基于近红外光谱技术的内燃机油鉴别研究

针对常规近红外光谱技术测试内燃机油时光谱信号响应低,对大分子基团分辨率不高,以及光谱信息与其结构组成之间存在非线性关系等难点,提出了一种基于电压为外扰方式的内燃机油二维近红外光谱测试技术,介绍了近红外光谱具有分形的特征.运用小波变换将近红外光谱分解至不同分辨尺度,然后计算各尺度分量的分形维数(盒维数),用近红外光谱的小波基分形参量替代近红外光谱的采样值.计算结果表明,在不同小波基和不同分解尺度下,内燃机油近红外光谱具有不同的盒维数,得到了近红外光谱在分形意义下的特征信息.以美孚、埃索和壳牌3种内燃机油品种鉴别分类问题为实例,比较研究了近红外光谱采样值与小波基分形参量,K近邻法的交互验证计算结果表明,小波基分形参量的分类效果优于近红外光谱采样值.采用近红外光谱技术测试内燃机油的结构组成信息是可行的.     

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。     

一种基于二进小波变换的自适应滤波方法

根据信号和噪声经小波变换后在不同尺度上有不同的特征，将相邻尺度二进小波变换值的相关量进行归一化处理并与小波变换值比较来判断信号与噪声，以噪声在各尺度的方差作为终止迭代的标准，提出了一种基于二进小波变换小波域选择噪声的自适应滤波方法。研究了模拟信号的去噪过程、半峰宽和信噪比对去噪结果的影响，并对模拟含噪信号和含噪毛细管电泳信号去噪前后的结果进行了比较。实验结果表明：由于该方法具有良好的自适应性和显著的滤波效果，必将得到广泛的应用。     

中药色谱指纹图谱的小波变换及分形表达方法

提出一种基于小波变换的色谱指纹图谱分形表达方法。该法运用小波变换将色谱谱线分解至不同分辨尺度，然后计算各尺度分量的分形维数，用色谱的小波基分形参量替代色谱指纹图谱的采样值。仿真实验结果表明，色谱小波基分形参量对谱峰保留时间漂移具有较好的抗干扰能力。以当归和川芎两种中药材的品种及道地性鉴别分类问题为实例，比较研究色谱采样值与小波基分形参量，k-近邻法的交叉验证计算结果表明，小波基分形参量的分类效果优于色谱采样值。     

分析化学信号在多尺度空间的滤噪

从多分辨率分析出发，结合离散正交小波变换(DWT)的理论，通过对各尺度下未抽取前的小波系数进行非线性滤波处理，达到在保护信号边缘的同时，有效实现白噪声及脉冲噪声的滤除。实验数据仿真结果研究也证明了该方法的可行性。     

基于小波的恒电量瞬态响应信号的滤波处理

利用小波变换的阈值法对恒电量响应信号进行滤波处理, 同时与传统的时域和频域的滤波方法进行分析比较, 并且讨论了小波变换的分解层数对恒电量响应信号滤波效果的影响. 结果表明, 利用小波变换可以在时域和频域同时对恒电量响应信号取得良好的去噪效果. 这不仅能提高时域曲线拟合的精度, 还大大地提高了恒电量频谱解析的可靠性. 在实际应用中, 小波变换的分解层数取5～7层可以收到满意的效果.     

基于小波变换的电位滴定法测定喷气燃料总酸值

在电位滴定法测定喷气燃料总酸值中采用小波变换法检测电位滴定终点。以二次样条小波为基函数对电位滴定曲线进行离散二进小波分解,在适当尺度上基本滤除曲线的干扰噪声,通过分析细节信号得到滴定曲线的突跃点,从而判定滴定终点。与指示剂法相比,本方法的测定结果具有相当的准确度和更高的精密度。本方法可快速测定喷气燃料的总酸值。     

基于分形维数的分析信号自适应中值滤波

提出一种面向分析仪器谱图信号处理的分形维自适应中值波方法（AMeFFD）。该法延拓运用分形理论，定义了相对点盒维数概念，由此建立建立判定脉冲型噪声的特异性指标，从而可自动调节中值滤波窗口宽度，有效地滤除脉冲型噪声及其它类噪声。对仿真色谱信号及实测色谱图的处理结果表明：AMeFFD法克服了经典中值滤波算法的缺陷，无论在信号的均方根偏差还是谱峰差等指标上，均明显优于后者，能在确保谱峰不畸变的同时更有效地滤除脉冲型常见噪声，是处理化学谱图信号的有力工具。     

用墨西哥帽小波研究DNA序列的分形特征

结合小波分析和分形理论，采用分数布朗运动(FBM)建立数学模型，研究脱氧 核糖核酸(DNA)序列的自相似性。用DNA walk的方式将DNA序列表达成为一个数字信 号，通过不同小波变换的尺度对应不同特征长度的碱基，选择墨西哥帽小波为母小 波，进行实验考察，结果发现小波系数的图形在许多尺度看上去很相似，大尺度对 应较多的碱基(小波变换尺度为2^7时，对应512个碱基)，能看到概貌；小尺度对应 较少的碱基(小波变换尺度为2^3时，对应32个碱基)，可看到细节。这表明其DNA序 列中存在分形结构，可以用分维数来作为定量描述。这种算法为进一步研究与基因 序列自相似结构有关的基因进化信息提供一种选择的途径。     

基于小波变换的航空润滑油酸值红外光谱分析

为利用红外光谱分析技术快速、准确测定在用航空润滑油酸值,将小波变换用于润滑油红外光谱预处理中,结合均值中心化法,提取光谱有效信息建模。光谱的小波变换选择符合光谱特征的db4小波为基函数,在分解尺度9下进行光谱分解,利用软阈值法滤除各层干扰噪声,重构消噪信号。该光谱预处理法与传统的Savitzky-Golay平滑导数结合均值中心化法相比,滤噪效果好,有效压缩了建模数据量。采用偏最小二乘法,选择最佳主因子数5,建立酸值模型,并对10个在用航空润滑油油样进行了分析。     

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.     

化学信号的近似高阶导数计算

提出了化学信号近似四阶导数计算的新方法——小波卷积法。该法通过信号与二阶样条小波函数的卷积运算对信号求导,能用于高噪音信号的直接求导,避免了普通导数运算将噪音放大的缺陷,即使对信噪比低至0．5的信号也能得到光滑的导数信号。详细讨论了尺度值、噪音、信号类型对求导的影响并建立了参数确定规则。将该法用于含噪音重叠分析化学信号的求导,能同时提高信号的分辨率和信噪比,结果满意。     

小波变换-偏最小二乘算法及其在复方甲硝唑注射液分析中的应用

提出了结合小波变换的偏最小二乘法（ＷＰＬＳ）,即先对光谱信号进行小波变换,去除噪声,再用偏最小二乘法对多组分同时测定。将该法用于模拟体系及复方甲硝唑注射液体系,结果表明,该法优于偏最小二乘法。     

Orthogonal Wavelets Analysis of Electroanalytical Signals

ABSTRACT

A signal processing technique based on orthogonal wavelet analysis is applied to process various simulated electroanalytical signals. The results indicate that if the scale parameters are selected, the orthogonal wavelet processing method (OWPM) can remove high-frequency noise. Experimental signal was recorded by computer and used to test the OWPM procedure. After processing with OWPM, the processed data was used to analyze the mechanism of the electrode reactions. Processed results of the experimental data are also satisfactory.     

Spline wavelet analysis for voltammetric signals

Application of wavelet multifrequency channel decomposition (WMCD) in electroanalytical chemistry is presented in this paper. A new approach on this digital processing technique, to extract useful information from high noise signals in voltammetry, is described in detail. The method of constructing a model of B-spline WMCD and its application with 2nd, 3rd and 4th order B-spline in the linear scan voltammetry are thoroughly discussed and several results obtained. If the suitable optimal wavelet basis and frequency scale value are selected, the absolute values of the peak relative errors are less than 2% when the signal-to-noise ratio (S/N) is greater than 0.2, and the absolute values of the peak potential relative errors are less than 12% when S/N is greater than 0.3. The processed results of experimental data with high noise are also satisfactory. The whole computation is simple and needs shorter time than other signal processing methods.     

Network analysis based on low-rank method for mining information on integrated data of multi-cancers

The noise problem of cancer sequencing data has been a problem that can’t be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers.     

基于小波变换平滑主成分分析

小波变换具有很强的信号分离能力,很容易把随机噪音从信号中分离出来,从而提高信号的信噪比。本文把小波变换引入到因子分析中,提出了基于小波变换平滑主成分分析,该算法既保留普通主成分分析的正交分解,又具备了小波变换的信号分离能力。模拟数据和实验数据的结果表明,该算法具有从低信噪比的数据中提取出有用信息,并提高信号的信噪比。迭代目标变换因子分析处理实验数据的结果表明,基于小波变换平滑主成分分析的处理结果优     

Studies of spline wavelet least square in processing electrochemical signals

The application of spline wavelet least square (SWLS) in analytical chemistry signals is presented in this paper. As a new technique in signal processing, to extract useful signals from high noise, the influences of different parameters on the results of processing is discussed in details. If the suitable parameters are selected, useful signals can be filtered from the noise of S/N=0.5. The relative error of peak current is less than 3.0%, and that of peak potential is less than 10%. Comparison of this method with wavelet multifrequency channel decomposition (WMCD) and spline least square (SLS) has also been made and it indicates that SWLS can solve some problems in WMCD and SLS. The experimental results are also satisfactory.     

随机共振与仪器分析中弱信号的检测

样品分析过程中，往往得到背景噪声很强的信号，尤其在微量和痕量水平；当信号的信噪比很小时，通常的方法不能满足检测要求，该文基于随机共振理论提出了一种新的弱信号的检测方法并对相关参数进行了讲座主；方法是通过噪声的协调作用，获得系统的“共振”，从而实现弱信号的检测，应用于模拟和实验信号的处理结果证明了方法的可行性。     

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.