Secure and robust image hashing via compressive sensing

Image hash functions find extensive applications in content authentication, database search. This paper develops a novel algorithm for generating a secure and robust image hash based on compressive sensing and Fourier-Mellin transform. Firstly, we incorporate Fourier-Mellin transform into our method to improve its performance under rotation, scale, transition attacks. Secondly, we exploit the property of dimension reduction inherent in compressive sensing for hash design. The statistic structure and sparse of the wavelet coefficients assure efficient compression in situation of including maximum the image features. The hashing method is computationally secure without additional randomization process. Such a combined approach is capable of tackling all types of attacks and thus can yield a better overall performance in multimedia identification. To demonstrate the superior performance of the proposed schemes, receiver operating characteristics analysis over a large image database is performed. Experimental results show that the proposed image hashing is robust to a wide range of distortions and attacks. When compared with the current state-of-the-art methods, the proposed method yields better identification performances under geometric attacks such as rotation attacks and brightness changes.     

XML keyword search with promising result type recommendations

Keyword search enables inexperienced users to easily search XML database with no specific knowledge of complex structured query languages and XML data schemas. Existing work has addressed the problem of selecting data nodes that match keywords and connecting them in a meaningful way, e.g., SLCA and ELCA. However, it is time-consuming and unnecessary to serve all the connected subtrees to the users because in general the users are only interested in part of the relevant results. In this paper, we propose a new keyword search approach which basically utilizes the statistics of underlying XML data to decide the promising result types and then quickly retrieves the corresponding results with the help of selected promising result types. To guarantee the quality of the selected promising result types, we measure the correlations between result types and a keyword query by analyzing the distribution of relevant keywords and their structures within the XML data to be searched. In addition, relevant result types can be efficiently computed without keyword query evaluation and any schema information. To directly return top-k keyword search results that conform to the suggested promising result types, we design two new algorithms to adapt to the structural sensitivity of the keyword nodes over the keyword search results. Lastly, we implement all proposed approaches and present the relevant experimental results to show the effectiveness of our approach.     

The min-dist location selection and facility replacement queries

We propose and study a new type of location optimization problem, the min-dist location selection problem: given a set of clients and a set of existing facilities, we select a location from a given set of potential locations for establishing a new facility, so that the average distance between a client and her nearest facility is minimized. The problem has a wide range of applications in urban development simulation, massively multiplayer online games, and decision support systems. We also investigate a variant of the problem, where we consider replacing (instead of adding) a facility while achieving the same optimization goal. We call this variant the min-dist facility replacement problem. We explore two common approaches to location optimization problems and present methods based on those approaches for solving the min-dist location selection problem. However, those methods either need to maintain an extra index or fall short in efficiency. To address their drawbacks, we propose a novel method (named MND), which has very close performance to the fastest method but does not need an extra index. We then utilize the key idea behind MND to approach the min-dist facility replacement problem, which results in two algorithms names MSND and RID. We provide a detailed comparative cost analysis and conduct extensive experiments on the various algorithms. The results show that MND and RID outperform their competitors by orders of magnitude.     

基于多特征融合和SVM分类器的植物病虫害检测方法

针对农业领域植物病虫害检测问题,提出一种基于高清视频图像融合特征的支持向量机(SVM)的检测方法,实现农业生产中植物病虫害的快速检测。对每幅植物叶片图像的颜色、HSV、纹理和方向梯度直方图四种特征采用基于特征包的多特征融合方法,形成特征向量,并利用SVM分类器进行训练分类。对单特征与融合特征的SVM分类器性能进行试验比较,所提出的方法具有较高的准确率。     

新闻传播中动画语言的应用

随着数字时代的到来,数字技术不仅使动画在传统影视和游戏领域如虎添翼,其特有的仿真能力使得动画逐渐在电视新闻与网络新闻中也频繁运用,不仅为动画在新闻传播领域开辟了新的应用范围,也打通了各领域间的界限,一方面成为动画本身多元化的拓展,另一方面也为新闻传播方式的多样化提供了一种新的可能。文章旨在梳理动画在新闻传播中的历史沿革,研究数字时代下动画在新闻传播中的应用,并在此基础上思考动画在新闻传播中亟待解决的诸多问题。     

多台机器下的最小期望延误的随机排序问题

论文针对多台机器下,任务的预期时间为随机变量的排序问题,首先用LPT排序方法把任务安排到不同的机器上,然后用简单的随机方法来确定任务在机器上的特殊加工次序。由于随机预期时间是相互独立的并且服从指数分布,考虑将参数进行最大延误。     

一种基于ZigBee的分布式井盖监控系统

随着我国现代经济发展水平的不断提高,城市井盖数目逐渐增加。由于较多井盖被盗,致使路面形成陷阱,危害到过往车辆和行人安全。本文提出了一种基于ZigBee无线网络的智能安全监控系统,解决分布式布控困难、不能实时监控、成本较高的问题。融合GPRS网络,设计相应的嵌入式网关和Web服务器,实现了对缺失井盖的精确定位、自动报警功能。     

关系数据库技术在计算机网络设计中的运用

随着我国社会经济建设水平的不断提高和计算机网络技术的推广普及,关系数据库技术在计算机网络设计中的运用成为社会各领域关注的焦点,本文将从关系数据库技术的角度出发,结合计算机网络设计的实际,对关系数据库技术在计算机网络设计中的运用进行深入的研究和探讨。     

基于OpenGL的图形引擎的设计与实现

近年过,随着图形硬件及图形接口的更新换代,用于游戏的实时图形引擎得到了突飞猛进的发展,各种开源引擎与商业引擎层出不穷。该文旨在运用计算机图形学理论知识,基于OpenGL 3.3的标准以及GLSL着色语言,使用C＋＋语言编写和Visual Studio 2010平台,完成一个拥有模型载入、贴图载入、UI界面、地形系统以及人机交互系统的图形引擎,能够用于游戏场景与漫游类的应用。     

TD-LTE与TD-SCDMA切换判决机制的探究与仿真

不同网络之间的切换问题是网络间互操作的重要组成部分,尤其是当前备受关注的TD-LTE与TD-SCDMA的切换问题。该文在对常规切换机制剖析的基础上,通过详尽的理论分析,根据现有切换机制的不足,提出了新的切换判决算法机制。新机制中结合了多属性切换判决中的层次分析算法和二维函数收益法,考虑了移动用户的业务喜好,并使用Matlab搭建了网络模型着重针对数据业务进行仿真,根据不同的网络收益值来实现切换的选择,验证了数据业务切换判决算法的有效性。