Optimal sampling and estimation in PASL perfusion imaging

Pulsed arterial spin labeling (PASL) techniques potentially allow the absolute, noninvasive quantification of brain perfusion using MRI. This can be achieved by fitting a kinetic model to the data acquired at a number of sampling times. However, the intrinsically low signal-to-noise ratio of PASL measurements usually requires substantial signal averaging, which may result in undesirably long scanning times. A judicious choice of the sampling points is, therefore, crucial in order to minimize scanning time, while optimizing estimation accuracy. On the other hand, a priori information regarding the model parameters may improve estimation performance. Here, we propose a Bayesian framework to determine an optimal sampling strategy and estimation method for the measurement of brain perfusion and arterial transit time (ATT). A Bayesian Fisher information criterion is used to determine the optimal sampling points and a MAP criterion is employed for the estimation of the model parameters, both taking into account the uncertainty in the model parameters as well as the amount of noise in the data. By Monte Carlo simulations, we show that using optimal compared to uniform sampling strategies, as well as the Bayesian estimator relative to a standard least squares approach, improves the accuracy of perfusion and ATT measurements. Moreover, we also demonstrate the applicability of the proposed approach to real data, with the advantage of reduced intersubject variability relative to conventional sampling and estimation approaches.     

基于EM算法的低剂量CT图像去噪

提高低剂量CT图像的信噪比是使其获得有效临床应用的关键.文中针对低剂量CT投影数据极低信噪比特性以及投影数据噪声所特有的非平稳高斯特性,提出采用EM(Expectation-Maximization)算法通过求解图像后验概率的条件期望值最大的方法达到图像复原目的,同时在算法中实现了图像模型参数的估计,并且引入Gibbs采样技术,很好的解决了算法计算问题.计算机仿真及真实投影数据的实验表明,本文算法无论从复原图像的可视化效果上还是从噪声-分辨率关系的定量分析上,都具有一定优势.     

Wavelet-Domain Medical Image Denoising Using Bivariate Laplacian Mixture Model

In this paper, we proposed novel noise reduction algorithms that can be used to enhance image quality in various medical imaging modalities such as magnetic resonance and multidetector computed tomography. The noisy captured 3-D data are first transformed by discrete complex wavelet transform. Using a nonlinear function, we model the data as the sum of the clean data plus additive Gaussian or Rayleigh noise. We use a mixture of bivariate Laplacian probability density functions for the clean data in the transformed domain. The MAP and minimum mean-squared error (MMSE) estimators allow us to efficiently reduce the noise. The employed prior distribution is mixture and bivariate, and thus accurately characterizes the heavy-tail distribution of clean images and exploits the interscale properties of wavelets coefficients. In addition, we estimate the parameters of the model using local information; as a result, the proposed denoising algorithms are spatially adaptive, i.e., the intrascale dependency of wavelets is also well exploited in the enhancement process. The proposed approach results in significant noise reduction while the introduced distortions are not noticeable as a result of accurate statistical modeling. The obtained shrinkage functions have closed form, are simple in implementation, and efficiently enhances data. Our experiments on CT images show that among our derived shrinkage functions usually BiLapGausMAP produces images with higher peak SNR. However, BiLapGausMMSE is preferred especially for CT images, which have high SNRs. Furthermore, BiLapRayMAP yields better noise reduction performance for low SNR MR datasets such as high-resolution whole heart imaging while BiLapGauMAP results in better performance in MR data with higher intrinsic SNR such as functional cine data.      

A laser speckle imaging technique for measuring tissue perfusion

Laser Doppler imaging (LDI) has become a standard method for optical measurement of tissue perfusion, but is limited by low resolution and long measurement times. We have developed an analysis technique based on a laser speckle imaging method that generates rapid, high-resolution perfusion images. We have called it laser speckle perfusion imaging (LSPI). This paper investigates LSPI output and compares it to LDI using blood flow models designed to simulate human skin at various levels of pigmentation. Results show that LSPI parameters can be chosen such that the instrumentation exhibits a similar response to changes in red blood cell concentration (0.1%-5%, 200 microL/min) and velocity (0-800 microL/min, 1% concentration) and, given its higher resolution and quicker response time, could provide a significant advantage over LDI for some applications. Differences were observed in the LDI and LSPI response to tissue optical properties. LDI perfusion values increased with increasing tissue absorption, while LSPI perfusion values showed a slight decrease. This dependence is predictable, owing to the perfusion algorithms specific to each instrument, and, if properly compensated for, should not influence each instrument's ability to measure relative changes in tissue perfusion.     

低信噪比条件下天基预警雷达目标运动参数估计方法

天基预警雷达目标回波包络峰值的距离徙动会导致FFT相参积累增益降低,为了对回波信号进行运动补偿,需要对目标运动参数进行估计。由于天基雷达对目标的观察距离较远,因此回波信噪比较低,直接影响了运动参数的估计精度。针对这一问题,本文提出了一种低信噪比条件下天基预警雷达目标运动参数估计方法。首先建立了目标回波模型,并对雷达观测距离范围内的回波信噪比进行了计算分析;然后利用Keystone变换对回波包络距离徙动进行校正,并对校正后的回波信号作离散短时傅里叶变换,得到回波信号方位向上的时频分布;最后利用Hough变换将时频域变换到参数域,得到运动参数的估计值。仿真结果表明,本文方法在低信噪比条件下能有效地估计目标运动参数,并且估计精度对信噪比的变化不敏感,具有良好的鲁棒性。      

低信噪比下对称三角线性调频信号参数估计

低截获概率雷达的广泛应用导致电子侦察系统截获到的雷达信号大多处于低信噪比环境中。针对目前雷达信号参数估计算法在此环境中性能急剧下降,甚至失效的问题,提出一种基于多相滤波器组和高阶累积量联合处理的对称三角线性调频连续波信号参数估计算法。该算法利用多相滤波器组实现信号在频域上的快速均匀划分,对输出的每个子带信号进行三阶累积量对角切片的短时估计,有效抑制了高斯噪声的干扰,并经过包络检波后得到信号完整的时频矩阵,通过对时频图像进行Radon变换得到信号带宽、周期以及调频斜率的估计,频率曲线的提取得到信号起止频率的估计。仿真结果表明:方法在信噪比大于-12dB时的估计正确率较高。     

A robust method for parameter estimation of AR systems using empirical mode decomposition

This paper presents a robust algorithm for parameter estimation of autoregressive (AR) systems in noise using empirical mode decomposition (EMD) method. The basic idea is to represent the autocorrelation function of the noise-free AR signal as the summation of damped sinusoidal functions and use EMD for extracting these component functions as intrinsic mode functions (IMFs). Unlike conventional correlation-based techniques, the proposed scheme first estimates the damped sinusoidal model parameters from the IMFs of autocorrelation function using a least-squares based method. The AR parameters are then directly obtained from the extracted sinusoidal model parameters. Simulation results show that EMD is a very promising tool for AR system identification at a very low signal-to-noise ratio (SNR).     

基于周期模糊函数理论的信噪比估计

周期模糊函数表示周期调制连续波信号的时延-频偏平面的相关,不同的信号具有不同的周期模糊函数。针对雷达中频信号的信噪比估计,提出了基于周期模糊函数理论的信噪比估计方法。根据信号和噪声在周期模糊函数中的不同位置具有不同的表现特征,推导了在高斯白噪声背景下基于周期模糊函数的信噪比估计公式,给出了实现算法,同时和其他信噪比估计算法进行了对比仿真分析。结果表明,基于周期模糊函数的信噪比估计方法,特别是对低信噪比情况下,估计精度高,算法简单,易于工程实现。     

一种基于支持向量机的含噪语音的清/浊/静音分类的新方法

本文将支持向量机(SVM)方法应用于语音信号的清/浊/静音检测中,提出并验证了一种在各种信噪比等级下将语音信号有效地分为清音、浊音和静音三类信号的新型分类算法.首先,在高信噪比情况下,本文采用了G.729B VAD中的四个差分参数作为SVM分类器的输入特征参数,进行了静音分类的对比实验,得到了优于G.729B VAD和BP神经网络传统算法的实验结果,说明引入这种机器学习方法做语音分类是可行的,并分析讨论了在核函数不同的情况下支持向量机在实验中所表现出的性能.其次,又讨论了在低信噪比条件下,如何通过对含噪语音建立统计模型,提取对噪音免疫的统计特征参数,并给出了一种对时变背景噪声自适应的估计方法.最后,通过在不同噪音环境下的对比实验结果,验证了本文所提出的算法在中低信噪比情况下的分类性能要优于其他传统算法.     

基于多随机森林的低信噪比声音事件检测

论文针对各种背景声音中低信噪比声音事件的检测问题，提出把背景声音与声音事件混合，形成带噪声样本来训练分类器.在预处理阶段，使用基于经验模态分解与2-6级固有模态函数的投票方法，对背景声音与声音事件端点进行预测并估算信噪比.接着使用子带能量分布方法，提取声音数据的特征.最后，论文将背景声音与声音事件样本库中所有声音样本按照估算的信噪比相混合，生成混合声音特征训练多随机森林，用于低信噪比声音事件的检测.实验证实，所提出的方法可以用于各种声场景下低信噪比声音事件的检测，并能在信噪比为-5dB的情况下保持67.1%的平均检测率.     

Clustered components analysis for functional MRI

A common method of increasing hemodynamic response (SNR) in functional magnetic resonance imaging (fMRI) is to average signal timecourses across voxels. This technique is potentially problematic because the hemodynamic response may vary across the brain. Such averaging may destroy significant features in the temporal evolution of the fMRI response that stem from either differences in vascular coupling to neural tissue or actual differences in the neural response between two averaged voxels. Two novel techniques are presented in this paper in order to aid in an improved SNR estimate of the hemodynamic response while preserving statistically significant voxel-wise differences. The first technique is signal subspace estimation for periodic stimulus paradigms that involves a simple thresholding method. This increases SNR via dimensionality reduction. The second technique that we call clustered components analysis is a novel amplitude-independent clustering method based upon an explicit statistical data model. It includes an unsupervised method for estimating the number of clusters. Our methods are applied to simulated data for verification and comparison to other techniques. A human experiment was also designed to stimulate different functional cortices. Our methods separated hemodynamic response signals into clusters that tended to be classified according to tissue characteristics.     

雷达脉冲信号检测及参数估计新方法

基于信号包络的信号检测是从信号与噪声能量差异的角度来区分两者,因此在信噪比较低的情况下包络检测效果不好。针对这个问题,提出利用信号的特征值——奇异谱斜率来区分信号和噪声,并从脉冲宽度角度来检测信号。在低信噪比环境中该方法可以有效地检测脉冲信号的有无,而在较高信噪比时该方法可以估计脉冲信号的到达时间和脉冲宽度。仿真试验证明了新方法在信号检测和参数估计方面的优越性。     

基于谱相关的CBOC信号参数盲估计

针对低信噪比下组合二进制偏移载波(CBOC)调制信号的参数盲估计问题,提出了利用谱相关对CBOC信号进行参数估计方法。首先给出了CBOC信号模型,然后根据CBOC信号的数据通道和导频通道之间有良好的正交性特点,详细推导出其谱相关函数可以化简为两个BOC信号谱相关函数的叠加,最后根据CBOC信号循环频率截面的特点进行峰值检索后,实现对伪码速率、载频速率和副载波速率的盲估计。推导结果和计算机仿真分析表明,该方法可以实现在低信噪比下对伪码速率、载频速率和副载波速率的有效估计。     

线性调频连续波信号检测与参数估计算法

针对长时间积累的方法较难在线性调频连续波信号的检测和参数估计中应用的问题,该文基于归一化变窗长相干平均法,提出一种联合帧间相关法与循环平稳法的线性调频连续波信号检测与参数估计算法。该方法首先利用归一化变窗长相干平均法实现噪声方差的一致性,然后在此基础上利用帧间相关法实现周期的精确估计,最后利用循环平稳法估计信号的相位参数。该算法以较低的计算复杂度实现信号的长时间积累,在低信噪比条件下具有较好的估计性能。仿真结果验证了该方法的有效性。     

滤波与正交基分解在磁性目标检测中的应用

通过对磁性目标磁异常信号时域和频域特性的分析可知,磁异常信号属于低频信号,且在实际磁异常探测中磁性目标信号被噪声淹没,信噪比较低。针对这种情况提出了基于低通滤波与正交基分解的检测方法,先分析数字滤波器特性,设计约束最小二乘FIR滤波器滤除高频噪声,再利用正交基分解检测算法,大幅提升了磁性目标信号的信噪比,从而实现了磁性目标检测。仿真试验表明,该方法可滤除高频噪声,提升信噪比,增强对磁性目标的检测能力     

Estimation of time varying signal parameters using an improved Adaline learning algorithm

This paper presents an improved adaptive linear combiner (Adaline) structure for fast estimation of time varying power signal parameters corrupted by noise. Unlike the conventional Adaline approach, the new algorithm minimizes an objective function based on weighted square of the error and uses a modified recursive Gauss Newton (MRGN) method. The Hessian matrix, obtained by minimizing the objective function, was simplified using certain approximations. A weight adjustment procedure for the Adaline is defined in a decoupled manner for direct current (DC), fundamental, harmonic components and system frequency. The new improved Adaline, thus produces a faster convergence and tracking accuracy for the time varying distorted power system signals. To test the effectiveness of the algorithm, several time varying power network signals were simulated with abrupt change in system frequency, harmonics, decaying dc components with low signal to noise ratio (SNR), and the changing parameters were estimated. The performance of proposed Adaline structure is compared with the standard Adaline structure in terms of accuracy.     

基于正交基函数-编辑距离的低信噪比下磁异常信号相似性度量方法

针对低信噪比下磁异常信号相似性难以度量的问题,该文提出基于正交基函数(OBF)分解和编辑距离法(EDR)相结合的OBF-EDR磁异常信号相似性度量方法.该方法通过对磁异常信号进行正交基函数分解得到离散基函数系数,根据背景噪声与基函数不相关的特性提高离散基函数系数信噪比,利用编辑距离法对离散基函数系数进行相似性计算从而间...     

基于决策理论的信号调制识别改进算法

针对基于决策判决理论的传统算法对通信信号调制识别在低信噪比下正确识别率较低的问题,提出了一种基于多种特征参数判决的改进算法。利用高阶矩取代了利用零中心归一化瞬时幅值绝对值的标准偏差来区分ASK信号,对FSK信号采用功率谱谱峰数进行识别。仿真使用了包括模拟调制信号在内的多达11种不同类型的调制信号,仿真结果表明,在信噪比不低于8 dB下,该方法具有较高的正确识别率。     

Application of expectation-maximization algorithm to the detectionof a direct-sequence signal in pulsed noise jamming

The detection of a direct-sequence spread-spectrum signal received in a pulsed noise jamming environment is considered. The expectation-maximization algorithm is used to estimate the unknown jammer parameters and hence obtain a decision on the binary signal based on the estimated likelihood functions. The probability of error performance of the algorithm is simulated for a repeat code and a (7, 4) block code. Simulation results show that at low signal-to-thermal-noise ratio and high jammer power, the EM detector performs significantly better than the hard limiter and somewhat better than the soft limiter. Also, at low SNR, there is little degradation as compared to the maximum-likelihood detector with true jammer parameters. At high SNR, the soft limiter outperforms the EM detector     

Parameter Blind Estimation of Frequency-Hopping Signal Based on Time–Frequency Diagram Modification

To effectively estimate the parameters of the multiple frequency-hopping signals, a blind parameter estimation method based on time–frequency diagram modification is proposed. Firstly, the observed signal is transformed to the time–frequency domain, using short time Fourier transform with overlapping windows. Then an energy detection method based on adaptive threshold is used to modify the time–frequency diagram, and the parameters of the frequency-hopping signals are finally obtained from the modified spectrogram. Theoretical analysis and simulation results show that the method proposed can get a clear time–frequency diagram at low signal-to-noise ratio (SNR), and its accuracy of parameter estimated is higher than that of previous methods. When SNR is ?10 dB, estimation errors of frequency, hopping time and hop duration is 0.0002, 0.0008 and 0.0013, respectively, which are about 1–2 orders of magnitude lower over the previous method.