Robust Video Text Detection with Morphological Filtering Enhanced MSER

Video text detection is a challenging problem, since video image background is generally complex and its subtitles often have the problems of color bleeding, fuzzy boundaries and low contrast due to vi...     

反作用控制系统电磁阀PWM控制技术研究

针对反作用控制系统快速响应电磁阀频繁工作的特性,从节能控制的角度出发,设计了一种基于脉宽调制技术的电磁阀控制驱动方案;建立电磁阀电路模型,通过仿真计算,确定了PWM频率及占空比对电磁阀线圈电流的影响,为电磁阀驱动电路参数的选取提供依据;搭建RCS电磁阀电流测试平台.设定PWM占空比为50％,试验结果表明:流经电磁阀的保持电流仅为开启电流的1/2,既能保证电磁阀正常开启,又避免了电磁阀处于高负载工作状态,有效地验证了该节能控制方案的正确性.     

一种SaaS模式防空作战仿真想定编辑系统实现

针对目前仿真想定编辑系统在通用性、灵活性以及更新升级等方面存在的问题,设计了一种SaaS(Software-as-a-service)模式的防空作战仿真想定编辑系统。通过为防空武器建立统一的数据模型(数据类集)实现数据处理的独立性;通过设计与具体武器独立的想定编辑相关的业务逻辑模块,实现业务逻辑的相对独立性;利用MapXtreme Java智能多线程机制解决多用户的并发编辑问题。另外,将系统部署在云计算基础设施平台上,解决了传统C/S模式系统与平台相关联、更新升级困难等缺点,也提升了系统的使用灵活性和用户体验。最后通过实例验证了系统的科学性和有效性。     

用JAVA实现HELLO WORLD入门和常见错误分析

本文论述了JAVA语言及编译过程,并且对其中的常见错误进行了详尽分析。     

计算机硬件教学方法改革研究

介绍了计算机硬件教学的基本情况．提出了目前存在的一些问题。通过对结合网络教学、逆向思维、软硬件结合实验三方面的改革．取得了明昱的效果。最后总结了发展的趋势。     

浅谈个性化校园交易网站的设计

提出设计一种基于J2EE多层体系结构开发的网上校园物品交易系统。     

Analysis and Implementation of an Open Programmable Router Based on Forwarding and Control Element Separation

A router architecture based upon ForCES (Forwarding and Control Element Separation), which is being standardized by IETF ForCES working group, gains its competitive advantage over traditional router architectures in flexibility, programmability, and cost-effectiveness. In this paper, design and implementation of a ForCES-based router (ForTER) is illustrated. Firstly, the implementation architecture of ForTER is discussed. Then, a layered software model, which well illustrates ForCES features, is proposed. Based on the model, design and implementation of Control Element (CE) and Forwarding Element (FE) in ForTER are introduced in detail. Moreover, security for ForTER is considered and an algorithm to prevent DoS attacks is presented. Lastly, experiments of ForTER are illustrated for routing and running routing protocols, network management, DoS attack prevention, etc. The experimental results show the feasibility of the ForTER design. Consequently, the ForTER implementation basically testifies the feasibility of ForCES architecture and some IETF ForCES specifications.     

JUnit的类测试应用研究

测试对于保证软件开发质量有着非常重要的作用,单元测试更是必不可少。JUnit是一个非常强大的单元测试包,可以对类及其方法测试,还可以将不同的TestCase整合成TestSuit,使测试任务自动化。本文对JUnit的应用进行了分析研究并以一个例子来阐述开发类测试驱动的过程。     

Matching pursuit based on nonparametric waveform estimation

Matching pursuit (MP) extracts signal feature components by decomposing the observed signal into a linear expansion of waveforms that are selected from a redundant dictionary of basis functions. It is difficult to predefine a parametric basis function that contains all prior information about the observed signal in practice, which restricted the application of MP. Focusing on this defect of MP, we present a modified algorithm of MP which decomposes the observed signal into a series combinations of waveforms, these waveforms are calculated by the nonparametric waveform estimation (NWE) method and used to best match the signal local structures. As a result, it is not needed to predefine the parametric basis function. With the NWE method, the adaptive adjustment of template signals makes it unnecessary for the method to require priori information, so that in practical applications it has better flexibility and adaptability. The extraction results of simulation signals are compared with the traditional MP decomposition, thus verifying the function of the proposed method. The extraction of testing signals on the rotor rig has again verified the feasibility and effectiveness of the method.     

Intensity standardization simplifies brain MR image segmentation

Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model-based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.