分析信号处理方法的比较研究

研究了高效液相色谱－火焰原子吸收光谱联用系统分析信号处理方法，比较了各种处理方法的优缺点，用积分与适应平滑法综合处理色谱峰分析信号，不仅能有效地消除噪声，而且能使分析信号得到加强，以测定镍为例，经积分与适应平滑法综合处理后，检出限改善了２．７倍。     

应用相关分析技术检测噪声中的微弱色谱信号

]本文介绍了相关分析的基本原理,运用相关分析方法从噪声中检测出微弱色谱峰,并考察了保留时间、峰面积计算值与实际值的偏差。实践证明,应用相关分析技术处理色谱信号可使色谱分析检测限量降低一个数量级左右。本文为色谱微量分析提供了新的数据处理方法。     

激光拉曼光谱解谱和处理的方法研究

研究一种拉曼光谱解谱和处理的方法。以化学计量学为基础,信号处理技术为工具,配合计算机算法的数据处理方法。具体为基线校正:对拉曼光谱原始信号进行基于自适应迭代重加权惩罚最小二乘法的基线校正;平滑:对进行完基线校正的拉曼光谱信号进行基于惩罚最小二乘法的平滑;峰检测:对进行完基线校正和平滑的信号进行基于连续小波变换的峰检测。这种基于惩罚最小二乘法的光谱平滑具有快速,可以连续控制平滑度并且可以进行交叉验证得到最客观的平滑值。改善了基于非对称最小二乘法的传统基线校正方法的两个缺陷。同时,基于连续小波变换的峰检测算法可以自动地并且同时考虑峰形和峰高对峰进行检测,最大限度地降低了峰检测假阳性的概率。     

傅立叶变换-主成份分析方法用于二维色谱数据的压缩和重构

结合傅立叶变换（ＦＴ）和主成份分析（ＰＣＡ）技术,实现了二维高效液相色谱数据的压缩和重构,并对不同信噪比的二维色谱测量数据进行了处理,即使对原始数据的压缩比高达２００倍,仍能很好地重构原始数据,当原始数据的信噪比较低时,阈值的选择受大振幅噪音的影响,使压缩比略有下降,但仍然能够保持信号基本不失真。     

强噪声背景中微弱信号检测的初步研究

为了提高信噪比,以往大都设法滤除噪声。根据随机共振理论,本文对用添加噪声来提高测量信号信噪比的方法进行了初步的尝试,并将此方法应用于强噪声背景中微弱信号的检测,得出了一些有意义的初步结构。     

自适应中值滤波用于色谱信号去噪的研究

在分析化学领域中，如何从被干扰的信号中有效去除噪声并恢复有用信号，具有重要的意义。此文从中值滤波基本概念出发，针对经典中值滤波器滑动窗口长度固定对信号去噪的影响，提出了自适应中值滤波器，通过对某元素色谱曲线进行去噪处理，并与移动均值法和小波变换法进行比较表明，此方法不仅能有效地滤除脉冲和高斯噪声，而且使色谱峰的边缘得到良好保护，从而提高了色谱数据分析的精确性。     

小波降噪技术在差分吸收光谱浓度检测中的应用

为了实现对工业气体SO₂的浓度进行监控,基于紫外差分吸收光谱法开发了SO₂在线检测系统。针对系统噪声和Mie散射使吸收光谱叠加带来的误差,本文提出采用小波变换降噪技术代替传统光谱处理方法中的多项式平滑滤波技术来提高检测精度。通过对应用了Symlets、Daubechies、Coiflet和Biorthogonal这4种不同小波函数的实验数据分析和对传统小波阈值选取方式的改进,最终确定了基于rigisure阈值的小波阈值去噪的信号处理方法,并提出一种新的信噪比量来衡量信号处理的效果。这种方法可以快速可靠地处理光谱信号,处理后所得的监测浓度准确度基本控制在1.5%以内。在实验室环境下和工业现场环境下的大量实验结果表明本方法能有效的减小噪声对SO₂浓度监测带来的影响。     

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.     

自适应形态滤波器用于色谱信号的处理

建立了一种能自适应地调整尺度的形态滤波器对色谱信号中混杂的离散冲型噪声加以滤除的方法。结果表明,自适应形态滤波器在滤除脉冲型噪声进,比目前文献报道的中值滤波方法呈现出更好的效果,它能保存高度完整的信号信息,是处理色谱信号脉冲型噪声的有力工具。     

计算机快速富里叶变换应用于气相色谱痕量分析

将计算机快速富里叶变换(FFT)应用于高分辨率气相色谱痕量分析中，以增加色谱峰的S／N比(信号与噪声比)，提高分析精度，对S／N非常低的色谱峰采用的这种计算机快速富里叶变换平滑新方法，可以提取出几乎被噪声淹没的色谱信号，使S／N比提高12倍以上，而色谱峰面积误差非常小。     

Coil Combination of Multichannel Single Voxel Magnetic Resonance Spectroscopy with Repeatedly Sampled In Vivo Data

Magnetic resonance spectroscopy (MRS), as a noninvasive method for molecular structure determination and metabolite detection, has grown into a significant tool in clinical applications. However, the relatively low signal-to-noise ratio (SNR) limits its further development. Although the multichannel coil and repeated sampling are commonly used to alleviate this problem, there is still potential room for promotion. One possible improvement way is combining these two acquisition methods so that the complementary of them can be well utilized. In this paper, a novel coil-combination method, average smoothing singular value decomposition, is proposed to further improve the SNR by introducing repeatedly sampled signals into multichannel coil combination. Specifically, the sensitivity matrix of each sampling was pretreated by whitened singular value decomposition (WSVD), then the smoothing was performed along the repeated samplings’ dimension. By comparing with three existing popular methods, Brown, WSVD, and generalized least squares, the proposed method showed better performance in one phantom and 20 in vivo spectra.     

A new single-well potential stochastic resonance algorithm to detect the weak signal

Based on the stochastic resonance theory, a new single-well potential stochastic resonance algorithm (SSR) to improve the signal-to-noise ratio (SNR) is presented. In the new algorithm, stochastic resonance takes place in a single-well potential driven only by the noise. The effect on the proposed algorithm is discussed. By using simulated and experimental data sets, it is proven that the signal-to-noise ratio (SNR) of the weak signal can be greatly enhanced by this method. The new single-well potential stochastic resonance algorithm (SSR) may be a promising tool to extend instrumental linear range and to improve the accuracy of trace analysis. The research enlarges the application scope of single-well potential to nonlinear signal processing.     

Application of empirical mode decomposition in the field of polymer physics

Noisy data has always been a problem to the experimental community. Effective removal of noise from data is important for better understanding and interpretation of experimental results. Over the years, several methods have evolved for filtering the noise present in the data. Fast Fourier transform (FFT) based filters are widely used because they provide precise information about the frequency content of the experimental data, which is used for filtering of noise. However, FFT assumes that the experimental data is stationary. This means that: (i) the deterministic part of the experimental data obtained from a system is at steady state without any transients and has frequency components which do not vary with respect to time and (ii) noise corrupting the experimental data is wide sense stationary, that is, mean and variance of the noise does not statistically vary with respect to time. Several approaches, for example, short time Fourier transform (STFT) and wavelet transform‐based filters, have been developed to handle transient data corrupted with nonstationary noise (mean and variance of noise varies with respect to time) data. Both these approaches provide time and frequency information about the data (time at which a particular frequency is present in the signal). However, these filtering approaches have the following drawbacks: (i) STFT requires identification of an optimal window length within which the data is stationary, which is difficult and (ii) there are theoretical limits on simultaneous time and frequency resolution. Hence, filtering of noise is compromised. Recently, empirical mode decomposition (EMD) has been used in several applications to decompose a given nonstationary data segment into several characteristic oscillatory components called intrinsic mode functions (IMFs). Fourier transform of these IMFs identifies the frequency content in the signal, which can be used for removal of noisy IMFs and reconstruction of the filtered signal. In this work, we propose an algorithm for effective filtering of noise using an EMD‐based FFT approach for applications in polymer physics. The advantages of the proposed approach are: (i) it uses the precise frequency information provided by the FFT and, therefore, efficiently filters a wide variety of noise and (ii) the EMD approach can effectively obtain IMFs from both nonstationary as well as nonlinear experimental data. The utility of the proposed approach is illustrated using an analytical model and also through two typical laboratory experiments in polymer physics wherein the material response is nonstationary; standard filtering approaches are often inappropriate in such cases. © 2010 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2011     

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     

THE DETERMINATION OF PERIODICITIES IN SHORT-TERM TIME-SERIES DATA IN THE PRESENCE OF HIGH FREQUENCY NOISE AND LONG-TERM TREND

The presence of periodicities in biological data may often be obscured by superposition of a background trend or high frequency oscillations. A method of least squares curve fitting was utilized to remove the trend in signal level. Subsequent removal of higher frequency noise utilizing smoothing convolution functions allows the determination of rhythmic components. A new “noise” convolution function gives a measure of the noise in data containing both signal and noise and allows the estimation of a limit above which an observed oscillation is considered significant. The effects of treating data with these convolution functions are discussed with reference to amplitude-frequency response curves. The results of data analysis utilizing convolution functions are compared with results obtained utilizing a moving interval Fourier technique (Blume, 1965). The convolution analysis have the advantages that they are easily programmed and rapidly calculated by programmable calculators, give results relatively insensitive to the length of convolution interval, and allow estimation of the variation in period and amplitude of the rhythm investigated.     

光谱滤波法提高激光诱导击穿光谱对蔬菜中元素Pb的检测精度

激光诱导击穿光谱(Laser induced breakdown spectroscopy, LIBS)原始光谱中包含较多噪声信号, 为探究不同滤波方法对LIBS光谱预处理的影响, 本研究以实验室Pb污染处理的蔬菜为研究对象, 采集波长范围在400.45~410.98 nm的LIBS谱线信息, 分别利用相邻平均(Adjacent averaging)、Savitzky-Golay(S-G)滤波器、快速傅里叶变换(Fast Fourier transformation, FFT)对采集的LIBS光谱进行平滑、去噪, 并结合偏最小二乘法(PLS)定量分析模型对光谱处理效果进行评价.结果表明, S-G平滑效果最优, 当S-G滤波器窗口宽度为15, 拟合阶次为3时, PLS定量模型效果最佳, 其验证集均方根误差(RMSEP)为0.26、平均相对误差(ARE)为3.7%.结果表明, 选择适合的滤波方法有助于提高LIBS光谱质量以及检测模型的精度.     

小波滤噪用于示波计时电位信号处理的研究

根据信号（Ｓ）和噪声（Ｎ）在小波变换下表现出的截然不同的性质,提出了一种新的滤噪方法,研究了噪音在不同细节的性质、滤噪阈值的确定及平滑与滤噪的特点。对不同Ｓ／Ｎ的示波计时电位信号进行了实验结果表明;小波滤噪在大大提高Ｓ／Ｎ的同时,信号强度基本不损失,优于传统的滤噪方法。     

GC-MS Assisted with Chemometric Methods Applied for Investigation of Migration Behavior of Phthalate Plasticizers in Fatty Foods Simulant

PVC films with different thickness and different contents of 9 phthalate plasticizers (PAEs) were manufactured according to a uniform design method. The migration contents of PAEs in isooctane, which was set as a fatty foods simulant, were first determined using GC-MS analysis. It was found that the chromatographic peaks of diisononyl ortho-phthalate (DINP) and diisodecyl ortho-phthalate (DIDP) very considerably overlapped, with a lower signal to noise ratio (SNR). For resolution of the noisy overlapped peaks, twice continuous wavelet transform (CWT) was adopted. Then, the migration models of PAEs were obtained by the non-negative least squares method (NNLS). The results show that the noisy overlapped peaks were successfully resolved with increasing the SNR 10 times; the migration ratio of the 9 PAEs at 70 °C are located with the range 0.0556–0.3383 %; the migration amount of a specific plasticizer is proportional to its content and some coexisting plasticizers; temperature is a significant impact factor for the migration process. The proposed chemometric-assisted GC-MS analysis provides a new approach for investigation of this complex process.     

新极谱法实验数据的卡尔曼滤波

本文提出的"1:n+2"新卡尔曼滤波器,具有算法简单、占用计算机内存少、运行速度快等优点;同时又能有效地消除信号中的随机噪音和电容电流,讨论了"1:3"滤波器的结构性能,采用Cd⁺离子的阶梯扫描伏安法的实测数据来分析滤波结果的最优性,用于处理TI⁺、pb²⁺、In³⁺等离子的实测数据,均获得理想的滤波效果。