A new adaptive subband decomposition approach for automatic analysis of NMR data

This paper presents a non-iterative, fast, and almost automated time-data analysis method for NMR spectroscopy, based on a new adaptive implementation of high resolution methods used in spectral subbands. It is intended to avoid the choice of the decimation factor (or the width of the spectral windows) which, in the case of a uniform decomposition, strongly conditions the estimation results, and to diminish the computational burden. It is achieved through successive decimation/estimation stages each followed by a test procedure in order to decide whether or not the process should continue. The proposed test is based on a local spectral flatness measure of the estimation residuals. This stop-criterion involves an a posteriori validation of the estimation, thus the method proposed allows one to obtain a better detection rate at a lower complexity comparatively to other stopping rules, while preserving a reasonable estimation variance. Moreover, the reliability of the fitting algorithms considered is improved, by decreasing the influence of the model order and the number of false detections. Finally, the method is more efficient than Fourier transform (FT) at low signal-to-noise ratio (SNR). The effectiveness of the method is demonstrated by analyzing a simulation signal and raw carbon-13 experimental data.     

Spectral estimation of NMR relaxation

In this paper, spectral estimation of NMR relaxation is constructed as an extension of Fourier Transform (FT) theory as it is practiced in NMR or MRI, where multidimensional FT theory is used. nD NMR strives to separate overlapping resonances, so the treatment given here deals primarily with monoexponential decay. In the domain of real error, it is shown how optimal estimation based on prior knowledge can be derived. Assuming small Gaussian error, the estimation variance and bias are derived. Minimum bias and minimum variance are shown to be contradictory experimental design objectives. The analytical continuation of spectral estimation is constructed in an optimal manner. An important property of spectral estimation is that it is phase invariant. Hence, hypercomplex data storage is unnecessary. It is shown that, under reasonable assumptions, spectral estimation is unbiased in the context of complex error and its variance is reduced because the modulus of the whole signal is used. Because of phase invariance, the labor of phasing and any error due to imperfect phase can be avoided. A comparison of spectral estimation with nonlinear least squares (NLS) estimation is made analytically and with numerical examples. Compared to conventional sampling for NLS estimation, spectral estimation would typically provide estimation values of comparable precision in one-quarter to one-tenth of the spectrometer time when S/N is high. When S/N is low, the time saved can be used for signal averaging at the sampled points to give better precision. NLS typically provides one estimate at a time, whereas spectral estimation is inherently parallel. The frequency dimensions of conventional nD FT NMR may be denoted D₁, D₂, etc. As an extension of nD FT NMR, one can view spectral estimation of NMR relaxation as an extension into the zeroth dimension. In nD NMR, the information content of a spectrum can be extracted as a set of n-tuples (ω₁, … ω_n), corresponding to the peak maxima. Spectral estimation of NMR relaxation allows this information content to be extended to a set of (n + 1)-tuples (λ, ω₁, … ω_n), where λ is the relaxation rate.     

Recent progress on laser-induced breakdown spectroscopy for the monitoring of coal quality and unburned carbon in fly ash

Our recent progress on developments of laser-induced breakdown spectroscopy (LIBS) based equipments for on-line monitoring of pulverized coal and unburned carbon (UC) level of fly ash are reviewed. A fully software-controlled LIBS equipment comprising a self-cleaning device for on-line coal quality monitoring in power plants is developed. The system features an automated sampling device, which is capable of elemental (C, Ca, Mg, Ti, Si, H, Al, Fe, S, and organic oxygen) and proximate analysis (Q _ad and A _ad) through optimal data processing methods. An automated prototype LIBS apparatus has been developed for possible application to power plants for on-line analysis of UC level in fly ash. New data processing methods are proposed to correct spectral interference and matrix effects, with the accuracy for UC level analysis estimated to be 0.26%.     

湿地土壤硝化微生物群落的高光谱研究

氮循环是土壤生态系统元素循环的重要过程,其中硝化作用对于土壤氮循环有重要影响。硝化作用的主要完成者是硝化微生物群落,土壤微生物是湿地生态系统的重要组成部分,其可以指示湿地生态环境变化,对正确认识湿地生态系统氮循环和湿地污染净化功能具有重要意义。尝试从高光谱遥感技术角度,基于土壤氮素光谱监测机理,探索湿地土壤硝化微生物群落高光谱估算技术,进而为估测其时空分布状况提供新技术途径。研究对硝化作用中两个独立阶段的主要完成者氨氧化细菌和亚硝酸氧化细菌,采用最大可能数法分别计数,并以两者计数测量结果的合计,作为各采样区域土壤硝化微生物的数量值。采用光谱倒数的对数(LR)、光谱一阶微分(FD)、光谱二阶微分(SD)、包络线去除(CR)和光谱波段深度(BD)光谱变换技术,以及基于再抽样(bootstrap)技术的多元逐步回归(SMLR)和偏最小二乘回归(PLSR)建模方法,构建湿地土壤硝化微生物数量和全氮含量估算模型。研究结果表明：在采用bootstrap SMLR建模方法时,湿地土壤硝化微生物数量和全氮含量的估算波段位置存在一定的相似性(尤其对于原光谱实测数据R和SD光谱);对于湿地土壤硝化微生物数量和全氮含量的估算,bootstrap PLSR相比于bootstrap SMLR建模方法,具有较高的估算精度;对湿地土壤硝化微生物数量的估算,最高估算精度产生于SD光谱变换技术结合bootstrap PLSR建模;对湿地土壤全氮含量的估算,最高估算精度产生于CR光谱变换技术结合bootstrap PLSR建模。     

一种基于授权信道特性的认知无线电频谱检测算法

针对认知无线电系统中频谱检测的频率直接影响系统容量以及与授权用户产生冲突的概率问题,分析了授权用户频谱使用的特性, 对授权用户行为进行统计建模, 提出一种自适应频谱检测算法. 引入控制因子, 在保证认知无线电系统稳定性的约束下, 自适应调整频谱感知的频率从而提高频谱利用率并减小系统冲突概率和检测开销, 进而降低了系统的能量消耗. 仿真结果表明, 该算法在保证不对授权用户产生干扰和一定的系统稳定性条件下, 有效地提高了系统的容量,并且具有良好的实用性和灵活性. 关键词： 认知无线电 自适应频谱检测 绿色通信 最大似然     

Selection of optimal combinations of inputs in a partial least squares model for prediction of soil organic matter

Abstract

Partial least squares model is widely used in estimation of soil physical and chemical parameters such as soil organic matter and moisture content, due to its advantages in dealing with collinearity of variables like hyperspectral reflectance. However, it is hard to determine optimal combination of partial least squares model input for soil organic matter prediction since there are lots of possibilities such as, different mathematical transformation of spectral reflectance, wavelength ranges, and spectral resolution. Laboratory hyperspectral reflectance of soils in Songnen plain were analyzed in this study, and the orthogonal experimental design method for deriving optimal combination of input variables for soil organic matter prediction models was introduced. For intercalating orthogonal experimental design table, five different levels which commonly used by researchers were assigned to factors. Results show that the optimal combination input for single black soil is using the derivative logarithmic reciprocal reflectance in the wavelength range selected by multiple stepwise regression at a spectral resolution of 5?nm (R²=?0.95, RMSE?=?0.21, and RPD?=?4.49), and different soils is using continuum removed in the wavelength range selected by MSR at a spectral resolution of 5?nm (R²?=?0.77, RMSE?=?0.74, and RPD?=?2.08). With optimal combination input, the partial least squares model prediction ability was evaluated as excellent for single black soil, possible for different soils. This study illustrates the orthogonal experimental design method can be an effective way to identify the optimal input variables of a partial least squares model for soil organic matter prediction, and multiple stepwise regression can be a preprocessing step to reduce hyperspectral data redundancy before using partial least squares to predict soil organic matter. Overall, this study provides a new approach for determining optimal input of partial least squares predicting model.     

Spectrograms of single-type modes and their application to problems of sound source localization in oceanic waveguides

The previously proposed method for selecting groups of single-type wave fieldmodes in oceanic waveguides, based on a double Fourier transform of the interference pattern formed by a moving sound source, is theoretically substantiated. This procedure, which is possible due to the invariance of the frequency shift of field maxima with respect to the numbers of interfering single-type modes, leads to spectral density localization. The sizes of acceptable localization domain and spectral density distribution are estimated. The results of a natural experiment on application of this procedure to problems of detection of a sound source and estimation of its radial velocity and distance from receiver are reported.     

考虑水分光谱吸收特征的水稻叶片SPAD预测模型

叶绿素是植被光合作用的重要色素,传统实验室方法测定叶绿素含量需破坏性取样且操作复杂。通过构建高精度SPAD光谱估算模型,可以实现对水稻叶片叶绿素含量的实时无损监测。以黑龙江省不同施氮水平下水稻为研究对象,采用SVC HR768i型光谱辐射仪共获取移栽后、分蘖期、拔节期、孕穗期、抽穗期共五个关键时期水稻叶片反射光谱数据。光谱探测范围350~2 500 nm。利用自带光源型手持叶片光谱探测器直接测定叶片光谱,光源为内置卤素灯。采用SPAD-502型手持式叶绿素仪同步测定水稻叶片的SPAD值。叶片水分是植物光合作用的基本原料,也间接影响着叶绿素含量。叶片含水量降低则会影响植物正常的光合作用,导致其叶绿素含量随之降低。因此将叶绿素敏感波段与水分吸收范围结合作为SPAD估算的输入量。随机森林模型是一个基于多个分类树的算法。算法在采样的过程中包括两个完全随机的过程,一是有放回抽样,可能会得到重复的样本,二是选取自变量是随机的。因此本文对叶片光谱反射率进行去包络线（CR）处理,综合考虑可见光近红外波段提取水稻叶片反射光谱特征参数和植被指数,综合分析光谱指标与SPAD相关关系,采用随机森林算法构建不同输入量的SPAD高光谱估算模型。结果表明: （1）水稻叶片SPAD与光谱反射率的相关系数在叶绿素敏感波段红波段范围（600～690 nm）、红边范围（720~760 nm）、水分吸收波段范围（1 400~1 490和1 900~1 980 nm）均为0.75以上;（2）在光谱参数与SPAD 的相关分析中,NDVI,DP2与水稻叶片SPAD值相关性最好,相关系数为0.811和0.808;（3）以结合水分光谱信息后的CR_{(V1, V2, V3, V4)}为自变量所建立的随机森林模型精度最高,R2为0.715,RMSE为2.646,可作为水稻叶片叶绿素预测模型。研究结果揭示了不同品种水稻的光谱响应机制,提供了水稻叶片SPAD值高精度反演的技术方法,为监测与调控东北地区水稻正常生育进程提供技术支持。     

土壤重金属砷的高光谱估算模型

砷是严重危害人体的重金属之一。利用高光谱技术进行土壤重金属砷含量的估测具有很大的应用潜力,但受区域和土壤背景的影响,估算模型适用性和精度都会有很大的差异。针对石家庄市地表水源地保护区土壤砷含量的高光谱估算,在水源地保护区的主要采矿地和冶炼企业进行了土壤实地采样和实验室重金属分析,对土壤样本的原始光谱反射率采用Savitzky-Golay 7点平滑处理,进行一阶微分（FD）、二阶微分（SD）、倒数（RT）、倒数一阶微分（RTFD）、倒数二阶微分（RTSD）、倒数对数（AT）、倒数对数一阶微分（ATFD）、倒数对数二阶微分（ATSD）、连续统去除（CR）9种光谱变换后,再对重金属砷实测含量与经光谱变换后的光谱指标进行相关分析,并提取各光谱指标的最大敏感波段。运用多元线性逐步回归（MLSR）、单光谱变换指标偏最小二乘回归（U-PLSR）和多光谱变换指标偏最小二乘回归（M-PLSR）方法构建土壤重金属砷含量估算模型,最后通过相关系数r、均方根误差（root mean square error, RMSE）和统计值F来比对建模效果。结果表明：研究区部分土壤样本重金属砷含量已经出现了轻度污染,大部分样本处于污染的临界状态;经连续统去除变换后的光谱特征与砷的相关性最大,一阶微分与砷含量存在最大负相关性;相较于多元线性逐步回归和单光谱变换指标偏最小二乘回归,采取多光谱变换指标偏最小二乘回归方法土壤重金属砷含量模型估算值与实测值最为接近,建模R2达到0.852,RMSE和F值分别达到0.147和32.384,多光谱变换指标建模集成效果显著。因此研究结果可以为石家庄水源地保护区主要采矿地和冶炼企业重金属砷污染高光谱快速监测提供科学依据。     

光谱特征提取对近似矿物光谱差异性影响分析

矿物光谱特征是基于光学遥感数据对矿物进行种类识别及定量反演的理论基础,光谱特征提取是高光谱数据常用的技术手段,但在多光谱数据中较少涉及。近似矿物识别是矿物光谱分类应用中的难点,目前还缺少有效指标来指示近似矿物类别光谱的差异性。光谱特征提取有望提高矿物分类精度,但该处理对近似矿物光谱差异性的影响还缺少相关研究。本文从矿物光谱差异性的原理出发,通过类间和类内光谱角的比值体现不同类别群体差异,并引入样本量因素,提出了类别可分比作为近似矿物光谱差异性的指标。以明矾石和高岭石两种近似矿物为例,对USGS光谱库光谱及Hyperion,ASTER,OLI等传感器的模拟数据进行光谱特征提取处理,通过对比处理前后矿物光谱差异性的变化,分析光谱特征提取对近似矿物光谱差异性的影响。实验结果表明,有效的光谱特征提取可以显著提高近似矿物光谱差异性,并且光谱分辨率越高,近似矿物光谱差异性越大。此外,光谱分辨率及中心波长设置对于包络线去除结果有很大影响,多光谱数据吸收特征提取效果有待进一步提高。该研究为今后近似矿物光谱识别精度的提高奠定了基础,也为未来新型遥感找矿传感器参数设置提供了参考。     

The auto-measurement of the gap of LCD glass plates using sub-pixel accuracy estimation

In this paper, the distance between laser spots has been measured using sub-pixel accuracy estimation and the accuracy has been tested in a noise environment. Here, the correlation like method (CLM) is used for the distance measurement between two laser spots and to count the estimation precision effect under noise factor by relevant CLM theorems. From the maximum value of the data series, then the position of the center of laser spot can be derived by interpolation. The estimation precision effect under noise factor and DC offset are tested and analyzed by a computer system. Also, for improving the estimation precision, a Kalman filter, which is often applied for random estimation and control theorem, is adopted. The innovation research combines CLM, and Kalman filter in sub-pixel accuracy estimation is more precise. This method can be used for measuring the gap of LCD glass plates, of optical strain gauge and the characteristics of spectral, quickly and can be automated.     

Finite temperature effect in infrared-improved AdS/QCD model with back reaction of bulk vacuum

Based on an IR-improved soft-wall AdS/QCD model for mesons,which provides a consistent prediction for the mass spectra of resonance scalar,pseudoscalar,vector and axial-vector mesons,we investigate its finite temperature effect.By analyzing the spectral function of mesons and fitting it with a Breit-Wigner form,we perform an analysis for the critical temperature of mesons.The back-reaction effects of bulk vacuum are considered and the thermal mass spectral function of resonance mesons is calculated based on the back-reaction improved action.A reasonable melting temperature is found to be T_c≈150 ± 7 MeV.which is consistent with the recent results from lattice QCD simulations.     

Statistical properties of turbidity, oxygen and pH fluctuations in the Seine river estuary (France)

We consider here, the fluctuations of turbidity, oxygen and pH time series recorded from the MAREL system (Ifremer, France), which is based on the deployment of data buoys equipped with water analysis capabilities in an automated mode. We perform a spectral analysis (from 10 min to years) of these time series, and we estimate their probability density functions. Oxygen, turbidity and pH are important quantities for ecosystem studies and physics-biology couplings, and their fluctuations reveal the possible influence of environmental factors such as tides and turbulence. Turbidity variability is highly complex and does not appear to be directly coupled to turbulence, since no clear range is visible. On the other hand, oxygen percentage of saturation and pH data show remarkably nice scaling ranges, indicating an important coupling with turbulence, but also biological or chemical activity, since the statistics differ markedly from passive scalars. Several possible sources of this variability are discussed.     

Improved spectral quality for 3D MR spectroscopic imaging using a high spatial resolution acquisition strategy

Spectral quality in (1)H MR spectroscopic imaging (MRSI) of the brain is often significantly degraded in regions subject to local magnetic susceptibility variations, which results in broadened and distorted spectral lineshapes. In this report, a modified acquisition strategy for volumetric echo-planar spectroscopic imaging (3D EPSI) is presented that extends the region of the brain that can be observed. The data are sampled at higher spatial resolution, then corrected for local B(0) shifts and reconstructed such that the final spatial resolution matches that of 3D EPSI data acquired with the conventional lower spatial resolution. Comparison of in vivo data obtained at 1.5 T with these two acquisition schemes shows that the high spatial resolution acquisition provides considerable reduction of spectral linewidths in many problematic brain regions, though with a reduction in signal-to-noise ratio by a factor of approximately 1.4 to 1.6 for the matrix sizes used in this study. However, the effect of the increased noise was largely offset by the improved spectral quality, leading to an overall improvement of the metabolite image quality obtained using automated spectral analysis.     

Acoustic classification of Australian anurans based on hybrid spectral-entropy approach

A new hybrid method for automated frog sound identification, using spectral centroid, Shannon entropy and Rényi entropy is proposed. The advantage of using entropy based information theoretic approach for analyzing complexity of bioacoustics signals in animal vocalization is discussed. Sound samples from nine species of Microhylidae frogs are first segmented into syllables. Fourier spectral centroid, Shannon entropy and Rényi entropy of the syllables are then determined. Finally, nonparametric k-th nearest neighbour (k-NN) classifier is used to recognize the frog species based on these three extracted features. Result shows that the k-NN classifier based on these selected features is capable to identify the species of the frogs with an average accuracy of 98%. It is found that the accuracy reduces significantly only when the noise levels higher than −20 dB.     

Estimation of cut-off wavelength of rare earth doped single-mode fibers

A new empirical relation is proposed describing spectral variation of mode-field radius (MFR) as inferred from measurements in the far-field of the fiber. It is shown that using this relation, it is possible to estimate the cut-off wavelength (λ_c) of the fiber. The proposed technique is successfully tested through measurements made on two standard step index single-mode fibers, as well as on an erbium doped fiber (EDF) having λ_c falling within its strong absorption band around 980 nm. This empirical formula is more accurate than the widely used Marcuse's formula to describe spectral dependence of MFR determined through measurements made in the fiber's far-field. The proposed technique is especially suited for estimation of λ_c of doped fibers in which λ_c falls within an absorption band.     

连续小波变换定量反演土壤有机质含量

以北京市东部地区96个潮土土样的土壤参数及对应光谱数据为数据源,采用连续小波多尺度分析处理与分析。首先将土壤光谱进行初步处理,生成小波系数,其次将土样的有机质含量与小波分解系数开展相关性分析,提取特征波段,最后采用特征波段建立预测耕层有机质含量的模型。结果表明：经连续小波处理后,光谱对耕层有机质含量的预测能力明显优于传统光谱变换技术;经连续小波分解后,对土壤有机质含量的预测能力随光谱分辨率降低呈先降后升再降的趋势;连续小波分析算法可提升土壤光谱对有机质含量的估测能力,与土壤高光谱反射率相比,基于连续小波变换的土壤有机含量最佳的精度提高19%;由于光谱分辨率为80 nm建立的模型精度较高,其R2达到0.632,这表明在连续小波算法下,光谱分辨率较低的宽波段数据可用于土壤有机质含量的监测。     

Determination of temperature and emissivity of opaque heated bodies via thermal radiation spectrum: simulation of measurements in spectral window

Computer simulation of emitted radiation intensity spectrum of tantalum object was carried out. Simulation measurements occurred in a narrow spectral window, which moved along the spectrum with the given step. In this way, spectral ranges at which the dependence of the emissivity (or its logarithm) on the wavelength is the simplest and fairly accurate, in particular linear, were sought for. In the case of successful search for the specified spectral range, the required temperature was determined in the spectral window with the least squares technique. If the emissivity (or its logarithm) is linearly dependent on the wavelength then the alternative estimation of the true temperature is possible. In this case, the required temperature is determined via the change of the convexity of the spectral emissivity dependence at selection of its numerical value from the values lower than the true temperature value to the values higher than the true temperature value. This approach is simple, does not require solution of the system of equations and can significantly narrow temperature range to which the true temperature belongs.