基于TD SCDMA的虚拟外场测试平台

目前在TD外场测试过程中,存在着设备操作不便、无线环境复杂、测试情景不能再现等诸多难题。为解决这些问题,通过对2G及3G相关理论的研究,给出适合TD网络的业务模型和传输模型。在此基础上提出一种利用软硬件仿真TD网络外场测试环境,在实验室环境中构建虚拟外场测试平台的方法。对该平台的结构和工作原理进行分析论述,利用该平台可使大部分外场测试能方便地在实验室中进行,提高了测试效率。     

基于数据场的大规模本体映射

针对已有的本体映射方法在处理大规模本体映射任务时效率和有效性较低的问题,文中提出了一个基于数据场的本体映射算法.该算法首先使用高效的相似度算法,建立本体中元素对另一本体的初始相关度;然后,利用数据场势函数引入周围本体元素对当前元素的影响,修正初始相关度,并最终确定本体间的相关子本体;最后,利用针对性的方法对上述相关子本体进行更有效的映射.实验结果表明,该算法可以在提高映射结果质量的同时保证较高的映射效率.     

我国煤矿自动化现状及发展趋势

根据先进的煤矿自动化产品的特点,分析了我国煤矿自动化产品存在的问题,指出了建立具有统一网络平台的煤矿综合自动化系统是我国煤矿自动化与信息化的发展趋势;介绍了我国煤矿综合自动化系统的现有基础及其在建设中主要涉及的方面,给出了基于工业以太网和基于现场总线的煤矿综合自动化系统的网络平台结构及特点,以及实施煤矿综合自动化系统需要注意的问题。     

基于PhotoModeler Scanner的普通数码相机快速检校研究

随着分辨率的不断提高与价格的日益降低,数码相机已成为近景摄影测量的基本设备,而相机的准确检校是保证量测精度的关键因素.借助于PhotoModeler Scanner软件,本文实现了相机的快速准确检校,并对野外检校的稳定性和质量进行了分析,说明该检校方法简便可靠.     

Gröbner basis of the alternating galoisian ideal

This article proposes an efficient and simple algebraic method of computation of a Gröbner basis generating the alternating galoisian ideal of a univariate separable polynomial. We named this method “the descent of the Vandermonde determinants”.     

Study of magnetic ranging technology in horizontal directional drilling

Intersection technology plays a key role in long-distance horizontal directional drilling (HDD). Magnetic ranging technology is an effective way to realize the intersection. A three-dimensional magnetic field model suitable for the cylindrical magnet connected with drilling bit was established. New formulae of magnetic induction intensity were deduced from the fundamental formulae to suit to the new magnetic ranging method. Based on the new formulae, a curve fitting model was established to complete the ranging. The whole process is simple and effective. The result of the experiment proved the feasibility of the new method and the accuracy is high enough for HDD.     

Image segmentation and bias correction via an improved level set method

Intensity inhomogeneity causes considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus bias field estimation is a necessary pre-processing step before quantitative analysis of MR data. This paper presents a variational level set approach for bias correction and segmentation for images with intensity inhomogeneities. Our method is based on the observation that local intensity variations in relatively smaller regions are separable, despite the inseparability of the whole image. In the beginning we define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. Generally the local intensity variations are described by the Gaussian distributions with different means and variances. In this work the objective functions are integrated over the entire domain with local Gaussian distribution of fitting energy, ultimately analyzing the data with a level set framework. Our method is able to capture bias of quite general profiles. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results.     

Pattern analysis of dermoscopic images based on Markov random fields

In this paper a method for detecting different patterns in dermoscopic images is presented. In order to diagnose a possible skin cancer, physicians assess the lesion based on different rules. While the most famous one is the ABCD rule (asymmetry, border, colour, diameter), the new tendency in dermatology is to classify the lesion performing a pattern analysis. Due to the colour textured appearance of these patterns, this paper presents a novel method based on Markov random field (MRF) extended for colour images that classifies images representing different dermatologic patterns. First, each image plane in L^*a^*b^* colour space is modelled as a MRF following a finite symmetric conditional model (FSCM). Coupling of colour components is taken into account by supposing that features of the MRF in the three colour planes follow a multivariate Normal distribution. Performance is analysed in different colour spaces. The best classification rate is 86% on average.