基于快速ICA和样本熵的胎儿心电提取

目的针对快速ICA(独立成分分析)算法提取胎儿心电信号通道不确定的问题,提出一种新的胎儿心电提取方法。方法先用快速ICA算法提取胎儿心电信号,再用样本熵评估快速ICA算法提取的3通道心电数据,从而识别出胎儿心电信号,最后去除胎儿心电基线漂移并采用小波软阈值去噪得到纯净的胎儿心电信号。结果相比自适应滤波算法、小波阈值去燥算法、径向基神经网络算法,该算法能更好的提取高信噪比的胎儿心电信号。结论应用快速ICA和样本熵可以准确的提取胎儿心电信号。     

基于完全抗混叠DTCWPT和包络谱熵的轴承故障诊断

针对经典小波包和双树复小波包(dual tree complex wavelet package transform,DTCWPT)能量泄漏和频率混叠的缺陷,提出完全抗混叠的DTCWPT改进算法,该算法解决了经典小波包存在负频率以及经典小波包和DTCWPT滤波器频率不完全截止问题。根据高斯白噪声频率充满整个频带的特性,通过小波包变换对高斯白噪声进行分解,利用频带能量泄漏的定量分析方法,验证了改进DTCWPT具有完全的抗频带能量泄漏特性。将改进DTCWPT方法和包络谱熵引入到轴承故障诊断中,该方法的核心是:对轴承振动信号进行改进DTCWPT变换得到不同尺度的分解信号,分别计算各分解信号的包络谱熵,合并熵值较小的几个分量信号的包络谱,最后根据合并的包络谱来检测轴承故障。该方法在消除经典小波包变换和DTCWPT频率混叠和能量泄漏的同时还解决了小波包分量选择盲目的问题。最后应用轴承故障试验数据对该方法进行试验验证,结果表明:改进DTCWPT结合包络谱熵选择的方法能够很好提取出轴承故障特征频率的基频、倍频,提高了轴承故障的诊断效果。     

超声波测距系统中的小波去噪方法研究

针对远距离超声波测距系统中回波信号信噪比低的问题,采用小波变换对超声波的回波信号进行去噪处理。为取得较好的去噪效果,对小波变换的参数选取进行了研究。根据小波基的特性,通过能量与能量熵选取最优小波基;基于回波信号噪声的白噪声特征,采用白噪声检验自适应确定分解层数;引入参考噪声信号,确定小波系数处理阈值,并选用一种结合软、硬阈值函数的改进阈值函数进行小波系数处理。为验证方法的有效性,搭建基于NI数据采集卡和LabVIEW的超声回波信号采集平台,利用MATLAB小波工具包完成回波信号的去噪处理,并通过信噪比、均方根误差等指标对去噪效果进行综合评判。实验表明小波去噪可以达到很好的去噪效果,为大量程超声测距提供理论基础。     

角加速度信号自适应小波的去噪方法

针对多扰动、大负载环境下角加速度计输出信号中含有脉冲噪声和高斯白噪声的情况,提出一种改进的离散小波阈值法与中值滤波算法相结合的角加速度计信号自适应去噪算法。首先,使用中值滤波对原始信号进行去除脉冲噪声的预处理;其次,使用分解层数的自适应确定方法与改进的阈值选取准则,通过离散小波阈值去噪法去除高斯白噪声。仿真结果表明,该算法能够有效地提高信噪比,降低最小均方误差。实验结果表明,该算法既能去除分子型液环式角加速度计信号中噪声,又能很好地保留真实信号中的高动态部分。     

EMD分解与小波阈值联合去噪在蔗地路谱信号处理中的应用

结合EMD分解与小波阈值两种去噪方法,并针对所采集的甘蔗地路面谱信号进行去噪处理,发挥了两种去噪方法各自的优势,得到了路面随机激励信号的有效频带;同时对比了三种去噪方法的结果,验证了该方法的可靠性。该信号的去噪预处理为数据后期拟合位移信号或者编制载荷谱的正确性提供了可靠的保证。     

基于小波变换的非接触式电导信号去噪研究

耦合毛细管电泳技术的非接触式电导检测器在检测痕量化学物质时具有较高的灵敏度,然而噪声会导致获得的信号检测结果存在偏差。针对非接触式电导检测信号的数据特点,提出基于小波变换的阈值去噪方法:利用高斯函数模型和高斯白噪声模拟仿真非接触式电导信号曲线,继而逐步选取不同的参数来得到多个去噪结果,并选择若干评价指标对去噪结果进行评价,从而确定最优去噪参数。同时,将小波变换算法与其他常用去噪算法进行对比,验证其优越性。最后将最优参数代入小波阈值去噪算法,对三种痕量无机离子的非接触式电导信号进行去噪处理。实验结果表明:三种痕量无机离子的电导信号经过去噪处理后,信噪比显著提高,数据曲线光滑,且保留了数据特征,具有可行性及有效性。     

基于傅里叶相位差的抗噪声位移估计算法

提出了一种利用连续图像相位谱的差估计运动目标位移的抗噪声算法。根据傅里叶相移特性,用连续两幅图像相位谱差的周期性变化或剖面的斜率计算图像中运动物体的位移。向量滤波的应用在去除噪声的同时,有效保留了周期性信号在一个周期结束点的跳变性质。分段拟合是从周期性信号中计算斜率的合理方法。本算法在频域估计运动位移,能够克服背景光照变化的影响,克服10％的高斯白噪声,分辨率达到一个像素。     

一种综合小波变换的心电信号消噪算法

针对心电信号中混有的基线漂移、工频干扰、肌电干扰等噪声,比较了适于心电信号的4种基于小波变换的心电信号消噪算法,结合消噪后的信噪比和信号失真度,提出一种综合小波变换的心电信号消噪算法.该算法先使用小波分解法消除心电信号中的基线漂移,再利用模极大值法消除工频干扰、肌电干扰等噪声.并且运用该算法对MIT-BIH心律失常数据库中的含有多种噪声的心电数据进行了仿真与实验,结果表明噪声被有效地消除并且失真度较小,可满足临床分析与诊断对心电波形的要求.     

能量重心法的改进FFT算法分析及应用研究

研究噪声引起的谱线定位错误对能量重心法校正精度的影响。为了提高频率校正精度,在分析能量重心法理论和现有谱线选择方法的基础上,提出了一种减少谱线定位错误率的改进FFT校正算法。该算法在D. Macleod法基础上,利用谱线间的相位差确定谱线,减少了谱线定位错误。最后给出了通用的频率校正公式。仿真结果表明,改进算法谱线定位错误率低,抗噪性强,高斯白噪声条件下进行频率估计时较其他方法具有更小的均方根误差。在模拟井下套管的管道长度检测应用中进一步表明,改进方法具有更高的校正精度,有效地减少了长度测量误差。     

基于噪声拟合的核磁共振二维谱反演新方法

针对现有二维反演算法对噪声敏感或过度依赖于对噪声的估计的问题,提出了一种基于噪声拟合的核磁共振二维谱反演新方法.首先,使用小波滤波进行噪声提取;然后将噪声分布按照高斯白噪声的数学模型进行拟合,获取噪声方差;最后依据制定的新的停机准则和进化算法进行拟合,拟合残差与估计的噪声水平相当或最接近时,迭代停止并得到二维谱.通过使用该算法对不同信噪比的仿真数据和多组实验采集数据进行处理后发现,该算法具有很高的鲁棒性和准确性.这种新的反演算法能够满足不同应用领域、不同信噪比数据的反演要求,具有很高的实际应用价值.     

Signal-to-noise ratio constrained feedback control: Robust stability analysis

The explicit consideration of a communication channel model in a feedback control loop is known to be constrained by a fundamental limitation for stabilizability on the channel signal-to-noise ratio (SNR) when the linear time invariant (LTI) plant model is unstable. The LTI modelling approach for real, usually nonlinear, processes compromises accuracy versus complexity of the resulting model. This in turn introduces a gap between the proposed model and the real process, which is known as the model uncertainty. In this paper we then study SNR limitations by considering the continuous-time scenario and the case of an additive coloured Gaussian noise (ACGN) channel with bandwidth limitation, for which we then quantify the infimal SNR subject to the simultaneous presence of plant, channel and noise model uncertainties. We observe, for the special case of memoryless additive white Gaussian noise (AWGN) channels, that the obtained SNR limitation subject to plant model uncertainty can be redefined as a channel capacity limitation for stabilization.     

基于信号延拓的采样信号频谱泄漏抑制

提出一种基于信号数据延拓的采样信号离散傅里叶变换频谱泄漏抑制方法,分析了经典谱估计方法中频谱泄漏产生的原因,得出含噪声周期信号的无泄漏条件,进而提出频谱泄漏抑制方法:对采样信号作周期估计和整数周期数据延拓处理,使DFT计算窗内信号的边界连续或近似连续,以达到抑制频谱泄漏的目的。对通信、电力系统中常出现的加性高斯白噪声恶化信号的仿真表明:该方法可以显著减少频谱能量泄漏,获得更高质量的频谱。     

改进 S 变换谱估计的动态手势肌电特征处理

手语是各种手势动态变化的一种复杂运动模式,手势特征处理效果直接关系到手语识别的准确性。本文提出一种基于改进S变换谱估计的动态手势肌电特征处理新方法。对采集的表面肌电信号进行S变换,引入优化因子调节时频分辨率并生成改进S变换谱;定义谱的时间和频率分量为二维随机变量,以改进S变换谱元素为二维随机变量样本,通过高斯核密度估计得到二维核密度函数。仿真和实验均表明,改进S变换谱估计方法有效抑制了白噪声,并使动态手势的肌电暂态突变特征得到加强。与经验模态分解、自排序熵、奇异值排序熵等方法对比,基于该方法的动态手势识别率分别提高了10.0%、6.67%和11.67%,特征处理方法的效果明显。     

基于多尺度隐马尔可夫模型的CR影像降噪方法研究

在CR成像过程中不可避免的要引入各种干扰和噪声,只有弄清干扰图像信息的各种噪声来源、特征及其与信号的相互关系,才能有效地将之消除.在分析CR成像系统的基础上,文章指出影响CR图像质量的噪声主要是固有噪声和X线量子噪声,在统计规律上它们分别服从高斯分布和泊松分布.本文针对CR的固有噪声从小波系数的统计规律出发,根据固有噪声的特点,结合混合高斯模型描述小波系数的统计特征,采用两个状态的隐马尔可夫模型描述小波系数在尺度之间的相关性和依赖性,用最大期望值( EM)算法估计隐马尔可夫模型在各个尺度上的参数,然后按照尺度大小逐级对小波系数进行维纳滤波,最后是小波逆变换恢复图像.文章最后还给出了实验结果,并与其它降噪算法进行了比较.     

A new velocity estimation method using spectral identification of noise

In all measurement techniques one seeks accuracy and precision. In ultrasonic Doppler velocimetry, those qualities strongly depend on signal to noise ratio of the Doppler signal and on the performance of the velocity estimator. The most widely used estimation method in ultrasonic coherent Doppler velocimetry is the pulse pair method. Its success is due to the computation efficiency of the algorithm combined to an unbiased estimator. Unfortunately, for a wide range of experimental fluid flows, the pulse pair estimation is less efficient, especially for clear water or concentrated mud where the signal to noise ratio can be very low, or for highly turbulent flows where the Doppler signal has a broad spectrum. Our approach is based on the treatment of the Doppler spectral information. It uses a simple parametric identification inspired by theoretical models and experimental observations. It acts through noise subtraction and subsequent cutting. Thus, we have developed a fast velocity estimation algorithm superior to the pulse pair one in terms of accuracy. The robustness of the method was evaluated by adding different levels of white Gaussian noise to an experimental Doppler signal. The results demonstrate an increase of noise immunity up to one decade compared to the pulse pair method.     

OPTIMAL AUTOREGRESSIVE MODELLING OF A MEASURED NOISY DETERMINISTIC SIGNAL USING SINGULAR-VALUE DECOMPOSITION

A new simple method using singular-value decomposition (SVD) to find the optimal order for an autoregressive (AR) model of a deterministic time series is proposed. The method is particularly effective when the signal is contaminated with additive noise, and it is shown that the choice of sampling rate is also important when the signal is contaminated with noise. In this paper, the signal of interest is the impulse response of a second-order differential system, and various levels of white noise are also added to the signal, to show the robustness of the method. Simulation results show the method to be very reliable even when the noise level is high (e.g. a signal-to-noise ratio of 6 dB). To validate the method on experimental data the method is applied to the impulse response of a cantilever beam contaminated with additive white noise.     

Ellipsoid bounding estimation for kinematic geodetic positioning

In kinematic position estimation, a Kalman filter procedure is often used to provide improved solution benefiting from the history information. However, the optimal Kalman filtering solutions are subject to precise function models and statistic knowledge of noises, which may be difficult to obtain in advance. As a result, Kalman filter does not necessarily provide better performance for kinematic positioning solutions. In real world situations, a bound of the noise distribution would be easily and more reasonably determined than noise statistics. This paper studies ellipsoid bounding estimation for kinematic position estimation. In this estimation, neither process nor measurement noise characteristics are necessary, as long as the noises at each sample points can be confined in a bound (ellipsoid). A general trace criteria is adopted to choose the optimal estimator. For a the special case that only scalar measurement is available, e.g., a position measurement, we designed a modified intersection approach to reduce the estimation conservatism. Numerical results are given in each estimation step to illustrate the algorithm. A flight trajectory data is processed and the estimation results are compared under three different measurement noise cases: Gaussian white noise, uniformly noise (non-Gaussian) and the real measurement noise. Kalman filter results are also given for comparison. Results demonstrate the ellipsoid estimation indeed offers improved kinematic position solution in the sense of robustness for non-Gaussian noises, and retains nearly the same estimation error variance.     

离散频谱能量重心法频率校正精度分析及改进

研究噪声对离散频谱能量重心法的频率校正精度的影响,推导了在高斯白噪声背景下用能量重心法对加对称窗的离散频谱进行校正的频率误差理论公式,分析找错和找对最大值谱线情况下的理论误差和某些情况下校正误差较大的原因,为了提高能量重心校正法的频率校正精度,提出用谱线间相位差为阈值作为选择用3条或4条谱线进行校正依据的改进措施。通过对加Hanning窗的离散频谱进行计算机仿真计算,结果表明在大噪声背景下改进的能量重心校正法有很高的频率校正精度,与理论推导十分吻合,验证了理论推导的正确性,表明改进后的能量重心法具有更高的抗噪性能,扩大了能量重心校正法的工程应用范围。     

Image signal‐to‐noise ratio estimation using adaptive slope nearest‐neighbourhood model

A new technique based on nearest neighbourhood method is proposed. In this paper, considering the noise as Gaussian additive white noise, new technique single‐image‐based estimator is proposed. The performance of this new technique such as adaptive slope nearest neighbourhood is compared with three of the existing method which are original nearest neighbourhood (simple method), first‐order interpolation method and shape‐preserving piecewise cubic hermite autoregressive moving average. In a few cases involving images with different brightness and edges, this adaptive slope nearest neighbourhood is found to deliver an optimum solution for signal‐to‐noise ratio estimation problems. For different values of noise variance, the adaptive slope nearest neighbourhood has highest accuracy and less percentage estimation error. Being more robust with white noise, the new proposed technique estimator has efficiency that is significantly greater than those of the three methods.