Wavelet-based ultra-high compression of multidimensional NMR data sets

The application of a lossy data compression algorithm based on wavelet transform to 2D NMR spectra is presented. We show that this algorithm affords rapid and extreme compression ratios (e.g., 800:1), providing high quality reconstructed 2D spectra. The algorithm was evaluated to ensure that qualitative and quantitative information are retained in the compressed NMR spectra. Whilst the maximum compression ratio that can be achieved depends on the number of signals and on the difference between the most and the least intense peaks (dynamic range), a compression ratio of 80:1 is affordable even for the challenging case of homonuclear 2D experiments of large biomolecules.     

基于小波变换的便携式X射线荧光光谱仪检测模型的建立与改进

应用便携式X射线荧光光谱仪对土壤中Cr,Cu,Zn,As,Pb等重金属含量进行检测, 每个样品扫描检测3次,利用小波阈值滤噪的方法对所测谱线进行光滑去噪处理后, 根据土壤重金属的标准值和相应的计数率(取三次处理后检测谱线的平均值)建立各重金属的标准曲线。运用小波阈值滤噪方法时, 为确定最佳的小波基和小波分解层数, 以信噪比(SNR),均方根误差(MSE)和信息熵(H)作为评估指标评价降噪效果。为验证仪器的稳定性, 根据土壤样品中重金属浓度的不同挑选部分样品并同时选用H₃BO₃(空白对照)进行重测。结果表明: 运用小波变换方法时,选取coif3小波基对谱线进行三层分解, 取得了最佳的去噪效果; 建立好模型后,仪器的决定系数R2范围是0.990~0.996, 表明在0~1 500 mg·kg-1范围内, 土壤样品中各重金属元素含量与X射线荧光光谱特征峰强度之间的线性关系良好; 经过重复检测和计算得知仪器的检出限均低于国家一级土壤标准。将小波变换的方法实际运用到X射线荧光光谱仪检测模型的建立与改进中,有效的提高了模型的准确性,同时经验证,仪器具有良好的精密度,可运用于实际土壤重金属污染的现场快速筛查。     

静态小波变换荧光检测水中矿物油信号的去噪方法

荧光分析法具有灵敏度高、选择性好、易于设计等优点,是检测水中油类污染物的重要手段。光电探测器产生的噪声会影响荧光检测系统的灵敏度,荧光信号的噪声消除一直是研究的热点问题。由于荧光信号增加了支集长度,dbN族小波能够解决信号的边界问题,通过比较dbN族不同小波基的去噪效果,选择db7为最优小波基,对含噪荧光信号作5层静态小波分解。根据小波熵理论自适应地选择阈值,高频系数经过阈值量化并重构得到纯净的荧光信号。与离散小波变换相比,静态小波变换去噪后信号具有信息完整性和时移不变性。     

Wavelet decomposition method decoupled boiling/evaporation oscillation mechanisms over two to three timescales: A study for a microchannel with pin fin structure

Boiling/evaporation heat transfer in a microchannel with pin fin structure was performed with water as the working fluid. Simultaneous measurements of various parameters were performed. The chip wall temperatures were measured by a high spatial-time resolution IR image system, having a sensitivity of 0.02 °C. The flow pattern variations synchronously changed wall temperatures due to ultra-small Bi number. The wavelet decomposition method successfully identified the noise signal and decoupled various temperature oscillations with different amplitudes and frequencies. Three types of temperature oscillations were identified according to heat flux q and mass flux G. The first type of oscillation occurred at q/G < 0.62 kJ/kg. The approximation coefficient of wavelet decomposition decided the dominant cycle period which was ∼3 times of the fluid residence time in the microchannel, behaving the density wave oscillation characteristic. The detail coefficients of wavelet decomposition decided the dominant cycle period, which matched the flow pattern transition determined value well, representing the flow pattern transition induced oscillation. For the second type of oscillation, the wavelet decomposition decoupled the three oscillation mechanisms. The pressure drop oscillation caused the temperature oscillation amplitudes of 5–10 °C and cycle periods of 10–15 s. The density wave oscillation and flow pattern transition induced oscillation are embedded with both the pressure rise and decrease stages of the pressure drop oscillation. The third type of oscillation happened at q/G > 1.13 kJ/kg, having the density wave oscillation coupled with the varied liquid film evaporation induced oscillation. The liquid island, retention bubble induced nucleation sites and cone-shape two-phase developing region are unique features of microchannel boiling with pin fin structure. This study illustrated that pressure drop oscillation and density wave oscillation, usually happened in large size channels, also take place in microchannels. The flow pattern transition and varied liquid film evaporation induced oscillations are specific to microchannel boiling/evaporation flow.     

运用RBF径向基神经网络识别基于小波变换的淫羊藿苷红外光谱指纹特征

运用RBF径向基神经网络对基于傅里叶变换红外光谱（FTIR）法、相关系数比对法及多层次小波变换所提取的淫羊藿药材样本有效成分指纹特征进行识别。样品包括药典选用的５个物种（淫羊藿Epimedium brevicornu Maxim.、箭叶淫羊藿E.sagittatum（Sieb. et Zucc.） Maxim.、柔毛淫羊藿E.pubescens Maxim.、朝鲜淫羊藿E.koreanum Nakai和巫山淫羊藿E.wushanense T.S.Ying）和其他物种共计250个样本。淫羊藿指标成分淫羊藿苷在原药材和甲醇提取物的红外光谱图上,具有较明显的1 259 cm-1特征峰,经典的HPLC法的定量分析结果也佐证了淫羊藿苷的含量与1 259 cm-1峰的位置具有很好的一致性。为此该峰可以用来作为判别各物种淫羊藿药材是否含有淫羊藿苷的重要依据。在此基础上,采用相关系数比对法和多层次的小波变换法消除了因淫羊藿苷含量低、吸收峰弱,信号和噪声大的问题,增强了RBF径向基神经网络的识别效果。初步建立了基于小波变换和RBF径向基神经网络淫羊藿原药材红外光谱快速识别淫羊藿苷指标成分的一种新方法。     

一种海空背景下红外小目标检测新算法

针对海空背景下红外小目标图像信噪比低、难以检测的情况以及传统检测方法需要图像帧数多、实时性差等缺点,基于Donoho的小波变换阈值化方法,提出了一种更加适合海空背景下红外小目标检测的新算法.该算法采用一种新的阈值求取方法以及更加简单的硬阈值化函数对图像进行处理.通过仿真实验将新算法与传统检测算法进行比较,实验结果表明,无论在处理效果还是实时性上,新算法都优于传统算法.     

一种基于噪声修整Contourlet变换的低码率图像压缩算法

为了克服冗余性的Contourlet变换不利于图像压缩的缺陷,提出了噪声修整的Contourlet变换结构,或称为NS-Contourlet.该结构通过迭代的方式减少了量化后的非零系数数量,并且提高了非零系数的逼近能力.设计了一种可采用提升小波实现的拉普拉斯金字塔变换,有效地提高了Contourlet中拉普拉斯金字塔变换部分的速度.提出的NS-Contourlet结构结合EBCOT编码器实现了一种图像压缩算法,并且通过实验验证了该算法的有效性.尤其当低码率压缩(小于0.2 bpp)或者待压缩图像呈现直线纹理特征时,提出算法在主观视觉质量和PSNR指标上均优于JPEG2000,平均PSNR值提高了0.1～0.5 dB.     

薄膜宽带监控中光谱信号随机噪音处理的研究

利用小波变换在时频域具有一定时间和频率多分辨率的特性,设计了小波阈值优化方法.根据信号与随机噪音在小波变换下各尺度不同的性质,同时减小拒真概率和虚报概率,在有效抑制随机噪音基础上,很好地保留了信号的细节成分.峰值误差为0.7%~1.0%,峰位误差为0.1%~0.3%.提高了光谱信号分析的准确性及系统的判停准确度.     

Wavelet-modified maximum average correlation height filter for out-of-plane rotation invariance

In this paper, we implement a wavelet-modified maximum average correlation height (WaveMACH) filter for recognition of 0-360° out-of-plane rotated targets employing hybrid digital-optical correlator architecture. A chirp function is used with the synthesized WaveMACH filter, which focuses the correlation signals in three different planes, thereby providing the choice of recording a single correlation peak at a particular plane. Signal-to-noise ratio and peak-to-sidelobe ratio have been calculated as metrics of goodness of the proposed WaveMACH filter.     

Wavelet Transformation and Wigner-Husimi Distribution Function

We find that the optical wavelet transformation can be used to study the Husimi distribution function in phase space theory of quantum optics. We prove that the Husimi distribution function of a quantum state |ψ〉 is just the modulus square of the wavelet transform of with ψ(x) being the mother wavelet up to a Gaussian function. Thus a convenient approach for calculating various Husimi distribution functions of miscellaneous quantum states is presented.