Sum rates for multi-user MIMO vector perturbation precoding with regularization

In this paper, we analyze the sum rate performance of multiuser multi-antenna downlink channel. We consider Rayleigh fading environment when regularized vector perturbation precoding (R-VPP) method is used at the transmitter. We derive expressions for the sum rate in terms of the variance of the received signal. We also provide a closed form approximation for the mean squared error (MSE) which is shown to work well for the whole range of SNR. Further, we also propose a simpler expression for R-VPP sum rate based on MSE. The simulation results show that the proposed expressions for R-VPP sum rate closely match the sum rate found by the entropy estimation. Our results show that when compared with other linear and non-linear precoding methods (like zero-forcing precoder, linear minimum mean square error (MMSE) precoder and VPP), R-VPP sum rate performance is very close to DPC for all SNR values. It is also noted that the sum rate performance of the linear MMSE precoder is very close to the R-VPP at low to medium SNR range. Finally we also compared the merits of performing regularization for VPP as compared to the greedy rate maximizing user scheduling. It turns out that the R-VPP with or without user selection performs better than the VPP systems with user selection.     

Monotonically increasing functions of any quantum correlation can make all multiparty states monogamous

Monogamy of quantum correlation measures puts restrictions on the sharability of quantum correlations in multiparty quantum states. Multiparty quantum states can satisfy or violate monogamy relations with respect to given quantum correlations. We show that all multiparty quantum states can be made monogamous with respect to all measures. More precisely, given any quantum correlation measure that is non-monogamic for a multiparty quantum state, it is always possible to find a monotonically increasing function of the measure that is monogamous for the same state. The statement holds for all quantum states, whether pure or mixed, in all finite dimensions and for an arbitrary number of parties. The monotonically increasing function of the quantum correlation measure satisfies all the properties that are expected for quantum correlations to follow. We illustrate the concepts by considering a thermodynamic measure of quantum correlation, called the quantum work deficit.     

Einstein-aether theory with a Maxwell field: General formalism

We extend the Einstein-aether theory to include the Maxwell field in a nontrivial manner by taking into account its interaction with the time-like unit vector field characterizing the aether. We also include a generic matter term. We present a model with a Lagrangian that includes cross-terms linear and quadratic in the Maxwell tensor, linear and quadratic in the covariant derivative of the aether velocity four-vector, linear in its second covariant derivative and in the Riemann tensor. We decompose these terms with respect to the irreducible parts of the covariant derivative of the aether velocity, namely, the acceleration four-vector, the shear and vorticity tensors, and the expansion scalar. Furthermore, we discuss the influence of an aether non-uniform motion on the polarization and magnetization of the matter in such an aether environment, as well as on its dielectric and magnetic properties. The total self-consistent system of equations for the electromagnetic and the gravitational fields, and the dynamic equations for the unit vector aether field are obtained. Possible applications of this system are discussed. Based on the principles of effective field theories, we display in an appendix all the terms up to fourth order in derivative operators that can be considered in a Lagrangian that includes the metric, the electromagnetic and the aether fields.     

An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine

Based on the techniques of Hilbert–Huang transform (HHT) and support vector machine (SVM), a noise-based intelligent method for engine fault diagnosis (EFD), so-called HHT–SVM model, is developed in this paper. The noises of a sample engine under normal and several fault states are first measured and denoised by using the wavelet packet threshold method to initially lower the noise level with negligible signal distortion. To extract fault features of the engine, then, the HHT is selected and applied to the measured noise signals. A nine-dimensional vector, which consists of seven intrinsic mode functions (IMFs) from the empirical mode decomposition (EMD), maximum value of HHT marginal spectrum and its corresponding frequency component, is specified to represent each engine fault feature. Finally, an optimal SVM model is established and trained for engine failure classification by using the fault feature vectors of the noise signals. Cross-validation results show that the proposed noise-based HHT–SVM method is accurate and effective for engine fault diagnosis. Due to outstanding time–frequency characteristics and pattern recognition capacity of the HHT and SVM, the newly proposed HHT–SVM can be used to deal with both the stationary and nonstationary signals, and even the transient ones. In the view of applications, the HHT–SVM technique may be suggested not only to detect the abnormal states of vehicle engines, but also to be extended to other fields for failure diagnosis in engineering.     

The estimation of the degree of coherence of mutually orthogonal beams: New approaches

A theoretical model for calculating the motion dynamics of the particles of different light-scattering mechanisms in the energy inhomogeneous optical field is suggested. The direct relationship between the motion velocity of the tested particles in the created field and the degree of coherence of mutually orthogonal linearly polarized plane waves is demonstrated.     

A framework for classification with single feature kernel matrix

In this paper, we propose a novel classification framework using single feature kernel matrix. Different from the traditional kernel matrices which make use of the whole features of samples to build the kernel matrix, this research uses features of the same dimension of any two samples to build a sub-kernel matrix and sums up all the sub-kernel matrices to get the single feature kernel matrix. We also use single feature kernel matrix to build a new SVM classifier, and adapt SMO (Sequential Minimal Optimization) algorithm to solve the problem of SVM classifier. The results of the experiments on several artificial datasets and some challenging public cancer datasets display the classification performance of the algorithm. The comparisons between our algorithm and L₂-norm SVM on the cancer datasets demonstrate that the accuracy of our algorithm is higher, and the number of support vectors selected is fewer, indicating that our proposed framework is a more practical approach.     

Effects of enhanced electronic correlation on magnetic properties of light non-metallic element (B,C, N,and O)-doped CuCl: A first-principles study

Based on density functional calculations within both standard generalized gradient approximation and plus on-site Coulomb interactions approaches, we have investigated the electronic structure and magnetic properties of the first-row element-doped CuCl semiconductors. The electronic correlations in both 2p and 3d orbitals are enhanced by adding the on-site Coulomb repulsion (Hubbard U and Hund exchange J). After a comparative study, we find that, for both standard and beyond approaches, B-doped CuCl is a half-metallic magnet with majority-spin impurity bands touching the Fermi level, C-doped CuCl is a magnetic semiconductor, and N-doped CuCl is a half-metallic magnet with minority-spin impurity bands crossing the Fermi level. Nevertheless, for O-doped CuCl, it transforms from a nonmagnetic semiconductor to a half-metallic magnet with metallic up-spins by considering the correlation effects. The calculation shows that the enhanced electronic correlation not only corrects the error of band-gap, but also influences the magnetic ground state and the distribution of local magnetic moments. The location of impurity bands with different dopants was understood based on the elements' electronegativity and interaction between dopant and host atoms. Strong hybridization between the dopant's 2p states and the filled 3d orbitals of adjacent Cu yields the main contribution to magnetization.     

Stress‐dependent correlations for resonant Raman bands in graphite with defects

We have studied the Raman features characteristics of defects generated in graphite under high stress conditions. Defects are generated in pristine highly oriented pyrolytic graphite by squeezing the samples in a high‐pressure anvil cell and monitored in situ by Raman spectroscopy. On the basis of our Raman measurements and existing literature correlations, we conclude that vacancies and grain boundaries are generated during compression–decompression cycles, being the defects mostly generated during the decompression stage. Our results demonstrate that the relative intensities of the D, D′, and (D + D′) bands are strongly correlated. Which is important for practical application of Raman spectroscopy in the characterization of carbon materials is that such correlations are essentially constant over the whole stress range covered in the experiments (~7 GPa). An additional interesting result concerns the relative intensities of the denoted 2D₁ and 2D₂ contributions, which are correlated with the intensity of the G band; the intensity ratio between both features is modified by stress indicating that the stress affects the stacking order of pristine graphite. Finally, we find that the decrease in intensity of the 2D₂ band with decreasing crystallite size found in existing studies on unstrained graphite remains under stress conditions. Copyright © 2014 John Wiley & Sons, Ltd.     

Using Copula distributions to support more accurate imaging-based diagnostic classifiers for neuropsychiatric disorders

Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. > 10) relative to the number of participants who provide the MRI data (< 100). Sparse data in a high dimensional space increase the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging measures or their multivariate Gaussian distributions. Thus, our findings demonstrate that estimated multivariate Copula distributions can generate dense sets of brain imaging measures that can in turn be used to train classifiers, and those classifiers are significantly more accurate and more reproducible than are those generated using real-world imaging measures alone.     

基于核方法的模拟电路故障诊断

核方法通过非线性映射将原始数据嵌入到高维特征空间,然后进行线性分析和处理,为基于知识的数据分析带来新的方法和模式;传统方法无法解决故障特征数据维数高、在故障样本交叠严重时多分类性能较差的问题,因此在电路故障特征数据预处理阶段,提出了分步骤分别在时域对电路输出电压波形进行小波包分析和在频域测量电路幅频特性的方法来提取电路故障特征;预处理后的故障特征向量只是8维向量,减少了SVM的训练时间;将该方法应用于国际标准电路中的CTSV滤波器电路的故障诊断,结果表明:该方法能突出不同故障的特性,故障诊断正确率达到98.57%(414/420)。