High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

Perfluorocarbons in Chemical Biology

Perfluorocarbons, saturated carbon chains in which all the hydrogen atoms are replaced with fluorine, form a separate phase from both organic and aqueous solutions. Though perfluorinated compounds are not found in living systems, they can be used to modify biomolecules to confer orthogonal behavior within natural systems, such as improved stability, engineered assembly, and cell-permeability. Perfluorinated groups also provide handles for purification, mass spectrometry, and ¹⁹F NMR studies in complex environments. Herein, we describe how the unique properties of perfluorocarbons have been employed to understand and manipulate biological systems.     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

基于特征融合的中文简历解析方法研究

针对基于规则和统计的传统中文简历解析方法效率低、成本高、泛化能力差的缺点，提出一种基于特征融合的中文简历解析方法，即级联Word2Vec生成的词向量和用BLSTM（Bidirectional Long Short-Term Memory）建模字序列生成的词向量，然后再结合BLSTM和CRF（Conditional Random Fields）对中文简历进行解析（BLSTM-CRF）。为了提高中文简历解析的效率，级联包含字序列信息的词向量和用Word2Vec生成的词向量，融合成一个新的词向量表示；再由BLSTM强大的学习能力融合词的上下文信息，输出所有可能标签序列的分值给CRF层；再由CRF引入标签之间约束关系求解最优序列。利用梯度下降算法训练神经网络，使用预先训练的词向量和Dropout优化神经网络，最终完成对中文简历的解析工作。实验结果表明，所提的特征融合方法优于传统的简历解析方法。     

A Least Squares Approach to Escalator Algorithms for Adaptive Filtering

In this paper, we introduce an escalator (ESC) algorithm based on the least squares (LS) criterion. The proposed algorithm is relatively insensitive to the eigenvalue spread ratio (ESR) of an input signal and has a faster convergence speed than the conventional ESC algorithms. This algorithm exploits the fast adaptation ability of least squares methods and the orthogonalization property of the ESC structure. From the simulation results, the proposed algorithm shows superior convergence performance.     

CO2吞吐工艺操作参数的整体优化设计

针对ZY油田原油CO2吞吐的工艺过程，建立了CO2吞吐数值模拟模型。应用数值模拟方法对CO2吞吐进行了实例分析，首先对该井的CO2吞吐参数进行了单因素敏感性分析，分析了CO2周期注入量、注入速度、焖井时间、采液速度等因素对CO2吞吐效果的影响。在此基础上进一步运用正交试验方法对CO2吞吐参数进行了优化，通过计算表明用正交试验优选法得到的优化方案比单因素敏感性分析所得的优化结果更为合理。     

Cu基络合物催化甲醇液相氧化羰化反应

针对甲醇液相氧化羰基化法合成碳酸二甲酯(DMC)工艺,开发了新型Cu基络合催化剂(CuB rnLm)。对CuB rnLm催化剂的活性及其稳定性进行了研究。实验结果表明,采用该催化剂,甲醇转化率和DMC选择性较高;元素价态和物质结构分析表明,CuB rnLm催化剂循环使用5次后仍保持较好的稳定性。采用正交设计和中心响应曲面法设计实验,并使用S tatistica软件进行统计分析,寻求出反应的主要影响因素,并得到优化的工艺条件:反应温度100~110℃、反应压力3.0~3.5M Pa、反应时间4~6h、CuB rnLm催化剂质量浓度(以甲醇的体积计)0.15~0.20g/mL。在此工艺条件下,甲醇转化率可达23%以上,DMC的选择性为96%~98%。     

应用TEXTIO和MATLAB进行复杂数字系统仿真

在基于FPGA的复杂数字系统的仿真测试中,设计者常常面临各种挑战.以数字图像处理系统的仿真为例,如果采用完备性仿真测试方法,那么测试矢量的数量将是非常巨大的;如果采用常规方法,不仅效率不高,而且可能无法达到仿真测试的目的.针对这个难题,提出一种进行仿真测试的新方法,该方法应用TEXTIO和MATLAB来辅助仿真测试过程,使问题得到较好的解决.以电视图像实时多目标捕获单元的仿真测试为例,全面、细致地讨论这种新方法.     

下一代无线通信概念和技术

下一代无线通信(NextG:Next-generationwireless)的主流是随时随地的无线通信系统和无缝的高质量无线业务。概述了随时随地的无线通信和业务发展情况,并且介绍了为下一代无线通信的发展提供基础的关键技术。     

一种实现非等带宽多载波调制的新方法

传统的多载波调制都是采用对传输信道进行等带宽划分的方式。为了更好地适应信道传输特性，一个很有应用前景的发展方向是采用非等带宽划分信道的多载波调制方式。本文提出了一种利用正交小波包变换实现的非等带宽划分信道的多载波调制方法。理论分析和实验结果表明，这种方法对于提高信道的传输性能有很大的潜力。