微带宽带匹配3GHZ低通滤波器设计【毕业论文说明书】
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毕 业 设 计（论 文）开 题 报 告1结合毕业设计（论文）课题情况，根据所查阅的文献资料，每人撰写不少于 1000 字的文献综述：文 献 综 述1、课题背景滤波器是一种可以有效地抑制带外噪声，均衡所使用频带的幅度、相位特性的仪器。因此，它在通信、控制及信号处理等领域都发挥着重要的作用，被广泛地应用于通信、数据采集、控制等各类电子系统中。在低功耗应用中，微带滤波器由于其低成本和可重复性的优点而被广泛使用 1。微波滤波器是微波电路中非常重要的一种器件，整个微波电路系统的运行直接受到微波滤波器的影响。现如今，人们对电磁波频带划分的要求越来越高，频带资源被划分的越来越精细，手持式移动设备的广泛应用也要求微波器件高性能、小型化、成本低等。微带滤波器在这种环境下应运而生，它的高性能、尺寸小、成本低、易于加工制作的特点，使其成为微波滤波器的主流形式之一 2。二、微带低通滤波器的特点微波低通滤波器通常应用在系统的前端，用来隔离高频信号，保护后面的工作电路。人们总希望它有陡峭的衰减和足够宽的阻带范围，因而，高性能，宽阻带成为当今微波低通滤波器研究的热点 2。现今的低通滤波器有 3 种典型结构：最平坦式、切比雪夫式、椭圆函数式，下面大致介绍 3 种低通滤波器的特点：1.最平坦式：在通带以内幅频曲线的幅度最平坦，由通带到阻带衰减陡度较缓，截止频率以后的衰减速率为 6MDB/倍频程，相频特性是非线性的。对阶跃信号有过冲和振铃现象。2.切比雪夫式：在通带内，具有相等的波纹。截频衰减陡度比同阶数巴特沃斯特性更陡度比同阶数程时的衰减就超过 6NDB。在阶数 N 一定时，波纹越大，截频衰减陡度越陡。相位响应也是非线性，但较之比前者为差。3.椭圆函数式：在通带和阻带内均出现相等的纹波，椭圆函数低通滤波器响应的幅频特性曲线阻带纹波的出现使椭圆函数滤波器获得了从通带到阻带的最大的衰减速率。若给定滤波器的阶数 N，椭圆函数滤波器较其他类型的滤波器具有最陡的截频衰减陡度。但它的延时特性不如前 2 种。三、微带低通滤波器的国内外发展现状1915 年，德国科学家 K.W.Wagn-er 首次提出了一种瓦格纳滤波器的滤波器设计方法，这种方法一经提出，就在业界得到迅速推广；与此同时，美国科学家G.A.Canbell 则提出了另一种独创的设计方法-图像参数法。1917 年，两国科学家分别发明了 LC 集总滤波器，次年美国发明了世界上第一个多路复用系统。从此以后，各国的科研人员们开始了对采用集总元件电感、电容的滤波器设计理论的全面系统的研究。随着滤波器设计理论研究的不断深入、材料领域的不断进步与突破以及工作频率的日益提高，所设计的滤波器从刚开始的由集总参数元件构成逐步扩展到由分布参数元件构成。P.D.Rich-tmever 在 1939 年报道并介绍了介质滤波器；然而，当时材料的温度稳定性不高，这种滤波器不能有任何的实际应用。1970 年后，新材料领域取得了重大突破，特别陶瓷材料的发展，介质滤波器有了实际的用武之地并且得到了迅速发展。近几年以来，小型化趋势使得各种类型的微带线滤波器的研发得到了蓬勃的发展。1980 年以后出现的高温超导材料被业界一致看好，它极有可能被应用于设计损耗极低、尺寸极小的新型微波滤波器。眼下许多发达国家都在新材料、新技术方面大做文章，试图利用新材料、新技术来提高器件的各项性能指标和集成度；同时，尽可能降低研发成本、减小器件尺寸、降低功耗等。目前，我国的微波滤波器的研制与发达国家相比，还存在一定差距，所以我国的微波工程和科技人员任重道远。总的来说，目前为了满足无线通信系统对微波滤波器所要求的高性能、小型化，国内外主要从材料、工艺、技术以及设计方法等方面进行了广泛而深入的研究 3。四、参考文献1 Auther B.Williams，Fred J.Taylor.the Electronic Filter DesignM.科学出版社,2008:530-5312 吕绪敬.宽阻带微带低通滤波器的设计与研究D.成都：电子科技大学，2013.3 吴姣.微带低通滤波器的设计与研究D.湖北：湖北大学,2010.4李全利.单片机原理及应用M. 清华大学出版社. 2004-15胡汉才.单片机原理及其接口技术（第 2 版）M. 清华大学出版社. 2004-116李建民.模拟电子基础M. 清华大学出版社. 2005-117康华光.电子基础(模拟部分)M. 高等教育出版社. 2001-48康华光.电子基础(数字部分)M. 高等教育出版社. 2001-49邹振春.MCS-51 系列单片机原理及接口技术M.机械工业出版社.2005-610杨西明、朱骐. 单片机编程与应用入门M. 机械工业出版社.2004-611张永瑞,刘振起.电子测量技术基础M.西安：电子科技大学出版社，199412李朝青.单片机原理与接口技术M.北京：北京航空航天大学出版社，2005 13陈光明,施金鸿,桂金莲.电子技术课程设计与综合实例M.北京：北京航空航天大学出版社，200314卿太全,李萧,郭明琼.常用数字集成电路原理与应用M.北京：人民邮电出版社,200315王俊峰.电子产品开发设计与制作M.北京：人民邮电出版社,200516陈传军.微带宽带匹配 3GHz 低通滤波器设计J.现代电子技术,2005(15)毕 业 设 计（论 文）开 题 报 告2本课题要研究或解决的问题和拟采用的研究手段（途径）：一、课题研究的问题设计一种微带宽带匹配 3GHz 低通滤波器,要求结构设计合理，功能电路实现较好，性能稳定。其框图如图 1 所示。宽带匹配低通滤波器电路两路频分器电路宽带匹配低通滤波器电路图图 1：总体设计框图二、课题要解决的问题 结合总体设计框图,需要解决的问题如下:宽带匹配低通滤波器串联两部分滤波器在空间中较难，但采用 ADS 仿真软件可以轻松实现，该软件的使用较冷门，所以熟练使用该软件也是本课题一个难点。三、本课题的研究途径1、开发环境：1）、PC 机：处理器：Intel Pentium 4 1.60G 或更高内存：2G硬盘空间：100G操作系统：Windows 72）、开发工具: ADS 20112、通过测试，完成宽带匹配的低通滤波，达到预期效果：通过仿真测量输入输出端的驻波比以及插入衰减，验证宽带匹配的低通滤波效果。四、课题设计工作进度计划表起 讫 日 期 工 作 内 容2015.12.162016.1.10查阅课题相关文献资料，英文资料翻译，完成开题报告，分析清楚系统功能。2016.1.112016.1.201、参加开题答辩；2. 在老师的指导下修改开题报告、英文翻译、进行毕业设计（论文）工作。2016.1.212016.3.201、完成理论分析，模块设计，程序调试、测试等工作；2、完成论文提纲或设计说明书提纲。2016.3.212016.3.311、提交中期课题完成情况报告。2、参加中期答辩。2016.4.12016.4.251、在老师指导下撰写毕业论文（修改两稿），根据论文定稿格式要求，完成论文定稿。2、完成系统使用说明书。2016.4.262016.5.6根据指导教师和评阅教师的意见进一步修改论文，并进行答辩准备工作。2016.5.7-2016.5.181、完成答辩准备（含答辩 PPT 的制作）。2、参加毕业设计（论文）答辩。2016.5.192016.6.5完成毕业设计全套材料（含电子稿）并提交（含网上提交）。毕 业 设 计（论 文）开 题 报 告指导教师意见：1对“文献综述”的评语：该生 对所设计的课题的研究目的、意义及国内外研究现状和发展趋势都有所了解，对设计课题的文献综述较全面。2对本课题的深度、广度及工作量的意见和对设计（论文）结果的预测：该生 毕业设计课题的深度、广度符合本科学生毕业设计要求，毕业设计工作量适中，该生对所设计课题的所要解决的问题、研究方法及手段较清楚，预计能完成毕业设计任务。3.是否同意开题：同意 不同意指导教师： 年 月 日所在专业审查意见：同意负责人： 年 月 日文章摘自：IEEE International Conference on Computer Science volume rendering; 3D texture mapping; graphics processing unitI. INTRODUCTIONVolume rendering is an important method of three-dimensional data visualization, in which the three-dimensional data are transformed directly into two-dimensional image without generating intermediate geometry1. For reflecting the feature of the volume data directly, the method is widely used in scientific computing visualization, engineering and so on. Usually it is roughly classified as image-based (or backward projective), e.g., ray-casting 2, and object-based (or forward projective), e.g., cell projection 3, shear-warp4, splatting, or texture-based algorithms 5 6. To obtain high-quality images, texture-based volume rendering with graphics hardware acceleration has attracted the attention. At present, programmable graphics hardware has the rapid development, and high-speed floating-point operationsChang-hui Sun, MeiKe WangInstitute of MeteorologyPLA University of Science and TechnologyNanJing, ChinaEmail: sunch_and data transmission bandwidth can be achieved by utilizing highly parallel structure and streaming computing model. Meanwhile, with the increasing display memory, even the large-scale volume data can be loaded by one time in the form of three-dimensional texture, for the memory is limited, the volume data must be segmented into blocks and transferred between system memory and texture memory and the efficiency declining. Therefore, currently three-dimensional texture mapping with graphics hardware is seemed to be the most efficient method.Nowadays, a lot of graphics hardware acceleration technologies have been presented. One of the major break through is the introduction of programmable graphics hardware capabilities, which allows the user to program custom shading programs 7 8 to replace the original modules in graphics rendering pipeline, so it expanded the ability of processors and graphics applications. In this paper, we presented a novel volume rendering method in which the sampling points are computed along the viewing ray in the vertex processor with current generic programmable graphics processor, and each fragment is filtered real-time for color and opacity in programmable fragment processor. In our method, the artifacts of volume rendering due to inadequate sampling are suppressed and can be used in interactive volume rendering applications.II. RELATED WORKA. Texture-based volume rendering3D texture-based volume rendering was proposed by Cabral 6 and it adopted the three-dimensional texture mapping features in advanced graphics workstation. While the texture mapping function is supported by more and more graphics hardware, the method expanded itsapplication fields and promoted volume rendering research.Texture-based volume rendering samples a set of data points that are parallell to the projection plane from the volume data, and then produces the result images. The method is consists of three-dimensional texture creation, the polygon facades rendering, texture mapping and facades blending and so on. At first, the volume data is loaded into memory and converted into three-dimensional texture that can be processed by graphics hardware. Then a series of facades parallel each other and oriented with viewing ray are defined by sampling the volume data with trilinear interpolation in graphics hardware. At last, the result images are produced by blending three-dimensional texture maps. As the graphics hardware plays a decisive role which is responsible for interpolation, sampling and blending throughout the rendering process and it can process parallel, so interactive volume rendering can be nearly real-time.B. Pre-Integrated ClassificationDirect volume rendering techniques differ considerably in the way the color c(s) and the opacity transfer functions (s) evaluate. While only the color and opacity at the sampling point participate in volume rendering integral in traditional classification methods, the order of interpolation and the application of the transfer functions define the difference between pre- and post-classification, the former was called slab-by-slab volume rendering integral, and the latter was called slice-by-slice volume rendering integral method.According to the sampling theorem, a correct reconstruction is only possible with sampling rates larger than the Nyquist frequency. However, a correct evaluation of the volume rendering integral as the Nyquist frequency of the data fields with color and opacity features for the sampling along the viewing ray, is approximately the product of the Nyquist frequencies of the scalar field s(x) and the maximum of the Nyquist frequencies of the color transfer functions c(s) and the opacity transfer functions(s). Therefore, it is by no means sufficient to sample a volume with the Nyquist frequency of the scalar field if non-linear transfer functions are allowed. Otherwise artifacts resulting from this kind of undersampling can befrequently observed unless using by very smooth transfer functions.To overcome the limitations discussed above, a method called Pre-integrated classification in was represented by Klaus9, in which, for the color andopacity at sampling point sf , it can be obtained by theintegrals of c(s) and (s) according to Equation (1) and (2) in the range of sf, sb where sb is the adjacent sampling point in viewing ray. Usually the color C and opacity atthe sample points can is efficiently lookup by sf and sb ,in one pre-integrated lookup table which ispre-computed by transfer function c(s) and (s) according to Equation (1) and (2).dC ( s , s , d ) ( K ( s ) K ( s )f b b fsb sf (1)K ( s ) 0s c ( s )dsd( s , s , d) 1exp (T ( s ) T ( s )s s (2)f b b fbfT ( s ) 0s ( s )dsWhere d is distance between the sampling slices. In the method, required Nyquist frequency was effectively reduced for avoiding the product of the s(x) and c(s) and(s) of the Nyquist frequency, therefore, pre-integrated classification can reduce artifacts caused by insufficient sample and improve the image quality. However, when the transfer function is changed, it needs to re-comupted the pre-integration lookup table, which impacts on the performance of real-time interaction.III. OUR METHODSAs the above discussion, artifacts in volume rendering was mainly caused by the less integral sampling frequency than required Nyquist frequency for non-linear transfer functions or to balance rendering speed. Considering the directional integral sampling, we improved the accuracy and the performance of volume rendering by low-pass filtering for color and opacity along viewing ray without increasing the sampling rate, and the artifacts in volume rendering are significantly reduced. In our methods, the filter sample points can be obtained at the front and back of the integral sample points along the ray, and the points color and opacity are filtered with low-pass filter kernel,as the integral sample points color and opacity features. B. Low-pass FilterAbove all, we implement the equation It is difficult to realize filtering operation fori N 1 i N 1 traditional graphics rendering pipeline. But with thec(sp i )Wi ( sp i )Wii 0 i 0 (3) development of graphics hardware, vertex processor andC ( P)i N 1 ( P) i N 1Wi Wi fragment processor are both separated into programmablei 0 i 0 units, and filter operation can be realized in suchwhere P is the integral sampling points, pi is the filter processors with advanced shading program. In our method,sample points, and Wi is the weight of the filter sampling points and the filtering are both processedcorresponding filter kernel. real-time by the graphics hardware, and consideringA. Filter sample point rendering speed and other reasons, the amount of shadingprograms should be limited. So we selected Box filterRayDir kernel and Gaussian filter kernel to carry on low-passP2P filter, which 1D Box filter kernels is 1,1,1 and 1, 1, 1, 1,P1V ViewDir 1 and Gaussian filter kernel is 1,2,1. Accordingequation (3), we can get the result of volume rendering asshown as Figure 2.Different volume rendering results for Engine datasetD are shown in above four pictures in Figure 2, andSlicei-1 Slicei Slicei+1following four pictures show enlarged effect for specificFigure.1 The illustration of sampling points computingregion. Comparing Figure 2 (b), Figure 2 (c) and Figure 2For any ray RayDir from the view point, point P (a), we can find that the volume rendering results usingdenotes the integral sample point in the ray, and the our methods have smoother and more delicate faadepositions of points P1 and P2 can be calculated from the effects, and the artifacts are significantly decreased, evenpositions of P with following equation there is no artifact in some regions. Comparing (b) and (c),cos RayDir ViewDir Gaussian filter kernel needs more a multiplicationPP PP (D / cos ) RayDir operation than Box filter kernel while they have similar1 2 (4)PV PV PP visualization results. For (c) and (d), though the image1 1 quality has improved after increasing the number ofPV PV PP2 2 sample points, it is not obvious and rendering speedwhere the viewpoint coordinate V and the viewing decreases for more addition and multiplication. So wedirection ViewDir are known, and the ray direction selected three point Box filter kernel as sample pointsfilter.RayDir can be obtained by the point P and V, ViewDirand RayDir are denoted in unit vector, D is the distancebetween slices. We can select a different distance D and calculate position of the new filter samples, noted that the position of filter sample points is coincide with position of volume rendering integral sample points. If post-classification method is chosen, we need lookup the table for filter sample points color and opacity and finally have it filtering operation.IV. EXPERIMENTAL RESULTSIn the following, we implemented the experiments in OpenGL and CG Shader Language on the machine with Intel P8600 2.4GHz CPU, 2G memory machines and 256M ATI 3470 Video card, display resolution of 400 400. We selected two sets of volume dataset, respectively, 256 256 128 of the Engine data and 256 256 225 of the Head data, in which three points Box low-pass filter are used while linear transfer function was used in Engine dataset and non-linear transfer function was used in Headdataset. We can obtain volume rendering result images as shown as Figure 3.In Figure 3, the standard volume rendering algorithm results in a large number of artifacts, while our method can effectively inhibit artifacts, in which the object has smoother effects, more natural colors. While it have similar visual effects with pre-integration volume rendering method, even better effects could be obtained in some directions with the perspectives changed. In terms of rendering speed, because the sampling and filter need be processed both in vertex processor and fragment processor, it slightly slower than the standard algorithm and the pre-integration method, but it still meets the requirement of real-time rendering.TAB.1 CONTRAST OF RENDERING SPEED FRAMES / SECONDPre-integration OurDataset Standardmethod methodEngine 28 24 22Head 19 16 15V. CONCLUSIONIn this paper, a new volume rendering improvement is represented based on the traditional 3D texture-based volume rendering method:(1)Low-pass filters are introduced into volume rendering. After investigating two common filter kernels such as Box filter kernel and Gaussian filter kernel, we tended to select one-dimensional Box filter with 3 points for its efficiency and accuracy.(2)Compared with pre-integration classification volume rendering method, the rendering results are similar for linear transfer function, and our method is even better than the former method sometimes with the perspective transform. But for non-linear transfer function, there are some bright points in some regions in our rendering image, so the method needs to be improved in the future.(3) Although rendering speed in our method slightly lower than the pre-integration method, but it does not needto generate two-dimensional pre-integration lookup tablewhich reduces the memory spending, and it can avoid re-calculation of pre-integration lookup table problems caused by real-time response caused by adjusted the transfer function.In the future, we will still improving the quality of methods, mainly focused on the impact on the rendering of low-pass filtering, including the selection of filter kernel and sampling distance, And one-dimensional or two-dimensional filtering method selection, the goal is to find the most suit
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