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unwrap_uv xatlas python 原理一、引言在计算机图形学和三维建模领域，UV映射是一个至关重要的过程，它涉及到将纹理坐标从模型表面展平到二维平面。
unwrap_uv和xatlas是两种常见的UV展开工具，而Python作为一种通用编程语言，为这两者提供了强大的集成环境。
本文将深入探讨unwrap_uv和xatlas在Python环境中的工作原理，以便更好地理解其应用和优势。
二、unwrap_uv的工作原理在三维建模过程中，UV映射的主要目标是使纹理能够平滑地映射到模型表面，同时保持纹理的连续性和完整性。
unwrap_uv作为一款专门用于解决UV问题的软件，其工作原理基于几何和纹理的优化算法。
具体来说，unwrap_uv通过分析模型的几何特征和纹理信息，自动寻找最优的UV坐标展开方案，以最小化纹理扭曲并保持纹理的自然流动。
在Python环境中，unwrap_uv通常通过调用命令行接口或使用Python绑定库来调用。
用户可以通过编写Python脚本来自动化UV展开过程，并根据需要定制和调整参数。
这样，Python成为了一个灵活的工具，使得对unwrap_uv的使用更加高效和便捷。
三、xatlas的工作原理xatlas是一款高效的UV展开工具，其工作原理主要基于优化的纹理空间算法。
与unwrap_uv不同的是，xatlas更加注重自动化的纹理排列和优化。
它通过对输入的纹理图像进行分析和处理，自动生成最佳的UV坐标映射方案，从而使得纹理在模型表面能够均匀分布并减少纹理扭曲。
在Python环境中，xatlas同样可以通过Python绑定库或命令行接口进行调用。
通过编写Python脚本，用户可以集成xatlas的功能到自己的项目中，实现自动化纹理映射和处理。
Python的灵活性和可扩展性使得xatlas的应用更加广泛和高效。
四、Python环境中的应用在Python环境中，unwrap_uv和xatlas都可以被用于自动化处理三维模型的UV映射问题。
opencv金字塔构建buildpyramid源码解读（原创实用版）目录1.OpenCV 简介2.图像金字塔的概念3.OpenCV 构建图像金字塔的方法4.构建图像金字塔的代码实例5.总结正文1.OpenCV 简介OpenCV（Open Source Computer Vision Library）是一个开源的计算机视觉库，它包含了大量的图像处理和计算机视觉方面的算法。
OpenCV 的主要目的是提供一组通用的图像处理和计算机视觉算法，以便开发人员能够更加方便地实现图像处理和计算机视觉方面的功能。
2.图像金字塔的概念图像金字塔是一种多尺度表示方法，它包含了一组不同分辨率的图像。
这些图像都是通过对原始图像进行不同程度的降采样和上采样得到的。
图像金字塔在计算机视觉领域中有着广泛的应用，例如目标检测、图像分割和图像融合等。
3.OpenCV 构建图像金字塔的方法OpenCV 提供了一系列的函数来构建图像金字塔，其中最主要的函数是 pyrUp() 和 pyrDown()。
pyrUp() 函数用于向上采样，它可以将一幅图像放大到更高的分辨率。
pyrUp() 函数的原理是对原始图像进行上采样，然后通过插值方法得到更高分辨率的图像。
pyrDown() 函数用于向下采样，它可以将一幅图像缩小到更低的分辨率。
pyrDown() 函数的原理是对原始图像进行下采样，然后通过插值方法得到更低分辨率的图像。
4.构建图像金字塔的代码实例以下是一个使用 OpenCV 构建图像金字塔的代码实例：```pythonimport cv2# 读取原始图像img = cv2.imread("original_image.jpg")# 创建一个高斯金字塔gauss_pyr = cv2.pyrUp(img)# 创建一个拉普拉斯金字塔laplacian_pyr = cv2.pyrDown(gauss_pyr)# 显示原始图像和金字塔cv2.imshow("Original Image", img)cv2.imshow("Gaussian Pyramid", gauss_pyr)cv2.imshow("Laplacian Pyramid", laplacian_pyr)# 等待按键，然后关闭窗口cv2.waitKey(0)cv2.destroyAllWindows()```5.总结OpenCV 提供了一种方便的方式来构建图像金字塔，通过使用 pyrUp() 和 pyrDown() 函数，可以轻松地实现图像的放大和缩小。
BrochureMore information from /reports/2175408/VLSI Digital Signal Processing Systems. Design and ImplementationDescription:Digital audio, speech recognition, cable modems, radar, high–definition television–these are but a few of the modern computer and communications applications relying on digital signal processing (DSP) and theattendant application–specific integrated circuits (ASICs). As information–age industries constantly reinventASIC chips for lower power consumption and higher efficiency, there is a growing need for designers whoare current and fluent in VLSI design methodologies for DSP.Enter VLSI Digital Signal Processing Systems–a unique, comprehensive guide to performance optimizationtechniques in VLSI signal processing. Based on Keshab Parhi's highly respected and popular graduate–levelcourses, this volume is destined to become the standard text and reference in the field. This text integratesVLSI architecture theory and algorithms, addresses various architectures at the implementation level, andpresents several approaches to analysis, estimation, and reduction of power consumption.Throughout this book, Dr. Parhi explains how to design high–speed, low–area, and low–power VLSI systemsfor a broad range of DSP applications. He covers pipelining extensively as well as numerous othertechniques, from parallel processing to scaling and roundoff noise computation. Readers are shown how toapply all techniques to improve implementations of several DSP algorithms, using both ASICs andoff–the–shelf programmable digital signal processors.The book features hundreds of graphs illustrating the various DSP algorithms, examples based on digitalfilters and transforms clarifying key concepts, and interesting end–of–chapter exercises that help matchtechniques with applications. In addition, the abundance of readily available techniques makes this anextremely useful resource for designers of DSP systems in wired, wireless, or multimedia communications.The material can be easily adopted in new courses on either VLSI digital signal processing architectures orhigh–performance VLSI system design.An invaluable reference and practical guide to VLSI digital signal processing.A tremendous source of optimization techniques indispensable in modern VLSI signal processing, VLSIDigital Signal Processing Systems promises to become the standard in the field. It offers a rich trainingground for students of VLSI design for digital signal processing and provides immediate access tostate–of–the–art, proven techniques for designers of DSP applications–in wired, wireless, or multimediacommunications.Topics include:* Transformations for high speed using pipelining, retiming, and parallel processing techniques* Power reduction transformations for supply voltage reduction as well as for strength or capacitancereduction* Area reduction using folding techniques* Strategies for arithmetic implementation* Synchronous, wave, and asynchronous pipelining* Design of programmable DSPs.Contents:Introduction to Digital Signal Processing Systems.Iteration Bound.Pipelining and Parallel Processing.Retiming.Unfolding.Folding.Systolic Architecture Design.Fast Convolution.Algorithmic Strength Reduction in Filters and Transforms.Pipelined and Parallel Recursive and Adaptive Filters.Scaling and Roundoff Noise.Digital Lattice Filter Structures.Bit–Level Arithmetic Architectures.Redundant Arithmetic.Numerical Strength Reduction.Synchronous, Wave, and Asynchronous Pipelines.Low–Power Design.Programmable Digital Signal Processors.Appendices.Index.Ordering:Order Online - /reports/2175408/ Order by Fax - using the form belowOrder by Post - print the order form below and send toResearch and Markets,Guinness Centre,Taylors Lane,Dublin 8,Ireland.Fax Order FormTo place an order via fax simply print this form, fill in the information below and fax the completed form to 646-607-1907 (from USA) or +353-1-481-1716 (from Rest of World). 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离散傅里叶变换的算术傅里叶变换算法张宪超1，武继刚1，蒋增荣2，陈国良1（1.中国科技大学计算机科学与技术系，合肥230027；2.国防科技大学系统工程与数学系，长沙410073）摘要：离散傅里叶变换（DFT）在数字信号处理等许多领域中起着重要作用.本文采用一种新的傅里叶分析技术—算术傅里叶变换（AFT）来计算DFT.这种算法的乘法计算量仅为0（N）；算法的计算过程简单，公式一致，克服了任意长度DFT传统快速算法（FFT）程序复杂、子进程多等缺点；算法易于并行，尤其适合VLSI设计；对于含较大素因子，特别是素数长度的DFT，其速度比传统的FFT方法快；算法为任意长度DFT的快速计算开辟了新的思路和途径.关键词：离散傅里叶变换（DFT）；算术傅里叶变换（AFT）；快速傅里叶变换（FFT）中图分类号：TN917文献标识码：A文章编号：0372-2112（2000）05-0105-03An Algorithm for Computing DFT Using Arithmetic Fourier TransformZHANG Xian-chao1，WU Ji-gang1，JIANG Zeng-rong2，CHEN Guo-iiang1（1.Dept.of CompUter Science&Technology，Unio.of Science&Technology of China，Hefei230027，China；2.Dept.of System Engineering&Mathematics，National Unio.of Defense Technology，Changsha410073，China）Abstract：The Discrete Fourier Transform（DFT）piays an important roie in digitai signai processing and many other fieids.In this paper，a new Fourier anaiysis technigue caiied the arithmetic Fourier transform（AFT）is used to compute DFT.This aigorithm needs oniy0（N）muitipiications.The process of the aigorithm is simpie and it has a unified formuia，which overcomes the disadvantage of the traditionai fast method that has a compieX program containing too many subroutines.The aigorithm can be easiiy performed in paraiiei，especiaiiy suitabie for VLSI designing.For a DFT at a iength that contains big prime factors，especiaiiy for a DFT at a prime iength，it is faster than the traditionai FFT method.The aigorithm opens up a new approach for the fast computation of DFT.Key words：discrete Fourier transform（DFT）；arithmetic Fourier transform（AFT）；fast Fourier transform（FFT）!引言离散傅里叶变换（DFT）在数字信号处理等许多领域中起着重要作用.但DFT的计算量很大（N点DFT需0（N2）乘法和加法）.因此，DFT的快速计算问题非常重要.1965年，Cooiey 和Tukey开创了快速傅里叶变换（FFT）方法，使N点DFT的计算量从0（N2）降到0（N iog N），开辟了DFT的快速计算时代.但FFT的计算仍较复杂，且对不同长度的DFT其计算公式不一致，致使任意长DFT的FFT程序非常复杂，包含大量子进程.1988年，Tufts和Sadasiv［1］提出了一种用莫比乌斯反演公式（Mibius inversion formuia）计算连续函数的傅立叶系数的方法并命名为算术傅立叶变换（AFT）.AFT有许多良好的性质：其乘法量仅为0（N）；算法简单，并行性好，尤其适合VLSI设计.因此很快得到广泛关注，并在数字图像处理等领域得到应用.AFT已成为继FFT后一种新的重要的傅立叶分析技术［2~5］.根据DFT和连续函数的傅立叶系数的关系，可以用AFT 计算DFT.这种方法保持了AFT的良好性质，且具有公式一致性.大量实验表明，同直接计算相比，AFT方法可以将DFT的计算时间减少90%，对含较大素因子，特别是其长度本身为素数的DFT，它的速度比传统的FFT快.从而它为DFT快速计算开辟了新的途径."算术傅立叶变换本文采用文［3］中的算法.设A（t）为周期为T的函数，它的傅立叶级数只含有限项，即：A（t）=a0+!Nn=1a n cos2!f0t+!Nn=1b n sin2!f0t（1）其中：f0=1/T，a0=1T"TA（t）dt.令：B（2n，!）=12n!2n-1m=0（-1）m A（m2nT+!T），-1<!<1（2）则傅立叶系数a n和b n可以由下列公式计算：a n=!［N/n］l=1，3，5，…U（l）B（2nl，0）b n=!［N/n］l=1，3，5，…U（l）（-1）（l-1）/2B（2n，14nl），n=1，…，N（3）第5期2000年5月电子学报ACTA ELECTR0NICA SINICAVoi.28No.5May2000其中：!（l ）=I ，（-I ）r ，0{，l =I l =p I p 2…p r 3p 使p 2\l为莫比乌斯（M bioLS ）函数.这就是AFT ，其计算量为：加法：N 2+［N /2］+［N /3］+…+I -2N ；乘法：2N.AFT 需要函数大量的不均匀样本点，而在实际应用中，若计算函数前N 个傅立叶系数，根据奈奎斯特（NygLiSt ）抽样定律，只需在函数的一个周期内均匀抽取2N 个样本点.这时可以用零次插值解决样本不一致问题.文献［2、3］已作了详细的分析，本文不再重复.3DFT 的AFT 算法3.1DFT 的定义及性质定义1设X I 为一长度为N 的序列，它的DFT 定义为：Y I =Z N-II =0X I w II ，I =0，I ，…，N -I ；w =e -i 2!/N（4）性质1用记号X I 、=、Y I 表示序列Y I 为序列X I 的DFT ，G I 、=、H I ，则：pX I +gG I 、=、pY I +gH I （5）因此，一个复序列的DFT 可以用两个实序列的DFT 计算.故本文只讨论实序列DFT 的计算问题.性质2设X I 为一实序列，X I 、=、Y I ，则：Re Y I =Re Y N -I ，Im Y I =-Im Y N -I （Re Y I 和Im Y I 分别代表Y I 的实部和虚部）（6）因此，对N 点实序列DFT ，只需计算：Re Y I 和Im Y I （I =0，…，「N /2）.3.2DFT 的AFT 算法离散序列的DFT 和连续函数的傅立叶系数有着密切的联系.事实上，若序列X I 是一段区间［0，T ］上的函数A （t ）经过离散化后得到的，再设A （t ）的傅立叶级数只含前N /2项，即：A （t ）=a 0+Z「N /2-II =Ia I coS2!f 0t +Z「N /2-II =I6I Sin2!f 0t（7）则DFT Y I 和傅立叶系数的关系为：Re Y I =「N /2a I /2Im Y I =「N /26I /{2，I =0，…，「N /2（8）式（7）中函数代表的是一种截频信号.对一般函数，式（8）中的“=”要改为“匀”［7］.因此，序列X I 的DFT 可以通过函数A （t ）的傅里叶系数计算.对于一般给定序列X I ，注意到在任意一个区间上，经过离散后能得到序列X I 的函数有无穷多个.对所有这些插值函数，公式（8）都近似地满足（仅式（7）中的函数精确地满足式（8））［7］.AFT 的零次插值实现实质上就是用这些插值函数中的零次插值函数代替原来的函数进行计算的.而从AFT 的零次插值实现方法可知，用AFT 计算傅里叶系数，实际上参与计算的只是函数经离散化后得到的序列，而不必知道函数本身.因此，我们可以任取一个区间，在这个区间上，把序列X算（8）中的“傅里叶系数”，再通过式（8），就可以计算出序列的DFT .算法描述如下（采用［0，I ］区间）：for I =I to 「N /2for m =0to 2I -IB （2I ，0）：=B （2I ，0）+（-I ）mX［Nm /2I +0.5］B （2I ，I /4I ）：=B （2I ，I /4I ）+（-I ）mX［Nm /2I +N /4I +0.5］endforB （2I ，0）：=B （2I ，0）/2I B （2I ，I /4I ）：=B （2I ，I /4I ）/2I endforfor =0to N -I a 0：=a 0+X （ ）/N for I =I to 「N /2for I =I to［「N /2/I ］by 2a I ：=a I +!（I ）B （2II ，0）6I ：=6I +!（I ）（-I ）（K -I ）/2B （2II ，I /4II ）endforRe Y I ：=「N /2a I /2Re Y N -I ：=Re Y I Im Y I ：=「N /2a I /2Im Y N -I ：=-Im Y I endfor endfor图IDFT 的AFT 算法程序AFT 方法的误差主要是由零次插值引起的，大量实验表明，同FFT 相比，其误差是可以接受的（部分实验结果见附录）.4算法的性能4.1算法的程序DFT 的AFT 算法具有公式一致性，且公式简单，因此算法的程序也很简单（图I ）.图2DFT 的AFT 算法进程示意为便于比较，不妨看一下FFT 的流程.图3FFT 算法进程示意可以看出，FFT 的程序中包含大量子进程，且这些子程序都较复杂.其中素数长度DFT 的FFT 算法程序尤其复杂.因此，任意长DFT 的FFT 算法其程序是非常复杂的.4.2算法的计算效率AFT 方法把DFT 的乘法计算量从0（N 2）降到0（N ），它2电子学报2000年计算时间减少90%.当DFT的长度!为2的幂时，FFT比AFT 方法快"对一般长度的DFT，当!含较大素因子时，AFT方法比FFT快；当!的因子都较小时，AFT方法不如FFT快.当DFT长度!本身为一较大素数时，AFT方法比FFT快"附录中给出部分实验结果以便比较"特别指出，对素数长度DFT，FFT的计算过程非常复杂，很难在实际中应用.而AFT方法算法简单，提供了较好的素数长度DFT快速算法"表1是两种算法计算效率较详细的比较"表1长度52191197114832417FFT效率67.30%68.03%72.50%71.23%76.22% AFT方法效率91.39#91.78#91.63#91.81#91.83# 4.3算法的并行性AFT具有良好的并行性，尤其适合VLSI设计，已有许多VLSI设计方案被提出，并在数字图像处理等领域得到应用.DFT的AFT算法继承了AFT优点，同样具有良好的并行性"5结论和展望本文采用算术傅里叶变换（AFT）计算DFT.这种方法把AFT的各种优点引入DFT的计算中来，开辟了DFT快速计算的新途径.把AFT方法同FFT结合起来，还可以进一步提高DFT的计算速度"参考文献［1］ D.W.Tufts and G.Sadasiv.The arithmetic Fourier transform.IEEE ASSP Mag，Jan.1988：13~17［2］I.S.Reed，D.W.Tufts，Xiao Yu，T.K.Troung，M.T.Shih and X.Yin.Fourier anaiysis and signai processing by use of Mobius inversion for-muiar.IEEE Trans.Acoust.Speech Speech Processing，Mar，1990，38（3）：458~470［3］I.S.Reed，Ming Tang Shih，T.K.Truong，R.Hendon and D.W.Tufts.A VLSI architecture for simpiified arithmetic fourier transform aigo-rithm.IEEE Trans.Signai Processing，May，1993，40（5）：1122~1132［4］H.Park and V.K.Prasanna.Moduiar VLSI architectures for computing the arithmetic fourier transform.IEEE.Signai Processing，June，1993，41（6）：2236~2246［5］Lovine.F.P，Tantaratanas.Some aiternate reaiizations of the arithmetic Fourier transform.Conference on Signai，system and Computers，1993，（Cat，93，CH3312-6）：310~314［6］蒋增荣，曾泳泓，余品能.快速算法.长沙：国防科技大学出版社，1993［7］E.0.布赖姆.快速傅立叶变换.上海：上海科学技术出版社，1976附录：较详细的实验结果（机型：586微机，主频：166MHz单位：秒）2的幂长度长度AFT方法基-2FFT直接算法2560.005160.002400.115120.018600.004400.4410240.075800.01100 1.81素数长度长度AFT方法FFT直接算法5210.03790.14390.449710.13400.4400 1.6014830.3103 1.0904 3.7924170.8206 2.389910.75任意长度长度因子分解AFT方法FFT直接算法13462!6370.270.44 3.1429862!1483 1.26 2.1414.8235793!1193 1.81 1.9222.1646374637 3.0821.4237.4755742!3!929 4.45 2.4752.2964364!1609 5.94 3.5772.6278933!3!8778.96 1.92105.49最大相对误差长度AFT方法FFT1024实部 2.1939>10-2 2.3328>10-2虚部 2.1938>10-29.9342>10-2 2048实部 4.2212>10-3 1.1967>10-2虚部 6.1257>10-3 4.9385>10-2 4096实部 2.3697>10-3 6.0592>10-3虚部 2.0422>10-3 2.4615>10-3张宪超1971年生"1994年、1998年分别获国防科技大学学士、硕士学位"现在中国科技大学攻读博士学位"主要研究方向为信号处理的快速、并行计算等"武继刚1963年生"烟台大学副教授，现在中国科技大学攻读博士学位"主要研究方向为算法设计和分析等"3第5期张宪超：离散傅里叶变换的算术傅里叶变换算法。
Python中的Pyramid框架1.简介Pyramid是一个开源的Python Web应用程序框架，它旨在使快速、可扩展和可维护的Web应用程序的开发变得更加容易。
Pyramid是由Pylons社区在2010年创建的，并在2011年正式发布。
它的目标之一是继承Pylon的核心理念，同时还加入了更现代、更Pythonic的设计理念，以便能够更好地解决Web开发中的现实问题。
2.设计理念Pyramid的设计理念是简洁、灵活和可扩展的。
它不像其他Web框架那样独占式的选择集成整个开发栈，而是让你可以根据需求，一步一步地选择你需要的组件。
这种设计哲学使得Pyramid很容易与其他Python库集成，并且可以方便地定制和扩展。
Pyramid的另一个设计理念是分层架构。
这个架构将Web应用程序分为多个层，从而将应用程序解耦和分离出来，便于开发和维护。
Pyramid的分层架构包括以下层：-视图层：处理来自Web客户端的请求，并产生响应。
视图可以使用Python函数、类或Pylons中的标准控制器（`controllers`）来实现。
-路由层：将请求路由到适当的视图函数或控制器类。
-模型层：与应用程序的数据交互。
-服务层：提供访问其他服务、API或外部资源的方法。
3.核心组件Pyramid框架包括以下核心组件：-路由系统（Routing system）：决定如何将URL请求路由到代码的适当部分。
路由系统可以使用模式匹配、正则表达式或其他技术实现。
-视图系统（View system）：负责处理URL请求并返回响应。
视图将HTTP请求分派给适当的Python函数或类。
-模板系统（Template system）：将动态生成的HTML或其他文档渲染为标准格式。
-会话系统（Session system）：让Web应用可以记住与特定客户端相关的数据。
-认证和授权系统（Authentication and authorization system）：确保只有授权用户才能访问特定页面或执行特定操作。
人工智能英文文献译文在计算机科学里许多现代研究都致于两个方面:一是怎样制造智能计算机,二是怎样制造超高速计算机.硬件成本的降低,大规模集成电路技术(VLSI)不可思议的进步以及人工智能(AI)所取得的成绩使得设计面向AI应用的计算机结构极为可行,这使制造智能计算机成了近年来最”热门”的方向.AI 提供了一个崭新的方法,即用计算技术的概念和方法对智能进行研究,因此,它从根本上提供了一个全新的不同的理论基础.作为一门科学,特别是科学最重要的部分,AI的上的是了解使智能得以实现的原理.作为一种技术和科学的一部分,AI的最终目的是设计出能完全与人类智能相媲美的智能计算机系统.尽管科学家们目前尚未彀这个目的,但使计算机更加智能化已取得了很大的进展,计算机已可用来下出极高水平的象棋,用来诊断某种疾病,用来发现数学概念,实际上在许多领域已超出了高水平的人类技艺.许多AI计算机应用系统已成功地投入了实用领域.AI是一个正在发展的包括许多学科在内的领域,AI的分支领域包括:知识表达,学习,定理证明,搜索,问题的求解以及规划,专家系统,自然语言(文本或语音)理解,计算机视觉,机器人和一些其它方面/(例如自动编程,AI教育,游戏,等等).AI是使技术适应于人类的钥匙,将在下一代自动化系统中扮演极为关键的角色.据称AI应用已从实验室进入到实用领域,但是传统的冯·诺依曼计算机中,有更大的存储容量与处理能力之比,但最终效率也不是很高.无论使处理器的速度多快也无法解决这个问题,这是因为计算机所花费的时间主要取决于数据的处理器和存储器之间传送所需的时间,这被称之为冯·诺依曼瓶颈.制造的计算机越大,这个问题就越严重.解决的方法是为AI应用设计出不同于传统计算机的特殊结构.在未来AI结构的研究中,我们可以在计算机结构中许多已有的和刚刚出现的新要领的优势,比如数据流计算,栈式计算机,特征,流水线,收缩阵列,多处理器,分布式处理,数据库计算机和推理计算机.无需置疑,并行处理对于AI应用是至关重要的.根据AI中处理问题的特点,任何程序,哪怕只模拟智能的一小部分都将是非常复杂的.因此,AI仍然要面对科学技术的限制,并且继续需要更快更廉价的计算机.AI的发展能否成为主流在很大程度上取决于VLSI技术的发展.另一方面,并行提供了一个在更高性能的范围内使用廉价设备的方法.只要使简单的处理单元完全构成标准模式,构成一个大的并行处理系统就变得轻而易举,由此而产生的并行处理器应该是成本低廉的.在计算机领域和AI中,研究和设计人员已投入大量精力来考查和开发有效的并行AI结构,它也越来越成为吸引人的项目.目前,AI在表达和使用大量知识以及处理识别问题方面仍然没有取得大的进展,然而人脑在并行处理中用大量相对慢的(与目前的微电子器件比较)神经元却可十分出色地完成这些任务.这启发了人们或许需要某种并行结构来完成这些任务.将极大地影响我们进行编程的方法.也许,一旦有了正确的结构,用程序对感觉和知识表达进行处理将变得简单自然.研究人员因此投入大量努力来寻求并行结构.AI中的并行方法不仅在廉价和快速计算机方面,而且在新型计算方法方面充满希望.两种流行的AI语言是函数型编程语言,即基于λ算子的和逻辑编程语言,即基于逻辑的.此外,面向对象的编程正在引起人们的兴趣.新型计算机结构采用了这些语言并开始设计支持一种或多种编程形式的结构.一般认为结合了这三种编程方式可为AI应用提供更好的编程语言,在这方面人们已经作了大量的研究并取得了某些成就.人工智能的发展1 经典时期:游戏和定理证明人工智能比一般的计算机科学更年轻,二战后不久出现的游戏程序和解迷宫程序可以看作是人工智能的开始,游戏和解迷宫看起来距专家系统甚远,也不能为实际应用提供理论基础.但是,基于计算机的问题的最基本概念可以追溯到早期计算机完成这些任务的程序设计方法.(1)状态空间搜索早期研究提出的基本叫做状态空间搜索,实质非常简单.很多问题都可以用以下三个组成部分表述:1. 初始状态,如棋盘的初始态;2. 检查最终状态或问题解的终止测试;3. 可用于改变问题当前状态的一组操作,如象棋的合法下法.这种概念性状态空间的一种思路是图,图中节点表示状态, 弧表示操作.这种空间随着思路的发展而产生,例如,可以从棋盘的初始状态开始构成图的第一个节,白子每走一步都产生连向新状态的一条弧,黑子对白子每步棋的走法,可以认为是改变了棋盘状态的情况下连向这些新节点的操作,等等.(2)启发式搜索如果除小范围搜索空间以外,彻底的搜索不可能的话,就需要某些指导搜索的方法.用一个或多项域专门知识去遍历状态空间图的搜索叫做启发式搜索.启发是凭经验的想法,它不像算法或决策程序那样保证成功,它是一种算法或过程,但大多数情况下是有用的.2 现代时期:技术与应用所谓现代时期是从70年代半期延续到现在,其特征是日益发展的自意识和自批判能力以及对于技术和应用的更强的定位.与理解的心理学概念相联系似已不占据核心地位.人们也渐渐不再对一般问题方法(如启发式搜索)心存幻想,研究者们已经认识到,这种方法过高估计了”一般智能”的概念,这一概念一向为心理学家喜欢,其代价是未考虑人类专家所具有的某一领域内的能力.这种方法也过低地估计了人的简单常识,特别是人能够避免,认识和纠正错误的能力.解决问题的启发能力程序能够处理的相关知识的清晰表达,而非某些复杂的推理机制或某些复杂的求值函数,这一观点已被证实并接受.研究者已经研制出以模块形式对人的知识进行编码的技术,此种编码可用模式启动.这些模式可以代表原始的或处理过的数据,问题说明或问题的部分解.早期模拟人们解决问题的努力试图达到知识编码的一致性和推理机制的简单性.后来将该结果应用于万家系统的尝试主要是允许自身的多样性.INTRODCTION TO ARTIFICIALMuch modern research effort in computer science goes along two directions. One is how to make intelligent computers,the other how to make ultraly high-speed computers. The former has become the newest “hot ” direction in recent years because the decreasing hardware costs, the marvelous progress in VLSI technology,and the results achieved in Artificial Intelligence(AI) have made it feasible to design AI applications oriented computer architectures.AI,which offers a mew methodology, is the study of intelligence using the idead and methods of computation, thus offering a radically new and different basis for theory formation. As a science, essentially part of Cognitive Science, the goal of AI is to understand the principles thatmake intelligence possible. As a technology and as a part of computer science,the final goal of AI is to design intelligent computer systems that behave with the complete intelligence of human mind.although scientists are far from achieving this goal, great progress dose hae been made in making computers more intelligent . computers can be made to play excellint chess, to diagnose certain types of diseases, to discover mathematical comcepts, and if fact , to excel in many other areas requiring a high level of human expertise. Many Aiapplication computer systems have been successfully put into practical usages.AI is a growing field that covers many disciplines. Subareas of AI include knowledge representation ,learning, theorem proving,search,problem solving, and planning, expert systems, natural-language(text or speech)understanding,computer vision,robotics, and several others (such as automatic programming ,AI education,game playing, etc.) .AI is the key for making techmology adaptable to people. It will play a crucial role in the next generation of automated systems.It is a growing field that covers many disciplines.subbareas of AI include knowledge representation,learing,theorem proving,search,prroblem solving, and planning,expert systems,natural_language(text or speech ) understanding,computer vision,robotics , and severalothers (such as automatic programming, AI education, game playing,etc.).AI is the key for making technology adaptable to people. It will play a crucial role in the next generation of automated systems.It is claimed that AI applications have moved from laboratories to the real wortld. However ,conventional von Neumann computers are unsuitable for AI applications,because they are designed mainly for numerical processing. In a larger von Neumann computer, there is a larger tatio of memory to processing power and consequently it is even less efficient. This inefficiency remains no matter how fast we make the processor because the length of the computation becomes dominated by the time required to move data between processor and memory. This is called the von Neumann bottleneck. The bigger we build machines, the worse it gets. The way to solve the problem is to diverse from the traditional architectures and to design special ones for AI applications. In the research of future AI architectures, we can take advantages of many existing or currentlyemerging concepts in computer architecture, such as dataflow computation, stack machines, tagging,pipelining, systolic array,multiprocessing,distrbuted processing,database machines ,and inference machines.No doubt, parallel processing is of crucial importance for AI applications.due to the nature of problems dealt with in AI, any program that will successfully simulate even a small part of intelligence will be very complicated. Therefor,AI continuously confronts the limits of computer science technology,and there id an instatiable demand for fastert and cheaper computers.the movement of AI into mainstream is largely owned to the addevent of VLSI technology.parallel architectures,on the other han,provide a way of using the inexpensive device technology at much higher performance ranges.it ix becoming easier and cheaper to construc large parallel processing systems as long as they are made of fairly regular patterns of simpl processwing elements,and thus parallel processors should become cost effective.a great amount of effort has been devoted to inverstigating and developing effictive parallel AI architectures,ans this topic id becoming more and more attractive for reaseachers and designersin the areas of computers and AI.Currently, very little success has been achieved in AI in representing and using large bodies of knowledge and in dealing with recognition problems. Whereas human brain can perform these tasks temarkably well using a large number of relatively slow (in comparison with todays microelectronic devices) neurons in parallel. This suggests that for these tasks some kind of parllel architecture may be needed. Architectures can significantly influence the way we programming it for perception and knowledge representation would be easy and natural. This has led researchers to look into massively parallel architectures. Parallelism holds great promise for AI not only in terms of cheaper and faster computers. But also as a novel way of viewingcomputation.Two kinds of popular AI languages are functoional programming languages, which are lambda-based ,and logic programming is attracting a growing interest. Novel computer architects have considered these languages seriously and begun to design architectures supporting one or more of the programming styles. It has been recognized that a combination of the three programming styles mingt provide a better language for AI applications. There have already been a lot of research effort and achievements on this topic.Development of AI1 the classical period: game playing and theorem provingartificial inteligence is scarcely younger than conventional computer science;the bebinnings of AI can be seen in the first game-playing and puzzle-solving programs written shortly after World War Ⅱ. Gameplaying and puzzle-solving may seem somewhat remote from espert systems, and insufficiently serious to provide a theoretical basis for real applications. However, a rather basic notion about computer-based problem solving can be traced back to early attempts to program computers to perform shuch tasks.(1)state space searchThe fundamental idea that came out of early research is called state space search,and it is essentially very simple. Many kinds of problem can be formulated in terms of three important ingredients:（1）a starting state,such as the initial state of the chess board;（2）a termination test for detecing final states or sulutions to the problem,such as the simple rule for detecting checkmate in chess;（3）a set of operations that can be applied to change the current state of theproblem,such as the legal moves of chess.One way of thinking of this conceptual space of states is as a graph in which the states are nodes and the operations are arcs. Such spaces can be generated as you go . gor exampe, you coule gegin with the starting state of the chess board and make it the first node in the graph. Each of White’s possilbe first moves would then be an arc connecting this node to a new state of the board. Each of Black’s legal replies to each of these f irst moves could then be considered as operations which connect each of these new nodes to a changed statd of the board , and so on .(2)Heuristic searchHiven that exhaustive search is mot feasible for anything other than small search spaces, some means of guiding the search is required. A search that uses one or more items of domain-specific knowledge to traverse a state space graphy is called a heuristic search. Aheuristic is best thought of as a rule of thumb;it is not guaranteed to succeed,in the way that an algorithm or decision procedure is ,but it is useful in the majority of cases .2 the romantic period: computer understandingthe mid-1960s to the mid-1970s represents what I call the romantic period in artificial intelligence reserch. Atthis time, people were very concerned with making machines “understand”, by which they usually meant the understanding of natural language, especially stories and dialogue. Winograd’s (1972)SHRDLU system was arguably the climax of this epoch : a program which was capable of understanding a quite substantial subset of english by representing and reasoning about a very restricted domain ( a world consisting of children’s toy blocks).The program exhibited understanding by modifying its “blocksworld” represent ation in respinse to commands , and by responding to questions about both the configuration of blocks and its “actions” upon them. Thus is could answer questions like:What is the colour of the block supporting the red pyramid?And derive plans for obeying commands such as :Place the blue pyramid on the green block.Other researchers attempted to model human problem-solving behaviour on simple tasks ,such as puzzles, word games and memory tests. The aim war to make the knowledge and strategy used by the program resemble the knowledge and strategy of the human subject as closely as possible. Empirical studies compared the performance of progran and subject in an attempt to see how successful the simulation had been.。
基于FPGA的Rijndael-Ecc加密系统的实现吉兵;陈振娇;王兆尹;范炯【摘要】针对目前物联网易被攻击的特点,提出一种基于FPGA的Rijndael-ECC 混合加密系统.方案采用Rijndael模块对数据进行加密,用散列函数加密算法处理数据得到数据摘要,用ECC加密算法实现对摘要的签名和私钥的加密,各模块采用并行执行的处理方式.同时利用流水线思想对Rijndael的轮单元结构进行了改进,提高了整个加密系统的工作效率,完全满足了物联网对于稳定性、功耗以及处理速度的要求,给数据传输的安全性提供了高强度的保障.【期刊名称】《电子与封装》【年(卷),期】2017(017)002【总页数】6页(P28-32,36)【关键词】FPGA;Rijndael;椭圆曲线加密算法;数字签名【作者】吉兵;陈振娇;王兆尹;范炯【作者单位】中国电子科技集团公司第58研究所,江苏无锡214072;中国电子科技集团公司第58研究所,江苏无锡214072;中国电子科技集团公司第58研究所,江苏无锡214072;中国电子科技集团公司第58研究所,江苏无锡214072【正文语种】中文【中图分类】TP309物联网的广泛使用带来了诸多类型的安全威胁，随着片上系统(SoC)和低功耗嵌入式技术的飞速发展，使得物联网广泛应用于国防军事、生物医疗、城市交通、国际反恐等诸多需严格保障信息安全的领域，因此如何为物联网络提供更可靠的安全保障已经成为研究的热点[1]。
自2013年以来，随着物联网概念的火热，市面上出现了大量基于物联网开发的智能传感器设备，从智能家居到医疗卫生，这些设备与我们的安全密不可分。
近年来关于黑客频繁入侵智能终端的报道越来越多，对于人们的安全隐私造成了极大的威胁，由于安全系统本身商业价值较低的原因，使得物联网安全发展十分缓慢。
值得注意的是自2015年开始，美国已出台了一系列关于物联网设备安全和隐私的规定。
我国目前对于物联网设备安全还没有明确规定，安全问题一直是现代生活中的一部分，对于物联网设备的保护应当出现在其发展中的各个阶段，新的安全漏洞会不断涌现，因此有关于物联网安全问题的研究具有十分重要的价值。
University of GlasgowDegrees of MEng, BEng, MSc and BSc in EngineeringVLSI DESIGN AND CAD (ENG5092)Friday 07 December 201213:00-15:00Answer FOUR questionsAnswer only TWO questions from each of sections A and BEach question is worth 25 marksThe numbers in square brackets in the right-hand margin indicate the marks allotted to the part of the question against which the mark is shown. These marks are for guidance only. An electronic calculator may be used provided that it does not have a facility for either textual storage or display, or for graphical display.If a calculator is used, intermediate steps in the calculation should be indicated.Page 1 of 5SECTION A: Attempt any TWO questions [50 marks]Q1 (a) Sketch the transistor-level schematic circuit diagram for a CMOS cell with thefollowing function, using the smallest number of transistors possible.Z=ABC+D[8](b) Draw a stick diagram for your circuit of Q1(a), including features such asmerged active and bulk connections. Clearly label every part of your diagramto show each of the layers you draw. [10](c) The circuit of Q1(a) is to be implemented as part of a standard cell librarywith a ‘unit’ inverter using W ni = 1 and W pi = 2. Correctly size the transistorsin your circuit for Q1(a) to match this inverter’s output characteristics, using alinear T-sizing method. [7] Q2 (a) Sketch a transistor circuit for a CMOS inverter, including a parasitic loadcapacitance (C L), labeling its input, output and supply voltage connections. [4](b) The input of a CMOS inverter is subjected to a square edge transition frominput LOW to input HIGH at a time t0. Sketch a timing diagram for its inputand output waveforms indicating the 10 % and 90 % output voltage valuesand corresponding times. [4](c) Using your answer to Q2(b), annotate the timing diagram with the operatingmodes of both the NMOS and PMOS transistors, and the portion of the timeaxis over which the transistor modes you identify apply. [5](d) Using your timing diagram for Q2(a) and (b), and the charging equation for acapacitor, show that the fall-time for the voltage at the output of a CMOSinverter is given by:t f =knCL βnVDDwhere k n is a constant and all the other symbols have their usual meaning. [12]Page 2 of 5Q3 (a) Using the logic symbol below for a 1-bit full-adder, design and sketch the circuit for a 4-bit ripple carry adder.A 1-bit adder symbol. [5](b) If the propagation delay from any input to any output of a 1-bit full-adder is t d,derive a formula for the complete logic evaluation time for a n-bit ripple carryadder. [2](c) Pipelining is a widely used method in VLSI design for improving overallcircuit performance. Using the D-type flip-flop symbol below, design apipelined 4-bit full adder with a latency of 4 clock cycles, assuming the inputsare already held in input registers at the start of the clock sequence.A D-type flip-flop symbol. [14](d) If a 1-bit full-adder has a propagation delay of t d, and a latch has a set-up time,t s and a clock-to-Q time, t CQ, provide a formula for the maximum possibleclock frequency at which the pipeline of Q3(c) could be operated, assumingthat there were no other delays. [4]Page 3 of 5SECTION B: Attempt any TWO questions [50 marks]Q4 (a) Most digital-to-analogue converter (DAC) architectures are based on the popular resistor-ladder (R-2R) network. Draw the circuit diagram for a 3-bitR-2R based DAC. [5](b) By analysis of the circuit of Q4(a) show mathematically how the input bits ofthe DAC relate to the analogue output voltage. [6](c) What are the advantages and disadvantages of using an R-2R DAC whencompared with other DAC architectures? [4](d) The digital input 11011011 applied to an 8-bit R-2R ladder DAC causes it toproduce an output current of I0= 4.275mA. Find the value of the DACreference current and the value of the complimentary output current, i.e. thecurrent flowing to ground. [6](e) Another popular DAC architecture, potentiometric DAC, is based on selectingone tap of a segmented resistor string by a switch network. State the keyadvantage of this type of DAC. Briefly explain how high resolution convertersexploit this advantage. [4] Q5 (a) Sketch the schematic diagram of a flash analogue-to-digital converter (ADC) using a 2-bit ADC to illustrate your answer. [6](b) Consider a 2-bit flash ADC with a reference voltage of 8 V.(i) How many comparators does this ADC use and what are their voltagereference levels? [4] (ii) What is the digital output for an input voltage of 3.5 V? [2](c) A typical flash converter resolves analogue voltages to 8 bits.(i) Briefly explain why this type of converter is not suited to higherresolution. [3] (ii) Show with the aid of a clearly labeled block diagram how a (two-step) subranging ADC can achieve 8-bit conversion. Briefly compare andcontrast this with an 8-bit flash ADC. [7](d) In a pipelined ADC architecture, each stage could be used to find just a singlebit. Identify the key component in this architecture which ensures thatpipelining is possible. [3]Page 4 of 5Q6 (a) Draw a clearly labeled block diagram of a sigma-delta (Σ-∆) analogue-to-digital converter (ADC). [5](b) Compared to Nyquist rate ADCs, explain how oversampling improves systemperformance. [3](c) Draw the linear system model of a first order Σ-∆ ADC. Excluding the effectof oversampling, explain how this architecture reduces quantization noise. [6](d) The integrator function in a Σ-∆ADC is realized using switched-capacitor(SC) techniques.(i) Sketch the circuit diagram of a non-inverting SC integrator. [4](ii) List the advantages of SC integrators compared to conventional resistor-capacitor integrators. [3](e) The signal-to-noise ratio of a first order Σ-∆ ADC is 6.02(n + 1.5m) - 3.41 dB,where the basic ADC is n-bit and the oversampling ratio is 2m. What samplerate is required to obtain 16-bit resolution if the system uses a 1-bit ADC andthe Nyquist sampling rate is 25 kHz? [4]Page 5 of 5。
