案例驱动下数据结构教材的编写

介绍数据结构的基本概念,讨论对数据结构课程的认知,提出教材编写过程中的几个要点,强调案例驱动教学模式在教材编写中的重要性,从而形成教材自身的特色。     

数据结构与算法核心课程建设

针对数据结构与算法课程理论联系实际的特点,文章提出在课程建设过程中应不断完善课程体系,加强团队建设,深入教学改革以及丰富教学资源,从培养学生的兴趣入手,注重学生思维能力、动手能力和创新能力的培养,最终达到提高教学质量的目的。     

应用型本科院校数据结构创新教学模式

文章针对计算机专业的核心课程数据结构,以培养创新应用型人才为目标,从教与学的角度探讨案例驱动式教学、形象教学与启发式教学结合、教学立体化、课程设计与大项目作业结合等观点,使学生能更好地学习数据结构课程。     

用案例教学法指导数据结构与算法教学

针对传统数据结构与算法教学中存在的问题,在分析案例教学法在程序设计类课程教学中的地位与作用的基础上,本文提出"精选典型案例—设计生动课件—激活课堂教学"的观点,结合我院学生与课程团队教师的实际,在数据结构与算法课程教学设计与改革方面进行教学实践,取得了显著的效果。     

一种XML文档结构相似度计算方法

对XML文档树路径模型进行扩展,加入了路径的频率信息.基于此路径-频率模型,提出一种带有位置仅重的基于路径的结构相似度计算方法(WLCS),并在此基础上提出基于路径频率的XML文档结构向量化方法.在真实数据集上的实验结果表明,WLCS方法召回率和准确率均高于当前存在的基于路径计算相似度的方法,适合于对来自不同DTD的XML文档的相似度比较.     

“数据结构”课程实验环节教学的改革与实践

"数据结构"是计算机专业的一门核心课程,在计算机专业中起着承上启下的作用。本文作者总结多年的教学经验,就这门课实验环节的教学改革与实践进行了分析研究,意在加强对学生编程能力和上机解题能力的训练,提高学生的编程能力和分析问题的思维能力,激发学生的创新能力。     

高职高专院校数据结构与算法教学改革与实践

针对数据结构与算法教学中存在的问题,根据自己多年教改的实践与探索,分析数据结构教学改革中一些观点和做法,提出教改中应该注意的一些问题。希望对该课程教学改革具有参考价值和促进意义。     

也谈“数据结构”课程教学的几个问题

本文针对困扰"数据结构"课程教与学的几个问题,作者根据自己的认识和多年的实际教学经验,进行深入浅出的分析和论述,以其对提高课程的认识和兴趣、牢固掌握课程知识及其灵活运用、提高利用计算机解决问题的能力具有很好的指导作用。     

Model order reduction methods for coupled systems in the time domain using Laguerre polynomials

In this paper, based on Laguerre polynomials, we present new methods for model reduction of coupled systems in the time domain. By appropriately selected projection matrices, a reduced order system is produced to retain the topology structure of the original system. Meanwhile, it preserves a desired number of Laguerre coefficients of the system’s output, thereby providing good approximation accuracy. We also study the two-sided projection method in the time domain, as well as the stability of reduced order systems. Two numerical examples are used to illustrate the efficiency of the proposed methods.     

3D Target Scale Estimation and Target Feature Separation for Size Preserving Tracking in PTZ Video

To achieve size preserving tracking, in addition to controlling the camera’s pan and tilt motions to keep the object of interest in the camera’s field of view (FOV), the camera’s focal length is adjusted automatically to compensate for the changes in the target’s image size caused by the relative motion between the camera and the target. The estimation accuracy of these changes determines the effectiveness of the resulting zoom control. The existing method of choice for real-time target scale estimation applies structure from motion (SFM) based on the weak perspective projection model. In this paper we propose a target scale estimation algorithm with a linear solution based on the more advanced paraperspective projection model, which improves the accuracy of scale estimation by considering center offset. Another key issue in SFM based algorithms is the separation of target and background features, especially when composite camera (pan/tilt/zoom) and target motions are involved. This paper designs a fast target feature separation/grouping algorithm, the 3D affine shape method. The resulting separation automatically adapts to the target’s 3D geometry and motion and is able to accommodate a large amount of off-plane rotation, which most existing separation/grouping algorithms find difficult to achieve. Experimental results illustrate the effectiveness of the proposed scale estimation and feature separation algorithms in tracking translating and rotating objects with a PTZ camera while preserving their sizes. In comparison with the leading size preserving tracking algorithm described by Tordoff and Murray, our algorithm is able to reduce the cumulative tracking error significantly from 17.4% to 3.3%.