HREELS signal processing via wavelets

In spectroscopy, the recorded spectra can often be modelled as the noisy convolution product of an instrumental function with the ‘true’ signal to be estimated. Such models have often been used for high‐resolution electron energy‐loss spectroscopy (HREELS). In this article, a new method is suggested to estimate the ‘true’ HREELS signal, i.e. the original electronic diffusion function with ‘true’ peak intensities. Our method relies upon the use of wavelets that, because they exhibit simultaneous time and frequency localization, are well‐suited for signal analysis. Firstly, a wavelet shrinkage algorithm is used to filter the noise. This is achieved by decomposing the noisy signal into an appropriate wavelet basis and then thresholding the wavelet coefficients that contain noise. This algorithm has a particular threshold related to frequency and time. Secondly, the broadening due to the instrumental response is eliminated through a deconvolution process. This step mainly rests on the existing relation between the Lipschitz regularity of the signal and the decay with scale of its wavelet coefficients and on least squares. The efficiency of this technique is highlighted by comparing the results obtained with those provided by other published methods. This work is the second in a series of three papers in this issue. The first one presents background knowledge on the wavelets required to understand the estimation methods. The third paper explores the application of wavelet filtering and deconvolution techniques to x‐ray photoelectron spectroscopy. Copyright © 2004 John Wiley & Sons, Ltd.     

Deconvolving compactly supported densities

This paper addresses the statistical problem of density deconvolution under the condition that the density to be estimated has compact support. We introduce a new estimation procedure, which establishes faster rates of convergence for smooth densities as compared to the optimal rates for smooth densities with unbounded support. This framework also allows us to relax the usual condition of known error density with non-vanishing Fourier transform, so that a nonparametric class of densities is valid; therefore, even the shape of the noise density need not be assumed. These results can also be generalized for fast decaying densities with unbounded support. We prove optimality of the rates in the underlying experiment and study the practical performance of our estimator by numerical simulations.      

Efficiency of four different techniques in coupled HPLC-UV/VIS to quantify overlapping peaks with known spectral features

Summary Four different techniques to quantify unresolved chromatographic peaks with known spectral features combined with photodiode array detection, are investigated as regards their efficiency for the accurate and precise determination of drugs in the low g-range. The comparison includes peak suppression utilising difference chromatograms, first-order derivative chromatograms, selective chromatograms, generated by the calculation of orthogonal polynomial shares, and the powerful least-squares multicomponent analysis approach. Each of these methods uses UV-spectra taken throughout, the peak. The results presented and conclusions reached should enable the chromatographer to come to a decision about the reasonable use of these options now provided by multichannel detection in HPLC.     

FTIR-spectroscopy as a highly sensitive technique to study adsorption and desorption on ionic film and single crystal surfaces

Sample/spectrum relationships are investigated using both a low resolution rapid-scanning NIR monochromator and a Fourier transform instrument capable of high resolution and are evaluated in terms of whether or not the resolution of the instrument is sufficient for measuring the natural bandwidths corresponding to the sample. Based on the sample/spectrum relationship a criterion is developed which must be followed in order to apply either derivative spectroscopy or deconvolution to enhance the resolution of overlapped bands without generating spectral artifacts.     

A mathematical and physical Study of multiscale deconvolution models of turbulence

This work presents a rigorous analysis of mathematical and physical properties for solutions of multiscale deconvolution turbulence models. We show that solutions of these models exactly conserve model quantities for the integral invariants of fundamental physical importance: kinetic energy, helicity, and (in two dimensions) enstrophy. The kinetic energy conservation is the key that allows us to next apply the phenomenology of homogeneous, isotropic turbulence to establish the existence of a model energy cascade and, in particular, that the cascade exhibits enhanced energy dissipation in a secondary accelerated cascade, which ends at the model's microscale (which we establish is larger than the Kolmogorov microscale). We also prove that the model dissipates energy at the same rate as true turbulent flow, ～ O(U³ ∕ L), independent of Reynolds number. Lastly, we prove the existence of global attractors for the model solutions; the proof of which also shows that solutions are actually one degree of regularity higher than previously known. Copyright © 2012 John Wiley & Sons, Ltd.     

Experimental shift allowance in the deconvolution of SIMS depth profiles

We study the deconvolution of the secondary ion mass spectrometry (SIMS) depth profiles of silicon and gallium arsenide structures with doped thin layers. Special attention is paid to allowance for the instrumental shift of experimental SIMS depth profiles. This effect is taken into account by using Hofmann's mixing‐roughness‐information depth model to determine the depth resolution function. The ill‐posed inverse problem is solved in the Fourier space using the Tikhonov regularization method. The proposed deconvolution algorithm has been tested on various simulated and real structures. It is shown that the algorithm can improve the SIMS depth profiling relevancy and depth resolution. The implemented shift allowance method avoids significant systematic errors of determination of the near‐surface delta‐doped layer position. Copyright © 2013 John Wiley & Sons, Ltd.     

Spectral analysis of Sr 3d XPS spectrum in Sr-containing hydroxyapatite

The Sr 3d X-ray photoelectron spectroscopy (XPS) spectrum of Sr-containing hydroxyapatite (SHAp) overlaps completely with the P 2p spectrum. Thus, the chemical state identification of Sr in SHAp is a challenging task. In this work, the Sr 3d spectrum was isolated from the overlapping spectra for analyzing the chemical state of Sr using a generic peak-fitting method. The SHAp layers containing various Sr concentrations were produced on a Ti substrate using a chemical treatment process with a calcium phosphate slurry that included Sr (NO₃)₂. The distribution of the constituent elements changed according to the Sr concentration, implying that the chemical state of Sr varied with concentration. The isolation of the Sr 3d XPS spectrum was conducted via spectral deconvolution using the P 2p spectrum corresponding to HAp. The isolated Sr 3d spectrum revealed that the chemical state of Sr was in SrO and Sr-substituted HAp states, and their ratio varied with the Sr concentration in the layer. The SrO to Sr-substituted HAp ratios affected the Sr ion releasing behavior from the SHAp layer, supporting the validity of the obtained analytical results.     

盲目反卷积光谱图超分辨复原算法

反卷积是实现光谱图超分辨复原的重要手段,与常规反卷积相比,盲目反卷积具有不需要预先准确获取卷积核函数的优势。着眼于充分利用光谱信号的特点和已有的光谱图反卷积成果,详细讨论了空域迭代盲目反卷积方法用于光谱图反卷积时的算法实现问题,并在分析光谱图卷积退化过程的基础上,针对光谱图反卷积算法特点,提出了光谱图卷积退化简化计算模型和最小二乘高斯拟合模型,以解决算法中相应的计算问题。基于Matlab平台的仿真表明,对于所用的高斯型谱线和点扩散函数,空域迭代盲目反卷积算法效果良好,在信噪比为50 dB时,分辨率提高约30％。     

用神经网络鉴别退化图像的模糊类型

提出了一种用神经网络鉴别退化图像的模糊类型的方法。由于采用不同降质方法得到退化图像的频谱差异较大,以此作为判别依据,用概率神经网络实现了对四种模糊类型:离焦,矩形,运动和高斯模糊的鉴别。根据神经网络的鉴别结果决定点扩散函数的初始估计值,可大大地提高盲解恢复算法的复原质量和系统点扩散函数的估计精度,扩大了算法的实用范围。     

Generalization of Strang's Preconditioner with Applications to Toeplitz Least Squares Problems

In this paper, we propose a method to generalize Strang's circulant preconditioner for arbitrary n-by-n matrices A_n. The th column of our circulant preconditioner Sn is equal to the th column of the given matrix A_n. Thus if A_n is a square Toeplitz matrix, then Sn is just the Strang circulant preconditioner. When Sn is not Hermitian, our circulant preconditioner can be defined as . This construction is similar to the forward-backward projection method used in constructing preconditioners for tomographic inversion problems in medical imaging. We show that if the matrix A_n has decaying coefficients away from the main diagonal, then is a good preconditioner for An. Comparisons of our preconditioner with other circulant-based preconditioners are carried out for some 1-D Toeplitz least squares problems: min ∥ b - Ax∥₂. Preliminary numerical results show that our preconditioner performs quite well, in comparison to other circulant preconditioners. Promising test results are also reported for a 2-D deconvolution problem arising in ground-based atmospheric imaging.