能量收集双向中继网络的高能效联合中继选择和功率分配算法

为了实现双向中继系统在满足传输速率要求时的最小功率消耗,基于功率分割中继协议,在完美和非完美的信道估计两种不同的情况下,提出了能量收集双向中继网络的高能效联合中继选择和功率分配算法,得到了两个信源的最优功率分配和中继节点最优的能量收集比例.仿真结果表明,信道估计误差会增加系统的功率消耗;与传统双向中继比较发现,能量收集双向中继能够实现更少的系统功率消耗.     

双相黏弹VTI介质一阶速度—应力方程正演模拟及双程波照明研究

地层岩石具有各向异性、黏弹性及双相性等特性,因此建立更加精确的地下介质模型,研究地震波传播规律,对认识复杂介质中地震波的传播特征和实际资料的解释有着重要的实际意义.为此,基于黏弹性广义标准线性体(GSLS)模型,首先推导了双相黏弹VTI介质的一阶速度—应力方程并进行正演模拟,与单相弹性各向同性介质相比,双相黏弹VTI介...     

采用核相关滤波器的长期目标跟踪

针对核相关滤波器(KCF)跟踪算法在目标跟踪中存在尺度变化、严重遮挡、相似目标干扰和出视野情况下跟踪失败等问题,提出了一种基于KCF的长期目标跟踪算法。该算法在分类器学习中加入空间正则化,利用基于样本区域空间位置信息的空间权重函数调节分类器系数,使分类器学习到更多负样本和未破坏的正样本,从而增强学习模型的判别力。然后,在检测区域利用Newton方法完成迭代处理,求取分类器最大响应位置及其目标尺度信息。最后,对最大响应位置的目标进行置信度比较,训练在线支持向量机(SVM)分类器,以便在跟踪失败的情况下,重新检测到目标而实现长期跟踪。采用OTB-2013评估基准50组视频序列验证了本文算法的有效性,并与30种其他跟踪方法进行了对比。结果表明:本文提出的算法跟踪精度为0.813,成功率为0.629,排名第一,相比传统KCF算法分别提高了9.86%和22.3%。在目标发生显著尺度变化、严重遮挡、相似目标干扰和出视野等复杂情况下,本文方法均具有较强的鲁棒性。     

Representation of High Resolution Images from Low Sampled Fourier Data: Applications to Dynamic MRI

In this work we propose the use of B-spline functions for the parametric representation of high resolution images from low sampled data in the Fourier domain. Traditionally, exponential basis functions are employed in this situation, but they produce artifacts and amplify the noise on the data. We present the method in an algorithmic form and carefully consider the problem of solving the ill-conditioned linear system arising from the method by an efficient regularization method. Two applications of the proposed method to dynamic Magnetic Resonance images are considered. Dynamic Magnetic Resonance acquires a time series of images of the same slice of the body; in order to fasten the acquisition, the data are low sampled in the Fourier space. Numerical experiments have been performed both on simulated and real Magnetic Resonance data. They show that the B-splines reduce the artifacts and the noise in the representation of high resolution Magnetic Resonance images from low sampled data. This work was supported by the Italian MIUR project Inverse Problems in Medical Imaging 2004–2006 (grant no 2004015818). Germana Landi received the BS degree in Mathematics from the University of Bologna in 1997 and the Ph.D. degree in Computational Mathematics from the University of Padova in 2000. She is currently a postdoctoral researcher in Numerical Analysis at the Department of Mathematics of the University of Bologna. Her research interests include medical imaging and inverse ill-posed problems. Elena Loli Piccolomini received the BS degree in Mathematics from the University of Bologna in 1988. She is an associate professor in Numerical Analysis at the Department of Mathematics of the University of Bologna. Her research interests include numerical methods for the regularization of discrete ill-posed problems with application to medical imaging (MR, TAC, SPECT, PET).     

非线性时域识别方程的不适定性与正则化方法研究

研究了非线性时域识别方程的不适定性及其正则化求解方法。雅可比矩阵的性态能够反映非线性识别方程的性态，因此雅可比矩阵的条件数是非线性识别方程的不适定性的度量。阻尼最小二乘法只是一种强迫正定的计算方法，其识别结果仍然对测试噪声很敏感，解决该问题的有效途径是将阻尼最小二乘法与正则化方法两者结合使用。算例表明，将先验的参数预估值引入Tikhonov镇定泛函可以得到稳定的参数解，且识别误差与原始数据的测试噪声基本保持在同一水平。     

A Full Curvature Based Algorithm for Image Registration

Image registration, i.e., finding an optimal displacement field u which minimizes a distance functional D(u) is known to be an ill-posed problem. In this paper a novel variational image registration method is presented, which matches two images acquired from the same or from different medical imaging modalities. The approach proposed here is also independent of the image dimension. The proposed variational penalty against oscillations in the solutions is the standard H²(Ω) Sobolev semi-inner product for each component of the displacement. We investigate the associated Euler-Lagrange equation of the energy functional. Furthermore, we approach the solution of the underlying system of biharmonic differential equations with higher order boundary conditions as the steady-state solution of a parabolic partial differential equation (PDE). One of the important aspects of this approach is that the kernel of the Euler-Lagrange equation is spanned by all rigid motions. Hence, the presented approach includes a rigid alignment. Experimental results on both synthetic and real images are presented to illustrate the capabilities of the proposed approach. Stefan Henn obtained his diploma (1997) and his Ph.D. in mathematics (2001), both from the Heinrich-Heine University (HHU) of Düsseldorf (Germany). From 1997–1999 he had a researcher position at the Institute for Brain Research at the HHU Düsseldorf. Since 1999 he is a research assistant at the Institute of Mathematics at the HHU Düsseldorf. He received the SIAM outstanding paper prize in 2003 for the paper (Iterative Multigrid Regularization Techniques for Image Matching, SIAM Journal on Scientific Computing, 23(4), pp. 1077-1093). His research interests include Multiscale methods in Scientific Computing and Image Processing, nonlinear large-scale optimization, and numerical analysis of partial differential equations.     

An entropic optimization approach for a parameter identification problem in quasibrittle fracture

The cohesive crack model is a widely used idealization to represent simply and reliably the quasibrittle fracture behavior of concrete-like materials. However, knowledge of the parameters characterizing this model is of prime importance and cannot all be obtained directly from experiments. Typically, recourse is made to some inverse numerical approach. Our particular formulation can be elegantly cast as an instance of a challenging optimization problem known as a mathematical program with equilibrium (or, more precisely in our case, complementarity) constraints (MPEC). The present paper investigates application of an entropic optimization approach to solve the MPEC, and compares its performance with our previously proposed Fischer–Burmeister smoothing scheme. We use actual experimental data for the comparison.     

Numerical simulation of sedimentation in the presence of 2D compressible convection and reconstruction of the particle-radius distribution function

Numerical simulation of the sedimentation of a polydisperse suspension in a convectively unstable medium is presented. For the simulation of 2D compressible convection, the full system of hydrodynamic equations is solved by the explicit MacCormack scheme. Velocities and positions of suspension particles are calculated simultaneously with the solution of the equations. Initially, the particles are randomly distributed in the computational region. The total weight of sedimented matter is recorded during the numerical experiment. The results are compared with the sedimentation of the same suspension without convection. To reconstruct the particle-radius distribution function from the sedimentation curve, a new method is used. This method is based on the solution of the sedimentation integral equation by the Tikhonov regularization method and was recently developed by the author. To illustrate this technique, sedimentation of cement powder in air is simulated. The suspension contains 50000 particles. The particle radii are assumed to be log-normally distributed. Heat-driven convection is completely determined by the top and bottom boundary temperatures of the computational region and lateral boundary conditions. It is shown that convective motions of a medium with sedimented particles lead to the following effect: the fine disperse fraction of the suspension remains suspended much longer than without convection. Some particles will not sediment at all. The maximum radius of the particles of this fraction depends on the convection parameters (e.g. on convection cell size and convection velocities). These parameters, in their turn, depend only on the temperature difference of the top and bottom boundaries. The results of these calculations can be applied in geology and meteorology for studying dust sedimentation in air as well as in technology. Heat-driven convection can be used for separation of suspensions with the cut-off particle radius depending on temperature difference only.     

On Semismooth Newton’s Methods for Total Variation Minimization

In [2], Chambolle proposed an algorithm for minimizing the total variation of an image. In this short note, based on the theory on semismooth operators, we study semismooth Newton’s methods for total variation minimization. The convergence and numerical results are also presented to show the effectiveness of the proposed algorithms. The research of this author is supported in part by Hong Kong Research Grants Council Grant Nos. 7035/04P and 7035/05P, and HKBU FRGs. The research of this author is supported in part by the Research Grant Council of Hong Kong. This work was started while the author was visiting Department of Applied Mathematics, The Hong Kong Polytechnic University. The research of this author is supported in part by The Hong Kong Polytechnic University Postdoctoral Fellowship Scheme and the National Science Foundation of China (No. 60572114). Michael Ng is a Professor in the Department of Mathematics at the Hong Kong Baptist University. As an applied mathematician, Michael’s main research areas include Bioinformatics, Data Mining, Operations Research and Scientific Computing. Michael has published and edited 5 books, published more than 140 journal papers. He is the principal editor of the Journal of Computational and Applied Mathematics, and the associate editor of SIAM Journal on Scientific Computing. Liqun Qi received his B.S. in Computational Mathematics at Tsinghua University in 1968, his M.S, and Ph.D. degree in Computer Sciences at University of Wisconsin-Madison in 1981 and 1984, respectively. Professor Qi has taught in Tsinghua University, China, University of Wisconsin-Madison, USA, University of New South Wales, Australia, and The Hong Kong Polytechnic University. He is now Chair Professor of Applied Mathematics at The Hong Kong Polytechnic University. Professor Qi has published more than 140 research papers in international journals. He established the superlinear and quadratic convergence theory of the generalized Newton method, and played a principal role in the development of reformulation methods in optimization. Professor Qi’s research work has been cited by the researchers around the world. According to the authoritative citation database ISIHighlyCited.com, he is one of the world’s most highly cited 300 mathematicians during the period from 1981 to 1999. Yu-Fei Yang received the B.Sc., M.S. and Ph.D. degrees in mathematics from Hunan University, P. R. China, in 1987, 1994 and 1999, respectively. From 1999 to 2001, he stayed at the University of New South Wales, Australia as visiting fellow. From 2002 to 2005, he held research associate and postdoctoral fellowship positions at the Hong Kong Polytechnic University. He is currently professor in the College of Mathematics and Econometrics, at Hunan University, P. R. China. His research interests includes optimization theory and methods, and partial differential equations with applications to image analysis. Yu-Mei Huang received her M.Sc. in Computer science from Lanzhou University in 2000. She is now pursuing her doctoral studies in computational mathematics in Hong Kong Baptist University. Her research interests are in image processing and numerical linear algebra.     

基于波叠加的噪声源识别实验研究

提出了基于波叠加的噪声源识别和定位方法。该方法克服了边界元法声全息固有的奇异性和非唯一性问题，原理上容易理解，计算上容易实现。从实验出发，建立了实验所需的采集和分析系统，根据实测数据利用自谱和互谱获取全息面上的复声压，结合正则化处理给出了同一平面三个音箱源识别的效果图，实验验证了该方法在进行噪声源识别和定位时具有较好的效果。