小波系数区域相关阈值滤噪方法研究

报道了一种基于小波分析的新的滤噪方法小波系数区域相关阈值滤噪法的基本原理 ,并将其用于模拟信号与含噪色谱信号的滤噪研究。系统的描述了该方法的滤噪过程 ,讨论了相关运算区域大小、软硬阈值法、信号的频率、信噪比对滤噪结果的影响。对高效液相色谱信号的滤噪结果表明 ,该方法在保留信号的特征信息的同时能够有效地滤除信号中的噪音     

二进小波变换极大模法用于分析化学信号的滤噪

实验数据的滤噪在分析化学领域中具有重要的意义。小波变换技术具有很强的信号分离能力，容易把随机噪声从信号中分离出来，从而提高信号的信噪比。本文使用滤噪方法不同于传统离散小波变换方法，而是通过引入二进小波变换和李氏指数的概念，根据噪声与有用信号的极大模截然不同的特征，实现信号滤噪。实验数据的仿真结果研究也证明该方法的可行性。     

一种新的小波滤波方法在化学谱图信号滤噪中的应用

仪器分析测定中，噪声的存在往往影响分析的准确度和仪器的检出限。小波变换多分辨分析的特性使得它成为一种很好的滤噪方法。基于小波分解后信号与噪声的小波系数随尺度变化规律不同的特性，提出了一种新的滤波滤方法-空域相关法，即通过不同尺度上相关系数模值与小波系数模模值的比较，达到滤波滤的目的。本文提出的方法具有无需人为选定无需人为选定滤噪阈值和小波函数、方法简单、失真度小等优点，可以大在提高信号的信噪比。模拟数据和ICP-AES实验数据证明了该方法的有效性。     

毛细管电泳激光诱导荧光检测信号的小波滤噪

采用小波滤噪方法对毛细管电泳激光诱导荧光检测信号进行了处理，研究了小波变换中小波基的选择及噪声阈值的选择对滤噪的影响。结果表明，采用DB4小波基能有效消除毛细管电泳激光诱导荧光检测信号中存在的噪声，使信噪比得到较大改善。     

小波神经网络用于色谱数据的滤噪与压缩

介绍了小波神经网络（Ｗａｖｅｌｅｔ Ｎｅｕｒａｌ Ｎｅｔｗｏｒｋ）的结构和算法，并将其应用于色谱数据的滤噪与压缩。在小波神经网络中，采用了Ｂｕｂｂｌｅ母小波和一维搜索变步长共轭梯度优化方法。结果表明，小波神经网络将原始数据压缩至１：２０时，仍能得到很好的重建谱图。重建的色谱图不仅能保留原始的谱图特征而且有光滑滤噪的效果。对于色谱数据的存储与平衡滤噪具有重要意义。     

毛细管电脉在柱安培检测信号的小波滤噪研究

采用一种新的小波滤噪方法对毛细管电泳在柱安培检测信号进行了处理,研究了小波基的选择、噪音在不同细节中的特征以及噪音阈值的确定等,用此方法对酚类定量分析,结果信噪比、检测限和线性范围均有较大改善。     

含氢聚甲基硅氧烷／丙烯酸丁酯／羟甲基丙烯酰胺复合乳液的制备方法对 …

采用自由基引发剂用３种不同的聚合方法制备了新型的含氢聚甲基硅氧烷（ＰＨＭＳ）／丙烯酸丁酯（ＢＡ）／羟甲基丙烯酰胺（ＮＭＡ）复合聚合物乳液，详细他不同聚合方法对乳液稳定性和乳胶粒子的影响。同时对聚合反应的机理，产物的结构作了考察，结果表明：通过乳液聚合，得到了ＰＨＭＳ／ＢＡ／ＮＭＡ共聚物，单体乳液滴加法所得乳液粒径较小，乳液性能稳定，是制备ＰＨＭＳ／ＢＡ／ＮＭＲ复合聚合物乳淤较适宜的方法。     

含氢聚甲基硅氧烷/丙烯酸丁酯/羟甲基丙烯酰胺复合乳液的制备方法对乳液性质的影响

采用自由基引发剂用３种不同的聚合方法制备了新型的含氢聚甲基硅氧烷（ＰＨＭＳ）／丙烯酸丁酯（ＢＡ）／羟甲基丙烯酰胺（ＮＭＡ）复合聚合物乳液。详细讨论了不同聚合方法对乳液稳定性和乳胶粒子的影响，同时对聚合反应的机理、产物的结构作了考察．结果表明：通过乳液聚合，得到了ＰＨＭＳ／ＢＡ／ＮＭＡ共聚物；单体乳液滴加法所得乳液粒径较小，乳液性能稳定，是制备ＰＨＭＳ／ＢＡ／ＮＭＡ复合聚合物乳液较适宜的方法．     

小波变换与分析化学信号处理

介绍了小波变换的基本理论并对小波变换的常用算法和应用进行了评述。由于小波变换的时2频局部化性质, 使其成为信号处理的强有力工具。在分析化学领域中, 小波变换在流动注射分析、伏安分析、高效液相色谱、红外光谱、质谱、核磁共振谱、可见-紫外光谱、光声光谱、扩展X-射线吸收精细结构(EXAFS) 谱等分析化学信号的平滑滤噪、数据压缩、重叠信号解析等方面都有成功的应用。     

PbS/Ru(Ⅱ)配合物敏化Cd(Ⅱ)掺杂TiO_2纳米晶电极的光电化学

合成了Ｃｄ（Ⅱ）掺杂ＴｉＯ２纳米粒子（掺杂５％Ｃｄ（Ⅱ））,用光电化学方法测定了ＰｂＳ、ＲｕＬ２（ＮＣＳ）２（Ｌ＝２,２′联吡啶４,４′二羧酸）分别敏化及ＰｂＳ／ＲｕＬ２（ＮＣＳ）２复合敏化该纳米晶膜电极的光电化学行为,实验证明,ＰｂＳ、ＲｕＬ２（ＮＣＳ）２单独敏化和ＰｂＳ／ＲｕＬ２（ＮＣＳ）２复合敏化的Ｃｄ掺杂电极比纯的ＴｉＯ２电极的光电流产生的起始波长都向长波方向移动;在３８０～６００ｎｍ范围内,ＰｂＳ／ＲｕＬ２（ＮＣＳ）２复合敏化Ｃｄ掺杂电极的效果比ＰｂＳ和ＲｕＬ２（ＮＣＳ）２单独敏化的效果更好     

微流控电泳芯片中化学发光信号的分段门限小波降噪

采用分段门限小波降噪(STWD)方法对化学信号中的异方差噪声进行降噪处理.用STWD法和统一门限小波降噪法同时处理两种模拟信号(其中之一包含异方差噪声).结果显示,优化参数的STWD法能够更有效地提高降噪效果.采用STWD法对微流控芯片化学发光检测信号中的异方差噪声进行处理,取得了满意的降噪效果.     

一种基于光谱信息计算解析的液相色谱滤噪新方法

提出一种基于光谱信息计算解析的高效液相色谱滤噪新方法.对于HPLC/DAD所获得的二维量测数据,取半峰高以上色谱数据点所对应的各列光谱数据进行计算处理,求得在检测波长下的单位标准光谱,并根据光谱维数据与单位标准光谱的关系,采用最小二乘法重构计算求出色谱维数据,从而去除色谱噪声.仿真实验和实测谱图的滤噪结果表明,该方法能有效滤除常见噪声,与中值滤波算法相比较,滤噪性能明显优于后者,是提高色谱定量分析准确度的有力工具.     

Sub-optimal wavelet denoising of coaveraged spectra employing statistics from individual scans

This paper proposes a novel wavelet denoising method, which exploits the statistics of individual scans acquired in the course of a coaveraging process. The proposed method consists of shrinking the wavelet coefficients of the noisy signal by a factor that minimizes the expected square error with respect to the true signal. Since the true signal is not known, a sub-optimal estimate of the shrinking factor is calculated by using the sample statistics of the acquired scans. It is shown that such an estimate can be generated as the limit value of a recursive formulation. In a simulated example, the performance of the proposed method is seen to be equivalent to the best choice between hard and soft thresholding for different signal-to-noise ratios. Such a conclusion is also supported by an experimental investigation involving near-infrared (NIR) scans of a diesel sample. It is worth emphasizing that this experimental example concerns the removal of actual instrumental noise, in contrast to other case studies in the denoising literature, which usually present simulations with artificial noise. The simulated and experimental cases indicate that, in classic denoising based on wavelet coefficient thresholding, choosing between the hard and soft options is not straightforward and may lead to considerably different outcomes. By resorting to the proposed method, the analyst is not required to make such a critical decision in order to achieve appropriate results.     

Adaptive approach for variable noise suppression on laser-induced breakdown spectroscopy responses using stationary wavelet transform

Spectral signals are often corrupted by noise during their acquisition and transmission. Signal processing refers to a variety of operations that can be carried out on measurements in order to enhance the quality of information. In this sense, signal denoising is used to reduce noise distortions while keeping alterations of the important signal features to a minimum. The minimization of noise is a highly critical task since, in many cases, there is no prior knowledge of the signal or of the noise. In the context of denoising, wavelet transformation has become a valuable tool. The present paper proposes a noise reduction technique for suppressing noise in laser-induced breakdown spectroscopy (LIBS) signals using wavelet transform. An extension of the Donoho's scheme, which uses a redundant form of wavelet transformation and an adaptive threshold estimation method, is suggested. Capabilities and results achieved on denoising processes of artificial signals and actual spectroscopic data, both corrupted by noise with changing intensities, are presented. In order to better consolidate the gains so far achieved by the proposed strategy, a comparison with alternative approaches, as well as with traditional techniques, is also made.     

Wavelet transforms in separation science for denoising and peak overlap detection

Wavelet transform is a versatile time‐frequency analysis technique, which allows localization of useful signals in time or space and separates them from noise. The detector output from any analytical instrument is mathematically equivalent to a digital image. Signals obtained in chemical separations that vary in time (e.g., high‐performance liquid chromatography) or space (e.g., planar chromatography) are amenable to wavelet analysis. This article gives an overview of wavelet analysis, and graphically explains all the relevant concepts. Continuous wavelet transform and discrete wavelet transform concepts are pictorially explained along with their chromatographic applications. An example is shown for qualitative peak overlap detection in a noisy chromatogram using continuous wavelet transform. The concept of signal decomposition, denoising, and then signal reconstruction is graphically discussed for discrete wavelet transform. All the digital filters in chromatographic instruments used today potentially broaden and distort narrow peaks. Finally, a low signal‐to‐noise ratio chromatogram is denoised using the procedure. Significant gains (>tenfold) in signal‐to‐noise ratio are shown with wavelet analysis. Peaks that were not initially visible were recovered with good accuracy. Since discrete wavelet transform denoising analysis applies to any detector used in separation science, researchers should strongly consider using wavelets for their research.     

Application of wavelet transforms and an approximate deconvolution method for the resolution of noisy overlapped peaks in DNA capillary electrophoresis

A new procedure for resolving noisy overlapped peaks in DNA separations by capillary electrophoresis (CE) is developed. The procedure combines both a wavelet-based denoising method that effectively denoises the signal and a novel approximate deconvolution technique that resolves the fragment peaks and improves the ability to separate highly overlapped peaks early in the electrophoresis process. Different kinds of overlapped peaks with and without noise simulated by computer as well as some DNA experimental electropherograms were submitted to the new procedure. A second order differential operator with variable coefficients is applied to the entire electrophoresis signal at any given time and approximate deconvolutions of the individual Gaussian peaks are performed. The operator incorporates the effect of the superposition and gives exact annihilation in the neighborhood of each peak. Overlapped peaks with a resolution higher than 0.46 can be resolved directly. Also, the method can determine the peak components of signals with a signal to noise ratio higher than 1.4     

A partial least squares and wavelet-transform hybrid model to analyze carbon content in coal using laser-induced breakdown spectroscopy

A partial least squares (PLS) and wavelet transform hybrid model are proposed to analyze the carbon content of coal by using laser-induced breakdown spectroscopy (LIBS). The hybrid model is composed of two steps of wavelet analysis procedures, which include environmental denoising and background noise reduction, to pretreat the LIBS spectrum. The processed wavelet coefficients, which contain the discrete line information of the spectra, were taken as inputs for the PLS model for calibration and prediction of carbon element. A higher signal-to-noise ratio of carbon line was obtained after environmental denoising, and the best decomposition level was determined after background noise reduction. The hybrid model resulted in a significant improvement over the conventional PLS method under different ambient environments, which include air, argon, and helium. The average relative error of carbon decreased from 2.74 to 1.67% under an ambient helium environment, which indicated a significantly improved accuracy in the measurement of carbon in coal. The best results obtained under an ambient helium environment could be partly attributed to the smallest interference by noise after wavelet denoising. A similar improvement was observed in ambient air and argon environments, thereby proving the applicability of the hybrid model under different experimental conditions.     

液相色谱-电喷雾电离质谱法检测尿液中的3种合成类固醇药物

研究建立了合成类固醇药物群勃龙、四氢孕三烯炔酮和孕三烯酮的液相色谱-电喷雾质谱的检测方法。尿样经过β-葡萄糖醛酸酶酶解和叔丁基甲醚提取后,采用Zorbax SB-C18分析柱(150 mm×2.1 mm,5 μm),在流动相为pH 3.5的甲酸铵缓冲液-乙腈的条件下进行梯度洗脱,在正离子模式下检测。考察了不同的质谱条件对这类化合物检测结果的影响。建立了人尿液中合成类固醇药物的液相色谱-电喷雾电离质谱的初筛和确证方法。