语音模糊消噪算法

针对加性有色噪声,提出了语音信号模糊消噪算法;建立并训练了一个语音模糊消噪系统——自适应神经模糊推理系统（ANFIS）;用其对含噪语音中的有色噪声进行模糊估计,从而提取出干净的语音。对算法进行了仿真实验,结果表明,对模拟有色噪声在-17dB时能提取出清晰的语音。     

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.     

VS-MFxLMS算法及其在汽车车内噪声有源控制中的应用

为提高经典VS-FxLMS算法的收敛性能以及规避MFxLMS算法不能同时兼顾收敛速度和稳态误差的缺陷,结合修反正切函数和归一化的方法,提出了一种可用于汽车车内噪声有源控制的VS-MFxLMS算法。应用MFxLMS算法、VS-FxLMS算法和VS-MFxLMS算法分别进行汽车车内噪声有源控制仿真实验。噪声有源控制结果的比较表明,与MFxLMS算法相比,VS-MFxLMS算法的收敛速度提高了1.5倍以上,稳态误差降低55%以上;与VS-FxLMS算法相比,VS-MFxLMS算法的收敛速度提高了25%以上,稳态误差降低了28%以上;为汽车车内噪声的有源控制提供了一种新方法。     

Data-Aided Timing Recovery for Recording Channels With Data-Dependent Noise

In high-density data storage systems, noise becomes highly correlated and data dependent as a result of media noise, channel nonlinearities, and front-end filters. In such environments, conventional timing recovery schemes will exhibit large residual timing jitter and, especially, data-dependent timing jitter. This paper presents a new data-aided timing recovery algorithm for data storage systems with data-dependent noise. We derive a maximum-likelihood timing recovery scheme based on a data-dependent Gauss-Markov model of the noise. The timing recovery algorithm incorporates data-dependent noise prediction parameters in the form of linear prediction filters and prediction error variances. Moreover, because noise can be nonstationary in practice, we propose an adaptive algorithm to estimate and track the noise prediction parameters. Simulation results, for an idealized optical storage channel incorporating a simple model of media noise, illustrate the merits of our algorithm     

基于梯度的自适应椒盐噪声滤波算法

针对传统中值滤波方法不能有效保持图像细节信息和对图像适应能力差的问题,提出一种改进的椒盐噪声滤除算法。算法基于先检测、后滤波的思想,根据图像的极小梯度矩阵自适应计算噪声阈值,提高了噪声检测的准确性;为了更好地保持图像细节,对检测出的噪声像素进行多窗口中值滤波。多组去噪实验表明：所提算法对污染程度不同的图像具有良好的适应性,在滤除噪声的同时还可以有效还原图像边缘等细节信息。     

一种计算航船对近海海洋环境噪声贡献的算法

为了研究浅海中低频段的海洋环境噪声，文章构建了一种计算航船对近海海洋环境噪声贡献的算法，利用某海域航船信息，结合实际水文参数，对该海域的航运噪声进行了仿真计算。航船信息通过访问船舶自动识别系统数据库获取。主要关注50~400 Hz的中低频段，将仿真计算结果和实验数据进行对比，验证了算法的可靠性，并进行了误差分析。利用该算法可获取接收点处航船噪声的水平方向分布特点，并可初步定量分析航船噪声对海洋环境噪声的贡献。     

基于FxLMS的汽车车内噪声变步长主动均衡算法

提出了一种可用于汽车车内噪声主动均衡控制的变步长主动噪声均衡（Active Noise Equalization,ANE）算法,与传统车内噪声主动抵消控制方法所采用的滤波x最小均方（Filtered-x Least Mean Square,FxLMS）算法相比具有更好的实用性。应用固定步长主动噪声均衡（Active Noise Equalization,ANE）算法、所提出变步长ANE算法和已有变步长ANE算法分别进行汽车车内噪声主动均衡控制。结果表明,所提出变步长ANE算法具有更快的收敛速度和较低的稳态误差,并且能进一步降低汽车车内噪声响度,为汽车车内声品质主动控制提供了一种新方法。     

有源脉冲噪声控制的反正切变换算法

近年来,针对有源脉冲噪声控制,提出一些较为有效的算法。由于脉冲噪声的高尖峰特性,给算法带来了不稳定。为克服这些算法的不足,提出一种基于反正切变换的滤波x最小均方差算法。该算法不需要根据脉冲噪声的先验知识估测阈值和选择参数,并且算法结构简单、易于实现。仿真结果表明该算法能有效地消除脉冲噪声,与其他几种算法相比表现了更好的收敛性和稳定性。     

Performance estimation for maximum-likelihood detection forchannels with transition noise

Transition noise is known to be a major cause of errors for high density magnetic recording. This noise is signal dependent and can be modeled as multiplicative noise in a linear channel model. A maximum-likelihood algorithm was considered for detection of signals in such noise. In this work, the performance of the detector, based on this algorithm, is compared to the traditional Viterbi algorithm (VA) and a modified Viterbi algorithm (MVA) by computer simulations. Results show an improvement of up to 5 dB In signal-to-noise-ratio (SNR) under typical conditions with a reasonable complexity     

环境噪声主动控制算法的研究与实现

噪声主动控制技术是环境降噪的新技术,主动控制算法是其核心的问题之一。在研究滤波-X LMS主动控制算法基础上,提出了基于ⅡR滤波器的滤波-U LMS主动控制算法。对存在声反馈时的噪声主动控制中,可以提高系统的稳定性。设计了自适应主动噪声控制系统,对滤波-X LMS与滤波-U LMS主动控制算法进行了仿真和实验,结果表明,两种算法均能有效地应用于噪声主动自适应控制中,滤波-U LMS具有更好的宽频降噪效果。     

去除脉冲噪声的自适应开关中值滤波

为消除图像中的脉冲噪声,提出了自适应开关中值(ASM)滤波算法。该算法采用一种新的噪声检测方法将图像中的像素分为信号点和噪声点两类。对检测出的噪声点统计其个数并由此估算图像中的噪声密度,根据估计的噪声密度自适应确定滤波窗口尺寸,采用改进的中值滤波对检测出的噪声点进行处理;而信号点则保留其灰度值不予处理。对ASM滤波进行仿真实验,结果表明,它能在有效去除噪声的同时很好地保护图像细节,较传统中值滤波及其它改进中值滤波算法有更优的滤波性能。     

图象序列中检测运动小目标的递归算法

分析了一种基于卡尔曼滤波理论的时域递归低通滤波算法。这种算法根据运动小目标,背景干扰和噪声在图象序列中的差异,能够抑制背景,增强小目标并将其从相对静止的背景中有效地分离出来。在恒虚警概率条件下,该算法可以在低信噪比的情况下,减小背景干扰和随机噪声的影响,提高信噪比,选取适当的阈值,能够得到清晰的小目标轮廓,通过仿真验证了这种算法的有效性     

Noise reduction using mean shift algorithm for estimating 3D shape

Abstract

The technique to estimate the three-dimensional (3D) geometry of an object from a sequence of images obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus — sharpness — is the crucial part for final 3D shape estimation. However, it is difficult to compute accurate and precise focus value because of the noise presence during the image acquisition by imaging system. Various noise filters can be employed to tackle this problem, but they also remove the sharpness information in addition to the noise. In this paper, we propose a method based on mean shift algorithm to remove noise introduced by the imaging process while minimising loss of edges. We test the algorithm in the presence of Gaussian noise and impulse noise. Experimental results show that the proposed algorithm based on the mean shift algorithm provides better results than the traditional focus measures in the presence of the above mentioned two types of noise.     

汽车车内噪声主动控制变步长NFB-LMS算法

为规避最小均方（Least Mean Square,LMS）算法不能同时提高收敛速度和降低稳态误差的固有缺陷,以及已有变步长LMS算法存在收敛速度慢和稳态误差估计精度差的问题,文中提出了一种基于变步长归一化频域块（Normalized Frequency-domain Block,NFB） LMS算法的汽车车内噪声主动控制方法。为了比较,应用传统的LMS算法、基于反正切函数的变步长LMS算法和变步长NFB-LMS算法分别进行实测汽车车内噪声的主动控制。结果表明,与其他两个算法相比,变步长NFB-LMS算法的收敛速度提高了70%以上,稳态误差减小了90%以上。变步长NFB-LMS算法在处理车内噪声信号时具有很高的效率,为进行汽车车内噪声主动控制提供了一种新方法。     

自适应神经网络模糊小波语音消噪算法

针对有色噪声,采用自适应神经网络模糊系统模糊(Auto Neural Fuzzy Inference System,ANFIS)逼近有色噪声,利用自适应神经模糊推理系统ANFIS对噪声的非线性动态特性进行建模,提出了语音自适应神经网络模糊小波消噪算法,建立并训练了消噪系统。对被有色噪声污染的测量信号经模糊消噪后,根据信号和噪声的小波系数在不同分解尺度上的传递性,进行中值滤波和小波重构,得到了干净的语音。对算法进行了仿真实验,结果表明,消噪效果明显。     

环形子孔径拼接算法的精度影响因素分析

优化的拼接算法是环形子孔径扫描测量大口径非球面光学元件的关键问题。针对一种基于离散相位值的环形子孔径拼接算法,从精度评定判据入手,对随机噪声、高阶噪声、重叠区宽度及子孔径数目这几个主要影响因素进行了数值仿真分析。结果表明,该算法对高阶噪声和随机噪声均不灵敏,高阶噪声的影响略大于随机噪声的影响;对口径和相对口径较大的非球面,相邻子孔径间重叠系数应大于 0.15,对于非球面度不大的非球面,重叠系数可大于 0.25, 能以较高精度求得拼接参量。     

脉冲噪声的最小均值M－估计有源控制算法

经典的滤波―X最小均方算法(Fx LMS)已经被广泛应用于有源噪声控制(ANC)领域。 但是当存在脉冲噪声时, 它的性能就会严重退化。基于鲁棒统计的概念介绍了一种新型自适应算法,采用的目标函数为M－估计函数, 而不是传统的最小均方误差。该算法分别采用了Huber 函数、Hampel 三段下降M估计函数等四种不同的M－估计函数作为目标函数,仿真结果表明所采用的算法能有效地消除脉冲噪声,并且与日本学者Akhtar 改进的加窗算法相比表现了更好的收敛性。     

一种新的应用于助听器指向性传声器的自适应算法

在噪声环境中助听器的性能会受到严重影响。但当噪声与期望信号处在不同方向时,在助听器中使用指向性传声器系统能够有效地抑制噪声,使助听器的使用者受益。在复杂环境中采用自适应指向性的传声器系统能够动态调整指向性模式,以适应噪声的变化情况。基于自适应最小均方（LMS）算法提出了一种新的适用于助听器的自适应算法,用以动态调整传声器系统中滤波器的系数,使指向性模式的灵敏度最低点朝向噪声源方向,达到降噪的目的。相比于经典的LMS算法,该算法有效改善了期望信号存在时的失调情况。通过仿真结果讨论了算法中关键参数的选取对于失调并和收敛速度的影响。     

General methods for generating phase-shifting interferometry algorithms

Two completely independent systematic approaches for designing algorithms are presented. One approach uses recursion rules to generate a new algorithm from an old one, only with an insensitivity to more error sources. The other approach uses a least-squares method to optimize the noise performance of an algorithm while constraining it to a desired set of properties. These properties might include insensitivity to detector nonlinearities as high as a certain power, insensitivity to linearly varying laser power, and insensitivity to some order to the piezoelectric transducer voltage ramp with the wrong slope. A noise figure of merit that is valid for any algorithm is also derived. This is crucial for evaluating algorithms and is what is maximized in the least-squares method. This noise figure of merit is a certain average over the phase because in general the noise sensitivity depends on it. It is valid for both quantization noise and photon noise. The equations that must be satisfied for an algorithm to be insensitive to various error sources are derived. A multivariate Taylor-series expansion in the distortions is used, and the time-varying background and signal amplitudes are expanded in Taylor series in time. Many new algorithms and families of algorithms are derived.     

Filtering algorithm for noise reduction in phase-map images with 2pi phase jumps

A filtering algorithm is proposed for processing images generated by TV holography that contain phase jumps and a high noise level. This algorithm first performs phase unwrapping without removing the noise. After that, it removes the noise by use of a conventional low-pass filter. The new approach allows for using low-pass filters with narrow passbands, leading to a better signal-to-noise ratio in the desired signal. Simulation results are presented and discussed. The new algorithm has been applied successfully under real conditions in a holographic station.