually not synchronized across the midline separating the hemisphere or across the sulcus between the frontal and parietal lobes. I believe that the widespread synchrony of the jumps in analytic phase manifest a metastable cortical state in accord with the theory of self-organized criticality. The jumps appear to be subcritical bifurcations. They reflect the aperiodic evolution of brain states through sequences of attractors that on access support the experience of remembering.     

Principal component and sampling analysis of color spectra

We studied the effect and relation between the optimal sampling interval and the optimal number of principal component analysis eigenvectors for color spectra. The effect of different type of light sources and illuminants are included in the study. Several reflectance spectra sets under different real light sources and standard illuminants were used as a test set. The needed sampling interval and number eigenvectors with different light sources and illuminants are found. The used bandwidth in measurements causes effect to the resulted optimal sampling interval, which is also shown.     

光阴极电子枪微波与激光同步系统研究

光阴极微波电子枪中,微波与激光脉冲的同步问题是其能否稳定工作的关键之一.我们自行设计、采用了一种取样锁相电路,用频率不很稳定的激光脉冲的基波与S波段高频微波直接取样鉴相,提高了锁相精度,实现了超快激光脉冲与高频微波的精准同步,这在国内还是第一次.均方根时间抖动为0.57ps,时间抖动的最大值为2.6ps,锁相精度完全满足SDUV项目光阴极微波电子枪的要求.     

An independent component analysis-based approach on ballistocardiogram artifact removing

Interest about simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data acquisition has rapidly increased during the last years because of the possibility that the combined method offers to join temporal and spatial resolution, providing in this way a powerful tool to investigate spontaneous and evoked brain activities. However, several intrinsic features of MRI scanning become sources of artifacts on EEG data. Noise sources of a highly predictable nature such as those related to the pulse MRI sequence and those determined by magnetic gradient switching during scanning do not represent a major problem and can be easily removed. On the contrary, the ballistocardiogram (BCG) artifact, a large signal visible on all EEG traces and related to cardiac activity inside the magnetic field, is determined by sources that are not fully stereotyped and causing important limitations in the use of artifact-removing strategies. Recently, it has been proposed to use independent component analysis (ICA) to remove BCG artifact from EEG signals. ICA is a statistical algorithm that allows blind separation of statistically independent sources when the only available information is represented by their linear combination. An important drawback with most ICA algorithms is that they exhibit a stochastic behavior: each run yields slightly different results such that the reliability of the estimated sources is difficult to assess. In this preliminary report, we present a method based on running the FastICA algorithm many times with slightly different initial conditions. Clustering structure in the signal space of the obtained components provides us with a new way to assess the reliability of the estimated sources.