信号处理中英文对照外文翻译文献

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外文参考文献翻译-中文

外⽂参考⽂献翻译-中⽂基于4G LTE技术的⾼速铁路移动通信系统KS Solanki教授，Kratika ChouhanUjjain⼯程学院，印度Madhya Pradesh的Ujjain摘要：随着时间发展，⾼速铁路（HSR）要求可靠的，安全的列车运⾏和乘客通信。

为了实现这个⽬标，HSR的系统需要更⾼的带宽和更短的响应时间，⽽且HSR的旧技术需要进⾏发展，开发新技术，改进现有的架构和控制成本。

为了满⾜这⼀要求，HSR采⽤了GSM的演进GSM-R技术，但它并不能满⾜客户的需求。

因此采⽤了新技术LTE-R，它提供了更⾼的带宽，并且在⾼速下提供了更⾼的客户满意度。

本⽂介绍了LTE-R，给出GSM-R与LTE-R之间的⽐较结果，并描述了在⾼速下哪种铁路移动通信系统更好。

关键词：⾼速铁路，LTE，GSM，通信和信令系统⼀介绍⾼速铁路需要提⾼对移动通信系统的要求。

随着这种改进，其⽹络架构和硬件设备必须适应⾼达500公⾥/⼩时的列车速度。

HSR还需要快速切换功能。

因此，为了解决这些问题，HSR 需要⼀种名为LTE-R的新技术，基于LTE-R的HSR提供⾼数据传输速率，更⾼带宽和低延迟。

LTE-R能够处理⽇益增长的业务量，确保乘客安全并提供实时多媒体信息。

随着列车速度的不断提⾼，可靠的宽带通信系统对于⾼铁移动通信⾄关重要。

HSR的应⽤服务质量（QOS）测量，包括如数据速率，误码率（BER）和传输延迟。

为了实现HSR的运营需求，需要⼀个能够与 LTE保持⼀致的能⼒的新系统，提供新的业务，但仍能够与GSM-R长时间共存。

HSR系统选择合适的⽆线通信系统时，需要考虑性能，服务，属性，频段和⼯业⽀持等问题。

4G LTE系统与第三代（3G）系统相⽐，它具有简单的扁平架构，⾼数据速率和低延迟。

在LTE的性能和成熟度⽔平上，LTE- railway（LTE-R）将可能成为下⼀代HSR通信系统。

⼆ LTE-R系统描述考虑LTE-R的频率和频谱使⽤，对为⾼速铁路（HSR）通信提供更⾼效的数据传输⾮常重要。

语音信号处理中英文翻译

附录：中英文翻译15SpeechSignalProcessing15.3AnalysisandSynthesisJ esseW. FussellA fte r an acousti c spee ch s i gnal i s conve rte d to an ele ctri cal si gnal by a mi crophone, i t m ay be desi rable toanalyzetheelectricalsignaltoestimatesometime-varyingparameterswhichprovideinformationaboutamodel of the speech producti on me chanism. S peech a na ly sis i s the process of e stim ati ng such paramete rs. Simil arl y , g ive n some parametri c model of spee ch production and a se que nce of param eters for that m odel,speechsynthesis istheprocessofcreatinganelectricalsignalwhichapproximatesspeech.Whileanalysisandsynthesistechniques maybedoneeitheronthecontinuoussignaloronasampledversionofthesignal,mostmode rn anal y sis and sy nthesis methods are base d on di gital si gnal processing.Atypicalspeechproductionmodelisshownin Fig.15.6.Inthismodeltheoutputoftheexcitationfunctionisscaledbythegainparam eterandthenfilteredtoproducespeech.Allofthesefunctionsaretime-varying.F IGUR E 15 .6 A ge ne ra l spee ch productionmodel.F IGUR E 1 5 .7 W ave form of a spoken phone me /i/ as i nbeet.Formanymodels,theparametersarevariedataperiodicrate,typically50to100timespersecond.Mostspee ch inform ati on is containe d i n the porti on of the si gnal bel ow about 4 kHz.Theexcitationisusually modeledaseitheramixtureorachoiceofrandomnoiseandperiodicwaveform.For hum an spee ch, v oi ced e x citati on occurs w hen the vocal fol ds in the lary nx vibrate; unvoi ce d e x citati onoccurs at constri cti ons i n the vocal tract w hi ch cre ate turbulent a i r fl ow [Fl anagan, 1965] . The rel ati ve mi x ofthesetw o type s ofexcitationisterme d ‚v oicing.‛In addition,theperiodi c e xcitation i s characterizedby afundamentalfrequency,termed pitch orF0.Theexcitationisscaledbyafactordesignedtoproducetheproperampli tude or level of the spee ch si gnal . The scaled ex citati on function i s then fi ltere d to produce the properspe ctral characte risti cs. W hile the filter m ay be nonli near, i t i s usuall y m odele d as a li nearfunction.AnalysisofExcitationInasimplifiedform,theexcitationfunctionmaybeconsideredtobepurelyperiodic,forvoicedspeech,orpurel y random, for unvoi ce d. T hese tw o states correspond to voi ce d phoneti c cl asse s such as vow elsand nasalsandunvoicedsoundssuchasunvoicedfricatives.Thisbinaryvoicingmodelisanoversimplificationforsounds such as v oi ced fri cati ves, whi ch consist of a mi xture of peri odi c and random compone nts. Fi gure 15.7is an ex ample of a time w ave form of a spoke n /i/ phoneme , w hi ch is w ell m odeled by onl y pe riodi c e x citation.B oth ti me dom ai n and frequency dom ai n anal y s is te chni ques have bee n used to esti m ate the de greeofvoi ci ng for a short se gme nt or frame of spee ch. One ti me dom ain fe ature, te rme d the ze ro crossing rate,i sthenumberoftimesthesignalchangessigninashortinterval.AsshowninFig.15.7,thezerocrossingrateforvoicedsoundsisrelativ elylow.Sinceunvoicedspeechtypicallyhasalargerproportionofhigh-frequencyenergy than voi ce d spee ch, the ratio of high-fre que ncy to low -frequency e nergy is a fre que ncy dom aintechni que that provi des i nform ation on voi cing.A nothe r measure use d to estim ate the de gree of voi ci ng is the autocorrel ation functi on, w hi ch is de fine d fora sam pled speech se gment, S ,aswheres(n)isthevalueofthenthsamplewithinthesegmentoflengthN.Sincetheautocorrelationfunctionofa periodi c functi on is i tsel f pe ri odi c, voi ci ng can be e sti mated from the de gree of pe ri odi city oftheautocorrel ati on function. Fi gure 15. 8 i s a graph of the nonne gati ve te rms of the autocorrel ation functi on for a64 -ms frame of the w aveform of Fi g . 15. 7. Ex cept for the de cre ase i n amplitude w ith i ncre asi ng lag, whi chresultsfromtherectangularwindowfunctionwhichdelimitsthesegment,theautocorrelationfunctionisseento be quite pe riodi c for thi s voi ce dutterance.F IGUR E 1 5 .8 A utocorrel ati on functi on of one frame of /i/. Ifananalysisofthevoicingofthespeechsignalindicatesavoicedorperiodiccomponentispresent,another ste p i n the anal y si s process m ay be to estim ate the freque ncy ( or pe ri od) of the voi ce d component.Thereareanumberofwaysinwhichthismaybedone.Oneistomeasurethetimelapsebetweenpeaksinthetime dom ai n si gnal. For ex am ple i n Fi g . 15.7 the m aj or peaks are separate d by about 0. 00 71 s, for afundamentalfrequencyofabout141Hz.Note,itwouldbequitepossibletoerrintheestimateoffundamentalfre quency by mistaki ng the sm aller pe aks that occur betwee n the m a jor pe aks for the m aj or pe aks. Thesesmallerpeaksareproducedbyresonanceinthevocaltractwhich,inthisexample,happentobeatabouttwicethe ex citation fre quency . T his ty pe of e rror w ould re sult in an e sti m ate of pitch approxi m atel y tw i ce the corre ct fre quency.The di stance betw ee n m ajor pe ak s of the autocorrel ation functi on is a closel y rel ate d fe ature thatisfre quentl y use d to esti m ate the pitch pe ri od. In Fi g . 15. 8, the di stance between the m aj or peaks in the autocorrelationfunctionisabout0.0071s.Estimatesofpitchfromtheautocorrelationfunctionarealsosusce pti ble to mistaking the fi rst vocal track resonance for the g l ottal e x citati on frequency.The absol ute m agnitude di ffere nce functi on ( AM DF), de fi nedas,is another functi on w hi ch is often use d i n estim ating the pitch of voi ce d spee ch. A n ex ample of the AM DF isshownin Fig.15.9forthesame64-msframeofthe/i/phoneme.However,theminimaoftheAMDFisusedasanindicatorofthepitchperiod.TheAMDFhasbeenshownt obeagoodpitchperiodindicator[Rossetal.,19 74 ] and does not requi re multi pli cations.FourierAnalysisOne of the m ore comm on processe s for e stim ating the spe ctrum of a se gme nt of spee ch is the Fourie rtransform [ Oppenheim and S chafer, 1 97 5 ]. T he Fourie r transform of a seque nce is m athem ati call y de fine daswheres(n)representsthetermsofthesequence.Theshort-timeFouriertransformofasequenceisatimedependentfunction,definedasF IGUR E 1 5 .9 A bsolute m agnitude diffe rence functi on of one frame of /i/.wherethewindowfunctionw(n)isusuallyzeroexceptforsomefiniterange,andthevariablemisusedtoselectthesectionofthesequ enceforanalysis.ThediscreteFouriertransform(DFT)isobtainedbyuniformlysam pling the short-ti me Fourie r transform i n the fre quency dime nsi on. Thus an N-point DFT is computedusingEq.(15.14),wherethe setofNsamples,s(n),may have firstbeenmultiplied by a window function.Anexampleofthemagnitudeofa512-pointDFTofthewaveformofthe/i/from Fig.15.10isshowninFig.15.10.Noteforthisfi gure, the 512 poi nts in the se que nce have been m ulti plied by a Ham ming w i ndow de fi nedbyF IGUR E 1 5 .1 0 M agnitude of 51 2-point FFT of Ham mi ng window e d/i/.S ince the spe ctral characteristi cs of spee ch m ay change dram a ti call y in a fe w milli se conds, the le ngth, type,and l ocation of the wi ndow function are im portant consi derati ons. If the w indow is too long, changi ng spe ctralcharacteristicsmaycauseablurredresult;ifthewindowistooshort,spectralinaccuraciesresult.AHammingwi ndow of 16 to 32 m s durati on is com m onl y use d for spee ch analysis.S everal characte risti cs of a speech utte rance m ay be dete rmine d by ex amination of the DFT m agnitude. InFig.15.10,theDFTofavoicedutterancecontainsaseriesofsharppeaksinthefrequencydomain.Thesepeaks, caused by the peri odi c sampl ing acti on of the g lottal ex ci tation, are separated by the fundame ntalfrequencywhichisabout141Hz,inthisexample.Inaddition,broaderpeakscanbeseen,forexampleatabout300 Hz and at about 2300 Hz. T hese broad peaks, calle d formants, result from resonances in the vocaltract. LinearPredictiveAnalysisGivenasampled(discrete-time)signals(n),apowerfulandgeneralparametric modelfortimeseriesanalysisiswheres(n)istheoutputandu(n)istheinput(perhapsunknown).Themodelparametersare a(k)fork=1,p,b( l ) for l = 1, q, and G. b( 0) is assume d to be unity. Thi s m odel , describe d as an autore g ressi ve m ov ing average(ARM A)orpole-zeromodel,formsthefoundationfortheanalysismethodtermedlinearprediction.Anautoregressive(AR) orall-polemodel,forwhichallofthe‚b‛coe fficientsexceptb(0)arezero,isfrequentlyused for spee ch anal y si s [M arkel and Gray, 1976].In the standard A R formul ati on of li ne ar predi ction, the model paramete rs are sele cte d to mi ni mizethemean-squarederrorbetweenthemodelandthespeechdata.Inoneofthevariantsoflinearprediction,theautocorrelationmethod,themini mizationiscarriedoutforawindowedsegmentofdata.Intheautocorrelationmethod,minimizingthemean-squareerror of the time domain samples is equivalentto minimizing theintegratedratioofthesignalspectrumtothespectrumoftheall-polemodel.Thus,linearpredictiveanalysisisagoodmethod forspectralanalysiswheneverthesignalisproducedby an all-pole system.M ost speechsounds fi t thi s model w ell.One ke y consi deration for li near pre dicti ve anal y si s is the order of the model, p. For spee ch, if the orde ristoosmall,theformantstructureisnot well represented. If the orderis too large, pitch pulses as well asformantsbegintoberepresented.Tenth- or twelfth-order analysis is typical forspeech.Figures15.11 and15.12 provideexamplesof the spectrum produced by eighth-order and sixteenth-order linear predictiveanalysisofthe/i/waveformofFig.15.7.Figure15.11showstheretobethreeformantsatfrequenciesofabout30 0, 23 00, and 3200 Hz , whi ch are ty pi cal for an/i/.Homomorphic(Cepstral)AnalysisFor the speech m odel of Fi g. 15. 6, the e x citati on and filter i mpulse response are convol ved to produce thespeech.Oneoftheproblemsofspeechanalysisistoseparateordeconvolvethespeechintothesetw ocom ponents. Onesuch te chni que is called hom omorphi c filte ri ng [ Oppe nheim and S chafer, 1968 ]. Thecharacte risti c sy ste mfor a sy ste m for hom om orphi c deconvol ution conve rts a convolution operation to anadditi on ope ration. The output of such a characteristi c sy stem is calle d the com ple x cep str u m . The complexcepstrumisdefinedastheinverseFouriertransformofthecomplexlogarithmoftheFouriertransformoftheinput.Iftheinputseque nceisminimumphase(i.e.,thez-transformoftheinputsequencehasnopolesorzerosoutside the unit ci rcle), the se quence can be represe nted by the real portion of the transforms. Thus, the re alcepstrum can be com pute d by cal cul ati ng the inve rse Fourie r transform of the log- spe ctrum of theinput.FIGURE15.11Eighth-orderlinearpredictiveanalysisofan‚i‛.FIGURE15.12Sixteenth-orderlinearpredictiveanalysisofan‚i‛.Fi gure 1 5.1 3 show s an e x ample of the cepstrum for the voi ced /i/ utterance from Fi g. 15.7 . The cepstrum ofsuch a voi ce d utterance i s characte rized by rel ati vel y la rge v alues in the fi rst one or tw o milli se conds as w ellas。

传感器技术论文中英文对照资料外文翻译文献

传感器技术论文中英文对照资料外文翻译文献Development of New Sensor TechnologiesSensors are devices that can convert physical。

chemical。

logical quantities。

etc。

into electrical signals。

The output signals can take different forms。

such as voltage。

current。

frequency。

pulse。

etc。

and can meet the requirements of n n。

processing。

recording。

display。

and control。

They are indispensable components in automatic n systems and automatic control systems。

If computers are compared to brains。

then sensors are like the five senses。

Sensors can correctly sense the measured quantity and convert it into a corresponding output。

playing a decisive role in the quality of the system。

The higher the degree of n。

the higher the requirements for sensors。

In today's n age。

the n industry includes three parts: sensing technology。

n technology。

and computer technology。

外文文献翻译译稿和原文

外文文献翻译译稿1卡尔曼滤波的一个典型实例是从一组有限的，包含噪声的，通过对物体位置的观察序列（可能有偏差）预测出物体的位置的坐标及速度。

在很多工程应用（如雷达、计算机视觉）中都可以找到它的身影。

同时，卡尔曼滤波也是控制理论以及控制系统工程中的一个重要课题。

例如，对于雷达来说，人们感兴趣的是其能够跟踪目标。

但目标的位置、速度、加速度的测量值往往在任何时候都有噪声。

卡尔曼滤波利用目标的动态信息，设法去掉噪声的影响，得到一个关于目标位置的好的估计。

这个估计可以是对当前目标位置的估计（滤波），也可以是对于将来位置的估计（预测），也可以是对过去位置的估计（插值或平滑）。

命名[编辑]这种滤波方法以它的发明者鲁道夫.E.卡尔曼（Rudolph E. Kalman）命名，但是根据文献可知实际上Peter Swerling在更早之前就提出了一种类似的算法。

斯坦利。

施密特（Stanley Schmidt）首次实现了卡尔曼滤波器。

卡尔曼在NASA埃姆斯研究中心访问时，发现他的方法对于解决阿波罗计划的轨道预测很有用，后来阿波罗飞船的导航电脑便使用了这种滤波器。

关于这种滤波器的论文由Swerling（1958）、Kalman (1960)与Kalman and Bucy（1961）发表。

目前，卡尔曼滤波已经有很多不同的实现。

卡尔曼最初提出的形式现在一般称为简单卡尔曼滤波器。

除此以外，还有施密特扩展滤波器、信息滤波器以及很多Bierman, Thornton开发的平方根滤波器的变种。

也许最常见的卡尔曼滤波器是锁相环，它在收音机、计算机和几乎任何视频或通讯设备中广泛存在。

以下的讨论需要线性代数以及概率论的一般知识。

卡尔曼滤波建立在线性代数和隐马尔可夫模型（hidden Markov model）上。

其基本动态系统可以用一个马尔可夫链表示，该马尔可夫链建立在一个被高斯噪声（即正态分布的噪声）干扰的线性算子上的。

系统的状态可以用一个元素为实数的向量表示。

通信工程专业英语文献翻译

Multi-Code TDMA (MC-TDMA) for Multimedia Satellite Communications用于多媒体卫星通信的MC--TDMA（多码时分多址复用）R. Di Girolamo and T. Le-NgocDepartment ofa Electricl and Computer Engineering - Concordia University1455 de Maisonneuve Blvd. West, Montreal, Quebec, Canada, H3G 1M8 ABSTRACT摘要In this paper, we propose a multiple access scheme basedon a hybrid combination of TDMA and CDMA,在这篇文章中，我们提出一种基于把时分多址复用和码分多址复用集合的多址接入方案。

referred toas multi-code TDMA (MC-TDMA). 称作多码—时分多址复用The underlying TDMAframe structure allows for the transmission of variable bitrate (VBR) information,以TDMA技术为基础的帧结构允许传输可变比特率的信息while the CDMA provides inherentstatistical multiplexing.和CDMA提供固有的统计特性多路复用技术The system is studied for a multimediasatellite environment with long-range dependentdata traffic,and VBR real-time voice and video traffic研究这个系统是为了在远程环境下依赖数据传输和可变比特率的语音和视频传输的多媒体卫星通信系统 . Simulationresults show that with MC-TDMA, the data packetdelay and the probability of real-time packet loss can bemaintained low. 仿真结果表明：采用MC-TDMA的多媒体卫星通信，数据包延时和实时数据丢失的可能性可以保持很低。

DSP滤波器中英文对照外文翻译文献

中英文对照外文翻译文献(文档含英文原文和中文翻译)译文:GA算法优化IIR滤波器的设计摘要本文提出了运用遗传算法(GA)来优化无限脉冲响应数字滤波器(IIR)的设计。

IIR滤波器本质上是一个递归响应的数字滤波器。

由于IIR 数字滤波器的表面误差通常是非线性的和多峰的，而全局优化技术需要避免局部最小值。

本文提出了启发式方式来设计IIR滤波器。

GA是组合优化问题中一种功能强大的全局优化算法，该论文发现IIR数字滤波器的最佳系数可以通过GA 优化。

该设计提出低通和高通IIR数字滤波器的设计，以提供过渡频带的估计值。

结果发现，所计算出的值比可用于过滤器的在MATLAB设计FDA工具更优化。

举个例子，采用的仿真结果表明在过渡带和均方误差(MSE)的改善。

零极点的位置也被提出来用来描述系统的的稳定性，以便将结果与模拟退火(SA)的方法相比较。

关键词：数字滤波器;无限冲激响应(IIR);遗传算法(GA);优化1.说明在过去的几十年中的数字信号处理(DSP)领域已经成长太重要的理论和技术。

在DSP中，有两个重要的类型系统。

第一类型的系统是执行信号滤波的时域，因此它被称为数字滤波器。

第二类型的系统提供的信号表示频域，被称为频谱分析仪。

数字滤波是DSP的最有力的工具之一。

数字滤波器能够性能规格，最好的同时也是极其困难的，而且不可能的是，先用模拟滤波器实现。

另外，数字滤波器的特性，可以很容易地在软件控制下发生变化。

数字滤波器被分类为有限持续时间脉冲响应(FIR)滤波器或无限持续时间脉冲响应(IIR)滤波器，这取决于该系统的脉冲响应的形式。

在FIR系统中，脉冲响应序列是有限的持续时间，即，它具有非零项的数量有限。

数字无限脉冲响应(IIR)滤波器通常可以提供比其等效有限脉冲响应(FIR)滤波器更好的性能和更少的计算成本，并已成为越来越感兴趣的目标。

但是，由于IIR滤波器的误差表面通常是非线性的，多式联运，传统的基于梯度的设计方法可以很容易地陷入错误的表面。

专业英语词汇(信号与系统)

《信号与系统》专业术语中英文对照表第1 章绪论信号（signal）系统（system）电压（voltage）电流（current）信息（information）电路（circuit）网络（network）确定性信号（determinate signal）随机信号（random signal）一维信号（one–dimensional signal）多维信号（multi–dimensional signal）连续时间信号（continuous time signal）离散时间信号（discrete time signal）取样信号（sampling signal）数字信号（digital signal）周期信号（periodic signal）非周期信号（nonperiodic（aperiodic）signal）能量（energy）功率（power）能量信号（energy signal）功率信号（power signal）平均功率（average power）平均能量（average energy）指数信号（exponential signal）时间常数（time constant）正弦信号（sine signal）余弦信号（cosine signal）振幅（amplitude）角频率（angular frequency）初相位（initial phase）周期（period）频率（frequency）欧拉公式（Euler’s formula）复指数信号（complex exponential signal）复频率（complex frequency）实部（real part）虚部（imaginary part）抽样函数Sa(t)（sampling（Sa）function）偶函数（even function）奇异函数（singularity function）奇异信号（singularity signal）单位斜变信号（unit ramp signal）斜率（slope）单位阶跃信号（unit step signal）符号函数（signum function）单位冲激信号（unit impulse signal）广义函数（generalized function）取样特性（sampling property）冲激偶信号（impulse doublet signal）奇函数（odd function）偶分量（even component）奇分量（odd component）正交函数（orthogonal function）正交函数集（set of orthogonal function）数学模型（mathematics model）电压源（voltage source）基尔霍夫电压定律（Kirchhoff’s voltage law（KVL））电流源（current source）连续时间系统（continuous time system）离散时间系统（discrete time system）微分方程（differential function）差分方程（difference function）线性系统（linear system）非线性系统（nonlinear system）时变系统（time–varying system）时不变系统（time–invariant system）集总参数系统（lumped–parameter system）分布参数系统（distributed–parameter system）偏微分方程（partial differential function）因果系统（causal system）非因果系统（noncausal system）因果信号（causal signal）叠加性（superposition property）均匀性（homogeneity）积分（integral）输入–输出描述法（input–output analysis）状态变量描述法（state variable analysis）单输入单输出系统（single–input and single–output system）状态方程（state equation）输出方程（output equation）多输入多输出系统（multi–input and multi–output system）时域分析法（time domain method）变换域分析法（transform domain method）卷积（convolution）傅里叶变换（Fourier transform）拉普拉斯变换（Laplace transform）第2 章连续时间系统的时域分析齐次解（homogeneous solution）特解（particular solution）特征方程（characteristic function）特征根（characteristic root）固有（自由）解（natural solution）强迫解（forced solution）起始条件（original condition）初始条件（initial condition）自由响应（natural response）强迫响应（forced response）零输入响应（zero-input response）零状态响应（zero-state response）冲激响应（impulse response）阶跃响应（step response）卷积积分（convolution integral）交换律（exchange law）分配律（distribute law）结合律（combine law）第3 章傅里叶变换频谱（frequency spectrum）频域（frequency domain）三角形式的傅里叶级数（trigonomitric Fourier series）指数形式的傅里叶级数（exponential Fourier series）傅里叶系数（Fourier coefficient）直流分量（direct composition）基波分量（fundamental composition）n 次谐波分量（n th harmonic component）复振幅（complex amplitude）频谱图（spectrum plot（diagram））幅度谱（amplitude spectrum）相位谱（phase spectrum）包络（envelop）离散性（discrete property）谐波性（harmonic property）收敛性（convergence property）奇谐函数（odd harmonic function）吉伯斯现象（Gibbs phenomenon）周期矩形脉冲信号（periodic rectangular pulse signal）周期锯齿脉冲信号（periodic sawtooth pulse signal）周期三角脉冲信号（periodic triangular pulse signal）周期半波余弦信号（periodic half–cosine signal）周期全波余弦信号（periodic full–cosine signal）傅里叶逆变换（inverse Fourier transform）频谱密度函数（spectrum density function）单边指数信号（single–sided exponential signal）双边指数信号（two–sided exponential signal）对称矩形脉冲信号（symmetry rectangular pulse signal）线性（linearity）对称性（symmetry）对偶性（duality）位移特性（shifting）时移特性（time–shifting）频移特性（frequency–shifting）调制定理（modulation theorem）调制（modulation）解调（demodulation）变频（frequency conversion）尺度变换特性（scaling）微分与积分特性（differentiation and integration）时域微分特性（differentiation in the time domain）时域积分特性（integration in the time domain）频域微分特性（differentiation in the frequency domain）频域积分特性（integration in the frequency domain）卷积定理（convolution theorem）时域卷积定理（convolution theorem in the time domain）频域卷积定理（convolution theorem in the frequency domain）取样信号（sampling signal）矩形脉冲取样（rectangular pulse sampling）自然取样（nature sampling）冲激取样（impulse sampling）理想取样（ideal sampling）取样定理（sampling theorem）调制信号（modulation signal）载波信号（carrier signal）已调制信号（modulated signal）模拟调制（analog modulation）数字调制（digital modulation）连续波调制（continuous wave modulation）脉冲调制（pulse modulation）幅度调制（amplitude modulation）频率调制（frequency modulation）相位调制（phase modulation）角度调制（angle modulation）频分多路复用（frequency–division multiplex（FDM））时分多路复用（time–division multiplex（TDM））相干（同步）解调（synchronous detection）本地载波（local carrier）系统函数（system function）网络函数（network function）频响特性（frequency response）幅频特性（amplitude frequency response）相频特性（phase frequency response）无失真传输（distortionless transmission）理想低通滤波器（ideal low–pass filter）截止频率（cutoff frequency）正弦积分（sine integral）上升时间（rise time）窗函数（window function）理想带通滤波器（ideal band–pass filter）第4 章拉普拉斯变换代数方程（algebraic equation）双边拉普拉斯变换（two-sided Laplace transform）双边拉普拉斯逆变换（inverse two-sided Laplace transform）单边拉普拉斯变换（single-sided Laplace transform）拉普拉斯逆变换（inverse Laplace transform）收敛域（region of convergence（ROC））延时特性（time delay）s 域平移特性（shifting in the s-domain）s 域微分特性（differentiation in the s-domain）s 域积分特性（integration in the s-domain）初值定理（initial-value theorem）终值定理（expiration-value）复频域卷积定理（convolution theorem in the complex frequency domain）部分分式展开法（partial fraction expansion）留数法（residue method）第5 章策动点函数（driving function）转移函数（transfer function）极点（pole）零点（zero）零极点图（zero-pole plot）暂态响应（transient response）稳态响应（stable response）稳定系统（stable system）一阶系统（first order system）高通滤波网络（high-low filter）低通滤波网络（low-pass filter）二阶系统（second system）最小相移系统（minimum-phase system）维纳滤波器（Winner filter）卡尔曼滤波器（Kalman filter）低通（low-pass）高通（high-pass）带通（band-pass）带阻（band-stop）有源（active）无源（passive）模拟（analog）数字（digital）通带（pass-band）阻带（stop-band）佩利－维纳准则（Paley-Winner criterion）最佳逼近（optimum approximation）过渡带（transition-band）通带公差带（tolerance band）巴特沃兹滤波器（Butterworth filter）切比雪夫滤波器（Chebyshew filter）方框图（block diagram）信号流图（signal flow graph）节点（node）支路（branch）输入节点（source node）输出节点（sink node）混合节点（mix node）通路（path）开通路（open path）闭通路（close path）环路（loop）自环路（self-loop）环路增益（loop gain）不接触环路（disconnect loop）前向通路（forward path）前向通路增益（forward path gain）梅森公式（Mason formula）劳斯准则（Routh criterion）第6 章数字系统（digital system）数字信号处理（digital signal processing）差分方程（difference equation）单位样值响应（unit sample response）卷积和（convolution sum）Z 变换（Z transform）序列（sequence）样值（sample）单位样值信号（unit sample signal）单位阶跃序列（unit step sequence）矩形序列(rectangular sequence)单边实指数序列（single sided real exponential sequence）单边正弦序列（single sided exponential sequence）斜边序列（ramp sequence）复指数序列（complex exponential sequence）线性时不变离散系统（linear time-invariant discrete-time system）常系数线性差分方程（linear constant-coefficient difference equation）后向差分方程（backward difference equation）前向差分方程（forward difference equation）海诺塔（Tower of Hanoi）菲波纳西（Fibonacci）冲激函数串（impulse train）第7 章数字滤波器（digital filter）单边Z 变换（single-sided Z transform）双边Z 变换(two-sided (bilateral) Z transform)幂级数（power series）收敛（convergence）有界序列（limitary-amplitude sequence）正项级数（positive series）有限长序列（limitary-duration sequence）右边序列（right-sided sequence）左边序列（left-sided sequence）双边序列（two-sided sequence）Z 逆变换（inverse Z transform）围线积分法（contour integral method）幂级数展开法（power series expansion）z 域微分（differentiation in the z-domain）序列指数加权（multiplication by an exponential sequence）z 域卷积定理（z-domain convolution theorem）帕斯瓦尔定理（Parseval theorem）传输函数（transfer function）序列的傅里叶变换（discrete-time Fourier transform：DTFT）序列的傅里叶逆变换（inverse discrete-time Fourier transform：IDTFT）幅度响应（magnitude response）相位响应（phase response）量化（quantization）编码（coding）模数变换（A/D 变换：analog-to-digital conversion）数模变换（D/A 变换：digital-to- analog conversion）第8 章端口分析法（port analysis）状态变量（state variable）无记忆系统（memoryless system）有记忆系统（memory system）矢量矩阵（vector-matrix ）常量矩阵（constant matrix ）输入矢量（input vector）输出矢量（output vector）直接法（direct method）间接法（indirect method）状态转移矩阵（state transition matrix）系统函数矩阵（system function matrix）冲激响应矩阵（impulse response matrix）朱里准则（July criterion）。

通信类英文文献及翻译

附录一、英文原文：Detecting Anomaly Trafﬁc using Flow Data in the realVoIP networkI. INTRODUCTIONRecently, many SIP[3]/RTP[4]-based VoIP applications and services have appeared and their penetration ratio is gradually increasing due to the free or cheap call charge and the easy subscription method. Thus, some of the subscribers to the PSTN service tend to change their home telephone services to VoIP products. For example, companies in Korea such as LG Dacom, Samsung Net- works, and KT have begun to deploy SIP/RTP-based VoIP services. It is reported that more than ﬁve million users have subscribed the commercial VoIP services and 50% of all the users are joined in 2009 in Korea [1]. According to IDC, it is expected that the number of VoIP users in US will increase to 27 millions in 2009 [2]. Hence, as the VoIP service becomes popular, it is not surprising that a lot of VoIP anomaly trafﬁc has been already known [5]. So, Most commercial service such as VoIP services should provide essential security functions regarding privacy, authentication, integrity and non-repudiation for preventing malicious trafﬁc. Particu- larly, most of current SIP/RTP-based VoIP services supply the minimal security function related with authentication. Though secure transport-layer protocols such as Transport Layer Security (TLS) [6] or Secure RTP (SRTP) [7] have been standardized, they have not been fully implemented anddeployed in current VoIP applications because of the overheads of implementation and performance. Thus, un-encrypted VoIP packets could be easily sniffed and forged, especially in wireless LANs. In spite of authentication,the authentication keys such as MD5 in the SIP header could be maliciously exploited, because SIP is a text-based protocol and unencrypted SIP packets are easily decoded. Therefore, VoIP services are very vulnerable to attacks exploiting SIP and RTP. We aim at proposing a VoIP anomaly trafﬁc detection method using the ﬂow-based trafﬁc measurement archi-tecture. We consider three representative VoIP anomalies called CANCEL, BYE Denial of Service (DoS) and RTP ﬂooding attacks in this paper, because we found that malicious users in wireless LAN could easily perform these attacks in the real VoIP network. For monitoring VoIP packets, we employ the IETF IP Flow Information eXport (IPFIX) [9] standard that is based on NetFlow v9. This trafﬁc measurement method provides a ﬂexible and extensible template structure for various protocols, which is useful for observing SIP/RTP ﬂows [10]. In order to capture and export VoIP packets into IPFIX ﬂows, we deﬁne two additional IPFIX templates for SIP and RTP ﬂows. Furthermore, we add four IPFIX ﬁelds to observe packets which are necessary to detect VoIP source spooﬁng attacks in WLANs.II. RELATED WORK[8] proposed a ﬂooding detection method by the Hellinger Distance (HD) concept. In [8], they have pre- sented INVITE, SYN and RTP ﬂooding detection meth-ods. The HD is the difference value between a training data set and a testing data set. The training data set collected trafﬁc over n sampling period of duration Δ testing data set collected trafﬁc next the training data set in the same period. If the HD is close to ‘1’, this testing data set is regarded as anomaly trafﬁc. For using this method, they assumed that initial training data set didnot have any anomaly trafﬁc. Since this method was based on packet counts, it might not easily extended to detect other anomaly trafﬁc except ﬂooding. On the other hand, [11] has proposed a VoIP anomaly trafﬁc detection method using Extended Finite State Machine (EFSM). [11] has suggested INVITE ﬂooding, BYE DoS anomaly trafﬁc and media spamming detection methods. However, the state machine required more memory because it had to maintain each ﬂow. [13] has presented NetFlow-based VoIP anomaly detection methods for INVITE, REGIS-TER, RTP ﬂooding, and REGISTER/INVITE scan. How-ever, the VoIP DoS attacks considered in this paper were not considered. In [14], an IDS approach to detect SIP anomalies was developed, but only simulation results are presented. For monitoring VoIP trafﬁc, SIPFIX [10] has been proposed as an IPFIX extension. The key ideas of the SIPFIX are application-layer inspection and SDP analysis for carrying media session information. Yet, this paper presents only the possibility of applying SIPFIX to DoS anomaly trafﬁc detection and prevention. We described the preliminary idea of detecting VoIP anomaly trafﬁc in [15]. This paper elaborates BYE DoS anomaly trafﬁc and RTP ﬂooding anomaly trafﬁc detec-tion method based on IPFIX. Based on [15], we have considered SIP and RTP anomaly trafﬁc generated in wireless LAN. In this case, it is possible to generate the similiar anomaly trafﬁc with normal VoIP trafﬁc, because attackers can easily extract normal user information from unencrypted VoIP packets. In this paper, we have extended the idea with additional SIP detection methods using information of wireless LAN packets. Furthermore, we have shown the real experiment results at the commercial VoIP network.III. THE VOIP ANOMALY TRAFFIC DETECTION METHOD A. CANCEL DoS Anomaly Trafﬁc DetectionAs the SIP INVITE message is not usually encrypted, attackers could extract ﬁelds necessary to reproduce the forged SIP CANCEL message by snifﬁng SIP INVITE packets, especially in wireless LANs. Thus, we cannot tell the difference between the normal SIP CANCEL message and the replicated one, because the faked CANCEL packet includes the normal ﬁelds inferred from the SIP INVITE message. The attacker will perform the SIP CANCEL DoS attack at the same wireless LAN, because the purpose of the SIP CANCEL attack is to prevent the normal call estab-lishment when a victim is waiting for calls. Therefore, as soon as the attacker catches a call invitation message for a victim, it will send a SIP CANCEL message, which makes the call establishment failed. We have generated faked SIP CANCEL message using sniffed a SIP INVITE in SIP header of this CANCEL message is the same as normal SIP CANCEL message, because the attacker can obtain the SIP header ﬁeld from unencrypted normal SIP message in wireless LAN environment. Therefore it is impossible to detect the CANCEL DoS anomaly trafﬁc using SIP headers, we use the different values of the wireless LAN frame. That is, the sequence number in the frame will tell the difference between a victim host and an attacker. We look into source MAC address and sequence number in the MAC frame including a SIP CANCEL message as shown in Algorithm 1. We compare the source MAC address of SIP CANCEL packets with that of the previously saved SIP INVITE ﬂow. If the source MAC address of a SIP CANCEL ﬂow is changed, it will be highly probable that the CANCEL packet is generated by a unknown user. However, the source MAC address could be spoofed. Regarding source spooﬁng detection, we employ the method in [12] that uses sequence numbers of frames. We calculate the gap between n-th and (n-1)-th frames. As the sequence number ﬁeld in a MAC header uses 12 bits, it varies from 0 to 4095. When we ﬁnd that the sequence number gap between a single SIP ﬂow is greater than the threshold value of N that willbe set from the experiments, we determine that the SIP host address as been spoofed for the anomaly trafﬁc.B. BYE DoS Anomaly Trafﬁc DetectionIn commercial VoIP applications, SIP BYE messages use the same authentication ﬁeld is included in the SIP IN-VITE message for security and accounting purposes. How-ever, attackers can reproduce BYE DoS packets through snifﬁng normal SIP INVITE packets in wireless faked SIP BYE message is same with the normal SIP BYE. Therefore, it is difﬁcult to detect the BYE DoS anomaly trafﬁc using only SIP header snifﬁng SIP INVITE message, the attacker at the same or different subnets could terminate the normal in- progress call, because it could succeed in generating a BYE message to the SIP proxy server. In the SIP BYE attack, it is difﬁcult to distinguish from the normal call termination procedure. That is, we apply the timestamp of RTP trafﬁc for detecting the SIP BYE attack. Generally, after normal call termination, the bi-directional RTP ﬂow is terminated in a bref space of time. However, if the call termination procedure is anomaly, we can observe that a directional RTP media ﬂow is still ongoing, whereas an attacked directional RTP ﬂow is broken. Therefore, in order to detect the SIP BYE attack, we decide that we watch a directional RTP ﬂow for a long time threshold of N sec after SIP BYE message. The threshold of N is also set from the 2 explains the procedure to detect BYE DoS anomal trafﬁc using captured timestamp of the RTP packet. We maintain SIP session information between clients with INVITE and OK messages including the same Call-ID and 4-tuple (source/destination IP Address and port number) of the BYE packet. We set a time threshold value by adding Nsec to the timestamp value of the BYE message. The reason why we use the captured timestamp is that a few RTP packets are observed under second. If RTP trafﬁc is observed after the time threshold, this willbe considered as a BYE DoS attack, because the VoIP session will be terminated with normal BYE messages. C. RTP Anomaly Trafﬁc Detection Algorithm 3 describes an RTP ﬂooding detection method that uses SSRC and sequence numbers of the RTP header. During a single RTP session, typically, the same SSRC value is maintained. If SSRC is changed, it is highly probable that anomaly has occurred. In addition, if there is a big sequence number gap between RTP packets, we determine that anomaly RTP trafﬁc has happened. As inspecting every sequence number for a packet is difﬁcult, we calculate the sequence number gap using the ﬁrst, last, maximum and minimum sequence numbers. In the RTP header, the sequence number ﬁeld uses 16 bits from 0 to 65535. When we observe a wide sequence number gap in our algorithm, we consider it as an RTP ﬂooding attack.IV. PERFORMANCE EVALUATIONA. Experiment EnvironmentIn order to detect VoIP anomaly trafﬁc, we established an experimental environment as ﬁgure 1. In this envi-ronment, we employed two VoIP phones with wireless LANs, one attacker, a wireless access router and an IPFIX ﬂow collector. For the realistic performance evaluation, we directly used one of the working VoIP networks deployed in Korea where an 11-digit telephone number (070-XXXX-XXXX) has been assigned to a SIP wireless SIP phones supporting , we could make calls to/from the PSTN or cellular phones. In the wireless access router, we used two wireless LAN cards- one is to support the AP service, and the other is to monitor packets. Moreover, in order to observe VoIP packets in the wireless access router, we modiﬁed nProbe [16], that is an open IPFIX ﬂow generator, to create and export IPFIX ﬂows related with SIP, RTP, and information. As the IPFIX collector, we have modiﬁed libipﬁx so that it could provide the IPFIX ﬂow decoding function for SIP, RTP, and templates. We used MySQL for the ﬂow DB.B. Experimental ResultsIn order to evaluate our proposed algorithms, we gen-erated 1,946 VoIP calls with two commercial SIP phones and a VoIP anomaly trafﬁc generator. Table I showsour experimental results with precision, recall, and F-score that is the harmonic mean of precision and recall. In CANCEL DoS anomaly trafﬁc detection, our algorithm represented a few false negative cases, which was related with the gap threshold of the sequence number in MAC header. The average of the F-score value for detecting the SIP CANCEL anomaly is %.For BYE anomaly tests, we generated 755 BYE mes-sages including 118 BYE DoS anomalies in the exper-iment. The proposed BYE DoS anomaly trafﬁc detec-tion algorithm found 112 anomalies with the F-score of %. If an RTP ﬂow is terminated before the threshold, we regard the anomaly ﬂow as a normal one. In this algorithm, we extract RTP session information from INVITE and OK or session description messages using the same Call-ID of BYE message. It is possible not to capture those packet, resulting in a few false-negative cases. The RTP ﬂooding anomaly trafﬁc detection experiment for 810 RTP sessions resulted in the F score of 98%.The reason of false-positive cases was related with the sequence number in RTP header. If the sequence number of anomaly trafﬁc is overlapped with the range of the normal trafﬁc, our algorithm will consider it as normal trafﬁc.V. CONCLUSIONSWe have proposed a ﬂow-based anomaly trafﬁc detec-tion method against SIP and RTP-based anomaly trafﬁc in this paper. We presented VoIP anomaly trafﬁc detection methods with ﬂow data on the wireless access router. We used the IETF IPFIX standard to monitor SIP/RTP ﬂows passing through wireless access routers, because its template architecture is easily extensible to several protocols. For this purpose, we deﬁned two new IPFIX templates for SIP and RTP trafﬁc and four new IPFIX ﬁelds for trafﬁc. Using these IPFIX ﬂow templates,we proposed CANCEL/BYE DoS and RTP ﬂooding trafﬁc detection algorithms. From experimental results on the working VoIP network in Korea, we showed that our method is able to detect three representative VoIP attacks on SIP phones. In CANCEL/BYE DoS anomaly trafﬁcdetection method, we employed threshold values about time and sequence number gap for classﬁcation of normal and abnormal VoIP packets. This paper has not been mentioned the test result about suitable threshold values. For the future work, we will show the experimental result about evaluation of the threshold values for our detection method.二、英文翻译：交通流数据检测异常在真实的世界中使用的VoIP网络一 .介绍最近,许多SIP[3],[4]基于服务器的VoIP应用和服务出现了,并逐渐增加他们的穿透比及由于自由和廉价的通话费且极易订阅的方法。

计算机网络中英文对照外文翻译文献

中英文资料外文翻译计算机网络计算机网络，通常简单的被称作是一种网络，是一家集电脑和设备为一体的沟通渠道，便于用户之间的沟通交流和资源共享。

网络可以根据其多种特点来分类。

计算机网络允许资源和信息在互联设备中共享。

一．历史早期的计算机网络通信始于20世纪50年代末，包括军事雷达系统、半自动地面防空系统及其相关的商业航空订票系统、半自动商业研究环境。

1957年俄罗斯向太空发射人造卫星。

十八个月后，美国开始设立高级研究计划局(ARPA)并第一次发射人造卫星。

然后用阿帕网上的另外一台计算机分享了这个信息。

这一切的负责者是美国博士莱德里尔克。

阿帕网于来于自印度，1969年印度将其名字改为因特网。

上世纪60年代，高级研究计划局(ARPA)开始为美国国防部资助并设计高级研究计划局网(阿帕网)。

因特网的发展始于1969年，20世纪60年代起开始在此基础上设计开发，由此，阿帕网演变成现代互联网。

二．目的计算机网络可以被用于各种用途:为通信提供便利:使用网络，人们很容易通过电子邮件、即时信息、聊天室、电话、视频电话和视频会议来进行沟通和交流。

共享硬件:在网络环境下，每台计算机可以获取和使用网络硬件资源，例如打印一份文件可以通过网络打印机。

共享文件：数据和信息: 在网络环境中，授权用户可以访问存储在其他计算机上的网络数据和信息。

提供进入数据和信息共享存储设备的能力是许多网络的一个重要特征。

共享软件:用户可以连接到远程计算机的网络应用程序。

信息保存。

安全保证。

三．网络分类下面的列表显示用于网络分类：3．1连接方式计算机网络可以据硬件和软件技术分为用来连接个人设备的网络，如：光纤、局域网、无线局域网、家用网络设备、电缆通讯和G.hn（有线家庭网络标准）等等。

以太网的定义，它是由IEEE 802标准，并利用各种媒介，使设备之间进行通信的网络。

经常部署的设备包括网络集线器、交换机、网桥、路由器。

无线局域网技术是使用无线设备进行连接的。

STC89C52处理芯片中英文对照外文翻译文献

STC89C52处理芯片中英文对照外文翻译文献本文介绍了国产STC89C52单片机的基本参数和特性。

该单片机与MCS-51单片机兼容，具有8K字节可编程视频存储器、1000次擦写周期、全静态操作、三级加密程序存储器、32个可编程I/O接口线、三个16位定时器、八个中断源、低功耗空闲和掉电模式、掉电后间断可唤醒、看门狗定时器、双数值指针和掉电标识符等特性。

该单片机广泛应用于智能仪表、工控、通讯设备、导航系统、家用电器等领域。

STC89C52单片机是一种高性能、低功耗的CMOS8位微控制器，采用高密度非易失性存储器技术制造，具有8K在系统可编程视频播放存储器。

该微控制器与工业80C51产物指令和引脚完全兼容，片上反射速度允许程序存储器在系统可编程，也适用于常规的程序编写器。

STC89C52微控制器拥有灵敏小巧的八位中央处理器和在线系统可编程反射，为嵌入式控制应用系统提供高度矫捷的、更加有用的解决方案。

该微控制器具有8K字节的反射速度、256字节的随机存取储存器、32位I/O串口线、看门狗定时器、2个数值指针、三个16为定时器、计数器、一个6向量2级间断结构、片内晶振及钟表电路。

此外，STC89C52支持两种软件可选择节电模式、间断继续工作，可降至0HZ静态逻辑操作。

在空闲模式下，CPU停止工作，允许RAM、定时器/计数器、串口、间断继续工作。

在掉电保护体式格局下，RAM内容被生成，振动器被冻结，单片机一切的工作停止，直到下一个间断或者硬件复位为止。

P0口是一个8位漏极开路的双向I/O口。

作为输出口，每位能驱动8个TTL逻辑电平。

当P0端口写“1”时，引脚用作高阻抗输入。

当访问外部程序和数值存储器时，P0口也被作为低八位/数值复用。

在这种模式下，P0具有内部上拉电阻。

在Flash编程时，P0口也用来接收指令字节；在程序校验时，输出指令字节。

在程序校验时，需要外部上拉电阻。

P1口是一个具有内部上拉电阻的八位双向I/O口，P1输出缓冲器驱动四个TTL逻辑电平。

小波分析中英文对照外文翻译文献

小波分析中英文对照外文翻译文献(文档含英文原文和中文翻译)译文：一小波研究的意义与背景在实际应用中，针对不同性质的信号和干扰，寻找最佳的处理方法降低噪声，一直是信号处理领域广泛讨论的重要问题。

目前有很多方法可用于信号降噪，如中值滤波，低通滤波，傅立叶变换等，但它们都滤掉了信号细节中的有用部分。

传统的信号去噪方法以信号的平稳性为前提，仅从时域或频域分别给出统计平均结果。

根据有效信号的时域或频域特性去除噪声，而不能同时兼顾信号在时域和频域的局部和全貌。

更多的实践证明，经典的方法基于傅里叶变换的滤波，并不能对非平稳信号进行有效的分析和处理，去噪效果已不能很好地满足工程应用发展的要求。

常用的硬阈值法则和软阈值法则采用设置高频小波系数为零的方法从信号中滤除噪声。

实践证明，这些小波阈值去噪方法具有近似优化特性，在非平稳信号领域中具有良好表现。

小波理论是在傅立叶变换和短时傅立叶变换的基础上发展起来的，它具有多分辨分析的特点，在时域和频域上都具有表征信号局部特征的能力，是信号时频分析的优良工具。

小波变换具有多分辨性、时频局部化特性及计算的快速性等属性，这使得小波变换在地球物理领域有着广泛的应用。

随着技术的发展，小波包分析(Wavelet Packet Analysis)方法产生并发展起来，小波包分析是小波分析的拓展，具有十分广泛的应用价值。

它能够为信号提供一种更加精细的分析方法，它将频带进行多层次划分，对离散小波变换没有细分的高频部分进一步分析，并能够根据被分析信号的特征，自适应选择相应的频带，使之与信号匹配，从而提高了时频分辨率。

小波包分析(wavelet packet analysis)能够为信号提供一种更加精细的分析方法，它将频带进行多层次划分，对小波分析没有细分的高频部分进一步分解，并能够根据被分析信号的特征，自适应地选择相应频带,使之与信号频谱相匹配，因而小波包具有更广泛的应用价值。

利用小波包分析进行信号降噪，一种直观而有效的小波包去噪方法就是直接对小波包分解系数取阈值，选择相关的滤波因子，利用保留下来的系数进行信号的重构，最终达到降噪的目的。

信号与系统信号术语中英文对照精选全文

精选全文完整版（可编辑修改）AAbsolutely integrable 绝对可积Absolutely integrable impulse response 绝对可积冲激响应Absolutely summable 绝对可和Absolutely summable impulse response 绝对可和冲激响应Accumulator 累加器Acoustic 声学Adder 加法器Additivity property 可加性Aliasing 混叠现象All-pass systems 全通系统AM (Amplitude modulation ) 幅度调制Amplifier 放大器Amplitude modulation (AM) 幅度调制Amplitude-scaling factor 幅度放大因子Analog-to-digital (A-to-D) converter 模数转换器Analysis equation 分析公式（方程）Angel (phase) of complex number 复数的角度（相位）Angle criterion 角判据Angle modulation 角度调制Anticausality 反因果Aperiodic 非周期Aperiodic convolution 非周期卷积Aperiodic signal 非周期信号Asynchronous 异步的Audio systems 音频（声音）系统Autocorrelation functions 自相关函数Automobile suspension system 汽车减震系统Averaging system 平滑系统BBand-limited 带（宽）限的Band-limited input signals 带限输入信号Band-limited interpolation 带限内插Bandpass filters 带通滤波器Bandpass signal 带通信号Bandpass-sampling techniques 带通采样技术Bandwidth 带宽Bartlett (triangular) window 巴特利特（三角形）窗Bilateral Laplace transform 双边拉普拉斯变换Bilinear 双线性的Bilinear transformation 双线性变换Bit （二进制）位，比特Block diagrams 方框图Bode plots 波特图Bounded 有界限的Break frequency 折转频率Butterworth filters 巴特沃斯滤波器C“Chirp” transform algorithm “鸟声”变换算法Capacitor 电容器Carrier 载波Carrier frequency 载波频率Carrier signal 载波信号Cartesian (rectangular) form 直角坐标形式Cascade (series) interconnection 串联，级联Cascade-form 串联形式Causal LTI system 因果的线性时不变系统Channel 信道，频道Channel equalization 信道均衡Chopper amplifier 斩波器放大器Closed-loop 闭环Closed-loop poles 闭环极点Closed-loop system 闭环系统Closed-loop system function 闭环系统函数Coefficient multiplier 系数乘法器Coefficients 系数Communications systems 通信系统Commutative property 交换性（交换律）Compensation for nonideal elements 非理想元件的补偿Complex conjugate 复数共轭Complex exponential carrier 复指数载波Complex exponential signals 复指数信号Complex exponential(s) 复指数Complex numbers 复数Conditionally stable systems 条件稳定系统Conjugate symmetry 共轭对称Conjugation property 共轭性质Continuous-time delay 连续时间延迟Continuous-time filter 连续时间滤波器Continuous-time Fourier series 连续时间傅立叶级数Continuous-time Fourier transform 连续时间傅立叶变换Continuous-time signals 连续时间信号Continuous-time systems 连续时间系统Continuous-to-discrete-time conversion 连续时间到离散时间转换Convergence 收敛Convolution 卷积Convolution integral 卷积积分Convolution property 卷积性质Convolution sum 卷积和Correlation function 相关函数Critically damped systems 临界阻尼系统Crosss-correlation functions 互相关函数Cutoff frequencies 截至频率DDamped sinusoids 阻尼正弦振荡Damping ratio 阻尼系数Dc offset 直流偏移Dc sequence 直流序列Deadbeat feedback systems 临界阻尼反馈系统Decibels (dB) 分贝Decimation 抽取Decimation and interpolation 抽取和内插Degenerative (negative) feedback 负反馈Delay 延迟Delay time 延迟时间Demodulation 解调Difference equations 差分方程Differencing property 差分性质Differential equations 微分方程Differentiating filters 微分滤波器Differentiation property 微分性质Differentiator 微分器Digital-to-analog (D-to-A) converter 数模转换器Direct Form I realization 直接I型实现Direct form II realization 直接II型实现Direct-form 直接型Dirichlet conditions 狄里赫利条件Dirichlet, P.L. 狄里赫利Discontinuities 间断点，不连续Discrete-time filters 离散时间滤波器Discrete-time Fourier series 离散时间傅立叶级数Discrete-time Fourier series pair 离散时间傅立叶级数对Discrete-time Fourier transform （DFT）离散时间傅立叶变换Discrete-time LTI filters 离散时间线性时不变滤波器Discrete-time modulation 离散时间调制Discrete-time nonrecursive filters 离散时间非递归滤波器Discrete-time signals 离散时间信号Discrete-time systems 离散时间系统Discrete-time to continuous-time离散时间到连续时间转换conversionDispersion 弥撒（现象）Distortion 扭曲，失真Distribution theory（property）分配律Dominant time constant 主时间常数Double-sideband modulation (DSB) 双边带调制Downsampling 减采样Duality 对偶性EEcho 回波Eigenfunctions 特征函数Eigenvalue 特征值Elliptic filters 椭圆滤波器Encirclement property 围线性质End points 终点Energy of signals 信号的能量Energy-density spectrum 能量密度谱Envelope detector 包络检波器Envelope function 包络函数Equalization 均衡化Equalizer circuits 均衡器电路Equation for closed-loop poles 闭环极点方程Euler, L. 欧拉Euler’s relation 欧拉关系（公式）Even signals 偶信号Exponential signals 指数信号Exponentials 指数FFast Fourier transform (FFT) 快速傅立叶变换Feedback 反馈Feedback interconnection 反馈联结Feedback path 反馈路径Filter(s) 滤波器Final-value theorem 终值定理Finite impulse response (FIR) 有限长脉冲响应Finite impulse response (FIR) filters 有限长脉冲响应滤波器Finite sum formula 有限项和公式Finite-duration signals 有限长信号 First difference一阶差分First harmonic components 基波分量（一次谐波分量） First-order continuous-time systems 一阶连续时间系统 First-order discrete-time systems一阶离散时间系统First-order recursive discrete-time filters 一阶递归离散时间滤波器 First-order systems 一阶系统 Forced response 受迫响应 Forward path 正向通路 Fourier series 傅立叶级数 Fourier transform 傅立叶变换 Fourier transform pairs 傅立叶变换对Fourier, Jean Baptiste Joseph 傅立叶（法国数学家，物理学家） Frequency response频率响应Frequency response of LTI systems线性时不变系统的频率响应Frequency scaling of continuous-time Fourier transform连续时间傅立叶变化的频率尺度（变换性质） Frequency shift keying (FSK) 频移键控 Frequency shifting property频移性质 Frequency-division multiplexing (FDM) 频分多路复用 Frequency-domain characterization频域特征Frequency-selective filter 频率选择滤波器Frequency-shaping filters 频率成型滤波器Fundamental components 基波分量Fundamental frequency 基波频率Fundamental period 基波周期GGain 增益Gain and phase margin 增益和相位裕度General complex exponentials 一般复指数信号Generalized functions 广义函数Gibbs phenomenon 吉伯斯现象Group delay 群延迟HHalf-sample delay 半采样间隔时延Hanning window 汉宁窗Harmonic analyzer 谐波分析议Harmonic components 谐波分量Harmonically related 谐波关系Heat propagation and diffusion 热传播和扩散现象Higher order holds 高阶保持Highpass filter 高通滤波器Highpass-to-lowpass transformations 高通到低通变换Hilbert transform 希尔波特滤波器Homogeneity (scaling) property 齐次性（比例性）IIdeal 理想的Ideal bandstop characteristic 理想带阻特征Ideal frequency-selective filter 理想频率选择滤波器Idealization 理想化Identity system 恒等系统Imaginary part 虚部Impulse response 冲激响应Impulse train 冲激串Incrementally linear systems 增量线性系统Independent variable 独立变量Infinite impulse response (IIR) 无限长脉冲响应Infinite impulse response (IIR) filters 无限长脉冲响应滤波器Infinite sum formula 无限项和公式Infinite taylor series 无限项泰勒级数Initial-value theorem 初值定理Inpulse-train sampling 冲激串采样Instantaneous 瞬时的Instantaneous frequency 瞬时频率Integration in time-domain 时域积分Integration property 积分性质Integrator 积分器Interconnection 互联Intermediate-frequency (IF) stage 中频级Intersymbol interference (ISI) 码间干扰Inverse Fourier transform 傅立叶反变换Inverse Laplace transform 拉普拉斯反变换Inverse LTI system 逆线性时不变系统Inverse system design 逆系统设计Inverse z-transform z反变换Inverted pendulum 倒立摆Invertibility of LTI systems 线性时不变系统的可逆性Invertible systems 逆系统LLag network 滞后网络Lagrange, J.L. 拉格朗日（法国数学家，力学家）Laplace transform 拉普拉斯变换Laplace, P.S. de 拉普拉斯（法国天文学家，数学家）lead network 超前网络left-half plane 左半平面left-sided signal 左边信号Linear 线性Linear constant-coefficient difference线性常系数差分方程equationsLinear constant-coefficient differential线性常系数微分方程equationsLinear feedback systems 线性反馈系统Linear interpolation 线性插值Linearity 线性性Log magnitude-phase diagram 对数幅－相图Log-magnitude plots 对数模图Lossless coding 无损失码Lowpass filters 低通滤波器Lowpass-to-highpass transformation 低通到高通的转换LTI system response 线性时不变系统响应LTI systems analysis 线性时不变系统分析MMagnitude and phase 幅度和相位Matched filter 匹配滤波器Measuring devices 测量仪器Memory 记忆Memoryless systems 无记忆系统Modulating signal 调制信号Modulation 调制Modulation index 调制指数Modulation property 调制性质Moving-average filters 移动平均滤波器Multiplexing 多路技术Multiplication property 相乘性质Multiplicities 多样性NNarrowband 窄带Narrowband frequency modulation 窄带频率调制Natural frequency 自然响应频率Natural response 自然响应Negative (degenerative) feedback 负反馈Nonanticipatibe system 不超前系统Noncausal averaging system 非因果平滑系统Nonideal 非理想的Nonideal filters 非理想滤波器Nonmalized functions 归一化函数Nonrecursive 非递归Nonrecursive filters 非递归滤波器Nonrecursive linear constant-coefficient非递归线性常系数差分方程difference equationsNyquist frequency 奈奎斯特频率Nyquist rate 奈奎斯特率Nyquist stability criterion 奈奎斯特稳定性判据OOdd harmonic 奇次谐波Odd signal 奇信号Open-loop 开环Open-loop frequency response 开环频率响应Open-loop system 开环系统Operational amplifier 运算放大器Orthogonal functions 正交函数Orthogonal signals 正交信号Oscilloscope 示波器Overdamped system 过阻尼系统Oversampling 过采样Overshoot 超量PParallel interconnection 并联Parallel-form block diagrams 并联型框图Parity check 奇偶校验检查Parseval’s relation 帕斯伐尔关系（定理）Partial-fraction expansion 部分分式展开Particular and homogeneous solution 特解和齐次解Passband 通频带Passband edge 通带边缘Passband frequency 通带频率Passband ripple 通带起伏（或波纹）Pendulum 钟摆Percent modulation 调制百分数Periodic 周期的Periodic complex exponentials 周期复指数Periodic convolution 周期卷积Periodic signals 周期信号Periodic square wave 周期方波Periodic square-wave modulating signal 周期方波调制信号Periodic train of impulses 周期冲激串Phase (angle) of complex number 复数相位（角度）Phase lag 相位滞后Phase lead 相位超前Phase margin 相位裕度Phase shift 相移Phase-reversal 相位倒置Phase modulation 相位调制Plant 工厂Polar form 极坐标形式Poles 极点Pole-zero plot(s) 零极点图Polynomials 多项式Positive (regenerative) feedback 正（再生）反馈Power of signals 信号功率Power-series expansion method 幂级数展开的方法Principal-phase function 主值相位函数Proportional (P) control 比例控制Proportional feedback system 比例反馈系统Proportional-plus-derivative 比例加积分Proportional-plus-derivative feedback 比例加积分反馈Proportional-plus-integral-plus-differenti比例－积分－微分控制al (PID) controlPulse-amplitude modulation 脉冲幅度调制Pulse-code modulation 脉冲编码调制Pulse-train carrier 冲激串载波QQuadrature distortion 正交失真Quadrature multiplexing 正交多路复用Quality of circuit 电路品质（因数）RRaised consine frequency response 升余弦频率响应Rational frequency responses 有理型频率响应Rational transform 有理变换RC highpass filter RC 高阶滤波器RC lowpass filter RC 低阶滤波器Real 实数Real exponential signals 实指数信号Real part 实部Rectangular (Cartesian) form 直角（卡笛儿）坐标形式Rectangular pulse 矩形脉冲Rectangular pulse signal 矩形脉冲信号Rectangular window 矩形窗口Recursive (infinite impulse response) 递归（无时限脉冲响应）滤波器filtersRecursive linear constant-coefficient递归的线性常系数差分方程difference equationsRegenerative (positive) feedback 再生（正）反馈Region of comvergence 收敛域right-sided signal 右边信号Rise time 上升时间Root-locus analysis 根轨迹分析（方法）Running sum 动求和SS domain S域Sampled-data feedback systems 采样数据反馈系统Sampled-data systems 采样数据系统Sampling 采样Sampling frequency 采样频率Sampling function 采样函数Sampling oscilloscope 采样示波器Sampling period 采样周期Sampling theorem 采样定理Scaling (homogeneity) property 比例性（齐次性）性质Scaling in z domain z域尺度变换Scrambler 扰频器Second harmonic components 二次谐波分量Second-order 二阶Second-order continuous-time system 二阶连续时间系统Second-order discrete-time system 二阶离散时间系统Second-order systems 二阶系统sequence 序列Series (cascade) interconnection 级联（串联）Sifting property 筛选性质Sinc functions sinc函数Single-sideband 单边带Single-sideband sinusoidal amplitude单边带正弦幅度调制modulationSingularity functions 奇异函数Sinusoidal 正弦（信号）Sinusoidal amplitude modulation 正弦幅度调制Sinusoidal carrier 正弦载波Sinusoidal frequency modulation 正弦频率调制Sliding 滑动Spectral coefficient 频谱系数Spectrum 频谱Speech scrambler 语音加密器S-plane S平面Square wave 方波Stability 稳定性Stabilization of unstable systems 不稳定系统的稳定性（度）Step response 阶跃响应Step-invariant transformation 阶跃响应不定的变换Stopband 阻带Stopband edge 阻带边缘Stopband frequency 阻带频率Stopband ripple 阻带起伏（或波纹）Stroboscopic effect 频闪响应Summer 加法器Superposition integral 叠加积分Superposition property 叠加性质Superposition sum 叠加和Suspension system 减震系统Symmetric periodic 周期对称Symmetry 对称性Synchronous 同步的Synthesis equation 综合方程System function(s) 系统方程TTable of properties 性质列表Taylor series 泰勒级数Time 时间，时域Time advance property of unilateral单边z变换的时间超前性质z-transformTime constants 时间常数Time delay property of unilateral单边z变换的时间延迟性质z-transformTime expansion property 时间扩展性质Time invariance 时间变量Time reversal property 时间反转（反褶）性Time scaling property 时间尺度变换性Time shifting property 时移性质Time window 时间窗口Time-division multiplexing (TDM) 时分复用Time-domain 时域Time-domain properties 时域性质Tracking system (s) 跟踪系统Transfer function 转移函数transform pairs 变换对Transformation 变换（变形）Transition band 过渡带Transmodulation (transmultiplexing) 交叉调制Triangular (Barlett) window 三角型（巴特利特）窗口Trigonometric series 三角级数Two-sided signal 双边信号Type l feedback system l 型反馈系统UUint impulse response 单位冲激响应Uint ramp function 单位斜坡函数Undamped natural frequency 无阻尼自然相应Undamped system 无阻尼系统Underdamped systems 欠阻尼系统Undersampling 欠采样Unilateral 单边的Unilateral Laplace transform 单边拉普拉斯变换Unilateral z-transform 单边z变换Unit circle 单位圆Unit delay 单位延迟Unit doublets 单位冲激偶Unit impulse 单位冲激Unit step functions 单位阶跃函数Unit step response 单位阶跃响应Unstable systems 不稳定系统Unwrapped phase 展开的相位特性Upsampling 增采样VVariable 变量WWalsh functions 沃尔什函数Wave 波形Wavelengths 波长Weighted average 加权平均Wideband 宽带Wideband frequency modulation 宽带频率调制Windowing 加窗zZ domain z域Zero force equalizer 置零均衡器Zero-Input response 零输入响应Zero-Order hold 零阶保持Zeros of Laplace transform 拉普拉斯变换的零点Zero-state response 零状态响应z-transform z变换z-transform pairs z变换对。

光纤通信技术外文翻译中英对照

Optical Fiber Communication TechnologyOptical fiber communication is the use of optical fiber transmission signals, the transmission of information in order to achieve a means of communication. 光导纤维通信简称光纤通信。

Referred to as optical fiber communication optical fiber communications. 可以把光纤通信看成是以光导纤维为传输媒介的“有线”光通信。

Can be based on optical fiber communication optical fiber as transmission medium for the "wired" optical communication. 光纤由内芯和包层组成,内芯一般为几十微米或几微米,比一根头发丝还细;外面层称为包层,包层的作用就是保护光纤。

Fiber from the core and cladding of the inner core is generally a few microns or tens of microns, than a human hair; outside layer called the cladding, the role of cladding is to protect the fiber. 实际上光纤通信系统使用的不是单根的光纤,而是许多光纤聚集在一起的组成的光缆。

In fact the use of optical fiber communication system is not a single fiber, but that brings together a number of fiber-optic cable componentsOptical fiber communication is the use of light for the carrier with fiber optics as a transmission medium to spread information from one another means of communication. 1966年英籍华人高锟博士发表了一篇划时代性的论文,他提出利用带有包层材料的石英玻璃光学纤维,能作为通信媒质。

毕业设计论文外文文献翻译智能交通信号灯控制中英文对照

英语原文Intelligent Traffic Light Controlby Marco Wiering The topic I picked for our community project was traffic lights. In a community, people need stop signs and traffic lights to slow down drivers from going too fast. If there were no traffic lights or stop signs, people’s lives would be in danger from drivers going too fast.The urban traffic trends towards the saturation, the rate of increase of the road of big city far lags behind rate of increase of the car.The urban passenger traffic has already become the main part of city traffic day by day and it has used about 80% of the area of road of center district. With the increase of population and industry activity, people's traffic is more and more frequent, which is unavoidable. What means of transportation people adopt produces pressure completely different to city traffic. According to calculating, if it is 1 to adopt the area of road that the public transport needs, bike needs 5-7, car needs 15-25, even to walk is 3 times more than to take public transits. So only by building road can't solve the city traffic problem finally yet. Every large city of the world increases the traffic policy to the first place of the question.For example，according to calculating, when the automobile owning amount of Shanghai reaches 800,000 (outside cars count separately ), if it distributes still as now for example: center district accounts for great proportion, even when several loop-lines and arterial highways have been built up , the traffic cannot be improved more than before and the situation might be even worse. So the traffic policy Shanghai must adopt , or called traffic strategy is that have priority to develop public passenger traffic of city, narrow the scope of using of the bicycle progressively , control the scale of growth of the car traffic in the center district, limit the development of the motorcycle strictly.There are more municipals project under construction in big city. the influence on the traffic is greater.Municipal infrastructure construction is originally a good thing of alleviating the traffic, but in the course of constructing, it unavoidably influence the local traffic. Some road sections are blocked, some change into an one-way lane, thus the vehicle can only take a devious route . The construction makes the road very narrow, forming the bottleneck, which seriously influence the car flow.When having stop signs and traffic lights, people have a tendency to drive slower andlook out for people walking in the middle of streets. To put a traffic light or a stop sign in a community, it takes a lot of work and planning from the community and the city to put one in. It is not cheap to do it either. The community first needs to take a petition around to everyone in the community and have them sign so they can take it to the board when the next city council meeting is. A couple residents will present it to the board, and they will decide weather or not to put it in or not. If not put in a lot of residents might be mad and bad things could happened to that part of the city.When the planning of putting traffic lights and stop signs, you should look at the subdivision plan and figure out where all the buildings and schools are for the protection of students walking and riding home from school. In our plan that we have made, we will need traffic lights next to the school, so people will look out for the students going home. We will need a stop sign next to the park incase kids run out in the street. This will help the protection of the kids having fun. Will need a traffic light separating the mall and the store. This will be the busiest part of the town with people going to the mall and the store. And finally there will need to be a stop sign at the end of the streets so people don’t drive too fast and get in a big accident. If this is down everyone will be safe driving, walking, or riding their bikes.In putting in a traffic light, it takes a lot of planning and money to complete it. A traffic light cost around $40,000 to $125,000 and sometimes more depending on the location. If a business goes in and a traffic light needs to go in, the business or businesses will have to pay some money to pay for it to make sure everyone is safe going from and to that business. Also if there is too many accidents in one particular place in a city, a traffic light will go in to safe people from getting a severe accident and ending their life and maybe someone else’s.The reason I picked this part of our community development report was that traffic is a very important part of a city. If not for traffic lights and stop signs, people’s lives would be in danger every time they walked out their doors. People will be driving extremely fast and people will be hit just trying to have fun with their friends. So having traffic lights and stop signs this will prevent all this from happening.Traffic in a city is very much affected by traffic light controllers. When waiting for a traffic light, the driver looses time and the car uses fuel. Hence, reducing waiting times before traffic lights can save our European society billions of Euros annually. To make traffic light controllers more intelligent, we exploit the emergence of novel technologies such as communication networks and sensor networks, as well as the use of more sophisticated algorithms for setting traffic lights. Intelligent traffic light control does not only mean thattraffic lights are set in order to minimize waiting times of road users, but also that road users receive information about how to drive through a city in order to minimize their waiting times. This means that we are coping with a complex multi-agent system, where communication and coordination play essential roles. Our research has led to a novel system in which traffic light controllers and the behaviour of car drivers are optimized using machine-learning methods.Our idea of setting a traffic light is as follows. Suppose there are a number of cars with their destination address standing before a crossing. All cars communicate to the traffic light their specific place in the queue and their destination address. Now the traffic light has to decide which option (ie, which lanes are to be put on green) is optimal to minimize the long-term average waiting time until all cars have arrived at their destination address. The learning traffic light controllers solve this problem by estimating how long it would take for a car to arrive at its destination address (for which the car may need to pass many different traffic lights) when currently the light would be put on green, and how long it would take if the light would be put on red. The difference between the waiting time for red and the waiting time for green is the gain for the car. Now the traffic light controllers set the lights in such a way to maximize the average gain of all cars standing before the crossing. To estimate the waiting times, we use 'reinforcement learning' which keeps track of the waiting times of individual cars and uses a smart way to compute the long term average waiting times using dynamic programming algorithms. One nice feature is that the system is very fair; it never lets one car wait for a very long time, since then its gain of setting its own light to green becomes very large, and the optimal decision of the traffic light will set his light to green. Furthermore, since we estimate waiting times before traffic lights until the destination of the road user has been reached, the road user can use this information to choose to which next traffic light to go, thereby improving its driving behaviour through a city. Note that we solve the traffic light control problem by using a distributed multi-agent system, where cooperation and coordination are done by communication, learning, and voting mechanisms. To allow for green waves during extremely busy situations, we combine our algorithm with a special bucket algorithm which propagates gains from one traffic light to the next one, inducing stronger voting on the next traffic controller option.We have implemented the 'Green Light District', a traffic simulator in Java in which infrastructures can be edited easily by using the mouse, and different levels of road usage can be simulated. A large number of fixed and learning traffic light controllers have already been tested in the simulator and the resulting average waiting times of cars have been plotted and compared. The results indicate that the learning controllers can reduce average waiting timeswith at least 10% in semi-busy traffic situations, and even much more when high congestion of the traffic occurs.We are currently studying the behaviour of the learning traffic light controllers on many different infrastructures in our simulator. We are also planning to cooperate with other institutes and companies in the Netherlands to apply our system to real world traffic situations. For this, modern technologies such as communicating networks can be brought to use on a very large scale, making the necessary communication between road users and traffic lights possible.中文翻译：智能交通信号灯控制马克·威宁我所选择的社区项目主题是交通灯。

外文翻译基于锁相环的测量信号的处理与仿真

外文翻译毕业设计题目：基于锁相环的测量信号的处理与仿真原文1：Frequency Modulation in Microwave Phase Lock Loop Synthesizers译文1：微波锁相回路合成器的调频原文2：The Design of A Low-Power Low-Noise Phase Lock Loop译文2：低功率低噪声的锁相环的设计Frequency Modulation in Microwave Phase Lock Loop SynthesizersAbstract —This paper shows, that frequency modulation bandwidth of phase locked controlled oscillator (CO) can be simple expanded using precorrecting circuit (corrector) connected to control port of oscillator. A method is presented of calculation of corrector according to exact PLL and frequency response of modulation channel, with experimental demonstration presented of adequacy of described technique being shown.Index Terms —Microwave PLL synthesizer, frequency modulation, maximum deviation, modulation bandwidth.I. INTRODUCTIONIn many microwave systems the synthesizer must generate frequency modulated signal in addition to monochromatic signal generation, its main function. Solution of this problem in case of phase lock loop (PLL) synthesizer becomes complicated due resistance of PLL to the CO modulation, as an automatic control system. The most difficulty is the expansion of modulation band and the modulation index range. The purpose of this paper is contribution in solution of these problems.II. TARGET SETTINGIt is well known that frequency modulation possibility of phase locked CO is limited by cutoff band. Modulation bandwidth corner is equal to PLL angular frequency [1]. In band above cutoff the loop makes no resistance to the CO modulation, but below cutoff its resistance increases when modulating frequency decreases. Thus, modulation bandwidth of CO must be widened up to down the PLL angular frequency. It can be made by three issues:• By decrease of PLL cutoff frequency;•by impact modulating signal into PLL: modulation of the reference frequency, manipulation of feedback division ratio, addition of the modulating signal to control signal of phase detector;•by application of linear precorrection to modulating signal for compensation of high-pass properties of PLL [2,3].Further the last method is considered. It is more effective as it makes no worse on dynamic and spectral purity characteristics of PLL synthesizer like first method and has no limitation of modulation bandwidth above like second way.III. MATHEMA TICAL DESCRIPTION OF CORRECTOR MODEL To improve the modulation sensitivity of CO an active corrector instead the passive corrector [2] is proposed in Fig. 1.Fig. 1. Corrector schematicModulating signal comes to input 1. PLL control signal comes to input 2. Driving signal for CO goes out through output 3.A. Small signal modelCorrector transfer function K1(p) from input 1 to output 3 is represented by:where a, c are gain factors of third stage at low and high frequencies respectively; τ is high frequency time constant of third stage; k is depth of dip of response curve in PLL corner frequency area;b is gain factor of first stage at high frequencies; τ1, τ2 are low and high frequency time constants of dip of response curve respectively. Parameters in (1) can be selected in case of an exact PLL and modulation channel requirements.B. Large signal modelMaximum deviation ΔFmax is limited by several facto rs, which are bound with nonlinear distortions of modulated signal envelope. These distortions appear in such cases as:-voltage or current operational amplifier (opamp) saturation;-CO frequency obtain the corner of regulation curve;-appearance of dynamic distortion of opamp.In first case the maximum deviation with voltage saturation is:where Usat is the saturation voltage of opamp; Kv is CO tuning sensitivity; KL(p) is closed PLL transfer function.In second case maximum deviation is constant equal to distance between average CO frequency and nearest corner of CO regulation curve. In third case maximum deviation is represented by [4]where S is slew rate of opamp.IV. CORRECTOR DESIGN AND TESTFig. 2 shows the calculated and experimental frequency responses of modulation channel with and without corrector. PLL cutoff frequency is 100 kHz, phase margin – 45°, CO tuning sensitivity –95 MHz/V. CO lag is not allowed.Fig. 2. Frequency responses of modulation channel normalized to CO tuning sensitivity Fig. 3 shows calculated and experimental frequency responses of maximum deviation for all types of distortions: solid curve – for first, dotted curve – for second and chain line – for third. Calculation was made for opamp AD829 with Usat=12V. Distance between average CO frequency and nearest corner of CO regulation curve is 50 MHz.From Fig. 2 and 3 is seen that modulation cannel bandwidth with corrector at maximum deviation 100 kHz is of 1,5 kHz facing 100 kHz without corrector. Dynamic distort ions in opamp don’t appear in comparison with two other types. In the fig. 2 experimental curve is close to calculated one. In Fig. 3 experimental curve differs from calculated one because current saturation of opamp has been appeared.V. CONCLUSIONSApplying an introduced corrector in PLL synthesizer one can expand the modulation bandwidth considerably. Here the simple schematic solution and low-cost elements can be used. A calculation method is simple and unlike described one in [3] incorporates the calculation of maximum frequency deviation.Fig. 3. Maximum deviation frequency responses作者：Andrew V. Gorevoy国籍：Russia出处：Siberian Conference on Control and Communications SIBCON–2009微波锁相环合成器的频率调制摘要：本论文表明，通过使用连接预先校正的电路来控制振荡器的端口，柏锁可控制振荡器的调频宽带就能够很容易被扩展。

《信号与系统》信号术语中英文对照.

AAbsolutely integrable 绝对可积Absolutely integrable impulse response 绝对可积冲激响应Absolutely summable 绝对可和Absolutely summable impulse response 绝对可和冲激响应Accumulator 累加器Acoustic 声学Adder 加法器Additivity property 可加性Aliasing 混叠现象All-pass systems 全通系统AM (Amplitude modulation ) 幅度调制Amplifier 放大器Amplitude modulation (AM) 幅度调制Amplitude-scaling factor 幅度放大因子Analog-to-digital (A-to-D) converter 模数转换器Analysis equation 分析公式（方程）Angel (phase) of complex number 复数的角度（相位）Angle criterion 角判据Angle modulation 角度调制Anticausality 反因果Aperiodic 非周期Aperiodic convolution 非周期卷积Aperiodic signal 非周期信号Asynchronous 异步的Audio systems 音频（声音）系统Autocorrelation functions 自相关函数Automobile suspension system 汽车减震系统Averaging system 平滑系统BBand-limited 带（宽）限的Band-limited input signals 带限输入信号Band-limited interpolation 带限内插Bandpass filters 带通滤波器Bandpass signal 带通信号Bandpass-sampling techniques 带通采样技术Bandwidth 带宽Bartlett (triangular) window 巴特利特（三角形）窗Bilateral Laplace transform 双边拉普拉斯变换Bilinear 双线性的Bilinear transformation 双线性变换Bit （二进制）位，比特Block diagrams 方框图Bode plots 波特图Bounded 有界限的Break frequency 折转频率Butterworth filters 巴特沃斯滤波器C“Chirp” transform algorithm“鸟声”变换算法Capacitor 电容器Carrier 载波Carrier frequency 载波频率Carrier signal 载波信号Cartesian (rectangular) form 直角坐标形式Cascade (series) interconnection 串联，级联Cascade-form 串联形式Causal LTI system 因果的线性时不变系统Channel 信道，频道Channel equalization 信道均衡Chopper amplifier 斩波器放大器Closed-loop 闭环Closed-loop poles 闭环极点Closed-loop system 闭环系统Closed-loop system function 闭环系统函数Coefficient multiplier 系数乘法器Coefficients 系数Communications systems 通信系统Commutative property 交换性（交换律）Compensation for nonideal elements 非理想元件的补偿Complex conjugate 复数共轭Complex exponential carrier 复指数载波Complex exponential signals 复指数信号Complex exponential(s) 复指数Complex numbers 复数Conditionally stable systems 条件稳定系统Conjugate symmetry 共轭对称Conjugation property 共轭性质Continuous-time delay 连续时间延迟Continuous-time filter 连续时间滤波器Continuous-time Fourier series 连续时间傅立叶级数Continuous-time Fourier transform 连续时间傅立叶变换Continuous-time signals 连续时间信号Continuous-time systems 连续时间系统Continuous-to-discrete-time conversion 连续时间到离散时间转换Convergence 收敛Convolution 卷积Convolution integral 卷积积分Convolution property 卷积性质Convolution sum 卷积和Correlation function 相关函数Critically damped systems 临界阻尼系统Crosss-correlation functions 互相关函数Cutoff frequencies 截至频率DDamped sinusoids 阻尼正弦振荡Damping ratio 阻尼系数Dc offset 直流偏移Dc sequence 直流序列Deadbeat feedback systems 临界阻尼反馈系统Decibels (dB) 分贝Decimation 抽取Decimation and interpolation 抽取和内插Degenerative (negative) feedback 负反馈Delay 延迟Delay time 延迟时间Demodulation 解调Difference equations 差分方程Differencing property 差分性质Differential equations 微分方程Differentiating filters 微分滤波器Differentiation property 微分性质Differentiator 微分器Digital-to-analog (D-to-A) converter 数模转换器Direct Form I realization 直接I型实现Direct form II realization 直接II型实现Direct-form 直接型Dirichlet conditions 狄里赫利条件Dirichlet, P.L. 狄里赫利Discontinuities 间断点，不连续Discrete-time filters 离散时间滤波器Discrete-time Fourier series 离散时间傅立叶级数Discrete-time Fourier series pair 离散时间傅立叶级数对Discrete-time Fourier transform （DFT）离散时间傅立叶变换Discrete-time LTI filters 离散时间线性时不变滤波器Discrete-time modulation 离散时间调制Discrete-time nonrecursive filters 离散时间非递归滤波器Discrete-time signals 离散时间信号Discrete-time systems 离散时间系统Discrete-time to continuous-time conversion 离散时间到连续时间转换Dispersion 弥撒（现象）Distortion 扭曲，失真Distribution theory（property）分配律Dominant time constant 主时间常数Double-sideband modulation (DSB) 双边带调制Downsampling 减采样Duality 对偶性EEcho 回波Eigenfunctions 特征函数Eigenvalue 特征值Elliptic filters 椭圆滤波器Encirclement property 围线性质End points 终点Energy of signals 信号的能量Energy-density spectrum 能量密度谱Envelope detector 包络检波器Envelope function 包络函数Equalization 均衡化Equalizer circuits 均衡器电路Equation for closed-loop poles 闭环极点方程Euler, L. 欧拉Euler’s relation欧拉关系（公式）Even signals 偶信号Exponential signals 指数信号Exponentials 指数FFast Fourier transform (FFT) 快速傅立叶变换Feedback 反馈Feedback interconnection 反馈联结Feedback path 反馈路径Filter(s) 滤波器Final-value theorem 终值定理Finite impulse response (FIR) 有限长脉冲响应Finite impulse response (FIR) filters 有限长脉冲响应滤波器Finite sum formula 有限项和公式Finite-duration signals 有限长信号First difference 一阶差分First harmonic components 基波分量（一次谐波分量）First-order continuous-time systems 一阶连续时间系统 First-order discrete-time systems 一阶离散时间系统 First-order recursive discrete-time filters 一阶递归离散时间滤波器 First-order systems 一阶系统 Forced response 受迫响应 Forward path 正向通路 Fourier series 傅立叶级数 Fourier transform 傅立叶变换 Fourier transform pairs 傅立叶变换对Fourier, Jean Baptiste Joseph 傅立叶（法国数学家，物理学家） Frequency response频率响应Frequency response of LTI systems线性时不变系统的频率响应Frequency scaling of continuous-time Fourier transform连续时间傅立叶变化的频率尺度（变换性质） Frequency shift keying (FSK) 频移键控 Frequency shifting property频移性质 Frequency-division multiplexing (FDM) 频分多路复用 Frequency-domain characterization 频域特征 Frequency-selective filter 频率选择滤波器 Frequency-shaping filters 频率成型滤波器 Fundamental components基波分量Fundamental frequency 基波频率Fundamental period 基波周期GGain 增益Gain and phase margin 增益和相位裕度General complex exponentials 一般复指数信号Generalized functions 广义函数Gibbs phenomenon 吉伯斯现象Group delay 群延迟HHalf-sample delay 半采样间隔时延Hanning window 汉宁窗Harmonic analyzer 谐波分析议Harmonic components 谐波分量Harmonically related 谐波关系Heat propagation and diffusion 热传播和扩散现象Higher order holds 高阶保持Highpass filter 高通滤波器Highpass-to-lowpass transformations 高通到低通变换Hilbert transform 希尔波特滤波器Homogeneity (scaling) property 齐次性（比例性）IIdeal 理想的Ideal bandstop characteristic 理想带阻特征Ideal frequency-selective filter 理想频率选择滤波器Idealization 理想化Identity system 恒等系统Imaginary part 虚部Impulse response 冲激响应Impulse train 冲激串Incrementally linear systems 增量线性系统Independent variable 独立变量Infinite impulse response (IIR) 无限长脉冲响应Infinite impulse response (IIR) filters 无限长脉冲响应滤波器Infinite sum formula 无限项和公式Infinite taylor series 无限项泰勒级数Initial-value theorem 初值定理Inpulse-train sampling 冲激串采样Instantaneous 瞬时的Instantaneous frequency 瞬时频率Integration in time-domain 时域积分Integration property 积分性质Integrator 积分器Interconnection 互联Intermediate-frequency (IF) stage 中频级Intersymbol interference (ISI) 码间干扰Inverse Fourier transform 傅立叶反变换Inverse Laplace transform 拉普拉斯反变换Inverse LTI system 逆线性时不变系统Inverse system design 逆系统设计Inverse z-transform z反变换Inverted pendulum 倒立摆Invertibility of LTI systems 线性时不变系统的可逆性Invertible systems 逆系统LLag network 滞后网络Lagrange, J.L. 拉格朗日（法国数学家，力学家）Laplace transform 拉普拉斯变换Laplace, P.S. de 拉普拉斯（法国天文学家，数学家）lead network 超前网络left-half plane 左半平面left-sided signal 左边信号Linear 线性Linear constant-coefficient difference线性常系数差分方程equationsLinear constant-coefficient differential线性常系数微分方程equationsLinear feedback systems 线性反馈系统Linear interpolation 线性插值Linearity 线性性Log magnitude-phase diagram 对数幅－相图Log-magnitude plots 对数模图Lossless coding 无损失码Lowpass filters 低通滤波器Lowpass-to-highpass transformation 低通到高通的转换LTI system response 线性时不变系统响应LTI systems analysis 线性时不变系统分析MMagnitude and phase 幅度和相位Matched filter 匹配滤波器Measuring devices 测量仪器Memory 记忆Memoryless systems 无记忆系统Modulating signal 调制信号Modulation 调制Modulation index 调制指数Modulation property 调制性质Moving-average filters 移动平均滤波器Multiplexing 多路技术Multiplication property 相乘性质Multiplicities 多样性NNarrowband 窄带Narrowband frequency modulation 窄带频率调制Natural frequency 自然响应频率Natural response 自然响应Negative (degenerative) feedback 负反馈Nonanticipatibe system 不超前系统Noncausal averaging system 非因果平滑系统Nonideal 非理想的Nonideal filters 非理想滤波器Nonmalized functions 归一化函数Nonrecursive 非递归Nonrecursive filters 非递归滤波器Nonrecursive linear constant-coefficient 非递归线性常系数差分方程difference equationsNyquist frequency 奈奎斯特频率Nyquist rate 奈奎斯特率Nyquist stability criterion 奈奎斯特稳定性判据OOdd harmonic 奇次谐波Odd signal 奇信号Open-loop 开环Open-loop frequency response 开环频率响应Open-loop system 开环系统Operational amplifier 运算放大器Orthogonal functions 正交函数Orthogonal signals 正交信号Oscilloscope 示波器Overdamped system 过阻尼系统Oversampling 过采样Overshoot 超量PParallel interconnection 并联Parallel-form block diagrams 并联型框图Parity check 奇偶校验检查Parseval’s relatio n 帕斯伐尔关系（定理）Partial-fraction expansion 部分分式展开Particular and homogeneous solution 特解和齐次解Passband 通频带Passband edge 通带边缘Passband frequency 通带频率Passband ripple 通带起伏（或波纹）Pendulum 钟摆Percent modulation 调制百分数Periodic 周期的Periodic complex exponentials 周期复指数Periodic convolution 周期卷积Periodic signals 周期信号Periodic square wave 周期方波Periodic square-wave modulating signal 周期方波调制信号Periodic train of impulses 周期冲激串Phase (angle) of complex number 复数相位（角度）Phase lag 相位滞后Phase lead 相位超前Phase margin 相位裕度Phase shift 相移Phase-reversal 相位倒置Phase modulation 相位调制Plant 工厂Polar form 极坐标形式Poles 极点Pole-zero plot(s) 零极点图Polynomials 多项式Positive (regenerative) feedback 正（再生）反馈Power of signals 信号功率Power-series expansion method 幂级数展开的方法Principal-phase function 主值相位函数Proportional (P) control 比例控制Proportional feedback system 比例反馈系统Proportional-plus-derivative 比例加积分Proportional-plus-derivative feedback 比例加积分反馈Proportional-plus-integral-plus-differential比例－积分－微分控制(PID) controlPulse-amplitude modulation 脉冲幅度调制Pulse-code modulation 脉冲编码调制Pulse-train carrier 冲激串载波QQuadrature distortion 正交失真Quadrature multiplexing 正交多路复用Quality of circuit 电路品质（因数）RRaised consine frequency response 升余弦频率响应Rational frequency responses 有理型频率响应Rational transform 有理变换RC highpass filter RC 高阶滤波器RC lowpass filter RC 低阶滤波器Real 实数Real exponential signals 实指数信号Real part 实部Rectangular (Cartesian) form 直角（卡笛儿）坐标形式Rectangular pulse 矩形脉冲Rectangular pulse signal 矩形脉冲信号Rectangular window 矩形窗口Recursive (infinite impulse response) filters 递归（无时限脉冲响应）滤波器Recursive linear constant-coefficient递归的线性常系数差分方程difference equationsRegenerative (positive) feedback 再生（正）反馈Region of comvergence 收敛域right-sided signal 右边信号Rise time 上升时间Root-locus analysis 根轨迹分析（方法）Running sum 动求和SS domain S域Sampled-data feedback systems 采样数据反馈系统Sampled-data systems 采样数据系统Sampling 采样Sampling frequency 采样频率Sampling function 采样函数Sampling oscilloscope 采样示波器Sampling period 采样周期Sampling theorem 采样定理Scaling (homogeneity) property 比例性（齐次性）性质Scaling in z domain z域尺度变换Scrambler 扰频器Second harmonic components 二次谐波分量Second-order 二阶Second-order continuous-time system 二阶连续时间系统Second-order discrete-time system 二阶离散时间系统Second-order systems 二阶系统sequence 序列Series (cascade) interconnection 级联（串联）Sifting property 筛选性质Sinc functions sinc函数Single-sideband 单边带Single-sideband sinusoidal amplitude单边带正弦幅度调制modulationSingularity functions 奇异函数Sinusoidal 正弦（信号）Sinusoidal amplitude modulation 正弦幅度调制Sinusoidal carrier 正弦载波Sinusoidal frequency modulation 正弦频率调制Sliding 滑动Spectral coefficient 频谱系数Spectrum 频谱Speech scrambler 语音加密器S-plane S平面Square wave 方波Stability 稳定性Stabilization of unstable systems 不稳定系统的稳定性（度）Step response 阶跃响应Step-invariant transformation 阶跃响应不定的变换Stopband 阻带Stopband edge 阻带边缘Stopband frequency 阻带频率Stopband ripple 阻带起伏（或波纹）Stroboscopic effect 频闪响应Summer 加法器Superposition integral 叠加积分Superposition property 叠加性质Superposition sum 叠加和Suspension system 减震系统Symmetric periodic 周期对称Symmetry 对称性Synchronous 同步的Synthesis equation 综合方程System function(s) 系统方程TTable of properties 性质列表Taylor series 泰勒级数Time 时间，时域Time advance property of unilateral单边z变换的时间超前性质z-transformTime constants 时间常数Time delay property of unilateral单边z变换的时间延迟性质z-transformTime expansion property 时间扩展性质Time invariance 时间变量Time reversal property 时间反转（反褶）性Time scaling property 时间尺度变换性Time shifting property 时移性质Time window 时间窗口Time-division multiplexing (TDM) 时分复用Time-domain 时域Time-domain properties 时域性质Tracking system (s) 跟踪系统Transfer function 转移函数transform pairs 变换对Transformation 变换（变形）Transition band 过渡带Transmodulation (transmultiplexing) 交叉调制Triangular (Barlett) window 三角型（巴特利特）窗口Trigonometric series 三角级数Two-sided signal 双边信号Type l feedback system l 型反馈系统UUint impulse response 单位冲激响应Uint ramp function 单位斜坡函数Undamped natural frequency 无阻尼自然相应Undamped system 无阻尼系统Underdamped systems 欠阻尼系统Undersampling 欠采样Unilateral 单边的Unilateral Laplace transform 单边拉普拉斯变换Unilateral z-transform 单边z变换Unit circle 单位圆Unit delay 单位延迟Unit doublets 单位冲激偶Unit impulse 单位冲激Unit step functions 单位阶跃函数Unit step response 单位阶跃响应Unstable systems 不稳定系统Unwrapped phase 展开的相位特性Upsampling 增采样VVariable 变量WWalsh functions 沃尔什函数Wave 波形Wavelengths 波长Weighted average 加权平均Wideband 宽带Wideband frequency modulation 宽带频率调制Windowing 加窗zZ domain z域Zero force equalizer 置零均衡器Zero-Input response 零输入响应Zero-Order hold 零阶保持Zeros of Laplace transform 拉普拉斯变换的零点Zero-state response 零状态响应z-transform z变换z-transform pairs z变换对。

数字信号处理词汇英文翻译

150
complex conjugate pairs复共轭对
151
quantization effects in digital filters数字滤波器中的量化效应
152
roundofferror舍入误差
153
sample-by-sample processing algorithm逐个样本处理算法
96
unit step单位阶跃信号
97
alternating step正负交替的阶跃信号
98
Z-transform Z变换
99
positive正的
100
negative负的
101
region of convergence收敛域
102
marginally stable临界稳定
103
polynomial多项式
208
Decimation-in-time radix-2 FFT algorithm按时间抽取的基二FFT算法
209
butterfly merging equations蝶形组合公式
210
shuffling重排
211
bit reversal码位倒置
212
fast convolution快速卷积
213
104
denominator分母
105
numerator分子
106
peak峰
107
dip谷
108
partial fraction expansion method部分分式展开法
109
unit circle单位圆
110
double sided complex sinusoid双边复正弦

数字信号处理英文文献及翻译

Digital Signal Processing数字信号处理院系:专业:学号:姓名:【英文原文】Digital Signal Processing1、IntroductionDigital signal processing is will signal to digitally says and deal with the theory and technology. Digital signal processing and analog signal processing is signal processing subset.Digital signal processing algorithm need to use special processing equipment such as computer or digital signal processor and application-specific integrated circuits, etc. Digital signal processing technology and equipment with flexible, precies anti-jamming of strong, equipment of small size, low cost, speed such outstanding advantages, these are simulation signal processing technology and equipment and incomparable.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 SamplingWith 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 ofsignals 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 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 cond ition, 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 “n on-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 f ilters 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 pr evious 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 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 transformis 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 presentin 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 (n log n) 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、Signal ProcessingSignals commonly need to be processed in a variety of ways. For example, the output signal from a transducer may well be contaminated with unwanted electrical “noise”. The electrodes attached to a patient’s chest when an ECG is taken measure tiny electrical voltage changes due to the activity of the heart and other muscles. The signal is often strongly affected by “mains pickup” due to electrical interference from the mains supply. Processing the signal using a filter circuit can remove or at least reduce the unwanted part of the signal. Increasingly nowadays, the filtering of signals to improve signal quality or to extract important information is done by DSP techniques rather than by analog electronics.6、Development of DSPThe development of digital signal processing dates from the 1960’s with the use of mainframe digital computers number-crunching applications such an the Fast Fourier Transform (FFT), which allows the frequency spectrum of a signal to be computed rapidly. These techniques are not widely used at that time, because suitable computing equipment was generally available only in universities and other scientific research institutions.7、Digital Signal Processors (DSPs)The introduction of the microprocessor in the late 1970’s and early 1980’s made it possible for DSP techniques to be used in a much wider range of applications. However, general-purpose microprocessors such as the Inter x86 family are not ideally suited to the numerically-intensive requirements of DSP, and during the 1980’s the increasing importance of DSP led several major electronics manuf acturers (such as Texas Instruments, Analog Devices and Motorola) to develop Digital Signal Processor chips-specialized microprocessors with architectures designed specifically for the types of operations required in digital signal processing.(Note that the acronym DSP can variously mean Digital Signal Processing, the term used for a wide range of techniques for processing signals digitally, or Digital Signal Processor,a specialized type of microprocessor chip). Like a general-purpose microprocessor, a DSP is a programmable device, with its own native instruction code. DSP chip are capable of carrying out millions of floating point operations per second, and like their better-known general-purpose cousins, faster and more powerful versions are continually being introduced. DSPs can also be embedded within complex “system-on-chip” devices, often containing both analog and digital circuitry.8、Applications of DSPGeneral speaking, digital signal processing is the study method of using a digital signal, analysis, transformation, filtering, detetion, modulation and a fast aloorithm door technology subject.DSP technology is nowadays commonplace in such devices as mobile phones, multimedia computers, video recorders, CD players, hard disc drive controllers and modems, and will soon replace analog circuitry in TV sets and telephones. An important application of DSP is in signal compression and decompression. Signal compression is used in digital cellular phones to allow a greater number of calls to be handled simul taneously within each local “cell”. DSP signal compression technology allows people not only to talk to one another but also to see one anther on their computer screens, using small video cameras mounted on the computer monitors, with only a conventional telephone line linking them together. In audio CD systems, DSP technology is used to perform complex error detection and correction on the raw data as it is read from the CD.Although some of the mathematical theory underlying DSP techniques, such as Fourier and Hilbert transforms, digital filter design and signal compression, can be fairly complex, the numerical operations required actually to implement these techniques are very simple, consisting mainly of operations that could be done on a cheap four-function calculator. The architecture of a DSP chip is designed to carry out such operations incredibly fast, processing hundreds of millions of samples every second, to provided real-time performance: that is , the ability to process a signal “live” as it is sampled and then output the processed signal, for example to a loudspeaker or video display. All of the practical examples of DSP applications mentioned earlier, such as hard disc drives and mobile phones, demand real-time operation.The major electronics manufacturers have invested heavily in DSP technology. Because they now find application in mass-market products, DSP chips account for a substantial proportion of the world market for electronic devices. Sales amount to billions of dollars annually, and seem likely to continue to increase rapidly.The 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 hi-fi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes.数字信号处理一、数字信号处理的概述数字信号处理是将信号以数字方式表示并处理的理论和技术。

脉宽调制器与通用定时器中英文对照外文翻译文献

中英文资料外文翻译文献(文档含英文原文和中文翻译)ARM Cortex-M3脉宽调制器(PWM)与通用定时器1.PWM脉宽调制（PWM）是一项功能强大的技术，它是一种对模拟信号电平进行数字化编码的方法。

在脉宽调制中使用高分辨率计数器来产生方波，并且可以通过调整方波的占空比来对模拟信号电平进行编码。

PWM通常使用在开关电源（switching power）和电机控制中。

StellarisPWM模块由3个PWM发生器模块1个控制模块组成。

每个PWM 发生器模块包含1个定时器（16位递减或先递增后递减计数器），2个PWM比较器，PWM信号发生器，死区发生器和中断/ADC-触发选择器。

而控制模块决定了PWM 信号的极性，以及将哪个信号传递到管脚。

每个PWM发生器模块产生两个PWM信号，这两个PWM信号可以是独立的信号（基于同一定时器因而频率相同的独立信号除外），也可以是一对插入了死区延迟的互补（complementary）信号。

这些PWM发生模块的输出信号在传递到器件管脚之前由输出控制模块管理。

StellarisPWM模块具有极大的灵活性。

它可以产生简单的PWM信号，如简易充电泵需要的信号；也可以产生带死区延迟的成对PWM信号，如供半-H桥（half-H bridge）驱动电路使用的信号。

3个发生器模块也可产生3相反相器桥所需的完整6通道门控。

PWM定时器每个PWM发生器的定时器都有两种工作模式：递减计数模式或先递增后递减计数模式。

在递减计数模式中，定时器从装载值开始计数，计数到零时又返回到装载值并继续递减计数。

在先递增后递减计数模式中，定时器从0开始往上计数，一直计数到装载值，然后从装载值递减到零，接着再递增到装载值，依此类推。

通常，递减计数模式是用来产生左对齐或右对齐的PWM信号，而先递增后递减计数模式是用来产生中心对齐的PWM信号。

PWM定时器输出3个信号，这些信号在生成PWM信号的过程中使用：方向信号（在递减计数模式中，该信号始终为低电平，在先递增后递减计数模式中，则是在低高电平之间切换）；当计数器计数值为0时，一个宽度等于时钟周期的高电平脉冲；当计数器计数值等于装载值时，一个宽度等于时钟周期的高电平脉冲。

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信号处理中英文对照外文翻译文献(文档含英文原文和中文翻译)译文：一小波研究的意义与背景在实际应用中，针对不同性质的信号和干扰，寻找最佳的处理方法降低噪声，一直是信号处理领域广泛讨论的重要问题。

目前有很多方法可用于信号降噪，如中值滤波，低通滤波，傅立叶变换等，但它们都滤掉了信号细节中的有用部分。

传统的信号去噪方法以信号的平稳性为前提，仅从时域或频域分别给出统计平均结果。

根据有效信号的时域或频域特性去除噪声，而不能同时兼顾信号在时域和频域的局部和全貌。

更多的实践证明，经典的方法基于傅里叶变换的滤波，并不能对非平稳信号进行有效的分析和处理，去噪效果已不能很好地满足工程应用发展的要求。

常用的硬阈值法则和软阈值法则采用设置高频小波系数为零的方法从信号中滤除噪声。

实践证明，这些小波阈值去噪方法具有近似优化特性，在非平稳信号领域中具有良好表现。

小波变换具有多分辨性、时频局部化特性及计算的快速性等属性，这使得小波变换在地球物理领域有着广泛的应用。

随着技术的发展，小波包分析(Wavelet Packet Analysis)方法产生并发展起来，小波包分析是小波分析的拓展，具有十分广泛的应用价值。

运用小波包分析进行信号消噪、特征提取和识别是小波包分析在数字信号处理中的重要应用。

二小波分析的发展与应用小波包分析的应用是与小波包分析的理论研究紧密地结合在一起的。

近年来，小波包的应用范围也是越来远广。

小波包分析能够把任何信号映射到一个由基本小波伸缩、平移而成的一组小波函数上去。

实现信号在不同时刻、不同频带的合理分离而不丢失任何原始信息。

这些功能为动态信号的非平稳描述、机械零件故障特征频率的分析、微弱信号的提取以实现早期故障诊断提供了高效、有力的工具。

（1）小波包分析在图像处理中的应用在图像处理中，小波包分析的应用是很成功的，而这一方面的著作和学术论文也特别多。

二进小波变换用于图像拼接和镶嵌中，可以消除拼接缝。

利用正交变换和小波包进行图像数据压缩。

可望克服由于数据压缩而产生的方块效应，获得较好的压缩效果。

利用小波包变换方法可进行边缘检测、图像匹配、图像目标识别及图像细化等。

（2）小波包分析在故障诊断中的应用小波包分析在故障诊断中的应用已取得了极大的成功。

小波包分析不仅可以在低信噪比的信号中检测到故障信号，而且可以滤去噪声恢复原信号，具有很高的应用价值。

小波包变换适用于电力系统故障分析，尤其适用于电动机转子鼠笼断条以及发电机转子故障分析。

用二进小波Mallat算法对往复压缩机盖振动信号进行分解和重构，可诊断出进、排气阀泄漏故障。

利用小波包对变速箱故障声压信号进行分解，诊断出了变速箱齿根裂纹故障等。

（3）小波包分析在语音信号处理中的应用语音信号处理的目的是得到一些语音参数以便高效地传输或存储。

利用小波包分析可以提取语音信号的一些参数，并对语音信号进行处理。

小波包理论应用在语音处理方面的主要内容包括：清浊音分割、基音检测、去躁、重建与数据压缩等几个方面。

小波包应用于语音信号提取、语音台成语音增加波形编码已取得了很好的效果。

三基础知识介绍近年来,小波理论得到了非常迅速的发展,而且由于其具备良好的时频特性,实际应用也非常广泛。

这里希望利用小波的自身特性,在降低噪声影响的同时,尽量保持图像本身的有用细节和边缘信息,从而保证图像的最佳效果。

小波合成连续小波变换是一种可逆的变换，只要满足方程2。

幸运的是，这是一个非限制性规定。

如果方程2得到满足，连续小波变换是可逆的，即使基函数一般都是不正交的。

重建可能是使用下面的重建公式：公式1小波逆变换公式其中C_psi是一个常量，取决于所使用的小波。

该重建的成功取决于这个叫做受理的常数，受理满足以下条件：公式2受理条件方程这里 psi^hat(xi) 是 FT 的psi(t)，方程2意味着psi^hat(0) = 0，这是:公式3如上所述，公式3并不是一个非常严格的要求，因为许多小波函数可以找到它的积分是零。

要满足方程3，小波必须振荡。

连续小波变换连续小波变换作为一种替代快速傅里叶变换办法来发展，克服分析的问题。

小波分析和STFT 的分析方法类似，在这个意义上说，就是信号和一个函数相乘，{它的小波}，类似的STFT的窗口功能，并转换为不同分段的时域信号。

但是，STFT和连续小波变换二者之间的主要区别是：1、Fourier转换的信号不采取窗口，因此，单峰将被视为对应一个正弦波，即负频率是没有计算。

2、窗口的宽度是相对于光谱的每一个组件变化而变化的，这是小波变换计算最重要的特征。

连续小波变换的定义如下：公式4从上面的方程可以看出，改变信号功能的有两个变量，τ和s，分别是转换参数和尺度参数。

psi(t)为转化功能。

小波包分析的基本原理目前大多数数字图像系统中，输入图像都是采用先冻结再扫描方式将多维图像变成一维电信号，再对其进行处理、存储、传输等加工变换。

最后往往还要在组成多维图像信号，而图像噪声也将同样受到这样的分解和合成。

噪声对图像信号幅度、相位的影响非常复杂，有些噪声和图像信号是相互独立不相关的，而有些则是相关的，并且噪声本身之间也可能相关。

因此要有效降低图像中的噪声，必须针对不同的具体情况采用不同方法，否则就很难获得满意的去噪效果。

一般图像去噪中常见的噪声有以下几种：1）加性噪声：加性噪声和图像信号强度是不相关的，如图像在传输过程中引进的“信道噪声”电视摄像机扫描图像的噪声等。

这类带有噪声的图像可看成是理想的没有被噪声“污染”的图像与噪声。

2）乘性噪声：图像的乘性噪声和图像的加性噪声是不一样的，加性噪声和图像信号强度是不相关的，而乘性噪声和图像信号是相关的，往往随着图像信号的变化而发生变化，如飞点扫描图像中的噪声、电视扫描光栅、胶片颗粒噪声等。

3）量化噪声：量化噪声是数字图像的主要噪声源，它的大小能够表示出数字图像和原始图像的差异程度，有效减少这种噪声的最好办法就是采用按灰度级概率密度函数选择量化级的最优量化措施。

4）“椒盐”噪声：此种噪声很多，例如在图像切割过程中引起的黑图像上的白点、白图像上的黑点噪声等，还有在变换域引入的误差，在图像反变换时引入的变换噪声等。

实际生活中还有多种多样的图像噪声，如皮革上的疤痕噪声、气象云图上的条纹噪声等。

这些噪声一般都是简单的加性噪声，不会随着图像信号的改变而改变。

这为实际的去噪工作提供了依据。

2.图像去噪效果的评价在图像去噪的处理中，常常需要评价去噪后图像的质量。

这是因为一个图像经过去噪处理后所还原图像的质量好坏，对于人们判断去噪方法的优劣有很重要的意义。

目前对图像的去噪质量评价主要有两类常用的方法：一类是人的主观评价，它由人眼直接观察图像效果，这种方法受人为主观因素的影响比较大。

目前由于对人的视觉系统性质还没有充分的理解，对人的心理因素还没有找到定量分析方法。

因此主观评价标准还只是一个定性的描述方法，不能作定量描述，但它能反映人眼的视觉特性。

另一类是图像质量的客观评价。

调试环境-MATLAB开发平台MATLAB是Math Works公司开发的一种跨平台的，用于矩阵数值计算的简单高效的数学语言，与其它计算机高级语言如C, C++, Fortran, Basic, Pascal等相比，MATLAB语言编程要简洁得多，编程语句更加接近数学描述，可读性好，其强大的圆形功能和可视化数据处理能力也是其他高级语言望尘莫及的。

四综述众所周知，由于图像在采集、数字化和传输过程中常受到各种噪声的干扰，从而使数字图像中包含了大量的噪声。

能否从受扰信号中获得去噪的信息，不仅与干扰的性质和信号形式有关，也与信号的处理方式有关。

在实际应用中，针对不同性质的信号和干扰，寻找最佳的处理方法降低噪声，一直是信号处理领域广泛讨论的重要问题。

小波包分析的应用是与小波包分析的理论研究紧密地结合在一起的。

现在，它已经在科技信息产业领域取得了令人瞩目的成就。

如今，信号处理已经成为当代科学技术工作的重要组成部分，信号处理的目的就是：准确的分析、诊断、编码、压缩和量化、快速传递或存储、精确的恢复（或重构）。

从数学的角度来看，信号与图像处理可以统一看作是信号处理，在小波包分析的许多分析的许多应用中，都可以归结为信号处理问题。

小波包分析的应用领域十分广泛，它包括：信号分析、图象处理、量子力学、理论物理、军事电子对抗与武器的智能化、计算机分类与识别、音乐与语言的人工合成、医学成像与诊断、地震勘探数据处理、大型机械的故障诊断等方面。

例如，在数学方面，它已用于数值分析、构造快速数值方法、曲线曲面构造、微分方程求解、控制论等。

在信号分析方面的滤波、去噪、压缩、传递等。

在图像处理方面的图象压缩、分类、识别与诊断，去污等。

在医学成像方面的减少B超、CT、核磁共振成像的时间，提高分辨率等。

小波包分析用于信号与图像压缩是小波包分析应用的一个重要方面。

它的特点是压缩比高，压缩速度快，压缩后能保持信号与图像的特征不变，且在传递中可以抗干扰。

基于小波包分析的压缩方法很多，比较成功的有小波包最好基方法，小波域纹理模型方法，小波变换零树压缩，小波变换向量压缩等。

小波包在信号分析中的应用也十分广泛。

它可以用于边界的处理与滤波、时频分析、信噪分离与提取弱信号、求分形指数、信号的识别与诊断以及多尺度边缘检测等。

A ·The wavelet study the meaning and backgroundIn practical applications, the different nature of the signal and interference, to find the best processing method to reduce noise, the important issue is widely discussed in the field of signal processing. Currently, there are many methods can be used to signal noise reduction, such as median filtering, low pass filtering, Fourier transform, etc., but they are filtered out by the useful part of the signal details. The traditional signal de-noising method smooth signal only from the time domain or frequency domain are given the results of the statistical average. Time domain or frequency domain characteristics of the effective signal to noise removal, but not taking into account the local and the whole picture of the signal in the time domain and frequency domain. More Practice has proved that the classical approach based on the Fourier transform of the filter, and can not be non-stationary signal analysis and processing, denoising effect can not meet the requirements of engineering application development. In recent years, many papers non-stationary signal wavelet threshold de-noising method. Donoho and Johnstone contaminated with Gaussian noise signalde-noising by thresholding wavelet coefficients. Commonly used hard threshold rule and soft threshold rule set to filter out the noise from the signal high-frequency wavelet coefficients to zero. Practice has proved that these wavelet thresholding method with approximate optimization features, has a good performance in the field of non-stationary signals. The threshold rule mainly depends on the choice of parameters. For example, the hard threshold and soft threshold depends on the choice of a single parameter - global threshold lambda lambda adjustment is critical However, due to the non-linearity of the wavelet transform. Threshold is too small or too large, will be directly related to the pros and cons of the signal de-noising effect. When the threshold value is dependent on a number of parameters, the problem will become more complex. In fact, the effective threshold denoising method is often determined based on wavelet decomposition at different levels depending on the threshold parameter, and then determine the appropriate threshold rule. Compared with the wavelet analysis, wavelet packet analysis (Wavelet Packet Analysis) to provide a more detailed analysis for the signal, it will band division of multi-level, multi-resolution analysisis no breakdown of the high-frequency part of the further decomposition, and according to the characteristic of the signal being analyzed, adaptive selection of the corresponding frequency band, to match with the signal spectrum, thereby increasing the time - frequency resolution. The wavelet packet transform is the promotion of the wavelet transform in signal with more flexibility than the wavelet transform. Using wavelet packet transform to the signal decomposition, the low-frequency part andhigh-frequency components are further decomposed. Wavelet packet signal de-noising threshold method combined with good application value.At present, both in engineering applications and theoretical study, removal of signal interference noise is a hot topic. Extract valid signal is band a wide interference or white noise pollution signal mixed with noise signal, has been an important part of signal processing. The traditional digital signal analysis and processing is to establish the basis of Fourier transform, Fourier transform stationary signals in the time domain and frequency domain algorithm to convert each other, but can not accurately represent the signal time-frequency localization properties. For non-stationary signals people use short-time Fourier transform, but it uses a fixed short-time window function is a single-resolution signal analysis method, there are some irreparable defect. Wavelet theory is developed on the basis of Fourier transform and short-time Fourier transform, and it has the characteristics of multi-resolution analysis, have the ability to characterize the local signal characteristics in the time domain and frequency domain, is an excellent tool for signal analysis . Wavelet transform (Wavelet transform) emerged in the mid 1980s when the frequency domain signal analysis tools, since 1989 S.Mallat the first time since the introduction of wavelet transform image processing, wavelet transform its excellent time-frequency local capacity and good to go related capacity in the field of image compression coding has been widely used, and achieved good results. Multi-resolution wavelet transform, time-frequency localization characteristics and calculation speed and other attributes, which makes the wavelet transform has been widely applied in the field of geophysics. Such as: using wavelet transform gravity and magnetic parameters of the extraction, the magnitude of the error of the reconstructed signal with the original signal after the wavelet analysis as a standard to select the wavelet basisSeismic data denoising. As technology advances, the wavelet packet analysis (Wavelet Packet Analysis) method developed wavelet packet analysis is the expansion of the wavelet analysis, with a very wide range of application. It is able to signal to provide a more detailed analysis of the method, it is the bandmulti-level framing is not broken down at high frequency portion of the discrete wavelet transform isfurther analyzed, and according to the characteristics of the signal to be analyzed, adaptively selecting the frequency band corresponding to , with the signal matching, thereby increasing the time-frequency resolution. The wavelet packet analysis (wavelet packet analysis) signal to be able to provide a more detailed analysis of the method, it is divided band multi-level wavelet analysis no breakdown of the high frequency portion is further decomposed, and according to the characteristic of the signal being analyzed, adaptively select the appropriate frequency band, the signal spectrum to match, thus wavelet packet has a wider range of applications. Fractal theory of wavelet packet by U.S. scientists BBMandelbrot in themid-1970s the creation of "self-similarity" and "self-affine fractal object, dimension to quantitatively describe the complexity of the signal, it is mainly research, widely used in many fields of science, including the recent wavelet analysis and fractal theory, is used to determine the overlap complex chemical signals in the group scores and the peak position and fractal characteristics of the DNA sequence. Using wavelet packet analysis for signal noise reduction, an intuitive and effective wavelet packet de-noising method is the direct thresholding wavelet packet decomposition coefficients, select the filter factor coefficient signal reconstruction preserved, and ultimately to drop The purpose of the noise. Signal de-noising using wavelet packet analysis, feature extraction and recognition is an important application of wavelet packet analysis in digital signal processing.B·The development and application of wavelet analysisWavelet packet analysis of the application of theoretical research and wavelet packet analysis closely together. Now, it has been made in the field of science and technology information industry made remarkable achievements. Electronic information technology is an area of six high-tech focus, image and signal processing. Today, the signal processing has become an important part of the contemporary scientific and technical work, the purpose of signal processing: an accurate analysis, diagnosis, compression coding and quantization, rapid transfer or storage, accurately restore (or reconstructed). From the point of view of mathematically, signal and image processing can be unified as a signal processing, wavelet packet analysis many many applications of the analysis, can be attributed to the signal processing problem. Now, for its nature with practice is stable and unchanging signal processing ideal tool still Fourier analysis. However, in practical applications, the vast majority of the signal is stable, while the tool is especially suitable fornon-stationary signal is wavelet packet analysis.In recent years, the combined fund research projects and corporate research projects. China in theapplication of wavelet packet analysis carried out some exploration.First, wavelet packet signal analysis, the the boundary singularity processing method and wavelet packet processing in the frequency domain positioning is perfect from the application point of view. Harmonic wavelet packet analysis method, and the harmonic wavelet packet and fractal combined to solve practical problems in engineering.Secondly, in the operation of the rotor vibration signal detection of the fault feature analysis simulation and practical research. Motor noise analysis method using wavelet packet analysis theory to identify the impact threshold to noise singular signal of the acceleration of the vehicle, using the method of wavelet packet analysis and come to a satisfactory conclusion, while the harmonic wavelet packet combined with the fractal theory. Automobile gearbox nonlinear crack fault feature, the first application of the method of combining wavelet analysis and fractal theory and the technical design of the vehicle driveline. Middle and low agricultural transport light goods vehicle driveline job stability is not good, the problem of short working life, in the practical application of engineering to explore a new way.Next, using theoretical analysis, experiments and software implementation phase junction station, namely the use of wavelet packet analysis and computer programs to achieve the digital signal processing. In the analysis of non-stationary signals, respectively, using existing technology and wavelet packet analysis method, the fractal method is used, expect improvements in digital signal processing. To reflect the complex characteristics of the information to improve the accuracy of the signal analysis and detection, reached the advanced level. On the basis of cooperation with others to complete a set of signal processing methods and techniques of high-speed data processing system.In recent years, the range of applications of the wavelet packet is increasingly far and wide. Wavelet packet analysis any signal can be mapped to a basic wavelet telescopic pan from the wavelet function up. Signal to achieve a reasonable separation of the different frequency bands at different times, without losing any of the original information. These features for non-stationary dynamic signal description, analysis of the mechanical parts fault characteristic frequency, weak signal extraction provides an efficient and powerful tool to achieve early fault diagnosis. In recent years, through the continuous efforts of the scientific and technical personnel in China have achieved encouraging progress, successfully developed a wavelet transform signal analyzer, to fill the gap with the international advanced level. In theoretical and applied research on the basis of the generally applicable to non-stationary detection and diagnosis of mechanical equipment online and offline technologies and devices to obtain economic benefits. The National Scienceand Technology Progress Award.(1) wavelet packet analysis applications in image processingIn image processing, the application of wavelet packet analysis is very successful, and this aspect of books and academic papers are particularly high. Dyadic wavelet transform for image mosaic and mosaic, can eliminate the seam. Orthogonal transform and wavelet packet image data compression. Is expected to overcome the the blocking effects arising due to compression of data, to obtain better compression results. Wavelet packet transform method for edge detection, image matching, image target recognition and image thinning.(2) The wavelet packet analysis application in fault diagnosisWavelet packet analysis in fault diagnosis has been made a great success. Wavelet packet analysis can not only be detected in the low signal-to-noise ratio of the signal to the fault signal, and can filter out the noise to restore the original signal has a high application value. Wavelet packet transform is applied to power system fault analysis, particularly suitable for motor rotor cage broken bars and generator rotor failure analysis. With the dyadic wavelet Mallat algorithm reciprocating compressor cover vibration signal decomposition and reconstruction can be diagnosed into the exhaust valve leakage fault. Gearbox failure sound pressure signal using wavelet packet decomposition, diagnose gearbox root crack fault.Wavelet packet analysis in speech signal processing. The purpose of the speech signal processing is to get some of the speech parameters for efficient transmission or storage. Wavelet packet analysis can extract some of the parameters of the speech signal, speech signal processing. The main contents include: the theory of wavelet packet used in voice processing V oicing segmentation, pitch detection, to impatient to rebuild data compression and other aspects. Wavelet Packet used in speech signal extraction, the voice station into increased voice waveform coding has achieved very good results.Wavelet packet analysis in mathematics and physics. In the field of mathematics, wavelet packet analysis is a powerful tool for numerical analysis, a simple and effective way to solve partial differential equations and integral equations. Also good for solving linear and nonlinear problems. The resulting wavelet finite element method and wavelet boundary element method, greatly enriched the contents of the numerical analysis method.In the field of physics, wavelet packet represents a new condensed matter in quantum mechanics. In the adaptive optics. There are currently study wavelet packet transform wavefront reconstruction. In addition, the suitability of wavelet packet transform to portray irregularities, provides a new tool for turbulenceresearch.Wavelet analysis in medical applications. Micronucleus identification has important applications in medicine. Environmental testing, pharmaceutical and other sets of objects can be used for toxin detection. In the micronucleus computer automatic identification, continuous wavelet can accurately extract the edge of the nucleus. Currently, it is being studied by using wavelet packet transform brain signal analysis and processing, This will effectively eliminate the transient interference and EEG short-term, low-energy transient pulse is detected.Wavelet packet analysis neural network. Wavelet packet theory provides a prequel network analysis and theoretical framework that the wavelet form in the network structure is used to make specific spectral information contained in the training data. Wavelet packet transform designed to handle network training can greatly simplified. Unlike traditional agoThe case of a neural network structure, where the function is convex. Global grant urinate only the wavelet packet analysis and neural network node sets up the equipment intelligent diagnosis. The use of wavelet packet analysis can be given the initial alignment of the linear and nonlinear models of the inertial navigation system.Wavelet packet analysis in engineering calculations. The matrix operations frequently encountered problems in the project, such as dense matrix acting on the vector (discrete) or integral operator acting on the calculation of the function (continuous). Sometimes computation great, fast wavelet transform, so that the operator is greatly reduced. In addition, CAD / C AM, large-scale engineering finite element analysis, mechanical engineering optimization design, automatic test system design aspects of wavelet packet analysis should be examples.Wavelet packet analysis equipment protection and status detection system can also be used, such ashigh-voltage line protection and generator stator inter-turn short circuit protection. In addition, the wavelet packet analysis is also used in astronomical research, weather analysis, identification and signal sending.C·BASIC THEORYIn recent years,wavelet theory has been very rapid development,but also because of its goodtime-frequency character istics of awide range of practical applications. Here wish to take advantage of the self-wavelet features,in the reduction of noise at the same time,to keep the details of the image itself and the edge of useful information,thus ensuring the best image.one of image wavelet thresholding denoising method can be said that many image denoising methods are the best.THE W A VELET THEORY: A MATHEMATICAL APPROACHThis section describes the main idea of wavelet analysis theory, which can also be considered to be the underlying concept of most of the signal analysis techniques. The FT defined by Fourier use basis functions to analyze and reconstruct a function. Every vector in a vector space can be written as a linear combination of the basis vectors in that vector space , i.e., by multiplying the vectors by some constant numbers, and then by taking the summation of the products. The analysis of the signal involves the estimation of these constant numbers (transform coefficients, or Fourier coefficients, wavelet coefficients, etc). The synthesis, or the reconstruction, corresponds to computing the linear combination equation.All the definitions and theorems related to this subject can be found in Keiser's book, A Friendly Guide to Wavelets but an introductory level knowledge of how basis functions work is necessary to understand the underlying principles of the wavelet theory. Therefore, this information will be presented in this section.THE WA VELET SYNTHESISThe continuous wavelet transform is a reversible transform, provided that Equation 2 is satisfied. Fortunately, this is a very non-restrictive requirement. The continuous wavelet transform is reversible if Equation 2 is satisfied, even though the basis functions are in general may not be orthonormal. The reconstruction is possible by using the following reconstruction formula:Equation 1 Inverse Wavelet Transformwhere C_psi is a constant that depends on the wavelet used. The success of the reconstruction depends on this constant called, the admissibility constant , to satisfy the following admissibility condition :Equation 2 Admissibility Conditionwhere psi^hat(xi) is the FT of psi(t). Equation 2 implies that psi^hat(0) = 0, which is:Equation 3As stated above, Equation 3 is not a very restrictive requirement since many wavelet functions can be found whose integral is zero. For Equation 3 to be satisfied, the wavelet must be oscillatory.THE CONTINUOUS W AVELET TRANSFORMThe continuous wavelet transform was developed as an alternative approach to the short time Fourier transform to overcome the resolution problem. The wavelet analysis is done in a similar way to the STFT analysis, in the sense that the signal is multiplied with a function, {it the wavelet}, similar to the windowfunction in the STFT, and the transform is computed separately for different segments of the time-domain signal. However, there are two main differences between the STFT and the CWT:1. The Fourier transforms of the windowed signals are not taken, and therefore single peak will be seen corresponding to a sinusoid, i.e., negative frequencies are not computed.2. The width of the window is changed as the transform is computed for every single spectral component, which is probably the most significant characteristic of the wavelet transform.The continuous wavelet transform is defined as followsEquation4As seen in the above equation , the transformed signal is a function of two variables,τ and s ,the translation and scale parameters, respectively. psi(t) is the transforming function, and it is called the mother wavelet . The term mother wavelet gets its name due to two important properties of the wavelet analysis as explained below:The term wavelet means a small wave . The smallness refers to the condition that this (window) function is of finite length (compactly supported). The wave refers to the condition that this function is oscillatory . The term mother implies that the functions with different region of support that are used in the transformation process are derived from one main function, or the mother wavelet. In other words, the mother wavelet is a prototype for generating the other window functions.The term translation is used in the same sense as it was used in the STFT; it is related to the location of the window, as the window is shifted through the signal. This term, obviously, corresponds to time information in the transform domain. However, we do not have a frequency parameter, as we had before for the STFT. Instead, we have scale parameter which is defined as $1/frequency$. The term frequency is reserved for the STFT. Scale is described in more detail in the next section.MULTIRESOLUTION ANALYSISAlthough the time and frequency resolution problems are results of a physical phenomenon (the Heisenberg uncertainty principle) and exist regardless of the transform used, it is possible to analyze any signal by using an alternative approach called the multiresolution analysis (MRA) . MRA, as implied by its name, analyzes the signal at different frequencies with different resolutions. Every spectral component is not resolved equally as was the case in the STFT.MRA is designed to give good time resolution and poor frequency resolution at high frequencies and good frequency resolution and poor time resolution at low frequencies. This approach makes sense especially when the signal at hand has high frequency components for short durations and low frequency components for long durations. Fortunately, the signals that are encountered in practical applications are often of this type. For example, the following shows a signal of this type. It has a relatively low frequency component throughout the entire signal and relatively high frequency components for a short duration somewhere around the middle.he basic principle of wavelet packet analysisimage noise classificationMost digital imaging systems, the input image are based on the first freeze and then scan the multi-dimensional image into a one-dimensional electrical signal, its processing, storage, transmission and processing transform. Finally, they often have in the composition of multi-dimensional image signal, image noise will be equally subject to such decomposition and synthesis. The impact of noise on the image signal amplitude and phase is very complicated, some noise and image signals are independent of each other Irrelevant, while others are related to, and the noise itself may also be relevant. Therefore, to effectively reduce the noise in the image, using different methods must be specific for the type, otherwise it is difficult to obtain a satisfactory denoising effect. Common in the general image denoising noise are the following: 1) is not relevant to additive noise: the additive noise and the image signal intensity, such as the image introduced during transmission channel noise of the scanned image of the television camera noise. Such with noise of the image can be seen as the ideal no noise pollution "image noise.2) multiplicative noise: image multiplicative noise and image additive noise is not the same, the additive noise and image signal strength is not related to the multiplicative noise and image signals are related, often with the image signal change change, flying point in a scanned image noise, the TV raster scanned film grain noise.3) quantization noise: the quantization noise is the main noise source of a digital image, its size can show the degree of difference of the digital image and the original image, effectively reducing this noise the best way is to select grayscale probability density function quantified level optimal quantitative measures.4) "salt and pepper" noise: Many of such noise, such as white spots on the black image in the the image cutting process caused the white image on the black point noise, the error introduced in the transform domain, the inverse transform of the image introducing the transformed noise.Real life there are a variety of image noise, such as leather scar noise, weather maps stripe noise. These noises are generally simple additive noise will not change with the change of the image signal. This provides a basis for actual denoising.2. Evaluation of the effectiveness of image denoisingIn the image denoising processing is often necessary to evaluate the quality of the image denoising. This is because an image after denoising restore the image quality is good or bad, has a very important significance for the people to judge the merits of de-noising method. Current image denoising quality evaluation mainly there are two commonly used methods: one is the subjective evaluation, it is directly observed by the human eye image effects, which, due to the relatively large human subjective factors. Due to the nature of the human visual system is not fully understood, the psychological factors have yet to find a quantitative analysis method. Subjective evaluation criteria is only a qualitative description can not be quantitative description, but it reflects the human visual characteristics. The other is an objective evaluation of the image quality. It is a mathematical statistics on the processing method, its disadvantage is that it does not always reflect the human eye's real feeling. A compromise approach in assessing the pros and cons of image denoising algorithm, the subjective and objective two standards considered together.debugging environment-MATLAB development platformMATLAB Math Works, Inc. to develop a cross-platform, used for the the matrix numerical calculation of the simple and efficient mathematical language, compared with other high-level computer language such as。