Structure Aware Visual Cryptography

Visual cryptography is an encryption technique that hides a secret image by distributing it between some shared images made up of seemingly random black‐and‐white pixels. Extended visual cryptography (EVC) goes further in that the shared images instead represent meaningful binary pictures. The original approach to EVC suffered from low contrast, so later papers considered how to improve the visual quality of the results by enhancing contrast of the shared images. This work further improves the appearance of the shared images by preserving edge structures within them using a framework of dithering followed by a detail recovery operation. We are also careful to suppress noise in smooth areas.     

Pattern synthesis of time‐modulated conformal arrays by an improved differential evolution algorithm

In this article, an improved differential evolution algorithm (IDE) based on two different colonies is proposed and applied to time‐modulated conformal arrays syntheses. The whole population of IDE is divided into two parts. The one part searches the solution globally while the other searches the neighborhood of the solution provided by the previous one. Benchmark functions are provided to testify IDE. Furthermore, IDE is applied to synthetize sum‐difference patterns with a 1 × 16 elements time‐modulated circular array and low sidelobe level (SLL) patterns with an 8 × 12 elements time‐modulated cone array. After optimization, the sideband level (SBL) of the circular array at the first sideband frequency is ?1.00 dB. The SLL and SBL at the first sideband frequency of the cone array are lower than ?30.00 and ?20.00 dB, respectively. Experiment results verify the superior performance of IDE. Moreover, to accelerate the computation speed, graphics processing unit parallel computing technique is introduced into pattern synthesis and the acceleration ratios of more than 23 times can be achieved. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:697–705, 2014.     

Synthesis of antenna arrays with an efficient multiobjective differential evolution algorithm

An efficient multiobjective differential evolution algorithm for array synthesis is proposed in this article. By introducing novel strategies in the generalized differential evolution (GDE3), the proposed algorithm can generate a better converged and distributed Pareto front than the standard GDE3. Five typical benchmark functions are optimized by the proposed improved GDE3 (IGDE) to verify its effectiveness. Furthermore, the proposed algorithm is applied in two‐pattern synthesis problems, including an unequal spaced linear array and a conformal array. The superior performance of the proposed IGDE demonstrates that it is an efficient and competitive algorithm in the function optimization, pattern synthesis, and other electromagnetic problems. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:161–169, 2014.     

A Rough RBF Neural Network Based on Weighted Regularized Extreme Learning Machine

Adjusting parameters iteratively is a traditional way of training neural networks, and the Rough RBF Neural Networks (R-RBF-NN) follows the same idea. However, this idea has many disadvantages, for instance, the training accuracy and generalization accuracy etc. So how to change this condition is a hot topic in Academics. On the basis of Extreme Learning Machine (ELM), this paper proposes a Weighted Regularized Extreme Learning Machine (WRELM), taking into account both minimizing structured risk and weighted least-squares principle, to train R-RBF-NN. The traditional iterative training method is replaced by the minimal norm least-squares solution of general linear system. The method proposed in this paper, increasing controllability of the entire learning process and considering the structured risk and empirical risk, can improve the performance of learning and generalization. Experiments show that it can reach a very superior performance in both time and accuracy when WRELM trains the Rough RBF Neural Networks in pattern classification and function regression, especially in pattern classification, which can improve the generalization accuracy more than 3.36 % compared with ELM. Obviously, the performance of the method proposed in this paper is better than the traditional methods.     

Image super-resolution based on multi-space sparse representation

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.     

Prediction-based realistic 3D model compression

The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced.     

集成电路密封用高纯邻甲酚醛环氧树脂的制备

ECN树脂中的可水解氯含量是决定树脂质量的—个重要指标。本文研究了反应温度、ECH/OCN配比、反应时间和碱用量等因素的影响,取得了规律性的认识,并获得最佳配方,制备出高纯度的ECN树脂系列产品。     

A formal framework for service mashups with dynamic service selection

The service mashup programming paradigm is a blooming faction of service oriented Architecture for developing web applications. A mashup application constructs its functionality by combining data, presentation and functionalities obtained from online services published by service providers such as Google and Amazon. This paradigm significantly facilitates the implementation of application and reduces the workload. But it also makes the application rely on the availability and qualities of the online services which beyond its control in which case the robustness of the system requires more concern. This paper proposes a formal model to specify and analyze the behavior and robustness of service mashups under a certain environment where some services may become unavailable. The specification contains both system specification and environment situation. Refinement theory are employed to specify the correctness of dynamic service selection. The framework realizes the service selection by allowing virtual service requests to be handled by any online service having consistent interface and refined functionality. The framework provides a clear definition of the robustness of mashup systems and proposes several rules as well as approaches to preserve the robustness during the development and maintenance of the system.     

十二烷基苯磺酸钠水溶液聚集性质的研究

用滴体积法测出十二烷基苯磺酸钠（ＳＤＢＳ）稀水溶液的表面张力；然后求出其临界胶束浓度（ＣＭＣ）。同时通过溶液的电导率、吸收光谱以及荧光光谱测定求出其ＣＭＣ。这样测出的ＣＭＣ值为１．２～１．６×１０－３ｍｏｌ／Ｌ，与文献值相符。也研究了部分水解的聚丙烯酰胺（ＰＨＰＡＭ）对ＳＤＢＳ聚集性质的影响；发现ＳＤＢＳ的ＣＭＣ值随ＰＨＰＡＭ的加人而减少并且ＰＨＰＡＭ水溶液的粘度随ＳＤＢＳ的存在而急剧降低（类似盐效应）。这些方法与性质对强化采油（ＥＯＲ）是重要的。     

一种新的BOC(n,n)型信号的精确捕获算法

针对BOC(n,n)型信号自相关函数存在三个峰值导致伪码捕获模糊性的问题,提出了一种新的基于相关函数的精确无模糊的捕获算法。该算法将信号进行频域的多普勒补偿,通过本地增设QBOC(n,n)支路,利用BOC(n,n)信号自相关以及BOC(n,n)和QBOC(n,n)互相关,经移位±TC/4、取模、求和、平方运算而实现。理论与仿真分析表明,该算法在不改变自相关主峰宽度的情况下完全消除相关副峰的同时,显著提高了主峰峰值;比传统捕获算法的性能好4.5 dB左右,并且捕获精度良好、易于实现。