基于多途信道的舰船辐射噪声功率谱密度估计*

针对低信噪比下利用单水听器估计辐射噪声功率谱密度精度较差的问题,提出一种基于多途信道传输函数估计的垂直阵测量估计1 m处舰船辐射噪声功率谱密度的方法。该方法将信道传输函数表示为多途路径近场阵列流形向量的叠加,较快地估计了信道传输函数,将其用于舰船辐射噪声功率谱密度估计,可较简便地估计距声中心1 m处辐射噪声的功率谱密度,即谱源级。分析了产生功率谱密度估计误差的原因,包括信道估计误差和环境噪声引起的误差,为降低估计误差提供了理论依据。仿真结果表明,该方法估计1 m处辐射噪声功率谱密度的性能良好。     

基于二元PCA特征谱的星系光谱成分快速扣除

实际观测天体目标光谱如超新星和活动星系核光谱常常混有寄主星系成分,这对目标天体光谱的类别和性质证认识别会造成困难。文章提出了一种快速有效的称为二元PCA特征谱分解的星系扣除算法。该算法首先计算了星系样本模板库和超新星样本模板库各自的PCA特征光谱,然后对特征光谱组通过正交变换得到混合空间的一组标准正交基,进而利用混合光谱在该标准正交基上的分解系数计算该光谱在原特征光谱组的分解系数,获得星系超新星混合光谱的快速分解,系数计算也可通过SVD矩阵分解得到,但计算效率较低。实验表明,该方法优于常用的直接PCA投影重构分解方法,与另一种χ2模板拟合方法扣除星系成分相比,在保持分解效果基本不变的前提下,时间消耗则大大降低,从而使该方法可应用到大规模光谱数据处理中。     

基于极值统计的拉曼光谱信噪比评估方法与应用

随着近年来便携式光谱仪技术的迅速发展,CCD光谱仪相对于传统光谱仪在光谱收集方式上发生了很多变化：（1）采集到的光谱对信号进行叠加积分,传统信噪比评估方法无法通过单次检测获得探测器波动;（2）对于谱图噪声（谱线随机波动）,由探测器响应随机波动和扫描重复误差转变为CCD探测器像素响应差异、探测器随机噪声和与光学系统分辨力有关的模式噪声。噪声类型发生改变,导致原有的光谱质量评价方法适用性变差,基于实测光谱提出更具适应性的光谱质量评价方法具有很强的现实意义。根据拉曼光谱仪检测器的变化,对采集光谱信号的成分进行分析,在该分析的基础上提出了CCD光谱仪的噪声模型假设,根据该假设使用不同的信号极值点频率对不同的噪声进行像素分离,并对噪声频率模式进行了数值模拟,模拟结果与假设相符;在此基础上提出并实验验证了通过谱线极值间距评估谱线噪声的拉曼光谱信噪比评估方法,该方法包括以下两个步骤：（1）通过采集多次实测光谱进行叠加,叠加过程中对对应不同频次的光谱极值点数量进行统计,得到统计结果后基于文中规律分离光谱仪中的环境噪声和暗噪声;（2）应用上述分离结果,对实测光谱中对应暗噪声的谱线极值点作统计平均,再将该值应用于文中公式,计算得到信噪比。该方法在进行了步骤（1）的前期准备后,可以通过单张谱图评估CCD拉曼光谱仪的随机噪声,并用于评估光谱的信噪比。基于光学构架相同、CCD探测器不同的三个拉曼光谱系统进行实验,采用该方法通过设定信噪比阈值对谱图质量进行控制,获得了一致的光谱曲线;基于该方法对同步叠加平均法进行信噪比拟合,拟合优度达到98%。该方法可用于拉曼光谱仪的性能评估和获取拉曼光谱谱图的质量实时控制。理论和实验表明：对于基于CCD探测器的拉曼光谱仪器,当确定样品和特征峰时,可以基于此方法获得信噪比。该方法还可用于比对不同配置的拉曼光谱设备,以及作为控制谱图质量一致性的标准,并对基于拉曼光谱技术的智能鉴别系统的开发具有指导意义。     

Optimized basis function for spectral reflectance recovery from tristimulus values

Spectral reflectance is a significant physical property of materials. It plays an important role in color constancy, illumination modeling, and color reproduction. Spectral reflectance basis functions are the most important impact factors for spectral reflectance recovery. Previous methods mainly calculated basis functions for the reflectance spectra data sets by employing the principle component analysis (PCA) and its improved methods. In this paper, we present a new method to solve this problem. Specifically, we propose a new cost function and some constraint conditions to convert the problem into an optimization problem by minimizing the cost function. Unlike the PCA method which yields the orthogonal basis functions for approximating the reflectance spectra, our method yields the nonorthogonal basis functions for better approximating the reflectance spectra. Experimental results show that our derived basis functions are better than those obtained by the PCA method for reflectance spectra recovery.     

Noise estimation based on time–frequency correlation for speech enhancement

As a fundamental part of speech enhancement, noise estimation is particularly challenging in highly non-stationary noise environments. In this work, we propose an effective algorithm on the basis of the “Improved Minima Controlled Recursive Averaging (IMCRA)” with the objective to improve the performance of noise estimation. The main contributions of this work are: (i) in the algorithm, a rough decision about speech presence is proposed by calculating the autocorrelation and cross-channel correlation of the T–F (Time–Frequency) units; (ii) with this decision, we refine the smoothing parameters for the smoothing of noisy power spectrum and the recursive averaging in noise spectrum estimation as well as the weighting factor for the a priori SNR (Signal to Noise Ratio) estimation in the IMCRA; (iii) we improve the search of local minima during spectral bursts by adding a minimum search with a shorter window. Extensive experiments are carried out to evaluate the performance of our proposed algorithm. The experimental results illustrate that, compared with the IMCRA, the proposed approach significantly improves the accuracy of noise spectrum estimation and the quality of enhanced speech in the typical noise situations.     

基于能谱CT的材料组分彩色表征研究

基于光子计数探测器的X射线能谱CT,通过增加能谱分辨率实现了CT图像由灰度向彩色的转变,提高了材料识别能力。然而随着能谱通道数量的增加,通道中的噪声也显著增加,降低了材料识别的准确性。为充分利用能谱CT图像的稀疏性以及能谱通道之间图像的相关性,提出一种多约束窄谱CT迭代重建算法,可在降低噪声的同时有效保留图像的边缘及细节部分。进一步利用主成分分析对窄谱CT图像中的能谱信息进行估计,并建立主成分图像与R,G和B颜色分量之间的映射关系,最后获取彩色CT图像。该方法通过材料组分的彩色表征可以有效识别材料,同时降低图像中的背景噪声。仿真实验和实际实验结果验证了多约束窄谱CT迭代重建算法的有效性以及利用主成分分析进行材料组分彩色表征的可行性。     

Principal component analysis‐assisted energy dispersive X‐ray fluorescence spectroscopy for non‐invasive quality assurance characterization of complex matrix materials

The analytical challenges in direct quality assurance analysis of complex matrices (extreme matrix effects, spectral overlap, poor signal‐to‐noise ratio (SNR) for trace analytes, ‘dark matrix’, imprecise geometry, need for sample integrity) by energy dispersive X‐ray fluorescence (EDXRF) spectrometry necessitate development of novel techniques for material characterization. We demonstrate the utility of principal component analysis (PCA) in isotope‐excited EDXRF spectrometry of a complex matrix (in this case lubricating oil) in the context of a newly developed EDXRF and scattering (EDXRFS) technique. Lubricating oil quality may be interpreted in terms of its viscosity, anti‐wear, anti‐oxidation, and anti‐rust properties, which are detectable via B, Ca, Mg, Zn, Fe, Na additives (quality markers). Our method involves simultaneous non‐invasive acquisition of both fluorescence and scatter spectra from samples held in a propylene dish, and their modeling in a reduced multidimensional space for an interpretable overview that is analytically more useful than, and complementary to, fluorescence peak‐based quantitation of the additives; by this method, only Fe and Zn are directly detectable, but with SNR of the fluorescence peak 15–20 times poorer compared with analysis after sample digestion. Although Fe and Zn cannot distinguish the various lubricating oil brands, it can differentiate authentic from adulterated. The method was however found to be analytically useful when combined with PCA: various brands of lubricating oil were discriminated in addition to the detection of adulteration. PCA processing of the spectra showed that the most important quality assurance spectral signature information responsible for the success is contained in the scatter region (low‐Z elements). Evaluation of the performance of the method with respect to SNR (i.e. analysis time and therefore speed) showed that there was no significant difference in method performance of analysis live time in the range 100–1000 s, showing proof of concept for rapid characterization of complex matrix materials by PCA‐assisted EDXRFS. Copyright © 2012 John Wiley & Sons, Ltd.     

空间人造目标混合光谱材料数目确定方法仿真研究

在空间人造目标光谱分析领域,受到观测距离和观测设备空间分辨率的限制,通常在观测空间人造目标光谱信号时,目标某个瞬时视场中的多种纯物质材料的光谱特征信息组合在一个像元中,形成“混合光谱”。因此,将这些混合光谱分解为每个单一材料的光谱并估计出相应的组成比例是空间人造目标光谱分析研究的重点。大多数现有空间目标光谱分解方法都假设空间人造目标混合光谱中包含的纯物质材料种类个数（即“端元数目”）是先验已知的,这对于未知空间人造目标而言是不现实的。因此,纯物质材料数目正确估计对后续光谱数据分析处理的准确性起着至关重要的作用。目前,现有的端元数目确定方法的设计均在高斯白噪声的假设下进行,而对于噪声信号的分布存在频谱相关性的情况下,会提供较差的结果。采用一种基于数据内在维度和似然最大化理论的方法--鲁棒特征值极大似然方法。由于数据内在维数与信号协方差矩阵和信号相关矩阵特征值差异的统计分布特性高度相关,因此通过分析该特征值差异的统计分布特性,构建一个极大似然函数,可以实现空间人造目标混合光谱端元数目的确定。该方法包含两个步骤：首先,采用基于多元回归和改进最小噪声分离方法对原始光谱数据进行预处理完成噪声特性估计和噪声白化过程,从而有效抑制具有频谱相关性的噪声的干扰;接下来,通过求解一个离散对数联合似然函数的极大值问题来实现空间人造目标混合光谱端元数目的确定,该方法完全不需要输入任何参数,并且运行速度比较快。分别利用实验室实测的五种空间人造目标材料的可见/近红外光谱数据和美国地质勘测局光谱数据构建混合光谱仿真数据进行实验。结果表明,该方法能有效抑制相关噪声和白噪声的干扰,空间人造目标纯物质材料数目确定结果具有很好的准确性和稳定性。     

白矮主序双星光谱的卷积特征提取

通过卷积运算提取白矮主序双星的光谱特征是提高识别精度的有效手段。通过设计一维卷积神经网络,以判别学习的方式从大量混合光谱中拟合出具有稳定分布的12个卷积核,有效提取白矮主序双星的卷积特征。通过引入相对松弛的光谱类别先验分布,提出反贝叶斯学习策略以解决由于光谱抽样有偏带来的问题,显著提高识别精度。通过比较光谱在不同信噪比下的交叉熵测试误差,分析卷积特征的提取过程对光谱信噪比的鲁棒性。实验发现,基于反贝叶斯学习策略的一维卷积神经网络对白矮主序双星的识别准确率达到99.0(±0.3),超过了经典的PCA+SVM模型。卷积特征谱的池化过程以降低光谱分辨率的形式缓解了光谱噪声对识别精度的影响。当信噪比小于3时,必须通过增加模型在光谱上的迭代次数以形成稳定的卷积核;当信噪比介于3与6之间时,光谱卷积特征较为稳定;当信噪比大于6时,光谱卷积特征的稳定性显著上升,信噪比对于模型识别精度带来的影响可以忽略。     

基于噪声谱结构特性的谱减法

提出了基于噪声谱结构特性的谱减法,在不增加语音失真的情况下,抑制传统谱减法的“音乐噪声”。首先,依据噪声谱结构特性在频带间自适应平滑周期图,减小谱估计方差的同时,避免噪声非连续谱的能量泄露;其次,依据噪声谱的结构特性,对增益函数进行自适应调整以更有效的抑制有调噪声。测试结果表明,不论对宽带噪声还是对窄带噪声,本文算法在信噪比提高和噪声抑制量等客观评价指标上都明显优于传统谱减法。非正式主观测听进一步验证了本文算法的有效性。      

The Marple algorithm for the autoregressive spectral estimates of the SMMW fourier transform spectroscopy data

The Marple algorthm for the autoregressive spectral estimates has been applied to the SMMW Fourier transform spectrum analysis. The experimental results have shown that this method yields AR spectra with three times higher resolution than the FFT method does. The improvements obtained from the Marple algorithm over the maximum entropy algorithm include higher resolution, less bias in the spectral peak frequency estimation and absence of observed spectral line splitting. The effects of the structure of the spectral lines and the noise on the resolution are discussed.Projects supported by National Natural Science Foundation of China     

水中矿物油三维荧光谱的特征提取与光谱重构

基于荧光激发-发射矩阵的奇异值分解方法,提取矿物油三维荧光谱的特征序列并进行光谱重构研究。对数十个水中污染油的三维荧光谱进行了奇异值分解和参数计算,表明奇异值参数具有明显的能量聚拢特性; 在适当截取奇异值特征参数后,将奇异值与相应的伴随特征向量组合构成三维荧光谱泛基因序列,实现了三维光谱反演重构。作为比较典型的示例,给出了水中柴油的原始三维荧光谱和重构三维荧光谱的对照图。重构的三维荧光谱与原始三维荧光谱基本相同。研究表明,矿物油三维荧光谱的奇异值特征谱能够代表三维荧光谱主能量的分布特征,有限的奇异值泛基因序列具有三维光谱反演重构能力。该结论对于水环境中的污染油模式识别以及矿物油荧光信息文库的建设有重要意义。     

哈达玛变换光谱成像仪光谱响应非均匀性修正

哈达玛变换光学是近些年发展起来的一门新技术,已经被广泛应用于目标设别、微弱信号检测等领域。基于DMD的哈达玛变换光谱成像仪是一种新型的色散型光谱成像仪。介绍了哈达玛变换成像原理,在自行研制的哈达玛变换光谱成像仪基础上,从光谱定标的角度,研究了哈达玛变换光谱成像仪获得的编码图像的光谱响应非均匀性。针对哈达玛变换光谱成像仪获得的光谱图像中叠加非均匀性噪声及光谱混叠影响光谱复原精度的问题,首次在该光谱成像仪上利用辐射度相对光谱修正和绝对光谱修正算法,对光谱响应非均匀性进行修正。仿真计算和实验结果表明,对于哈达玛变换光谱仪采集的七个波段的光谱图像,修正后的光谱曲线与辐射度计获得的谱线精度非常接近,使复原光谱的误差在2.4%～4.2%的范围,满足实验室和工程应用的技术要求。     

基于相似性度量的星系光谱红移估计

光谱的自动分析对大规模的光谱巡天有着非常重要的意义。文章提出了一种基于相似性度量的星系光谱红移测量方法。方法中采用主分量分析构造星系光谱的静止模板,利用谱线特征确定观测光谱的红移候选,然后根据红移候选进行观测光谱与模板光谱间的相似性度量,所采取的相似性度量策略类似于证据积累的思想,定义为几个相似证据的加权和,从而降低了观测光谱与模板光谱之间的误匹配,提高了红移估计的正确率。通过实验将所提出方法与基于谱线匹配的方法和传统的交叉相关方法进行了比较,实验结果表明：本文方法的正确率较之基于谱线匹配的方法和传统相关法有较大提高。     

A Mathematical Assessment of the Precision of Parameters in Measuring Resonance Spectra

The accurate interpretation ofin vivomagnetic resonance spectroscopy (MRS) spectra requires a complete understanding of the associated noise-induced errors. In this paper, we address the effect of complex correlated noise patterns on the measurement of a set ofpeakparameters. This is examined initially at the level of a single spectral analysis followed by addressing the noise-induced errors associated with determining thesignalparameters from thepeakparameters. We describe a relatively simple method for calculating these errors for any correlated noise pattern in terms of the noise standard deviation and correlation length. The results are presented in such a way that an estimate of the errors may be made from a single MRS spectrum. We also explore how, under certain circumstances, the lineshape of the signal may be determined. We then apply these results to reexamine a set ofin vivo³¹P MRS spectra obtained from rat brain prior to and following moderate fluid percussion injury. The approach outlined in this paper will demonstrate how meaningful results may be obtained from spectra where the signal-to-noise ratio (SNR) is quite small and where knowledge of the precise shape of the signal and the detail of the noise pattern is unknown. In essence, we show how to determine the expected errors in the spectral parameters from an estimate of the SNR from a single spectrum, thereby allowing a more discriminative interpretation of the data.     

Robust and accurate fundamental frequency estimation based on dominant harmonic components

This paper presents a new method for robust and accurate fundamental frequency (F0) estimation in the presence of background noise and spectral distortion. Degree of dominance and dominance spectrum are defined based on instantaneous frequencies. The degree of dominance allows one to evaluate the magnitude of individual harmonic components of the speech signals relative to background noise while reducing the influence of spectral distortion. The fundamental frequency is more accurately estimated from reliable harmonic components which are easy to select given the dominance spectra. Experiments are performed using white and babble background noise with and without spectral distortion as produced by a SRAEN filter. The results show that the present method is better than previously reported methods in terms of both gross and fine F0 errors.     

光谱成像检测法鉴别中药材

使用基于液晶可调谐滤光器的光谱成像检测系统,对西洋参及市面上常用做假冒西洋参的伪品桔梗进行了在体检测.该系统能对检品进行连续光谱成像,光谱分辨率可达0.5 nm,配合使用Matlab软件对图像信号进行处理和分析.获取了400 nm～680 nm之间的连续荧光光谱图像,通过对比西洋参、桔梗的特征光谱曲线,及进行相关系数的计算,有效地区分了西洋参及桔梗.为西洋参的真伪鉴别提供了一定的帮助.     

像增强器噪声频谱特性测试技术研究

对一台进口的像增强器信噪比（SNR）测试仪进行履行，重新研制和设计了测试仪的信号处理模块、计算机系统和测试软件，采用硬件滤波、软件数字滤波和快速傅里叶变换（FFT）相结合的方法实现了像增强器噪声的频谱特性测试。利用所研制的测试仪对多种型号的进口和国产像增强器的噪声频谱进行了测试分析，并给出了分析测试结果。     

Phase-sensitive spectral estimation by the hybrid filter diagonalization method

A more robust way to obtain a high-resolution multidimensional NMR spectrum from limited data sets is described. The Filter Diagonalization Method (FDM) is used to analyze phase-modulated data and cast the spectrum in terms of phase-sensitive Lorentzian "phase-twist" peaks. These spectra are then used to obtain absorption-mode phase-sensitive spectra. In contrast to earlier implementations of multidimensional FDM, the absolute phase of the data need not be known beforehand, and linear phase corrections in each frequency dimension are possible, if they are required. Regularization is employed to improve the conditioning of the linear algebra problems that must be solved to obtain the spectral estimate. While regularization smoothes away noise and small peaks, a hybrid method allows the true noise floor to be correctly represented in the final result. Line shape transformation to a Gaussian-like shape improves the clarity of the spectra, and is achieved by a conventional Lorentzian-to-Gaussian transformation in the time-domain, after inverse Fourier transformation of the FDM spectra. The results obtained highlight the danger of not using proper phase-sensitive line shapes in the spectral estimate. The advantages of the new method for the spectral estimate are the following: (i) the spectrum can be phased by conventional means after it is obtained; (ii) there is a true and accurate noise floor; and (iii) there is some indication of the quality of fit in each local region of the spectrum. The method is illustrated with 2D NMR data for the first time, but is applicable to n-dimensional data without any restriction on the number of time/frequency dimensions.     

采用L1/2稀疏约束的梅尔倒谱系数语音重建方法

提出了一种利用L_1/2稀疏约束从梅尔倒谱系数重建语音时域信号方法。从梅尔倒谱系数估计语音幅度谱是一个欠定问题,现有的方法均采用幅度谱最小均方误差估计或采用L1正则化进行幅度谱的稀疏约束。相比于L₁正则化模型,L_1/2的稀疏约束特性更强,为此,本文在从梅尔倒谱系数估计语音幅度谱时引入L_1/2正则化约束,并利用求解的稀疏幅度谱估计相位谱,最后利用估计的频谱重建时域语音信号。实验结果表明,与幅度谱最小均方误差法相比,本文算法所估计出的语音信号具有更高的语音质量;在噪声环境下进行语音重建实验,与L₁正则化幅度谱估计方法相比,本文算法重建的语音质量更好,表现出更好抗噪性。