改进的乘积合并接收机部分频带抗干扰性能分析

乘积合并接收机以其简单有效的特性在快速跳频通信系统中得到了广泛关注。对于实际应用中普遍遇到的非最坏部分频带干扰环境,传统乘积合并算法有其自身的不足。针对这种情况,提出了一种改进的乘积合并方法,它保留了传统乘积合并算法的主要特征,通过增加对各跳频信号能量差异的检测信息,改善了传统乘积合并接收机在部分频带干扰下的传输性能,仿真验证了这种改进合并方法的有效性。     

Dynamically modulated intensity interrogation scheme using waveguide coupled surface plasmon resonance sensors

An electro-optically modulated intensity interrogation method based on tunable waveguide coupled surface plasmon resonance sensors has been proposed. It has been theoretically and experimentally demonstrated that the proposed scheme can enable sensitive measurement of measurand variations. By modulating the refractive index in the waveguide layer, this interrogation method yields modulated signal whose amplitude is related to measurand's refractive index. This amplitude modulated signal offers a higher signal to noise ratio and eliminates additive noise in the sensor system. A preliminary investigation using saline buffers with different NaCl concentrations shows a resolution of 2.3 × 10^?6 refractive index unit by our approach. Resolution can be controlled by the amplitude of the applied modulation voltage and can be further enhanced by optimizing the device structure or improving the electro-optical (E-O) coefficient of the E-O material. This approach is simple, stable, and promising for low-cost or multi-channel SPR biosensor applications.     

针对低轮PRESENT的代数攻击

基于MiniSAT 2.0软件,研究对低轮PRESENT的代数攻击问题。提出将S盒表示为单项式个数较少的无冗余等效方程组的方法,将PRESENT的S盒表示为由14个单项式个数均≤6的8元布尔方程构成的等效方程组,并基于不同的已知明文量,利用MiniSAT软件对PRESENT进行代数攻击实验,获得了较好的攻击效果。实验表明,在已知明文条件下可以在121 h内求出80 bit密钥的5轮PRESENT的全部密钥比特,在选择明文条件下可以在203 h内求出6轮PRESENT的全部密钥比特。     

基于优化目标可调控的免疫网络算法的分析与应用

针对目前几种免疫网络模型在数据聚类方面应用的一些不足,在aiNet免疫算法的基础上结合函数优化的思想提出一种基于目标可调控的免疫模型。并在算法中给出目标控制函数,和细胞记忆库的概念。本算法提高了免疫学习质量并从整体上对免疫网络进行优化。     

面向构件接口变异的模型检验技术研究

随着构件技术在软件开发中的广泛应用,人们对构件化软件系统质量的要求也在不断提高。该文运用模型检验技术进行接口变异测试的相关研究工作。该文首先详细介绍了基于模型检验技术的变异测试的基本原理,随后通过对构件集成时接口之间的交互关系进行分析后,提出了通过构造接口函数调用关系模型进行基于模型检验的接口变异测试方法。最后,通过实例进行了对JavaBean构件的接口测试用例的自动生成。     

Robust Feature-Preserving Mesh Denoising Based on Consistent Subneighborhoods

In this paper, we introduce a feature-preserving denoising algorithm. It is built on the premise that the underlying surface of a noisy mesh is piecewise smooth, and a sharp feature lies on the intersection of multiple smooth surface regions. A vertex close to a sharp feature is likely to have a neighborhood that includes distinct smooth segments. By defining the consistent subneighborhood as the segment whose geometry and normal orientation most consistent with those of the vertex, we can completely remove the influence from neighbors lying on other segments during denoising. Our method identifies piecewise smooth subneighborhoods using a robust density-based clustering algorithm based on shared nearest neighbors. In our method, we obtain an initial estimate of vertex normals and curvature tensors by robustly fitting a local quadric model. An anisotropic filter based on optimal estimation theory is further applied to smooth the normal field and the curvature tensor field. This is followed by second-order bilateral filtering, which better preserves curvature details and alleviates volume shrinkage during denoising. The support of these filters is defined by the consistent subneighborhood of a vertex. We have applied this algorithm to both generic and CAD models, and sharp features, such as edges and corners, are very well preserved.     

Skew detection in document images based on rectangular active contour

The digitalization processes of documents produce frequently images with small rotation angles. The skew angles in document images degrade the performance of optical character recognition (OCR) tools. Therefore, skew detection of document images plays an important role in automatic document analysis systems. In this paper, we propose a Rectangular Active Contour Model (RAC Model) for content region detection and skew angle calculation by imposing a rectangular shape constraint on the zero-level set in Chan–Vese Model (C-V Model) according to the rectangular feature of content regions in document images. Our algorithm differs from other skew detection methods in that it does not rely on local image features. Instead, it uses global image features and shape constraint to obtain a strong robustness in detecting skew angles of document images. We experimented on different types of document images. Comparing the results with other skew detection algorithms, our algorithm is more accurate in detecting the skews of the complex document images with different fonts, tables, illustrations, and layouts. We do not need to pre-process the original image, even if it is noisy, and at the same time the rectangular content region of a document image is also detected.     

A collaborative platform for complex product design with an extended HLA integration architecture

How to integrate the heterogeneous, autonomous models in a distributed environment, running on Internet, integration based on multilayer federation architecture and simulation resources dynamic reuse are the major difficulties for complex product collaborative design and simulation. An extended HLA-based collaborative simulation platform for multidisciplinary collaborative design is proposed. The architecture of platform is first given. The idea and realization of four key technologies, the dynamic creation of simulation federation based on resource management federation (RMF), HLA enabled template, collaborative simulation algorithm based on stage-data synchronization and TH_RTI, an extensible HLA runtime infrastructure (RTI), which is a web enabled RTI and can be used at Internet, are emphasized. Finally, an industry case is given. The demonstration of these prototype systems shows that extended HLA integration architecture is effective infrastructure for multidisciplinary collaborative modeling and simulation (M&S).     

Multistage System Planning for Hydrogen Production and Distribution

Hydrogen is an energy carrier that has the potential to improve the sustainability of transportation fuels and reduce oil dependence. This paper presents a stochastic dynamic programming model for sequentially building a hydrogen production and distribution system. The decision variables are the sequence and locations of the central production sites and the corresponding distribution systems from supply to demand sites. A case study based on the geographic setting of Northern California is included, in which the hydrogen is produced via coal gasification and transported from plant to city gates (demand sites) by cryogenic liquid hydrogen trucks. Future demands for hydrogen are modeled as uncertain parameters, with an assumption that hydrogen fuel cell vehicle (HFCV) market penetration rate increases from 1% to 25% over a 20-year period. This model provides multistage decision support for long term transportation energy planning at national and regional levels.     

Solving Stochastic Transportation Network Protection Problems Using the Progressive Hedging-based Method

This research focuses on pre-disaster transportation network protection against uncertain future disasters. Given limited resources, the goal of the central planner is to choose the best set of network components to protect while allowing the network users to follow their own best-perceived routes in any resultant network configuration. This problem is formulated as a two-stage stochastic programming problem with equilibrium constraints, where the objective is to minimize the total expected physical and social losses caused by potential disasters. Developing efficient solution methods for such a problem can be challenging. In this work, we will demonstrate the applicability of progressive hedging-based method for solving large scale stochastic network optimization problems with equilibrium constraints. In the proposed solution procedure, we solve each modified scenario sub-problem as a mathematical program with complementary constraints and then gradually aggregate scenario-dependent solutions to the final optimal solution.