环境监测系统报表打印设计

环境监测系统报表打印程序,是要求完成对环保局监测系统的各个科室所需要的报表的打印工作,其操作过程是要求在用户界面中,输入或查询到需要打印的报表后,系统的操作者,点击对应的功能按钮,实现报表的连打和套打的功能。所谓的连打即表单的连续打印控制,当表单过长,一个页面打印不下,可以将其连续打印到几个页面。所谓的套打即按照印刷好的表格形式进行固定格式的打印。     

计量检定管理系统

文章介绍了采用中文VFP6．0开发的计量检定形难管理系统，该系统具备录入、查询、打印、统计、维护等功能。可以完全替代手工自动编辑证书、打印收费单、统计工作量，实现检定/校准文档管理的计算机化。     

基于 Labview的材料力学综合试验台测试系统研究

针对原来的力学实验设备老化，测试数据误差较大的情况，设计开发了基于Labview的材料力学综合试验台测试系统。该测试系统能进行材料力学的各种非破坏性实验项目，具有加载方便、使用灵活、可移植性好等特点，并且具备测试数据的自动采集、分析、显示、存储、历史数据查询、打印等功能。     

LED点阵显示屏字符信息显示方法及应用

本文介绍一种在点阵显示屏上显示西文字符、汉字等信息的方法,将该方法应用于LED屏上,可使显示相关信息变得更加简便,生活中可方便公布各种表格数据信息。     

Access数据库管理和打印出租车计价器检定证书

出租车计价器检定的Access数据库管理系统,主要实现对出租车计价器检定工作的信息化管理,包括对基本信息的登记、查询、统计、报表和证书打印等功能.本文详细介绍该系统的创建:表、查询和证书报表的设计、建立方法和注意事项.     

计量管理信息及其网络发布系统

文章介绍了采用DELPHI和VB设计的计算机计量管理信息及网络发布系统,该系统具备录入、查询、报表统计和在企业内部网的客户计算机上用IE浏览器进行查询和打印的功能。     

公路动态汽车衡证书统一打印系统的开发

本系统主要是基于VB、Access、Word及Excel等技术开发的动态汽车衡证书及附件统一打印系统.该系统具备录入、查询、打印、提醒等功能.在规范化管理的同时,提高工作效率.     

LED显示屏系统设计与proteus仿真

设计一种单片机控制的LED显示屏系统,通过AT89C52单片机控制由74HC154和74HC595组成的行列外围电路实现字符、汉字、数字显示,并且使用由DS1302组成的电路进行实时时间控制;通过Proteus软件实现显示屏系统的仿真,在Proteus环境下绘制电路原理图,井与Keil实现连接,通过仿真实现了实时时间显示,汉字静态显示和动态显示．并且按键切换显示内容的显示状态。     

基于CATIA平台的板料成形后置处理系统

基于CATIA平台和CAA二次开发技术,开发了完全集成于CATIA的后置处理系统,采用CATIA可视化显示技术与动态响应技术,实现了视图显示和查询功能。该系统可以在CATIA上直观地反映数据结果,并采用动态存储提高显示速度,操作简单,有效地提高了设计效率,在CATIA平台上实现了CAD/CAE真正意义上的无缝集成,适用...     

利用VB开发影剧院技术管理系统

开发影剧院技术管理数据库 ,能对影剧院的各个技术环节实行全面、高效、信息化的科学管理 ,具有重要的实用价值。本文介绍了采用Access和VB6 .0开发影剧院技术管理系统的设计思想、系统结构及其实现方法。该系统能够实现管理信息录入、查询、修改、打印数据和表格的功能 ,以及管理图片、声音、影像等多媒体功能 ,进而实现信息动态发布的网络化功能。     

Recognition of online unconstrained handwritten Gurmukhi characters based on Finite State Automata

This paper presents a language-based efficient post-processing algorithm for the recognition of online unconstrained handwritten Gurmukhi characters. A total of 93 stroke classes have been identified to recognize the Gurmukhi character set in this work. Support Vector Machine (SVM) classifier has been employed for stroke classification. The main objective of this paper is to improve the character level recognition accuracy using an efficient Finite State Automata (FSA)-based formation of Gurmukhi characters algorithm. A database of 21,945 online handwritten Gurmukhi words is primarily collected in this experiment. After analysing the collected database, we have observed that a character can be written using one or more strokes. Therefore, a total of 65,946 strokes have been annotated using the 93 identified stroke classes. Among these strokes, 15,069 stroke samples are considered for training the classifier. The proposed system achieved promising recognition accuracy of 97.3% for Gurmukhi characters, when tested with a new database of 8,200 characters, written by 20 different writers.     

Efficient Analysis of Vertical Projection Histogram to Segment Arabic Handwritten Characters

The paper discusses the segmentation of words into characters, which is an essential task in the development process of character recognition systems, as poorly segmented characters will automatically be unrecognized. The segmentation of offline handwritten Arabic text poses a greater challenge because of its cursive nature and different writing styles. In this article, we propose a new approach to segment handwritten Arabic characters using an efficient analysis of the vertical projection histogram. Our approach was tested using a set of handwritten Arabic words from the IFN/ENIT database, and promising results were obtained.     

对两种汉字编码输入法效率的比较研究

为了找出提高汉字输入效率的方法，通过理论分析和实验室实验方法，抽取１２名被试测试了当前最常用的两种汉字输入法，即新智慧全拼输入方法和智能五笔输入法的输入效率。结果发现两种方法需要大量的学习时间才能掌握，其中智能五笔需要的学习时间更长，更容易被遗忘。实验揭示，各种汉字输入方法还都需要进一步改进。     

基于ARCS的ASD儿童绘本角色设计研究

目的 为提升ASD儿童情绪学习动机,探究不同角色形象在基本情绪学习中的激发作用,以绘本角色造型为切入点,为绘本角色设计和干预训练提供参考。提出一种基于ARCS动机模型的角色设计研究方法。方法 首先,结合ARCS动机维度,建立了一套适用于绘本角色研究的指标体系。在文献研究的基础上,筛选出7项代表性眼动指标和1项情绪匹配数据,并利用眼动仪收集这些指标的实验数据;其次,采用熵权法计算眼动实验客观指标的权重,并使用逼近理想解法(TOPSIS)确定三大角色样本类型的综合排序。结果 研究发现,与其他类型的角色相比,在ASD儿童中,器物角色的动机反馈效果更佳,并且这几类角色对ASD儿童的眼动指标都有不同程度的影响,因此每个角色的干预效果也不尽相同。结论 将ARCS动机理论模型引入绘本角色设计中,结合眼动实验解析动机策略与角色特征元素之间的关联性,能更有效地改善干预训练中缺乏学习动机的情况;建立评价体系有助于为后续相关设计提供研究思路。     

中英文字可视化信息与形态编排设计比较研究

目的 中英文字字形构造不同,各自有其发展规律。文字可视化信息与形态是研究文字编排的重要维度。方法 以中英文字体为研究对象,以其可视化程度与形态比较为研究中心,分别从文字的大小、密度、构造、形态、识别特征及认知特点6个方面展开研究,探寻其各自编排设计特征与优势,以求合理借鉴及从中得以启示。结论 调节文字密度、笔划粗细、内部结构比例等因素,可使汉字编排更为统一规整;借助弧线改变关键词下端笔划形态可使词语流畅,语义重点突出;强化汉字边缘特征,是其快速认知的方法之一。对汉字进行减法设计时,文字的中心部位是其首选位置之一;英文字母上端及左端识别性较强,便于快速阅读。可借鉴其特征优势,强化汉字上端及左侧汉字部件特征,起到快速阅读 的作用;在中英文组合编排时,可参照中文或英文任意一种文字内部比例,统一调整,使两者搭配更为协调。     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Handwriting segmentation of unconstrained Oriya text

Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper we propose a water reservoir concept-based scheme for segmentation of unconstrained Oriya handwritten text into individual characters. Here, at first, the text image is segmented into lines, and the lines are then segmented into individual words. For line segmentation, the document is divided into vertical stripes. Analysing the heights of the water reservoirs obtained from different components of the document, the width of a stripe is calculated. Stripe-wise horizontal histograms are then computed and the relationship of the peak-valley points of the histograms is used for line segmentation. Based on vertical projection profiles and structural features of Oriya characters, text lines are segmented into words. For character segmentation, at first, the isolated and connected (touching) characters in a word are detected. Using structural, topological and water reservoir concept-based features, characters of the word that touch are then segmented. From experiments we have observed that the proposed “touching character” segmentation module has 96.7% accuracy for two-character touching strings.