A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

THE METHOD OF SEPARATING HARMONIC SIGNALS FROM MULTIPLICATIVE AND ADDITIVE NOISES

This paper focuses on the extraction of a harmonic signal from multiplicative and additive noises. A method is proposed in two stages： （1） to square the original discrete time series, which includes both signals and noises, and form a new time series. By this means, the multiplicative noise is converted to additive noise; and （2） to filter out the noise by using existing noise removal schemes. With a large amount of simulation, experimental results demonstrated the efficiency and effectiveness of this newly developed method in terms of Signal-to-Noise Ratio （SNR） and other criteria. Prom the experiment, it is also found that： the two kinds of noises affect the SNR differently. In general, the SNR is not influenced by multiplicative Gaussian noise regardless of its variance. However, if both kinds of noise exist, the SNR decreases with the incensement of the Variance of Additive Noise to Multiplicative Noise Ratio （VAMNR）. This analysis is also supported by simulation work.

Local Correlated Noise Improvement of Signal-to-Noise Ratio Gain in an Ensemble of Noisy Neuron

We theoretically investigate the collective response of an ensemble of leaky integrate-and-fire neuron units to a noisy periodic signal by including local spatially correlated noise. By using the linear response theory, we obtained the analytic expression of signal-to-noise ratio (SNR). Numerical simulation results show that the rms amplitude of internal noise can be increased up to?an optimal value where the output SNR reaches a maximum value. Due to the existence of the local spatially correlated noise in the units of the ensemble, the SNR gain of the collective ensemble response can exceed unity and can be optimized when the nearest-neighborhood correlation is negative. This nonlinear collective phenomenon of SNR gain amplification in an ensemble of leaky integrate-and-fire neuron units can be related to the array stochastic resonance (SR) phenomenon. Furthermore, we also show that the SNR gain can also be optimized by tuning the number of neuron units, frequency and?amplitude of the weak periodic signal. The present study illustrates the potential to utilize the local spatially correlation noise and the number of ensemble units for optimizing the collective response of the neuron to inputs, as well as a guidance in the design of information processing devices to weak signal detection.

Application of local polynomial estimation in suppressing strong chaotic noise

In this paper, we propose a new method that combines chaotic series phase space reconstruction and local polynomial estimation to solve the problem of suppressing strong chaotic noise. First, chaotic noise time series are reconstructed to obtain multivariate time series according to Takens delay embedding theorem. Then the chaotic noise is estimated accurately using local polynomial estimation method. After chaotic noise is separated from observation signal, we can get the estimation of the useful signal. This local polynomial estimation method can combine the advantages of local and global law. Finally, it makes the estimation more exactly and we can calculate the formula of mean square error theoretically. The simulation results show that the method is effective for the suppression of strong chaotic noise when the signal to interference ratio is low.

Optimization of wear parameters of binary Al-25Zn and Al-3Cu alloys using design of experiments

Zinc-aluminum alloys have been used as bearing materials in the past. In recent years, binary Al-Zn alloys and Al-Zn-Cu alloys are being used as an alternative to the Zn-Al alloys for bearing applications. In this study, both binary Al-25 Zn and Al-3 Cu were prepared using stir casting process. Homogenization of the as-cast alloys was performed at 350oC for 8 h and then, the alloys were furnace-cooled to 50oC. The homogenization led to the removal of the dendritic structure of the as-cast alloys. After homogenization, wear parameters optimization was carried out using Taguchi technique. For this purpose, L9 orthogonal array was selected, and the control parameters selected are load, velocity, and sliding distance. The optimum parametric condition was obtained using signal-to-noise(S/N) ratio analysis, and specific wear rate(SWR) is the selected response. The "smaller-the-better" is the goal of the experiment for S/N ratio analysis. After the optimization, confirmation tests were carried out using analysis of variance(ANOVA) from the developed regression equation. Finally, wear mechanism studies were conducted using scanning electron microscopy(SEM) and energy-dispersive X-ray spectroscopy(EDX) images.

Application of Wavelet Packet De-noising in Time-Frequency Analysis of the Local Wave Method

The local wave method is a very good time-frequency method for nonstationaryvibration signal analysis. But the interfering noise has a big influence on the accuracy oftime-frequency analysis. The wavelet packet de-noising method can eliminate the interference ofnoise and improve the signal-noise-ratio. This paper uses the local wave method to decompose thede-noising signal and perform a time-frequency analysis. We can get better characteristics. Finally,an example of wavelet packet de-noising and a local wave time-frequency spectrum application ofdiesel engine surface vibration signal is put forward.

通信干扰下无线传感器网络中微弱信号检测

微弱信号检测是保证无线传感器网络高效使用的重要环节,但检测过程易受噪声信号、传感器性能、虚拟信号等因素的干扰,从而导致误检。为了解决上述问题,提出一种通信干扰下无线传感器网络微弱信号检测方法。通过局部投影降噪法剔除信号中的噪声,避免噪声对检测过程产生影响。采用主分量分析算法提取去噪信号的特征,并根据遗传算法优化支持向量参数,将提取的特征输入到向量机中,通过特征的分类完成通信干扰下无线传感器网络微弱信号的检测。实验结果表明,所提方法的信号检测结果与实际结果基本一致,检测时间在30ms内,且抗噪性能强。

光学电流传感消抖卡尔曼本征信号高精度提取方法研究

本征信号是光学电流传感长期运行稳定性和高精度测量的重要性能参数。目前,传统方法如双光路法并不能消除本征信号的抖动,对光学电流传感高测量精度产生消极影响。为此,提出了光学电流传感消抖卡尔曼本征信号高精度提取方法来对本征信号进行消噪处理。首先对光学电流传感的噪声特性以及光路结构进行分析,建立了光学电流传感本征信号模型。引入消抖函数修正卡尔曼增益K,提出了消抖卡尔曼(Debouncing Kalman,DBK)算法,解决了标准卡尔曼增益K对初始状态依赖性强以及测量过程不确定性非常敏感带来的状态估计值严重抖动问题。并且引入了噪声方差递推估计,来保证在滤波过程中可以实时对噪声方差进行修正。在MATLAB进行了消抖卡尔曼滤波算法的验证并与标准卡尔曼算法进行了滤波效果的对比分析,验证了该算法的可行性,并结合李雅普诺夫稳定性分析法推导验证了该算法的稳定性。最后,搭建光学电流传感实验平台,将所提算法在LabVIEW FPGA硬件平台并行实现,验证了该算法的实时性,且能够满足实际工程需求。

非平稳强噪声环境中的音频信号端点检测系统

为提高音频信号端点识别能力,设计一种非平稳强噪声环境中的音频信号端点检测系统。构建音频信号端点检测硬件单元,利用预处理单元对音频信号进行预加重、分帧以及加窗处理后,端点检测单元在提取处理音频信号的MFCC倒谱距离特征、频带方差特征的基础上,依据动态阈值估计策略确定恰当阈值;通过双特征参数双门限法来实现对音频信号起止点的确定以及语音帧和非语音帧的分离;利用包络确定延时单元,防止噪声段被错误识别为语音段,避免出现拖尾太长问题。实验结果表明,所设计系统可实现非平稳强噪声环境音频信号端点检测,检测误差满足设定要求。

金属齿轮内部缺陷超声检测信号去噪方法

针对金属齿轮内部缺陷检测信号易受噪声干扰导致缺陷特征提取不准确问题,提出一种基于动态局部熵与自适应分解的齿轮缺陷超声检测信号去噪方法。采用经验模态分解对齿轮缺陷超声检测信号进行自适应分解,结合相关系数模态聚类指标得到预处理信号;基于检测信号动态局部熵理论确定缺陷回波区间,得到缺陷回波信号;采用经验小波变换实现缺陷回波信号去噪,结合二次多项式平滑滤波算法提升去噪信号平滑度,得到金属齿轮缺陷超声检测信号的最终去噪结果。仿真超声检测信号去噪实验结果显示,对于不同信噪比条件下的仿真信号,本文方法去噪信号平均信噪比值和均方误差分别为21.34 dB和0.0002 V,去噪效果明显优于经验模态分解和小波变换,两种方法的平均信噪比和均方误差分别为10.43 dB、12.56 dB和0.0019 V、0.0014 V,且在不同信噪比条件下的本文方法均具有更优的去噪效果和更强的鲁棒性。金属齿轮内部缺陷实测实验结果表明,本文方法能够去除齿轮缺陷超声检测信号中的复杂噪声干扰,有效提高了金属齿轮内部缺陷超声检测信号质量。

基于拉曼光谱数据处理和谱峰识别的变压器油绝缘老化研究

针对变压器油的拉曼光谱分析通常受到噪声和荧光背景等的干扰以及谱峰位置难以识别的问题,提出了一种改进的数据处理和谱峰识别算法,用于变压器油老化评估时的拉曼光谱分析。提出一种自适应Savitzky-Golay滤波法,引入自适应窗口大小拉曼光谱数据进行去噪处理。采用改进的多项式拟合算法对去噪后的数据进行去除荧光背景处理,减小荧光背景对拟合结果的影响。通过数据点与期望的拉曼信号的接近程度为每个数据点赋予权重,以实现更准确的去荧光背景处理。利用谱峰识别技术判别变压器油的老化程度,采用大小两种尺度高斯窗口判别法来识别谱峰,并结合局部加权信噪比(local weighted signal-to-noise ratio,LW_SNR)来判断疑似拉曼谱峰的真实性。最后通过实验验证了所提算法在变压器油老化评估中的有效性。

基于集合经验模态分解和排列熵的核电厂信号降噪研究

本文提出了一种基于集合经验模态分解和排列熵的电站信号降噪方法。该方法流程如下,首先,采用集合经验模态分解对电站典型实测信号进行了分解,获得对应的本征模态分量。其次,采用排列熵对本征模态分量进行混沌度的定量评价,从而实现实测信号中的有用信号和噪声信号的区分。对于后者,采用改进的小波软阈值降噪法进行降噪。最后,根据排列熵筛分后的有用信号和改进的小波软阈值降噪后的噪声信号进行重构,得到降噪后的信号。另外,本文也采用了主流的经验模态分解和局部均值分解对该信号进行了处理,并将分析结果进行对比。对比结果表明,基于本文所提方法得到的降噪后信号排列熵较小,表明降噪效果要优于以上两种方法。

OFDM系统中TURBO码译码算法研究

研究多径衰落信道下TURBO编码OFDM系统的特性,推导出一种TURBO码在OFDM系统具体应用方式,得到OFDM系统中TURBO的译码算法。并从推导结果确定,平均信噪比和信噪比弥散度是影响系统性能的两个重要参数。仿真结果表明,与IEEE 802.11 A定义的物理层性能相比,采用提出的TURBO译码算法,可以提高整个系统性能,信噪比改善2.9DB;在大平均信噪比和小信噪比弥散度条件下,系统性能较好,误帧率较低。

基于改进Canny算子的齿轮边缘缺陷检测方法

针对Canny算子在齿轮边缘缺陷检测中存在识别相似度低、因图像噪声影响导致微弱边缘难以提取、边缘连续性与鲁棒适应性差的问题,提出了一种改进Canny算子的齿轮边缘缺陷检测技术。改进滤波器,使用梯度双边滤波进行图片预处理,平滑图像并减少图像噪声;改进了卷积核,在45°和135°梯度方向上对像素点进行非极大值抑制,增加了微弱边缘被保留的概率;采用最大类间方差法(Otsu算法)计算图像的高阈值,通过双阈值的办法自适应寻找图像强弱边缘;将Prewitt算子和Canny算法以及改进算法进行对比实验验证。结果表明,改进算法可以提取更完整的齿轮边缘,处理后图片的峰值信噪比(PSNR)相比Canny算法提升了16%,检测效果提升了30%,重叠系数高达81.9%,提升了25.1%,为齿轮边缘缺陷检测提供了一定的参考价值。

一种快速连续跳频的超宽带多功能频综模块设计

基于快速连续跳频和超宽带射频收发电路的应用,设计了一种快速连续跳频的超宽带多功能频综模块,可实现0.1~9.8 GHz工作频段的快速连续或非连续跳频功能,以及FDD、TDD收发电路中上下变频的处理。采用“乒乓”锁相环(PLL)作为整体架构,结合多路复用开关(MUX)实现快速跳频功能,并产生收发电路所需的本振信号。最终实现适用于通信、雷达无线电跳频、软件无线电、干扰抗扰等相关领域的频综模块。结果表明,6 GHz相位噪声不大于-110 dBc/Hz@100 kHz,快速连续跳频可达至少30 000跳/s,具有优良的时钟性能。

The effect of different number of diffusion gradients on SNR of diffusion tensor-derived measurement maps

Diffusion tensor imaging (DTI) is mainly applied to white matter fiber tracking in human brain, but there is still a debate on how many diffusion gradient directions should be used to get the best results. In this paper, the performance of 7 protocols corresponding to 6, 9, 12, 15, 20, 25, and 30 noncollinear number of diffusion gradi-ent directions (NDGD) were discussed by com-paring signal-noise ratio (SNR) of tensor- de-rived measurement maps and fractional ani-sotropy (FA) values. All DTI data (eight healthy volunteers) were downloaded from the website of Johns Hopkins Medical Institute Laboratory of Brain Anatomi-cal MRI with permission. FA, apparent diffusion constant mean (ADC-mean), the largest eigen-value (LEV), and eigenvector orientation (EVO) maps associated with LEV of all subjects were calculated derived from tensor in the 7 proto-cols via DTI Studio. A method to estimate the variance was presented to calculate SNR of these tensor-derived maps. Mean ±standard deviation of the SNR and FA values within re-gion of interest (ROI) selected in the white mat-ter were compared among the 7 protocols. The SNR were improved significantly with NDGD increasing from 6 to 20 (P<0.05). From 20 to 30, SNR were improved significantly for LEV and EVO maps (P<0.05), but no significant dif-ferences for FA and ADC-mean maps (P>0.05). There were no significant variances in FA val-ues within ROI between any two protocols (P>0.05). The SNR could be improved with NDGD in-creasing, but an optimum protocol is needed because of clinical limitations.

Optimizing electrophoretic deposition conditions for enhancement in electrical conductivity of carbon fiber/carbon nanotube/epoxy hybrid composites

The effectiveness of optimizing electrical conductivity of carbon fiber/carbon nanotube （CNT）/epoxy hybrid composites via Taguchi method was demonstrated. CNTs were induced on carbon fabric by electrophoretic deposition （EPD） technique. The essential deposition parameters were identified as l） the deposition time, 2） the deposition voltage, 3） the mass fraction of CNTs in suspension, and 4） the distance between the electrodes. An experimental design was then performed to establish the appropriate levels for each factor. An orthogonal array of L9 （34） was designed to conduct the experiments. Electrical conductivity results were collected as the response. The relative influences of design parameters on the response were discussed. Using the model, signal to noise （S/N） ratio and response characteristics for the optimized deposition parameter combination were predicted. The results show clearly that the optimum condition of electrophoretic deposition （EPD） process improves the electrical conductivity of carbon/epoxy hybrid composites.

基于粗糙集的微小缺陷图像对比度增强算法

设计了一种微小缺陷图像对比度增强算法。采用粗糙集理论将图像划分为不同类别,同一类别中像素间为不可分辨关系。并依据图像灰度值和噪声属性进一步将图像划分为子图,在子图中得到图像的明暗区域。对图像不同区域进行直方图均衡变换和直方图指数变换处理,处理后即可增强图像的对比度。仿真实验表明,处理后可突出显示图像中的微小细节,图像不同区域之间的划分效果更加明显,可以有效提升图像的对比度和视觉效果。

基于NLM-CEEMDAN和样本熵的水电机组振动信号去噪

振动监测分析是水电机组故障诊断的重要手段,如何从振动信号中滤除噪声以便于故障特征有效提取是关键问题,为此提出了基于非局部均值滤波(NLM)和自适应噪声完备集合经验模态分解方法(CEEMDAN)结合的振动信号去噪新方法,并在水电机组摆度监测分析中进行了应用。该方法利用NLM-CEEMDAN对信号进行降噪处理,获得若干个固有模态分量(IMF),并且计算各分量样本熵值来进行分量归类。最后通过将高频噪声分量和信噪混合分量中的噪声成分从原始信号中滤除来完成对振动信号的去噪。仿真和实例分析,该方法优于常用的分解分量重构法和小波去噪算法,具有更好的去噪效果,为水电机组故障特征提取提供了新思路。

基于局域均值分解的行星齿轮箱故障诊断方法

在低转速工况下,容易出现行星齿轮箱故障的微弱信号和强信号难以分离的情况,导致行星齿轮箱存在微弱故障诊断精度较差的问题,为此,提出了一种基于局域均值分解(LMD)的行星齿轮箱故障诊断方法。首先,采用DASP数据采集系统,采集了行星齿轮箱不同工况下的振动信号,采用平移不变量小波降噪方法,对其振动信号进行了降噪处理;然后,采用局域均值分解方法分解了其振动信号,分别采用了能量算子和循环频率对其进行了解调处理,获取了微弱故障信号分量所对应的幅值和相位调制信息,准确提取了行星齿轮箱的微弱故障信号特征;最后,采用最小二乘支持向量机(LSSVM)识别了齿轮箱不同故障特征,判断了行星齿轮箱的运行状态,实现了行星齿轮箱的故障诊断。研究结果表明:采用基于LMD的方法,可以对行星齿轮箱的微弱异常信号及强异常信号进行准确诊断,获得满意的行星齿轮箱故障诊断结果,有效保障行星齿轮箱的安全、稳定运转。