A residual‐based Allen–Cahn phase field model for the mixture of incompressible fluid flows

The hydrodynamics of fluid mixtures is receiving more and more attention in many science and engineering applications. Within the techniques for dealing with front displacements and moving boundaries between different density and/or viscosity fluids, phase fields are a class of models in which a diffusive transition region is taken into account instead of a steep interface. Although these models have a physical motivation, they require the definition of extra parameters. In order to make it less parameter dependent, the classic Allen–Cahn phase field model is modified, exploring its similarities with residual‐based discontinuity‐capturing schemes, making the phase field equation dependent on its own residual. We solve the coupling between incompressible viscous fluid flow and the phase field advective–diffusive–reactive transport to simulate the main processes in interface tension and/or buoyancy driven problems. For the solution of the Navier–Stokes and transport equations, we use a stabilized finite element formulation. The implementation has been performed using the libMesh finite element library, written in C++ , which provides support for adaptive mesh refinement and coarsening. A chemical convection benchmark problem is used to validate the proposed model, and then we solve two bubble interaction problems. Copyright © 2014 John Wiley & Sons, Ltd.     

An adaptive multilevel wavelet framework for scale‐selective WENO reconstruction schemes

We put forth a dynamic computing framework for scale‐selective adaptation of weighted essential nonoscillatory (WENO) schemes for the simulation of hyperbolic conservation laws exhibiting strong discontinuities. A multilevel wavelet‐based multiresolution procedure, embedded in a conservative finite volume formulation, is used for a twofold purpose. (i) a dynamic grid adaptation of the solution field for redistributing grid points optimally (in some sense) according to the underlying flow structures, and (ii) a dynamic minimization of the in built artificial dissipation of WENO schemes. Taking advantage of the structure detection properties of this multiresolution algorithm, the nonlinear weights of the conventional WENO implementation are selectively modified to ensure lower dissipation in smoother areas. This modification is implemented through a linear transition from the fifth‐order upwind stencil at the coarsest regions of the adaptive grid to a fully nonlinear fifth‐order WENO scheme at areas of high irregularity. Therefore, our computing algorithm consists of a dynamic grid adaptation strategy, a scale‐selective state reconstruction, a conservative flux calculation, and a total variation diminishing Runge‐Kutta scheme for time advancement. Results are presented for canonical examples drawn from the inviscid Burgers, shallow water, Euler, and magnetohydrodynamic equations. Our findings represent a novel direction for providing a scale‐selective dissipation process without a compromise on shock capturing behavior for conservation laws, which would be a strong contender for dynamic implicit large eddy simulation approaches.     

基于三角形循环二分算法的图像去雾研究

为了提高图像去雾效果,采用三角形循环二分算法处理图像。首先分析基本暗通道算法图像去雾;接着基于三角形循环二分算法对大气光值精确计算,将图像沿对角线分成4个三角形区域,只对雾浓度最较高的区域进行二分图形划分,选取三角形最长边的中点,连接到对应三角形最长边的顶点,循环执行直到满足预先设定的面积阈值;然后自适应调节透射率,避免色彩失真;最后给出了算法流程。实验仿真显示,本文算法去雾后的图像在视觉上更加清晰,可见边集合数目比、平均梯度比值、直方图相似度系数、综合评价指标相比其他算法较好,能够满足图像去雾对清晰度、信息量的要求。     

基于正弦控制因子的Lateral变异鲨鱼优化算法

针对传统鲨鱼优化算法在求解高维目标函数时,易早熟收敛,陷入局部最优的缺陷.提出一种基于正弦控制因子的Lateral变异鲨鱼优化算法.通过正弦曲线的特性和自适应惯性权重,改善了传统鲨鱼优化算法中由于随机选取控制因子数值大小可能导致算法在迭代后期全局搜索能力降低的问题,提高了算法在迭代后期的全局收敛能力,并对最佳鲨鱼位置引入Lateral变异策略,加强了算法跳出局部最优的可能性.改进后的算法对多个shifted单峰,多峰以及固定维测试函数进行求解,实验结果表明,对比多种不同优化算法而言,本文所提LSSO算法具有更高的收敛精度和搜索速度.     

宽带数字接收机IQ不平衡估计与自适应补偿算法

针对宽带调制解调技术中广泛存在的IQ不平衡问题,在IQ不平衡模型及补偿原理的分析基础上提出了一种宽带数字接收机IQ不平衡估计与自适应补偿算法。首先利用解调数据对IQ不平衡参数进行实时估计,然后利用估计参数对接收信号进行自适应补偿。实验结果表明,所提算法可以有效解决宽带调制解调系统中普遍存在的IQ不平衡问题,提升系统误码性能。     

Comparison of self‐adaptive dynamic differential evolution and particle swarm optimization for smart antennas in wireless communication

In this paper, ultrawide band (UWB) communication systems with eight transmitting and receiving ring antenna arrays are implemented to test the bit error rate and capacity performance. By using the ray‐tracing technique to compute any given indoor wireless environment, the impulse response of the system can be calculated. The synthesized beamforming problem can be reformulated into a multiobjective optimization problem. Self‐adaptive dynamic differential evolution (SADDE) and particle swarm optimization (PSO) are used to find the excitation current and the feed line length of each antenna to form the appropriate beam pattern. This pattern can then reduce the bit error rate and increase the channel capacity and receiving energy. Numerical results show that the fitness value and the convergence speed by the SADDE are better than those by the PSO. Moreover, the SADDE had better results for both line‐of‐sight and nonline‐of‐sight cases. In other words, compared with PSO, SADDE has improved more effectively the main beam radiation energy and reduced the multipath interference.     

Energy Harvesting in Cognitive Networks with Cooperative Beamforming: Power Allocation and Stability Analysis

Energy harvesting (EH) is a promising technology to improve both energy efficiency and spectral efficiency in cognitive radio (CR) networks. However, due to the randomness of the harvested energy and the interference constraint at the primary users (PUs), the limited transmission power of secondary users (SUs) may reduce the service rate of SUs. To solve this problem, this paper investigates a cooperative transmission method where a zero-forcing beamforming method is used in the EH based secondary network. Considering the transmission power constraint and energy causality, we derive the closed-form solution of the optimal transmission power for the secondary source and relays, which achieves the maximal stable throughput of the secondary network. Numerical results show the impact of different system parameters to the maximal stable throughput. In addition, compared with the traditional decode-and-forward (DF) scheme, the cooperative beamforming method achieves higher stable throughput under an high quality source-to-relay channel.     

LLN中基于能量均衡的高效低时延路由算法

针对有损低功耗网络(Low-power and Lossy Network,LLN)中由于无线链路的有损特性以及节点部署不均匀无法有效实现网络能量均衡,提出一种基于能量均衡的高效低时延路由算法(Efficient Low Latency Routing Algorithm Based on Energy Balance in Low-Power and Lossy Networks,ELLEB-RPL)。该算法采用“ELLEB组网”策略,使得节点能够有效避免在DODAG(Destination Oriented Directed Acyclic Graph)间来回切换;通过采用“最优父节点选择”策略,使得节点选择的父节点达到最优;通过采用“流量自适应分配”策略,达到网络能量均衡的目的。仿真结果表明,ELLEB-RPL在Sink节点平均吞吐量、网络平均寿命以及端到端时延上均有提升。     

融合层次聚类的高分辨率遥感影像超像素分割方法

为解决遥感影像分割尺度自动选取难的问题,提出了融合层次聚类的高分辨率遥感影像超像素分割方法。首先采用自适应形态重建的分水岭分割算法将影像分割成多个超像素;然后提取各超像素的灰度特征向量;最后利用层次聚类方法进行超像素合并,实现高分辨率遥感影像的精确分割。实验选用4组景遥感影像;采用定性和定量相结合的方法评价实验结果。实验结果表明,该方法有效提高了遥感影像分割精度,并取得了较好的分割视觉效果。     

空时误差情况下的STAP方法性能分析

空时自适应处理（Space-Time Adaptive Processing,STAP）技术可以实现对机载雷达杂波的有效抑制,显著提高机载雷达对运动目标的检测性能。但在实际工程应用过程中,STAP技术不可避免地会受到各种空时误差的影响,导致其性能严重下降。本文首先给出了各种空时误差的数学模型,然后从目标导向矢量失配和杂波自由度增加两方面系统分析了误差影响STAP性能的内在机理,并以信杂噪比（Signal to Clutter plus Noise Ratio,SCNR）损失为指标分析了不同误差对STAP性能的影响,最后通过仿真实验对相关分析进行了验证。本文工作量化了不同误差对STAP性能的影响程度,可为机载脉冲多普勒雷达空时误差补偿提供重要的理论支撑。