测控技术与仪器科技英语word范文 12页.docx

1、测控技术与仪器科技英语word范文 12页本文部分内容来自网络整理，本司不为其真实性负责，如有异议或侵权请及时联系，本司将立即删除！= 本文为word格式，下载后可方便编辑和修改！ = 测控技术与仪器科技英语篇一：测控技术与仪器科技英语第四课翻译与课文Unit 4Digital Signal Processing (DSP)Having heard a lot about digital signal processing (DSP) technology , investigate why DSP is preferred to analog circuitry for many types

2、 of operations , and discover how to learn enough to design your own DSP system .This article , the first of a series , is an opportunity to take a substantial first step towards finding answers to your question .This series is an introduction to DSP topics from the point of analog system designers

3、seeking additional tools for handing analog signal. Designers reading this series can lean about the possibilities of DSP to deal with analog signals and where to find additional sources of information and assistance. 4.1 What Is DSP?In brief, DSPs are processors or microcomputers whose hardware, so

4、ftware, and instruction sets are optimized high-speed numeric processing applications-an essential for processing digital data representing analog signals in real time. What a DSP does is straightforward. When acting as a digital filter, for example, the DSP receives digital values based on samples

5、of a signal, calculates the results of a filter function operating on these values, and provides digital values that represent the filter output; it can also provide system control signals based on properties of these values. The DSPs high-speed arithmetic and logical hardware is programmed to rapid

6、ly execute algorithms modeling the filter transformation. The combination of design elements a arithmetic operators, memory handling, instruction set, parallelism, data addressing that provide this ability forms the key difference between DSPs and other kinds of processors. Understanding the relatio

7、nship between real-time signal and DSP Calculation speed provides some background on just how special this combination is .The real-time signal comes to the DSP as a train of individualsamples from an analog-to-digital converter (ADC) .To do filtering in real-time, the DSP must complete all the calc

8、ulations and operations required for processing each samples (usually updating a process involving many previous samples ) before the next sample arrives. To perform high-order filtering of real-world signals having significant frequency content calls for really fast processors. 4.2 Why Use a DSP?To

9、 get an ideal of the type of calculations of DSP dose and get an ideal of how an analog circuit compares with a DSP system , one could compare the two systems in terms of a filter function. The familiar analog filter uses resistors ,capacitors,inductors ,amplifiers .It can be cheap and easy to assem

10、ble ,but difficult to calibrate,modify, and maintain a difficulty that increases exponentially with filter order .For many purposes, one can more easily design ,modify,and depend on filters using a DSP because the filter function on the DSP is software-based, flexible ,and repeatable.Further,to crea

11、te flexibly adjustable filter s with higher-order response requires only software modifications,with no additional hardware unlike purely analog circuits .An ideal bandpass filter,with the frequency response shown in Fig.4.1,would have the following characteristics: ? a response within the passband

12、that is completely flat with zero phase shift? infinite attenuation in the stopband.Useful additions would include:? passband tuning and width control? Stopband rolloff controlAs Fig.4.1 shows, an analog approach using second-order filters would require quite a few staggered high-Q sections; the dif

13、ficulty of tuning and adjusting it can beimagined.With DSP software ,there are two basic approaches to filter design : finite impulse response (FIR) and infinite impulse response (IIR) .The FIR filters time response to an impulse is the straightforward weighted sum of the present and a finite number

14、 of previous input samples. Having no feedback,its response to a given sample ends when the sample reaches the end of the line (Fig. 4 . 2). An FIR filters frequency response has no poles, only zeros. The IIR filter , by comparison, is called infinite because it is a recursive function:its output is

15、 a weighed sum of inputs and outputs. Since it is recursive , its response can continue indefinitely . An IIR filter frequency response has both poles and zeros. . The x(s) are the input samples, y(s) are the output samples, a(s) are input sample weighings, and b(s) are sample weighings. Nis the pre

16、sent sample time, and M and N are the number of samples programmed (the filters order). Note that the arithmetic operations indicated for both types are simply sums and products in potentially great number. In fact ,multiply-and-add is the case for many DSP algorithms that represent mathematical ope

17、rations of great sophistication and complexity. Approximating an ideal filter consists of applying a transfer function with appropriate coefficients and a high enough order , or number of taps (considering the train of input samples as tapped delay line). Fig. 4.3shows the response of a 90-tap FIR f

18、ilter compared with sharp-cutoff Chebyshev filters of various orders. The 90-tap example suggests how close the filter can come to approximating an ideal filter. Within a DSP system, programming a 90-tap FIR filter like the one in Fig. 4.3 is not a difficult task. By comparison, it would no be cost-

19、effective to attempt this level of approximation with a purely analog circuit. Another crucial point in favor of using a DSP to approximate the ideal fillter is long-term stability. With an FIR (or an IIR having sufficient resolution to avoid truncation-error buildup), the programmable DSP achieves

20、the same response,time after time. Purely analog filter responses of high order are less stable with time.Mathematical transform theory and practice are the core requirement for creating DSP application and understanding their limits. This article series walks through a few signal-analysis and-proce

21、ssing examples to introduce DSP concepts. The series also provides references to texts for further study and identifies software tools that case the development of signal-processing software. 4.3 Sampling Real-world SignalsReal-world phenomena are analog the continuously changing energy levels of ph

22、ysical processes like sound, light, heat, electricity, magnetism, A transducer converts these levels into manageable electrical voltage and current signals, and an ADC sampling frequency, of the ADC is critically important in digital processing processing of real-world signals. This sampling rate is

23、 determined by the amount of signal information that is needed for processing the signal adequately for a given application. In order for an ADC to provide enough samples to accurately describe the real-world signal, the sampling rate must be at least twice the highest-frequency component of the ana

24、log signal. For example, to accurately describe an audio signal containing frequencies up to 20kHz, the ADC must sample the signal at a minimum of 40kHz. Since arriving signal can easily contain component frequencies above 20kHz (including noise), they must be removed before sampling by feeding the

25、signal through a low-pass filter, is intend to remove the frequencies above 20kHz that could corrupt the converted signal. However, the anti-aliasing filter has a finite frequency rolloff, so additional bandwidth must be provided for the filters transition band. For example, with an inputsignal band

26、width of 20kHz, one might allow 2 to 4kHz of extra bandwidth.Figure 4.4 depicts the filter needed to reject any signals with frequencies above half of a 48kHz sampling rate.Second sample .The time between samples is the time budget for the DSP to preform all processing tasks.For the audio example ,a

27、 48kHz sample rate corresponds to a 20.833vs sampling interval. Fig.4.5 relates the the analog signal and digital sample rate .图Next consider the relation between the speed of the DSP and complexity of the algorithm (the software containing the transform or other set of numeric operations ).Complex

28、algorithm require more processing tasks.Because the time between samples is fixed ,the higher complexity calls for faster processing .For example ,suppose that the algorithm requires 50 processing operations to be performed between samples .Using the previous examples 48kHz sampling rate (20.833 vs

29、sampling interval),one can calculate the minimum required DSP processor speed ,in millions of operations per second (MOPS) as follow: DSP Speed=Operations50=2.4 mops SamplingInterval20.833vs 数字信号处理器因为听到了很多关于数字信号处理(DSP)技术, 你可能想找出是什么可以用DSP, ,并发现如何学习足够你自己设计DSP系统。本文,系列的第一,是一个机会来承担高额的第一步,寻找答案到你的问题, 。本系列介

30、绍DSP主题而言,从模拟系统设计师寻求额外的工具将模拟信号. 设计者可以依赖阅读本系列文章对可能性的DSP处理模拟信号和在哪里能够找到更多的信息来源和援助。 4.1 什么是数字信号处理器总之, DSP 处理器或者是微机的硬件、软件和指令集的优化高速数字处理必不可少的,模拟信号处理数字数据代表在真正的时间, 什么是简单易懂的DSP, 当作为数字, 例如,DSP接收数字值基于样本的一个信号,计算结果一个过滤器的功能操作这些值,并提供数字值,代表了过滤器的输出, 它还可以提供系统控制信号基于这些值的属性, DSP的高速算术和逻辑的硬件被编程来迅速执行算法建模过滤器转换。 设计元素的组合一个算术操作符

31、,内存处理、指令集、并行性、数据处理提供这种能力形式的关键区别dsp和其他类型的处理器。了解实时信号之间的关系和DSP计算速度提供了一些背景对这种组合是有多么特别, 。实时信号来DSP作为个人的样本训练的模数转换器(ADC). 做实时过滤,DSP必须完成所有操作所需的计算和处理每个样本(通常是更新过程涉及许多之前的样本)之前到达下一个示例。执行高阶过滤现实世界的信号频率有重要内容要求真正的快速处理器。 4.2为什么使用一个数传信号处理器？得到数传信号处理器剂量的计算的类型的理想而且得到一个类比线路如何与一个数传信号处理器系统相较的理想，一会在一个过滤器功能方面比较这两个系统。熟悉类比过滤器使用

32、电阻、电容器、授职者，喇叭筒。集合能是廉宜、容易的，但是困难的校正，修正，而且维持一种指数地以过滤器次序增加的困难。对于许多目的，因为在数传信号处理器上的过滤器功能是以软件为基础的、有柔性、和可重复的，所以一个罐子更容易设计，修正，而且取决于使用一个数传信号处理器的过滤器。更进一步，用比较高次序回应产生易曲可调整过滤器 s 需要唯一的软件修正，藉由没有另外硬件不像纯粹地类比线路。一个藉由在 Fig.4.1 被显示的频率回应的理想通带过滤器会有下列的特性： 在通带里面的一个回应哪一是完全平坦的与零状态变化在阻带中的无限变薄。有用的附加会包括：通带调音和宽度控制阻带滚边控制作为 Fig.4.1 表，使用第二次序的过滤器的类比方法会需要相当多的蹒跚高 Q 区段；调音的困难而且调整它能被想像。由数传信号处理器软件，有两基本方法要过滤设计：有限的冲动回应 （枞树）和无限推动回应 （IIR）。对一种冲动的枞树过滤器的时间回应是礼物的笔直的重量总数和有限数字早先的输入抽取样品。没有回应，当样品达成 线的结束 时，它的回应对一个给定的样品结束。 (图 4.2)枞树过滤器的频率回应没有杆，只有零。因为它是一个回归的功能，所以被比较的 IIR 过滤器叫做无限。因为它是回归的，它的回应能不确定继续。IIR 过滤器频率回应有杆和零。 x （s）是输入样品， y（s）是输出样