智能公交移动平台实时查询客户端

随着智能手机和高速移动网络的普及,智能公交查询系统作为一个城市化的公共信息平台,可较大程度上方便广大民众的日常出行．目前,Android平台上的公交查询客户端分为两种,一种是查询静态信息,如换乘方案,线路信息等,另外一种是公交实时运行状况的查询．本文在传统设计的基础上,采用本地数据库和HTTP连接双重查询方式,通过解析JSON数据,设计并实现了具有自动刷新功能的移动平台公交实时查询软件．在查询站点信息和线路信息基础上,将线路上即将到站车辆的运行位置信息实时显示在页面上,从而使用户清晰的了解当前公交运行情况,适当调节自己的出行计划,具有较高的现实意义．     

卫星定位系统的手机终端公交实时查询

为了提高城市化智能公交水平,方便智能手机用户实时查询公交线路信息,该文提出基于Android开发平台的手机终端实时查询系统,实现公交站点查询、车次查询和换乘方案查询等功能。该系统使用Android软件开发工具包和Mysql数据库进行开发,应用程序代码实现基于Java语言。用户通过手机终端可获取想要乘坐的公交车的具体信息,从而给用户的出行带来便捷。     

基于J2ME的嵌入式公交查询系统的研究与实现

介绍了一个基于J2ME平台用于嵌入式设备的无线公交线路查询系统的总体结构和功能,该系统用于向移动设备用户提供各种公交信息的查询服务,重点讨论了系统的分层设计和公交换乘算法的实现。     

基于位置的手机博客系统设计与实现

随着移动产业发展和移动技术提高,基于用户位置的业务迅速发展,如:紧急援助、信息查询等,基于位置业务创新已经成为移动产业发展的巨大推动力。文中在ISG平台上设计和实现基于位置的手机博客系统。与传统的手机博客系统相比较,文中引入用户位置信息。用户写博客时,系统自动记录用户的位置信息,并把用户位置与其所写博客动态绑定存储;用户可以根据自己的位置动态搜索博客。     

基于位置的手机博客系统设计与实现

随着移动产业发展和移动技术提高,基于用户位置的业务迅速发展,如:紧急援助、信息查询等,基于位置业务创新已经成为移动产业发展的巨大推动力.文中在ISG平台上设计和实现基于位置的手机博客系统.与传统的手机博客系统相比较,文中引入用户位置信息.用户写博客时,系统自动记录用户的位置信息,并把用户位置与其所写博客动态绑定存储;用户可以根据自己的位置动态搜索博客.     

多端实时同步的公交电子智能站牌系统

结合时下流行的物联网技术,文中设计并实现了一种电子站牌与移动端应用实时同步显示的公交电子智能站牌系统。利用GPS技术与GPRS技术将公交车位置数据发送到后台中心服务器进行处理,服务器将数据处理封装为数据接口,电子站牌和移动端通过调用数据接口实现电子站牌公交信息数据在移动端的实时同步显示与远程同步更新。设计的公交电子智能站牌系统具有实现成本低廉、推广简单、信息同步更新便捷等优点。     

基于天地图的智慧公交移动APP设计与实现

针对太原市智慧公交系统建设的需求,基于天地图移动API和IOS平台,研发了一款智慧公交移动APP.APP提供静态公交查询、实时公交查询、周边公交查询和公交换乘查询等公交查询服务和周边兴趣点查询、天气查询和交通管理通知公告等增值服务.解决传统公交运营管理不能满足公众对出行信息的需求问题,做到了智慧出行.     

基于环境信息的移动搜索个性化查询扩展

与传统搜索相比,移动搜索对位置、温度、速度等环境信息更为敏感.为了有效利用环境信息推断用户查询意图,提出了一种基于环境信息的查询扩展方法并应用在移动搜索系统Clever Search Engine(CSE)中.该方法利用专家系统对分词后的查询词和收集到的用户环境信息进行推理和融合,扩展查询词,实现个性化搜索.实验证明,基于环境信息的移动搜索个性化查询扩展方法能有效改善移动用户的搜索体验,比现有的公共搜索引擎(如Google)具有更高的查准率.     

基于手机短信的公交动静态信息查询系统研究

为解决城市因难以获取公交车辆实时动态信息而使出行者盲目等待的问题,建立了公交动静态信息数据库,利用GSM设备与应用软件构建了短信中心,应用VB程序语言实现了数据库的访问以及公交乘客与公交信息中心的通信,完成了基于手机短信的福州市公交信息查询系统,有利于提高公交服务质量,吸引大量出行者使用公交.     

水情信息移动查询系统的设计与实现

为了方便快捷地获取实时水情信息,对水情信息移动查询系统进行了设计开发.该系统客户端基于开源的嵌入式Linux和Qt/Embcdded开发GIS查询软件,经GPRS移动接入GIS应用服务器,采用Oracle9i数据库统一管理服务器端空间数据和水情数据,GIS应用服务器以JDBC方式连接数据库,实现空间分析和水情信息查询,并以Socket方式将结果传回客户移动终端.应用实例表明了使用终端的GIS软件实现实时水情信息移动查询的可行性.     

基于LBS的自绘地图实时漫游系统的设计与研究

随着移动计算技术、网络通信技术及LBS技术的快速发展和普及,移动端搭载的LBS应用也越来越受到研究者的关注。然而,市面上原有的移动端LBS应用并不适合低成本环境下为特殊地点提供特殊信息服务的需求。为了解决这种问题,经过基于LBS的自绘地图实时漫游系统的设计与研究,提出三种关于自绘地图与经纬度信息结合提供特殊信息的实现思路,并且以浙江工业大学的校园地图为例制作Android应用进行演示。     

基于智能手机的校园巴士运行位置实时共享系统

当前很多学校的校园内部已经采用电瓶车等新能源汽车来作为公共的交通工具为教师和学生提供交通便利.本文采用校园巴士的司机自身所携带的智能手机作为采集终端,通过手机APP采集司机的经纬度位置数据,以此作为该台校园巴士的位置信息,利用移动互联网将该位置信息传递给后台.后台则借助于地图服务器,根据不同司机手机所传递过来的位置信息以Web的方式呈现给用户.本系统直接使用司机的智能手机作为校园巴士的位置采集终端,省却了购买专门的获取巴士位置数据的智能终端设备费用,同时可以利用学校的Wi-Fi网络节省移动通信流量,具有成本低、部署快、使用费用低廉、维护简单方便等优点.     

一种基于位置的移动信息服务测试数据随机生成方法

基于位置的移动信息服务通过智能移动终端的GPS定位,为用户提供了位置相关的、个性化、方便、有趣的信息。由于位置区域广袤、网络和定位环境复杂,兴趣点数量众多、以及位置服务更新频繁等因素,使得基于位置的移动信息服务测试非常困难。本文探索基于蒙特卡罗随机模拟抽样方法,按照兴趣点位置分布概率随机生成位置点测试数据,实现对地理区域和兴趣点的高覆盖测试。实验表明,与简单随机测试数据生成方法相比,该方法能够有效提高测试效率和测试质量。     

基于GPS和GIS技术的公交车辆实时监控系统的设计与实现

本文介绍了一种基于GIS和GPS技术的实时监控公交车辆的位置和运行状态的方法。在利用GPS系统对车辆的精确定位基础上，进一步利用GIS提供的强大的地图显示功能，在电子地图上动态显示公交车辆的位置，查询有关某一车辆的信息，以实现车辆的实时监控，为交通管理系统智能化和动态跟踪车辆的实现提供了有力的保证。     

融合语义激光与地标信息的SLAM技术研究

针对移动机器人在SLAM（即时定位与地图构建）过程中出现的定位失真问题,提出一种通过搭建地标数据库和位姿推导模型,修正机器人错误定位的方法。建图过程中,融合视觉信息与激光数据,得到语义激光,赋予地标语义标签并记录其在地图上的位置信息。导航过程中,当产生定位偏差时,结合多种位姿数据和相对位置关系,推算出机器人在地图上的实际位置,完成重定位。通过实验测试可知,该方法克服了现有机器人在实际室内动态环境下,单一地采用激光或视觉进行定位或重定位技术的缺点和不足,能有效解决“机器人位置漂移问题”。将机器人从当前位置劫持到另一位置,也能根据提出的算法迅速重定位,且定位精度高。     

On the planning of collision-free movements of manipulators

This paper proposes an efficient position identification method for mobile robots in the environment of a building corridor using colour images and map information. A robot can usually estimate its position from its motion history (so-called dead reckoning); however, there are occasions when the robot's position needs to be estimated without the motion history, for example when self-tracking of the motion has failed, or just after the robot power is on. The proposed method is to identify the robot's position without the motion history. The method consists of the following three steps: (1) map information for the mobile robot is prepared; (2) a colour image is processed to detect a vanishing point and to generate an abstracted image. The robot moves to an appropriate position for the identification, if it is unable to identify the current position; and (3) the current robot position is identified from the map information, the vanishing point, and the abstracted image. The effectiveness of the proposed method is shown by the experimental results.     

Sonar mapping of a mobile robot considering position uncertainty

This paper suggests a new sonar mapping method considering the position uncertainty of a mobile robot. Sonar mapping is used for recognizing the unknown environment for a mobile robot during navigation. Usually accumulated position error of a mobile robot causes considerable deterioration of the quality of a constructed map. In this paper, therefore, a new Bayesian probability map construction method is proposed, which considers estimation of the position error of a mobile robot. In this method, we applied approximation transformation theory to estimate the position uncertainty of a real mobile robot, and introduced cell ordering uncertainty caused by the position uncertainty of a robot in cell-based map updating. Through simulation we showed the effect of a robot's position uncertainty on the quality of a reconstructed map. Also, the developed methods were implemented on a real mobile robot, AMROYS-II, which was built in our laboratory and shown to be useful enough in a real environment.     

App store mining is not enough for app improvement

The rise in popularity of mobile devices has led to a parallel growth in the size of the app store market, intriguing several research studies and commercial platforms on mining app stores. App store reviews are used to analyze different aspects of app development and evolution. However, app users’ feedback does not only exist on the app store. In fact, despite the large quantity of posts that are made daily on social media, the importance and value that these discussions provide remain mostly unused in the context of mobile app development. In this paper, we study how Twitter can provide complementary information to support mobile app development. By analyzing a total of 30,793 apps over a period of six weeks, we found strong correlations between the number of reviews and tweets for most apps. Moreover, through applying machine learning classifiers, topic modeling and subsequent crowd-sourcing, we successfully mined 22.4% additional feature requests and 12.89% additional bug reports from Twitter. We also found that 52.1% of all feature requests and bug reports were discussed on both tweets and reviews. In addition to finding common and unique information from Twitter and the app store, sentiment and content analysis were also performed for 70 randomly selected apps. From this, we found that tweets provided more critical and objective views on apps than reviews from the app store. These results show that app store review mining is indeed not enough; other information sources ultimately provide added value and information for app developers.     

Continuous mobile robot localization by using structured light and a geometric map

A mobile robot needs to know its position and orientation with accuracy in order to decide the control actions that permit it to finish the entrusted tasks successfully. To obtain this information, dead-reckoning-based systems have been used, and more recently inertial navigation systems. However, these systems have some errors that grow bigger as time goes by, therefore a moment comes when the information provided is useless. Because of this, there should be a periodic process that updates the robot position and orientation of the vehicle. The process to determine the robot position and orientation by using information originated from the external sensors is defined as the mobile robot relocalization. It is obvious that the greater the frequency of this process, the better the knowledge of its position the robot will have, and therefore its movements will be better directed to the point it must reach. The algorithm to achieve this can be classified in two large groups: relocalization through an a priori map of the environment and relocalization through the detection of landmarks present in that environment. The algorithm presented in the paper belongs to the first case. The sensor used is a combination of a laser diode and a CCD camera. The sensorial information is modelled as straight lines that will be matched with an a priori map of the environment. With this, the position of the mobile robot is estimated. The matching process is accomplished within an extended Kalman filter. The algorithm is able to work in real time, and it actualizes the position of the robot in a continuous way.     

Real-time building of a thinning-based topological map

An accurate and compact map is essential to an autonomous mobile robot system. A topological map, one of the most popular map types, can be used to represent the environment in terms of discrete nodes with edges connecting them. It is usually constructed by Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map by using a thinning algorithm. This algorithm, when combined with the application of the C-obstacle, can easily extract only the meaningful topological information in real-time and is robust to environment change, because this map is extracted from a local grid map generated based on the Bayesian update formula. In this paper, position probability is defined to evaluate the quantitative reliability of the end node extracted by the thinning process. Since the thinning process builds only local topological maps, a global topological map should be constructed by merging local topological maps according to nodes with high position probability. For real and complex environments, experiments showed that the proposed map building method based on the thinning process can accurately build a local topological map in real-time, with which an accurate global topological map can be incrementally constructed.