英文资料翻译
Image processing is not a one step process．We are able to distinguish between several steps which must be performed one after the other until we can extract the data of interest from the observed scene．In this way a hierarchical processing scheme is built up as sketched in Fig．The figure gives an overview of the different phases of image processing.
Image processing begins with the capture of an image with a suitable，not necessarily optical，acquisition system．In a technical or scientific application，we may choose to select an appropriate imaging system．Furthermore，we can set up the illumination system，choose the best wavelength range，and select other options to capture the object feature of interest in the best way in an image．Once the image is sensed，it must be brought into a form that can be treated with digital computers.This process is called digitization.
With the problems of traffic are more and more serious. Thus Intelligent Transport System (ITS) comes out. The subject of the automatic recognition of license plate is one of the most significant subjects that are improved from the connection of computer vision and pattern recognition. The image imputed to the computer is disposed and analyzed in order to localization the position and recognition the characters on the license plate express these characters in text string form The license plate recognition system (LPSR) has important application in ITS. In LPSR, the first step is for locating the license plate in the captured image which is very important for character recognition. The recognition correction rate of license plate is governed by accurate degree of license plate location. In this paper, several of methods in image manipulation are compared and analyzed, then come out the resolutions for localization of the car plate. The experiences show that the good result has been got with these methods. The methods based on edge map and frequency analysis is used in the process of the localization of the license plate, that is to say, extracting t he characteristics of the license plate in the car images after being checked up for
the edge, and then analyzing and processing until the probably area of license plate is extracted.
The automated license plate location is a part of the image processing ,it’s also an important part in the intelligent traffic system．It is the key step in the Vehicle License Plate Recognition(LPR).A method for the recognition of images of different backgrounds and different illuminations is proposed in the paper.the upper and lower borders are determined through the gray variation regulation of the character distribution.The left and right borders are determined through the black-white variation of the pixels in every row.
The first steps of digital processing may include a number of different operations and are known as image processing．If the sensor has nonlinear characteristics, these need to be corrected．Likewise，brightness and contrast of the image may require improvement．Commonly，too，coordinate transformations are needed to restore geometrical distortions introduced during image formation．Radiometric and geometric corrections are elementary pixel processing operations．
It may be necessary to correct known disturbances in the image，for instance caused by a defocused optics，motion blur，errors in the sensor，or errors in the transmission of image signals．We also deal with reconstruction techniques which are required with many indirect imaging techniques such as tomography that deliver no direct image.
A whole chain of processing steps is necessary to analyze and identify objects．First，adequate filtering procedures must be applied in order to distinguish the objects of interest from other objects and the background．Essentially，from an image（or several images），one or more feature images are extracted．The basic tools for this task are averaging and edge detection and the analysis of simple neighborhoods and complex patterns known as texture in image processing．An important feature of an object is also its motion．Techniques to detect and determine motion are necessary．Then the object has to be separated from the background．This means that regions of constant features and discontinuities must be identified．This process leads to a
label image．Now that we know the exact geometrical shape of the object，we can extract further information such as the mean gray value，the area，perimeter，and other parameters for the form of the object[3]．These parameters can be used to classify objects．This is an important step in many applications of image processing，as the following examples show：In a satellite image showing an agricultural area，we would like to distinguish fields with different fruits and obtain parameters to estimate their ripeness or to detect damage by parasites．There are many medical applications where the essential problem is to detect pathologi-al changes．A classic example is the analysis of aberrations in chromosomes．Character recognition in printed and handwritten text is another example which has been studied since image processing began and still poses significant difficulties．
You hopefully do more，namely try to understand the meaning of what you are reading．This is also the final step of image processing，where one aims to understand the observed scene．We perform this task more or less unconsciously whenever we use our visual system．We recognize people，we can easily distinguish between the image of a scientific lab and that of a living room，and we watch the traffic to cross a street safely．We all do this without knowing how the visual system works．For some times now，image processing and computer-graphics have been treated as two different areas．Knowledge in both areas has increased considerably and more complex problems can now be treated．Computer graphics is striving to achieve photorealistic computer-generated images of three-dimensional scenes，while image processing is trying to reconstruct one from an image actually taken with a camera．In this sense，image processing performs the inverse procedure to that of computer graphics．We start with knowledge of the shape and features of an object—at the bottom of Fig. and work upwards until we get a two-dimensional image．To handle image processing or computer graphics，we basically have to work from the same knowledge．We need to know the interaction between illumination and objects，how a three-dimensional scene is projected onto an image plane，etc．
There are still quite a few differences between an image processing and a graphics workstation．But we can envisage that，when the similarities and interrelations between computergraphics and image processing are better understood and the proper hardware is developed，we will see some kind of general-purpose workstation in the future which can handle computer graphics as well as image processing tasks[5]．The advent of multimedia，i. e. ，the integration of text，images，sound，and movies，will further accelerate the unification of computer graphics and image processing.
In January 1980 Scientific American published a remarkable image called Plume2，the second of eight volcanic eruptions detected on the Jovian moon by the spacecraft Voyager 1 on 5 March 1979．The picture was a landmark image in interplanetary exploration—the first time an erupting volcano had been seen in space．It was also a triumph for image processing．
Satellite imagery and images from interplanetary explorers have until fairly recently been the major users of image processing techniques，where a computer image is numerically manipulated to produce some desired effect-such as making a particular aspect or feature in the image more visible.
Image processing has its roots in photo reconnaissance in the Second World War where processing operations were optical and interpretation operations were performed by humans who undertook such tasks as quantifying the effect of bombing raids．With the advent of satellite imagery in the late 1960s，much computer-based work began and the color composite satellite images，sometimes startlingly beautiful, have become part of our visual culture and the perception of our planet．
Like computer graphics，it was until recently confined to research laboratories which could afford the expensive image processing computers that could cope with the substantial processing overheads required to process large numbers of high-resolution images．With the advent of cheap powerful computers and image collection devices like digital cameras and scanners，we have seen a migration of image processing techniques into the public domain．Classical image processing techniques are routinely employed by
graphic designers to manipulate photographic and generated imagery，either to correct defects，change color and so on or creatively to transform the entire look of an image by subjecting it to some operation such as edge enhancement.
A recent mainstream application of image processing is the compression of images—either for transmission across the Internet or the compression of moving video images in video telephony and video conferencing．Video telephony is one of the current crossover areas that employ both computer graphics and classical image processing techniques to try to achieve very high compression rates．All this is part of an inexorable trend towards the digital representation of images．Indeed that most powerful image form of the twentieth century—the TV image—is also about to be taken into the digital domain．Image processing is characterized by a large number of algorithms that are specific solutions to specific problems．Some are mathematical or context-independent operations that are applied to each and every pixel．For example，we can use Fourier transforms to perform image filtering operations．Others are“algorithmic”—we may use a complicated recursive strategy to find those pixels that constitute the edges in an image．Image processing operations often form part of a computer vision system．The input image may be filtered to highlight or reveal edges prior to a shape detection usually known as low-level operations．In computer graphics filtering operations are used extensively to avoid abasing or sampling artifacts．
中文翻译
图像处理不是一步就能完成的过程。可将它分成诸多步骤，必须一个接一个地执行这些步骤，直到从被观察的景物中提取出有用的数据。依据这种方法，一个层次化的处理方案，该图给出了图像处理不同阶段的概观。
图像处理首先是以适当的但不一定是光学的采集系统对图像进行采集。在技术或科学应用中，可以选择一个适当的成像系统。此外，可以建立照明系统，选择最佳波长范围，以及选择其他方案以便用最好的方法在图像中获取有用的对象特征。一旦图像被检测到，必须将其变成数字计算机可处理的形式，这个过程称之为数字化。
随着交通问题的日益严重，智能交通系统应运而生。汽车牌照自动识别系统是近几年发展起来的计算机视觉和模式识别技术在智能交通领域应用的重要研究课题之一。课题的目的是对摄像头获取的汽车图像进行预处理,确定车牌位置,提取车牌上的字符串，并对这些字符进行识别处理,用文本的形式显示出来。车牌自动识别技术在智能交通系统中具有重要的应用价值。在车牌自动识别系统中，首先要将车牌从所获取的图像中分割出来，这是进行车牌字符识别的重要步骤，定位准确与否直接影响车牌识别率。本文在对各种车辆图像处理方法进行分析、比较的基础上,提出了车牌预处理、车牌粗定位和精定位的方法，并且取得了较好的定位结果。车牌定位采取的是边缘检测的频率分析法。从经过边缘提取后的车辆图像中提取车牌特征,进行分析处理,从而初步定出车牌的区域，再利用车牌的先验知识和分布特征对车牌区域二值化图像进行处理，从而得到车牌的精确区域。
汽车牌照的自动定位是图像处理的一种，也是智能交通系统中的重要组成部分之一，是实现车牌识别(LPR)系统的关键。针对不同背景和光照条件下的车辆图像，提出了一种基于灰度图像灰度变化特征进行车牌定位的方法。依据车牌中字符的灰度变化以峰、谷规律分布确定车牌上下边界，对扫描行采用灰度跳变法确定车牌左右边界。
数字化处理的第一步包含了一系列不同的操作并被称之为图像处理。如果传感器具有非线性特性，就必须予以校正，同样，图像的亮度和对比度也需要改善。通常，还需要进行坐标变换以消除在成像时产生的几何畸变。辐射度校正和几何校正是最基本的像素处理操作。
在图像中，对已知的干扰进行校正也是不可少的，比如由于光学聚焦不准，运动模糊，传感器误差以及图像信号传输误差所引起的干扰。在此还要涉及图像重构技术，它需要许多间接的成像技术，比如不直接提供图像的X 射线断层技术等。
一套完整的处理步骤对于物体的分析和识别是必不可少的。首先，应该采用适当的过滤技术以便从其他物体和背景中将所感兴趣的物体区分出来。实质上就是从一幅图像（或者数幅图像）中抽取出一幅或几幅特征图像。要完成这个任务最基本的工具就是图像处理中所使用的求均值和边缘检测、简单的相邻像素分析，以及复杂的被称为材质描述的模式分析。物体的一个重要特性就是它的运动性。检测和确定物体运动性的技术是必不可少的。随后，该物体必须从背景中分离出来，这就意味着具有同样特性和不同特性的区域必须被识别出来。这个过程产生出标志图像。既然已经知道了物体精确的几何形状，就可以抽取诸如平均灰度值、区域、边界以及形成物体的其他参数等更多的信息。这些参数可用来对物体进行分类，这是许多图像处理应用中至关重要的一步，比如下面一些应用：在一个显示农业地区的卫星图像中，想要区别出不同的果树，并获取参数以估算出成熟情况并监测害虫情况；
在许多的医学应用中，最基本的问题是检查病理变化，最典型的应用就是染色体畸变分析；印刷体和手写体识别是另一个例子，图像处理一出现，人们就开始对它进行着研究，现在依然困难重重。
人们希望能了解得更多一些，也就是试图理解所读到的内容。这也是图像处理的最后一个步骤，即理解所观察到的景象。当我们使用视觉系统时，实际上已或多或少无意识地在执行这个任务。我们能识别不同的人，可以很轻易地区分出实验室和起居室，可以观察车流以便安全地穿行马路。我们完成这样的任务而并不了解视觉系统工作的奥秘。
长久以来，图像处理和计算机图形学被看做两个不同的领域。现在，人们在这两个领域中的知识都有了极大的提高，并可以解决许多复杂的问题。计算机图形学正在努力使三维景物的计算机图像达到照片级效果。而图像处理则试图对用照相机实际拍摄的图像进行重构。从这个意义上讲，图像处理完成的是与计算机图形技术相反的过程。但从有关物体的形状和特性知识开始，向上直到获得一个二维图像要运用图像处理和计算机图形技术，所用到
的基本知识都是一样的。我们需要了解物体和照明之间的相互关系，三维景物是如何投影到图像平面上的等有关知识。
图像处理和计算机图形工作站之间仍然有一些不同之处。但我们应该看到，一旦较好地理解了计算机图形技术和图像处理之间的相似性和相互关系，并开发出了适当的硬件系统，一些既可处理计算机图形，又可完成图像处理任务的通用工作站就会出现。多媒体的出现，即文字、图像、声音和电影的综合，将进一步加速计算机图形学和图像处理的统一。
1980年元月《科学美国人》发表了一幅被称之为“Plume 2”的著名图像，它是1979年3月5日通过宇宙飞船旅行者1号在木星的卫星上探测到的8次火山爆发中的第二次。这幅图像在星际探险图像中是一个里程碑，人们第一次在宇宙中看到了正在爆发的火山。它也是图像处理领域的一次伟大胜利。
卫星图像以及宇宙探测器所获取的图像直到近年来才大量应用图像处理技术。在这些技术中，对计算机图像进行数字化处理以得到想要获得的效果，比如使图像的某一部分或某一特性更加明显。
图像处理源自于二战中的摄影侦察。当时，处理操作是通过光学方法来完成的，判读工作则是由专门精于此道并能确定炸弹袭击结果的人员来做。随着20世纪60年代后期卫星图像的出现，更多基于计算机的工作便开展起来彩色合成的卫星图像，有时的确漂亮得让人吃惊，它们已经成为人类视觉文化和对我们这个行星进行认知的一个组成部分。
正如计算机图形学一样，直到近几年，图像处理仍局限在一些实验室里使用，只有这些地方才能提供昂贵的图像处理计算机来满足处理大量高分辨率图像的需要。随着价格低廉的高性能计算机和诸如数码相机及扫描仪这样的图像采集设备的出现，我们已经看到图像处理技术在向公众领域转移。经典的图像处理技术很平常地被图像设计人员用来处理图片和生成图像，比如修复缺陷，改变色彩等或者通过图像边缘增强这样的处理来改变整个图片外观。
目前图像处理的主流应用是图像的压缩，即通过互联网进行传递或在可视电话和视频会议中进行移动视频图像的压缩。可视电话是当今结合计算机图像和传统图像处理技术，以期产生很高压缩比的交叉领域之一。所有这一
切都是图像的数字表达这一不可抗拒的发展趋势的组成部分。事实上，20世纪最强大的图像形式——电视图像，也将不可避免地融入数字领域。
图像处理的特点是针对不同问题有大量不同的算法。有一些是应用于每一个像素的、数学的或不依赖上下文的运算，比如，可以使用傅里叶变换来完成图像滤波操作；还有一些则是算法上的一一可以在图像中使用复杂的递归策略找出构成边缘的那些像素。
图像处理操作通常形成计算机视觉系统的一部分。比如，在形状检测操作中输入图像可过滤成高光或显示图像边缘。在计算机视觉系统中．这些处理通常认为是低级操作在计算机图形技术中，过滤操作广泛地用于防止图像毛边或采样失真。
中英文资料对照外文翻译 一、英文原文 A NEW CONTENT BASED MEDIAN FILTER ABSTRACT In this paper the hardware implementation of a contentbased median filter suitabl e for real-time impulse noise suppression is presented. The function of the proposed ci rcuitry is adaptive; it detects the existence of impulse noise in an image neighborhood and applies the median filter operator only when necessary. In this way, the blurring o f the imagein process is avoided and the integrity of edge and detail information is pre served. The proposed digital hardware structure is capable of processing gray-scale im ages of 8-bit resolution and is fully pipelined, whereas parallel processing is used to m inimize computational time. The architecturepresented was implemented in FPGA an d it can be used in industrial imaging applications, where fast processing is of the utm ost importance. The typical system clock frequency is 55 MHz. 1. INTRODUCTION Two applications of great importance in the area of image processing are noise filtering and image enhancement [1].These tasks are an essential part of any image pro cessor,whether the final image is utilized for visual interpretation or for automatic an alysis. The aim of noise filtering is to eliminate noise and its effects on the original im age, while corrupting the image as little as possible. To this end, nonlinear techniques (like the median and, in general, order statistics filters) have been found to provide mo re satisfactory results in comparison to linear methods. Impulse noise exists in many p ractical applications and can be generated by various sources, including a number of man made phenomena, such as unprotected switches, industrial machines and car ign ition systems. Images are often corrupted by impulse noise due to a noisy sensor or ch annel transmission errors. The most common method used for impulse noise suppressi on n forgray-scale and color images is the median filter (MF) [2].The basic drawback o f the application of the MF is the blurringof the image in process. In the general case,t he filter is applied uniformly across an image, modifying pixels that arenot contamina ted by noise. In this way, the effective elimination of impulse noise is often at the exp ense of an overalldegradation of the image and blurred or distorted features[3].In this paper an intelligent hardware structure of a content based median filter (CBMF) suita ble for impulse noise suppression is presented. The function of the proposed circuit is to detect the existence of noise in the image window and apply the corresponding MF
附录A Lathe fixture design and analysis Ma Feiyue (School of Mechanical Engineering, Hefei, Anhui Hefei 230022, China) Abstract: From the start the main types of lathe fixture, fixture on the flower disc and angle iron clamp lathe was introduced, and on the basis of analysis of a lathe fixture design points. Keywords: lathe fixture; design; points Lathe for machining parts on the rotating surface, such as the outer cylinder, inner cylinder and so on. Parts in the processing, the fixture can be installed in the lathe with rotary machine with main primary uranium movement. However, in order to expand the use of lathe, the work piece can also be installed in the lathe of the pallet, tool mounted on the spindle. THE MAIN TYPES OF LATHE FIXTURE Installed on the lathe spindle on the lathe fixture
广东工业大学华立学院 本科毕业设计（论文） 外文参考文献译文及原文 系部经济学部 专业经济学 年级 2007级 班级名称 07经济学6班 学号 16020706001 学生姓名张瑜琴 指导教师陈锶 2011 年05月
目录 1挑战：小额贷款中的进入和商业银行的长期承诺 (1) 2什么商业银行带给小额贷款和什么把他们留在外 (2) 3 商业银行的四个模型进入小额贷款之内 (4) 3.1内在的单位 (4) 3.2财务子公司 (5) 3.3策略的同盟 (5) 3.4服务公司模型 (6) 4 合法的形式和操作的结构比较 (8) 5 服务的个案研究公司模型：厄瓜多尔和Haiti5 (9)
1 挑战：小额贷款中的进入和商业银行的长期承诺 商业银行已经是逐渐重要的运动员在拉丁美洲中的小额贷款服务的发展2到小额贷款市场是小额贷款的好消息客户因为银行能提供他们一完整类型的财务的服务，包括信用，储蓄和以费用为基础的服务。整体而言，它也对小额贷款重要，因为与他们广泛的身体、财务的和人类。如果商业银行变成重的运动员在小额贷款，他们能提供非常强烈的竞争到传统的小额贷款机构。资源，银行能廉宜地发射而且扩张小额贷款服务rela tively。如果商业广告银行在小额贷款中成为严重的运动员，他们能提出非常强烈的竞争给传统的小额贷款机构。然而，小额贷款社区里面有知觉哪一商业银行进入进入小额贷款将会是短命或浅的。举例来说，有知觉哪一商业银行首先可能不搬进小额贷款因为时候建立小额贷款操作到一个有利润的水平超过银行的标准投资时间地平线。或，在进入小额贷款，银行之后可能移动在－上面藉由增加贷款数量销售取利润最大值－或者更坏的事，退出如果他们是不满意与小额贷款的收益性的水平。这些知觉已经被特性加燃料商业银行的情形进入小额贷款和后来的出口之内。在最极端的，一些开业者已经甚至宣布，”降低尺度死！”而且抛弃了与主意合作的商业银行。 在最 signific 看得到的地方，蚂蚁利益商业银行可能带给小额贷款，国际的ACCION 发展发射而且扩张的和一些商业银行的关系小额贷款操作。在这些情形的大部分方面， ACCION 和它的合伙人正在使用方法，已知的当做服务公司模型，表演早答应当做一个能工作的方法克服真正的。 商业银行的障碍进入和穿越建立长命的小额贷款操作一个商业银行 这论文描述如何服务公司模型、住址商业银行中的主要议题进入进小额贷款，监定成功建立的因素动作井小额贷款服务公司，和礼物结果和小额贷款的课servic e 公司用最长的经验，在海地和审判官席 del 的 SOGEBANK│ SOGESOL 初期结果指出那这服务公司模型表现一重要的突破在促成商业银行进入和留在小额贷款。在厄瓜多尔的 Pichincha│ CREDIFE。初期结果指出服务公司模型在促成商业广告中表现一次重要的突破银行进入而且留在小额贷款。
实时车辆的车牌识别系统 摘要 本文中阐述的是一个简炼的用于车牌识别系统的算法。基于模式匹配，该算法可以应用于对车牌实时检测数据采集，测绘或一些特定应用目的。拟议的系统原型已经使用C++和实验结果已证明认可阿尔伯塔车牌。 1.介绍 车辆的车牌识别系统已经成为在视频监控领域中一个特殊的热门领域超过10年左右。随着先进的用于交通管理应用的视频车辆检测系统的的到来，车牌识别系统被发现可以适合用在相当多的领域内，并非只是控制访问点或收费停车场。现在它可以被集成到视频车辆检测系统，该系统通常安装在需要的地方用于十字路口控制，交通监控等，以确定该车辆是否违反交通法规或找到被盗车辆。一些用于识别车牌的技术到目前为止有如BAM（双向联想回忆）神经网络字符识别[1]，模式匹配[2]等技术。应用于系统的技术是基于模式匹配，该系统快速，准确足以在相应的请求时间内完成，更重要的是在于阿尔伯塔车牌识别在字母和数字方位确认上的优先发展。由于车牌号码的字体和方位因国家/州/省份的不同而不同，该算法需要作相应的修改保持其结构完整，如果我们想请求系统识别这些地方的车牌。 本文其余部分的组织如下：第2节探讨了在识别过程中涉及的系统的结构和步骤，第3节解释了算法对于车牌号码的实时检测，第4节为实验结果，第5节总结了全文包括致谢和参考文献。 2.系统架构 系统将被用来作为十字路口的交通视频监控摄像系统一个组成部分来进行分析。图1显示了卡尔加里一个典型的交叉口。只有一个车牌用在艾伯塔，连接到背面的车辆照相机将被用于跟踪此背面车牌。 图1 卡尔加里一个的典型交叉口
系统架构包含三个相异部分：室外部分，室内部分和通信链路。室外部分是安装摄像头在拍摄图像的不同需要的路口。室内部分是中央控制站，从所有这些安装摄像头中，接收，存储和分析所拍摄图像。通信链路就是高速电缆或光纤连接到所有这些相机中央控制站。 几乎所有的算法的开发程度迄今按以下类似的步骤进行。一般的7个处理步骤已被确定为所有号牌识别算法[3] 共有。它们是： 触发：这可能是硬件或软件触发。硬件触发是旧的方式，即感应圈用于触发和这个表述了图像通过检测车牌的存在何时应该被捕获。硬件触发现在在操作上在许多地方被软件触发取代。在软件触发，图像分为区，通过图像对于分析的车辆的检测的执行。 图像采集：硬件或软件触发启动图像捕捉设备来捕捉和存储图像来进一步的分析。 车辆的存在：这一步是只需要如果在确认一定时间间隔后触发完成不需要知道车辆存在于捕获的图像中。这一步背景图像与捕获的图片作比较，并检测是否有任何重大改变。如果没有，拍摄的图像被忽略，否则进入到下一个步骤。 寻找车牌：此步骤是在捕获的图像中定位车牌。一些技术的可用于这一步，例如颜色检测[4]，特征分析[5]，边缘检测[6]等。在捕获的图像中的任何倾斜是纠正在这一步。一旦车牌已被定位，图像即准备进行字符识别。 字符分割：分割可以通过检测浓到淡或者淡到浓的过渡层。车牌中的每个灰色字符产生了一个灰色带。因此，通过检测类似灰度带每个字符可以被分割出来。 识别过程：这是光学字符识别的一步。一些技术可以被用于到这一步包括模式匹配[2]，特征匹配[7][8]和神经网络分类[9]。 发布过程：这是应用程序的特有的一步。根据应用此步骤可保存已被检测出来的车牌用于交通数据收集，尝试匹配号牌与被盗车辆数据库或在停车场中为认可停车的车辆打开汽车门等等。 3.算法 该算法用于在处理捕获的图像和车牌检测后的车牌字符识别。基于模式匹配，系统沿用了一个智能算法用于艾伯塔车牌字母和数字的识别。图2显示了一个艾伯塔省车牌样本其中包含三个字母，3个数字和破折号在内。所以通过基本的字符确认方法，模糊的字符比如有：数字'0'和字母'O'，数字'8'和字母'B已被解决。 此外，由于前三个字符是字母，所以只需与A-Z这一段的字母作比较比较。类似的，在最后三个字符，它门只需与0-9这一段数字作比较。
1. Introduction America is one of the countries that speak English. Because of the special North American culture, developing history and the social environment, American English has formed its certain unique forms and the meaning. Then it turned into American English that has the special features of the United States. American English which sometimes also called United English or U.S English is the form of the English language that used widely in the United States .As the rapid development of American economy, and its steady position and strong power in the world, American English has become more and more widely used. As in 2005, more than two-thirds of English native speakers use various forms of American English. The philologists of the United States had divided the English of the United States into four major types: “America n creating”; “Old words given the new meaning”; “Words that eliminated by English”;“The phonetic foreign phrases and the languages that are not from the English immigrates”[1]. Compared to the other languages, American English is much simple on word spelling, usage and grammar, and it is one of the reasons that American English is so popular in the world. The thesis analyzes the differences between American English and British English. With the main part, it deals with the development of American English, its peculiarities compared to that of British English, its causes and tendency. 2. Analyses the Differences As we English learners, when we learning English in our junior or senior school, we already came across some words that have different spellings, different pronunciations or different expressions, which can be represented by following contrasted words: spellings in "color" vs. "colour"; pronunciations in "sec-re-ta-ry" vs. "sec-re-try";
关于毕业设计说明书（论文）英文文献及中文翻译撰写格式 为提高我校毕业生毕业设计说明书（毕业论文）的撰写质量，做到毕业设计说明书（毕业论文）在内容和格式上的统一和规范，特规定如下: 一、装订顺序 论文（设计说明书）英文文献及中文翻译内容一般应由3个部分组成，严格按以下顺序装订。 1、封面 2、中文翻译 3、英文文献（原文） 二、书写格式要求 1、毕业设计（论文）英文文献及中文翻译分毕业设计说明书英文文献及中文翻译和毕业论文英文文献及中文翻译两种，所有出现相关字样之处请根据具体情况选择“毕业设计说明书” 或“毕业论文”字样。 2、毕业设计说明书（毕业论文）英文文献及中文翻译中的中文翻译用Word 软件编辑，英文文献用原文，一律打印在A4幅面白纸上，单面打印。 3、毕业设计说明书（毕业论文）英文文献及中文翻译的上边距:30mm；下边距:25mm；左边距:3Omm；右边距:2Omm；行间距1.5倍行距。 4、中文翻译页眉的文字为“中北大学2019届毕业设计说明书” 或“中北大学××××届毕业论文”，用小四号黑体字，页眉线的上边距为25mm；页脚的下边距为18mm。 5、中文翻译正文用小四号宋体，每章的大标题用小三号黑体，加粗，留出上下间距为：段前0.5行，段后0.5行；二级标题用小四号黑体，加粗；其余小标题用小四号黑体，不加粗。 6、文中的图、表、附注、公式一律采用阿拉伯数字分章编号。如图1.2，表2.3，附注3.2或式4.3。 7、图表应认真设计和绘制,不得徒手勾画。表格与插图中的文字一律用5号宋体。
每一插图和表格应有明确简短的图表名，图名置于图之下，表名置于表之上，图表号与图表名之间空一格。插图和表格应安排在正文中第一次提及该图表的文字的下方。当插图或表格不能安排在该页时，应安排在该页的下一页。 图表居中放置，表尽量采用三线表。每个表应尽量放在一页内，如有困难，要加“续表X.X”字样，并有标题栏。 图、表中若有附注时，附注各项的序号一律用阿拉伯数字加圆括号顺序排，如:注①。附注写在图、表的下方。 文中公式的编号用圆括号括起写在右边行末顶格，其间不加虚线。 8、文中所用的物理量和单位及符号一律采用国家标准，可参见国家标准《量和单位》(GB3100~3102-93)。 9、文中章节编号可参照《中华人民共和国国家标准文献著录总则》。
The research of digital image processing technique 1 Introduction Interest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception. This chapter has several objectives: (1)to define the scope of the field that we call image processing; (2)to give a historical perspective of the origins of this field; (3)to give an idea of the state of the art in image processing by examining some of the principal area in which it is applied; (4)to discuss briefly the principal approaches used in digital image processing; (5)to give an overview of the components contained in a typical, general-purpose image processing system; and (6) to provide direction to the books and other literature where image processing work normally is reporter. 1.1What Is Digital Image Processing? An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. We consider these definitions in more formal terms in Chapter2. Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike human who are limited to the visual band of the electromagnetic (EM) spectrum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that human are not accustomed to associating with image. These include ultrasound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of application. There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vision, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computer to
2604130359 CNC Cutting Technology Review Numerical control high speed cutting technology (High Speed Machining, HSM, or High Speed Cutting, HSC), is one of the advanced manufacturing technology to improve the machining efficiency and quality, the study of related technology has become an important research direction of advanced manufacturing technology at home and abroad. China is a big manufacturing country, in the world of industry transfer to accept the front instead of back-end of the transfer, to master the core technology of advanced manufacturing, or in a new round of international industrial structure adjustment, our country manufacturing industry will further behind. Imminent research on the theory and application of advanced technology. 1, high-speed CNC machining meaning High speed cutting theory put forward by the German physicist Carl.J.Salomon in the last century and early thirty's. He concluded by a lot of experiments: in the normal range of cutting speed, cutting speed if the increase, will cause the cutting temperature rise, exacerbating the wear of cutting tool; however, when the cutting speed is increased to a certain value, as long as more than the inflection point, with the increase of the cutting speed, cutting temperature can not rise, but will decline, so as long as the cutting speed is high enough, it can be solved very well in high cutting temperature caused by tool wear is not conducive to the cutting problem, obtained good processing efficiency. With the development of manufacturing industry, this theory is gradually paid more attention to, and attracted a lot of attention, on the basis of this theory has gradually formed the field of high-speed cutting technology of NC, relatively early research on NC High-speed Machining Technology in developed countries, through the theoretical basis of the research, basic research and applied research and development application, at present applications have entered the substantive stage in some areas. The high-speed cutting processing category, generally have the following several kinds of classification methods, one is to see that cutting speed, cutting speed over conventional cutting speed is 5-10 times of high speed cutting. Also has the scholar to spindle speed as the definition of high-speed processing standards, that the spindle speed is higher than that of 8000r\/min for high speed machining. And from the machine tool spindle design point of view, with the product of DN diameter of spindle and spindle speed, if the value of DN to (5~2000) * 105mm.r\/min, is considered to be of high speed machining. In practice, different processing methods, different materials, high speed cutting speed corresponding to different. Is generally believed that the turning speed of (700~7000) m\/min, milling speed reaches m\/min (300~6000), that is in the high-speed cutting. In addition, from the practical considerations, high-speed machining concept not only contains the high speed cutting process, integration and optimization also contains the process of cutting, is a
Advantages of Managed Code Microsoft intermediate language shares with Java byte code the idea that it is a low-level language witha simple syntax , which can be very quickly translated intonative machine code. Having this well-defined universal syntax for code has significant advantages. Platform independence First, it means that the same file containing byte code instructions can be placed on any platform; atruntime the final stage of compilation can then be easily accomplished so that the code will run on thatparticular platform. In other words, by compiling to IL we obtain platform independence for .NET, inmuch the same way as compiling to Java byte code gives Java platform independence. Performance improvement IL is actually a bit more ambitious than Java bytecode. IL is always Just-In-Time compiled (known as JIT), whereas Java byte code was ofteninterpreted. One of the disadvantages of Java was that, on execution, the process of translating from Javabyte code to native executable resulted in a loss of performance. Instead of compiling the entire application in one go (which could lead to a slow start-up time), the JITcompiler simply compiles each portion of code as it is called (just-in-time). When code has been compiled.once, the resultant native executable is stored until the application exits, so that it does not need to berecompiled the next time that portion of code is run. Microsoft argues that this process is more efficientthan compiling the entire application code at the start, because of the likelihood that large portions of anyapplication code will not actually be executed in any given run. Using the JIT compiler, such code willnever be compiled.
A review and analysis of current computer-aided fixture design approaches Iain Boyle, Yiming Rong, David C. Brown Keywords: Computer-aided fixture design Fixture design Fixture planning Fixture verification Setup planning Unit design ABSTRACT A key characteristic of the modern market place is the consumer demand for variety. To respond effectively to this demand, manufacturers need to ensure that their manufacturing practices are sufficiently flexible to allow them to achieve rapid product development. Fixturing, which involves using fixtures to secure work pieces during machining so that they can be transformed into parts that meet required design specifications, is a significant contributing factor towards achieving manufacturing flexibility. To enable flexible fixturing, considerable levels of research effort have been devoted to supporting the process of fixture design through the development of computer-aided fixture design (CAFD) tools and approaches. This paper contains a review of these research efforts. Over seventy-five CAFD tools and approaches are reviewed in terms of the fixture design phases they support and the underlying technology upon which they are based. The primary conclusion of the review is that while significant advances have been made in supporting fixture design, there are primarily two research issues that require further effort. The first of these is that current CAFD research is segmented in nature and there remains a need to provide more cohesive fixture design support. Secondly, a greater focus is required on supporting the detailed design of a fixture’s physical structure. 2010 Elsevier Ltd. All rights reserved. Contents 1. Introduction (2) 2. Fixture design (2) 3. Current CAFD approaches (4) 3.1 Setup planning (4) 3.1.1 Approaches to setup planning (4) 3.2 Fixture planning (4) 3.2.1 Approaches to defining the fixturing requirement (6) 3.2.2 Approaches to non-optimized layout planning (6) 3.2.3 Approaches to layout planning optimization (6) 3.3 Unit design (7) 3.3.1 Approaches to conceptual unit design (7)