数字信号专业英语翻译.docx
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数字信号专业英语翻译.docx
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数字信号专业英语翻译
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:
2012020217
Digital Signal Processing
1、Introduction
Digital signal processing (DSP) is concerned with the representation of the signals by a 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 subfields like audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, 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 analog 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 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 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 signal 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, 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 general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial applications such as motor control), and stream processors, among others.
In 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 from 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.
2、Signal Sampling
With the increasing use of computers the usage of and need for digital signal processing 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 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 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 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 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 use 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 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 converges to a constant value with time, or remains bounded within a finite interval. 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, 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.
4、Frequency Domain
Signals 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 determine which frequencies are present in the input signal and which are missing.
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 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 frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain Fourier transform, takes the logarithm, then applies another Fourier transform. This emphasizes the frequency components with smaller magnitude while retaining the order of magnitudes of frequency components.Frequency domain analysis is also called spectrum or spectral analysis.
5、signalprocessing,
Signalusuallyneedindifferentways.Forexample,fromasensoroutputsignalmaybecontaminatedtheredundantelectrical"noise".Electrodeisconnectedtoapatient'schest,electrocardiogram(ecg)ismeasuredbytheheartandothermusclesactivitycausedbysmallvoltagevariation.Duetothestrongeffectelectricalinterferencefromthepowersupply,signalpickedupthe"main"isusuallyadopted.Processingsignalfiltercircuitcaneliminateoratleastreduceunwantedpartofthesignal.Now,moreandmore,isbytheDSPtechnologytoextractthesignalfiltertoimprovethequalityofsignalorimportantinformation,ratherthantheanalogelectronictechnology.
6、thedevelopmentofDSP
Thedevelopmentofdigitalsignalprocessing(DSP)inthe1960stolargeNumbersofdigitalcomputingapplicationsusingfastFouriertransform(FFT),whichallowsthefrequencyspectrumofasignalcanbequicklycalculated.Thesetechniqueshavenotbeenwidelyusedatthetime,becausesuitablecomputingequipmentisusuallyonlyinuniversityandotherresearchinstitutionscanbeused.
7、thedigitalsignalprocessor(DSP)
Inthelate1970sandearly1980stheintroductionofmicroprocessormakesDSPtechnologyisusedinthewiderrange.Generalmicroprocessor,suchasIntelx86family,however,isnotsuitableforthecalculationofDSPintensivedemand,withtheincreaseofDSPimportanceinthe1980sledtoseveralmajorelectronicsmanufacturers(suchasTexasinstruments,analogdevicesandMOTOROLA)todevelopadigitalsignalprocessorchip,microprocessor,specificallydesignedforuseintheoperationofthedigitalsignalprocessingrequirementstypeofarchitecture.(notethatabbreviationDSPdigitalsignalprocessing(DSP)ofdifferentmeanings,thiswordisusedindigitalsignalprocessing,avarietyoftechnicalordigitalsignalprocessor,aspecialtypeofmicroprocessorchips).Asacommonmicroprocessors,DSPisonekindhasitsownlocalinstructioncodeofprogrammabledevices.DSPchipisabletomillionsoffloatingpointoperationspersecond,astheyareofthesametypemorefamousuniversaldevice,fasterandmorepowerfulversionsareintroduced.DSPcanalsobeembeddedinacomplex"systemchip"devices,usuallyincludesanaloganddigitalcircuit.
8、theapplicationofdigitalsignalprocessors
DSPtechnologyiswidespreadinmobilephones,multimediacomputers,videorecorders,CDplayers,harddiskdrivesandcontrollerofthemodemequipment,andwillsoonreplaceanalogcircuitsinTVandtelephoneservice.DSPisanimportantapplicationofsignalcompressionanddecompression.Signalcompressionisusedfordigitalcellularphone,ineveryplaceofthe"unit"letmorephoneisprocessedatthesametime.DSPsignalcompressiontechnologynotonlymakespeoplecant
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