数字信号专业英语翻译

1、电子与通信专业英语Digital Signal Processing（英文翻译）姓名：赵 豪 班级：信工 122 学号：2012020217Digital Signal Processing1、Introduction  Digital signal processing (DSP) is concerned with the representation of the signals by a 

2、sequence of numbers or symbols and the processing of these signals. Digital signal processing and analog signal processing are subfields of signal processing. DSP includes sub

3、fields like audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, 

4、;signal processing for communications, biomedical signal processing, seismic data processing, etc. Since the goal of DSP is usually to measure or filter continuous real-world analo

5、g signals, the first step is usually to convert the signal from an analog to a digital form, by using an analog to digital converter. Often, the required output

6、 signal is another analog output signal, which requires a digital to analog converter. Even if this process is more complex than analog processing and has a discrete

7、 value range, the stability of digital signal processing thanks to error detection and correction and being less vulnerable to noise makes it advantageous over analog sig

8、nal processing for many, though not all, applications. DSP algorithms have long been run on standard computers, on specialized processors called digital signal processors (DSP)s,&#

9、160;or on purpose-built hardware such as application-specific integrated circuit (ASICs). Today there are additional technologies used for digital signal processing including more powerful&#

10、160;general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial applications such as motor control), and stream processors, among

11、60;others. In DSP, engineers usually study digital signals in one of the following domains: time domain (one-dimensional signals), spatial domain (multidimensional signals), frequency d

12、omain, 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) 

13、;as to which domain best represents the essential characteristics of the signal. A sequence of samples from a measuring device produces a time or spatial domain represent

14、ation, whereas a discrete Fourier transform produces the frequency domain information that is the frequency spectrum. Autocorrelation is defined as the cross-correlation of the sig

15、nal with itself over varying intervals of time or space. 2、Signal Sampling  With the increasing use of computers the usage of and need for digital signal processing&

16、#160;has increased. In order to use an analog signal on a computer it must be digitized with an analog to digital converter (ADC). Sampling is usually carried out

17、60;in two stages, discretization and quantization. In the discretization stage, the space of signals is partitioned into equivalence classes and quantization is carried out by 

18、;replace the signal with representative signal values are approximated by values from a finite set. The Nyquist-Shannon sampling theorem states that a signal can be exactly

19、60;reconstructed from its samples if the samples if the sampling frequency is greater than twice the highest frequency of the signal. In practice, the sampling frequency 

20、is often significantly more than twice the required bandwidth. A digital to analog converter (DAC) is used to convert the digital signal back to analog signal. The u

21、se of a digital computer is a key ingredient in digital control systems. 3、Time and Space Domains The most common processing approach in the time or space domain

22、0;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 curre

23、nt 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&

24、#160;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 equ

25、ally 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&

26、#160;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 

27、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&#

28、160;a constant value with time, or remains bounded within a finite interval. An converges to a constant value with time, or remains bounded within a finite interval. 

29、;An unstable filter can produce an output that grows without bounds, with bounded or even zero input. A“Finite Impulse Response” (FIR) filter uses only the input signal,&

30、#160;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 filt

31、ers 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

32、60;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 

33、;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

34、 to derive a sample processing algorithm to implement the filter using hardware instructions. 4、Frequency Domain Signals are converted from time or space domain to the fr

35、equency domain usually through the Fourier transform. The Fourier transform converts the signal information to a magnitude and phase component of each frequency. Often the Fou

36、rier 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 

37、;frequency domain is analysis of signal properties. The engineer can study the spectrum to determine which frequencies are present in the input signal and which are missi

38、ng. Filtering, particularly in non real-time work can also be achieved by converting to the frequency domain, applying the filter and then converting back to the time

39、0;domain. This is a fast, O (nlogn) operation, and can give essentially any filter shape including excellent approximations to brickwall filters. There are some commonly used&

40、#160;frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain Fourier transform, takes the logarithm, then applies another Fourier transform

41、. This emphasizes the frequency components with smaller magnitude while retaining the order of magnitudes of frequency components.Frequency domain analysis is also called spectrum 

42、or spectral analysis. 5、 signal processing,Signal usually need in different ways.For example, from a sensor output signal may be contaminated the redundant electrical "noise".Electrode is connected to a patient's chest, electrocardiogram (ecg) is measured by the heart an

43、d other muscles activity caused by small voltage variation.Due to the strong effect electrical interference from the power supply, signal picked up the "main" is usually adopted.Processing signal filter circuit can eliminate or at least reduce unwanted part of the signal.Now, more and more

44、, is by the DSP technology to extract the signal filter to improve the quality of signal or important information, rather than the analog electronic technology.6、the development of DSPThe development of digital signal processing (DSP) in the 1960 s to large Numbers of digital computing applications

45、using fast Fourier transform (FFT), which allows the frequency spectrum of a signal can be quickly calculated.These techniques have not been widely used at the time, because suitable computing equipment is usually only in university and other research institutions can be used.7、 the digital signal p

46、rocessor (DSP)In the late 1970 s and early 1980 s the introduction of microprocessor makes DSP technology is used in the wider range.General microprocessor, such as Intel x86 family, however, is not suitable for the calculation of DSP intensive demand, with the increase of DSP importance in the 1980

47、 s led to several major electronics manufacturers (such as Texas instruments, analog devices and MOTOROLA) to develop a digital signal processor chip, microprocessor, specifically designed for use in the operation of the digital signal processing requirements type of architecture.(note that abbrevia

48、tion DSP digital signal processing (DSP) of different meanings, this word is used in digital signal processing, a variety of technical or digital signal processor, a special type of microprocessor chips).As a common microprocessors, DSP is one kind has its own local instruction code of programmable

49、devices.DSP chip is able to millions of floating point operations per second, as they are of the same type more famous universal device, faster and more powerful versions are introduced.DSP can also be embedded in a complex "system chip" devices, usually includes analog and digital circuit

50、.8、the application of digital signal processorsDSP technology is widespread in mobile phones, multimedia computers, video recorders, CD players, hard disk drives and controller of the modem equipment, and will soon replace analog circuits in TV and telephone service.DSP is an important application o

51、f signal compression and decompression.Signal compression is used for digital cellular phone, in every place of the "unit" let more phone is processed at the same time.DSP signal compression technology not only makes people can talk to each other, and can be installed on the computer by us

52、ing the small camera make people through the monitor to see each other, and these together is the only needs to be a traditional phone line.In audio CD system, DSP technology to perform complex error detection and correction of raw data, because it is read from CD.Although some of the underlying mat

53、hematical theory of DSP technology, such as Fourier transform and Hilbert transform, the design of digital filter and signal compression, can be quite complex, and the actual implementation of these technologies needed for numerical computation is very simple, mainly including operations can be in a

54、 cheap four function calculator.A kind of structure design of the DSP chip to operate very fast, deal with the sample of the hundreds of millions of every second, and provide real-time performance: that is, to a real-time signal processing, because it is sample, and then the output signal processing

55、, such as speakers or video display.All of the DSP applications mentioned above instance, such as hard disk drives and mobile phone, for real-time operation.Major electronics manufacturers have invested heavily in DSP technology.Because they now find application in mass-market products, DSP chip ele

56、ctronic device occupies very large proportion in the world market.Sales of billions of dollars a year, and may continue to grow rapidly.DSP is mainly used of audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital commun

57、ication, radar, sonar, earthquake, and biological medicine.Concrete example is in digital mobile telephone voice compression and transmission, space balanced stereo matching, amplification area, good weather forecasts, economic forecasts, seismic data processing, and analysis of industrial process c

58、ontrol, computer generated animation film, medical image such as CAT scans and magnetic resonance imaging (MRI), MP3compression, image processing, hi-fi speaker divider and equilibrium, and compared with electric guitar amplifier using audio effect.9、the experiment of digital signal processingDigita

59、l signal processing is often use special microprocessor, such as dsp56000 TMS320, or SHARC.These often processing data using the fixed point operation, although some versions can use floating-point arithmetic and more powerful.Faster application of FPGA can flow from a slow start the emergence of ap

60、plication processor Freescale company, traditional slower processors, such as single chip may be appropriate.数字信号处理1、介绍数字信号处理(DSP)的关心表示信号序列的数字或符号和处理这些信号。数字信号处理与模拟信号处理是信号处理的分支学科。DSP包括分支学科如音频和语音信号处理、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,数字图像处理,信号处理,通信、生物医学信号处理、地震数据处理等。由于DSP的目标通常是测量或过滤连续真实世界的模拟信号,第一步通常是将信号从模拟转换

61、成数字形式,通过使用一个模拟数字转换器。通常,所需的输出信号是另一个模拟输出信号,这就需要一个数字模拟转换器。即使这个过程比模拟加工和复杂的离散值范围,数字信号处理的稳定性由于错误检测和校正和不太容易受到噪声使它优于模拟信号处理对许多人来说,虽然并不是所有的应用程序。DSP算法一直是标准的计算机上运行,在专门的处理器称为数字信号处理器(DSP),或在专用硬件如专用集成电路(asic)。今天有额外的技术用于数字信号处理包括更强大的通用微处理器,现场可编程门阵列(fpga),数字信号控制器(主要是电机控制等工业应用),和流处理器等等。 在DSP,工程师通常在以下领域之一:研究数字信号时间

62、域(一维信号),空间域(多维信号),频域,自相关域,和小波域。他们选择的域来处理信号通过一个消息灵通的猜测(或尝试不同的可能性),域最能代表信号的基本特征。一个序列样本的测量装置产生一个时间或空间域表示,而离散傅里叶变换会产生频谱的频域信息。自相关是指信号的互相关与本身在不同时间间隔的时间和空间。 2、信号采样 随着计算机的应用越来越多地使用，对数字信号处理的需要增加了。为了在电脑上使用一个模拟信号必须数字化模拟到数字转换器(ADC)。抽样通常在两个阶段进行,离散化和量化,在离散化阶段信号的空间划分等价类和量化进行了信号替换为代表的信号值从一个有限集值来近似。 N

63、yquist-Shannon抽样定理指出,一个信号可以准确重建的样品如果样品采样频率大于信号最高频率的两倍。在实践中,采样频率往往远远超过所需的带宽的两倍。 数字模拟转换器（DAC）用于将数字信号转化到模拟信号。数字计算机的使用是数字控制系统中的一个关键因素。 3、时间域和空间域 在时间或空间域中最常见的处理方法是对输入信号进行一种称为滤波的操作。滤波通常包括对一些周边样本的输入或输出信号电流采样进行一些改造。现在有各种不同的方法来表征的滤波器，例如： 一个线性滤波器的输入样本的线性变换；其他的过滤器都是“非线性”。线性滤波器满足叠加条件，即如果一个输入

64、不同的信号的加权线性组合，输出的是一个同样加权线性组合所对应的输出信号。 “因果”滤波器只使用以前的样本的输入或输出信号；而“非因果”滤波器使用未来的输入样本。一个非因果滤波器通常可以通过增加一个延迟将它变成了一个因果滤波器。 “时间不变”滤波器随着时间的推移性具有稳定特性；其他滤波器如随时间变化的自适应滤波器。 一些滤波器是“稳定”的，别的是“不稳定的”。一个稳定的滤波器产生的输出信号随时间收敛于一个恒定值，或在一个有限的时间间隔内是有界的。一种不稳定的滤波器可以产生一个没有增长界限的输出，甚至零输入有界。 “有限脉冲响应”过滤器只使用输入信号,而一个“无限脉冲响应滤波器(IIR)使用的输入信号和之前的样本输出信号。冷杉过滤器总是稳定的,虽然IIR滤波器可能不稳定。 大多数滤波器可以被描述在z域（频域的一个超集）的传递函数。如果它是一个FIR滤波器的脉冲响应和阶跃响应，