Data-driven testing methodology for RFID systems

A radio-frequency identification (RFID) system including hardware and software may be updated from time to time after first time deployment. To ensure the reliability of the system, extensive tests are required. However, enumerating all test cases is infeasible, especially when the tests involve time-consuming hardware operations. To solve this problem, we propose a testing methodology for RFID systems which does not enumerate all test cases but rather those which are representative. A clustering method is adopted in selecting representative test cases. Although a small number of selected test cases are run, we can still obtain a relatively high bug detection rate compared with running the enumerated test cases. Our extensive experiments show the efficiency and effectiveness of our testing methodology.     

Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects

This paper presents a chaotic self-adaptive particle swarm optimization algorithm (CSAPSO) to solve dynamic economic dispatch problem (DED) with value-point effects. The proposed algorithm takes PSO as the main evolution method. The velocity, a sensitive parameter of PSO, is adjusted dynamically to increase the precision of PSO. To overcome the drawback of premature in PSO, chaotic local search is imported into proposed algorithm. Moreover, a new strategy is proposed to handle the various constraints of DED problem in this paper, the results solved by proposed strategy can satisfy the constraints of DED problem well. Finally, the high feasibility and effectiveness of proposed CSAPSO algorithm is validated by three test systems consisting of 10 and extended 30 generators while compared with the experimental results calculated by the other methods reported in this literature.     

A novel color image watermarking scheme in nonsampled contourlet-domain

Geometric distortion is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust color image watermarking scheme against geometric distortions. Based on the support vector regression (SVR) and nonsubsampled contourlet transform (NSCT), we propose a new color image watermarking algorithm with good visual quality and reasonable resistance toward geometric distortions in this paper. Firstly, the geometrically invariant space is constructed by using color image normalization, and a significant region is obtained from the normalized color image by utilizing the invariant centroid theory. Then, the NSCT is performed on the green channel of the significant region. Finally, the digital watermark is embedded into host color image by modifying the low frequency NSCT coefficients, in which the HVS masking is used to control the watermark embedding strength. In watermark detection, according to the high correlation among different channels of the color image, the digital watermark can be recovered by using SVR technique. Experimental results show that the proposed color image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc., but also robust against the geometrical distortions.     

Neighborhood rough set and SVM based hybrid credit scoring classifier

The credit scoring model development has become a very important issue, as the credit industry is highly competitive. Therefore, considerable credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicant’s credit score according to the applicant’s input features: (1) using neighborhood rough set to select input features; (2) using grid search to optimize RBF kernel parameters; (3) using the hybrid optimal input features and model parameters to solve the credit scoring problem with 10-fold cross validation; (4) comparing the accuracy of the proposed method with other methods. Experiment results demonstrate that the neighborhood rough set and SVM based hybrid classifier has the best credit scoring capability compared with other hybrid classifiers. It also outperforms linear discriminant analysis, logistic regression and neural networks.     

降低协同设计虚拟机启动开销的译码后指令缓存技术

协同设计虚拟机采用动态二进制翻译实现不同体系结构间的二进制兼容,对源指令的翻译和处理影响了协同设计虚拟机的启动性能.研究发现,在一个采用解释执行和翻译相结合的协同设计虚拟机中,处理非热点代码的解释执行是虚拟机启动开销的主要来源.发现了协同设计虚拟机中的解释例程局部性,并提出了一种硬件译码后指令缓存结构DICache(d...     

一种基于最小均方误差原理的位同步捕捉算法

提出一种数字通信系统中基于最小均方误差原理的位同步捕捉算法,对算法的提出、原理以及性能进行了理论分析,同时在加性高斯白噪声信道下进行了大量的计算机仿真,给出了在某无线通信系统中的应用结果.此算法通用性强,实现位同步捕捉速度快,易于高速单片机及DSP实现,具有良好的抗噪性能.     

基于双曲线映射签名的高效可分电子现金系统

基于双曲线映射的签名设计了一种新型可分电子现金方案,方案中有效地加入了压缩支付和批处理支付,计算时间复杂度较小,从而使得系统的整体效率较高。另外,方案中无需可信第三方(TTP)的参与,因而系统的整体开销较小。方案的安全性基于q-SDK假设、计算离散对数困难性假设以及单向散列函数存在性假设。     

一种基于人工神经网络的快速超分辨率方法

图像的超分辨率是指利用一幅或者多幅的低分辨率图像,通过相应的算法来获得一幅对应的清晰的高分辨率图像.针对现有的基于学习的超分辨率方法低效率的问题,提出一种基于人工神经网络的快速超分辨率方法,该方法试图利用人工神经网络学习到高分辨率图像和低分辨率图像之间的函数关系,其理论基础来自于人工神经网络能够很好地求解流形学习中高维流形和低维流形之间的映射关系.     

基于模糊推理的多智能体强化学习

以电子市场智能定价问题为研究背景,提出基于模糊推理的多智能体强化学习算法(FI-MARL).在马尔科夫博弈学习框架下,将领域知识初始化为一个模糊规则集合,智能体基于模糊规则选择动作,并采用强化学习来强化模糊规则.该方法有效融合应用背景的领域知识,充分利用样本信息并降低学习空间维数,从而增强在线学习性能.在电子市场定价的...     

一种安全高效的Java操作系统IPC机制的设计与实现

安全高效的进程间通信机制是操作系统中实现进程间协同工作的关键.针对现有Java操作系统普遍使用的共享堆和Java远程过程调用(RMI)等进程间通信机制在安全性、高效性等方面的缺陷,面向Java操作系统的结构特点和进程间通信需求,设计和实现了一种新的安全、高效的进程间通信机制MSP.MSP机制通过区分小尺寸和大尺寸消息,...