光谱分析图及应用

叶绿素a和叶绿素b的颜色，学生往往是通过死记硬背的方式记忆的，其实只要学会光谱分析，记忆起来非常容易，而且有助于对习题的研究．

药学科技论文中典型图谱的正确表达

针对药学专业科技论文中经常出现的图谱如:差示扫描量热分析图、X-射线衍射分析图、红外光谱分析图、高效液相色谱图谱中横、纵坐标的量符号和单位表达混乱的现象,以实例分析的方式进行阐述,并提出正确规范的写法。

Speed-up for N-FINDR algorithm

N-FINDR is a very popular algorithm of endmember (EM) extraction for its automated property and high efficiency. Unfortunately, innumerable volume calculation, initial random selection of EMs and blind searching for EMs lead to low speed of the algorithm and limit the applications of the algorithm. So in this paper two measures are proposed to speed up the algorithm. One of the measures is substituting distance calculation for volume calculation. Thus the avoidance of volume calculation greatly decreases the computational cost. The other measure is resorting dataset in terms of pixel purity likelihood based on pixel purity index (PPI) concept. Then, initial EMs can be selected well-founded and a fast searching for EMs is achieved. Numerical experiments show that the two measures speed up the original algorithm hundreds of times as the number of EMs is more than ten.

Muscle fatigue in women with primary biliary cirrhosis:Spectral analysis of surface electromyography

AIM： To evaluate the myoelectric manifestations of peripheral fatigability in patients with primary biliary cirrhosis in comparison to healthy subjects. METHODS： Sixteen women with primary biliary cirrhosis without comorbidity and 13 healthy women matched for age and body mass index （BMI） completed the selfreported questionnaire fatigue impact scale. All subjects underwent surface electromyography assessment of peripheral fatigability. Anterior tibial muscle isometric voluntary contraction was executed for 20 s at 80% of maximal voluntary isometric contraction. During the exercise electromyographic signal series were recorded and root mean square （expression of central drive） as well as mean and median of electromyographic signal frequency spectrum （estimates of muscle fatigability） were com- puted. Each subject executed the trial two times. EMG parameters were normalized, then linear regression was applied and slopes were calculated. RESULTS： Seven patients were fatigued （median fatigue impact scale score： 38, range： 26-66） and 9 were not fatigued （median fatigue impact scale score： 7, range： 0-17）. The maximal voluntary isometric contraction was similar in patients （82, 54-115 N） and controls （87, 74-101 N）, and in patients with high （81, 54-115 N） and low fatigue impact scale scores （86, 65-106 N）. Root mean square as well as mean and median of frequency spectrum slopes were compared with the Mann-Whitney U test, and no significant difference was found between fatigued and non-fatigued patients and controls. CONCLUSION： No instrumental evidence of peripheral fatigability can be found in women with primary biliary cirrhosis but no comorbidity, suggesting that fatigue in such patients may be of central origin.

Analysis of Spectral Characteristics Based on Optical Remote Sensing and SAR Image Fusion

Because of cloudy and rainy weather in south China, optical remote sens-ing images often can＇t be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model and effect of ENVISAT/SAR and HJ-1A satel ite multispectral remote sensing images. Based on the ARSIS strat-egy, using the wavelet transform and the Interaction between the Band Structure Model （IBSM）, the research progressed the ENVISAT satel ite SAR and the HJ-1A satel ite CCD images wavelet decomposition, and low/high frequency coefficient re-construction, and obtained the fusion images through the inverse wavelet transform. In the light of low and high-frequency images have different characteristics in differ-ent areas, different fusion rules which can enhance the integration process of self-adaptive were taken, with comparisons with the PCA transformation, IHS transfor-mation and other traditional methods by subjective and the corresponding quantita-tive evaluation. Furthermore, the research extracted the bands and NDVI values around the fusion with GPS samples, analyzed and explained the fusion effect. The results showed that the spectral distortion of wavelet fusion, IHS transform, PCA transform images was 0.101 6, 0.326 1 and 1.277 2, respectively and entropy was 14.701 5, 11.899 3 and 13.229 3, respectively, the wavelet fusion is the highest. The method of wavelet maintained good spectral capability, and visual effects while improved the spatial resolution, the information interpretation effect was much better than other two methods.

Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation

The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size.We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm.For each independent component we added two constraint conditions:non-negativity and constant sum.We use priority weighting by higher-order statistics,and then the spectral angle match method to overcome the order nondeterminacy.By these steps,endmembers can be extracted and abundance quantified simultaneously.To examine the coverage of a real oil spill and correct our estimate,a simulation experiment and a real experiment were designed using the algorithm described above.The result indicated that,for the simulation data,the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6.We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011.The total oil spill area was 0.224 km^2,and the oil spill rate was 22.89%.The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates.It also allows the accurate estimation of the oil spill area.

AN EVEN COMPONENT BASED FACE RECOGNITION METHOD

This paper presents a novel face recognition algorithm. To provide additional variations to training data set, even-odd decomposition is adopted, and only the even components (half-even face images) are used for further processing. To tackle with shift-variant problem,Fourier transform is applied to half-even face images. To reduce the dimension of an image,PCA (Principle Component Analysis) features are extracted from the amplitude spectrum of half-even face images. Finally, nearest neighbor classifier is employed for the task of classification. Experimental results on ORL database show that the proposed method outperforms in terms of accuracy the conventional eigenface method which applies PCA on original images and the eigenface method which uses both the original images and their mirror images as training set.

Phase Spectral Analysis of EEG Signals

A new method of phase spectral analysis of EEG is proposed for the comparative analysis of phase spectra between normal EEG and epileptic EEG signals based on the wavelet decomposition technique. By using multiscale wavelet decomposition,the original EEGs are mapped to an orthogonal wavelet space,such that the variations of phase can be observed at multiscale. It is found that the phase (and phase difference) spectra of normal EEGs are distinct from that of epileptic EEGs. That is the variations of phase (and phase difference) of normal EEGs have a distinct periodic pattern with the electrical activity proceeds in the brain,but do not the epileptic EEGs. For epileptic EEGs,only at those transient points,the phase variations are obvious. In order to verify these results with the observational data,the phase variations of EEGs in principal component space are observed and found that,the features of phase spectra is in correspondence with that the wavelet space. These results make it possible to view the behavior of EEG rhythms as a dynamic spectrum.

DIMENSIONALITY REDUCTION FOR HYPERSPECTRAL IMAGERY BASED ON FASTICA

The high dimensions of hyperspectral imagery have caused burden for further processing. A new Fast Independent Component Analysis (FastICA) approach to dimensionality reduction for hyperspectral imagery is presented. The virtual dimensionality is introduced to determine the number of dimensions needed to be preserved. Since there is no prioritization among independent components generated by the FastICA,the mixing matrix of FastICA is initialized by endmembers,which were extracted by using unsupervised maximum distance method. Minimum Noise Fraction (MNF) is used for preprocessing of original data,which can reduce the computational complexity of FastICA significantly. Finally,FastICA is performed on the selected principal components acquired by MNF to generate the expected independent components in accordance with the order of endmembers. Experimental results demonstrate that the proposed method outperforms second-order statistics-based transforms such as principle components analysis.

Elemental Analysis of Soil Phosphorus Neighborhoods Using SEM, Spectral Mapping and GIS

The utility of GIS （geographic information system） methods and spatial statistical analysis on spectral maps of sediment samples were examined. Detailed elemental maps are often constructed using energy dispersive X-ray techniques and SEM （scanning electron microscopy）. The elemental neighborhood associations of a single element, P （phosphorus）, were quantified at a magnification of 3,000 ×. For each of the 170,000 pixels on the images which displayed a strong P concentration, neighborhoods from 0.1μm^2 to 12 μm^2 were examined for associated elemental concentrations. PCA （principal component analysis） revealed two significant neighborhood types associated with P in samples of pH 4, and three neighborhood types at pH 8. These neighborhoods corresponded to Mg-P associations commonly found to be chemically prevalent in river sediments impacted by agricultural operations. Discriminant analysis showed that the greatest accuracy in predicting sample pH could be achieved by using a neighborhood size of 12 ~m2. Potassium at relatively large neighborhood sizes was the element most significant in predicting pH. While many of the chemical associations in close proximity to P could be predicted and explained through mineral solubility, spatial analysis provided some interesting insights into the structure of the samples. Results also indicted differences in the spatial scale associated with different processes.

Catalytic performance and kinetics of Au/γ-Al_2O_3 catalysts for low-temperature combustion of light alcohols

Au/γ-Al2O3 catalysts were prepared by deposition-precipitation method for the catalytic combustion of low concentration alcohol streams(methanol,ethanol,iso-propanol and n-propanol).The catalysts were characterized by X-ray photoelectron spectroscopy(XPS),X-ray diffractometry(XRD) and energy dispersive X-ray micro analysis(EDS) techniques.The XPS results showed that there was only Au0 on the surface of catalysts.The XRD patterns showed that Au was presumably highly dispersed over γ-Al2O3.The temperatures for complete conversion of methanol,ethanol,iso-propanol and n-propanol with concentration of 2.0 g/m3 were 60,155,170 and 137 ℃,respectively,but they were completely mineralized into CO2 and H2O at 60,220,260 and 217 ℃ respectively over the optimized catalyst.The activity of the catalyst was stable in 130 h.The kinetics for the catalytic methanol elimination followed quasi-first order reaction expressed as r=0.652 8c0+0.084 2.The value of apparent activation energy is 54.7 kJ/mol in the range of reaction temperature.