Estimating Basic Characteristics of Arrival Processes in Telecommunication Networks by Empirical Data

Considering the realistic teletraffic analysis in advanced telecommunication networks, the estimation of basic characteristics of arrival processes by empirical data is an important subject of current research. Using independent observations of the interarrival times between events and the mean numbers of events in intervals of fixed length, we propose methods to estimate the intensity of a nonhomogeneous arrival stream, particularly a Poisson process, and the renewal function of a renewal process. We formulate the estimation task as stochastically ill-posed problem and apply procedures for the stabilization of the estimates.     

An edge preserved image compression technique

An image compression technique that can preserve edges of objects is proposed here. This technique is developed by looking at small local regions of the image and classifying these regions as uniform, point, line or edge patterns from statistical likelihood ratio tests. The compression ratio depends on the size of the local region considered and the quality of the image will degrade as the size of the local region increases. Experimental results show good quality preservation when the sizes of the local region are 3 × 3, 4 × 4, 5 × 5 with the corresponding compression ratios less than or equal to 7/24, 1/5, 13/100 respectively. A comparison with discrete cosine transform is given here and shows that the new technique produces better quality in most cases.     

金鸡岭山体滑坡治理

介绍了金鸡岭山体滑坡概况,分析了滑坡产生的原因,通过采取排水-抗滑支挡加固为主的治理措施,滑坡体未见活动迹象,表明该治理措施是合理和成功的,可在类似山体滑坡治理中借鉴和参考.     

Regularization in model updating

This paper presents the theory of sensitivity‐based model updating with a special focus on the properties of the solution that result from the combination of optimization of the response prediction with a priori information about the uncertain parameters. Model updating, together with the additional regularization criterion, is an optimization with two objective functions, and must be linearized to obtain the solution. Structured solutions are obtained, based on the generalized singular value decomposition (GSVD), and specific features of the parameter and response paths as the regularization parameter varies are explored. The four different types of spaces that arise in the solution are discussed together with the characteristics of the regularized solution families. These concepts are demonstrated on a simulated discrete example and on an experimental case study. Copyright © 2007 John Wiley & Sons, Ltd.     

Incomplete oblique projections method for solving regularized least-squares problems in image reconstruction

In this paper we improve on the incomplete oblique projections (IOP) method introduced previously by the authors for solving inconsistent linear systems, when applied to image reconstruction problems. That method uses IOP onto the set of solutions of the augmented system Ax?r=b, and converges to a weighted least‐squares solution of the system Ax=b. In image reconstruction problems, systems are usually inconsistent and very often rank‐deficient because of the underlying discretized model. Here we have considered a regularized least‐squares objective function that can be used in many ways such as incorporating blobs or nearest‐neighbor interactions among adjacent pixels, aiming at smoothing the image. Thus, the oblique incomplete projections algorithm has been modified for solving this regularized model. The theoretical properties of the new algorithm are analyzed and numerical experiments are presented showing that the new approach improves the quality of the reconstructed images.     

A fast and adaptive method for complex-valued SAR image denoising based on l_k norm regularization

This paper developed a fast and adaptive method for SAR complex image denoising based on lk norm regularization, as viewed from parameters estimation. We firstly establish the relationship between denoising model and ill-posed inverse problem via convex half-quadratic regularization, and compare the difference between the estimator variance obtained from the iterative formula and biased Cramer-Rao bound, which proves the theoretic flaw of the existent methods of parameter selection. Then, the analytic expre...     

A Fast MAP Algorithm for High-Resolution Image Reconstruction with Multisensors

In many applications, it is required to reconstruct a high-resolution image from multiple, undersampled and shifted noisy images. Using the regularization techniques such as the classical Tikhonov regularization and maximum a posteriori (MAP) procedure, a high-resolution image reconstruction algorithm is developed. Because of the blurring process, the boundary values of the low-resolution image are not completely determined by the original image inside the scene. This paper addresses how to use (i) the Neumann boundary condition on the image, i.e., we assume that the scene immediately outside is a reflection of the original scene at the boundary, and (ii) the preconditioned conjugate gradient method with cosine transform preconditioners to solve linear systems arising from the high-resolution image reconstruction with multisensors. The usefulness of the algorithm is demonstrated through simulated examples.     

A new numerical method for the boundary optimal control problems of the heat conduction equation

A new numerical method is developed for the boundary optimal control problems of the heat conduction equation in the present paper. When the boundary optimal control problem is solved by minimizing the objective function employing a conjugate‐gradient method, the most crucial step is the determination of the gradient of objective function usually employing either the direct differentiation method or the adjoint variable method. The direct differentiation method is simple to implement and always yields accurate results, but consumes a large amount of computational time. Although the adjoint variable method is computationally very efficient, the adjoint variable does not have sufficient regularity at the boundary for the boundary optimal control problems. As a result, a large numerical error is incurred in the evaluation of the gradient function, resulting in premature termination of the conjugate gradient iteration. In the present investigation, a new method is developed that circumvents this difficulty with the adjoint variable method by introducing a partial differential equation that describes the temporal and spatial dynamics of the control variable at the boundary. The present method is applied to the Neumann and Dirichlet boundary optimal control problems, respectively, and is found to solve the problems efficiently with sufficient accuracy. Copyright © 2001 John Wiley & Sons, Ltd.     

地层电导率非线性反演方法—阵列感应数据反演

介绍一种由阵列感应成角测井仪的单频测量信号对实际地层电导率进行反演的新方法，该方法能体现电压与地层电导率的非线性关系。该方法是基于精确的非线性积分方程的迭代方法，用正则化方法求出地层电导率剖面分布。一些反演的数值模拟例子表明，仅利用ＡＩＴ单频测量信号就能很好地对复杂地层进行反演，且定的结果与真实电导率剖面分布吻合得比较好。     

Acceleration of the ICTM image restoration algorithm

We have implemented a simple modification of the ICTM image restoration algorithm that reduces the computational requirements considerably. The modified algorithm converges to the same solution as alternative approaches for computing non-negatively constrained Tikhonov solutions. It compares favourably with these algorithms in terms of speed.