A subspace time-domain algorithm for automated NMR spectral normalization

Recently, two methods have been proposed for quantitatively comparing NMR spectra of control and treated samples, in order to examine the possible occurring variations in cell metabolism and/or structure in response to numerous physical, chemical, and biological agents. These methods are the maximum superposition normalization algorithm (MaSNAl) and the minimum rank normalization algorithm (MiRaNAl). In this paper a new subspace-based time-domain normalization algorithm, denoted by SuTdNAl (subspace time-domain normalization algorithm), is presented. By the determination of the intersection of the column spaces of two Hankel matrices, the common signal poles and further on the components having proportionally varying amplitudes are detected. The method has the advantage that it is computationally less intensive than the MaSNAl and the MiRaNAl. Furthermore, no approximate estimate of the normalization factor is required. The algorithm was tested by Monte Carlo simulations on a set of simulation signals. It was shown that the SuTdNAl has a statistical performance similar to that of the MiRaNAl, which itself is an improvement over the MaSNAl. Furthermore, two samples of known contents are compared with the MiRaNAl, the SuTdNAl, and an older method using a standard. Finally, the SuTdNAl is tested on a realistic simulation example derived from an in vitro measurement on cells.     

基于热红外发射光谱的岩石SiO2定量反演模型研究

利用野外测量21种地表岩石的高光谱发射率数据,进行包络线去除和归一化处理后,运用逐步回归法进行波段选择,分析了SiO2含量与包络线去除后特征波段发射率的定量关系。在此基础上,通过比较12种SiO2光谱指数,建立了定量反演SiO2含量的最优模型。结果表明:构建的SiO2光谱指数能有效预测SiO2含量,其中基于11.18和12.36μm波段的归一化SiO2光谱指数(normalization silicon dioxide index,NSDI)的预测能力最高;与回归模型相比,光谱指数更简单、实用;研究结果在岩石种类鉴定及SiO2含量的高精度提取方面有重要应用价值。     

Improving iris recognition performance via multi-instance fusion at the score level

Fusion of multiple instances within a modality for biometric verification performance improvement has received considerable attention. In this letter, we present an iris recognition method based on multiinstance fusion, which combines the left and right irises of an individual at the matching score level. When fusing, a novel fusion strategy using minimax probability machine （MPM） is applied to generate a fused score for the final decision. The experimental results on CASIA and UBIRIS databases show that the proposed method can bring obvious performance improvement compared with the single-instance method. The comparison among different fusion strategies demonstrates the superiority of the fusion strategy based on MPM.     

基于叶片反射光谱特征的银杏健康量化评价技术

准确地诊断树木健康状况是城市森林树木管理工作的基础,也是目前生产中急需的技术。通过土壤和植物养分分析诊断树木健康可靠性差,通过形态特征调查诊断树木健康费时、费力,如何快速、准确、无损地诊断树木健康已经成为城市树木健康管理的重要技术瓶颈。以北京市银杏为研究对象,对基于叶片反射光谱特征的树木健康诊断技术进行了研究。通过13个外貌形态特征聚类将树木健康划分成健康木、亚健康木、一般健康木和不健康木4个等级,不同健康等级树木叶片色素含量差异极其显著(p<0.001) ,因叶绿素含量与光谱反射率之间存在相关关系,所以采用叶片反射光谱特征判断树木健康状况是可行的。采用因子分析法,通过15个叶片反射光谱指标构建了能够综合反映叶片反射光谱特征的绿度指数、色素指数、三边指数。不同健康等级间叶片反射光谱指标以及三个反射光谱指数均有极显著差异(p<0.001)。所以,采用三个反射光谱指数构建了银杏健康评价的多元二次模型,经检验模型预测精度达到79%,可以作为银杏树木健康快速诊断。选取的光谱指标较为全面,方法简洁,并通过综合分析,确定了不同健康等级树木核心形态指标以及叶片的绿度指数、色素指数、三边指数等综合得分以及得分范围,为生产中直接使用该方法诊断银杏健康状况提供了标准。     

汉语发音质量评估的实验研究

研究了发音评估系统中通用的置信度测度——后验概率算法,针对它存在的不足,提出了两种改进方案。首先,为了降低计算复杂度,传统算法采用了求最大值算法代替求和算法,在被测发音偏离目标音素集的情况下,这会严重降低后验概率的计算精度,本文提出基于扩展的音素混淆网络的后验概率算法。其次,为使置信度能评估不同语音段长的发音质量优劣,传统算法采用了后验概率的段长规整策略,研究分析发现声学似然值与时间的关系更为紧密,所以本文提出了基于声学似然值的时间规整方案。试验结果表明:与传统算法相比,采用改进的置信度算法能使平均打分错误率相对降低35%左右,有效地改善了计算机辅助语言学习系统的性能。      

Brightness-normalized Partial Least Squares Regression for hyperspectral data

Developed in the field of chemometrics, Partial Least Squares Regression (PLSR) has become an established technique in vegetation remote sensing. PLSR was primarily designed for laboratory analysis of prepared material samples. Under field conditions in vegetation remote sensing, the performance of the technique may be negatively affected by differences in brightness due to amount and orientation of plant tissues in canopies or the observing conditions. To minimize these effects, we introduced brightness normalization to the PLSR approach and tested whether this modification improves the performance under changing canopy and observing conditions. This test was carried out using high-fidelity spectral data (400–2510 nm) to model observed leaf chemistry. The spectral data was combined with a canopy radiative transfer model to simulate effects of varying canopy structure and viewing geometry. Brightness normalization enhanced the performance of PLSR by dampening the effects of canopy shade, thus providing a significant improvement in predictions of leaf chemistry (up to 3.6% additional explained variance in validation) compared to conventional PLSR. Little improvement was made on effects due to variable leaf area index, while minor improvement (mostly not significant) was observed for effects of variable viewing geometry. In general, brightness normalization increased the stability of model fits and regression coefficients for all canopy scenarios. Brightness-normalized PLSR is thus a promising approach for application on airborne and space-based imaging spectrometer data.     

Intensity normalization of additive and multiplicative spatially multiplexed patterns with n encoded phases

An efficient, robust and user-free intensity normalization method for patterns with n frequency-multiplexed phases of both additive and multiplicative types is proposed. For this, the parameter estimation approach by using the least-squares method is applied. The theoretical principles are given and the good performance of the proposal is verified by computer simulation and experimental results. Because the proposed method has good features such as robustness, simplicity, fast and user-free execution, it could be implemented in a wide variety of applications. This contribution motivates future generalization in both phase demodulation algorithms and experimental setups to exploit the benefits of additive/multiplicative patterns with multiple phases.     

Multimodal biometric authentication based on score level fusion using support vector machine

Fusion of multiple biometrics for human authentication performance improvement has received considerable attention. This paper presents a novel multimodal biometric authentication method integrating face and iris based on score level fusion. For score level fusion, support vector machine (SVM) based fusion rule is applied to combine two matching scores, respectively from Laplacianface based face verifier and phase information based iris verifier, to generate a single scalar score which is used to make the final decision. Experimental results show that the performance of the proposed method can bring obvious improvement comparing to the unimodal biometric identification methods and the previous fused face-iris methods.     

Assessment of scanner performance and normalization of estimated relaxation rate values

The purpose of this study was to develop and test a method for the assessment of Magnetic Resonance (MR) scanner performance suitable for routine brain MR studies and for normalization of calculated relaxation times. We hypothesized that regular monitoring of machine performance changes could provide a helpful normalization tool for calculating tissue MR parameters, thus contributing to support their use for longitudinal and comparative studies of both normal and diseased tissues.The method is based on the acquisition of phantom images during routine brain studies with standard spin-echo sequences. MR phantom and brain tissue parameters were used to assess the influence of machine related changes on relaxation parameter estimates. Experimental results showed that scanner performance may affect relaxation rate estimates. Phantom and in vivo results indicate that the correction method yields a reduction in variability of estimated phantom R1 values up to 29% and of R1 for different brain structures up to 17%. These findings support the validity of using brain coil phantoms for routine system monitoring and correction of tissue relaxation rates.     

Ar doping of CH<Subscript>4</Subscript> plasmas for carbon film deposition

In the classical box-counting method a partition sum is used for calculation of multifractal spectra. It is shown that differently normalized partition sums yield multifractal spectra that are not equivalent. Therefore, their direct comparison is not possible unless convenient transformations are found. This fact is illustrated with conventional and unconventional multifractal spectra of the dyadic Cantor cluster. So far the crucial influence of normalization procedure on multifractal spectra of the box-counting method has not been recognized. To remove this deficiency a generalized partition sum has been proposed. Common partition sums of the box-counting method are included in the generalized one as special normalization events. The multifractal spectra of the generalized partition sum are classified according to the normalization parameters. Transformation relations enabling mutual transitions and correct comparison among the generalized spectra of different normalization are also presented.     

红外光谱技术的三文鱼肉假冒鉴别

国内三文鱼市场鱼龙混杂,假冒问题严重,但鉴别方法有限。采用红外光谱技术结合偏最小二乘判别分析法(PLS-DA)研究了黑龙江大马哈鱼、淡水虹鳟、智利太平洋鲑三种鱼肉对挪威三文鱼的冒充问题。采用FITR光谱仪和KBr压片法采集四种肉类的原始光谱,并对原始光谱分别进行多元散射校正(MSC)、Savitzky-Golay平滑、一阶导数(first derivative)、标准正则变换(SNV)、峰面积归一化(peak area normalization)五种预处理来消除噪声等干扰因素并确定最佳预处理方法。为建立PLS-DA鉴别模型,将四种鱼肉的光谱分别赋予-3,-1,1和3四个参考分值,建模后通过预测检测集鱼肉得分来检验模型准确性。结果表明：采用峰面积归一化法时,PLS-DA检测模型的效果最好,校正集和交叉验证集的决定系数分别为0.97和0.95。RMSEC和RMSECV分别为0.37和0.52。该模型能显著区分四种鱼肉、检测集的预测分值分别聚集在各自的参考分值附近,在阈值为±1的判别条件下预测准确度为96%。同时采用马氏距离法进一步对四种鱼肉的光谱进行分析,发现相互之间差异明显,其中挪威三文鱼与其品种差别最大的淡水虹鳟距离最大,与其比较接近的智利太平洋鲑的距离最小,红外光谱信息能够反映不同鱼肉的品种、生活环境等差异。因此,采用红外光谱技术结合PLS-DA法能够准确的鉴别出其他鱼肉对挪威三文鱼的冒充问题,同时对其他肉类检测有一定借鉴意义。     

Zurek-Kibble domain structures: the dynamics of spontaneous vortex formation in annular Josephson tunnel junctions

Phase transitions create a domain structure with defects, which has been argued by Zurek and Kibble (ZK) to depend in a characteristic way on the quench rate. We present an experiment to measure the ZK scaling exponent sigma. Using long symmetric Josephson tunnel junctions, for which the predicted index is sigma=0.25, we find sigma=0.27+/-0.05. Further, we agree with the ZK prediction for the overall normalization.     

Iterative algorithm for spatial and intensity normalization of MEMRI images. Application to tract-tracing of rat olfactory pathways

Manganese (Mn)-enhanced magnetic resonance imaging (MEMRI) is an emerging technique for visualizing neuronal pathways and mapping brain activity modulation in animal models. Spatial and intensity normalizations of MEMRI images acquired from different subjects are crucial steps as they can influence the results of groupwise analysis. However, no commonly accepted procedure has yet emerged. Here, a normalization method is proposed that performs both spatial and intensity normalizations in a single iterative process without the arbitrary choice of a reference image. Spatial and intensity normalizations benefit from this iterative process. On one hand, spatial normalization increases the accuracy of region of interest (ROI) positioning for intensity normalization. On the other hand, improving the intensity normalization of the different MEMRI images leads to a better-averaged target on which the images are spatially registered. After automatic fast brain segmentation and optimization of the normalization process, this algorithm revealed the presence of Mn up to the posterior entorhinal cortex in a tract-tracing experiment on rat olfactory pathways. Quantitative comparison of registration algorithms showed that a rigid model with anisotropic scaling is the best deformation model for intersubject registration of three-dimensional MEMRI images. Furthermore, intensity normalization errors may occur if the ROI chosen for intensity normalization intersects regions where Mn concentration differs between experimental groups. Our study suggests that cross-comparing Mn-injected animals against a Mn-free group may provide a control to avoid bias introduced by intensity normalization quality. It is essential to optimize spatial and intensity normalization as the detectability of local between-group variations in Mn concentration is directly tied to normalization quality.     

The dynamic range normalization of dualband (3–5um and 8–12um) thermal image

A method of nonlinear dynamic range normalization of dual waveband thermal image has been developed which can limit the saturation phenomenon of thermal image and display detail information. Meanwhile it can acquire a dualband single enhanced thermal image with better system performance as well.     

黄土高原中部秋季干湿的年际和年代际环流异常特征及与海温的多尺度相关性研究

黄土高原地区作为气候敏感区和生态脆弱区地表干 湿状况的年际和年代际变化特征十分明显. 但以往主要是针对夏季进行分析, 而对黄土高原秋季干湿变化规律及大气环流机理的认识非常有限. 本文基于中国589站最近50 a (1961–2010年)月降水和气温月平均资料、NCEP/NCAR提供的再分析资料以及NOAA提供的海表温度(Sea Surface Temperature, SST) 资料, 运用带通/低通滤波、小波分析、EOF/REOF和回归分析等方法, 在对中国秋季干湿时空演化分类的基础上, 通过研究秋季黄土高原中部干湿演变周期、大气环流特征及与海温的多尺度相关关系, 以揭示影响黄土高原中部秋季干湿变化的物理机理, 并确定影响该区域干湿状况的前兆信号. 小波功率谱分析表明, 黄土高原中部秋季干湿指数存在准4 a和准8 a的周期, 1970–1990年准8 a尺度周期振荡尤为明显. 年际(周期≤ 8 a) 尺度上偏湿年的大气环流特征是, 欧亚大陆中高纬呈“双阻型”, 200 hPa西风急流显著北移, 日本海-鄂霍茨克海受反气旋控制, 其底部的偏东水汽输送带将水汽输入研究区. 年代际(周期 > 8 a)尺度上偏湿年的大气环流特征是, 东亚大陆为一致的低值系统; 200 hPa东亚副热带西风急流减弱北移, 研究区主要水汽来源由经孟加拉湾在中南半岛转向的南风水汽输送及中纬度的西风水汽输送组成. 整个序列上, Nino3区SST指数(Nino3I)超前5个月与秋季干湿指数已呈显著的负相关关系, 而孟加拉湾–中国南海SST指数(BayI)则超前3个月与干湿指数呈现显著的负相关关系. 年际尺度上, 秋季Nino3I, BayI均与秋季干湿指数存在显著相关(准4 a, 4–6 a), 而年代际尺度上, 只有BayI与秋季干湿指数存在显著相关性(准10 a). 黄土高原中部秋季干湿的年际和年代际周期的确定、大气环流异常特征的认识及与海温的多尺度相关关系的建立, 不仅揭示了影响该区域干湿变化的物理机理, 也为干旱气候预测提供了重要的前兆信号. 关键词： 黄土高原中部 干湿特征 海表温度 小波分析     

用图样匹配法在计算机上自动识别语音

本文中提出了一个机器识别语言的模型。根据这个模型,提出一种将图样在时间域上规正的新方法。最后给出一组实验结果,正确识别率超过99％。这表明,该方案能应用于某些自动控制系统中。     

层状GRIN板折射率分布的自动测量

采用溶液 凝胶扩散共聚法成功地合成了二元体系的甲基烯酸苄酯 甲基烯酸甲酯 (BZMA MMA)层状梯度折射率板 ,折射率分布是其主要性能指标。给出了一种测量该制品折射率分布的简便实验装置和自动实现方法。该测量方法基于激光干涉的原理 ,由计算机自动跟踪干涉条纹的移动来计算梯度折射率材料的折射率分布。实验表明 ,该方法简单易行 ,具有较高的折射率测量精度 ,为科学评价自研制梯度折射率材料的性能提供了保证     

Renormalization of Dyson's hierarchical vector valued φ4 model at low temperatures

We investigate Dyson's hierarchical vector valued ⁴ model at low temperatures. The case is considered. The pure phase is constructed, and the existence of its large scale limit is proved. The limit is Gaussian, but an unusual normalization has to be chosen. In the direction of the spontaneous magnetization one has to divide by the square root of the volume, but in the orthogonal direction one has to divide by a different power of the volume for all low temperatures.     

Design of a highly sensitive surface plasmon resonance sensor using aluminum-based diffraction grating

A highly-sensitive grating-based surface plasmon resonance (SPR) sensor is proposed. The angular interrogation method has been used to study the performance of the sensor, and high sensitivity is obtained if the ?1st diffraction order is used to excite surface plasmon (SP). The sensitivity as well as the width of the SPR curves and reflective amplitude is considered for designing the sensor. Compared with the conventional gold (Au)-based or silver (Ag)-based SPR sensor, it is found that the aluminum (Al)-based sensor has the best performance. The oxidation problem of an Al-based SPR sensor has been addressed by coating it with an ultrathin gold film on the surface. Numerical simulations based on rigorous coupled wave analysis (RCWA) show that the sensitivity of the optimized sensor is 245°/RIU (degree per refractive index unit).