(完整版)MATLAB外文资料翻译毕业设计论文
算法类外文资料翻译
Q-Learning By ExamplesIn this tutorial, you will discover step by step how an agent learns through training without teacher (unsupervised) in unknown environment. You will find out part of reinforcement learning algorithm called Q-learning. Reinforcement learning algorithm has been widely used for many applications such as robotics, multi agent system, game, and etc.Instead of learning the theory of reinforcement that you can read it from many books and other web sites (see Resources for more references), in this tutorial will introduce the concept through simple but comprehensive numerical example. You may also download the Matlab code or MS Excel Spreadsheet for free.Suppose we have 5 rooms in a building connected by certain doors as shown in the figure below. We give name to each room A to E. We can consider outside of the building as one big room to cover the building, and name it as F. Notice that there are two doors lead to the building from F, that is through room B and room E.We can represent the rooms by graph, each room as a vertex (or node) and each door as an edge (or link). Refer to my other tutorial on Graph if you are not sure about what is Graph.We want to set the target room. If we put an agent in any room, we want the agent to go outside the building. In other word, the goal room is the node F. To set this kind of goal, we introduce give a kind of reward value to each door (i.e. edge of the graph). The doors that lead immediately to the goal have instant reward of 100 (see diagram below, they have red arrows). Other doors that do not have direct connection to the target room have zero reward. Because the door is two way (from A can go to E and from E can go back to A), we assign two arrows to each room of the previous graph. Each arrow contains an instant reward value. The graph becomes state diagram as shown belowAdditional loop with highest reward (100) is given to the goal room (F back to F) so that if the agent arrives at the goal, it will remain there forever. This type of goal is called absorbing goal because when it reaches the goal state, it will stay in the goal state.Ladies and gentlemen, now is the time to introduce our superstar agent….Imagine our agent as a dumb virtual robot that can learn through experience. The agent can pass one room to another but has no knowledge of the environment. It does not know which sequence of doors the agent must pass to go outside the building.Suppose we want to model some kind of simple evacuation of an agent from any room in the building. Now suppose we have an agent in Room C and we want the agent to learn to reach outside the house (F). (see diagram below)How to make our agent learn from experience?Before we discuss about how the agent will learn (using Q learning) in the next section, let us discuss about some terminologies of state and action .We call each room (including outside the building) as a state . Agent's movement from one room to another room is called action . Let us draw back our state diagram. State is depicted using node in the state diagram, while action is represented by the arrow.Suppose now the agent is in state C. From state C, the agent can go to state D because the state C is connected to D. From state C, however, the agent cannot directly go to state B because there is no direct door connecting room B and C (thus, no arrow). From state D, the agent can go either to state B or state E or back to state C (look at thearrow out of state D). If the agent is in state E, then three possible actions are to go to state A, or state F or state D. If agent is state B, it can go either to state F or state D. From state A, it can only go back to state E.We can put the state diagram and the instant reward values into the following reward table, or matrix R .Action to go tostateAgent now inFstateA -B 100C -D -E 100F 100The minus sign in the table says that the row state has no action to go to column state. For example, State A cannot go to state B (because no door connecting room A and B, remember?)In the previous sections of this tutorial, we have modeled the environment and the reward system for our agent. This section will describe learning algorithm called Q learning (which is a simplification of reinforcement learning).We have model the environment reward system as matrix R.Now we need to put similar matrix name Q in the brain of our agent that will represent the memory of what the agent have learned through many experiences. The row of matrix Q represents current state of the agent, the column of matrix Q pointing to the action to go to the next state.In the beginning, we say that the agent know nothing, thus we put Q as zero matrix. In this example, for the simplicity of explanation, we assume the number of state is known (to be six). In more general case, you can start with zero matrix of single cell. It is a simple task to add more column and rows in Q matrix if a new state is found.The transition rule of this Q learning is a very simple formulaThe formula above have meaning that the entry value in matrix Q (that is row represent state and column represent action) is equal to corresponding entry of matrix Radded by a multiplication of a learning parameter and maximum value of Q for all action in the next state.Our virtual agent will learn through experience without teacher (this is called unsupervised learning). The agent will explore state to state until it reaches the goal. We call each exploration as an episode . In one episode the agent will move from initial state until the goal state. Once the agent arrives at the goal state, program goes to the next episode. The algorithm below has been proved to be convergence (See references for the proof)Q学习实例在本教程中，您将一步一步地发现在未知的环境中一个代理如何进行没有老师(非监督)的学习训练。
(完整版)MATLAB中英文对照
MATLAB7.1中文翻译MATLAB MATLAB 主程序模块Simulink 动态仿真模块Aerospace Blockset 航空模块集Bioinformatics Toolbox 生物信息学工具箱CDMA Reference Blockset CDMA 参考模块集Communications Blockset 通信模块集Communications Toolbox 通信工具箱Control System Toolbox 控制系统工具箱Curve Fitting Toolbox 曲线拟合工具箱DSP Blockset 数字信号模块集Data Acquisition Toolbox 数据采集工具箱Database Toolbox 数据库工具箱Datafeed Toolbox 数据反馈工具箱Distributed Computing Toolbox 分布式计算工具箱Dials and Gauges Blockset 刻度标尺模块集Embedded Target for Motorola MPC 555 摩托罗拉MPC555 嵌入对象Embedded Target for the TI C2000 DSP TI C2000 DSP嵌入对象Embedded Target for TI 6000 DSP TI 6000数字信号处理嵌入对象Embedded Target for Infineon C166 Microcontrollers Infineon C166 微控制器嵌入对象Embedded Target for Motorola? HC12 Motorola? HC12嵌入对象Embedded Target for OSEK/VDX? OSEK/VDX?嵌入对象Excel Link Excel 连接Extended Symbolic Math 扩展符号数学库Filter Design Toolbox 滤波器设计工具箱Filter Design HDL Coder 滤波器设计硬件描述语言编码器Financial Derivatives Toolbox 金融预测工具箱Financial Time Series Toolbox 金融时间系列工具箱Financial Toolbox 金融系统工具箱Fixed-Income Toolbox 定点收益工具箱Fixed-Point Blockset 定点模块集Fuzzy Logic Toolbox 模糊逻辑工具箱GARCH Toolbox GARCH 工具箱Genetic Algorithm and Direct Search Toolbox 遗传算法和直接搜寻工具箱Gauges Blockset Gauges模块集Image Processing Toolbox 图像处理工具箱Image Acquisition Toolbox 图像调节工具箱Instrument Control Toolbox 设备控制工具箱LMI Control Toolbox LMI 控制工具箱MATLAB Com Builder MATLAB COM 文件编辑器MATLAB Compiler MATLAB 编译器MATLAB Excel Builder MATLAB 外部编辑器MATLAB Link for code composer studio MATLAB 与代码设计工作室的连接MATLAB Link for ModelSim MATLAB与ModelSim的连接MATLAB Report Generator MATLAB 报告生成器MATLAB Runtime Server MATLAB 运行时间服务器MATLAB Webs Server MATLAB 支持Web 服务器MATLAB? Distributed Computing Engine MATLAB? 分布式计算引擎Mapping Toolbox 地图工具箱Model Predictive Control Toolbox 模型预测控制工具箱Model-Based Calibration Toolbox 基于模型标准工具箱Mu-Analysis and Synthesis ToolboxMU 分析与合成工具箱Neural Network Toolbox 神经网络工具箱Nonlinear Control Design Blockset 非线性控制设计模块集OPC Toolbox OPC工具箱Optimization Toolbox 优化工具箱Partial Differential Equation Toolbox 偏微分方程工具箱Real-Time Windows Target 实时视窗对象Real-Time Workshop 实时工作室Real-Time Workshop Embedded Coder 实时工作室内嵌编码器Requirements Management Interface 需求管理界面Robust Control Toolbox 强（鲁棒）控制工具箱RF Toolbox RF工具箱SB2SL(converts models to Simulink) 模型转换成Simulink工具Signal Processing Toolbox 信号处理工具箱Signal Processing Blockset 信号处理模块集Sim Driveline SIM动力传动系统Sim Mechanics SIM机械学Sim Power Systems SIM电力系统Simulink Performance ToolsSimulink 执行工具箱Simulink Report Generator Simulink 报表生成器Simulink Verification and Validation Simulink 核实与验证Simulink Response Optimization Simulink响应优化Simulink Parameter Estimation Simulink 参数估计Simulink Fixed Point Simulink不动点Simulink Control Design Simulink控制设计Simulink Accelerator Simulink加速器Spline Toolbox 样条工具箱Stateflow 状态流Stateflow Coder 状态流编码器Statistics Toolbox 统计工具箱Symbolic Math Toolbox 符号数学工具箱System Identification Toolbox 系统识别工具箱Virtual Reality Toolbox 虚拟现实工具箱Video and Image Processing Blockset 视频和图像处理模块集Wavelet Toolbox 小波分析工具箱xPC Target XPC对象xPC Target Embedded Option XPC对象内嵌属性。
图像压缩毕业设计
XXXXXXX大学毕业设计图像压缩编码系统设计实现(B)Design and Implementation of ImageCompression Encoding System (B)2011 届电气与电子工程学院专业电气工程及其自动化学号 xxxxxoooo学生姓名 xxxxxx指导教师 xxxxxxxx完成日期 2011年 6 月 2 日毕业设计成绩单毕业设计任务书毕业设计开题报告摘要近年来，随着现代通信技术、计算机技术、网络技术和信息处理技术的迅速发展，人们对各种信息的需求也不断增长，尤其是图像和多媒体信息。
未经处理的图像信号的数据量是很大的，使得图像信息的传输，处理和存储都受到一定的限制。
因此，研究高效的图像数据压缩编码方法，即怎样处理，组织图像数据，在应用领域中的作用是至关重要的，图像压缩编码技术已经成为多媒体及通讯领域中很关键的技术之一。
编码技术是图像压缩的基础，利用信息编码对图像进行压缩，使图像便于传输、存储。
本文就是运用编码技术中的游程长度编码对二值图像进行压缩的。
压缩前，先将图像转换成二值图像，然后再进行压缩，这样就达到很好的压缩效果。
最后通过MATLAB 进行仿真，来验证方案的合理性和可行性。
关键词：图像压缩二值图像MATLAB游程长度编码AbstractAlong with the rapid development of modern communication technology, computer technology, the network technology and information processing technology, rising incomes have created sharp growth in demand for some information especially image and multi-media resources, in recent years. Untreated image signal data quantity is big, which makes image information transmission, processing and storage are certain limits. Therefore, the effective image data compression coding method, i.e. how to deal with, the organization image data, the role in applications is of vital importance, image compression technology has become multimedia and communication field a key technical one. Therefore, the effective image data compression coding method, i.e. how to handle, organization the image data, the role in applications is of vital importance, image compression technology is one of key technicals in multimedia and communication field. Encoding technology is the basis of image compression, use the information encoding to do image compression, which make the image facilitate transmission and memory. This paper is to use the run-length encoding technology of length coding binary image compression. before compression, make the image become binary image, thus which can reach good compression effect. Finally through MATLAB, and simulation to verify the rationality and feasibility of schemes.Key words：image compression binary image MATLAB run-length length coding目录第1章绪论 (1)1.1研究背景 (1)1.2图像压缩综述 (2)1.3 图像压缩的必要性 (3)1.4图像压缩的可行性 (3)第2章图像的基本知识 (5)2.1图像与数字图像 (5)2.2图像的采样和量化 (5)2.3采样点数和量化级数的选取 (5)第3章图像压缩编码 (7)3.1概述 (7)3.2熵编码方法 (7)3.2.1基本概念 (7)3.2.2哈夫曼编码方法 (8)3.2.3香农编码法 (9)3.2.4算术编码方法 (9)3.2.4.1算术编码的方法 (9)3.2.4.2算术编码的特点 (10)3.3预测法编码 (10)3.4变换编码 (11)3.5常见的几种变换编码方法 (12)3.5.1离散余弦(DCT)变换 (12)3.5.2小波变换 (12)3.5.2.1二进小波变换 (12)3.5.2.2 离散小波变换(DWT) (13)第4章MATLAB简介 (14)4.1综述MATLAB (14)4.1.1MATLAB语言的功能 (14)4.1.2 MATLAB的特点 (15)4.2MATLAB在信号处理中的应用 (16)4.2.1信号及其表示 (16)4.2.2线性时不变系统的响应 (17)4.2.2.1线性时不变系统的时域响应 (17)4.2.2.2LTI系统的单位冲激响应 (18)4.2.2.3 时域响应的其它函数 (18)第5章图像压缩算法的实现 (19)5.1游程编码原理 (19)5.2游程编码图像压缩算法的实现 (20)5.3主要程序代码 (20)第6章功能验证 (22)第7章结束语 (27)参考文献 (28)致谢 (29)附录A外文资料翻译 (30)A.1英文资料 (30)A.2资料译文 (37)第1章绪论1.1 研究背景随着多媒体技术的迅速发展，静止图像的应用越来越广泛。
孙明明 外文资料翻译
毕业设计(论文)外文资料翻译学院：机械电子工程学院专业：机械设计制造及其自动化姓名：孙明明学号： 070501504外文出处： The advantages of PLC control,filed under PLC Articles附件： 1.外文资料翻译译文；2.外文原文。
(用外文写)附件1：外文资料翻译译文PLC的控制优势任何控制系统从概念到进入工厂工作都要经历四个阶段。
PLC系统在每一个阶段都有优势。
第一阶段是设计，对工厂的需要进行研究和制定控制策略，传统的运行平台的设计和制造必须在设计进行前完成。
PLC系统仅仅需要的是一个模糊的关于机器的可能大小的想法和I/O数量的要求（多少输入和输出接口）。
在这个阶段输入和输出芯片十分便宜，所以可以内置一个很健全的备用容量，它允许用来补充遗漏项目和为未来的扩充做准备。
其次是设计。
传统的方案是，每一项工作都是“一次成型”这不可避免的造成了工程拖延和增加成本。
一个的PLC系统使用最简单的标准件螺栓连接在一起。
在这样的连接下开始编写 PLC程序（或者至少是写入详细的程序规范）。
下一阶段是安装，安装是一种繁琐和昂贵的工作，例如安装传感器、执行器、限制开关系统和主机的连接。
分布式PLC系统使用串行链路式的预编译，测试界面可以简化安装它带来了巨大的成本优势。
PLC的程序多数在这个阶段完成。
最后是调试，而这正是PLC真正的优势被发掘的部分。
没有任何设备在第一次就正常工作。
人性就是这样，总会有一些疏漏。
与传统的系统变动情况的耗时和昂贵相比，PLC的设计师提供了系的内置备用内存容量、备用I/O和一些备用多芯电缆线，多数的变动能迅速和相对便宜的完成。
另外一个好处是，所有的变化PLC都有记录，程序的调试和修改不会因为没有被记录而遗失，这是一个经常发生在常规系统中的问题。
还有一个额外的第五阶段，维护，一旦启动工作，并移交生产就产生了维护的问题。
所有设备都有缺点，大多数设备在错误的模式中度过了它们的大部分的时间。
翻译专业毕业设计的规定(2010年5月20日(修改稿))
浙江师范大学外国语学院翻译系关于翻译专业毕业设计的规定及要求2010年5月20日（修改稿）《浙江师范大学本科生毕业设计（论文）工作条例（修订稿）》中指出：“毕业设计（论文）是实现本科培养目标的重要教学环节，它既是培养学生综合运用所学的基础理论、专业知识和基本技能进行科学研究工作的初步训练，也是对学生进行全面素质检测的重要手段之一。
做好毕业设计（论文）工作对全面衡量与提高教学质量具有重要的意义。
”本科生毕业设计（论文）是学生毕业及取得学位资格的重要依据。
加强本科毕业设计（论文）工作，对提高人才质量具有十分重要的意义。
为此，为规范本系翻译专业的毕业设计（论文）工作，进一步提高毕业设计的质量，特对本专业的毕业设计相关事宜做出如下规定。
一、毕业设计（论文）的目标(一)培养学生严肃认真的科学态度和务实求真的工作作风，形成正确的世界观，掌握科学的研究方法；(二)要求学生掌握翻译学科的基本理论、基础知识和翻译实践的能力。
提高学生发现、分析、解决与本专业相关的实际问题的敏锐性；(三)训练与提高学生查阅文献资料、运用各种工具搜集、整理材料和积累分析数据的能力，以及阅读、翻译本专业外文资料的能力；(四)培养学生良好的协作精神，提高学生勇于探索的创新能力；(五)综合检验学生用英文进行较为复杂的学术写作，以及用英语进行思辨的能力。
二、毕业设计方案(一)理论依据2009年5月第五届全国翻译系负责人联席会议于石家庄召开，在会上由广东外语外贸大学牵头成立的“高等学校翻译专业本科教学要求”编写组在广泛调研和大量数据分析的基础上编写了《高等学校翻译专业本科教学要求（讨论稿）》。
《高等学校翻译专业本科教学要求（讨论稿）》在第6页对“六、学位论文/毕业设计”做出如下规定：“学位论文/毕业设计是考察学生综合能力与创新能力、评估学业成绩的一个重要方式。
翻译专业应鼓励学生采用以下各种形式之一作为学位论文/毕业设计：学位论文、翻译实践报告、译作评论、研究报告等。
液位控制系统论文中英文资料对照外文翻译
液位控制系统论文中英文资料对照外文翻译The liquid level control system based ondde\matlab\simulinkProcess control is an important application field of automatic technology, it is to point to the level, temperature, flow control process variables, such as in metallurgy, machinery, chemical, electric power, etc can be widely used. Especially liquid level control technology in real life, played an important role in production, for example, the water supply, civil water tower if low water levels, can affect people's lives in water; Industrial enterprises with water, if the drainage water drainage or controlled properly or not, in relation to the workshop of condition; Boiler drum, if the control level boiler is too low, can make level boiler overheating, possible accident; Jing flow, liquid level control tower control accuracy and level of the craft can influence the quality of the products and the cost, etc. In these production field, are basically labor strength or the operation has certain risk nature of work, extremely prone to accidents caused by operating error, the losses, killing manufacturer. Visible, in actual production, liquid level control accuracy and control effects directly affect the factory production cost and economic benefit of safety coefficient. Even equipment So, in order to ensure safety, convenient operation, you have to research the development of a d v a n c e d l e v e l c o n t r o l m e t h o d s a n d s t r a t e g i e s.The graduation design topic is the liquid level control system based on dde\matlab\simulink\force control, Among them was controlled object for tank level, Communication mode for DDE communications , Matlab is mainly used in the simulation test ,And force control software used for modeling, This system mainly through combination of hardware and software device to achieve precise control of liquid level , In modern industry level control of important component, it influence upon production not allow to ignore, in order to ensure safety in production and the product quality and quantity, the level and perform effective control is very necessary, The following is a description of all aspects：一PID controllerA proportional–integral–derivative controller (PID controller) is a generic .control loop feedback mechanism widely used in industrial control systems.A PID controller attempts to correct the error between a measured process variable and a desired set point by calculating and then outputting a corrective action that can adjust the process accordingly.The PID controller calculation (algorithm) involves three separate parameters; the Proportional, the Integral and Derivative values. The Proportional value determines the reaction to the current error, the Integral determines the reaction based on the sum of recent errors and the Derivative determines the reaction to the rate at which the error has been changing. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element. By "tuning" the three constants in the PID controller algorithm the PID can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the set point and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability.Some applications may require using only one or two modes to provide the appropriate system control. This is achieved by setting the gain of undesired control outputs to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are particularly common, since derivative action is very sensitive to measurement noise, and the absence of an integral value may prevent the system from reaching its target value due to the control action.1.Control loop basicsA familiar example of a control loop is the action taken to keep one's shower water at the ideal temperature, which typically involves the mixing of two process streams, cold and hot water. The person feels the water to estimate its temperature. Based on this measurement they perform a control action: use the cold water tap to adjust the process. The person would repeat this input-output control loop, adjusting the hot water flow until the process temperature stabilized at the desired value.Feeling the water temperature is taking a measurement of the process value or process variable (PV). The desired temperature is called the set point (SP). The output from the controller and input to the process (the tap position) is called the manipulated variable (MV). The difference between the measurement and the set point is the error (e), too hot or too cold and by how much. As a controller, one decides roughly how much to change the tap position (MV) after one determines the temperature (PV), and therefore the error. This first estimate is the equivalent of the proportional action of a PID controller. The integral action of a PID controller can be thought of as gradually adjusting the temperature when it is almost right. Derivative action can be thought of as noticing the water temperature is getting hotter or colder, and how fast, and taking that into account when deciding how to adjust the tap，Making a change that is too large when the error is small is equivalent to a high gain controller and will lead to overshoot. If the controller were to repeatedly make changes that were too large and repeatedly overshoot the target, this control loop would be termed unstable and the output would oscillate around the set point in either a constant, growing, or decaying sinusoid. A human would not do this because we are adaptive controllers, learning from the process history, but PID controllers do not have the ability to learn and must be set up correctly. Selecting the correct gains for effective control is known as tuning the controller.If a controller starts from a stable state at zero error (PV = SP), then further changes by the controller will be in response to changes in other measured or unmeasured inputs to the process that impact on the process, and hence on the PV. Variables that impact on the process other than the MV are known as disturbances and generally controllers are used to reject disturbances and/or implement set point changes. Changes in feed water temperature constitute a disturbance to the shower process.In theory, a controller can be used to control any process which has a measurable output (PV), a known ideal value for that output (SP) and an input to the process (MV) that will affect the relevant PV. Controllers are used in industry to regulate temperature, pressure, flow rate, chemical composition, speed and practically every other variable for which a measurement exists. Automobile cruise control is an example of a process which utilizes automated control.Due to their long history, simplicity, well grounded theory and simple setup and maintenance requirements, PID controllers are the controllers of choice for many ofthese applications.2.PID controller theoryNote: This section describes the ideal parallel or non-interacting form of the PID controller. For other forms please see the Section "Alternative notation and PID forms".The PID control scheme is named after its three correcting terms, whose sum constitutes the manipulated variable (MV). Hence:where Pout, Iout, and Dout are the contributions to the output from the PID controller from each of the three terms, as defined below.2.1. Proportional termThe proportional term makes a change to the output that is proportional to the current error value. The proportional response can be adjusted by multiplying the error by a constant Kp, called the proportional gain.The proportional term is given by:WherePout: Proportional outputKp: Proportional Gain, a tuning parametere: Error = SP − PVt: Time or instantaneous time (the present)Change of response for varying KpA high proportional gain results in a large change in the output for a given change in the error. If the proportional gain is too high, the system can become unstable (See the section on Loop Tuning). In contrast, a small gain results in a small output response to a large input error, and a less responsive (or sensitive) controller. If the proportional gain is too low, the control action may be too small when responding to system disturbances.In the absence of disturbances, pure proportional control will not settle at its target value, but will retain a steady state error that is a function of the proportional gain and the process gain. Despite the steady-state offset, both tuning theory and industrial practice indicate that it is the proportional term that should contribute the bulk of the output change.2.2.Integral termThe contribution from the integral term is proportional to both the magnitude of the error and the duration of the error. Summing the instantaneous error over time (integrating the error) gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied by the integral gain and added to the controller output. The magnitude of the contribution of the integral term to the overall control action is determined by the integral gain, Ki.The integral term is given by:Iout: Integral outputKi: Integral Gain, a tuning parametere: Error = SP − PVτ: Ti me in the past contributing to the integral responseThe integral term (when added to the proportional term) accelerates the movement of the process towards set point and eliminates the residual steady-state error that occurs with a proportional only controller. However, since the integral term is responding to accumulated errors from the past, it can cause the present value to overshoot the set point value (cross over the set point and then create a deviation in the other direction). For further notes regarding integral gain tuning and controller stability, see the section on loop tuning.2.3 Derivative termThe rate of change of the process error is calculated by determining the slope of the error over time (i.e. its first derivative with respect to time) and multiplying this rate of change by the derivative gain Kd. The magnitude of the contribution of the derivative term to the overall control action is termed the derivative gain, Kd.The derivative term is given by:Dout: Derivative outputKd: Derivative Gain, a tuning parametere: Error = SP − PVt: Time or instantaneous time (the present)The derivative term slows the rate of change of the controller output and this effect is most noticeable close to the controller setpoint. Hence, derivative control isused to reduce the magnitude of the overshoot produced by the integral component and improve the combined controller-process stability. However, differentiation of a signal amplifies noise and thus this term in the controller is highly sensitive to noise in the error term, and can cause a process to become unstable if the noise and the derivative gain are sufficiently large.2.4 SummaryThe output from the three terms, the proportional, the integral and the derivative terms are summed to calculate the output of the PID controller. Defining u(t) as the controller output, the final form of the PID algorithm is:and the tuning parameters areKp: Proportional Gain - Larger Kp typically means faster response since thelarger the error, the larger the Proportional term compensation. An excessively large proportional gain will lead to process instability and oscillation.Ki: Integral Gain - Larger Ki implies steady state errors are eliminated quicker. The trade-off is larger overshoot: any negative error integrated during transient response must be integrated away by positive error before we reach steady state.Kd: Derivative Gain - Larger Kd decreases overshoot, but slows down transient response and may lead to instability due to signal noise amplification in the differentiation of the error.二Matlab IntroductionThe MATLAB® environment is well suited to rapid prototyping and application development. The interactive programming environment, built-in math functions, toolboxes, editing and debugging tools, and deployment options all contribute to reducing your overall development time.By using the built-in math functions and the many specialized functions contained within our toolboxes, MATLAB can significantly reduce the time it takes you to develop prototypes. In addition to integrated editing and debugging tools, MATLAB provides a performance profiler to help you further optimize your code when programming in MATLAB.Building applications around complex algorithms and graphics is easier than everwith the GUI builder, GUIDE. GUIDE was redesigned in MATLAB 6 to save you time. It offers all the drag and drop interface options you would expect, such as text boxes, radio buttons, check boxes, listboxes, sliders, pop-up menus, frames and more.When you're ready to deploy your application, the MathWorks offers a number of different options that allow you to either convert or interface your MATLAB application to other environments including C/C++ and the Web. MATLAB is the most productive development environment for creating scientific and engineering applications because it offers powerful tools for every step in the process to reduce your overall development time.MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses include• Math and computation• Algorithm development• Data acquisition• Modeling, simul ation, and prototyping• Data analysis, exploration, and visualization• Scientific and engineering graphics• Application development, including graphical user interface building三DDE IntroductionDynamic data exchange (DDE, Dynamic data exchange) is real-time exchange data between applications, it is the effective method between different applications to share data a agreement. DDE agreement is a kind of open, and language unrelated, based on protocol, it allows multiple applications to any human agreed format data exchange or command. It is application through Shared memory process of the communication between a form, also need not user intervention of good data exchange method.DDE applications can be divided into four types: client and server and client/server and the monitor. Conversation is a basic concept of DDE. DDE conversation happened in client applications and server application between. Customer is responsible for initializing and attendant session and control conversation flow, from the server application request data or services; The server applicationresponse client applications of data or service request. Client/server applications is both client applications and server application request, it can be and can provide information. Monitor application for debugging purposes. DDE applications can have multiple burst conversation, a service applications can also have multiple client applications, a client applications can to multiple requests data service applications, and an application can also act as client applications and services applications, when don't need the service application data or service, the customer will terminate session. DDE agreement must be synchronous control the news session, but in different application can switch between asynchronous session.DDE Application using the three layer identification system: Application name apply), theme name (from) and project name (Item). Application name (also called service name) is located at the top of the hierarchical structure, the service application registration for pointed out that particular DDE server application name, customer the application wants to establish session with the server application must be specified application name when this string marks; Name in every conversation topics is one and to identify logical data connection string, is the total classification, data it defines a server application conversation theme content, the server application can support one or more theme name; Project name identifies exchange unit of data string, furthermore confirm the conversation of detailed information, every theme name may have one or more project name. Example: for a database interface applications, will it supports database name as a theme name, and will all sorts of SQL commands as project name, because the server application can support one or more theme, and each theme name name may have one or more project name; So, when to change or reconstruct a conversation, just changing the subject name or project name can.四force control IntroductionForce control is Beijing SANWEI force control technology and "soft" control strategy software, real-time database and its management system, Web portal of tools and other products. These products are not isolated, and the force control is an application scale can free the system structure, the whole expansion force control system and its various products are made from some components procedures according to certain combinations and become. So this guide is not specifically targeted specific products separately describes the use of method, but the common use of all products introduced method. Force control configuration software is a can run on Windows 98/2000 / NT environment, and can run on Windows CE, DOSembedded environment control fu- nctions such as software modules. It USES function diagram way for users provide interface, possess and real-time database, graphical interface system and communication function.Force charged with monitoring configuration software is to the field production data acquisition and process control of specialized software, the biggest characteristic is to flexible "configuration mode" instead of programming approach to system integration, and it provides a good user interface and simple engineering development, as long as the realization method of software module of pre-settings simple "configuration", it can easily realization and complete monitoring layer each function, shorten the automation engineer system integration time, greatly improve the efficiency of integration.Force charged with monitoring configuration software is in the automatic control system monitoring layer level software platform, it can also and the domestic and foreign various industrial control network communication equipment manufacturer, it is ok with high reliable industrial controlling computer and network system integration, can achieve the purpose of the centralized management and monitoring, and can also be convenient to control layer and management for software and hardware to implement all the interface, with "third party" hardware and software systems for integration.The control strategy in the force control, an application in generator may have a lot of control strategy, but only one main strategy. The Lord, the Lord was first execution strategy calls. Other strategies strategy Strategy nested grade 4, namely for most 0 ~ 3 level, in this category 4, grade 3 0 level supreme, the lowest. Senior strategy can call low-level strategy, and low-level strategy can't call senior strategy. In addition to tier 3 most can have 127 strategy outside, other three grades maximum respectively are 255 strategy. Control strategy of by some basic function blocks, a function blocks represent an operation, algorithm or variables. Function blocks basic execution element is strategy, similar to an integrated circuit blocks, have several input and output, each input and output tube feet all have the only name.Force control control strategy is in control strategy, edited generated generators in automatic control strategy for strategies when inventory compiled, and check grammar mistakes, compile can also manually. Control strategy, and you can also call between if A strategy was B strategy calls, says A is B son strategy. A functional block can be repeated calls, each calls are automa- tically entitled to a name by. The executive order and function block in the position of screen on the upper left, position relevant function block, according to priority execution left after the first order under implementation.Force control control strategy of basic function blocks generator was divided into five categories: variable function blocks, mathematical operation function blocks, program control function blocks, logic function blocks function block and control algorithm.基于matlab\dde\simulink\力控的液位控制系统过程控制是自动技术的重要应用领域，它是指对液位、温度、流量等过程变量进行控制，在冶金、机械、化工、电力等方面得到了广泛应用。
基于matlab的图像形状与分类 任务书 翻译
毕业设计（论文）任务书题目(包括副标题)基于matlab的图像形状与分类教师姓名职称讲师系别学生姓名学号班级成果形式A论文B设计说明书C实物D软件E作品■□□■□任务下达时间2011年10月25日1．毕业设计（论文）课题任务的内容和要求：掌握数字图像处理的基本理论和实现方法，熟悉Matlab图像处理的常用工具箱，找出一种区别各种不同形状的特征算法，并编程对算出的参数逐个进行比对，识别出图像的具体形状，并进行分类。
具体要求如下：1.完成方案总体设计和实际需求分析；2.对基于形状的识别分类的现状及发展趋势进行调研；3.了解图像形状识别的基本原理和相关方法；4.构建本课题形状识别分类的软硬件平台，过程设计具体，详细，有独特之处；5.完成毕业论文的撰写。
2．毕业设计（论文）工作进度计划：时间工作内容第一周--第二周第三周-- 第五周第六周—第七周第八周—第十周第十一周—第十二周第十三周—第十四周收集课题信息及相关资料学习MATLAB软件相关知识，完成初步设计。
进行毕业设计，确定设计方案。
编写程序，进行软件调试。
逐步完善设计方案,实现功能。
将以上的工作编制成论文形式设计总结、完成论文、答辩准备教研室（学科组）主任签字：毕业设计（论文）中期报告学院班级学生姓名指导教师课题名称：基于matlab的图像形状与分类1、开题以来所做的具体工作和取得的进展和成果自开题以来，我所做的工作主要是阅读文献和练习软件使用，并补充前期设计方面没有考虑全面的问题，并大量阅读文献材料进行模拟仿真，比较相关各种方法的优缺点。
所取得的进展和成果：①在计算机上安装所要用到的开发工具matlab并调试成功，可以使用②阅读相关文献，了解图像识别与分类的常用方法③了解并比较这些常用方法的优缺点④完成一些程序代码的编译与仿真2、下一步的主要研究任务，具体设想和安排①继续学习matlab的相关知识，争取早日把相关函数功能模块做完做好②把仿真做完，查阅资料，解决遇到的问题③实际仿真出具体程序的运行结果，整理出过程，结果④进一步完善各种方法的优缺点的总结与比较⑤准备毕业设计答辩问题，整理整个毕业设计中的问题与收获3、存在的具体问题①对matlab程序的使用还不是很熟练，操作还要多加练习②对有些实现方法的理解不够透彻③已模拟仿真好的程序偶尔会出现问题④有些方法的具体程序还是看不太懂，理解不深，还要多加练习学生签字：年月日指导教师的建议与要求：指导教师签字：年月日注：本表格同毕业设计（论文）一同装订成册，由所在学院归档保存。
管理信息系统外文翻译
毕业设计(论文)外文资料翻译系 ：专 业：姓 名：学 号：外文出处： Madiha shah procedia-social and附 件 ：1.外文资料翻译译文；2.外文原文(用外文写)附件1：外文资料翻译译文管理信息系统(MIS)对学校的影响-----文献报告Madiha Shah Malaysia. Malaya大学马来西亚摘要鉴于其快捷和有效性,教育管理信息技术的使用已迅速增加。
在其发展的初始阶段,管理信息系统(MIS)的主要目的和使用是改善学校办公室活动的效率。
它是用于存储的学生和全体职工的数据。
最重要的的是重要数据录入和整理,而不是在数据传输或分析。
管理信息的价值当时被人们公认。
在集成阶段，全盘回顾文献，其强调积极影响学校管理和管理信息系统管理,包括更好的可访问性信息,更有效的管理,学校资源更高的利用率同时也减少了工作量,更好的时间管理,提高报告的质量。
对于信息管理系统，大量的抑制剂的使用在文献中很明显,其中最重要的是缺乏时间,缺乏信心或能力,缺乏培训,缺乏高层管理人员的支持,缺乏技术支持等。
管理信息系统可以提供所需的信息通知计划、决策和评估方面相关的管理员和教师。
管理信息系统改变了学校管理领域的领导、决策、工作负载、人力资源管理、沟通、责任,规划等方方面面。
这些系统可以帮助学校管理者在决定学校的目标,制定战略计划,分配资源,评估员工的绩效以及组织时更加顺利。
关键词: 管理信息系统、MIS 、学校管理、学校管理。
1、介绍电脑被视为有潜力在教学、学习和学校的管理方面做出重大的贡献。
信息和介绍通信技术(ICT)进入到学校包括硬件、软件、网络和员工发展的广泛的投资被认为是值得的前提。
如果有证据表明,它使在学校的表现和产生相应的影响有效性(Condie et al .,2007)真实存在。
利用信息技术在教育管理就会由于其效率和迅速增加有效性。
学校管理人员花大量的时间用于解决复杂的分配问题(如人员分配、资源分配、时间安排)和监控学校的操作已经有了更好的选择旨运用发展该技术。
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大连民族学院毕业设计外文资料翻译所在学院：机电信息工程学院专业 (班级)：自动化063学生姓名：徐睿指导教师：王培昌2010年6月4日复杂脊波图像去噪作者：G. Y. Chen and B. Kegl刊名：Pattern Recognition;出版日期：2007 1.介绍小波变换已成功地应用于许多科学领域，如图像压缩，图像去噪，信号处理，计算机图形，IC和模式识别，仅举几例。
Donoho和他的同事们提出了小波阈值去噪通过软阈值和阈值.这种方法的出现对于大量的应用程序是一个好的选择。
这是因为一个小波变换能结合的能量,在一小部分的大型系数和大多数的小波系数中非常小,这样他们可以设置为零。
这个阈值的小波系数是可以做到的只有细节的小波分解子带。
我们有一些低频波子带不能碰触，让他们不阈值。
众所周知，Donoho提出的方法的优势是光滑和自适应。
然而，Coifman和Donoho指出，这种算法展示出一个视觉产出：吉布斯现象在邻近的间断。
因此，他们提出对这些产出去噪通过平均抑制所有循环信号。
实验结果证实单目标识别小波消噪优于没有目标识别的情况。
Bui和Chen扩展了这个目标识别计划，他们发现多小波的目标识别去噪的结果比单小波去噪的结果要好。
蔡和西尔弗曼提出了一种阈值方案通过采取相邻的系数。
他们结果表现出的优势超于了传统的一对一小波消燥。
Chen和Bui扩展这个相邻小波阈值为多小波方法。
他们声称对于某些标准测试信号和真实图像相邻的多小波降噪优于相邻的单一小波去噪。
陈等人提出一种图像去噪是考虑方形相邻的小波域。
陈等人也尝试对图像去噪自定义小波域和阈值。
实验结果表明：这两种方法产生更好的去噪效果。
研究脊波变换的数多年来打破了小波变换的局限性。
将小波变换产生的二维图像在每个规模大的小波系数的分解。
有这么多的大系数，对于图像去噪有很多困难。
我们知道脊波变换已经成功用于分析数字图像。
不像小波变换,脊波变换过程首先计算积分在不同的方向和位置的数据。
沿着“x1cos_ + x2sin_ = 常数”一条线的脊波是不变的。
在这些脊的方向正交小波变换是一。
最近脊波已成功应用于图像去噪。
在本文中，我们结合二元树复小波的脊波变换中并将其应用到图像降噪。
这种近似二元树性能的复杂变性小波和良好性能的脊波使我们有更好的方法去图像去噪。
实验结果表明，采用二元树复杂脊波在所有去噪图像和许多不同噪音中我们的算法获得较高的峰值信噪比（PSNR）。
这篇文章大体是这样的。
在第二部分，我们将解释如何将二元树复杂的波变换成脊波去图像去噪。
实验结果在第3节。
第4节是最后得出的结论和未来需要做的工作。
2.用复杂脊波图像去噪离散脊波变换提供接近理想的稀松代表光滑的物体边缘。
高斯去噪是一个接近最优的方法。
脊波变换能够压缩图像能量成为少量的脊波系数。
在另一方面,利用小波变换产生的多大的小波系数对每个尺度边缘二维小波分解。
这句话的意思是说许多小波系数进行重构在图像的边缘。
我们知道近似氡转化为数字数据可以基于离散傅立叶变换。
普通的脊波变换即可达到如下：1.计算出二维FFT的图像。
2.替补的采样傅里叶广域上变换得到晶格和极性格的采样值。
3.计算一维逆FFT每一个角的线。
4.执行一维标量小波对角线结果，获取脊波系数。
众所周知，普通的离散小波变换在变换期间是不移位和不转变的。
输入信号的一个小小的改变能够引起输出小波系数很大的变化。
为了克服这个问题，Kingsbury发明了一种新型的小波变换，叫做二元树复杂小波变换，它能够转移性能和提高近似角分辨率不变。
由于标量波不是转移不变的，在脊波变换中就更好的应用二元树复杂小波变换这样我们就可以叫我们的复杂脊波。
这样可以通过取代一维标量小波的一维二元树复杂小波在最后一步进行脊波变换。
用这种方法我们可以优秀品质的脊波变换用来替换二元树发杂脊波。
这个复杂的脊波变换可以应用到整体图像，或者我们可以应用到分割图像大量重叠的平方或者在每一平方上运用脊波变换。
我们分解一组n*n的影像重叠顺利进入边长R的象素是重叠的是两个相邻长方形的数组大小为R2*R两者之间重叠的相邻区域就是一个长方形的大小R*R2。
对于一个n*n的图像，我们能够计数2n=R对于不同方向的模块，这个分区就引入了4倍的冗余。
为了得到降噪的复杂脊波系数我们通常在当前象素地位对降噪的复杂脊波系数进行平均4份。
复杂的脊波变换阈值类似于曲波阈值。
当我们求阈值时一个不同是我们采取的是复杂的脊波系数。
当yλ是带噪的脊波系数。
我们使用下列硬阈值规则估算未知的脊波系数。
当│yλ┃> kσ〜, 我们令λ= Ÿλ.否则, ^y_ = 0.在这里，〜σ是通过用蒙特卡罗模拟接近。
采用的系数k是依赖于噪声系数。
当这个小于30时，我们用k=5首先分解尺度和k=4分解其他尺度。
当这个噪音系数大于30时，我们用k=6首次分解尺度和k=5分解其他尺度。
这个复杂的脊波去噪算法能够被描述如下：1.图像分割成R*R块，两个垂直相邻的R 2*R重叠，两个水平象素块R*R2重叠。
2.对于每一块，应用所提出的复杂脊波，复杂脊波系数的阈值，复杂脊波的逆换算。
3.在同一位置以平均象素对图像去噪。
我们称这种算法叫，同时我们使用普通的脊波。
这个计算复杂度的ComRidgeletShrink是和小波RidgeletShrink的标量相似。
唯一的区别是我们取代了一维小波变换与一维二元树发杂小波变换。
这个数量的计算是一维二元树复数小波的变换是一维小波变换的两倍。
该算法的其他计算步骤保持相同。
我们的实验结果显示ComRidgeletShrink优于V isuShrink, RidgeletShink, and 过滤器wiener2等所有测试案例。
在某些情况下，我们在RidgeletShink中能够提高0.8db的信噪比。
通过V isuShrink,能够改善更大的去噪图像。
这表明ComRidgeletSrink对于自然图像去噪是一个很好的选择。
3.实验结果我们通过对众所周知的蕾娜进行处理，通过Donoho等人我们得到了这种图片的自由软体包WaveLab。
带有不同噪音的噪音图像时通过对原无噪音图像添加高斯白噪音得到的。
与之相比，我们实行VisuShrink, RidgeletShrink, ComRidgeletShrink and wiener2。
VisuShrink是通用软阈值去噪技术。
这个wiener2函数是可以从MatLab图像工具箱得到，我们用一个5*5的相邻图像在每个象素中。
该wiener2适用于一个维纳滤波器（一种线性的滤波器）图形自适应。
剪裁自己的图像局部方差。
峰值信噪比的实验结果显示的表1.我们发现对于分区块的大小32*32或者64*64是最好的选择。
表1是对蕾娜图像进行去噪，根据不同的噪声水平固定分区和一素块为32*32。
表格中的第一栏是原来带噪图片的信噪比，其他列是通过不同去噪方法得到的去噪图像信噪比。
这个信噪比被定义PSNR = 10 log10Pi;j (B(i; j) A(j))2n22552 :; 其中B是去噪图像A是无噪音图像。
从表1.我们可以看出VisuShrink ,ComRidgeletShrink是优于不同RidgeletShrink和wiener2在所有案例中。
当噪音低时VisuShrink没有去噪能力。
在这样的情况下，VisuShrink将产生比原来的去噪图像更糟的结果。
然而，ComRidgeletShrink在这种情况下取得较好的效果。
在某些情况下，ComRidgeletShrink能够比普通RidgeletShrink多提供给我们0.8db。
这表明，我们把二元树结合复数的小波变换成脊波变换能够明显的改善我们图像去噪的效果。
ComRidgeletShrink超越VisuShrink的表现更重要的是所有噪音水平和图像测试。
图一显示的是在无噪音图像，添加噪音的图像，用VisuShrink去噪的图像，用RidgeletShrink去噪的图像，用ComRidgeletShrink去噪的图像，用wiener2去噪的图像，在一个分区大小为32*32的象素块中。
ComRidgeletShrink在视觉上产生的效果比VisuShrink ，wiener2 RidgeletShrink更清晰具有高线性和恢复曲线的特点。
4．结论和未来工作在这篇文章中我们研究了用复杂脊波对图像去噪。
复杂脊波变换是通过执行一维二元树复杂小波在空气中氡的变换系数获得的。
氡变换是通过投影片定理得到的。
对于图像去噪近似转换不变性质的二元树复数小波变换对于复杂小波变换是一个很好的选择。
复杂脊波变换提供了近乎完美的对于光滑的物体和表现对象与边缘。
这使噪音阈值的脊波系数更接近高斯白噪音的消噪。
我们测试了我们新的去噪方法和几个标准图像和加入高斯白噪音的图像。
用一个非常简单的硬阈值复杂脊波系数。
在这些脊波实验中，实验结果表明复杂的脊波有更好的去噪能力比起VisuShrink和普通的wiener2。
我们建议ComRidgeletShrink用于实际的图像去噪中。
未来工作主要是考虑在复杂图像应用曲波复杂脊波。
同样，复杂脊波还可以应用的不变特征提取模式识别方法。
Complex Ridgelets for Image DenoisingG. Y. Chen and B. Kegl1 IntroductionWavelet transforms successfully used in many scientific fields such as image compression, image denoising, signal processing, computer graphics,and pattern recognition, to name only a few.Donoho and compact the energy of the image to only a small number of large coefficients and the majority of the wavelet coeficients are very small so that they can be set to zero. The thresholding of the wavelet coeficients can be done at only the detail wavelet decomposition subbands. We keep a few low frequency wavelet subbands untouched so that they are not thresholded. It is well known that Donoho's method offers the advantages of smoothness and adaptation. However, as Coifmanand Donoho pointed out, this algorithm exhibits visual artifacts: Gibbs phenomena in the neighbourhood of discontinuities. Therefore, they propose in a translation invariant (TI) denoising scheme to suppress such artifacts by averaging over the denoised signals of all circular shifts. The experimental results in confirm that single TI wavelet denoising performs better than the non-TI case. Bui and Chen extended this TI scheme to the multiwavelet case and they found that TI multiwavelet denoising gave better results than TI single wavelet denoising. Cai and Silverman proposed a thresholding scheme by taking the neighbour coeficients into account Their experimental results showed apparent advantages over the traditional term-by-term wavelet denoising.Chen and Bui extended this neighbouring wavelet thresholding idea to the multiwavelet case. Theyclaimed that neighbour multiwavelet denoising outperforms neighbour single wavelet denoising for some standard test signals and real-life images.Chen et al. proposed an image denoising scheme by considering a square neighbourhood in the wavelet domain. Chen et al. also tried to customize the wavelet _lter and the threshold for image denoising. Experimental results show that these two methods produce better denoising results. The ridgelet transform was developed over several years to break the limitations of the wavelet transform. The 2D wavelet transform of images produces large wavelet coeficients at every scale of the decomposition.With so many large coe_cients, the denoising of noisy images faces a lot of diffculties. We know that the ridgelet transform successfully used to analyze digital images. Unlike wavelet transforms, the ridgelet transform processes data by first computing integrals over different orientations and locations. A ridgelet is constantalong the lines x1cos_ + x2sin_ = constant. In the direction orthogonal to these ridges it is a wavelet.Ridgelets successfully applied in image denoising recently. In this paper, we combine the dual-tree complex wavelet in the ridgelet transform and apply it to image denoising. The approximate shift invariance property of the dual-tree complex wavelet and the good property of the ridgelet make our method a very good method for image denoising.Experimental results show that by using dual-tree complex ridgelets, our algorithms obtain of this paper is as follows. In Section 2, we explain Section 3. Finally we give the conclusion and future work to be done in section 4.2 Image Denoising by using ComplexRidgelets Discrete ridgelet transform provides near-ideal sparsity of representation of both smooth objects and of objects with edges. It is anear-optimal method of denoising for Gaussian noise. The ridgelet transform can compress the energy of the image into a smaller number of ridgelet coe_cients. On the other the edges on every scale of the 2D wavelet decomposition. This means that many wavelet coe_cients are needed in order to reconstruct the edges in the image. We know that approximate Radon transforms for digital data can be based on discrete fast Fouriertransform. The ordinary ridgelet transform can be achieved as follows:1. Compute the 2D FFT of the image.2. Substitute the sampled values of the Fourier transform obtained on the square lattice with sampled values on a polar lattice.3. Compute the 1D inverse FFT on each angular line.4. Perform the 1D scalar wavelet transform on the resulting angular lines in order to obtain the ridgelet coe_cients.It is well known that the ordinary discrete wavelet transform is not shift invariant because of the decimation operation during the transform.A small shift in the input signal can cause very di_erent output wavelet coe_cients. In order to overcome this problem, Kingsbury introduced a new kind of wavelet transform, called the dual-tree complex wavelet transform, that exhibits approximate shift invariant property and improved angular resolution. Since the scalar wavelet is not shift invariant,it is better to apply the dual-tree complex wavelet in the ridgelet transform so that we can be done by replacing the 1D scalar wavelet with the 1D dualtree complex wavelet transform in the last step of the ridgelet transform. In this way, we can combine the good property of the ridgelet transform with the approximate shift invariant property of the dual-tree complex wavelets.The complex ridgelet transform can be applied to the entire image or we can partition the image into a number of overlapping squares and we apply the ridgelet transform to each square. We decompose the original n _ n image into smoothly overlapping blocks of sidelength R pixels so that the overlap between two vertically adjacent blocks is a rectangular array of size R=2 _ R and the overlap between two n _ n image, we count 2n=R such blocks in each direction. This partitioning introduces a redundancy of 4 times. In order to get the denoised complex ridgelet coe_cient, we use the average of the four denoised complex ridgelet coe_cients in the current pixel location.The thresholding for the complex ridgelet transform is similar to the curvelet thresholding [10]. One difference is that we take the magnitude of the complex ridgelet coe_cients when we do the thresholding. Let y_ be the noisy ridgelet coe_cients. We use the following ridgelet coe_cients. When jy_j > k_~_, we let ^y_ = y_. Otherwise, ^y_ = 0. Here, ~It is approximatedby using Monte-Carlo simulations. The constant k used is dependent on the noise . When the noise is less than 30, we use k = 5 for the first decomposition scale and k = 4 for other decomposition scales. When the noise _ is greater than 30, we use k = 6 for the _rst decomposition scale and k = 5 for other decomposition scales.The complex ridgelet image denoising algorithm can be described as follows:1. Partition the image into R*R blocks with two vertically adjacent blocks overlapping R=2*R pixels and two .We call this algorithm ComRidgeletShrink,while the algorithm using the ordinary ridgelets RidgeletShrink. The computational complexity of ComRidgeletShrink is similar to that of RidgeletShrink by using the scalar wavelets. The only di_erence is that we replaced the 1D wavelet transform with the 1D dual-tree complex wavelet transform. The amount of computation for the 1D dual-tree complex wavelet is twice that of the 1D scalar wavelet transform. However, other steps of the algorithm keep the same amount of computation. Our experimental results show that ComRidgeletShrink outperforms V isuShrink, RidgeletShink, and wiener2 _lter for all testing cases. Under some case, we obtain 0.8dB improvement in Peak Signal to Noise Ratio (PSNR) over RidgeletShrink. The improvement over V isuShink is even bigger for denoising all images. Thisindicates that ComRidgeletShrink is an excellent choice for denoising natural noisy images.3 Experimental ResultsWe perform our experiments on the well-known image Lena. We get this image from the free software package WaveLab developed by Donoho et al. at Stanford University. Noisy images with di_erent noise levels are generated by adding Gaussian white noise to the original noise-free images. For comparison, we implement VisuShrink, RidgeletShrink, ComRidgeletShrink and wiener2. VisuShrink is the universal soft-thresholding denoising technique. The wiener2 function is available in the MATLAB Image Processing Toolbox, and we use a 5*5 neighborhood of each pixel in the image for it. The wiener2 function applies a Wiener _lter (a type of linear filter) to an image adaptively, tailoring itself to the local image variance. The experimental results in Peak Signal to Noise Ratio (PSNR) are shown in Table 1. We find that the partition block size of 32 * 32 or 64 *64 is our best choice. Table 1 is for denoising image Lena, for di_erent noise levels and afixed partition block size of 32 *32 pixels.The first column in these tables is the PSNR of the original noisy images, while other columns are the PSNR of the denoised images by using di_erent denoising methods. The PSNR is de_ned as PSNR = 10 log10 Pi;j (B(i; j) A(i; j))2 n22552 : where B is the denoised image and A is the noise-free image. From Table 1 we can see that ComRidgeletShrink outperforms VisuShrink, the ordinary RidgeletShrink, and wiener2 for all cases. VisuShrink does not the noise level is low. Under such a condition, VisuShrink produces even worse results than the original noisy images. However, ComRidgeletShrink performs very well in this case. For somecase, ComRidgeletShrink gives us about 0.8 dB improvement over the ordinary RidgeletShink. This indicates that by combining the dual-tree complex wavelet into the ridgelet transform we obtain signi_cant improvement in image denoising. The improvement of ComRidgeletShrink over V isuShrink is even more signi_cant for all noisy levels and testing images. Figure 1 shows the noise free image, the image with noise added, the denoised image with VisuShrink, the denoised image with RidgeletShrink, the denoised image with ComRidgeletShrink, and the denoised image with wiener2 for images Lena, at a partition block size of 32*32 pixels. It can be seen that ComRidgeletShrink produces visually sharper denoised images than V isuShrink, the ordinary RidgeletShrink, and wiener2 filter, in terms of this paper, we study image denoising by using complex ridgelets. Our complex ridgelet transform is obtained by performing 1D dual-tree complex wavelet onto the Radon transform coe_cients. The Radon transform is done by means of the projection-slice theorem. The approximate shift invariant property of the dual-tree complex wavelet transform makes the complex ridgelet transform an excellent choice for image denoising. The complex ridgelet transform provides near-ideal sparsity of representation for both smooth objects and objects with edges. This makes the thresholding of noisy ridgelet coe_cients a near-optimal method of denoising for Gaussian white noise. We test our new denoising method with several standard images with Gaussian white noise added to the images. A very simple VisuShrink, wiener2, and the ordinary ridgelets under all experiments. We suggest that ComRidgeletShrink be used for practical image denoising applications. Future work will be done by considering complex ridgelets in curvelet image denoising. Also, complex ridgelets could be applied to extract invariant features for pattern recognition.。