A method for local deconvolution

A new method for deconvolution of one-dimensional and multidimensional data is suggested. The proposed algorithm is local in the sense that the deconvolved data at a given point depend only on the value of the experimental data and their derivatives at the same point. In a regularized version of the algorithm the deconvolution is constructed iteratively with the help of an approximate deconvolution operator that requires only the low-order derivatives of the data and low-order integral moments of the point-spread function. This algorithm is expected to be particularly useful in applications in which only partial knowledge of the point-spread function is available. We tested and compared the proposed method with some of the popular deconvolution algorithms using simulated data with various levels of noise.     

最大相关峭度解卷积结合稀疏编码收缩的齿轮微弱故障特征提取

针对强背景噪声环境下齿轮早期故障诊断问题,提出了最大相关峭度解卷积结合稀疏编码收缩的微弱故障特征提取方法。由于最大相关峭度解卷积算法的处理结果同时受滤波器长度参数及解卷积周期参数的影响,为自适应地实现最佳的解卷积效果,利用粒子群算法优良的寻优特性,对最大相关峭度解卷积算法的最佳影响参数组合进行搜索。原故障信号经影响参数优化的最大相关峭度解卷积算法处理后,冲击特征会明显增强,为剔除剩余噪声,对所获解卷积信号做进一步稀疏编码收缩降噪处理,并通过分析降噪信号的包络谱来识别故障特征频率成分。实例分析结果验证了该方法的有效性和可靠性。     

Parallel-distributed blind deconvolution based on a self-organizing neural network

A parallel-distributed blind deconvolution method based on a self-organizing neural network is introduced. A large degraded image is segmented into smaller subpatterns. Each subpattern can be used to get a blur function. Moreover, we propose a two-step unsupervised learning method in the self-organizing neural network. The two-step learning method includes parallel learning and series learning operations. The series learning operation is similar to a typical learning operation in the self-organizing neural network. The parallel learning operation is used as a positive perturbation to let the learning operation leave a local minimum. Several improved blur functions can be estimated from the different subpatterns, and the optimized blur function is evolved by use of a genetic algorithm. As the blur function is estimated, the source image of the large degraded image can be easily restored by use of a Wiener-type filter or other deconvolution methods. Computer simulations show that the proposed parallel-distributed blind deconvolution method gives good reconstruction and that the two-step learning method in the self-organizing neural network can promote learning. Since the main computational cost is dependent on the size of the subpattern, the proposed method is effective for the restoration of the large image.     

一种利于工程应用的快速反卷积波束形成方法

针对传统的反卷积波束形成算法在处理宽带随机信号时计算量过大的问题,给出了一种利于工程应用的快速反卷积波束形成方法。利用不同频率阵列波束图的相似性,将宽带随机信号划分成非等间距的多个窄带,并在每个窄带取一个频率点的点扩散函数(Point Spread Function, PSF)进行反卷积的近似处理,极大地提高了反卷积波束形成的计算速度。通过在波束功率谱上进行边界扩展,解决了因 Richardson-Lucy(R-L)迭代算法带来的边界模糊问题,进一步提高了计算速度。仿真和海试结果表明,该方法相对于常规波束形成具有更高的分辨力、更高的处理增益和更好的旁瓣抑制能力;相对于传统反卷积波束形成计算速度提升了 50%以上。     

Frequency-estimation-based signal-processing algorithm for white-light optical fiber Fabry-Perot interferometers

A novel signal-processing algorithm based on frequency estimation of the spectrogram of single-mode optical fiber Fabry-Perot interferometric sensors under white-light illumination is described. The frequency-estimation approach is based on linear regression of the instantaneous phase of an analytical signal, which can be obtained by preprocessing the original spectrogram with a bandpass filter. This method can be used for a relatively large cavity length without the need for spectrogram normalization to the spectrum of the light source and can be extended directly to a multiplexed sensor system. Experimental results show that the method can yield both absolute measurement with high resolution and a large dynamic range. Performance analysis shows that the method is tolerant of background noise and variations of the source spectrum.     

基于APEX方法的湍流退化图像复原算法

在目标探测过程中,为了消除大气湍流带来的影响,提出了一种基于APEX方法的盲去卷积图像复原算法.该算法是一种非迭代的盲图像复原算法,以湍流退化系统具有G类点扩展函数为假设前提,通过模糊图像的频谱信息直接估计点扩展函数,并采用SECB方法实现目标图像的重建.本文对该算法的原理及其对湍流退化图像复原的可行性进行了深入研究,进行了真实的湍流退化图像的复原实验,其结果表明,该算法能够快速实现对湍流退化图像的重建,并具有一定的稳定性,能满足目标探测过程中的实时性要求.     

Automatic deconvolution of isotope-resolved mass spectra using variable selection and quantized peptide mass distribution

We present an algorithm for the deconvolution of isotope-resolved mass spectra of complex peptide mixtures where peaks and isotope series often overlap. The algorithm formulates the problem of mass spectrum deconvolution as a classical statistical problem of variable selection, which aims to interpret the spectrum with the least number of peptides. The LASSO method is used to perform automatic variable selection. The algorithm also makes use of the quantized distribution of peptide masses in the NCBInr database after in silico trypsin digestion as filters to aid the deconvolution process. Errors in the expected isotope pattern are accounted for to avoid spurious isotope series. The effectiveness of the algorithm is demonstrated with annotated ESI spectrum of known peptides for which the peaks and isotope series are highly overlapping. The algorithm successfully finds all correct masses in the experimental spectrum, except for one spectrum where an additional refinement procedure is required to obtain the correct results. Our results compare favorably to those from a widely used commercial program.     

基于分数阶功率谱熵的未知水声脉冲信号检测方法

水声侦察的核心问题是在无先验知识条件下捕获其他平台发射的脉冲信号,单频（Continuous Wave,CW）信号和调频（Frequency Modulation,FM）信号是常用的水声探测脉冲。功率谱熵算法能有效检测低信噪比的CW信号,但对FM信号性能不佳,分数阶傅里叶变换（Fractional Fourier Transform,FRFT）则能聚集FM信号能量。利用FRFT的性质,结合功率谱熵算法,设计了分数阶功率谱熵检测器,可在分数阶域实施未知脉冲信号检测。理论分析了FRFT对FM信号的能量聚集作用,优化了FRFT阶数搜索方法。仿真实验以及海试数据处理结果证实检测器对FM信号性能良好,且对CW信号的侦察检测性能无影响。通过门限学习,检测器可实现对未知水声脉冲信号的统一自动检测。     

自适应MED结合EMD诊断滚动轴承早期故障

针对最小熵解卷积(MED)降噪效果受滤波器长度影响问题,提出一种利用步长迭代算法和包络谱熵检验准则的自适应MED降噪方法,并结合经验模态分解(EMD)提取滚动轴承微弱故障特征。首先利用自适应MED降噪方法对原信号进行最优降噪处理,然后通过EMD将降噪信号分解为若干个本征模函数(IMF)分量,再选取峭度值最大的IMF进行包络谱分析,根据包络谱中故障特征频率实现故障诊断。仿真信号和实测信号分析结果表明其优于基于EMD的包络解调方法。     

Regularization of the image division approach to blind deconvolution

A problem of blind deconvolution arises when one attempts to restore a short-exposure image that has been degraded by random atmospheric turbulence. We attack the problem by using two short-exposure images as data inputs. The Fourier transform of each is taken, and the two are divided. The result is the quotient of the two unknown transfer functions. The latter are expressed, by means of the sampling theorem, as Fourier series in corresponding point-spread functions, the unknowns of the problem. Cross multiplying the division equation gives an equation that is linear in the unknowns. However, the problem has, initially, a multiplicity of solutions. This deficiency is overcome by use of the prior knowledge that the object and the point-spread functions have finite (albeit unknown) support extensions and also are positive. The result is a fixed-length, linear algorithm that is regularized to the presence of 4-15% additive noise of detection.     

Deconvolution of doppler-broadened positron annihilation lineshapes by fast fourier transformation using a simple automatic filtering technique

A deconvolution scheme for digital lineshapes using fast Fourier transforms and a filter based on background subtraction in Fourier space has been developed. In tests on synthetic data this has been shown to give optimum deconvolution without prior inspection of the Fourier spectrum. Although offering significant improvements on the raw data, deconvolution is shown to be limited. The contribution of the resolution function is substantially reduced but not eliminated completely and unphysical oscillations are introduced into the lineshape. The method is further tested on measurements of the lineshape for positron annihilation in single crystal copper at the relatively poor resolution of 1.7 keV at 512 keV. A two-component fit is possible yielding component widths in agreement with previous measurements.     

风力发电机轴承复合故障特征提取方法研究

针对较强背景噪声干扰下的风力发电机轴承复合故障特征难以准确提取的问题,提出了基于自适应最大相关峭度解卷积(AMCKD)的风力发电机轴承复合故障特征提取的方法。首先,利用人工鱼群算法(AFSA)以解卷积信号包络谱的相关峭度值为目标函数对最大相关峭度解卷积算法(MCKD)的影响参数自适应寻优;然后,将影响参数优化的MCKD对原始故障信号进行解卷积处理,对解卷积信号进行包络谱分析,通过包络谱中的主导频率成分与轴承各元件的故障特征频率相对比,准确实现轴承复合故障的诊断。仿真和工程应用实例验证了所提方法的有效性与实用性。     

A filter for on-line estimation of spectral content

A robust filter algorithm to extract, a posteriori, the rational signal model from a noisy measurement, with little a priori information, is proposed. The spectrum and the statistics of the signal and of the corrupting noise are assumed unknown, except that the signal is assumed to have a rational spectrum. An algorithm based on system and signal theory is derived to select a set of frequencies where the signal-to-noise ratio (SNR) is high from a given measurement spectrum. The density of selected frequencies weights the importance of the measurement as a function of frequency, An estimate of the signal model is obtained from the best weighted least-squares fit to the measurement spectrum at the selected frequencies. The proposed filter has applications to control and signal processing, and a wide variety of applications are presented. Applications include: system identification of a dc motor and a two-link manipulator, extraction of a myo-electric signal from a noisy measurement, the assignment of a rational model to a vegetation tissue's impedance, and to the number density profile of atmospheric oxygen     

A robust, fast, and accurate deconvolution algorithm for EM-field measurements around GSM and UMTS base stations with a spectrum analyzer

In this paper, we present a new robust, fast, and accurate deconvolution algorithm for correcting noise disturbed measurements and eliminating the influence of the resolution filter of a spectrum analyzer used for EM-field measurements around a GSM and UMTS base station. Our purpose is to be able to determine immediately the field values at a measurement site. Therefore the algorithm has to be fast and accurate. Furthermore, because of the presence of noise, our algorithm has to be robust. Our algorithm that uses windowing and filtering meets those demands and reduces the present noise by an additional convolution with a square wave function.     

Localized Fourier transform filter for noise removal in electronic speckle pattern interferometry wrapped phase patterns

This article is concerned with frequency filtering for electronic speckle pattern interferometry wrapped phase patterns. We propose a robust localized Fourier transform filter which is an extension of the root filtering method (RFM). We improve the RFM from a simple technical process and a filter function in the frequency domain. In our method, the proposed filter function is taken as the power spectrum of the convolution of an image and a Gaussian function to the power α. We test the proposed method on two computer-simulated wrapped phase fringe patterns and one experimentally obtained wrapped phase pattern, and compare our models with the widely used, well-known RFM and windowed Fourier filtering (WFF). The experimental results have demonstrated that our localized Fourier transform filter outperforms the RFM and is comparable to WFF. Our method depends on fewer parameters, as compared with WFF, and can achieve a better balance between the computational complexity and the filtered results.     

A new method for power measurements in digital wireless communication systems

Repeatability problems, usually accompanied by low accuracy, are often experienced in power measurements, especially in those concerning spread spectrum signals, peculiar to digital wireless communication systems, and involving a specified frequency range. For this concern, a new method for power measurements is presented. The method applies original and straightforward digital signal-processing algorithms to the input signal power spectrum, preliminarily provided by optimized versions of well-known estimators, such as those based on weighted overlapped segment averaging and multitaper approaches. The outcomes of many experiments, conducted on spread spectrum signals of third-generation telecommunication systems, confirm the efficacy of the proposed method in assuring reliable and accurate results in various types of power measurement.     

一种平稳地震地面运动的改进金井清谱模型

该文提出了一种描述平稳地震地面运动过程随机特性的功率谱密度函数模型。假定基岩地震动为某种过滤白谱过程, 通过两个特定频率参数控制该过滤白谱的低频和高频含量, 从而实现对基岩地震动物理特性的模拟。利用金井清滤波器对基岩过滤白谱进行过滤, 得到地震地面加速度的谱密度模型。通过数学推导解释了该模型的物理机制, 选择汶川地震强震观测数据拟合模型中的相关参数, 给出了汶川地震场地相应的模型参数建议取值。该文模型在物理机制上可以认为是金井清谱模型的改进方案, 但修正了金井清谱模型在零频处奇异的缺陷。     

Myopic deconvolution of adaptive optics images by use of object and point-spread function power spectra

Adaptive optics systems provide a real-time compensation for atmospheric turbulence. However, the correction is often only partial, and a deconvolution is required for reaching the diffraction limit. The need for a regularized deconvolution is discussed, and such a deconvolution technique is presented. This technique incorporates a positivity constraint and some a priori knowledge of the object (an estimate of its local mean and a model for its power spectral density). This method is then extended to the case of an unknown point-spread function, still taking advantage of similar a priori information on the point-spread function. Deconvolution results are presented for both simulated and experimental data.     

Pulse elongation and deconvolution filtering for medical ultrasonic imaging

Range sidelobe artifacts which are associated with pulse compression methods can be reduced with a new method composed of pulse elongation and deconvolution (PED). While pulse compression and PED yield similar signal-to-noise ratio (SNR) improvements, PED inherently minimizes the range sidelobe artifacts. The deconvolution is implemented as a stabilized inverse filter. With proper selection of the excitation waveform an exact inverse filter can be implemented. The excitation waveform is optimized in a minimum mean square error (MMSE) sense. An analytical expression for the power spectrum of the optimal pulse is presented and several techniques to numerically optimize the excitation pulse are shown. The effects of PED are demonstrated in computer simulations as well as ultrasonic images.     

The Photographic Emulsion as a Three-dimensional Recording Medium

Methods have been evolved for determining optical density, silver content, the modulation transfer function and the granularity of the elementary layers of a photographic emulsion coating. Preliminary data are presented. From the distribution in depth of the density, the distribution of inner exposure can be derived; this has a maximum a little below the surface. The modulation transfer function is more strongly degraded the lower the elementary layer. The power spectrum of granularity shows a relatively high contribution by the lower elementary layers for low spatial frequencies.