All-fiber self-mixing interferometer for displacement measurement based on the quadrature demodulation technique

The quadrature demodulation algorithm used for all-fiber self-mixing sensors is applied to displacement measurements. A fiber electro-optic modulator is used to introduce phase modulation, which can improve the accuracy of measurements. Although phase modulation is easily affected by the polarization state of light, this method can process signals by sampling the interference signals at specific time points. Such signals are insensitive to polarization, and thus, the calculation process and experimental devices are simplified. The feasibility of the algorithm is validated via a series of experiments under nonpolarization-maintaining conditions.     

Reflectivity function reconstruction using the frequency deconvolution technique

Deconvolution of ultrasonic echo signals improves resolution and quality of ultrasonic images. A frequency deconvolution algorithm depends on the Fast Fourier transform is proposed for ultrasonic data. The stability of the algorithm and the influence of the truncation effect on the deconvoluted results were investigated with respect to the duration time of reflectivity function reconstruction and the signal to noise ratio. Reliability of the separation of reconstructing the reflectivity of a biological tissue is estimated by frequency deconvolution of the echo ultrasound signals.     

A new algorithm of active suppression of noise signals in real time measurements

A new algorithm of the suppression of pickup noise and background in information signals in real time measurements is considered. This algorithm is based on a recurrent method of spectral-coefficient measurements of noise component in an analyzed signal and a recursive filtration algorithm for its suppression. Using recurrent calculations in the new algorithm makes it possible to perform a dynamic spectral measurements of information signals in real time, which are not possible to do by classical algorithms of spectral transformations due to time restrictions.     

一种适用于微弱信号提取的盲源分离算法

本文首次提出了适用于微弱信号提取的盲源分离算法,这种方法是在常用的自然梯度串行更新算法基础上采用了有监督的机制。通过观察两个信号互相关的程度自动判别激活函数的种类,实现了超高斯亚高斯强弱信号混合下的盲源分离。信号仿真表明,有监督的盲源分离技术收敛速度较快,精度较高。     

A locally optimal algorithm for detecting weak geophysical signals at the output of laser gravitational antennas

A locally optimal algorithm for detecting weak low-frequency geophysical signals by the use of long-baseline laser interferometric gravitational antennas that operate in the free spectral range mode is considered. The likelihood ratio is derived using the estimate-correlation online processing of signals. The main element of the optimal detector in the Gaussian approximation is the realizable Wiener filter. A calculation formula for the signal-to-noise ratio is presented.     

A blind separation algorithm for underdetermined convolutional mixed communication signals based on time–frequency soft mask

This paper focuses on the separation for time–frequency (TF) overlapped communication signals received by the sensors. A novel blind separation strategy is proposed to improve the poor performance of signal separation by traditional algorithms for convolutional mixtures in underdetermined cases. Firstly, the number of sources and cluster centers are obtained in the sparse domain by combining the density peak clustering (DPC) with fuzzy c-means (FCM) clustering algorithm; Then the GMM clustering algorithm is applied to calculate the membership degree of the source signal in the mixed signals, so as to construct a TF soft mask matrix to more precisely carry out separation for TF overlapped signals. In this paper, the separation simulations are conducted with the digital modulation signals of 2ASK, BPSK, QPSK, etc. The results show that the algorithm proposed in this paper has better anti-aliasing and anti-noise performance than the comparison algorithms.     

基于二次相关的语音信号时延估计改进算法

目前语音信号的时延估计研究,大部分采用的是广义互相关算法。然而,广义互相关时延估计算法易受噪声和混响环境影响。为此,本文提出了一种基于二次相关的语音信号时延估计改进算法,该算法对语音信号进行二次互相关运算,并结合Hilbert变换,对二次互相关峰值进行进一步的锐化处理,使得反映时延的峰值点检测更为准确。实验结果表明,改进的时延估计方法在非平稳的语音信号中能够有效地抑制噪声干扰,且在不同混响条件下时延估计具有更好的性能。     

The possibility of the classification of infrasonic signals using methods for checking statistical hypotheses

This work is devoted to the possibility of the classification of signals using methods for checking statistical hypotheses. On the basis of analysis of the typical peculiarities of signals that belong to the same class we performed an empirical derivation of the shape of the class. The mechanism of the definition of the separability of signals of each class, as well as the level of criterion for defining critical domain were suggested. A classification algorithm was obtained on the basis of the research. The efficiency of the suggested methodology was tested on the problem of separability of infrasonic signals recorded in the atmosphere.     

Fast affine projections and the regularized modified filtered-error algorithm in multichannel active noise control

In this paper, real-time results are given for broadband multichannel active noise control using the regularized modified filtered-error algorithm. As compared to the standard filtered-error algorithm, the improved convergence rate and stability of the algorithm are obtained by using an inner-outer factorization of the transfer path between the actuators and the error sensors, combined with a delay compensation technique using double control filters and a regularization technique that preserves the factorization properties. The latter techniques allow the use of relatively simple and efficient adaptation schemes in which filtering of the reference signals is unnecessary. Results are given for a multichannel adaptive feedback implementation based on the internal model control principle. In feedforward systems based on this algorithm, colored reference signals may lead to reduced convergence rates. An adaptive extension based on the use of affine projections is presented, for which real-time results and simulations are given, showing the improved convergence rates of the regularized modified filtered-error algorithm for colored reference signals.     

基于PCF-LPG的差分算法实现对光纤传感器的噪音消除

为了有效消除光源抖动及系统不稳定因素给光纤传感器带来的噪音干扰,提高光纤传感器的准确度,本文提出了一种基于光子晶体光纤长周期光栅的差分算法.利用光子晶体光纤长周期光栅良好的温度稳定性及宽光谱滤波特性,同时监测位于光子晶体光纤长周期光栅透射谱正负斜率线性区域内的两个信号功率变化.这两个信号是经同一路径到监测设备的,包含光源抖动以及系统其它不稳定因素带来的噪音干扰,对这两个信号进行差分处理即可有效消除噪音干扰,并将其应用于光纤环镜温度传感器.结果表明:通过基于光子晶体光纤长周期光栅的差分处理,在光源功率变化±10%的情况下,差分值基本保持不变,由此获得的温度测量值与真实值相对误差分别为0.04和0.03|与未引入差分算法相比,传感器准确度从约76%提高到约97%.本文提出的基于光子晶体光纤长周期光栅的差分算法可以有效消除光纤传感器内功率抖动所带来的噪音干扰,大大提高系统的准确度.     

CO在1. 578 μ m的可调谐二极管激光光谱信号小波去噪研究

研究了一氧化碳(CO)近红外波段直接吸收和波长调制信号去噪处理算法。从HATRAN数据库中得到CO气体的吸收信号作为仿真数据,提取直接吸收信号、1-f和2-f解调信号作为原始信号,研究了不同小波基以及不同的分解层数对叠加高斯白噪声的光谱信号去噪的效果。最后利用基长为0.95 m有效光程为55.1 m的Herriott型多光程池对CO在1.578 μm处的第二泛频带P(4)吸收谱线信号进行测量和信号处理,与原始信号相比,经过信号处理过的直接吸收、1-f和2-f解调信号的信噪比都提高1～2个数量级;通过选择不同小波基和优化小波变换层数,增加了系统的抗干扰能力。     

Automated correction of unwanted phase jumps in reference signals which corrupt MRSI spectra after eddy current correction

A commonly applied step in the postprocessing of gradient localized proton MR spectroscopy, is correction for eddy current effects using the water signal as a reference. However, this method can degrade some of the metabolite signals, in particular if applied on proton MR spectroscopic imaging data. This artifact arises from the water reference signal in the presence of a second signal which resonates close to the main water resonance. The interference of both resonances will introduce jumps in the phase of the reference time domain signal. Using this phase for eddy current correction will result in a ringing artifact in the frequency domain of the metabolite signal over the whole frequency range. We propose a moving window correction algorithm, which screens the phase of reference signals and removes phase jumps in time domain caused by interference of signals from multiple spin systems. The phase jumps may be abrupt or gradually distributed over several time data points. Because the correction algorithm only corrects time data points which contain phase jumps, the phase is minimally disrupted. Furthermore, the algorithm is automated for large datasets, correcting only those water reference signals which are corrupted. After correction of the corrupted reference signals, normal eddy current correction may be performed. The algorithm is compared with a method which uses a low-pass filter and tested on simulated data as well as on in vivo proton spectroscopic imaging data from a healthy volunteer and from patients with a brain tumor.     

Fourier algorithm for four-wave-mixing signals from optically dense systems with memory

A triple Fourier-transform algorithm for generating and propagating femtosecond four-wave-mixing signals in optically thick samples is demonstrated. The algorithm has a dynamic range that is useful for tests of theory and simulations of experiments with an arbitrary nonlinear response. Although two-pulse echoes decay faster as optical density increases for a Bloch model, we find that systems with memory exhibit the opposite trend.     

Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis

The sparse decomposition based on matching pursuit is an adaptive sparse expression of the signals. An adaptive matching pursuit algorithm that uses an impulse dictionary is introduced in this article for rolling bearing vibration signal processing and fault diagnosis. First, a new dictionary model is established according to the characteristics and mechanism of rolling bearing faults. The new model incorporates the rotational speed of the bearing, the dimensions of the bearing and the bearing fault status, among other parameters. The model can simulate the impulse experienced by the bearing at different bearing fault levels. A simulation experiment suggests that a new impulse dictionary used in a matching pursuit algorithm combined with a genetic algorithm has a more accurate effect on bearing fault diagnosis than using a traditional impulse dictionary. However, those two methods have some weak points, namely, poor stability, rapidity and controllability. Each key parameter in the dictionary model and its influence on the analysis results are systematically studied, and the impulse location is determined as the primary model parameter. The adaptive impulse dictionary is established by changing characteristic parameters progressively. The dictionary built by this method has a lower redundancy and a higher relevance between each dictionary atom and the analyzed vibration signal. The matching pursuit algorithm of an adaptive impulse dictionary is adopted to analyze the simulated signals. The results indicate that the characteristic fault components could be accurately extracted from the noisy simulation fault signals by this algorithm, and the result exhibited a higher efficiency in addition to an improved stability, rapidity and controllability when compared with a matching pursuit approach that was based on a genetic algorithm. We experimentally analyze the early-stage fault signals and composite fault signals of the bearing. The results further demonstrate the effectiveness and superiority of the matching pursuit algorithm that uses the adaptive impulse dictionary. Finally, this algorithm is applied to the analysis of engineering data, and good results are achieved.     

An algorithm for the superresolution of the sources of narrow-band signals on the basis of a solution of a system of observation equations

An algorithm for the superresolution of the sources of narrow-band signals on the basis of the solution of a system of observation equations is proposed. In accordance with this algorithm, the angular coordinates of sources are the roots of a single equation. To reliably estimate the coordinates, the number of time samples of observed signals must exceed the number of sources by at least one. In the case of a linear equidistant antenna array, a stable estimate of the angular coordinates requires that the number of time samples exceed the half-number of sources by at least one. A partial solution that provides minimization of the variance of the estimate errors is selected from the general solution. When the number of time samples that are used to estimate angular coordinates increases, the mathematical expectations of their errors tend to zero and the variances are larger than the minimally possible ones (determined from the Rao-Kramer inequality) by a factor of 1–1.2.     

一种可控的视频图像动态对比度增强算法

为增强显示设备进行视频信号显示时的图像对比度,提出了一种幅度可控的动态对比度增强算法。该算法利用一组归一化线性直方图数据和输入图像的归一化灰度直方图数据的结合,实现对输入图像的权重化直方图均衡化处理,进而达到幅度可控的动态对比度增强效果。该算法可以通过控制对比度增强幅度来避免传统直方图均衡化产生的过增强现象。将基于该算法的实时图像处理器应用于50英寸AC PDP上,实验结果表明,其对比度获得显著提升,并且能够根据外部接口的调节实现幅度可控的对比度增强效果。     

A simulation algorithm for ultrasound liver backscattered signals

In this study, we present a simulation algorithm for the backscattered ultrasound signal from liver tissue. The algorithm simulates backscattered signals from normal liver and three different liver abnormalities. The performance of the algorithm has been tested by statistically comparing the simulated signals with corresponding signals obtained from a previous in vivo study. To verify that the simulated signals can be classified correctly we have applied a classification technique based on an artificial neural network. The acoustic features extracted from the spectrum over a 2.5 MHz bandwidth are the attenuation coefficient and the change of speed of sound with frequency (dispersion). Our results show that the algorithm performs satisfactorily. Further testing of the algorithm is conducted by the use of a data acquisition and analysis system designed by the authors, where several simulated signals are stored in memory chips and classified according to their abnormalities.     

磁共振成像谱仪B0信号的高精度发生与测试

设计了一种基于现场可编程门阵列（FPGA）的磁共振成像（MRI）谱仪B₀信号的高精度发生方法,并对产生的B₀信号经高速采集卡采集之后进行测试和验证.FPGA从外部读取波形数据和参数,分别存储在双端口随机存取存储器（RAM）和参数寄存器中,根据预补偿算法实现B₀信号的发生,并通过对时间参数和幅度参数的控制,产生不同的B₀信号,时间分辨率为1 μs.对谱仪的B₀输出进行采集,再进行最小二乘拟合,以验证B₀信号发生的准确性.经实验证实,该设计可以产生正确、可控的高精度B₀信号.     

室内两步法监督式学习双耳声源距离估计

提出一种室内环境下两步法监督式学习双耳声源距离估计算法,该算法通过预先估计声源方位角信息以克服声源方位角的变化对声源距离估计性能的不利影响.该算法第1步利用深度神经网络模型估计声源的方位角,并将不同方位角的双耳信号分类;第2步中对每个方位角的双耳信号采用独立的深度神经网络模型进行声源距离估计,其中距离特征选用双耳信号的一些双耳特征和统计特性。在仿真和实际环境下,本文提出的两步法声源距离估计算法的距离估计准确率比现有算法提高了3%~5%左右,并且在各种不匹配环境下的距离估计准确率比现有算法高出5%~10%左右。实验结果表明利用声源方位角信息可以有效提高双耳声源距离估计算法的性能。      

声速不均匀介质的光声成像重建算法

为提高声速不均匀介质的光声成像精度,提出一种基于反卷积的光声成像重建算法.本算法不需预先知道介质的声速分布.先通过探测到的光声信号构造出一个新函数,并用不同位置探测到的光声信号间的相关性来估计空间两点间的声波传播时间,以补偿声速的不均匀性.然后基于反卷积方法由该函数解出待测组织内的电磁波吸收分布.仿真研究结果表明,当介质声速差异在10%以内时,重建图像能正确反映待测目标的大小、位置和电磁波吸收系数,算法具有良好的抗噪性能.由于生物软组织内的声速差异一般小于10%,因此本算法是一种有效的光声成像重建算法.