人脸识别文献翻译(中英双文)

4 Two-dimensional Face Recognition

4.1 Feature Localization

Before discussing the methods of comparing two facial images we now take a brief look at some at the preliminary processes of facial feature alignment. This process typically consists of two stages: face detection and eye localization. Depending on the application, if the position of the face within the image is known beforehand (for a cooperative subject in a door access system for example) then the face detection stage can often be skipped, as the region of interest is already known. Therefore, we discuss eye localization here, with a brief discussion of face detection in the literature review .

The eye localization method is used to align the 2D face images of the various test sets used throughout this section. However, to ensure that all results presented are representative of the face recognition accuracy and not a product of the performance of the eye localization routine, all image alignments are manually checked and any errors corrected, prior to testing and evaluation.

We detect the position of the eyes within an image using a simple template based method. A training set of manually pre-aligned images of faces is taken, and each image cropped to an area around both eyes. The average image is calculated and used as a template.

Figure 4-1 The average eyes. Used as a template for eye detection.

Both eyes are included in a single template, rather than individually searching for each eye in turn, as the characteristic symmetry of the eyes either side of the nose, provide a useful feature that helps distinguish between the eyes and other false positives that may be picked up in the background. Although this method is highly susceptible to scale (i.e. subject distance from the camera) and also introduces the assumption that eyes in the image appear near horizontal. Some preliminary experimentation also reveals that it is advantageous to include the area of skin just beneath the eyes. The reason being that in some cases the eyebrows can closely match the template, particularly if there are shadows in the eye-sockets, but the area of skin below the eyes helps to distinguish the eyes from eyebrows (the area just below the eyebrows contain eyes, whereas the area below the eyes contains only plain skin).

A window is passed over the test images and the absolute difference taken to that of the average eye image shown above. The area of the image with the lowest difference is taken as the region of interest containing the eyes. Applying the same procedure using a smaller template of the individual left and right eyes then refines each eye position.

This basic template-based method of eye localization, although providing fairly precise localizations, often fails to locate the eyes completely. However, we are able to improve performance by including a weighting scheme.

Eye localization is performed on the set of training images, which is then separated into two sets: those in which eye detection was successful; and those in which eye detection failed. Taking the set of successful localizations we compute the average distance from the eye template (Figure 4-2 top). Note that the image is quite dark, indicating that the detected eyes correlate closely to the eye template, as we would expect. However, bright points do occur near the whites of the eye, suggesting that this area is often inconsistent, varying greatly from the average eye template.

Figure 4-2 – Distance to the eye template for successful detections (top) indicating variance due to noise and failed detections (bottom) showing credible variance due to miss-detected features.

In the lower image (Figure 4-2 bottom), we have taken the set of failed localizations(images of the forehead, nose, cheeks, background etc. falsely detected by the localization routine) and once again computed the average distance from the eye template. The bright pupils surrounded by darker areas indicate that a failed match is often due to the high correlation of the nose and cheekbone regions overwhelming the poorly correlated pupils. Wanting to emphasize the difference of the pupil regions for these failed matches and minimize the variance of the whites of the eyes for successful matches, we divide the lower image values by the upper image to produce a weights vector as shown in Figure 4-3. When applied to the difference image before summing a total error, this weighting scheme provides a much improved detection rate.

Figure 4-3 - Eye template weights used to give higher priority to those pixels that best represent the

eyes.

4.2 The Direct Correlation Approach

We begin our investigation into face recognition with perhaps the simplest approach, known as the direct correlation method (also referred to as template matching by Brunelli and Poggio) involving the direct comparison of pixel intensity values taken from facial images. We use the term ‘Direct Correlation’ to encompass all techniques in which face images are compared directly, without any form of image space analysis, weighting schemes or feature extraction, regardless of the distance metric used. Therefore, we do not infer that Pearson’s correlation is applied as the similarity function (although such an approach would obviously come under our definition of direct correlation). We typically use the Euclidean distance as our metric in these investigations (inversely related to Pearson’s correlation and can be considered as a scale and translation sensitive form of image correlation), as this persists with the contrast made between image space and subspace approaches in later sections.

Firstly, all facial images must be aligned such that the eye centers are located at two specified pixel coordinates and the image cropped to remove any background information. These images are stored as grayscale bitmaps of 65 by 82 pixels and prior to recognition converted into a vector of 5330 elements (each element containing the corresponding pixel intensity value). Each corresponding vector can be thought of as describing a point within a 5330 dimensional image space. This simple principle can easily be extended to much larger images: a 256 by 256 pixel image occupies a single point in 65,536-dimensional image space and again, similar images occupy close points within that space. Likewise, similar faces are located close together within the image space, while dissimilar faces are spaced far apart. Calculating the Euclidean distance d, between two facial image vectors (often referred to as the query image q, and gallery image g), we get an indication of similarity. A threshold is then applied to make the final verification decision.

4.2.1 Verification Tests

The primary concern in any face recognition system is its ability to correctly verify a claimed identity or determine a person's most likely identity from a set of potential matches in a database. In order to assess a given system’s ability to perform these tasks, a variety of evaluation methodologies have arisen. Some of these analysis methods simulate a specific mode of operation (i.e. secure site access or surveillance), while others provide a more mathematical description of data distribution in some classification space. In addition, the results generated from each analysis method may be presented in a variety of formats. Throughout the experimentations in this thesis, we primarily use the verification test as our

method of analysis and comparison, although we also use Fisher’s Linear Discriminate to analyze individual subspace components in section 7 and the identification test for the final evaluations described in section 8. The ver ification test measures a system’s ability to correctly accept or reject the proposed identity of an individual. At a functional level, this reduces to two images being presented for comparison, for which the system must return either an acceptance (the two images are of the same person) or rejection (the two images are of different people). The test is designed to simulate the application area of secure site access. In this scenario, a subject will present some form of identification at a point of entry, perhaps as a swipe card, proximity chip or PIN number. This number is then used to retrieve a stored image from a database of known subjects (often referred to as the target or gallery image) and compared with a live image captured at the point of entry (the query image). Access is then granted depending on the acceptance/rejection decision.

The results of the test are calculated according to how many times the accept/reject decision is made correctly. In order to execute this test we must first define our test set of face images. Although the number of images in the test set does not affect the results produced (as the error rates are specified as percentages of image comparisons), it is important to ensure that the test set is sufficiently large such that statistical anomalies become insignificant (for example, a couple of badly aligned images matching well). Also, the type of images (high variation in lighting, partial occlusions etc.) will significantly alter the results of the test. Therefore, in order to compare multiple face recognition systems, they must be applied to the same test set.

However, it should also be noted that if the results are to be representative of system performance in a real world situation, then the test data should be captured under precisely the same circumstances as in the application environment. On the other hand, if the purpose of the experimentation is to evaluate and improve a method of face recognition, which may be applied to a range of application environments, then the test data should present the range of difficulties that are to be overcome. This may mean including a greater percentage of ‘difficult’ images than would be expected in the perceived operating conditions and hence higher error rates in the results produced. Below we provide the algorithm for executing the verification test. The algorithm is applied to a single test set of face images, using a single function call to the face recognition algorithm: Compare Faces (FaceA, FaceB). This call is used to compare two facial images, returning a distance score indicating how dissimilar the two face images are: the lower the score the more similar the two face images. Ideally, images of the same face should produce low scores, while images of different faces should produce high scores.

Every image is compared with every other image, no image is compared with itself and no pair is compared more than once (we assume that the relationship is symmetrical). Once two images have been compared, producing a similarity score, the ground-truth is used to determine if the images are of the same person or different people. In practical tests this information is often encapsulated as part of the image filename (by means of a unique person identifier). Scores are then stored in one of two lists: a list containing scores produced by comparing images of different people and a list containing scores produced by comparing images of the same person. The final acceptance/rejection decision is made by application of a threshold. Any incorrect decision is recorded as either a false acceptance or false rejection. The false rejection rate (FRR) is calculated as the percentage of scores from the same people that were classified as rejections. The false acceptance rate (FAR) is calculated as the percentage of scores from different people that were classified as acceptances.

These two error rates express the inadequacies of the system when operating at a specific threshold value. Ideally, both these figures should be zero, but in reality reducing either the FAR or FRR (by altering the threshold value) will inevitably result in increasing the other. Therefore, in order to describe the full operating range of a particular system, we vary the threshold value through the entire range of scores produced. The application of each threshold value produces an additional FAR, FRR pair, which when plotted on a graph produces the error rate curve shown below.

Figure 4-5 - Example Error Rate Curve produced by the verification test.

The equal error rate (EER) can be seen as the point at which FAR is equal to FRR. This EER value is often used as a single figure representing the general recognition performance of a biometric system and allows for easy visual comparison of multiple methods. However, it is important to note that the EER does not indicate the level of error

that would be expected in a real world application. It is unlikely that any real system would use a threshold value such that the percentage of false acceptances was equal to the percentage of false rejections. Secure site access systems would typically set the threshold such that false acceptances were significantly lower than false rejections: unwilling to tolerate intruders at the cost of inconvenient access denials.

Surveillance systems on the other hand would require low false rejection rates to successfully identify people in a less controlled environment. Therefore we should bear in mind that a system with a lower EER might not necessarily be the better performer towards the extremes of its operating capability.

There is a strong connection between the above graph and the receiver operating characteristic (ROC) curves, also used in such experiments. Both graphs are simply two visualizations of the same results, in that the ROC format uses the True Acceptance Rate (TAR), where TAR = 1.0 –FRR in place of the FRR, effectively flipping the graph vertically. Another visualization of the verification test results is to display both the FRR and FAR as functions of the threshold value. This presentation format provides a reference to determine the threshold value necessary to achieve a specific FRR and FAR. The EER can be seen as the point where the two curves intersect.

Figure 4-6 - Example error rate curve as a function of the score threshold

The fluctuation of these error curves due to noise and other errors is dependant on the number of face image comparisons made to generate the data. A small dataset that only allows for a small number of comparisons will results in a jagged curve, in which large steps correspond to the influence of a single image on a high proportion of the comparisons made. A typical dataset of 720 images (as used in section 4.2.2) provides 258,840 verification operations, hence a drop of 1% EER represents an additional 2588 correct decisions, whereas the quality of a single image could cause the EER to fluctuate by up to

4 二维人脸识别

4.1 特征定位

在讨论两幅人脸图像的比较之前，我们先简单看下面部图像特征定位的初始过程。这一过程通常有由两个阶段组成：人脸检测和眼睛定位。根据不同的应用，如果在面部图像是事先所知的（例如在门禁系统主题之中），因为所感知区域是已知的，那么人脸检测阶段通常是可以跳过的。因此，我们讨论眼睛定位的过程中，有一个人脸检测文献的简短讨论。眼睛定位适用于对齐的各种测试二维人脸图像的方法通篇使用于这一节。但是，为了确保所有的结果都代表面部识别准确率，而不是对产品功能的眼睛定位，所有图像结果都是手动检查的。若有错误，则需要更正测试和评价。我们发现在一个使用图像的眼睛一个简单的基于模板的位置方法。

在一个区域中对前脸手动对齐图像进行采取和裁剪，以两只眼睛周围的区域，平均计算图像作为模板。

图4-1 - 平均眼睛，用作模板的眼睛检测

两个眼睛都包括在一个模板，而不是单独的为单个搜索，因为眼睛在鼻子两边对称的特点，这样就提供了一个可用方法，可以帮助区分眼睛和其他可能误报的背景。虽然这种方法介绍了假设眼睛水平的形象出现后很容易受到小距离的影响（即主体和相机的距离），但初步试验显示，还是利于包括眼睛下方的皮肤区域得到校准去的结果。因为在某些情况下，眉毛可以密切配合模板，特别是如果在眼睛区域的阴影周围。此外眼睛以下的皮肤面积有助于区分眉毛（眉毛下方的面积眼中包含的眼睛，而该地区眼睛下面的皮肤只含有纯色）。窗口区域是通过对测试图像和绝对差采取的这一平均眼睛上面显示的图像。图像的最低差额面积作为含有眼中感知的区域。运用同样的程序使用小模板单人左，右眼，然后提取每只眼睛的位置。

这个基本模板的眼睛定位方法，尽管提供了相当精确的本地化，但往往不能找到完全的眼睛区域。但是，我们能够改善性能和加权值。

眼睛定位是在执行图像处理，然后被分成集两套：哪些眼睛检测成功的，和哪些眼睛检测失败的。以成功的本地化处理，我们在计算平均距离眼睛模板（图4-2丁部）时，请注意，该图像是非常黑暗的，这表明发现眼睛密切相关的眼睛模板，正如我们期望的那样。然而，亮点确实发生在眼睛区域，表明这方面经常是不一致的，不同于普通模板。

图4-2 对眼睛模板成功检测（左），由于方差噪音和失败的检测（右）显示

在右侧的图像（图4-2右），前额，鼻子图像，脸颊，背景等采用了虚假的检测，并再次从眼睛计算了平均距离。明亮点由暗区包围表明，一个失败的匹配往往和鼻子和颧骨地区绝大多数的高相关性差相关。我们排除以上价值较低的图像产生的重矢量，如图4-3所示。应用到差分图像在总结前的误差，这个比重计划大大提高了检出率。

图 4-3

4.2直接相关方法

我们把最简单的人脸识别调查方法称为直接相关方法（也称为模板匹配的布鲁内利和波焦），其中所涉及的像素亮度值直接比较取自面部图像。我们使用术语'直接关系'，以涵盖所有图像技术所面临的直接比较，以及没有任何形式的形象空间分析，加权计划或特征提取。因此，我们并不能推断皮尔逊函数的相关性，作为应用相似的功能（尽管这种做法显然会受到我们的直接相关的定义）。我们通常使用欧氏距离度量作为我们的调查结果（负相关，Pearson相关，可以考虑作为一个规模和翻译的图像相关敏感的形式），这还对比了后面的章节的空间和子空间图像方法。

首先，所有的面部图像必须保持一致，这样使眼睛在两个中心位于指定的像素坐标和裁剪，以消除任何背景中的图像信息。这些图像存储为65和82像素灰度位图前进入了5330元素（每个元素包含向量转换确认相应的像素强度值）。每一个对应的向量可以认为是在说明5330点的三维图像空间。这个简单的原则很容易被推广到更大的照片：由256像素的图像256占用一个在65,536维图像空间，并再次指出，类似的图像占据接近点在该空间。同样，类似的面孔靠近一起在图像空间，而不同的面间距相距甚远。计算欧几里得距离d，两个人脸图像向量（通常称为查询图像Q和画廊图像克），我们得到一个相似的迹象。然后用一个阈值，制作出最后核查结果。

4.2.1验证测试

任何一个人脸识别系统的主要关注点是它能够从一个潜在的集合数据库中正确地验证人脸的身份或确定一个人最可能的身份。为了评估一个给定的系统的能力是否能执行这些任务，我们可以采用不同的评价方法。其中一些分析方法模拟一个具体的运作模式（即安全网站的访问或监视），而其他人提供更多的数据分布（数学描述中

的一些分类空间）。此外，每个分析结果产生的方法可能提交的各种格式。在本论文的整个实验过程中，我们主要验证我们的方法分析和比较，以核查措施的测试系统的能力，是否能正确地接受或拒绝一个人的身份认证。在一个功能级别中，可以减少到两个图像的比较，该系统必须对任何一个接受返回（两个图像是同一人）或拒绝（两个不同的图像人）做出结论。该测试旨在模拟安全网站访问的应用领域。在这种情况下，主题将在某一入境点的身份证件，或是刷卡，接近芯片或PIN号码。用于检索数据库中的已知对象通常被称为目标（1存储的图像画廊或图像），并在入境点（捕获的现场图像比较查询图像）。能否通过访问是根据当时获得的接受/拒绝的决定而执行的。

测试结果计算出多少人的接受/拒绝决定是正确的。为了执行这项测试，我们必须首先确定我们的测试人脸图像集。虽然这些图像的测试集数量不会影响结果产生的准确性，但重要的是要确保测试集是足够大，这样才能使统计的异常变得不重要（例如，一个非常一致的匹配以及情侣的图像）。另外，影像的类型（照明高度变化，部分遮挡等）将明显改变的结果测试。因此，为了比较多个面部识别系统，这些图像必须适用于相同的测试集。还应该指出，如果系统性能的结果代表在现实世界中的情况，测试数据应在同样情况下所获得的。另一方面，如果该实验的目的是评估和完善人脸识别方法，是否可应用到产品所在的范围环境中，那么测试过程中困难，要尽量克服。这也可能意味着包含一个'难'的图片，产生较高的错误率的结果。

以下我们提供了执行验证测试的算法。该算法适用于单个测试人脸图像集，调用一个在人脸识别算法中的函数：CompareFaces(FaceA，FaceB)。这一函数是用来比较两个面部图像。返回距离的评分表明两个不同的人脸图像的相似度：得分越低越相似。理想情况下，相同的人脸图像生产低分数，而不同的人脸图像产生高分。每一个形象，与所有其他形象相比较，，并与自身比较不止一次（我们假设关系是对称的）。当两个图像进行比较，产生相似性评分，结果用于确定图像中是否为同一人或不同人。实际中这些信息往往是通过测试图片中部分独特的人标识符来确定结果。比较后存储在两个列表其中一个中：一份列出通过比较的不同人的形象清单，另一份列出通过分数比较产生的同一人图像清单。最终的接受/拒绝决定是由一个门槛决定。任何不正确的决定，记为虚假或错误拒绝接受。该错误拒绝率（FRR）的计算方法作为得分从被认为是在拒绝归类相同的百分比。该错误接受率（FAR）是按不同的分数比例被认为是在接受归类的人。

这两个错误率反映了系统的不足之处。理想情况下，这两个数字应该是零，但在现实中无论是远或近（通过改变阈值）将不可避免地导致在增加。因此，为了描述一个完整的工作范围尤其是在系统中的，我们通过不同的分数范围的阈值来产生数据。每个阈值应用程序产生一个额外的容积率，它绘制在图表上时产生的错误率曲线如下图所示。

图4-5 - 范例错误率曲线的验证测试生成

等错误率（能效比）可以被看作是点的远近所产生的值。这能效比值通常被用来作为一个单一的代表普遍承认的数字生物识别系统的性能和视觉比较容易允许多个方法。不过，重要的是要注意，能效比未注明级别错误，这将是在一个真实世界中的应用预期。这是不太可能有真正的系统将使用一个阈值，这样的虚假承兑百分比等于拒绝虚假的百分比。安全网站接入系统通常会设置的门槛，例如虚假承兑汇票均显着高于假的则拒绝：不容忍入侵者在访问不便否认成本。另一方面监控系统将要求低错误拒绝率成功地确定一个受控环境中的人少。因此，我们应该承担。记住，一个具有较低的能效比制度不一定是最好的表演实现其经营能力的极端。有一图形和接收器强大的连接操作特征（ROC）曲线，亦在此类实验中使用。这两个图是完全相同的结果，在这视觉效果，中国格式使用真验收率,其中有效地翻转图垂直。另一个验证试验结果的可视化的，而且同时显示了FRR和职能的阈值。此演示文稿格式提供一参照确定阈值要达到一个特定的FRR，该能效比可以被看作是点的两条曲线相交。

图4-6 - 范例错误作为得分率阈值函数曲线

这些错误的波动曲线，噪音和其他错误是由于人脸图像进行比较时产生的，便可生成数据。一个小的数据集包含一个比较小的数目和一条锯齿状曲线。720图像的一个典型的数据集所提供258840验证操作，因此下降1%的EER代表了一个额外的2588

的正确决策，而一个单一的图像质量可能会导致能效比波动达

中英文文献翻译

毕业设计（论文）外文参考文献及译文 英文题目Component-based Safety Computer of Railway Signal Interlocking System 中文题目模块化安全铁路信号计算机联锁系统 学院自动化与电气工程学院 专业自动控制 姓名葛彦宁 学号 200808746 指导教师贺清 2012年5月30日

Component-based Safety Computer of Railway Signal Interlocking System 1 Introduction Signal Interlocking System is the critical equipment which can guarantee traffic safety and enhance operational efficiency in railway transportation. For a long time, the core control computer adopts in interlocking system is the special customized high-grade safety computer, for example, the SIMIS of Siemens, the EI32 of Nippon Signal, and so on. Along with the rapid development of electronic technology, the customized safety computer is facing severe challenges, for instance, the high development costs, poor usability, weak expansibility and slow technology update. To overcome the flaws of the high-grade special customized computer, the U.S. Department of Defense has put forward the concept：we should adopt commercial standards to replace military norms and standards for meeting consumers’demand [1]. In the meantime, there are several explorations and practices about adopting open system architecture in avionics. The United Stated and Europe have do much research about utilizing cost-effective fault-tolerant computer to replace the dedicated computer in aerospace and other safety-critical fields. In recent years, it is gradually becoming a new trend that the utilization of standardized components in aerospace, industry, transportation and other safety-critical fields. 2 Railways signal interlocking system 2.1 Functions of signal interlocking system The basic function of signal interlocking system is to protect train safety by controlling signal equipments, such as switch points, signals and track units in a station, and it handles routes via a certain interlocking regulation. Since the birth of the railway transportation, signal interlocking system has gone through manual signal, mechanical signal, relay-based interlocking, and the modern computer-based Interlocking System. 2.2 Architecture of signal interlocking system Generally, the Interlocking System has a hierarchical structure. According to the function of equipments, the system can be divided to the function of equipments; the system

建筑类外文文献及中文翻译

forced concrete structure reinforced with an overviewRein Since the reform and opening up, with the national economy's rapid and sustained development of a reinforced concrete structure built, reinforced with the development of technology has been great. Therefore, to promote the use of advanced technology reinforced connecting to improve project quality and speed up the pace of construction, improve labor productivity, reduce costs, and is of great significance. Reinforced steel bars connecting technologies can be divided into two broad categories linking welding machinery and steel. There are six types of welding steel welding methods, and some apply to the prefabricated plant, and some apply to the construction site, some of both apply. There are three types of machinery commonly used reinforcement linking method primarily applicable to the construction site. Ways has its own characteristics and different application, and in the continuous development and improvement. In actual production, should be based on specific conditions of work, working environment and technical requirements, the choice of suitable methods to achieve the best overall efficiency. 1、steel mechanical link 1.1 radial squeeze link Will be a steel sleeve in two sets to the highly-reinforced Department with superhigh pressure hydraulic equipment (squeeze tongs) along steel sleeve radial squeeze steel casing, in squeezing out tongs squeeze pressure role of a steel sleeve plasticity deformation closely integrated with reinforced through reinforced steel sleeve and Wang Liang's Position will be two solid steel bars linked Characteristic: Connect intensity to be high, performance reliable, can bear high stress draw and pigeonhole the load and tired load repeatedly.

企业创新战略外文翻译文献

企业创新战略外文翻译文献(文档含中英文对照即英文原文和中文翻译) 翻译之一： Choosing an innovation strategy: theory and practice Author：Joseph T. Gilbert Nationality：America Derivation：Business Horizons, Nov-Dec, 1994 Innovations come as both inventions and adoptions. They come in many types and vary greatly in complexity and scope. Companies attempting to

make a profit cannot continue for long periods without innovating. If they try, their customers will leave them for firms with more up-to-date products or services. It is an observed fact that different companies take different approaches to the use of innovation in attempting to improve their performance. Both academic and practitioner publications in recent years have contained a great deal of writing about innovation, the subjects of which have ranged from comparisons of national patterns of innovation to studies of individual innovations. However, little has been published regarding one issue of both theoretical and practical importance: the innovation policy or strategy of individual firms. Business strategy as a field of study is concerned with how a company competes in its chosen business. It deals with the analysis of a firm's strengths and weaknesses and the opportunities and threats presented by the firm's environment. Strategy looks toward consistent execution of broad plans to achieve certain levels of performance. Innovation strategy determines to what degree and in what way a firm attempts to use innovation to execute its business strategy and improve its performance. To choose an innovation strategy, managers might logically start by thinking about various kinds of innovations and their requirements. We shall discuss three major features of innovation, and analyze each in terms of distinct opposites, even though innovations found in the real world more often appear at various points between these opposites. Innovation is sometimes used in a limited sense to refer only to inventions (products, services, or administrative procedures that no other firm has introduced). More often, however, it applies in a more general sense that includes both invention as described above and imitation (adoption by a firm of a product, service, or administrative procedure that is not an invention but is new to that firm). We use the term in this second sense. Innovations can be characterized in a variety of ways. In the following

文献翻译英文原文

https://www.wendangku.net/doc/a85016915.html,/finance/company/consumer.html Consumer finance company The consumer finance division of the SG group of France has become highly active within India. They plan to offer finance for vehicles and two-wheelers to consumers, aiming to provide close to Rs. 400 billion in India in the next few years of its operations. The SG group is also dealing in stock broking, asset management, investment banking, private banking, information technology and business processing. SG group has ventured into the rapidly growing consumer credit market in India, and have plans to construct a headquarters at Kolkata. The AIG Group has been approved by the RBI to set up a non-banking finance company (NBFC). AIG seeks to introduce its consumer finance and asset management businesses in India. AIG Capital India plans to emphasize credit cards, mortgage financing, consumer durable financing and personal loans. Leading Indian and international concerns like the HSBC, Deutsche Bank, Goldman Sachs, Barclays and HDFC Bank are also waiting to be approved by the Reserve Bank of India to initiate similar operations. AIG is presently involved in insurance and financial services in more than one hundred countries. The affiliates of the AIG Group also provide retirement and asset management services all over the world. Many international companies have been looking at NBFC business because of the growing consumer finance market. Unlike foreign banks, there are no strictures on branch openings for the NBFCs. GE Consumer Finance is a section of General Electric. It is responsible for looking after the retail finance operations. GE Consumer Finance also governs the GE Capital Asia. Outside the United States, GE Consumer Finance performs its operations under the GE Money brand. GE Consumer Finance currently offers financial services in more than fifty countries. The company deals in credit cards, personal finance, mortgages and automobile solutions. It has a client base of more than 118 million customers throughout the world

指纹识别系统(文献综述)

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流动的：一个快速的，多平台的开放源码的同步化多媒体整合语言唱机Dick C.A. Bulterman, Jack Jansen, Kleanthis Kleanthous, Kees Blom and Daniel Benden CWI: Centrum voor Wiskunde en Informatica Kruislaan 413 1098 SJ Amsterdam, The Netherlands +31 20 592 43 00 Dick.Bulterman@cwi.nl 摘要： 本文概述了一个出现在早期的流动性的同步化多媒体唱机。不同于其它同步化的实现，早期的播放器是一个可重组的同步化引擎，可以定制作为一个实验媒体播放器的核心。同步化唱机是一个引用了同步化多媒体引擎并可以集成在一个广泛的媒体播放器的项目。本文是以我们要创造一个新的同步化引擎为动机的综述开始的。然后论述的是早期媒体播放器的核心架构（包括可扩展性，播放器自定义的集成装置）。我们以一个关于我们在windows，Mac，Linux版本应用于台式机以及PDA设备上实施流动性例子的体验的讨论结束。 类别和主题描述符： H.5.2 多媒体的信息系统。 H.5.4 超级文本/超级媒体。 一般词汇： 试验，性能，验证。 关键词： 同步化多媒体整合语言，唱机，公开源代码，演示。 1.动机： 早期公开的同步化媒体播放器是一个非常有特色的公开源代码的同步化 2.0播放器，它以研究团体的意图被使用（在我们的研究团体内外）目的是为了研究项目的团体在需要源代码的时候可以访问生产特性的同步化播放器的网站。它也被用作一个独立的不需要专有的媒体格式的同步化播放器使用，播放器支持一系列同步化2.0配置文件（包括台式机和移动的配置）可以被分配利用在Linux，Macintosh，windows系统的台式机，PDA设备和掌上电脑。 同时现存的几个同步化播放器，包括网络视频播放软件,IE浏览器,小型同步化播放器, GRiNS ,X- GRiNS ，以及各种各样专有移动设备，我们发展流动性唱机有三个原因： 准许制作数字以及个人或者课堂使用中的的全部硬拷贝即时没有提供拷贝权限或者商业性的利益分摊，而且在第一页有这种拷贝的注意事项。服务器上有关于复制以及翻版的分发列表的通知。需要事先明确具体的许可权以及费用。 'MM’04, October 10-16, 2004, New Y ork, New Y ork, USA. Copyright 2004 ACM 1-58113-893-8/04/0010...$5.00. 现有的同步化播放器没有提供一个完整同步化2.0播放器的正确实现。早期的播放器所有的同步化工具，是以同步化2.0语言的属性为基础加上扩展功能能够支持高级的动画以及规范可移动设备以3GPP/PSS-6同步化使用. 所有的同步化播放器都是针对商业SMIL表达专有媒介。早期的播发器使用开源的媒体解码器和开源的网络传输协议,以便播放器可以轻松定制广泛的使用范围的研究计划。 我们的目标是建立一个鼓励发展类似的多媒体研究输出的平台,，我们期望的是一个标准的基线播放器的供给,其他研究人员和开发机构可以集中精力到基线播放器的集成扩展(从新媒体的解码器或新的网络控制算法任何一个中)。这些扩展可以在其它的平台上被共享。 在2004年中期，与螺旋形客户机对照，同时移动到一个GPL核心，早期的播放器支持一个广阔的范围的同步化应用指标构架，它提供了一个准确实现的更完整的同步化语言，它在低资源配置下提供了更好的性能，提供了更多可扩展的媒体播放器架构。它也提供了一个包含所有媒体解码作为部分开放的客户基础。

英文文献翻译

中等分辨率制备分离的 快速色谱技术 W. Clark Still,* Michael K a h n , and Abhijit Mitra Departm(7nt o/ Chemistry, Columbia Uniuersity,1Veu York, Neu; York 10027 ReceiLied January 26, 1978 我们希望找到一种简单的吸附色谱技术用于有机化合物的常规净化。这种技术是适于传统的有机物大规模制备分离，该技术需使用长柱色谱法。尽管这种技术得到的效果非常好，但是其需要消耗大量的时间，并且由于频带拖尾经常出现低复原率。当分离的样本剂量大于1或者2g时，这些问题显得更加突出。近年来，几种制备系统已经进行了改进，能将分离时间减少到1-3h，并允许各成分的分辨率ΔR f≥（使用薄层色谱分析进行分析）。在这些方法中，在我们的实验室中，媒介压力色谱法1和短柱色谱法2是最成功的。最近，我们发现一种可以将分离速度大幅度提升的技术，可用于反应产物的常规提纯，我们将这种技术称为急骤色谱法。虽然这种技术的分辨率只是中等（ΔR f≥），而且构建这个系统花费非常低，并且能在10-15min内分离重量在的样本。4 急骤色谱法是以空气压力驱动的混合介质压力以及短柱色谱法为基础，专门针对快速分离，介质压力以及短柱色谱已经进行了优化。优化实验是在一组标准条件5下进行的，优化实验使用苯甲醇作为样本，放在一个20mm*5in.的硅胶柱60内，使用Tracor 970紫外检测器监测圆柱的输出。分辨率通过持续时间（r）和峰宽（w,w/2）的比率进行测定的（Figure 1），结果如图2-4所示，图2-4分别放映分辨率随着硅胶颗粒大小、洗脱液流速和样本大小的变化。

土木工程外文文献翻译

专业资料 学院： 专业：土木工程 姓名： 学号： 外文出处：Structural Systems to resist (用外文写) Lateral loads 附件：1.外文资料翻译译文；2.外文原文。

附件1：外文资料翻译译文 抗侧向荷载的结构体系 常用的结构体系 若已测出荷载量达数千万磅重，那么在高层建筑设计中就没有多少可以进行极其复杂的构思余地了。确实，较好的高层建筑普遍具有构思简单、表现明晰的特点。 这并不是说没有进行宏观构思的余地。实际上，正是因为有了这种宏观的构思，新奇的高层建筑体系才得以发展，可能更重要的是：几年以前才出现的一些新概念在今天的技术中已经变得平常了。 如果忽略一些与建筑材料密切相关的概念不谈，高层建筑里最为常用的结构体系便可分为如下几类： 1.抗弯矩框架。 2.支撑框架，包括偏心支撑框架。 3.剪力墙，包括钢板剪力墙。 4.筒中框架。 5.筒中筒结构。 6.核心交互结构。 7. 框格体系或束筒体系。 特别是由于最近趋向于更复杂的建筑形式，同时也需要增加刚度以抵抗几力和地震力，大多数高层建筑都具有由框架、支撑构架、剪力墙和相关体系相结合而构成的体系。而且，就较高的建筑物而言，大多数都是由交互式构件组成三维陈列。 将这些构件结合起来的方法正是高层建筑设计方法的本质。其结合方式需要在考虑环境、功能和费用后再发展，以便提供促使建筑发展达到新高度的有效结构。这并

不是说富于想象力的结构设计就能够创造出伟大建筑。正相反，有许多例优美的建筑仅得到结构工程师适当的支持就被创造出来了，然而，如果没有天赋甚厚的建筑师的创造力的指导，那么，得以发展的就只能是好的结构，并非是伟大的建筑。无论如何，要想创造出高层建筑真正非凡的设计，两者都需要最好的。 虽然在文献中通常可以见到有关这七种体系的全面性讨论，但是在这里还值得进一步讨论。设计方法的本质贯穿于整个讨论。设计方法的本质贯穿于整个讨论中。 抗弯矩框架 抗弯矩框架也许是低，中高度的建筑中常用的体系，它具有线性水平构件和垂直构件在接头处基本刚接之特点。这种框架用作独立的体系，或者和其他体系结合起来使用，以便提供所需要水平荷载抵抗力。对于较高的高层建筑，可能会发现该本系不宜作为独立体系，这是因为在侧向力的作用下难以调动足够的刚度。 我们可以利用STRESS，STRUDL 或者其他大量合适的计算机程序进行结构分析。所谓的门架法分析或悬臂法分析在当今的技术中无一席之地，由于柱梁节点固有柔性，并且由于初步设计应该力求突出体系的弱点，所以在初析中使用框架的中心距尺寸设计是司空惯的。当然，在设计的后期阶段，实际地评价结点的变形很有必要。 支撑框架 支撑框架实际上刚度比抗弯矩框架强，在高层建筑中也得到更广泛的应用。这种体系以其结点处铰接或则接的线性水平构件、垂直构件和斜撑构件而具特色，它通常与其他体系共同用于较高的建筑，并且作为一种独立的体系用在低、中高度的建筑中。

营销策略外文翻译文献

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