RFID二进制树型折半搜索防碰撞算法

为提高RFID系统中多标签读取的工作效率,分析了二进制树型搜索防碰撞算法,提出一种改进型的二进制树型折半搜索算法．通过构建哈夫曼树,使用自定义编码,采用堆栈操作确定碰撞位,用折半查找方式减少重复路径访问量,并最终确定最短路径长度．数据分析及实验结果表明,二进制树型折半搜索防碰撞算法可以明显降低搜索深度,显著提高RFID多标签读取的工作效率。     

基于向量夹角的SIFT特征点匹配算法

提出一种基于向量夹角的近似最近邻搜索算法.该算法首先计算高维空间向量与随机选择的参考向量的夹角,并进行排序.计算出待查询向量与参考向量的夹角后,采用二分搜索算法在已排序夹角中查找对应的夹角,并以此夹角为中心,在一定范围内搜索给定向量的近似最近邻.实验结果表明,文中算法可显著提高尺度不变特征变换特征的匹配速度,并能获得满意的匹配效果.     

基于陷波器参数自调整的伺服系统谐振抑制

针对永磁交流伺服系统中的柔性传动环节,建立电机——负载二质量模型,通过对模型的Simulink仿真分析阐述系统机械谐振发生的机理。为抑制伺服系统的机械谐振,根据对伺服系统谐振的定量分析,结合对电机速度误差信号的快速傅里叶变换（FFT）分析,设计自动调整参数的陷波滤波器,并进行仿真实验与实际实验。实验结果证实理论分析的正确性和所设计陷波器的有效性,在传动环节的刚度、阻尼和负载惯量不尽准确的情况下,相关仿真结果验证所设计陷波器依旧有效。     

Hydrodynamic effects of reconnecting lake group with Yangtze River in China

The hydrodynamic effects of reconnecting a lake group with the Yangtze River were simulated using a three-dimensional hydrodynamic model. The model was calibrated and validated using the measured water temperature and total phosphorous. The circulation patterns, water temperature, and water exchange conditions between sub-lakes were simulated under two conditions: (1) the present condition, in which the lake group is isolated from the Yangtze River; and (2) the future condition, with a proposed improvement ...     

An ideal multi-secret sharing scheme based on MSP

A multi-secret sharing scheme is a protocol to share m arbitrarily related secrets s₁, … , s_m among a set of n participants. In this paper, we propose an ideal linear multi-secret sharing scheme, based on monotone span programs, where each subset of the set of participants may have the associated secret. Our scheme can be used to meet the security requirement in practical applications, such as secure group communication and privacy preserving data mining etc. We also prove that our proposed scheme satisfies the definition of a perfect multi-secret sharing scheme.     

Texture segmentation using independent-scale component-wise Riemannian-covariance Gaussian mixture model in KL measure based multi-scale nonlinear structure tensor space

This paper proposes a novel texture segmentation approach using independent-scale component-wise Riemannian-covariance Gaussian mixture model (ICRGMM) in Kullback-Leibler (KL) measure based multi-scale nonlinear structure tensor (MSNST) space. We use the independent-scale distribution and full-covariance structure to replace the covariant-scale distribution and 1D-variance structure used in our previous research. To construct the optimal full-covariance structure, we define the full-covariance on KL, Euclidean, log-Euclidean, and Riemannian gradient mappings, and compare their performances. The comparison experiments demonstrate that the Riemannian gradient mapping leads to its optimum properties over other choices when constructing the full-covariance. To estimate and update the statistical parameters more accurately, the component-wise expectation-maximization for mixtures (CEM²) algorithm is proposed instead of the originally used K-means algorithm. The superiority of the proposed ICRGMM has been demonstrated based on texture clustering and Graph Cuts based texture segmentation using a large number of synthesis texture images and real natural scene textured images, and further analyzed in terms of error ratio and modified F-measure, respectively.     

Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China

Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geo-environment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou.     

基于QCircuit的BB84窃听仿真与分析

该文研究量子线路模型仿真量子密钥分配协议.基于QCircuit软件运用量子线路模型设计不同攻击模型下的BB84量子密钥分配协议仿真模型,并引入rsec和I(α,E)两个指标,设计指标分析线路模型,仿真分析了不同噪声信道模型下BB84密钥分配协议在P/P、B/P和B/B三种不同截取/重发策略下的有效性及安全性.仿真结果表...     

Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization

This paper proposes an improved variational model, multiple piecewise constant with geodesic active contour (MPC-GAC) model, which generalizes the region-based active contour model by Chan and Vese, 2001 [11] and merges the edge-based active contour by Caselles et al., 1997 [7] to inherit the advantages of region-based and edge-based image segmentation models. We show that the new MPC-GAC energy functional can be iteratively minimized by graph cut algorithms with high computational efficiency compared with the level set framework. This iterative algorithm alternates between the piecewise constant functional learning and the foreground and background updating so that the energy value gradually decreases to the minimum of the energy functional. The k-means method is used to compute the piecewise constant values of the foreground and background of image. We use a graph cut method to detect and update the foreground and background. Numerical experiments show that the proposed interactive segmentation method based on the MPC-GAC model by graph cut optimization can effectively segment images with inhomogeneous objects and background.     

非理想运放增益误差的MathCAD定量分析

在推导非理想运算放大器增益误差表达式的基础上,利用MathCAD分析讨论运算放大器的非理想参数开环增益、输入和输出电阻对实际增益误差的影响,对比现代实际运放与理想运放的差别.结果表明:运放的差模输入电阻和输出电阻对增益误差的影响较小,而开环增益是主要影响因素.开环增益越大,误差越小;目标增益较小,增益误差也较小.对现代...