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。

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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。

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。