A wavelet-based multisensor data fusion algorithm

This paper presents a wavelet transform-based data fusion algorithm for multisensor systems. With this algorithm, the optimum estimate of a measurand can be obtained in terms of minimum mean square error (MMSE). The variance of the optimum estimate is not only smaller than that of each observation sequence but also smaller than the arithmetic average estimate. To implement this algorithm, the variance of each observation sequence is estimated using the wavelet transform, and the optimum weighting factor to each observation is obtained accordingly. Since the variance of each observation sequence is estimated only from its most recent data of a predetermined length, the algorithm is self-adaptive. This algorithm is applicable to both static and dynamic systems including time-invariant and time-varying processes. The effectiveness of the algorithm is demonstrated using a piecewise-smooth signal and an actual time-varying flow signal.     

改进的最小均方误差语音增强算法的研究

针对传统最小均方误差谱幅度估计（MMSE—STSA．minimum mean-square error-short time spectral amplitude）语音增强算法无法有效的跟踪非平稳噪声变化的问题,对一种改进的MMSE-STSA语音增强算法进行了研究和仿真。该算法对背景噪声的估计利用加权噪声估计方法：采用一个非线性函数根据带噪语音信噪比（SNR．signal—to-noise ratio）的变化计算得到相应的加权因子并作用于带噪语音信号,对加权的带噪语音求平均得到估计的背景噪声。算法中的谱增益修正,还可以抑制低信噪比时的残留噪声以及避免对带噪语音的过抵消。实验结果表明,该方法能很好的跟踪非平稳噪声的变化,不仅在增强性能上有很好的效果,同时降低了语音的失真。     

听觉掩蔽效应语音增强的改进算法

含噪信号利用掩蔽效应去噪后,噪声估计的误差导致语音失真。在利用听觉阈值计算谱减系数时提出了一种改进的计算方法,通过增加修改参数来抑制语音的过分衰减,减少了语音失真,然后基于MMSE准则对增强的语音谱再进行平滑处理,进一步抑制音乐噪声。实验表明该算法在不影响语音失真的基础上,提高了信噪比,消除了音-／乐噪声,主观测听的语音音质明显提高。     

采用离散余弦变换的小波图像去噪方法

提出一种通过对小波域中噪声能量的估计来进行去噪的新方法。算法采用离散余弦变换(DCT)提取小波系数的主要特征,无需对噪声方差进行估计。对图像进行小波分解,利用 DCT对高频子带进行局部特征提取;利用部分 DCT 系数对小波系数进行重建,并以重建系数的平均能量作为局部噪声能量的估计;去除原小波系数中的噪声分量后,进行小波逆变换,得到去噪后的图像。实验证明,其峰值信噪比(PSNR)比通常的阈值萎缩法提高了 2-4dB。     

Fusion of noisy images based on joint distribution model in dual-tree complex wavelet domain

Source images are frequently corrupted by noise before fusion, which will lead to the quality decline of fused image and the inconvenience for subsequent observation. However, at present, most of the traditional medical image fusion scheme cannot be implemented in noisy environment. Besides, the existing fusion methods scarcely make full use of the dependencies between source images. In this research, a novel fusion algorithm based on the statistical properties of wavelet coefficients is proposed, which incorporates fusion and denoising simultaneously. In the proposed algorithm, the new saliency and matching measures are defined by two distributions: the marginal statistical distribution of single wavelet coefficient fit by the generalized Gaussian Distribution and joint distribution of dual source wavelet coefficients modeled by the anisotropic bivariate Laplacian model. Additionally, the bivariate shrinkage is introduced to develop a noise robust fusion method, and a moment-based parameter estimation applied in the fusion scheme is also exploited in denoising method, which allows to achieve the consistency of fusion and denoising. The experiments demonstrate that the proposed algorithm performs very well on both noisy and noise-free images from multimodal medical datasets (computerized tomography, magnetic resonance imaging, magnetic resonance angiography, etc.), outperforming the conventional methods in terms of both fusion quality and noise reduction.     

Noise Parameter Estimation From Quantized Data

In this paper, the parametric estimation of the variance of white Gaussian noise is considered when available data are obtained from a quantized noisy stimulus. The Crameacuter-Rao lower bound is derived, and the statistical efficiency of a maximum-likelihood parametric estimator is discussed, along with the estimation algorithm proposed in IEEE Standard 1241     

频域响应函数估计的非参数辨识法

针对振动工程领域中频响函数的辨识估计,本文深入研究频响函数估计的非参数辨识法。在离散傅里叶变换中,考虑初始和终端状态带来的暂态泄露项和观测噪声谱项对频响函数估计的影响。为得到准确的频响函数估计值,联合频响函数、初始-终端状态和脉冲响应系数待辨识参数矢量,将频响函数估计问题转化为一个线性最小二乘优化问题。针对此线性最小二乘优化问题的特殊形式,提出一种可分离的求解过程。最后用仿真算例验证本文辨识方法的有效性。     

Optimum synthesis for time scale generation

A time scale can be regarded as a synthesis of readings from precise clocks. Usually such synthesis is based on the principle of weighted averaging, which balances the contribution of each clock according to its noise level. It is well known that there are five different noise processes in precise clocks. Therefore, a good synthesis should balance each of those noise levels. Most existing algorithms control only one or two noise types. If an algorithm can control all five noise types simultaneously, we consider it to be optimum. The key point of constructing an optimum algorithm is the separation of all five noise types. In this paper, an optimum algorithm is presented using the half-integrating/half-differentiating model by which the five noise types are separated correctly. Performances of the new algorithm are demonstrated with simulated and real data.     

自适应分解层数的小波域中值滤波振动信号降噪法

为降低结构健康监测加速度信号中常见的白噪声以及脉冲噪声,提出了中值滤波与小波阈值降噪相结合的方法。分解层数对降噪效果有着重要影响,为取得更好的降噪效果,提出了一种分解层数自适应确定法,并给出了各层阈值的取值方法。数值模拟以及国家游泳中心健康监测系统实测数据分析结果表明,所提出的方法克服了传统阈值降噪法对脉冲噪声拟制效果不理想的缺陷,能同时有效降低白噪声以及脉冲噪声,为模态参数识别等提供更高信噪比的分析数据。     

基于信息融合与FastICA的轴承故障提取方法

针对振动传感器监测信号易受噪声干扰的问题,提出一种基于FastICA算法与信息融合的轴承故障诊断方法。算法对各通道测得的信号采用FastICA算法进行降噪处理,采用自适应线性加权算法对降噪后信号进行数据层信息融合,最后基于谱峭度指标设计自适应带通滤波器,进行特征提取。此方法解决了低信噪比条件下的轴承故障特征提取问题。使用了仿真和实验轴承故障信号验证了算法的有效性。     

误差白化与Kalman滤波的微硅加速度传感器动态补偿

通过模型参考的系统辨识方法建立微硅加速度传感器的动态补偿器。由于测量噪声和补偿器对传感器的频带扩展,使得补偿器的输入/输出信号存在严重的噪声干扰。在噪声干扰下,采用均方误差为代价函数的系统辨识方法,无法得到补偿器参数的无偏估计。补偿器参数的偏差和传感器频带的扩展将会使补偿器的输出信号出现严重失真和高频噪声干扰。为解决噪声对硅加速度传感器的动态补偿的影响,研究了一种新的动态补偿方法,该方法采用误差白化为代价函数的系统辨识方法得到补偿器的参数,可消除补偿器的参数在估计中的测量噪声影响,并通过卡尔曼实时滤波减小因传感器频带扩展所产生的高频噪声干扰。     

Blind multichannel reconstruction of high-resolution images using wavelet fusion

We developed an approach to the blind multichannel reconstruction of high-resolution images. This approach is based on breaking the image reconstruction problem into three consecutive steps: a blind multichannel restoration, a wavelet-based image fusion, and a maximum entropy image interpolation. The blind restoration step depends on estimating the two-dimensional (2-D) greatest common divisor (GCD) between each observation and a combinational image generated by a weighted averaging process of the available observations. The purpose of generating this combinational image is to get a new image with a higher signal-to-noise ratio and a blurring operator that is a coprime with all the blurring operators of the available observations. The 2-D GCD is then estimated between the new image and each observation, and thus the effect of noise on the estimation process is reduced. The multiple outputs of the restoration step are then applied to the image fusion step, which is based on wavelets. The objective of this step is to integrate the data obtained from each observation into a single image, which is then interpolated to give an enhanced resolution image. A maximum entropy algorithm is derived and used in interpolating the resulting image from the fusion step. Results show that the suggested blind image reconstruction approach succeeds in estimating a high-resolution image from noisy blurred observations in the case of relatively coprime unknown blurring operators. The required computation time of the suggested approach is moderate.     

Multiple-tone estimation by IEEE standard 1057 and the expectation-maximization algorithm

The aim of this paper is to present an efficient algorithm for multiple-tone parameter estimation. The algorithm is inspired by the expectation-maximization algorithm, and it utilizes the IEEE standard 1057 for single-tone parameter estimation. In the derivation of the algorithm, it is assumed that the number of tones are known and that the frequencies are well separated. The algorithm is evaluated using noisy data consisting of multiple real-valued tones. The performance of the frequency estimator is studied and compared with the asymptotic Cramer-Rao bound (CRB). It is shown that the algorithm produces statistically efficient frequency estimates at high signal-to-noise ratios (SNRs), that is the variance of the estimates reaches the CRB. Finally, it is illustrated that the algorithm can produce efficient estimates independent of the number of tones in the input signal.     

A successive parameter estimation algorithm for chirplet signal decomposition

In ultrasonic imaging systems, the patterns of detected echoes correspond to the shape, size, and orientation of the reflectors and the physical properties of the propagation path. However, these echoes often are overlapped due to closely spaced reflectors and/or microstructure scattering. The decomposition of these echoes is a major and challenging problem. Therefore, signal modeling and parameter estimation of the nonstationary ultrasonic echoes is critical for image analysis, target detection, and object recognition. In this paper, a successive parameter estimation algorithm based on the chirplet transform is presented. The chirplet transform is used not only as a means for time-frequency representation, but also to estimate the echo parameters, including the amplitude, time-of-arrival, center frequency, bandwidth, phase, and chirp rate. Furthermore, noise performance analysis using the Cramer Rao lower bounds demonstrates that the parameter estimator based on the chirplet transform is a minimum variance and unbiased estimator for signal-to-noise ratio (SNR) as low as 2.5 dB. To demonstrate the superior time-frequency and parameter estimation performance of the chirplet decomposition, ultrasonic flaw echoes embedded in grain scattering, and multiple interfering chirplets emitted by a large, brown bat have been analyzed. It has been shown that the chirplet signal decomposition algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements. Numerical and analytical results show that the algorithm is efficient and successful in high-fidelity signal representation.     

A greatest common divisor approach to blind super-resolution reconstruction of images

An algorithm for blind super-resolution reconstruction of a single image from multiple degraded observations is developed. The algorithm depends on estimating the 2D greatest common divisor (GCD) between each observation and a combinational image generated by a weighted averaging process of the available observations. The purpose of generating this combinational image is to obtain a new image with a higher signal to noise ratio, and a blurring operator that is co-prime with all the blurring operators of the available observations. The 2D GCD is then estimated between the new image and each observation and thus the effect of noise on the estimation process is reduced. The results of each 2D GCD process are fused to form a single reconstructed image, which is then interpolated subject to local regularization to form a high-resolution (HR) image. Results show that the proposed algorithm succeeds in estimating an HR image from noisy blurred observations in the case of relatively co-prime unknown blurring operators.     

基于EEMD和参数自适应VMD的高速列车轮对轴承故障诊断

针对高速列车轮对轴承工作环境复杂,振动信号中时常伴有冲击性噪声和循环平稳性噪声,使得传统的参数自适应变分模态分解(variational modal decomposition,VMD)方法对轮对轴承的故障特征信息提取不准确的问题,提出了一种基于集成经验模态分解(ensemble empirical mode deco...     

一种基于Harris特征点和DWT-SVD的图像盲水印算法

目的针对第2代数字水印技术,提出一种基于Harris特征点和DWT-SVD的图像盲水印算法。方法提取归一化图像的Harris特征点;选取部分稳定特征点来确定要嵌入水印的特征区域;将特征区域作一次小波分解得到的低频子带,对低频子带进行分块,并对每一块进行奇异值分解,通过对每块中最大奇异值进行加权的方法来嵌入水印信息。结果 PSNR值均大于45 d B,NC值接近于1,说明该算法具有可行性。结论该算法对剪切攻击具有很好的鲁棒性,同时该算法也能很好地抵抗噪声、中值滤波攻击、提高亮度攻击、降低亮度攻击、基本图像处理操作的攻击。     

基于数据缩减的分频段小波模态参数快速识别

针对利用小波进行模态参数识别效率较低的问题,提出了一种基于数据缩减的分频段小波模态参数快速识别算法。利用奇异值分解对协方差信号在保留数据信息量的情况下进行缩减以减少参与计算的数据量,对正功率谱密度矩阵的奇异值分解确定识别系统的模态阶数及相应的频率范围,利用小波变换对缩减后的数据进行各阶模态逐频段识别。相比原始算法,文中方法减少了小波分析的数据量并避免了一些无用频带的小波分解从而减少计算量。通过对一个3阶线性时不变系统以及一个大桥模型的参数识别验证了文中方法在保持识别精度的情况下大幅度地提升了计算效率。     

原子钟频差数据去噪算法的研究

为降低原子钟频差的噪声,根据其数据非线性非平稳的特征,将整体经验模态分解用于原子钟频差去噪算法。首先将原子钟频差数据叠加一定强度的白噪声;然后进行经验模态分解,如此重复多次;最后将各分量叠加求平均得到去噪的原子钟频差序列。从时域和频域上分别分析了该算法的去噪效果,并与小波阈值去噪算法进行了比较。结果表明,该算法有效地去除了原子钟频差数据序列中的噪声,将方差由小波算法的2.707%降为0.7263%,数据变得更加平稳。     

Manifold Learning Using Linear Local Tangent Space Alignment (LLTSA) Algorithm for Noise Removal in Wavelet Filtered Vibration Signal

A denoising procedure is proposed to remove both out-band and in-band noise for extraction of weak bursts in signal obtained from defective bearing. Energy of continuous wavelet scalogram is computed and the band having higher energy is selected to remove the out-band noise. Signals of selected band are brought together to form a high-dimensional waveform feature space. Further, low dimensional waveform manifold is formed using linear local tangent space alignment (LLTSA) algorithm to remove in-band noise. A criterion, entitled as frequency factor is also proposed to determine the optimum neighbour size of LLTSA. The two complicated conditions are chosen to demonstrate the effectiveness of the technique in the extraction of bursts in the noisy situations. A significant improvement in the signal to noise ratio is observed when in-band noise is removed using manifold learning by LLTSA algorithm. The experimental result reveals the success of the proposed denoising procedure in extraction of defect features, even in the case of noisy condition.