專(zhuān)業(yè)英語(yǔ)翻譯之?dāng)?shù)字信號(hào)處理

1、Signal processingSignal processing is an area of electrical engineering and applied mathematics that deals with operations on or analysis of signals, in either discrete or continuous time, to perform useful operations on those signals. Signals of interest can include sound, images, time-varying meas

2、urement values and sensor data, for example biological data such as electrocardiograms, control system signals, telecommunication transmission signals such as radio signals, and many others. Signals are analog or digital electrical representations of time-varying or spatial-varying physical quantiti

3、es. In the context of signal processing, arbitrary binary data streams and on-off signalling are not considered as signals, but only analog and digital signals that are representations of analog physical quantities.HistoryAccording to Alan V. Oppenheim and Ronald W. Schafer, the principles of signal

4、 processing can be found in the classical numerical analysis techniques of the 17th century. They further state that the "digitalization" or digital refinement of these techniques can be found in the digital control systems of the 1940s and 1950s.2Categories of signal processingAnalog sign

5、al processingAnalog signal processing is for signals that have not been digitized, as in classical radio, telephone, radar, and television systems. This involves linear electronic circuits such as passive filters, active filters, additive mixers, integrators and delay lines. It also involves non-lin

6、ear circuits such as compandors, multiplicators (frequency mixers and voltage-controlled amplifiers), voltage-controlled filters, voltage-controlled oscillators and phase-locked loops.Discrete time signal processingDiscrete time signal processing is for sampled signals that are considered as defined

7、 only at discrete points in time, and as such are quantized in time, but not in magnitude.Analog discrete-time signal processing is a technology based on electronic devices such as sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. Th

8、is technology was a predecessor of digital signal processing (see below), and is still used in advanced processing of gigahertz signals.The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without

9、taking quantization error into consideration. Digital signal processingDigital signal processing is for signals that have been digitized. Processing is done by general-purpose computers or by digital circuits such as ASICs, field-programmable gate arrays or specialized digital signal processors (DSP

10、 chips). Typical arithmetical operations include fixed-point and floating-point, real-valued and complex-valued, multiplication and addition. Other typical operations supported by the hardware are circular buffers and look-up tables. Examples of algorithms are the Fast Fourier transform (FFT), finit

11、e impulse response (FIR) filter, Infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters1.Digital signal processingDigital signal processing (DSP) is concerned with the representation of signals by a sequence of numbers or symbols and the processing of thes

12、e 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 process

13、ing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, etc.The goal of DSP is usually to measure, filter and/or compress continuous real-world analog signals. The first step is usually to convert the signal from an analog to a digital fo

14、rm, by sampling it using an analog-to-digital converter (ADC), which turns the analog signal into a stream of numbers. However, often, the required output signal is another analog output signal, which requires a digital-to-analog converter (DAC). Even if this process is more complex than analog proc

15、essing and has a discrete value range, the application of computational power to digital signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression.1DSP algorithms have long been run on

16、standard computers, on specialized processors called digital signal processors (DSPs), 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 microprocess

17、ors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial apps such as motor control), and stream processors, among others.22. DSP domainsIn DSP, engineers usually study digital signals in one of the following domains: time domain (one-dimensional signals), spati

18、al 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.

19、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 i

20、ntervals of time or space.3. Signal samplingMain article: Sampling (signal processing)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. Sampl

21、ing 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 replacing the signal with representative signal of the corresponding equivalence class. In the quant

22、ization stage the representative signal values are approximated by values from a finite set.The NyquistShannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency of the signal; but requires an infi

23、nite number of samples . In practice, the sampling frequency is often significantly more than twice that required by the signal's limited bandwidth.A digital-to-analog converter is used to convert the digital signal back to analog. The use of a digital computer is a key ingredient in digital con

24、trol systems.4. Time and space domainsMain article: Time domainThe most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Digital filtering generally consists of some linear transformation of a number of surrounding samples a

25、round 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

26、 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 inpu

27、t 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 "unstab

28、le". A stable filter produces an output that 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

29、only the input signals, 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. Filters can be represented by block diagrams which can then be used to derive a

30、sample processing algorithm to implement the filter using hardware instructions. 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 a digital filter to any given input may be calcula

31、ted by convolving the input signal with the impulse response.5. Frequency domainMain article: 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

32、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 det

33、ermine which frequencies are present in the input signal and which are missing.In addition to frequency information, phase information is often needed. This can be obtained from the Fourier transform. With some applications, how the phase varies with frequency can be a significant consideration.Filt

34、ering, particularly in non-realtime 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(n log n) operation, and can give essentially any filter shape including excellent approximations to brickwall filter

35、s.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, then applies another Fourier transform. This emphasizes the frequency components with smaller magnitude while retaining

36、 the order of magnitudes of frequency components.Frequency domain analysis is also called spectrum- or spectral analysis.6. Z-domain analysisWhereas analog filters are usually analysed on the s-plane; digital filters are analysed on the z-plane or z-domain in terms of z-transforms.Most filters can b

37、e described in Z-domain (a complex number superset of the frequency domain) by their transfer functions. A filter may be analysed in the z-domain by its characteristic collection of zeroes and poles.7. ApplicationsThe main applications of DSP are audio signal processing, audio compression, digital i

38、mage 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 applic

39、ations, 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, MP3 compression, image manipulation, high fidelity loudspeaker crossovers and equalization, an

40、d audio effects for use with electric guitar amplifiers8. ImplementationDigital signal processing is often implemented using specialised microprocessors such as the DSP56000, the TMS320, or the SHARC. These often process data using fixed-point arithmetic, although some versions are available which u

41、se floating point arithmetic and are more powerful. For faster applications FPGAs3 might be used. Beginning in 2007, multicore implementations of DSPs have started to emerge from companies including Freescale and Stream Processors, Inc. For faster applications with vast usage, ASICs might be designe

42、d specifically. For slow applications, a traditional slower processor such as a microcontroller may be adequate. Also a growing number of DSP applications are now being implemented on Embedded Systems using powerful PCs with a Multi-core processor.翻譯信號(hào)處理信號(hào)處理是電氣工程與應(yīng)用數(shù)學(xué)領(lǐng)域，在離散的或連續(xù)時(shí)間域處理和分析信號(hào)，以對(duì)這些信號(hào)進(jìn)行所需的

43、有用的處理。這些信號(hào)可以包括聲音、圖像、時(shí)變實(shí)測(cè)值與傳感器的數(shù)據(jù),例如生物資料如心電圖、控制系統(tǒng)信號(hào),電信傳遞訊號(hào)如無(wú)線(xiàn)電信號(hào),以及其他許多種形式。模擬或數(shù)字信號(hào)代表空間變換或者時(shí)變的物理量。在信號(hào)處理中，任意二進(jìn)制數(shù)據(jù)流、開(kāi)關(guān)信號(hào)沒(méi)有被作為實(shí)質(zhì)的信號(hào)，而是被當(dāng)做代表模擬物理量的模擬和數(shù)字信號(hào)。發(fā)展歷史根據(jù)Alan V. Oppenheim和Ronald W. Schafer的研究，信號(hào)處理可以在十七世紀(jì)的經(jīng)典數(shù)據(jù)分析中被發(fā)現(xiàn)。他們進(jìn)一步研究說(shuō)明這種技術(shù)的數(shù)字化和數(shù)字精度在二十世紀(jì)的四十年代到五十年代的數(shù)控領(lǐng)域都得到了應(yīng)用。信號(hào)處理的類(lèi)別模擬信號(hào)處理模擬信號(hào)處理是針對(duì)那些沒(méi)有被數(shù)字化的信號(hào)所做

44、的處理，例如在老式的電臺(tái)、 、雷達(dá)和電視系統(tǒng)中的信號(hào)。這包括線(xiàn)性電路,如無(wú)源濾波器、有源濾波器、累加器、集成商和延遲線(xiàn)。同時(shí)也涉及到非線(xiàn)性電路,如(混頻器、壓控放大器、壓控過(guò)濾器、壓控振蕩器、鎖相環(huán)等。離散時(shí)間信號(hào)處理離散時(shí)間信號(hào)處理是針對(duì)在離散時(shí)間點(diǎn)上采樣的信號(hào)，但它們只是時(shí)間上離散，而在幅度上并不離散。模擬離散時(shí)間信號(hào)處理，如采樣和保持電路，模擬時(shí)分多路復(fù)用器，模擬延遲線(xiàn)和模擬反饋移位寄存器的電子裝置為基礎(chǔ)的技術(shù)。這項(xiàng)技術(shù)是一種數(shù)字信號(hào)處理見(jiàn)下文的前身，至今依然是在千兆赫信號(hào)先進(jìn)的加工使用。在離散時(shí)間信號(hào)處理概念也指的是一個(gè)理論學(xué)科，它建立了數(shù)字信號(hào)處理的數(shù)學(xué)基礎(chǔ)，而不考慮量化誤差。數(shù)字信

45、號(hào)處理數(shù)字信號(hào)處理是已經(jīng)數(shù)字化的信號(hào)。加工是由通用電腦或?qū)Ｓ眉呻娐返?，現(xiàn)場(chǎng)可編程門(mén)陣列或?qū)ｉT(mén)的數(shù)字信號(hào)處理器DSP芯片數(shù)字電路。典型的算術(shù)運(yùn)算包括定點(diǎn)和浮點(diǎn)，實(shí)數(shù)和復(fù)數(shù)，乘法和加法。由硬件支持的其他典型的操作循環(huán)緩沖器和查找表。對(duì)算法的例子是快速傅立葉變換FFT，有限脈沖響應(yīng)FIR濾波器，無(wú)限脈沖響應(yīng)IIR濾波器，以及諸如維納和卡爾曼濾波自適應(yīng)濾波器。1.數(shù)字信號(hào)處理數(shù)字信號(hào)處理DSP是關(guān)注的信號(hào)通過(guò)一組數(shù)字或符號(hào)序列，這些信號(hào)處理的代表性。數(shù)字信號(hào)處理和模擬信號(hào)處理是信號(hào)處理的子領(lǐng)域。 DSP的包括像子字段：音頻和語(yǔ)音信號(hào)處理，聲納和雷達(dá)信號(hào)處理，傳感器陣列處理，譜估計(jì)，統(tǒng)計(jì)信號(hào)處理，數(shù)字

46、圖像處理，通信信號(hào)處理，系統(tǒng)控制，生物醫(yī)學(xué)信號(hào)處理，地震數(shù)據(jù)處理。 DSP的目標(biāo)通常衡量，篩選器和/或壓縮連續(xù)現(xiàn)實(shí)世界的模擬信號(hào)。第一步通常是從模擬轉(zhuǎn)換到數(shù)字信號(hào)的形式通過(guò)抽樣它使用一個(gè)模擬數(shù)字轉(zhuǎn)換器ADC，它變成了數(shù)字流的模擬信號(hào)。不過(guò)，通常情況下，所需的輸出信號(hào)是另一個(gè)模擬輸出信號(hào)，這需要一個(gè)數(shù)字至模擬轉(zhuǎn)換器DAC。即使這個(gè)過(guò)程比模擬處理復(fù)雜，離散值范圍內(nèi)，計(jì)算能力為數(shù)字信號(hào)處理應(yīng)用允許通過(guò)模擬處理諸多優(yōu)點(diǎn)，在許多應(yīng)用，如錯(cuò)誤檢測(cè)和校正，以及數(shù)據(jù)傳輸，壓縮。 DSP算法一直運(yùn)行在標(biāo)準(zhǔn)的電腦，專(zhuān)用處理器上所謂的數(shù)字信號(hào)處理器DSP或?qū)Ｓ玫忍囟☉?yīng)用集成電路ASIC的硬件。今天，有更多的數(shù)字信號(hào)

47、處理技術(shù)包括更強(qiáng)大的通用微處理器，現(xiàn)場(chǎng)可編程門(mén)陣列FPGA，數(shù)字信號(hào)控制器主要用于工業(yè)，如馬達(dá)控制應(yīng)用程序和流處理器使用等。2.DSP的領(lǐng)域在數(shù)字信號(hào)處理器中，數(shù)字信號(hào)的研究工程師通常在以下領(lǐng)域之一：時(shí)域一維信號(hào)，空間域多維信號(hào)，頻域，自相關(guān)域和小波域。他們選擇的域的處理由作出知情預(yù)測(cè)或通過(guò)嘗試不同的可能性，以最能代表該域的信號(hào)的本質(zhì)特征的信號(hào)。一個(gè)從一個(gè)測(cè)量裝置的樣品順序會(huì)產(chǎn)生一個(gè)時(shí)間或空間域表示，而一個(gè)離散傅立葉變換產(chǎn)生頻域信息，那就是頻譜。自相關(guān)被定義為交叉信號(hào)的相關(guān)性與本身在不同的時(shí)間或空間的間隔。3.信號(hào)采樣主要文章：抽樣信號(hào)處理 隨著越來(lái)越多地使用電腦的使用情況和數(shù)字信號(hào)處理的需要有所增加。為了使用上，它必須與一個(gè)模擬數(shù)字轉(zhuǎn)換器數(shù)字化電腦模擬信號(hào)。抽樣通常分兩個(gè)階段進(jìn)行，離散化和量化。在離散化階段，對(duì)信號(hào)的空間劃分為等價(jià)類(lèi)和量化是由替換相應(yīng)的等價(jià)類(lèi)的代表信號(hào)的信號(hào)輸出。在量化階段，代表信號(hào)值是近似的值是從一個(gè)有限集合。 奈奎斯特- Shannon采樣定理指出，一個(gè)信號(hào)可以準(zhǔn)確地從它的采樣中重建如果采樣頻率大于兩倍的信號(hào)最高頻率更高，但是需要無(wú)限多的樣本。在實(shí)踐中，采樣頻