Research on fiber optic gyro signal de-noising based on wavelet packet soft-threshold

Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.

TRANSLATION-INVARIANT BASED ADAPTIVE THRESHOLD DENOISING FOR IMPACT SIGNAL

A translation-invariant based adaptive threshold denoising method formechanical impact signal is proposed. Compared with traditional wavelet denoising methods, itsuppresses pseudo-Gibbs phenomena in the neighborhood of signal discontinuities. To remedy thedrawbacks of conventional threshold functions, a new improved threshold function is introduced. Itpossesses more advantages than others. Moreover, based on utilizing characteristics of signal, aadaptive threshold selection procedure for impact signal is proposed. It is data-driven andlevel-dependent, therefore, it is more rational than other threshold estimation methods. Theproposed method is compared to alternative existing methods, and its superiority is revealed bysimulation and real data examples.

Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection

The chaotic oscillator has already been considered as a powerful method to detect weak signals, even weak signals accompanied with noises. However, many examples, analyses and simulations indicate that chaotic oscillator detection system cannot guarantee the immunity to noises （even white noise）. In fact the randomness of noises has a serious or even a destructive effect on the detection results in many cases. To solve this problem, we present a new detecting method based on wavelet threshold processing that can detect the chaotic weak signal accompanied with noise. All theoretical analyses and simulation experiments indicate that the new method reduces the noise interferences to detection significantly, thereby making the corresponding chaotic oscillator that detects the weak signals accompanied with noises more stable and reliable.

Denoising Method for Shear Probe Signal Based on Wavelet Thresholding

Shear probe works under a tough environment where the turbulence signals to be measured are very weak. The measured turbulence signals often contain a large amount of noise. Due to wide frequency band, noise signals cannot be effectively removed by traditional methods based on Fourier transform. In this paper, a wavelet thresholding denoising method is proposed for turbulence signal processing in that wavelet analysis can be used for multi-resolution analysis and can extract local characteristics of the signals in both time and frequency domains. Turbulence signal denoising process is modeled based on the wavelet theory and characteristics of the turbulence signal. The threshold and decomposition level, as well as the procedure of the turbulence signal denoising, are determined using the wavelet thresholding method. The proposed wavelet thresholding method was validated by turbulence signal denoising of the Western Pacific Ocean trial data. The results show that the propsed method can reduce the noise in the measured signals by shear probes, and the frequency spectrums of the denoised signal correspond well to the Nasmyth spectrum.

A Squared-Chebyshev wavelet thresholding based 1D signal compression

In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression;but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.

New Wavelet Thresholding Algorithm in Dropping Ambient Noise from Underwater Acoustic Signals

Underwater Wireless Communication, largely dependent on the acoustic communication between the machines, is largely affected by various types of noise in the shallow and deep water. However ambient noise which is due to multiple sources (e.g. shipping, wind) and no one source dominates. Ambient noise masks the acoustic signal to a large extent. Hence today it has drawn the attention of the experts to reduce its effect on the received signal. This paper discusses ambient noise problem and devises a new wavelet thresholding method to reduce its effect. Afterwards a comparative study on statistical parameters is shown to prove the efficiency of the devised method.

基于空间变换技术的脉冲激光声信号检测

针对目前脉冲激光声信号检测方法极易受到环境噪声的影响,导致检测精确度不理想的问题。提出基于空间变换技术的脉冲激光声信号检测方法。首先,采用小波阈值法去除脉冲激光声信号中的噪声;其次,利用球傅里叶空间变换技术对声信号中的声源展开定位,获取声信号的准确位置;最后,分别提取声信号中的比值特征、比重特征、频率特征三种参数,在此基础上通过SVM算法对其展开分类识别,以此实现脉冲激光声信号的有效检测。实验结果表明,所提方法的声信号检测精度超过96%。

门槛阈值自匹配的车载呼救系统设计

为了提高车辆碰撞自动呼救系统(Automatic Crash Notification System,ACNS)的抗干扰能力和事故信息的准确性,提出了一种具有自适应门槛阈值的ACNS触发算法。基于门槛阈值与路面国际平整度指数之间的关联模型,设计ACNS触发算法,并实现ACNS门槛阈值随路面状况和车速的动态匹配;基于STM32F103ZET6微控制器,利用加速度信号采集模块、定位模块和通信模块等模块设计一种车辆事故自动呼救系统终端;利用道路实车试验和触发测试试验测试系统性能;结果表明:ACNS终端能够实现门槛阈值根据不同路面状况的动态匹配,并对相关信息进行实时处理及发送呼救信息。

PMF+双门限Tong检测的北斗B1I信号捕获算法

针对传统Tong检测算法检测时间长、漏警概率大的问题,提出将PMF+双门限Tong检测算法应用于北斗B1I信号的捕获中。首先,将射频前端模块处理得到的中频信号下变频至基带,对基带信号进行降速等预处理;然后,利用部分匹配滤波(PMF)算法计算相关值,通过PMF算法将数据信号分成几个均等片段,对每一段信号都分别计算相关值,会出现三组具有同相位的相关结果;最后,利用双门限Tong检测算法对PMF算法计算得到的相关结果检测,达到捕获B1I信号的目的。仿真结果表明,利用该算法捕获北斗B1I信号,有效提高了捕获时的检测概率并缩短了检测时间,提高了捕获B1I信号的速度。

基于小波变换的语音信号去噪算法优化

深入研究基于小波变换的语音信号去噪方法,并针对传统方法在复杂噪声环境下处理效果不佳的问题,提出一种基于自适应阈值的小波变换去噪优化方法。首先,分析小波变换去噪的基本原理。其次,深入研究自适应阈值技术的数学模型,并将其应用于小波变换,通过动态调整阈值来适应不同噪声环境的需求。最后,采用Aurora数据集进行实验验证。实验结果表明,该方法能够有效去除噪声。

CEEMDAN-SE-WT降噪方法在航空发动机燃油流量信号中的应用

燃油流量信号是反映发动机状态和计算飞机排放物排放量的重要信号,但飞机飞行过程中传感器采集信号时不可避免地会受到外界环境以及内部因素干扰。提出一种结合样本熵(sample entropy,SE)的完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)与小波变换(wavelet transform,WT)的联合降噪方法。首先使用CEEMDAN对燃油流量信号进行分解得到本征模态分量,利用样本熵筛选含噪分量,并用相关系数与方差贡献率进行复核。对于含噪分量使用小波阈值降噪进行处理。最后将未处理的模态分量和完成降噪的模态分量重构得到最终燃油流量信号。通过与其他方法比较,CEEMDAN-SE-WT方法拥有最高信噪比为85.287,降噪后燃油消耗总量与飞机总重变化最为接近,可以认为该方法较大程度保留了燃油流量信号中的有效特征,为后续计算民机排放物排放总量提供了良好的数据支持。

基于改进小波阈值—CEEMDAN的变压器局部放电超声波信号白噪声抑制方法

为了有效去除变压器局部放电超声信号中的白噪声干扰,提高后续局部放电模式识别及定位的准确性,提出了一种基于改进小波阈值和自适应噪声完全集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)的变压器局部放电超声波信号去噪方法。首先,通过对放电信号进行CEEMDAN分解得到一系列由高频到低频的本征模函数IMF(intrinsic mode function);然后,利用多尺度排列熵(multi-scale permutation entropy,MPE)算法计算各阶IMF分量的排列熵PE(permutation entropy),根据各IMF的排列熵值确定信号的去噪阈值与有效阈值。对高于去噪阈值的IMF分量采用改进小波阈值法进行去噪处理,对低于有效阈值的IMF分量视为基线漂移进行剔除。最后,通过重构去噪分量与剩余分量来获得去噪后的超声波信号。仿真和实验结果均表明,文中所提出的去噪算法大大提高了信号的信噪比,并保留了原始超声波信号中的有效信息,对提高后续利用超声波信号进行局部放电模式识别及定位的精确性具有重要意义。

中文版hearWHO应用程序在听力筛查中的验证

目的验证中文版hearWHO应用程序,一款噪声下数字言语测试工具,在听力筛查中的有效性。方法招募听力正常者12名,听力损失患者92例。所有受试者依次完成纯音听阈测试、声导抗测试以及中文版hearWHO测试。分析双耳PTA0.5~4kHz和hearWHO得分的相关性,采用受试者工作特性曲线及曲线下面积等参数,确定hearWHO得分在最佳诊断截点下的灵敏度和特异度。结果hearWHO得分与较好耳和较差耳纯音听阈均值PTA0.5~4kHz均呈显著负相关性,相关系数分别为-0.552和-0.693(P<0.001)。按PTA0.5~4kHz≥20dBHL为存在听力损失的标准,当hearWHO得分≤60时,提示至少单耳出现听力下降,灵敏度和特异度分别为0.733、0.690;当hearWHO得分≤50时,提示受试者可能存在双耳听力下降,灵敏度和特异度分别为0.815、0.917。结论hearWHO对检出单耳或双耳听力损失的灵敏度和特异度均较高,当hearWHO得分≤60时,提示受试者有必要转诊进行进一步的听力检测及诊断。

腰椎间盘突出症模型大鼠疼痛的针刺干预

背景:针灸是缓解腰椎间盘突出症腰痛的有效方法,但其机制目前尚未明确。JAK2/STAT3信号通路相关因子可调节机体炎症反应,参与神经病理性疼痛的过程。目的:基于JAK2/STAT3信号通路研究针刺对腰椎间盘突出症模型大鼠的作用机制。方法:采用随机数字表法将40只SD大鼠分为假手术组、模型组、针刺组与针刺+激动剂组,每组10只。模型组、针刺组及针刺+激动剂组采用自体髓核移植法构建L5腰椎间盘突出症模型,造模3 d后,针刺组开始进行针刺治疗(作用于阳陵泉、肾俞、环跳、大肠俞等穴位),针刺+激动剂组造模后第6,12,18天针刺治疗前向L4/L5椎间隙鞘内注射JAK2激动剂香豆霉素A1,针刺治疗1次/d,20 min/次,连续治疗15 d。造模前及造模后3,6,9,12,15,18 d,检测大鼠机械缩足阈值;造模后18 d,检测血清炎症因子水平,苏木精-伊红染色观察L5-L6组织形态,RT-PCR检测L5-L6组织JAK2、STAT3 mRNA表达,Western Blot检测L5-L6组织JAK2、STAT3、p-JAK2及p-STAT3蛋白表达。结果与结论:①模型组大鼠造模后不同时间点的机械缩足阈值均低于假手术组(P<0.05),针刺组大鼠造模后9,12,15,18 d的机械缩足阈值均高于模型组(P<0.05),针刺+激动剂组大鼠造模后9,12,15,18 d的机械缩足阈值均低于针刺组(P<0.05);②与假手术组比较,模型组白细胞介素6、肿瘤坏死因子α、神经递质P物质、脑部神经肽Y水平均升高(P<0.05);与模型组比较,针刺组4种炎症因子水平均降低(P<0.05);与针刺组比较,针刺+激动剂组4种炎症因子水平均升高(P<0.05);③苏木精-伊红染色显示模型组大鼠腰椎退行性变化明显,针刺组与针刺+激动剂组大鼠腰椎退行性变化减轻,针刺组减轻更明显;④与假手术组比较,模型组JAK2与STAT3 mRNA表达、p-JAK2与p-STAT3蛋白表达均升高(P<0.05);与模型组比较,针刺组JAK2与STAT3 mRNA表达、p-JAK2与p-STAT3蛋白表达均降低(P<0.05);与针刺组比较,针刺+激动剂组JAK2与STAT3 mRNA表达、p-JAK2与p-STAT3蛋白表达均升高(P<0.05);⑤结果表明,针刺干预可通过降低腰椎间盘突出症模型大鼠的炎症反应来缓解疼痛,其作用机制可能与抑制JAK2/STAT3信号通路有关。

基于小波时频图和ResNet18的焊接状态监测方法研究

针对焊接过程中因外部干扰因素或焊接参数选择不当而导致的气孔和未熔合缺陷的问题,提出一种小波时频图和深度残差网络(ResNet18)相结合的焊接质量检测方法。首先,搭建管道全位置自动焊接试验平台,利用拾音器记录熔合良好、未熔合和气孔焊接状态下的声音信号,将采集到的声音信号进行小波阈值降噪处理并且计算信号的信噪比,从而得到合适的信号降噪方法。其次,使用连续小波变换得到小波时频图,对时频图进行压缩和预处理,将时频图的大小设置为224×224,并剔除时频图上的标题、坐标和能量等。最后,将处理好的小波时频图作为输入,以未熔合、熔合良好和气孔三种状态作为输出,利用ResNet18网络进行训练。结果表明,该模型对三种焊接状态下的声音信号具有良好的监测效果,其准确率为90.78%。

分布式光纤温度传感信号小波去噪方法

在分布式光纤拉曼温度传感系统中,反斯托克斯光包含了温度信息,但其强度相对较弱,易受到噪声干扰。传统的信号去噪方法往往会滤除系统所具有的原始特征。针对这一问题,本文提出了改进的小波阈值方法,将3sigm准则与传统算法相结合。在该算法中,对信号进行小波分解,再对分解的各层小波系数进行阈值处理,使用3sigm值来区分有用信号和噪声,然后进行小波重构,实现信号去噪。在7 km长的多模光纤上进行温度测量实验,使用改进的算法对数据进行分析。结果表明,改进的方法处理后的温度波动平均为0.991℃,相较于传统算法得到的2.422℃,提高了1.431℃。与传统算法相比,3sigm准则算法提高了测温精度,并为阈值的选择提供了新的思路,使阈值的选择更具统计意义。

多定频干扰下准随机跳频信号检测仿真

多定频干扰环境的复杂性会对准随机跳频信号产生影响,导致跳频信号检测效果不佳。为了提升检测性能,提出多定频干扰下准随机跳频信号检测方法。对多定频干扰下的准随机跳频信号开展分析,获取跳频信号,并对信号实行小波阈值去噪处理;根据去噪处理结果,对准随机跳频信号开展傅里叶变换,求解跳频信号的局部自适应阈值,以此消除定频干扰,完成跳频信号提取;检测提取信号在时域的驻留连续性,区分跳频信号及可疑目标信号,实现多定频干扰下准随机跳频信号的检测。实验结果表明,通过开展信噪比测试、检测误差测试,验证了该方法的检测效果较好,说明其具有实用性和有效性。

基于能量阈值的双参数阈值函数在生理信号降噪中的应用

针对弱生理信号在采集过程中易被噪声淹没,传统小波去噪算法存在去噪效果差和信号提取失真的问题,根据小波系数的能量分布特点,提出一种改进的小波阈值去噪算法。通过计算各层小波系数的能量来确定阈值,避免阈值计算的不平衡性,同时提高自适应性和弱信号的保真度;采用一种改进的可调节的双参数阈值函数对小波系数进行处理,在小波系数压缩程度可控的同时可以自由调节阈值函数的变化趋势。实验结果表明:改进的小波阈值去噪算法相较于两种传统去噪算法(经验模态分解算法和滤波器算法)以及12种传统小波阈值和阈值函数组合算法,在信噪比、均方根百分比和均方根误差上都具有明显的优势,并且在实测生理信号中取得了最小的平均相对误差和最小的波动性。

胎—路接触应力传感器的信号去噪方法研究

为了解决应变传感器在测量胎—路接触应力信号时的噪声干扰问题,提出一种基于自适应噪声完备集合经验模态分解(CEEMDAN)和小波软阈值的胎—路接触应力传感器的信号去噪方法。首先,将胎—路接触应力信号CEEMDAN分解为一系列从高频到低频排列的本征模态函数(IMF)分量;再根据能量值法判定出噪声主导IMF分量和信号主导IMF分量,并对噪声主导IMF分量进行小波软阈值去噪;最后,将去噪后的IMF分量和信号主导IMF分量进行重构,得到去噪后的胎—路接触应力信号。将所提方法分别与CEEMDAN去噪方法、小波软阈值去噪方法进行比较,结果表明:所提方法去噪后信号的信噪比(SNR)分别提高了33%和343%,均方根误差(RMSE)分别降低了64%和252%,说明所提方法的去噪效果更佳。

基于CEEMDAN和小波包分解的闸门振动信号降噪研究

针对闸门监测振动信号去噪问题,提出基于CEEMDAN(经验模态分解)和小波包分解的闸门振动信号降噪算法,通过采用CEEMDAN和小波包分解方法进行信号去噪,可以有效处理水电站泄洪闸门振动信号中受到的外部干扰。CEEMDAN方法能够将信号分解成多个本征模态函数(IMF),每个IMF代表不同频率的振动成分,使得外部干扰和真实信号成分可以分离。随后,小波包分解能够将每个IMF进一步分解成不同尺度和频率的子频带,这有助于更准确地定位和分离干扰成分。对每个子频带应用阈值去噪技术,可以有效去除噪声,保留真实信号。由测试结果可知,该算法能很好地剔除闸门振动信号中的无用噪声,有效提高闸门振动信号的准确性。