A general non-local denoising model using multi-kernel-induced measures

Noises are inevitably introduced in digital image acquisition processes, and thus image denoising is still a hot research problem. Different from local methods operating on local regions of images, the non-local methods utilize non-local information (even the whole image) to accomplish image denoising. Due to their superior performance, the non-local methods have recently drawn more and more attention in the image denoising community. However, these methods generally do not work well in handling complicated noises with different levels and types. Inspired by the fact in machine learning field that multi-kernel methods are more robust and effective in tackling complex problems than single-kernel ones, we establish a general non-local denoising model based on multi-kernel-induced measures (GNLMKIM for short), which provides us a platform to analyze some existing and design new filters. With the help of GNLMKIM, we reinterpret two well-known non-local filters in the united view and extend them to their novel multi-kernel counterparts. The comprehensive experiments indicate that these novel filters achieve encouraging denoising results in both visual effect and PSNR index.     

Preliminary evaluation of the anticorrosive properties of aircraft coatings by electrochemical methods

Strict regulations concerning the content of volatile organic compounds (VOCs) and heavy metals (Cr⁶⁺) in aircraft coating systems have increased the economic burden of the United States Air Force (USAF) in the area of coating maintenance. To this end, it is critical to have methods to characterize new coating systems in such a manner that the data can be used to predict accurately and reliably the expected lifetime of the coatings in service. Electrochemical noise method (ENM) and electrochemical impedance spectroscopy (EIS) are two techniques used to monitor extent and rate of corrosion. The USAF is currently employing these methods in order to supplement data acquired from traditional salt-spray methods. ENM and EIS are used to evaluate each component of the coating system and its contribution to corrosion prevention. Preliminary evaluations of an aircraft coating system on aluminum substrate (Al 2024-T3) produces resistance noise values of 10⁶ to 10⁷ Ω/cm². It is hoped that these results will form the basis of coatings that give increased USAF fleet service life and reduction in maintenance manpower and materials costs.     

ON THE ROBUST PREDICTION AND INTERPOLATION OF TIME SERIES IN THE PRESENCE OF CORRELATED NOISE

Abstract. We consider the problem of predicting and interpolating linearly a time series which can be represented as the sum of a model process with known spectral density and a noise process. The spectral density of the noise process is unknown with the exception of an upper bound for its integral. Some partial information of quite general kind about the cross spectral density of model and noise is available. We prove the existence of a robust predictor which minimizes the maximal mean-square error, where the maximum is taken over all spectral densities which may arise from the circumstances described above as spectral density of the predicted time series. An analogous result holds for the related interpolation problem. We describe how to derive the minimax robust predictor and interpolator in concrete situations. The method is illustrated by determining the robust predictor explicitly for three examples where model and noise may be arbitrarily, only causally or not at all correlated.     

三江盆地二维地震资料处理方法研究与应用

三江盆地二位地震资料处理存在着较多的难题。如:静校正复杂;在逐步压制各类干扰的同时,难于保护低频有效信息和波组特征;波场复杂,准确拾取叠加速度比较困难。利用各种新技术新方法提高该地区地震资料的精度,是该地区地震资料处理的目标。通过综合应用各种静校正、地表一致性处理、噪声衰减、偏移等技术和方法,解决了该地区资料处理的难题,提高了地震资料的精度。实际地震资料处理的结果表明,以上处理技术和方法对该区复杂的资料处理效果明显。     

New results on PWARX model identification based on clustering approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.     

Noise characterization of surface processes of the Li/organic electrolyte interface

The processes occurring in aprotic electrolyte on a lithium electrode in the steady state conditions and under polarization are studied using the method of electrochemical noise characterization. The evidence of the electro-chemical noise measurements on polarized lithium electrodes indicates that the discharge of lithium ions under cathodic polarization, as well as lithium anodic dissolution, is localized under the passive film rather than on its surface. An increase in the polarizing current results in local breakdown of the film; in this case, the electrochemical process emerges on the electrode surface affecting the character of potential fluctuations. The intensity of electrochemical noise significantly increases in the course of cathodic polarization with high currents. The reason is that lithium metal crystals, which are formed under the passive film, perforate the film, and dendrites grow on its surface. The method shows the dependence of electrochemical noise intensity on the nature of the electrolyte and establishes the correlation between the stability of the lithium electrode in the course of cycling and the intensity of fluctuations. This offers an opportunity of using the method of electrochemical noise for screening organic electrolytes for lithium batteries.     

基于背景噪声的图像盲篡改检测

数字图像在成像过程中会产生特定的背景噪声,如果两幅不同噪声的图像拼接在一起,篡改区域和其他区的噪声会有差异。提出一种基于偏度统计特性的背景噪声估计算法,其通过对图像分块计算每块的噪声标准差,从而检测出噪声异常部分以达到篡改检测的目的。算法利用DCT变换去除原图细节部分,利用偏度统计特性估计噪声,利用条件最小值法求出噪声的标准差。算法改进了迭代求条件最小值法,利用微分方法求取最小值,避免了初始值设定问题,提高了算法的准确率。实验结果表明,提出的噪声估计算法正确率高,且对拼接篡改图像篡改检测有明显效果。     

分布式光纤测温系统在煤矿火灾检测中的应用

摘要：针对煤矿安全存在潜在的火灾问题,文中主要介绍分布式光纤测温原理及其在煤矿火灾监测中的应用。在系统结构上,本文提出了一种优化设计方案。此外,在信号处理方面,本文提出了一种命名为RLN-ELAM的新颖算法,用于检测并消除瑞利噪声对系统测量精度的影响,提高拉曼分布式光纤系统的测量精度。实验结果表明,利用此设计方案和算法后,可以使系统的温度分辨率、空间分辨率均有所提高,测量误差从7%降低到不足2%。在实验室进行的模拟实验中,对温度进行实际测量,结果表明,该煤矿火灾监测系统能够提供详细的温度分布情况并能够及时准确地报警,使工作人员随时掌握煤矿温度分布情况,及时准确地找到火灾发生区。     

一种基于PCA的MPSK信号码元速率估计方法

针对MPSK信号的码元速率估计问题, 研究了有限数据条件下循环谱的谱线特征受到背景色噪声干扰的现象, 提出了一种基于主分量分析(PCA)的循环谱特征码元速率估计方法。PCA变换抑制了信号循环谱中的背景色噪声, 提高了估计精度, 减小了估计方差。仿真表明, 该方法在有限数据条件下具有良好的估计性能, 适用于不同成形滤波系数的MPSK信号。     

A minimum variance distortionless response filter based on the bifrequency spectrum for single-channel noise reduction

This paper deals with the problem of single-channel noise reduction in the short-time Fourier transform (STFT) domain. Many algorithms have been developed to solve this important problem, most of which generally assume that the STFT coefficients in different frequency bands are uncorrelated, so the noise reduction is achieved by applying a gain function to the STFT of the noisy speech in each frequency band. However, this assumption is not accurate and the STFT coefficients of speech signals between neighboring frequency bands are correlated in practice due to the use of small lengths of the fast Fourier transform (FFT) and overlap add/save techniques in implementation. This paper formulates the noise reduction problem by taking into account the interband correlation using the so-called bifrequency spectrum. Based on this formulation, a single-channel minimum variance distortionless response (MVDR) filter is derived, which is shown to be able to significantly improve the signal-to-noise ratio (SNR) and meanwhile maintain the desired speech not much distorted. Simulations are presented to justify the claimed merits of the developed MVDR filter.