Nonconvex plus quadratic penalized low-rank and sparse decomposition for noisy image alignment针对含噪图像对准问题的非凸加二次罚项约束的低秩和稀疏分解方法

This paper proposes a general method for dealing with the problem of recovering the low-rank structure, in which the data can be deformed by some unknown transformations and corrupted by sparse or nonsparse noises. Nonconvex penalization method is used to remedy the drawbacks of existing convex penalization method and a quadratic penalty is further used to better tackle the nonsparse noises in the data. We exploits the local linear approximation (LLA) method for turning the resulting nonconvex penalization problem into a series of weighted convex penalization problems and these subproblems are efficiently solved via the augmented Lagrange multiplier (ALM). Besides comparing with the method of robust alignment by sparse and low-rank decomposition for linearly correlated images (RASL), we also propose a nonconvex penalized lowrank and sparse decomposition (NLSD) model as comparison. Numerical experiments are conducted on both controlled and uncontrolled data to demonstrate the outperformance of the proposed method over RASL and NLSD.     

On the criteria for designing complex orthogonal space-time block codes

A complex orthogonal design (COD) used in space-time block codes is a kind of combinatorial design. It has been well studied because it has a fast maximum-likelihood decoding algorithm and achieves full diversity. When designing CODs, there are seven characteristics that should be considered, which include code rate, decoding delay, transceiver signal linearization, peak-to-average power ratio, etc. In this paper, we study the relationships among these design criteria. We prove that the maximum rate of generalized CODs (GCODs) that meet the last five criteria is 1/2. Moreover, we present a new method to construct GCODs based on CODs. Using this method on balanced complex orthogonal designs (BCODs), we obtain a novel class of GCODs that performs almost perfectly with respect to all the seven properties.     

Attribute reduction in interval-valued information systems based on information entropies

Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.     

Top-<Emphasis Type="Italic">k</Emphasis> probabilistic prevalent co-location mining in spatially uncertain data sets

A co-location pattern is a set of spatial features whose instances frequently appear in a spatial neighborhood. This paper efficiently mines the top-k probabilistic prevalent co-locations over spatially uncertain data sets and makes the following contributions: 1) the concept of the top-k probabilistic prevalent co-locations based on a possible world model is defined; 2) a framework for discovering the top-k probabilistic prevalent co-locations is set up; 3) a matrix method is proposed to improve the computation of the prevalence probability of a top-k candidate, and two pruning rules of the matrix block are given to accelerate the search for exact solutions; 4) a polynomial matrix is developed to further speed up the top-k candidate refinement process; 5) an approximate algorithm with compensation factor is introduced so that relatively large quantity of data can be processed quickly. The efficiency of our proposed algorithms as well as the accuracy of the approximation algorithms is evaluated with an extensive set of experiments using both synthetic and real uncertain data sets.     

嵌入式软件GUI自动化测试平台研究

为实现嵌入式软件的GUI自动化测试,对嵌入式软件GUI自动化测试现状深入分析,提出了一种无侵入式的GUI自动化测试框架,利用图像采集与信息识别技术实现测试用例结果判断;利用键盘鼠标模拟技术模拟测试用例执行的人机交互;利用脚本语言嵌入技术实现用例编辑与转换、利用嵌入式软件GUI测试自动化控制技术实现用例的自动驱动与执行;在此基础上,设计并研发了嵌入式软件GUI自动化测试平台,能够方便、有效地实现无侵入式的嵌入式GUI自动化测试.     

综合业务区二级光交的部署策略探讨

综合业务区对于实现客户预覆盖、节约管道和光缆资源、快速业务接入等方面具有重要的意义.对于不同场景下的二级光交部署策略进行探讨,旨在对综合业务区的建设起到借鉴作用.     

基于软构件的Web办公系统的设计与实现

该文对软构件进行了研究,提出了利用DSCIE（Distributed Software Component Integrated Environment）,即分布式软构件集成环境的框架开发Web办公系统的方法。Web办公系统通过.net技术来实现。并且基于这个框架成功开发出了铁路Web办公系统。     

基于Android的安防视频监控系统

安防监控是未来智慧城市的重要发展方向之一,基于Android的视频监控使安防应用更为便捷、智能。本系统采用高性能的ARM Cortex-A8处理器,在Android系统之上构建了一个安防视频监控系统。采用Android底层的Linux内核驱动实现视频数据的采集,并通过移植x264和JRTPLIB库实现了视频数据的H．264编码和RTP协议传输,保证了视频监控具有较好的实时性和可靠性。