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1．不久前我应邀参加了一次有关美国报业的作用的公众研讨会。
还有另外两个嘉宾也出席了。
一位是知名的电视节目主持人，另一位是美国一家主要报纸的编辑，他是一位彻头彻尾的新闻工作者---- 在据实报道的方式上坚忍不拔、积极进取且见识过人。
2．据我所知本次研讨旨在审查传媒的义务，并提出实现那些义务的最佳途径。
3在公开讨论时，观众席中的一位男士向两位嘉宾提问，“为什么报纸和电视新闻节目都充斥灾难？为什么新闻界的男男女女对悲剧、暴力、和失败是如此关注？”4．主持人和编辑作出了回答，好像是自己应该为坏消息的存在而受到谴责一样。
他们说，新闻工作者只负责报道新闻，而不负责制造或修改新闻。
5．我不认为这两位新闻工作者回答了这个问题。
提出这个问题的先生并没有因为世上的歪曲报道而谴责他们。
他只是想知道为什么报道得最多的是歪曲的事件。
新闻媒体的运作理念似乎是凡是新闻皆坏事。
为什么呢？是不是着重报道负面新闻是一种传统——是新闻工作者所习以为常的对日常事件作出的反应方式？6．在此或许分析一下我们如何定义“新闻”一词会有所帮助，因为这是问题的起因。
新闻应该是报道过去十二小时，最迟二十四小时内所发生的事情。
然而，突发事件往往具有爆炸性的：一名狙击手枪杀了几名行人，一名恐怖分子劫持了飞机上250名人质，欧佩克石油组织宣布原油价格上涨25%，英国货币又贬值10%，一辆载有放射性废料的卡车与一架水泥搅拌车相撞。
7．然而，一味聚焦这些报道，则是失真的画面。
人类文明成果远远多于灾难总和。
每种文明中最重要的成分就是进步。
但进步不会立即发生，也没有爆发性。
一般来说，它是一点一滴逐步发展的，在某个特定时刻是微不足察的。
但是所有微小的进步都参与了历史性的巨变的实现，使社会更加美好。
8．就是这类活生生的历史，绝大多数的新闻媒体没有予以充分的反映，结果导致我们对社会的正面发展了解不足，对灾难却知之甚多，这又使人产生失败和绝望的情绪，而这些情绪易于阻碍社会进步。
Unite 9 Digital signals and signal processingPart 1: Digital signal processingDigital signal processing (DSP) is the study of signals in a digital representation and the processing methods of these signals. DSP and analog signal processing are sub-fields of signal processing. DSP includes sub-fields like audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, image processing, signal processing for communications, biomedical signal processing, etc.Since the goal of DSP is usually to measure or filter continuous real-world analog signals, the first step is usually to convert the signal form an analog to a digital form, by using an analog to digital converter. Often, the required output signal is another analog output signal, which requires a digital to analog converter.The algorithms required for DSP are sometimes performed using specialized computers, which make use of specialized microprocessors called digital signal processors (also abbreviated DSP). These process signals in real time, and are generally purpose-designed application-specific integrated circuits (ASICs). When flexibility and rapid development are more important than unit costs at high volume, DSP algorithms may also be implemented using field-rogrammable gatearrays (FPGAs).DSP domainsIn DSP, engineers usually study digital signals in one of the following domains: time domain (one-dimensional signals), spatial domain (multidimensional signals), frequency domain, autocorrelation domain, and wavelet domains. They choose the domain in which to process a signal by making an informed guess (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal. A sequence of samples form a measuring device produces a time or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain information, that is, the frequency spectrum. Autocorrelation is defined as the cross-correlation of the signal with itself over varying intervals of time or space.Signal samplingWith the increasing use of computers the usage and need of digital signal processing has increased. In order to use an analog signal on computer it must be digitized with an analog to digital converter (ADC). Sampling is usually carried out in tow stages, discretization and quantization. In the discretization stage, the space of signals is partitioned into equivalence classes and discretization is carried out by replacing thesignal with representative signal of the corresponding equivalence class. In the quantization stage the representative signal values are approximated by values form a finite set.In order for a sampled analog signal to be exactly reconstructed, the Nyquist-Shannon sampling theorem must be satisfied. This theorem states that the sampling frequency must be greater than twice the bandwidth of the signal. In practice, the sampling frequency is often significantly more than twice the required bandwidth. The most common bandwidth scenarios are: DC~BW (“baseband”); and f BW, a frequency band centered on a carrier frequency (“direct demodulation”).Time and space domainsThe most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Filtering generally consists of some transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters; for example: - A “linear” filter is a linear transformation of input samples; other filters are “non-linear.” Linear filters satisfy the superposition condition, i.e., if an input is a weighted linear combination of different signals, the output is an equally weighted linear combination of the corresponding output signals.- A “causal” filter uses only previous samples of the input or output signals; while a “non-causal” filter uses future input samples. A non-causal filter can usually be changed into a causal filter by adding a delay to it.- A “time-invariant” filter has constant properties over time; other filters such as adaptive filters change in time.- Some filters are “stable”, others are “unstable”. A stable filter produces an output that converges to a constant value with time or remains bounded within a finite interval. An unstable filter produces output which diverges.- A “finite impulse response” (FIR) filter uses only the input signal, while an “infinite impulse response” filter (IIR) uses both the input signal and previous samples of the output signal. FIR filters are always stable, while IIR filters may be unstable.Most filters can be described in Z-domain (a superset of the frequency domain) by their transfer functions. A filter may also be described as a difference equation, a collection of zeroes and poles or, if it is an FIR filter, an impulse response or step response. The output of an FIR filter to any given input may be calculated by convolving the input signal with the impulse response. Filters can also be represented by block diagrams which can then be used to derive a sample processing algorithm to implement the filter using hardware instructions.Frequency domainSignals are converted from time or space domain to the frequency domain usually through the Fourier transform. The Fourier transform converts the signal information to a magnitude and phase component of each frequency. Often the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared.The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to get information of which frequencies are present in the input signal and which are missing.There are some commonly used frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain through Fourier transform, takes the logarithm, and then applies another Fourier transform. This emphasizes the frequency components with smaller magnitude while retaining the order of magnitudes of frequency components.ApplicationsThe main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, radar, sonar,seismology, and biomedicine. Specific examples are speech compression and transmission in digital mobile phones, room matching equalization of sound in HiFi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, computer-generated animations in movies, medical imaging such as CAT scans and MRI, image manipulation, high fidelity loudspeaker crossovers and equalization, and audio effects for use with electric guitar amplifiers.ImplementationDigital signal processing is often implemented using specialized microprocessors such as the MC560000 and the TMS320. These often process data using fixed-point arithmetic, although some versions are available which use floating point arithmetic and are more powerful. For faster applications FPGAs might be used. Beginning in 2007, multicore implementations of DSPs have started to emerge. For faster applications with vast usage, ASICs might be designed specifically. For slow applications, a traditional slower processor such as microcontroller can cope.Part 2: General concepts of digital signal processingThere have been tremendous demands in the use of digital computersand special-purpose digital circuitry for performing varied signal processing functions that were originally achieved with analog equipment. The continued evolution of inexpensive integrated circuits has led to a variety of microcomputers and minicomputers that can be used for various signal processing functions. It is now possible to build special-purpose digital processors within much smaller size and lower cost constraints of systems previously all analog in nature.We will provide a general discussion of the basic concepts associated with digital signal processing. To do so, it is appropriate to discuss some common terms and assumptions. Wherever possible the definitions and terminology will be established in accordance with the recommendations of the IEEEE Group on Audio and Electroacoustics.An analog signal is a function that is defined over a continuous range of time and in which the amplitude may assume a continuous range of values. Common examples are the sinusoidal function, the step function, the output of a microphone, etc. the term analog apparently originated from the field of analog computation, in which voltages and currents are used to represent physical variables, but it has been extended in usage.Continuous-time signal is a function that is defined over a continuous range of time, but in which the amplitude may either have a continuous ranger of values or a finite number of possible values. In this context, an analog signal could be considered as a special case of continuous-timesignal. In practice, however, the terms analog and continuous-time are interchanged casually in usage and are often used to mean the same thing. Because of the association of the term analog with physical analogies, preference has been established for the term continuous-time. Nevertheless, there will be cases in which the term analog will be used for clarity, particularly where it relates to the term digital.The term quantization describes the process of representing a variable by a set of distinct values. A quantized variable is one that may assume only distinct values.A discrete-time signal is a function that is defined only at a particular set of values of time. This means that the independent variable, time, is quantized. If the amplitude of a discrete-time signal is permitted to assume a continuous range of values, the function is said to be a sampled-data signal. A sampled-data signal could arise from sampling an analog signal at discrete values of time.A digital signal is a function in which both time and amplitude are quantized. A digital signal may always be represented by a sequence of numbers in which each number has a finite number of digits.The terms discrete-time and digital are often interchanged in practice and are often used to mean the same thing. A great deal of the theory underlying discreet-time signals is applicable to purely digital signals, so it is not always necessary to make rigid distinctions. The term。
龙文教育学科老师个性化教案教师学生姓名上课日期2013.11.24 学科英语年级八年级教材版本人教版学案主题新概念第二册lesson 9,10课时数量（全程或具体时间）第（4）课时授课时段15:00-17:00教学目标教学内容新概念第二册lesson 9,10个性化学习问题解决新概念第二册lesson 9,10教学重点、难点难点：新概念语法讲解词汇句子记忆重点：同上教学过程一．DiscussionWhich is more important, the process or the result?二．Test （Try your best and see your level）16. －Who's that young man on the poster?－Justin Bieber, excellent singer.A. aB. anC. theD. /17.－The sweater is not the right for me.－Well, shall I get you a bigger one or a smaller one?A. priceB. colorC. sizeD. material18.－I've left my keys in the meeting room. Please them for me.－All right.A. buyB. paintC. washD. fetch19. －Your aunt often walks the dog in the morning.－Yeah, bad weather stops her.A. whenB. unlessC. becauseD. since20. －You look . What's up, sir?－I can't find my ticket, but it's time to check in.A. sleepyB.hungryC. tiredD. worried21. The girl is afraid to dance in public because she thinks others may her.A. laugh atB. wait forC. hear ofD. agree with22.－Which magazine do you like better, Crazy Reading or Teens' Space?－I like of them. They are useful for English learners.A. noneB. neitherC. allD. both23. －Can Peter play games with us, Mrs. Hawking?－Wait a minute. He a shower.A. is takingB. takesC. tookD. was taking24.－Do you know Jane visits her grandparents?－Once a week. She loves them deeply.A. how soonB. how oftenC. how longD. how far25. －Bob, your room is a real mess!－. I'll clean it up right away.A. I'd love toB. I hope soC. I'm sorryD. I disagree三．上次内容检测写出以下动词的过去式过去分词四．课文讲解背景知识英国传统节日VS中国传统节日Lesson 9 A cold welcomeWelcomen. 欢迎eg. Thanks for your warm welcome.v. 欢迎welcome sb to sth.eg. Welcome you to my house.Welcome you to visit Beijing.What does ‘a cold welcome’ refer to?refer to 涉及，谈及，参考，指什么Eg. Don’t refer to the matter again. 别再提这件事了。
现代数字信号处理英文版课程设计IntroductionModern digital signal processing (DSP) is a rapidly growing field that has become essential for a wide range of applications including audio processing, image processing, communications, and control systems. This course ms to provide students with a comprehensive understanding of modern DSP techniques, including theory, algorithms, and practical implementation.Course ObjectivesBy the end of the course, students will be able to:•Understand the fundamental concepts of digital signal processing•Design and implement common DSP algorithms for various applications•Analyze and evaluate the performance of DSP algorithms•Use MATLAB to simulate and visualize DSP algorithms Course OutlineWeek 1: Introduction to DSP•Overview of DSP•Discrete-time signals and systems•Sampling and quantizationWeek 2: Time Domn Analysis•Convolution and correlation•Discrete Fourier Transform (DFT)•Fast Fourier Transform (FFT) Week 3: Frequency Domn Analysis•Fourier series•Fourier transform•Filter designWeek 4: Digital Filters•FIR Filters•IIR Filters•Filter design and implementation Week 5: Multirate Signal Processing•Downsampling and upsampling•M-Channel filter banks•Polyphase decompositionWeek 6: Applications of DSP•Audio processing•Image processing•Communications•Control systemsGrading Policy•30% Assignments•30% Quizzes•40% Final ProjectCourse Materials•Oppenheim, A. V., & Schafer, R. W. (2010). Discrete-time signal processing. Prentice Hall.•MATLAB.Prerequisites•Linear algebra•Calculus•Basic programming skills in MATLAB or other programming languages.ConclusionDigital signal processing is a rapidly evolving field that has become essential for many applications. This course provides students with a solid foundation in modern DSP techniques, including theory, algorithms, and practical implementation. By the end of the course, students will be able to apply their knowledge to a wide range of applications in audio processing, image processing, communications, and control systems.。
Digital Signal Processing Using Computer-Based Methods -Course Design for the 4th EditionIntroductionDigital Signal Processing (DSP) is an area of study that has witnessed significant growth and advancement in recent times. Technological advancements have made it possible to work with signals and signals processing methods more effectively and efficiently. The use of computers has also contributed significantly to the development of DSP methods. In this course design, we will provide an overview of the Digital Signal Processing course designed for the 4th edition of the book titled Digital Signal Processing Using Computer-Based Methods.Overview of the CourseThis course is designed to provide students with a fundamental understanding of digital signal processing concepts, their applications, and techniques for analyzing signals. The course is divided into eight modules, covering the following topics:1.Introduction to Digital Signal Processing2.Discrete-Time Signals and Systems3.Discrete Fourier Transform4.Z-Transform and Analysis of LTI Systems5.FIR Filter Design6.IIR Filter Design7.Multirate Signal Processing8.DSP Applications in Speech and Image ProcessingThe course will cover both theoretical and practical aspects of DSP, including hands-on experience with MATLAB software. The course involves lectures, discussions, and assignments, which will enable students to develop an in-depth understanding of DSP concepts and their applications.Course ObjectivesThe primary objectives of this course are to: - Develop an in-depth understanding of digital signal processing concepts and techniques - Familiarize students with the use of MATLAB for signal analysis and processing - Develop skills for designing digital filters and analyzing signals using the Fourier and Z-transforms - Provide practical experience with signal processing applications in speech and image processingCourse OutlineModule 1: Introduction to Digital Signal Processing •Basic concepts of digital signal processing•Analog-to-digital conversion•Sampling theorem•Signal quantizationModule 2: Discrete-Time Signals and Systems•Discrete-time signals and their characteristics•Discrete-time systems and their properties•Convolution and correlation of discrete-time signalsModule 3: Discrete Fourier Transform•Fourier series and Fourier Transform•Discrete Fourier Transform (DFT) and its properties •Fast Fourier Transform (FFT) algorithmsModule 4: Z-Transform and Analysis of LTI Systems •Z-Transform and its properties•Transfer function and Frequency Response of LTI systems•Analysis of LTI systems using Z-TransformModule 5: FIR Filter Design•Design of Finite Impulse Response (FIR) filters•Windowing techniques and their effects•Filter design using Fourier SeriesModule 6: IIR Filter Design•Design of Infinite Impulse Response (IIR) filters•Pole-zero locations and their effects•Butterworth and Chebyshev filter designs Module 7: Multirate Signal Processing•Sampling rate conversion using decimation and interpolation•Polyphase decomposition and filter banks•Multistage decimation and interpolation Module 8: DSP Applications in Speech and Image Processing •Speech analysis and synthesis•Speech coding and compression•Image enhancement and restoration•Image compressionEvaluationThe grading for this course will be based on your performance in the following components: - Regularassignments and quizzes: 20% - Mid-term examination: 30% - Final examination: 50%ConclusionThis course in Digital Signal Processing will provide students with a comprehensive understanding of digital signal processing concepts and their applications. The course will focus on fundamental principles, practical applications, and hands-on experience with digital signal processing using MATLAB. Upon successful completion of this course, students will have the skills and knowledge to analyze and design digital signal processing systems.。
Getting Started with the LabVIEW DSP Module Version 1.0ContentsIntroduction (1)Launching LabVIEW Embedded Edition and Selecting the Target (2)Looking at the Front Panel and Block Diagram (3)Running the VI (5)Where to Go for Support.........................................................................6IntroductionUse this tutorial to learn how to create, build, download, and run a DSP VI on a digital signal processor (DSP).This tutorial assumes you are familiar with basic LabVIEW concepts. Refer to the Getting Started with LabVIEW manual, available by selecting Start»Programs»National Instruments»LabVIEW 7.1 Embedded Edition»LabVIEW Manuals and opening gtstrtlv.pdf , for exercises that teach you basic LabVIEW concepts.This tutorial uses the Heterodyne VI, located in the labview embedded\ examples\EmbeddedDSP directory, and an NI SPEEDY-33 board. This example shows double-sideband modulation, also called signal heterodyning or signal mixing.Note You also can use a Texas Instruments 6711 DSK or a Spectrum Digital 6713 DSK target.™Getting Started with the LabVIEW DSP Module Launching LabVIEW Embedded Edition and Selecting the TargetComplete the following steps to launch LabVIEW Embedded Edition andselect the DSP target.1.Launch LabVIEW Embedded Edition.2.In the LabVIEW dialog box, shown in Figure 1, select SPEEDY33from the Execution Target pull-down menu.Note If you are using another supported target, select that target in the Execution Target pull-down menu instead of SPEEDY33.Figure 1. LabVIEW Dialog Box3.Click the Open button, navigate to labview embedded\examples\EmbeddedDSP , and open Heterodyne.vi.© National Instruments Corporation 3Getting Started with the LabVIEW DSP ModuleLooking at the Front Panel and Block DiagramFigure 2 shows the front panel of the Heterodyne VI. You create userinterfaces for DSP VIs in the same way you create user interfaces inLabVIEW for Windows.The waveform graph on the front panel displays the heterodyned signal.You can use the slider controls on the front panel to modify the carrierfrequency and baseband frequency of the signal.Figure 2. Heterodyne VI Front PanelSelect Window»Show Block Diagram and look at the VIs that theHeterodyne VI uses.Tip Press the <Ctrl-E> keys to switch from the front panel to the block diagram or fromthe block diagram to the front panel.Getting Started with the LabVIEW DSP Module Figure 3 shows the block diagram of the Heterodyne VI. The following VIs are used in the Heterodyne VI:•Simulate Signal Express VI —Generates sine waves. One instancegenerates the carrier frequency, and one instance generates the baseband frequency. The two sine waves are multiplied together, which results in a mixed signal. The product of these two signals is the input to the Default AO Elemental I/O Node.•Spectral Measurements Express VI —Computes the FFT and displays the signal on a waveform graph on the front panel.•Analog Output Elemental I/O Node —Writes data to theDigital-to-Analog (D/A) converter, also known as aCODEC (coder-decoder), on the SPEEDY-33 target. You canconfigure how the VI writes data to the analog output bydouble-clicking the Elemental I/O Node.Figure 3.Heterodyne VI Block DiagramRunning the VIClick the Run button to build, download, and run the DSP VI on theSPEEDY-33 target. When you click the Run button, the LabVIEW DSPModule Status Monitor window, shown in Figure 4, appears and displaysthe progress of the build, download, and execution of the DSP VI on theSPEEDY-33 target.Figure 4. LabVIEW DSP Module Status Monitor WindowWhen the VI is running on the DSP target, the front panel moves to thefront. You can modify the carrier frequency and baseband frequency usingthe slider controls on the front panel. When you change the carrierfrequency or baseband frequency, the DSP VI writes the values to the DSPtarget at run time without modifying any of the other code. The waveformgraph on the front panel shown in Figure 2 displays the frequency responseof the signal. If you plug in speakers or headphones to the analog output onthe SPEEDY-33 board, you also can hear the changes to the carrierfrequency and baseband frequency.Click the Stop button to stop the VI.Refer to the labview embedded\examples\EmbeddedDSP directoryfor additional DSP Module examples.© National Instruments Corporation5Getting Started with the LabVIEW DSP ModuleWhere to Go for SupportThe National Instruments Web site is your complete resource for technicalsupport. At /support you have access to everything fromtroubleshooting and application development self-help resources to emailand phone assistance from NI Application Engineers.National Instruments corporate headquarters is located at11500 North Mopac Expressway, Austin, Texas, 78759-3504.National Instruments also has offices located around the world to helpaddress your support needs. For telephone support in the United States,create your service request at /support and follow the callinginstructions or dial 512 795 8248. 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信号与系统g ySignals and Systems/eclass/控制系本科教学平台：http://www cse zju edu cn/eclass/搜索课程：信号与系统（乙）浙江大学控制科学与工程学系¾信号与系统所需的预修课程¾数学分析（微积分）、常微分方程、复变函数、电路原理等¾信号与系统课程的研究内容（1）信号的特性（包括连续和离散时间信号）信号的时域特性信号的频域特性：频谱（幅度谱，相位谱）等信号的变换域复频域信号的变换域：复频域（2）系统的特性系统的特性：线性、时不变、因果、稳定等系统的特性线性时不变因果稳定等刻画系统的参量：冲激响应、系统函数、频率响应等2¾目的:–介绍信号与系统的基本概念、理论–学习如何分析信号特性和线性时不变系统¾要求:–预习,听课和复习–按时完成所布置的作业（重要！每周上课时交上一次作业）¾评分标准–平时成绩（到课、作业、回答问题以及课堂练习）: 20-30%–期末考试: 70-80%¾答疑：课间、课程网站答疑论坛、电子邮件、电话均可3参考书目(Second Edition) 1998年，清华大学出版社•PRENTICE HALL(英文版）(S d Editi)1998年清华大学出版社PRENTICE HALL(英文版）Samebook¾Oppenheim A V, Willsky A S, Nawab S H.刘树棠译.信号与系统(第二版), 西安: 西安交通大学出版社, 1998.¾Oppenheim A V, Schafer R W, Buck J R.刘树棠,黄建国译.离散时间信号处理(第二版), 西安: 西安交通大学出版社, 2001.¾于慧敏主编. －－教材信号与系统（第二版）, 北京: 化学工业出版社, 2008.¾于慧敏,凌明芳,史笑兴,杭国强编. －－配套书信号与系统学习指导, 北京:化学工业出版社, 2004.¾其他同类书4¾本课程性质电类（弱电类）专业的专业基础课¾本课程教学内容第一章~第七章第一章信号与系统基本概念第二章LTI系统的时域分析：连续与离散信号第三章连续时间信号与系统的频域分析第四章离散时间信号与系统的频域分析第五章采样、调制与通信系统第六章信号与系统的复频域分析第七章Z变换5¾任课教师王慧hwang@130********徐祖华xuzh@135********陈曦xichen@138********周立芳lfzhou@139********lfzhou@iipc zju edu cn赵均jzhao@130********j本科教学课程中心：/eclass/搜索课程：信号与系统（乙）6本科教学课程网站：/eclass浙江大学控制科学与工程学系7资源下载浙江大学控制科学与工程学系8浙江大学控制科学与工程学系9号系g y信号与系统Signals and Systems第一章信号与系统的基本概念第章Chapter 1 Signals and Systems Basic Concepts浙江大学控制科学与工程学系本章主要内容（0）引言(Introduction)（1）信号的基本概念（2）连续时间与离散时间的基本信号（3）复指数信号与正弦信号（4）信号的运算与自变量变换（5）系统的描述及系统的基本性质基本概念——引言(0)消息运动或状态变化的直接反映待传输与处理的原始对象之含意如语音9消息（Message)、信息（Information) 、信号（signal ）消息：运动或状态变化的直接反映、待传输与处理的原始对象之含意，如语音、基本概念——引言(1)9重要性：三大资源（能源、材料、信息）9信息化——信息的流通、积累、处理和利用。
专业英语复习Lesson3Microprocessors (1)Lesson4Operational Amplifiers (2)Lesson8Clock Sources (3)Lesson12Personal Computer Systems (4)Lesson13Overview of Modern Digital Design (5)Lesson16Basic Concepts of DSP (6)Lesson19High Fidelity Audio (8)Lesson22Digital Image Fundamentals (9)Lesson25Choosing the right core (10)Lesson26Design Languages for Embedded Systems (11)Lesson27Choosing a Real-Time Operating System (12)Lesson28Signal Sources (13)Lesson3Microprocessors1.micron是“微米(百万分之一米)”2.data width是指算术逻辑单元ALU的字长3.MIPS Million Instructions Per Second每秒百万条指令4.Reset复位5.tri-state buffer三态缓冲器A tri-state buffer is a device that allows you to control when an output signal makes it to the bus.When the tri-state buffer's control bit is active,the input of the device makes it to the output.When it's not active,the output of the device is Z,which is high-impedance or,equivalently,nothing.There is no electrical signal is allowed to pass to the output.6.PipeliningA technique where the microprocessor fetches the next instruction before completing execution of the previous instruction,in order to increase processing speed.）流水线是一种在前一条指令全部执行完之前就开始取下一条指令，以提高处理速度的技术。
DSP 实训讲义目录：第一章电子系统设计总论第一节电子系统设计方法第二节电子系统的调试、组装与总结第三节电子系统的电磁兼容第二章DTMF信号发生和接受器系统设计第一节什么是DTMF信号第二节DTMF信号发生器的要求第三节DTMF信号检测方法第三章DTMF信号发生/检测器的设计第一节基于DSP的DTMF信号发生器硬件设计第二节基于DSP的DTMF 信号检测器硬件设计第三节DTMF信号发生器软件设计第四节DTMF 信号检测器软件设计附录一 protel 简介附录二 CCS2.0简介第一章电子系统设计总论从一般系统到电子系统以系统的观点分析电子系统，那么一个电子系统应该有输入，输出，以及输入输出之间的映射关系，如果，输入和输出之间的映射关系，那么输入输出之间有：对于一个物理可实现的系统来说首先确定的是系统输入输出之间的映射关系对于一个系统来说往往工作在某种环境或者是某些环境下因此往往要求系统能在这种环境下可靠工作也就是环境在一定范围变化的情况下系统输入输出之间的关系还要能保持。
因此在设计一个系统的时候应该考虑两个至少是两个特性：一系统输入和输出之间的关系这个关系可以说就是系统要完成的功能或者说是任务二当系统的工作环境在一定范围变化的时候，系统仍然能够完成设计时的输入和输出之间关系的能力这种能力就叫做可靠性电子系统的设计时应考虑的基本问题在电子系统设计阶段应考虑以下两个问题：一、功能设计二、可靠性设计电子系统的功能是一个电子系统的主要特性，在设计的时候是设计人员主要考虑问题。
设计一个电子系统是为了能在一定的环境和一定的时间段内完成一定的任务，因此设计者在设计电子系统的时候不但要考虑电子系统功能，还要考虑设计的电子系统能不能在规定的环境和时间段上完成设计的功能，也就是要考虑设计的电子系统在一定的环境变化范围内和期望的时间长度上能不能可靠的完成设计时的功能因此电子系统的可靠性是电子系统在规定的时间和环境条件下完成设计的功能的能力，度量可靠性能力的指标就是可靠性度量电子系统功能设计方法电子系统设计方法一般有：A 自下向上设计方法，B 自顶向下设计方法电子系统传统的设计方法是自下向上设计方法这种方法是采用中小规模集成电路和分立元件对电路板设计，采用这种方法对一个复杂电子系统进行设计的时候往往是先设计好底层的电路然后搭积木一般用设计好的底层电路搭建复杂的电子系统。
Unit 9 数字信号和信号处理Unit 9-1第一部分：数字信号处理数字信号处理（DSP）是研究数字表示的信号以及这些信号的处理方法。
数字信号处理和模拟信号处理是信号处理的子领域。
数字信号处理包括音频及语音信号处理、声纳和雷达信号处理、传感器阵列处理、谱估计、统计信号处理、图像处理、通信信号处理、生物医学信号处理等子领域。
数字信号处理的目标通常是测量连续的真实世界的模拟信号或对其滤波，因此，第一步常常是使用模数转换器将信号从模拟形式转换成数字形式。
通常，要求的输出信号为另一个模拟输出信号，这就需要数模转换器。
数字信号处理的算法有时通过使用专用计算机来实现，它们（专用计算机）利用被称为数字信号处理器的专用微处理器（简称DSP）。
这些数字信号处理器实时处理信号，通常是针对具体目的而设计的专用集成电路（ASIC）。
当灵活性和快速开发比大批量生产的成本更重要时，DSP算法也可以用现场可编程门阵列来实现。
数字信号处理域在数字信号处理中，工程师通常在下面几个域的一个域中来研究数字信号：时域（一维信号），空域（多维信号），频域，自相关域以及小波域。
他们按照某些依据来猜测（或试验不同的可能性）那一个域能够最好地表示信号的本质特性来选择在其中进行信号处理的域。
从测量设备得到的样本序列产生（信号的）时域或空域表示，而离散Fourier变换则产生频域表示即频谱。
自相关定义为信号与其自身经过时间或空间间隔变化后的互相关。
信号采样随着计算机应用的增长，数字信号处理的使用和需求日益增多。
为了能够在计算机上使用模拟信号，必须使用模数转换器（ADC）对其进行数字化。
采样通常分两步实现：离散化和量化。
在离散化阶段，信号空间被分割为相等的区间，用相应区间的代表性信号值代替信号本身。
在量化阶段，用有限集中的值来近似代表性的信号值。
为了能够正确地重建被采样的模拟信号，必须满足奈奎斯特-香农采样定理。
定理规定：采样频率必须大于两倍的信号带宽。
Digital Signal Processing Digital Signal Processing (DSP) is a crucial aspect of modern technology, encompassing a wide range of applications from audio and image processing to telecommunications and medical imaging. However, it also presents a myriad of challenges and issues that need to be addressed in order to ensure its effectiveness and reliability. One of the key problems in DSP is the trade-off between accuracy and computational complexity. As the demand for higher accuracy and faster processing speeds increases, engineers and researchers are faced with the challenge of developing algorithms and hardware that can meet theseconflicting requirements. Another significant problem in DSP is the issue of signal distortion and noise. In real-world applications, signals are often corrupted by various forms of interference, such as electrical noise, environmental factors, and imperfect transmission channels. This can lead to inaccuracies and errors in the processed signals, which can have serious consequences in critical applications such as medical diagnostics or telecommunications. As a result, researchers are constantly striving to develop new techniques and algorithms to mitigate the effects of signal distortion and noise in DSP systems. Furthermore, the implementation of DSP algorithms and systems also presents challenges in terms of hardware and software integration. Developing efficient and reliable DSP hardware requires a deep understanding of both the algorithmic and architectural aspects of signal processing, as well asthe ability to optimize performance and power consumption. Similarly, the integration of DSP algorithms into software applications requires careful consideration of real-time processing constraints, memory management, and compatibility with different operating systems and platforms. In addition to technical challenges, ethical and societal considerations also play a significant role in the development and deployment of DSP technologies. For example, the useof DSP in surveillance and security systems raises concerns about privacy andcivil liberties, as well as the potential for misuse and abuse of these technologies. Similarly, the use of DSP in medical imaging and diagnostics raises ethical questions about the accuracy and reliability of automated diagnostic systems, as well as the potential impact on healthcare professionals and patients.Moreover, the rapid advancement of DSP technologies also poses challenges in terms of education and training. As the field continues to evolve, there is a growing need for skilled professionals who can design, implement, and maintain DSP systems. This requires a comprehensive understanding of mathematical concepts, signal processing theory, and practical implementation skills, which can be challengingto acquire and teach effectively. In conclusion, the field of digital signal processing presents a wide range of technical, ethical, and educational challenges that need to be addressed in order to ensure its continued advancement and responsible use. From the trade-off between accuracy and computational complexity to the ethical considerations of surveillance and medical applications, DSP encompasses a complex and multifaceted landscape that requires careful consideration and collaboration from researchers, engineers, policymakers, and educators alike. As we continue to push the boundaries of DSP technology, it is essential to approach these challenges with a holistic and interdisciplinary perspective, in order to realize the full potential of digital signal processing while mitigating its potential risks and drawbacks.。
预备级Unit9 Unit 10 教材分析Ugns 教材分析一．本单元在全书中的地位分析在前面八个单元学完名词的单复数、谓语的单复数、祈使句、there be 结构、表示位置关系的介词、人称代词的主宾格、形容词性物主代词、名词性物主带刺的基础上，本单元继续学习基本语法：情态动词can和must. 为下一个单元学习第一种时态“一般现在时”打基础。
二．本单元的教学重点1．情态动词can和must的用法情态动词can和must的用法并不难，就是后面跟动词原型，没有人称和数的变化。
2．学习一些重要的动词词组，如：watch TV, lusic, flde a buse a book, bring my bag here 3．学习几个重要的交际用语：Let’s go. Why not? Why not bring your dog here? 和I see.三．本单元的教学难点1．要向学生讲明，Can的含义不止一种：表示请求和许可，例如：Can I bring my bag here? You can’t play football here.表示某人做事的能力，例如：I can swim.2. must的否定形式mustn’t表示“不许”，如果用must提问，肯定回答说Yes, you must.但是否定回答要说No, you needn’t.四．解决重点难点的方法1. 对于情态动词can和must的用法，要求学生做模仿对话，进行想象造句。
2．利用Reading课文进行问答，也可以加深对情态动词用法的了解。
3．对于几个难点，则要多做练习题，加以熟练。
Unit 10l day 教材分析一．本单元在全书中的地位分析通过前面九个单元学习基础语法的学习，本单元学习第一种时态“一般现在时”，时态一直是学生不容易掌握的语法，因为如此，后两个单元的语法专题仍然是一般现在时，不断加以重复、巩固。
二．本单元的教学重点1. 掌握句型：(1) Wha?(2) What daday?(3) Wur … lesson ?(4) What time do you …?2．学习一般现在时, 并学会用一般现在时表述自己在一天中的活动。
