基于PDA的GIS实时测图系统开发

讨论了WindowsCE环境下基于PDA的GIS实时测图系统开发原理和方法。简述了其开发基本理论 ,探讨了eM beddedVC ++开发环境下基于PDA的GIS实时测图系统开发具体技术。介绍了POCKETPC中实现的一个GIS实时测图系统及其进行的初步应用 ,这为基于PDA等移动设备进行移动制图或移动GIS研究和开发提供了有益的参考模式。

GPS Van一种实时测图系统简介

GPSVan是由美国俄亥俄州立大学测图中心（Centreformapping）研制的第二代移动式数据采集系统，它主要用于对道路及其附近的地物进行制图，并可直接与GIS后处理工作站作为一个整体来使用，以便快速、准确地编辑所采集的数据。

改进的单目视觉实时定位与测图方法

针对经典单目实时定位与测图(SLAM)采用卡尔曼滤波(EKF)滤波和FAST特征角点所存在的非线性误差和鲁棒性较差的问题,提出了一种改进的单目视觉实时定位与测图方法。该方法采用相机中心的迭代EKF(IEKF)滤波方法,将特征点在当前相机坐标系下表达,并在线性化展开点附近迭代更新,不断逼近最优位置,从而最小化线性化误差;针对特征点跟踪的鲁棒性、高效性及分布不均的问题,选用具有尺度和旋转不变性,且探测和匹配效率更高的ORB特征作为特征角点,并采用一种由探测到筛选阶段的整体网格化处理方法;另外,采用特征点逆深度参数化方法,避免了因深度信息未知而导致的局部地图初始化错误问题,并采用1点随机抽样一致方法(RANSAC)滤波更新方法剔除错误的特征匹配,保证滤波估计的准确与稳定。实验采用外符合精度对算法进行评价,结果表明:新方法具有更强的鲁棒性,绝对定位精度提升至2.24 m,误差轨迹比提升至1.3%,且满足实时性要求,是一种实用性较强的单目视觉实时定位与测图方法。

Real-time image processing and display in object size detection based on VC++

Real-time detection for object size has now become a hot topic in the testing field and image processing is the core algorithm. This paper focuses on the processing and display of the collected dynamic images to achieve a real-time image pro- cessing for the moving objects. Firstly, the median filtering, gain calibration, image segmentation, image binarization, cor- ner detection and edge fitting are employed to process the images of the moving objects to make the image close to the real object. Then, the processed images are simultaneously displayed on a real-time basis to make it easier to analyze, understand and identify them, and thus it reduces the computation complexity. Finally, human-computer interaction （HCI）-friendly in- terface based on VC ＋＋ is designed to accomplish the digital logic transform, image processing and real-time display of the objects. The experiment shows that the proposed algorithm and software design have better real-time performance and accu- racy which can meet the industrial needs.

Background Subtraction and Frame Difference Based Moving Object Detection for Real-Time Surveillance

A new real-time algorithm is proposed in this paperfor detecting moving object in color image sequencestaken from stationary cameras.This algorithm combines a temporal difference with an adaptive background subtraction where the combination is novel.Ⅷ1en changes OCCUr.the background is automatically adapted to suit the new conditions.Forthe background model,a new model is proposed with each frame decomposed into regions and the model is based not only upon single pixel but also on the characteristic of a region.The hybrid presentationincludes a model for single pixel information and a model for the pixel’s neighboring area information.This new model of background can both improve the accuracy of segmentation due to that spatialinformation is taken into account and salientl5r speed up the processing procedure because porlion of neighboring pixel call be selected into modeling.The algorithm was successfully used in a video surveillance systern and the experiment result showsit call obtain a clearer foreground than the singleframe difference or background subtraction method.

Method of Precise Auto Tool Setting for Micro Milling

Based on microscope and image processing, a new method of auto tool setting for micro milling was presented. Firstly, a realtime image of tool setting area was obtained by microscope and CCD camera, then image processing was carried out on this image and the gap between the tool and workpiece was calculated. The gap measurement was sent to motion controlling card to make the tool approach to the surface of workpiece. These steps were repeated until the gap is zero, which means that tool setting was finished. Moreover, a reliability verification test was conducted. Results indicated that the precision of tool setting is satisfactory.

Real-time Data Communication in Photoelectric Image Detection System

High speed data communication between digital signal processor and the host is required to meet the demand of most real-time systems. PCI bus technology is a solution of this problem. The principle of data communication based on PCI has been explained. Meanwhile, the technology of data transfer between synchronous dynamic RAM(SDRAM) and an mapping space of on-chip memory(L2) by expansion direct memory access(EDMA) has also been realized.

Global Solar Radiation Maps of Saudi Arabia

This paper uses Abductive network to predict global solar radiation in any location in the Kingdom of Saudi Arabia （KSA） based on sunshine duration, month number, latitude, longitude, and altitude of the location. Results indicate good agreement between measured and predicted GSR values for each of the 35 locations with known GSR values. Finally, the data from all available stations are used to train an abductive network to estimate the GSR values anywhere in the Kingdom based on latitude and longitude. GSR values are estimated using the developed method at 25 additional locations throughout the kingdom and used with the measured data from the 35 available measurement stations to draw a comprehensive contour map of GSR values for KSA.

Quantifying Dynamical Predictability:the Pseudo-Ensemble Approach

The ensemble technique has been widely used in numerical weather prediction and extended-range forecasting.Current approaches to evaluate the predictability using the ensemble technique can be divided into two major groups.One is dynamical,including generating Lyapunov vectors,bred vectors,and singular vectors,sampling the fastest error-growing directions of the phase space,and examining the dependence of prediction efficiency on ensemble size.The other is statistical,including distributional analysis and quantifying prediction utility by the Shannon entropy and the relative entropy.Currently,with simple models,one could choose as many ensembles as possible,with each ensemble containing a large number of members.When the forecast models become increasingly complicated,however,one would only be able to afford a small number of ensembles,each with limited number of members,thus sacrificing estimation accuracy of the forecast errors.To uncover connections between different information theoretic approaches and between dynamical and statistical approaches,we propose an (∈;T)-entropy and scale-dependent Lyapunov exponent——based general theoretical framework to quantify information loss in ensemble forecasting.More importantly,to tremendously expedite computations,reduce data storage,and improve forecasting accuracy,we propose a technique for constructing a large number of "pseudo" ensembles from one single solution or scalar dataset.This pseudo-ensemble technique appears to be applicable under rather general conditions,one important situation being that observational data are available but the exact dynamical model is unknown.