《软件工程》实验课程改革与实践

《软件工程》课程涵盖了软件开发的全过程,为将计算机专业的学生培养成高层次的IT人才,提供了理论依据和借鉴,实验课在《软件工程》课程体系中所占的位置尤为重要。通过分析《软件工程》实验课程的重要作用,详细阐述实验课程设计的方法和实施过程,说明《软件工程》实验改革具有重大意义。     

大学物理开放实验网络选课系统的优化设计

以大学物理开放实验课程为例,针对当前高校物理开放性实验网络选课系统在辅助教学方面的一些不足,提出改进措施,设计优化型网络选课系统,分析系统的功能和特点,并对该系统的几个功能模块进行简单介绍。     

《汇编语言程序设计》教学改革与体会

在计算机行业高速发展、高级语言日益使用普通的情况下,分析汇编语言的教学现状,针对汇编语言课程的特点和教学中存在的问题,提出具体解决办法,介绍该课程的教改思路和创新成果。     

Linux系统下用SCILAB对化学实验数据进行分析和处理

为简单、快速地分析大量的化学实验数据,介绍了自由科学计算软件SCILAB及其在Linux系统下的安装,并用实例讨论了如何用SCILAB分析实验数据。结果表明,用SCILAB所提供的函数对实验数据进行分析处理将非常方便,大大减小误差,SCILAB提供的的图形显示函数绘制实验数据曲线,可以使实验结果更加直观。     

数据链路多径传输模型的物理映射方法研究

根据无人机数据链路的多径传输要求,针对在传输中电磁波衰落,为保证精度,在微波暗室中建立与之相关的分析模型,采用仿真手段有助于提高无人机数据链的有效性。通过对数据链路的实际传输场景(散射点属性等)以及信道传输模型的多径参数(时延、衰落、多普勒频移)分析,解算出该场景下接收点处的合成功率,并在微波暗室中,依据仪表的衰落仿真机理和暗室环境中求得的合成功率。用两种方法对场景进行仿真,可从中选取一种精度更好的方法进行仿真验证有效性,实现了多径传输链路在微波暗室的准确物理映射。     

《计算机网络应用基础》教学浅谈

计算机网络应用基础在我院教学过程中占有重要位置,笔者从三本教学目标和学生特点出发,在总结分析传统计算机网络应用基础教学方法、考核方式中的弊端和不足,实践结合教学,对计算机网络应用基础教学模式提出新的思路,从而全面提高学生学习兴趣,提高教学质量。     

《计算机网络技术》实验教学的优化方案研究

根据<计算机网络技术>实验教学的特点,在总结目前<计算机网络技术>实验教学现状的基础上,从实验项目、实验过程以及实验考核方式三个方面进行优化研究,以培养学生对<网络技术>的综合实践技能运用和设计创新能力,并提高实验教学质量和效果.     

WCDMA空中接口的物理层测量

本文对空中接口的技术进行了简单的介绍,着重描述了WCDMA空中接口的结构与功能。并从测量可实现的角度出发,给出了对物理层性能指标和过程进行测量的方案,开发设计了测量分析软件。通过测量的结果,对网络的性能进行了分析。     

Who is LB1? Discriminant analysis for the classification of specimens

Many problems in paleontology reduce to finding those features that best discriminate among a set of classes. A clear example is the classification of new specimens. However, these classifications are generally challenging because the number of discriminant features and the number of samples are limited. This has been the fate of LB1, a new specimen found in the Liang Bua Cave of Flores. Several authors have attributed LB1 to a new species of Homo, H. floresiensis. According to this hypothesis, LB1 is either a member of the early Homo group or a descendent of an ancestor of the Asian H. erectus. Detractors have put forward an alternate hypothesis, which stipulates that LB1 is in fact a microcephalic modern human. In this paper, we show how we can employ a new Bayes optimal discriminant feature extraction technique to help resolve this type of issues. In this process, we present three types of experiments. First, we use this Bayes optimal discriminant technique to develop a model of morphological (shape) evolution from Australopiths to H. sapiens. LB1 fits perfectly in this model as a member of the early Homo group. Second, we build a classifier based on the available cranial and mandibular data appropriately normalized for size and volume. Again, LB1 is most similar to early Homo. Third, we build a brain endocast classifier to show that LB1 is not within the normal range of variation in H. sapiens. These results combined support the hypothesis of a very early shared ancestor for LB1 and H. erectus, and illustrate how discriminant analysis approaches can be successfully used to help classify newly discovered specimens.     

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76-0.90 μm wavelength) and band 5 (1.55-1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R² of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation.