Detection of an infrared near-field optical signal by attaching an infrared-excitable phosphor to the end of a photocantilever

To improve the signal-to-noise ratio of near-field scanning optical microscopy, we propose attaching an infrared-excitable phosphor (IEP) to a photocantilever. One source of noise is the light scattered from locations on the sample surface other than that of the probe tip. By detecting only the light scattered from the tip, we can obtain a near-field optical signal without noise. We attached an IEP particle to a photocantilever to convert infrared light to visible light and we used 1550-nm infrared illumination, so the light scattered from the sample was only infrared. The silicon photodiode of the photocantilever is 10⁶ times less sensitive to infrared light than to visible light. As a result, only the converted visible light from the IEP particle, i.e. the signal containing the near-field optical information from the tip, was detected. We verified that the photocantilever detected the signal in the evanescent light produced by infrared illumination and that the detected signal was the light converted by the IEP. The experimental results show the feasibility of detecting infrared light and not the background light through the use of the IEP.     

公共场所异常声源定位中时延估计方法研究

HB(Hassab-Boucher)加权广义互相关(generalized cross correlation based on HB weighted function,GCC-HB)是常用的时延估计方法,在环境为弱高斯噪声情况下,可获得较为精确的时延估计值用于声源定位。通过分析认为,通常公共场所异常声音是一种短时信号,背景噪声主要为粉红噪声与脉冲噪声,符合分数低阶α稳定分布(fractional lower order alpha-stable,FLOA)。在此背景噪声的低信噪比环境下,GCC-HB方法的时延估计性能急剧下降。为此,提出基于反正切变换的改进GCC-HB的时延估计方法(improved GCC-HB method based on arc tangent transform,ATAN-IHB)。该方法首先对加噪信号采用反正切变换抑制噪声中尖峰脉冲的影响,然后结合每帧的信噪比对HB加权函数进行改变,并由多帧HB加权后的峰值确定出时延估计值。理论分析和计算机仿真结果表明,所提出的方法即使在低信噪比的环境下,也可以获得比较满意的时延估计值,具有一定的实用性价值。     

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures.     

Noise cancellation of ultrasonic NDE signals using blind source separation

This paper proposes a new denoising method for ultrasonic NDE (nondestructive evaluation) signals using blind separation (BSS) technology. The proposed denoising method consists of four steps. First, a reconstructed phase space (RPS) is constructed from observed ultrasonic NDE signals. The information about the underlying sources (e.g., ultrasonic signal, noise, etc.) acting on this system is contained in this RPS. Second, independent component analysis (ICA) is performed on the RPS to recover all sources underlying the RPS. Next, the ultrasonic signal component is selected by a decision criterion related to the denoising application and, finally, is reconstructed to obtain the denoised ultrasonic signal. To validate the proposed method, it has been applied to the experimental ultrasonic NDE signals of the test sample and is compared with the wavelet denoising method in SNR (signal-to-noise ratio) enhancement. The experimental results show that the SNR of the ultrasonic NDE signals can be enhanced greatly using the proposed denoising method and the proposed method has almost the same denoising performance as the wavelet denoising method in SNR enhancement. A trait of the proposed denoising method is the ability to denoise ultrasonic NDE signals by separating the ultrasonic signal and noise using blind source separation technology. The text was submitted by the authors in English.     

Detection of an infrared near-field optical signal by attaching an infrared-excitable phosphor to the end of a photocantilever

To improve the signal-to-noise ratio of near-field scanning optical microscopy, we propose attaching an infrared-excitable phosphor (IEP) to a photocantilever. One source of noise is the light scattered from locations on the sample surface other than that of the probe tip. By detecting only the light scattered from the tip, we can obtain a near-field optical signal without noise. We attached an IEP particle to a photocantilever to convert infrared light to visible light and we used 1550-nm infrared illumination, so the light scattered from the sample was only infrared. The silicon photodiode of the photocantilever is 10(6) times less sensitive to infrared light than to visible light. As a result, only the converted visible light from the IEP particle, i.e. the signal containing the near-field optical information from the tip, was detected. We verified that the photocantilever detected the signal in the evanescent light produced by infrared illumination and that the detected signal was the light converted by the IEP. The experimental results show the feasibility of detecting infrared light and not the background light through the use of the IEP.     

基于过完备线性预测字典的压缩感知语音重构

基于语音信号帧内样值间的相关性和冗余域的稀疏性,针对采用离散余弦转换矩阵及基追踪方法对压缩感知采样语音进行重构时,语音稀疏性不够好导致大压缩比采样后重构效果差的缺点,提出采用过完备线性预测字典做转换矩阵,用基追踪重构算法对压缩感知采样语音进行高质量重构。该方法预先由训练语音的预测系数聚类构造过完备字典,不需要测试语音的预测系数;基于过完备线性预测字典重构信号性能良好。对利用基追踪重构的语音进行了主客观评价,得出结论:同样的观测数目下,基于过完备线性预测字典比基于离散余弦变换矩阵压缩感知采样语音重构信噪比高出3～8 dB。     

低信噪比情况下小目标检测技术

讨论了数字图像处理中跟踪移动目标的相关算法，提出了低信噪比情况下小目标检测的解决方案，提出了目标跟踪稳定性和准确性。     

Application of the L-Wigner distribution to the diagnosis of local defects of gear tooth

The detection of impulsive signals causing the fracture of gears is a significant task for the analysis of the characteristics of a damaged gear. However, this impulsive signal is hidden by background noise such as meshing frequencies and broadband noise. Recently, conventional time frequency methods have been used. In the case of a signal with a low SNR, these methods are not sufficient for the detection of impulsive signals; hence, the L-Wigner distribution with an S-method kernel is used and applied to the diagnosis of local defects of a tooth in a gear.     

Estimating ultrasonic time of flight using envelope and quasi maximum likelihood method for damage detection and assessment

Time of flight (TOF) is widely used to locate and size faults in ultrasonic non-destructive evaluation (NDE). In this paper, we propose a novel model-based method for ultrasonic echo parameter estimation. The ultrasonic signal is assumed to be composed of an unknown number of Gaussian echoes corrupted by white Gaussian noise. Firstly, the Hilbert transform is used to extract the envelope of the signal. It is shown that the parameter estimation of the signal is improved by using the envelope. To estimate the parameters of the envelope of the signal, quasi maximum likelihood method is used. The number of echoes is estimated using consistent Akaike information criterion. Two measures are used to evaluate the performance of the proposed method: (a) probability of detection of backscattered echoes and (b) the error of estimated time of flights. The proposed method is then compared to the cross-correlation method and the maximum likelihood method which uses the original signal. Simulated and experimental signals are used to evaluate the performance of each method. Both experimental and simulated results show that the proposed method can improve the parameter estimation which ultimately enhances the damage detection and assessment.     

Multiphoton fluorescent images with a spatially varying background signal: a ML deconvolution method

By means of multiphoton laser scanning microscopy, neuroscientists can look inside the brain deeper than has ever been possible before. Multiphoton fluorescent images, as all optical images, suffer from degradation caused by a variety of sources (e.g. light dispersion and absorption in the tissue, laser fluctuations, spurious photodetection and staining deficiency). From a modelling perspective, such degradations can be considered the sum of stochastic noise and a background signal. Among the methods proposed in the literature to perform image deconvolution in either confocal or multiphoton fluorescent microscopy, Vicidomini et al. (2009) were the first to incorporate models for noise (a Poisson process) and background signal (spatially constant) in the context of regularized inverse problems. Unfortunately, the so-called split-gradient deconvolution method (SGM) they used did not consider possible spatial variations in the background signal. In this paper, we extend the SGM by adding a maximum-likelihood estimation step for the determination of a spatially varying background signal. We demonstrate that the assumption of a constant background is not always valid in multiphoton laser microscopy and by using synthetic and actual multiphoton fluorescent images, we evaluate the face of validity of the proposed method, and compare its accuracy with the previously introduced SGM algorithm.     

Extending immunofluorescence detection limits in whole paraffin-embedded formalin fixed tissues using hyperspectral confocal fluorescence imaging

A major problem in microscopic imaging of ex vivo tissue sections stained with fluorescent agents (e.g. antibodies, peptides) is the confounding presence of background tissue autofluorescence. Autofluorescence limits (1) the accuracy of differentiating background signals from single and multiple fluorescence labels and (2) reliable quantification of fluorescent signals. Advanced techniques such as hyperspectral imaging and spectral unmixing can be applied to essentially remove this autofluorescent signal contribution, and this work attempts to quantify the effectiveness of autofluorescence spectral unmixing in a tumour xenograft model. Whole-specimen single-channel fluorescence images were acquired using excitation wavelengths of 488 nm (producing high autofluorescence) and 568 nm (producing negligible autofluorescence). These single-channel data sets are quantified against hyperspectral images acquired at 488 nm using a prototype whole-slide hyperspectral fluorescence scanner developed in our facility. The development and further refinement of this instrument will improve the quantification of weak fluorescent signals in fluorescence microscopy studies of ex vivo tissues in both preclinical and clinical applications.     

基于多稳随机共振的机电设备故障诊断研究

针对机械轴承早期故障诊断提出了多稳随机共振检测方法。分析了系统参数对多稳系统结构的影响,研究了高斯噪声背景下基于多稳随机共振的微弱信号检测方法。采用平均输出信噪比作为衡量指标,以多频微弱信号为待测信号进行数值仿真,并将其应用于滚动轴承故障信号检测中,实验结果均表明,该方法对早期故障振动信号具备准确的诊断能力,为其应用于工程实践奠定了基础。     

采用余弦调制滤波器组泄漏检信号处理研究

供水管道泄漏检测定位中,定位的不确定度由检测信号的信噪比决定,抑制噪声干扰,提高信噪比,是提高泄漏检测定位精度的关键。在充分分析泄漏信号特征的基础上,提出将改进的余弦调制滤波器组用于泄漏检测信号的噪声抑制。该方法利用分析滤波器组将信号分解为若干个带宽相同的子带信号,然后选择有效子带,再通过综合滤波器组将有效子带信号合成。该方法是一种不需要泄漏信号和噪声先验知识的自适应信号处理方法,能够有效抑制噪声,由于余弦调制滤波器分解的特性,即使泄漏信号处于不同频率段时,噪声抑制效果基本相同,处理后信噪比较处理前平均提高8 d B左右。实际工程应用验证表明,该方法去噪处理后的定位结果优于现有各种噪声抑制/信号增强方法的结果。     

相敏检波在空气耦合超声检测中的应用

空气耦合超声检测信噪比低、脉冲余振长,需要采用合适的信号处理技术增强接收信号的信噪比。根据空气耦合超声检测过程中影响接收信号的因素,提出先采用相敏检波技术对接收信号进行处理,获取相位信号,然后计算得到超声检测信息。介绍了超声检测中的超外差接收、相敏检波原理,并阐述了在超声检测应用中需注意的问题。根据分析研究结果,构建了相敏检波空气耦合超声检测系统,在常规超声检测系统的基础上进行了相应改造,实现了超声成像检测。碳纤维增强复合材料板的成像检测结果表明:相敏检波可以有效提高系统信噪比和检测效果。     

LTE同频小区检测及在扫频测试仪中的应用

在采用同频组网的3GPP长期演进(long term evolution,LTE)系统中,同频干扰会严重影响小区检测性能。提出在对FDD-LTE网络进行扫频测试时采用同频干扰抵消技术检测同频小区,并针对在实际组网中不同小区可能有相同的主同步码和辅同步码偶数序列的情况,提出了一种改进的同频干扰抵消技术。采用蒙特卡罗法对该方法检测同频小区的成功概率进行了仿真,仿真结果表明此方法对检测同频小区是非常有效的,当信噪比为0 dB,终端接收到的两小区信号比值达10 dB时,弱信号小区的检测成功概率超过93%。并且通过运用串行干扰抵消方法连续消除可以检测出尽可能多的同频小区,仿真结果表明在信噪比超过3 dB时,当接收到的4个同频小区信号间的最大比值为10 dB时,弱信号小区的检测成功概率都超过99%。最后在研制的FDD-LTE扫频测试仪中应用了此技术,实现了同频多小区的有效检测。     

基于自适应多尺度自互补Top-Hat变换的轴承故障增强检测

针对实际工程中滚动轴承冲击性故障特征难以提取的问题,提出一种自适应多尺度自互补Top-Hat(Adaptive multi-scale self-complementary Top-Hat, AMSTH)变换方法用于轴承故障的增强检测。自互补Top-Hat变换在消除信号中背景噪声的同时,能有效增强故障振动信号的冲击特性,而构造的多尺度自互补Top-Hat变换方法,可以较有效地兼顾抗噪性能和信号的细节保持。在分析形态学滤波的基础上,提出采用特征幅值能量比(Feature amplitude energy radio, FAER)的方法自适应确定最优结构元素的尺度,并应用于轴承的故障增强检测。通过对仿真信号和实测轴承滚动体、内圈故障信号进行分析,结果表明该方法可有效增强滚动轴承的故障检测,并且在运算效率和提取效果方面优于基于信噪比标准的多尺度形态学开-闭和闭-开组合变换方法。     

A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery

In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal–noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.     

一种基于宽带激励的虚拟时间反转方法

针对主动Lamb波监测中常见的低信噪比问题和传统时间反转方法降低监测信号空间分辨率的不足,提出一种采用宽带激励求取损伤散射路径传递函数的虚拟时间反转方法并应用于复杂结构的多损伤成像中.理论研究并经实验验证表明该方法不但能够有效实现Lamb波多损伤散射信号在相应损伤处聚焦增强,抑制了边界反射信号和噪声,提高了信噪比,而且与传统时间反转方法相比,提高了损伤聚焦波包的空间分辨率,增强了对于多个损伤的监测能力.最后的成像结果显示虚拟时间反转方法能够改善复杂板结构中两个近邻损伤的成像质量.     

Suppressing GPR Clutter from Randomly Rough Ground Surfaces to Enhance Nonmetallic Mine Detection

This study attempts to quantify the ground penetrating radar rough ground surface clutter by numerical modeling of wave scattering, and establish a strategy to suppress the clutter for given test signals. The goal is to improve the GPR detection statistics for small, buried, low-contrast nonmetallic antipersonnel mines. Using a model of an experimentally measured impulse GPR signal, we simulate the ground surface and buried low-contrast mine target scattered responses. We employ a 2D finite difference time domain (FDTD) method to analyze the pulse shape, delay, and amplitude characteristics of the scattered waves—with and without buried nonmetallic mine targets—as a function of roughness parameters. Five hundred Monte Carlo simulations of various test cases of specified ground root mean square height and correlation length were run to generate statistics for the clutter and target signal variations. In addition, the effectiveness of identifying and removing the ground surface clutter signal for detecting subsurface targets is presented. Results indicate that even with moderate roughness, statistics can be generated to enhance the detection of small, shallow, low-contrast targets.     

基于驰振的压电能量采集器建模与实验研究

针对低信噪比环境下核电站松动件的检测,以降低误报率、漏报率为目标,提出了一种基于盲解卷积算法的松动部件冲击响应提取方法,并进一步结合支持向量机分类辨识算法,给出了一种低信噪比环境下核电站松动部件检测方法。利用叠加实堆背景噪声的平板钢球跌落实验数据开展了报警研究,并对盲解卷积算法进行了参数优化设计。结果表明：优化后的盲解卷积算法能够很好地恢复出信噪比低至-20dB的冲击响应信号,并使噪声能量降低了75%,有效抑制了噪声;给出的松动部件检测方法在信噪比低至-14dB时,仍具有极低的漏报率,并且噪声误报率和脉冲干扰误报率为零,因而具有良好的抗误报、抗漏报能力。