Improvement of direct sampling method in transverse electric polarization

Recently, the mathematical structure of the direct sampling method (DSM) was investigated in transverse-electric (TE) polarization, and the reason why the exact locations of small inhomogeneities cannot be retrieved through traditional DSM has been revealed. In this paper, we present an improved DSM for identifying the exact locations of small inhomogeneities in TE polarization. Furthermore, we investigate a multi-frequency indicator function to obtain a better result. Corresponding mathematical analysis and simulations are performed to show the feasibilities of the proposed improvement techniques.     

基于活动重叠的DSM项目进度优化与仿真

项目活动之间的重叠虽然可以缩短项目工期,但又存在返工风险。为此,本文采用设计结构矩阵(DSM),提出活动超前重叠因子矩阵和活动赶工重叠因子矩阵,结合返工风险矩阵,完整地描述了项目活动的重叠及其对返工的影响。提出了基于活动重叠的混合粒子群项目进度优化方法。通过案例计算表明,该法可以有效缩短项目工期,同时为项目决策者合理选择活动重叠量提供了新思路。     

基于资源时间因子的DSM项目群进度优化研究

在项目实践中,由于资源约束,从而会延长项目群的工期。为此,本文引入资源时间因子概念,采用设计结构矩阵(DSM),利用资源进入和退出时间因子矩阵描述了项目任务对资源的需求。提出了基于资源时间因子的遗传粒子群项目群进度优化方法。通过案例计算表明,该法可以有效缩短项目群工期,同时为项目决策者合理增加资源提供了新思路。     

Application and analysis of direct sampling method in real-world microwave imaging

In this paper, a direct sampling method (DSM) is designed for a real-time detection of small anomalies from scattering parameters measured by a small number of dipole antennas. Applicability of the DSM is theoretically demonstrated by proving that its indicator function can be represented in terms of an infinite series of Bessel functions of integer order, Hankel function of order zero, and the antenna configurations. Experiments using real-data then demonstrate both the effectiveness and limitations of this method.     

Suppression of Defective Data Artifacts for Deblurring Images Corrupted by Random Valued Noise

For deblurring images corrupted by random valued noise, two-phase methods first select likely-to-be reliables (data that are not corrupted by random valued noise) and then deblur images only with selected data. Two-phase methods, however, often cause defective data artifacts, which are mixed results of missing data artifacts caused by the lack of data and noisy data artifacts caused mainly by falsely selected outliers (data that are corrupted by random valued noise). In this paper, to suppress these defective data artifacts, we propose a blurring model based reliable-selection technique to select reliables as many as possible to make all of to-be-recovered pixel values to contribute to selected data, while excluding outliers as accurately as possible. We also propose a normalization technique to compensate for non-uniform rates in recovering pixel values. We conducted simulation studies on Gaussian and diagonal deblurring to evaluate the performance of proposed techniques. Simulation results showed that proposed techniques improved the performance of two-phase methods, by suppressing defective data artifacts effectively.     

An efficient serial distributed arithmetic algorithm for FPGA implementation of digital up conversion

This article deals with a computationally efficient serial distributed arithmetic algorithm producing a device efficient field programmable gate array implementation. The proposed algorithm takes half the computations time as compared to the conventional symmetric algorithms. The algorithm is analyzed for a direct conversion transmitter of ionospheric radar. Matching and buffering criterion are used to reduce the arithmetic process. The algorithm can be extended to applications with similar characteristics, particularly for System On Chip (SOC) techniques. © 2005 Wiley Periodicals, Inc. Complexity 11: 24–29, 2005     

Image reconstruction model for the exterior problem of computed tomography based on weighted directional total variation

The exterior problem of computed tomography (CT) is a special imaging modality and it is an important research issue in Non-Destructive Testing (NDT). In the exterior problem, the missing projection data vary with the position of the pixels. Furthermore, amounts of theoretical and experimental results showed that the edges that are tangent to the radial direction are much harder to be reconstructed than edges that are tangent to the angular direction. In this paper, a weighted directional total variation (WDTV) based regularization model was proposed to better deal with the exterior problem. By introducing the WDTV regularization term, our model can reconstruct high quality images. First, WDTV of an image also describes the sparsity of image gradient magnitude which can enforce the reconstructed result to be a nearly flat image. Second, our model can preserve edges and reduce artifacts near edges that are tangent to the radial direction. Third, a numerical implementation called SART+WDTV algorithm was developed to solve our model. Simulated and real experiments demonstrated that our model was more capable of suppressing artifacts and preserving edges.     

Riemannian Newton-type methods for joint diagonalization on the Stiefel manifold with application to independent component analysis

The joint approximate diagonalization of non-commuting symmetric matrices is an important process in independent component analysis. This problem can be formulated as an optimization problem on the Stiefel manifold that can be solved using Riemannian optimization techniques. Among the available optimization techniques, this study utilizes the Riemannian Newton’s method for the joint diagonalization problem on the Stiefel manifold, which has quadratic convergence. In particular, the resultant Newton’s equation can be effectively solved by means of the Kronecker product and the vec and veck operators, which reduce the dimension of the equation to that of the Stiefel manifold. Numerical experiments are performed to show that the proposed method improves the accuracy of the approximate solution to this problem. The proposed method is also applied to independent component analysis for the image separation problem. The proposed Newton method further leads to a novel and fast Riemannian trust-region Newton method for the joint diagonalization problem.     

IMAGE DENOISING VIA TIME-DELAY REGULARIZATION COUPLED NONLINEAR DIFFUSION EQUATIONS

A novel nonlinear anisotropic diffusion model is proposed for image denoising which can be viewed as a novel regularized model that preserves the cherished features of Perona-Malik to some extent. It is characterized by a local dependence in the diffusivity which manifests itself through the presence of $p(x)$-Laplacian and time-delay regularization. The proposed model offers a new nonlinear anisotropic diffusion which makes it possible to effectively enhance the denoising capability and preserve the details while avoiding artifacts. Accordingly, the restored image is very clear and becomes more distinguishable. By Galerkin's method, we establish the well-posedness in the weak setting. Numerical experiments illustrate that the proposed model appears to be overwhelmingly competitive in restoring the images corrupted by Gaussian noise.     

Stereoscopic neuro-vision for three-dimensional object recognition

Neural network technology has provided new methodologies for solving difficult computational problems in many areas of science and engineering. Neural networks, along with their varied learning techniques, have replaced complicated mathematical models, complex estimation techniques, or optimization procedures in several applications. One particular area seeing much benefit from these new computational paradigms is machine vision. The machine vision field has long needed approaches offering robust operation, massive parallel and distributed computational capabilities, and graceful system degradation. Neural networks offer these capabilities along with the potential of direct hardware implementation.This article demonstrates several novel uses of artificial neural networks in the processing of stereoscopic images for three-dimensional object recognition. It will be shown that several different types of neural networks can be combined, with a rule base and conventional processing techniques, for the creation of a powerful 3-D object recognition system. This hybrid system has been tested on several simple objects and the results are presented.     

Image reconstruction model for limited-angle CT based on prior image induced relative total variation

Limited-angle computed tomography (CT) reconstruction has a great potential to reduce X-ray radiation dose or scanning time. Suppressing shading artifacts is challenging, but of great practical significance in limited-angle CT. Traditional methods based on total variation (TV) cannot effectively remove the shading artifacts, prior image constrained compressed sensing (PICCS) is a promising method, but is sensitive to the quality of the prior image. In micro-CT, a prior image reconstructed by filtered back-projection (FBP) may contain high-level noise. An image reconstructed by PICCS tends to inherit both structures and noise of the prior image. In this study, to suppress noise and shading artifacts, we propose a new limited-angle CT reconstruction model called prior image induced relative total variation (piiRTV), that uses the structure information of a prior image to guide limited-angle CT reconstruction. The proposed piiRTV is compared to TV and PICCS. Numerical simulations and experiments on real CT projections demonstrate the effectiveness of piiRTV in suppression of noise and shading artifacts. In addition, the proposed piiRTV is more robust to the prior image quality than PICCS.     

A new SOM-based method for profile generation: Theory and an application in direct marketing

The field of direct marketing is constantly searching for new data mining techniques in order to analyze the increasing available amount of data. Self-organizing maps (SOM) have been widely applied and discussed in the literature, since they give the possibility to reduce the complexity of a high dimensional attribute space while providing a powerful visual exploration facility. Combined with clustering techniques and the extraction of the so-called salient dimensions, it is possible for a direct marketer to gain a high level insight about a dataset of prospects. In this paper, a SOM-based profile generator is presented, consisting of a generic method leading to value-adding and business-oriented profiles for targeting individuals with predefined characteristics. Moreover, the proposed method is applied in detail to a concrete case study from the concert industry. The performance of the method is then illustrated and discussed and possible future research tracks are outlined.     

Accurate fourteenth-order methods for solving nonlinear equations

We establish new iterative methods of local order fourteen to approximate the simple roots of nonlinear equations. The considered three-step eighth-order construction can be viewed as a variant of Newton’s method in which the concept of Hermite interpolation is used at the third step to reduce the number of evaluations. This scheme includes three evaluations of the function and one evaluation of the first derivative per iteration, hence its efficiency index is 1.6817. Next, the obtained approximation for the derivative of the Newton’s iteration quotient is again taken into consideration to furnish novel fourteenth-order techniques consuming four function and one first derivative evaluations per iteration. In providing such new fourteenth-order methods, we also take a special heed to the computational burden. The contributed four-step methods have 1.6952 as their efficiency index. Finally, various numerical examples are given to illustrate the accuracy of the developed techniques.     

The Dissipative Spectral Methods for the First Order Linear Hyperbolic Equations

In this paper, we introduce the dissipative spectral methods (DSM) for the first order linear hyperbolic equations in one dimension. Specifically, we consider the Fourier DSM for periodic problems and the Legendre DSM for equations with the Dirichlet boundary condition. The error estimates of the methods are shown to be quasi-optimal for variable-coefficients equations. Numerical results are given to verify high accuracy of the DSM and to compare the proposed schemes with some high performance methods, showing some superiority in long-term integration for the periodic case and in dealing with limited smoothness near or at the boundary for the Dirichlet case.     

Kinematics of an offset 3-UPU translational parallel manipulator by the homotopy continuation method

For most parallel manipulators, the inverse kinematics is straightforward, while the direct kinematics is challenging. The latter requires the solution of a system of nonlinear equations. In this paper we use the homotopy continuation method to solve the forward and inverse kinematic problems of an offset 3-UPU translational parallel manipulator. The homotopy continuation method is a novel method which alleviates drawbacks of the traditional numerical techniques, namely; the acquirement of good initial guess values, the problem of convergence and computing time. The direct kinematics problem of the manipulator leads to 16 real solutions.     

A New Approach to Numerical Integration

A novel method of estimating integrals is introduced using thetheory of measure and Lebesgue integration. It is shown thatmultiple integrals reduce to the evaluation of a one-dimensionalintegral of a measure function. Comparison of the method andvarious conventional techniques is carried out for several integrals.     

一种具有平均累积优势度的排序方法

本文结合特征向量法（EM）及和法（SM）优点,提出了一种新的排序方法一具有平均累积优势度的和法（DSM）,同EM,SM,MDM[6]相比,此法简单,实用,可靠,计算权重所需时间少,且与EM总量得到相同的方案排序,而其它方法如平均优势度矩阵法（MDM）,对数最小二乘法（LISM）,最小偏差法（LDM）有时会产生逆序。     

AN ACCELERATION STRATEGY FOR RANDOMIZE-THEN-OPTIMIZE SAMPLING VIA DEEP NEURAL NETWORKS

Randomize-then-optimize (RTO) is widely used for sampling from posterior distribu-tions in Bayesian inverse problems.However,RTO can be computationally intensive for complexity problems due to repetitive evaluations of the expensive forward model and its gradient.In this work,we present a novel goal-oriented deep neural networks (DNN) sur-rogate approach to substantially reduce the computation burden of RTO.In particular,we propose to drawn the training points for the DNN-surrogate from a local approximated posterior distribution-yielding a flexible and efficient sampling algorithm that converges to the direct RTO approach.We present a Bayesian inverse problem governed by elliptic PDEs to demonstrate the computational accuracy and efficiency of our DNN-RTO ap-proach,which shows that DNN-RTO can significantly outperform the traditional RTO.     

A review of parallel finite element methods on the DAP

This paper reviews the research work that has been done to implement the finite element method for solving partial differential equations on the ICL distributed array processor (DAP). A brief outline of the principle features of the method is given, followed by details of the novel techniques required for implementation on the highly parallel architecture. Various methods of solution of the finite element equations are discussed; both direct and iterative techniques are included. The current state-of-the-art favours the use of the preconditioned conjugate gradient method. Some suggestions for future research work on parallel finite element methods are made.     

Local models—an approach to distributed multi-objective optimization

When solving real-world optimization problems, evolutionary algorithms often require a large number of fitness evaluations in order to converge to the global optima. Attempts have been made to find techniques to reduce the number of fitness function evaluations. We propose a novel framework in the context of multi-objective optimization where fitness evaluations are distributed by creating a limited number of adaptive spheres spanning the search space. These spheres move towards the global Pareto front as components of a swarm optimization system. We call this process localization. The contribution of the paper is a general framework for distributed evolutionary multi-objective optimization, in which the individuals in each sphere can be controlled by any existing evolutionary multi-objective optimization algorithm in the literature.