Nonlinear mixture‐wise expansion approach to underdetermined blind separation of nonnegative dependent sources

Underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed mixed signals into greater number of original nonnegative and dependent component (source) signals. That is an important problem for which very few algorithms exist. It is also practically relevant for contemporary metabolic profiling of biological samples, such as biomarker identification studies, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of complex multicomponent mixtures. This paper presents a method for underdetermined blind separation of nonnegative dependent sources. The method performs nonlinear mixture‐wise mapping of observed data in high‐dimensional reproducible kernel Hilbert space (RKHS) of functions and sparseness‐constrained nonnegative matrix factorization (NMF) therein. Thus, the original problem is converted into new one with increased number of mixtures, increased number of dependent sources, and higher‐order (error) terms generated by nonlinear mapping. Provided that amplitudes of original components are sparsely distributed, which is the case for mass spectra of analytes, sparseness‐constrained NMF in RKHS yields, with significant probability, improved accuracy relative to the case when the same NMF algorithm is performed on the original problem. The method is exemplified on numerical and experimental examples related respectively to extraction of 10 dependent components from five mixtures and to extraction of 10 dependent analytes from mass spectra of two to five mixtures. Thereby, analytes mimic complexity of components expected to be found in biological samples. Copyright © 2013 John Wiley & Sons, Ltd.     

The peculiarities of ion scattering by crystals due to surface halfchannels,formed by close packed atomic rows

The ion scattering by the surface of crystals has been studied when there are halfchannels on the crystal surface, which are formed by close packed atomic rows of the first and the second layers of crystals. The obtained results are compared with one of ion scattering by directions, which have no surface halfchannels.     

Non-blind image deconvolution using natural image gradient prior

Since non-blind image deconvolution is inherently ill-posed, the results of unregularized methods are often contaminated by noise and ringing artifacts. To reach a stable solution, we adopt the natural image gradient prior to regularize the latent image and obtain an improved version of the Richardson–Lucy (RL) algorithm. We use both synthetic and real world blurred images to test the proposed method. Experimental results show that the negative artifacts are significantly suppressed and the restored images are of high quality.     

Influence of Spectral Line Deconvolution on Measurement of Excitation Temperature in a Wall‐Stabilized Thermal Plasma by Optical Emission Spectroscopy

Abstract

The measurement of temperature in a wall‐stabilized thermal plasma is done by optical emission spectroscopy. The absolute intensity line method, relative intensity line method, and plot of the Boltzmann function are used. Another method, based on the measurement of electron density and calculations of concentrations, is also used. The influence of spectral line deconvolution on temperature profiles is studied. The results obtained by the four methods are compared. The influence of others parameters, such as the accuracy of spectroscopic constants, and the influence of the theoritical calculations of concentrations are also discussed. These methods employ Ar I, C I and O I lines to measure the temperature of an Ar–CO₂ mixture plasma produced in a wall‐stabilized arc.     

多径衰落环境中具有调制识别能力的盲均衡新算法

常数模盲均衡算法在对非常模信号进行均衡时稳态均方误差不能收敛至零,且不具备信号调制识别能力.因此,依照常数模算法代价函数的构造方法定义了一种余弦代价函数,提出了基于该代价函数的盲均衡新算法.该余弦代价函数可将BPSK、M-PAM、M-QAM信号的不同星座点映射至原点,从而使新算法在对上述常模或非常模信号进行均衡时稳态均方误差均能收敛至零,更重要的是新算法能够在多径衰落环境下实现对上述信号的调制识别.理论分析和仿真结果证明了新算法的优良性能.     

Floating-point scaling technique for sources separation automatic gain control

Based on the floating-point representation and taking advantage of scaling factor indetermination in blind source separation (BSS) processing, we propose a scaling technique applied to the separation matrix, to avoid the saturation or the weakness in the recovered source signals. This technique performs an automatic gain control in an on-line BSS environment. We demonstrate the effectiveness of this technique by using the implementation of a division-free BSS algorithm with two inputs, two outputs. The proposed technique is computationally cheaper and efficient for a hardware implementation compared to the Euclidean normalisation.     

基于奇异值分解的多卫星信号盲检测

针对非合作低信噪比环境下的卫星通信信号检测问题,在信号子空间维数估计的基础上,提出了一种基于奇异值分解的多卫星信号盲检测方法。该方法充分利用奇异值与特征值之间的关系,设计检测统计量将多个信号能量集中起来进行考虑,以适应更低的信噪比,并从理论上对检测性能进行了推导分析。仿真结果表明,该方法简单高效,针对不同的卫星信号,在虚警概率小于1%、信噪比为-11 dB时,盲检测概率均可达90%以上;同时能够在低信噪比环境下适应多信号环境,且其计算量相对特征值方法减少了一个数量级,更适合应用在星载设备上。     

一种变步长短波信道盲均衡算法

通过对短波通信中的自适应均衡(Adaptive Equalization)和盲均衡(Blind Equalization)技术进行比较研究,分析了自适应均衡和盲均衡中各种均衡算法的适用条件和优缺点,重点研究分析了盲均衡算法,并针对盲均衡算法进行改进,提出了将盲均衡算法与变步长思想结合起来,解决了收敛速度与收敛精度(均方剩余误差)的矛盾。用MATLAB对算法进行仿真,对仿真结果进行分析比较并得出结论,验证此改进算法的有效性。     

基于旋转阵列的多帧合成孔径图像复原

针对光学合成孔径成像系统的调制传递函数 (MTF)在中-低频段的衰减以及造成的合成图像降质问题,以6孔径Y型 阵列 为例,提出子孔径阵列旋转成像以获取包含互补信息的多帧图像;通过截止频率与中频能量 比确定旋转角度,并 利用多帧迭代盲解卷积算法进行图像复原。实验表明,与现有的代表性工作相比,本文方法 对于Y型阵列的合成孔径图像有较好的复原效果和重建的准确性。     

一种新颖的抗阶数过估计的增强子空间盲辨识算法

研究了子空间盲辨识算法在信道阶数过估计时失效的问题,提出一种新的鲁棒性强的增强算法。首先针对过估计模型,提出了"加权矩阵定理",理论证明通过构造一个加权矩阵来修正子空间算法,可消除阶数过估计的影响;然后,给出一种用指数序列构造近似的加权矩阵的增强算法。与子空间盲辨识算法相比,增强算法复杂度相当,对阶数过估计鲁棒性强。仿真实验验证了该算法的有效性。