Coupling subband decomposition and independent component regression for quantitative NIR spectroscopy

In this paper, a new method is proposed for coupling digital bandpass filtering with independent component regression (ICR) to improve the quality of the raw absorbance spectra in quantitative NIR spectroscopy. The proposed model, referred to as SDICR, is based on a subband decomposition independent component analysis (SDICA) model coupled with ICR regression. The SDICA is used to select the optimal parameters of the digital bandpass filter and to determine the most independent subcomponents of the original sources, so the standard ICA methods can be used. The efficiency of the proposed model is validated using mixtures composed of glucose, urea and triacetin in a phosphate buffer solution in a non-controlled environment. The proposed model decreases the standard error of prediction (SEP) from 29.1 mg/dL for ICR to only 18.6547 mg/dL using 10 subbands.     

子类独立分量分析在声目标识别中的应用

针对已有的特征提取方法在多目标识别中的不足,提出了基于高阶统计分析的独立分量分析法特征提取方法,通过对多种目标的声音信号进行子类特征提取,并应用决策导向无环图支持向量机实现对多目标的有效分类。结果表明该算法在通过声音信号对多目标识别上,具有很好的应用前景。     

Analysis of unsupervised learning techniques for face recognition

Face recognition has always been a potential research area because of its demand for reliable identification of a human being especially in government and commercial sectors, such as security systems, criminal identification, border control, etc. where a large number of people interact with each other and/or with the system. The last two decades have witnessed many supervised and unsupervised learning techniques proposed by different researchers for the face recognition system. Principal component analysis (PCA), self‐organizing map (SOM), and independent component analysis (ICA) are the most widely used unsupervised learning techniques reported by research community. This article presents an analysis and comparison of these techniques. The article also includes two SOM processing methods global SOM (GSOM) and local SOM (LSOM) for performance evaluation along with PCA and ICA. We have used two different databases for our analysis. The simulation result establishes the supremacy of GSOM in general among all the unsupervised techniques. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 261–267, 2010     

猕猴桃挤压损伤高光谱快速检测研究

目的 探究猕猴桃挤压损伤较优的快速无损判别方法。方法 利用高光谱成像系统获得所有猕猴桃的高光谱图像,并提取猕猴桃损伤区域以及完好无损区域的光谱反射率;运用多元散射校正方法对原始反射光谱进行预处理,并运用主成分分析对光谱数据降维;比较并分析Fisher判别分析方法以及简化的K最近邻(Simplified K Nearest Neighbor,SKNN)模式识别方法对猕猴桃挤压损伤的判别效果。结果 在710~850 nm和960~1 030 nm这2个波段内,猕猴桃损伤区域的平均光谱反射率与完好无损区域的平均光谱反射率存在较明显差异;采用主成分分析从256个全波段中筛选了前5个主成分作为新变量,识别模型的检测效率得到了提升;构建的SKNN和Fisher模型对预测集中样本的正确识别率均为93.3%,从SKNN识别模型的混淆矩阵中得出,预测集中仅有2个样本出现误判,并且SKNN模型对校正集中样本的正确识别率高于Fisher模型。结论 在判别猕猴桃挤压损伤时,SKNN识别模型具有相对较好的判别效果。     

纳米TiO2半导体溶胶的光生物学效应

通过溶胶-凝胶法制备了纳米TiO2半导体溶胶材料(nano-TiO2 semiconductor sol,NTSS),并测定了抗菌性能;以黄瓜为研究对象,初步探讨了NTSS在防治植物细菌性/真菌性病害及增加叶片光合色素含量方面的光生物学效应.试验结果表明,溶胶材料中TiO2颗粒的结晶型为锐钛矿型,平均粒径为30.6nm;TiO2溶胶材料可在叶片等固体表面形成连续、稳定的抗菌薄膜,具有很强的光氧化活性,抗菌率达到99.9%;通过人工接种病原菌试验及田间病害调查试验发现,黄瓜喷施一定浓度的NTSS后,可显著降低叶片病斑面积、发病率及病情指数,对黄瓜细菌性角斑病/霜霉病的发生具有抑制效果;测定叶片光合色素含量发现,NTSS对叶绿素(Chl)及类胡萝卜素(Car)的生成具有促进作用.     

Application of independent component analysis with mixture density model to localize brain alpha activity in fMRI and EEG

Independent component analysis (ICA) is an approach to solve the blind source separation problem. In the original and extended versions of ICA, nonlinearity functions are fixed to have specific density forms such as super‐Gaussian or sub‐Gaussian, thereby limiting their performance when sources with different classes of densities are mixed in multichannel data. In this article, we have incorporated a mixture density model such that no assumption about source density would be required. We show that this leads to better source separation due to increased flexibility in handling source‐ densities with flexible parametric nonlinearity. The algorithm was validated through simulation studies and its performance was compared to other versions of ICA. The modified mixture density ICA was then applied to functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data to localize independent sources of alpha activity in the human brain. A good spatial correlation was found in the spatial distribution of alpha sources derived independently from fMRI and EEG, suggesting that spontaneous alpha rhythm can be imaged by fMRI using ICA without concurrent acquisition of EEG. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 170–180, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20021     

ICA的近红外光谱分析软件的研制

研制了基于独立分量分析方法的近红外光谱分析软件.该软件包括光谱解析、光谱建模和未知成分含量测定三个模块,使用了小波分析、ICA和BP神经网络等数据处理方法.将这种软件用于实测的玉米近红外光谱分析,所得结果令人满意.使用LabVIEW与MATLAB软件混合编程,充分利用了各软件的优点,不仅程序简单,而且界面友好.     

Independent Component Analysis–Based Feature Extraction Technique for Defect Classification Applied for Pulsed Eddy Current NDE

With the development of nondestructive detection, the emerging testing techniques provide new challenges to signal analysis and interpretation approach applied to the inspection evaluation. Some researchers have developed the methods that focus on feature analysis of detected signals. This article presents a new feature analysis by the Independent Component Analysis (ICA) approach to evaluate the defects tested by the pulsed eddy current (PEC) technique. ICA is a high-order statistics technique used to separate multi-unknown sources, which has been successfully applied to facial image identification and separation of the components of 1D signal. In this article, the ICA approach is utilized to project the response signals of various defects into the independent components (ICs) feature subspace by signal representation model. Dependent on the selected ICs, each defect is represented by different projected coefficients, which are proposed to discriminate and classify the defects that belong to three categories. The improved ICA model is proposed to improve the classification of two similar categories of single defects: metal loss and subsurface defects. The evaluation using the series of experimental data has validated the classification of single defects and the defects with lift-off effect by our ICA approach. The comparison with Principal component analysis (PCA)–based approach further verified the better performance of the ICA-based model.     

Multiscale ICA for fundus image analysis

Independent component analysis is a technique used for separation of statistically independent sources. It can estimate unknown sources from a mixture of sources without any prior knowledge about them. The sources should be non‐Gaussian and independent with each other. In this work, multiscale ICA is proposed for medical images (fundus images, MRI Images). The data matrix is formed by considering the higher sub‐bands of multiscale decompositions. Performance of multiscale ICA is evaluated and compared with the ICA algorithms using simulated signals and different medical images using Amari performance index and Comon test values. Results show that API and Comon test values are less for multiscale ICA for simulated signals. In case of pathological images, the features are separated correctly by multiscale ICA. Multiscale ICA performs better than simple ICA for separation and detection of independent components from medical images (fundus images), such as blood vessels and artifacts. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 327–337, 2013     

Geometric transformation based independent component analysis for mixed image separation

Abstract

An independent component analysis (ICA) method for image separation by geometric transformation of a scatter diagram is proposed. Geometric transformation and normalization are used to project mixed image signals to independent component space. This method includes four procedures: data correction, whitening, geometric rotation, and slant compensation. Several synthetic mixed image and real applications are used to evaluate the performance of the proposed method. From experimental results, mixed images are separated accurately by the proposed method.