太赫兹的寡头时代(完整版)

TRTZ理论的40个发明原理原理1：分离法也称分割原理，即将整体切分。

有三方面的含义：1. 将物体分成相互独立的部分。

例如：火车车厢，分离成一个一个的单体车厢；用卡车加拖车代替大卡车；将垃圾箱分割为可回收及不可加收的部分；电冰箱分为冷冻室和冷藏室，并分多个层；运载火箭分为多个助推器；班级为了便于管理分成多个小组等等。

2. 将物体分成容易组装和拆卸的部分。

例如：组合式家具；移动房屋；活动帐篷；组合菜板等，如图2-3(b)所示。

3. 增加物体的分割程度。

例如：用百叶窗代替大的窗帘，输送高温玻璃时用熔化的锡代替滚轴等。

原理2：提取法也称抽取法、抽取原理，即将物体中有用或有害的部分提取出来进行相应的处理。

有两方面的含义：1. 从物体中抽出产生负面影响的部分或属性。

例如：避雷针将雷电引入地下，减少其危害；空调的压缩机分离出来放在室外；食品真空包装等。

例如：用狗的叫声做警报而不用真的养一条狗；把彩喷打印机中的墨盒分离出来以便更换；用光纤或光波导分离主光源，以增加照明点；成分献血，只采集血液中的血小板；微波滤波器；互联网上的搜索引擎等。

原理2：提取法2. 从物体中抽出必要的部分或属性。

原理3：局部质量改善法在物体的特定区域改变其特征，从而获得必要的特性。

有三方面的含义：1. 从物体或外部介质（外部作用）的一致结构过渡到不一致结构。

例如：采用温度、密度、压力的梯度，而不用恒定的温度、密度、压力；刀或斧子的刀刃部分进行特殊处理等。

2. 物体的不同部分应当具有不同的功能。

例如：起钉锤；指甲剪；多功能组合工具等。

3. 物体的每一部分均应处于最有利于其工作的条件。

例如：餐盒的隔间，防止串味；矿山坑道除尘等。

汤勺每一部分都有相应的工作条件。

原理4：非对称法 例如：电源插头做成不对称形式，防止插错；不对称搅拌叶片加强搅拌；铁道转弯处内外铁轨间有高度差以提供向心力，减少对轨道挤压造成的危害；在鞋跟易磨的一侧钉上“鞋掌”；为增强密封性，将圆形密封圈做成椭圆的，等等。

Engineering 2 (2016) xxx–xxxNews & FocusContents lists available at ScienceDirectjo u rn a l h om e pag e: w w w.else /locate/engEngineering一个时代的终结Lance A. DavisSenior Advisor, US National Academy of Engineering许多人都撰写过有关摩尔定律即将消亡和集成电路芯片晶体管密度将呈指数级扩展的文章。

当我们在思索这一即将到来的转变所带来的后果时，我们应该适当花些时间去关注在2016年3月21日逝世的享年79岁的安迪•格鲁夫。

1987年至1998年，格鲁夫担任英特尔公司的首席执行官(CEO)，之后，作为戈登•摩尔的继任者担任该公司的董事长，直到2005年。

和其他人一样，格鲁夫负责将摩尔定律和信息革命变成现实。

安迪•格鲁夫的人生十分引人关注，很多人都说是传奇式的。

在逃离了二战大屠杀和经历了匈牙利11年的政治动荡后，他以移民的身份逃到了美国，身无分文，不会说英语，并且还患有由于儿童时期的疾病导致的听力丧失。

但他成功地从纽约市立学院毕业，然后转到加利福尼亚大学伯克利分校，并于1963年在伯克利获得了化学工程博士学位。

此后不久，他加入了仙童半导体公司，并于1968年协助创立了英特尔。

显然，格鲁夫是一个令人敬畏的技术人才，但是他的专长似乎在管理方面。

这一点可以在1996年出版的《只有偏执狂才能生存》[1]一书中得到印证。

据他的CEO继任者克雷格•贝瑞特描述，他的管理方式就是采用激烈的个人竞争，这在当事者看来就像是被木板击中头部。

许多管理顾问或心理学家可能会说，这种方法过于极端，但正是这种方法多次将英特尔从灾难的边缘拉回来，使其成为历史上最成功的公司之一，成为计算机行业微处理器芯片的主要供应商。

成功并不总是等同于正确。

俄罗斯七大寡头主要内容寡头介绍 (1)金融寡头 (2)七大寡头 (2)弗拉基米尔.波塔宁 (5)罗曼.阿布拉莫维奇 (9)七大寡头——鲍里斯.别列佐夫斯基 (19)七大寡头——弗拉基米尔.古辛斯基 (24)七大寡头——米哈伊尔.鲍里斯维奇.霍多尔科夫斯基 (26)七大寡头——斯摩棱斯基 (30)七大寡头——诺格拉多夫 (30)七大寡头——马尔金 (31)七大寡头——弗里德曼 (31)寡头介绍寡头(Oligopoly)市场又称为寡头垄断市场，它是指少数几家厂商控制整个市场的产品的生产和销售的这样一种市场组织。

寡头市场被认为是一种较为普遍的市场组织，西方国家中不少行业都表现出寡头垄断的特点，例如，美国的汽车业、电气设备业、罐头行业等，都被几家企业所控制。

行成寡头市场的主要原因有：某些产品的生产必须在相当大的生产规模上进行才能达到最好的经济效益；行业中几家企业对生产所需的基本生产资源的供给的控制；政府的扶植和支持等等。

由此可见，寡头世行的成因和垄断市场是很相似的，只是在程度上有所差别而已。

寡头市场是比较接近垄断市场的一种市场组织。

寡头行业可按不同方式分类。

根据产品特征，可分为纯粹寡头行业和差别寡头行业两类。

还可按厂商的行动方式分为有勾结行为的（即合作的）和独立行动的（即不合作的）不同类型。

寡头厂商的价格和产量的决定是非常复杂的问题。

主要原因在于：在寡头市场上，每个寡头的产量都在全行业的总产量中占较大份额，从而每个厂商的产量和价格的变动都会对其他竞争对手以至整个行业的产量和价格产生举足轻重的影响。

从而每个寡头厂商在采取某项行动之前，必须首先推测或掌握自己这一行动对其他厂商的影响以及其他厂商可能做出的反应，考虑到这些因素之后，才能采取最有利的行动。

所以每个寡头厂商的利润都要受到行业中所有厂商的决策的相互作用的影响。

一般而言，不知道竞争对手的反应方式，就无法建立寡头厂商的模型。

或者说，有多少关于竞争对手的反映方式的假定，就有多少寡头厂商的模型，就可以得到多少不同的结果。

904N.E.Huang and others10.Discussion98711.Conclusions991References993 A new method for analysing has been devel-oped.The key part of the methodany complicated data set can be decomposed intoof‘intrinsic mode functions’Hilbert trans-This decomposition method is adaptive,and,highly efficient.Sinceapplicable to nonlinear and non-stationary processes.With the Hilbert transform,Examplesthe classical nonlinear equation systems and dataare given to demonstrate the power new method.data are especially interesting,for serve to illustrate the roles thenonlinear and non-stationary effects in the energy–frequency–time distribution.Keywords:non-stationary time series;nonlinear differential equations;frequency–time spectrum;Hilbert spectral analysis;intrinsic time scale;empirical mode decomposition1.Introductionsensed by us;data analysis serves two purposes:determine the parameters needed to construct the necessary model,and to confirm the model we constructed to represent the phe-nomenon.Unfortunately,the data,whether from physical measurements or numerical modelling,most likely will have one or more of the following problems:(a)the total data span is too short;(b)the data are non-stationary;and(c)the data represent nonlinear processes.Although each of the above problems can be real by itself,the first two are related,for a data section shorter than the longest time scale of a sta-tionary process can appear to be non-stationary.Facing such data,we have limited options to use in the analysis.Historically,Fourier spectral analysis has provided a general method for examin-the data analysis has been applied to all kinds of data.Although the Fourier transform is valid under extremely general conditions(see,for example,Titchmarsh1948),there are some crucial restrictions of Proc.R.Soc.Lond.A(1998)Nonlinear and non-stationary time series analysis905the Fourier spectral analysis:the system must be linear;and the data must be strict-ly periodic or stationary;otherwise,the resulting spectrum will make little physicalsense.to the Fourier spectral analysis methods.Therefore,behoves us review the definitions of stationarity here.According to the traditional definition,a time series,X (t ),is stationary in the wide sense,if,for all t ,E (|X (t )2|)<∞,E (X (t))=m,C (X (t 1),X (t 2))=C (X (t 1+τ),X (t 2+τ))=C (t 1−t 2),(1.1)in whichE (·)is the expected value defined as the ensemble average of the quantity,and C (·)is the covariance function.Stationarity in the wide sense is also known as weak stationarity,covariance stationarity or second-order stationarity (see,forexample,Brockwell &Davis 1991).A time series,X (t ),is strictly stationary,if the joint distribution of [X (t 1),X (t 2),...,X (t n )]and [X (t 1+τ),X (t 2+τ),...,X (t n +τ)](1.2)are the same for all t i and τ.Thus,a strictly stationaryprocess with finite second moments is alsoweakly stationary,but the inverse is not true.Both definitions arerigorous but idealized.Other less rigorous definitions have also beenused;for example,that is stationary within a limited timespan,asymptotically stationary is for any random variableis stationary when τin equations (1.1)or (1.2)approaches infinity.In practice,we can only have data for finite time spans;these defini-tions,we haveto makeapproximations.Few of the data sets,from either natural phenomena or artificial sources,can satisfy these definitions.It may be argued thatthe difficulty of invoking stationarity as well as ergodicity is not on principlebut on practicality:we just cannot have enough data to cover all possible points in thephase plane;therefore,most of the cases facing us are transient in nature.This is the reality;we are forced to face it.Fourier spectral analysis also requires linearity.can be approximated by linear systems,the tendency tobe nonlinear whenever their variations become finite Compounding these complications is the imperfection of or numerical schemes;theinteractionsof the imperfect probes even with a perfect linear systemcan make the final data nonlinear.For the above the available data are ally of finite duration,non-stationary and from systems that are frequently nonlinear,either intrinsicallyor through interactions with the imperfect probes or numerical schemes.Under these conditions,Fourier spectral analysis is of limited use.For lack of alternatives,however,Fourier spectral analysis is still used to process such data.The uncritical use of Fourier spectral analysis the insouciant adoption of the stationary and linear assumptions may give cy range.a delta function will giveProc.R.Soc.Lond.A (1998)906N.E.Huang and othersa phase-locked wide white Fourier spectrum.Here,added to the data in the time domain,Constrained bythese spurious harmonics the wide frequency spectrum cannot faithfully represent the true energy density in the frequency space.More seri-ously,the Fourier representation also requires the existence of negative light intensity so that the components can cancel out one another to give thefinal delta function. Thus,the Fourier components might make mathematical sense,but do not really make physical sense at all.Although no physical process can be represented exactly by a delta function,some data such as the near-field strong earthquake records areFourier spectra.Second,tions;wave-profiles.Such deformations,later,are the direct consequence of nonlinear effects.Whenever the form of the data deviates from a pure sine or cosine function,the Fourier spectrum will contain harmonics.As explained above, both non-stationarity and nonlinearity can induce spurious harmonic components that cause energy spreading.The consequence is the misleading energy–frequency distribution forIn this paper,modemode functions The decomposition is based on the direct extraction of theevent on the time the frequency The decomposition be viewed as an expansion of the data in terms of the IMFs.Then,based on and derived from the data,can serve as the basis of that expansion linear or nonlinear as dictated by the data,Most important of all,it is adaptive.As will locality and adaptivity are the necessary conditions for the basis for expanding nonlinear and non-stationary time orthogonality is not a necessary criterionselection for a nonlinearon the physical time scaleslocal energy and the instantaneous frequencyHilbert transform can give us a full energy–frequency–time distribution of the data. Such a representation is designated as the Hilbert spectrum;it would be ideal for nonlinear and non-stationary data analysis.We have obtained good results and new insights by applying the combination of the EMD and Hilbert spectral analysis methods to various data:from the numerical results of the classical nonlinear equation systems to data representing natural phe-nomena.The classical nonlinear systems serve to illustrate the roles played by the nonlinear effects in the energy–frequency–time distribution.With the low degrees of freedom,they can train our eyes for more complicated cases.Some limitations of this method will also be discussed and the conclusions presented.Before introducing the new method,we willfirst review the present available data analysis methods for non-stationary processes.Proc.R.Soc.Lond.A(1998)Nonlinear and non-stationary time series analysis9072.Review of non-stationary data processing methodsWe willfirstgivea brief survey of themethodsstationary data.are limited to linear systems any method is almost strictly determined according to the special field in which the application is made.The available methods are reviewed as follows.(a )The spectrogramnothing but a limited time window-width Fourier spectral analysis.the a distribution.Since it relies on the tradition-al Fourier spectral analysis,one has to assume the data to be piecewise stationary.This assumption is not always justified in non-stationary data.Even if the data are piecewise stationary how can we guarantee that the window size adopted always coincides with the stationary time scales?What can we learn about the variations longer than the local stationary time scale?Will the collection of the locally station-ary pieces constitute some longer period phenomena?Furthermore,there are also practical difficulties in applying the method:in order to localize an event in time,the window width must be narrow,but,on the other hand,the frequency resolu-tion requires longer time series.These conflicting requirements render this method of limited usage.It is,however,extremely easy to implement with the fast Fourier transform;thus,ithas attracted a wide following.Most applications of this methodare for qualitative display of speech pattern analysis (see,for example,Oppenheim &Schafer 1989).(b )The wavelet analysisThe wavelet approach is essentially an adjustable window Fourier spectral analysiswith the following general definition:W (a,b ;X,ψ)=|a |−1/2∞−∞X (t )ψ∗ t −b ad t,(2.1)in whichψ∗(·)is the basic wavelet function that satisfies certain very general condi-tions,a is the dilation factor and b is the translationof theorigin.Although time andfrequency do not appear explicitly in the transformed result,the variable 1/a givesthe frequency scale and b ,the temporal location of an event.An intuitive physical explanation of equation (2.1)is very simple:W (a,b ;X,ψ)is the ‘energy’of X ofscale a at t =b .Because of this basic form of at +b involvedin thetransformation,it is also knownas affinewavelet analysis.For specific applications,the basic wavelet function,ψ∗(·),can be modified according to special needs,but the form has to be given before the analysis.In most common applications,however,the Morlet wavelet is defined as Gaussian enveloped sine and cosine wave groups with 5.5waves (see,for example,Chan 1995).Generally,ψ∗(·)is not orthogonalfordifferent a for continuous wavelets.Although one can make the wavelet orthogonal by selecting a discrete set of a ,thisdiscrete wavelet analysis will miss physical signals having scale different from theselected discrete set of a .Continuous or discrete,the wavelet analysis is basically a linear analysis.A very appealing feature of the wavelet analysis is that it provides aProc.R.Soc.Lond.A (1998)908N.E.Huang and othersuniform resolution for all the scales.Limited by the size of thebasic wavelet function,the downside of the uniform resolution is uniformly poor resolution.Although wavelet analysis has been available only in the last ten years or so,it hasbecome extremelypopular.Indeed,it is very useful in analysing data with gradualfrequency changes.Since it has an analytic form for the result,it has attracted extensive attention of the applied mathematicians.Most of its applications have been in edge detection and image compression.Limited applications have also been made to the time–frequency distribution in time series (see,for example,Farge 1992;Long et al .1993)andtwo-dimensionalimages (Spedding et al .1993).Versatile as the wavelet analysis is,the problem with the most commonly usedMorlet wavelet is its leakage generated by the limited length of the basic wavelet function,whichmakesthe quantitativedefinitionof the energy–frequency–time dis-tribution difficult.Sometimes,the interpretation of the wavelet can also be counter-intuitive.For example,to define a change occurring locally,one must look for theresult in the high-frequencyrange,for the higher the frequency the more localized thebasic wavelet will be.If a local event occurs only in the low-frequency range,one willstill be forced to look for its effects inthe high-frequencyrange.Such interpretationwill be difficultif it is possible at all (see,for example,Huang et al .1996).Another difficulty of the wavelet analysis is its non-adaptive nature.Once the basic waveletis selected,one will have to use it to analyse all the data.Since the most commonlyused Morlet wavelet is Fourier based,it also suffers the many shortcomings of Fouri-er spectral analysis:it can only give a physically meaningful interpretation to linear phenomena;it can resolve the interwave frequency modulation provided the frequen-cy variationis gradual,but it cannot resolve the intrawave frequency modulation because the basic wavelet has a length of 5.5waves.Inspite of all these problems,wavelet analysisisstillthe bestavailable non-stationary data analysis method so far;therefore,we will use it in this paper as a reference to establish the validity and thecalibration of the Hilbert spectrum.(c )The Wigner–Ville distributionThe Wigner–Ville distribution is sometimes alsoreferred toas the Heisenberg wavelet.By definition,it is the Fourier transform of the central covariance function.For any time series,X (t ),we can define the central variance as C c (τ,t )=X (t −12τ)X ∗(t +12τ).(2.2)Then the Wigner–Ville distribution is V (ω,t )=∞−∞C c (τ,t )e −i ωτd τ.(2.3)This transform has been treated extensively by Claasen &Mecklenbr¨a uker (1980a ,b,c )and by Cohen (1995).It has been extremely popular with the electrical engi-neering community.The difficulty with this method is the severe cross terms as indicated by the exis-tence of negativepowerfor some frequency ranges.Although this shortcoming canbe eliminated by using the Kernel method (see,for example,Cohen 1995),the resultis,then,basically that of a windowed Fourier analysis;therefore,itsuffers all thelim-itations of the Fourier analysis.An extension of this method has been made by Yen(1994),who used the Wigner–Ville distribution to define wave packets that reduce Proc.R.Soc.Lond.A (1998)Nonlinear and non-stationary time series analysis909 a complicated data set to afinite number of simple components.This extension is very powerful and can be applied to a variety of problems.The applications to complicated data,however,require a great amount of judgement.(d)Evolutionary spectrumThe evolutionary spectrum wasfirst proposed by Priestley(1965).The basic idea is to extend the classic Fourier spectral analysis to a more generalized basis:from sine or cosine to a family of orthogonal functions{φ(ω,t)}indexed by time,t,and defined for all realω,the frequency.Then,any real random variable,X(t),can beexpressed asX(t)= ∞−∞φ(ω,t)d A(ω,t),(2.4)in which d A(ω,t),the Stieltjes function for the amplitude,is related to the spectrum asE(|d A(ω,t)|2)=dµ(ω,t)=S(ω,t)dω,(2.5) whereµ(ω,t)is the spectrum,and S(ω,t)is the spectral density at a specific time t,also designated as the evolutionary spectrum.If for eachfixedω,φ(ω,t)has a Fourier transformφ(ω,t)=a(ω,t)e iΩ(ω)t,(2.6) then the function of a(ω,t)is the envelope ofφ(ω,t),andΩ(ω)is the frequency.If, further,we can treatΩ(ω)as a single valued function ofω,thenφ(ω,t)=α(ω,t)e iωt.(2.7) Thus,the original data can be expanded in a family of amplitude modulated trigono-metric functions.The evolutionary spectral analysis is very popular in the earthquake communi-ty(see,for example,Liu1970,1971,1973;Lin&Cai1995).The difficulty of its application is tofind a method to define the basis,{φ(ω,t)}.In principle,for this method to work,the basis has to be defined a posteriori.So far,no systematic way has been offered;therefore,constructing an evolutionary spectrum from the given data is impossible.As a result,in the earthquake community,the applications of this method have changed the problem from data analysis to data simulation:an evo-lutionary spectrum will be assumed,then the signal will be reconstituted based on the assumed spectrum.Although there is some general resemblance to the simulated earthquake signal with the real data,it is not the data that generated the spectrum. Consequently,evolutionary spectrum analysis has never been very useful.As will be shown,the EMD can replace the evolutionary spectrum with a truly adaptive representation for the non-stationary processes.(e)The empirical orthogonal function expansion(EOF)The empirical orthogonal function expansion(EOF)is also known as the principal component analysis,or singular value decomposition method.The essence of EOF is briefly summarized as follows:for any real z(x,t),the EOF will reduce it toz(x,t)=n1a k(t)f k(x),(2.8)Proc.R.Soc.Lond.A(1998)910N.E.Huang and othersin whichf j·f k=δjk.(2.9)The orthonormal basis,{f k},is the collection of the empirical eigenfunctions defined byC·f k=λk f k,(2.10)where C is the sum of the inner products of the variable.EOF represents a radical departure from all the above methods,for the expansion basis is derived from the data;therefore,it is a posteriori,and highly efficient.The criticalflaw of EOF is that it only gives a distribution of the variance in the modes defined by{f k},but this distribution by itself does not suggest scales or frequency content of the signal.Although it is tempting to interpret each mode as indepen-dent variations,this interpretation should be viewed with great care,for the EOF decomposition is not unique.A single component out of a non-unique decomposition, even if the basis is orthogonal,does not usually contain physical meaning.Recently, Vautard&Ghil(1989)proposed the singular spectral analysis method,which is the Fourier transform of the EOF.Here again,we have to be sure that each EOF com-ponent is stationary,otherwise the Fourier spectral analysis will make little sense on the EOF components.Unfortunately,there is no guarantee that EOF compo-nents from a nonlinear and non-stationary data set will all be linear and stationary. Consequently,singular spectral analysis is not a real improvement.Because of its adaptive nature,however,the EOF method has been very popular,especially in the oceanography and meteorology communities(see,for example,Simpson1991).(f)Other miscellaneous methodsOther than the above methods,there are also some miscellaneous methods such as least square estimation of the trend,smoothing by moving averaging,and differencing to generate stationary data.Methods like these,though useful,are too specialized to be of general use.They will not be discussed any further here.Additional details can be found in many standard data processing books(see,for example,Brockwell &Davis1991).All the above methods are designed to modify the global representation of the Fourier analysis,but they all failed in one way or the other.Having reviewed the methods,we can summarize the necessary conditions for the basis to represent a nonlinear and non-stationary time series:(a)complete;(b)orthogonal;(c)local;and (d)adaptive.Thefirst condition guarantees the degree of precision of the expansion;the second condition guarantees positivity of energy and avoids leakage.They are the standard requirements for all the linear expansion methods.For nonlinear expansions,the orthogonality condition needs to be modified.The details will be discussed later.But even these basic conditions are not satisfied by some of the above mentioned meth-ods.The additional conditions are particular to the nonlinear and non-stationary data.The requirement for locality is the most crucial for non-stationarity,for in such data there is no time scale;therefore,all events have to be identified by the time of their occurences.Consequently,we require both the amplitude(or energy) and the frequency to be functions of time.The requirement for adaptivity is also crucial for both nonlinear and non-stationary data,for only by adapting to the local variations of the data can the decomposition fully account for the underlying physics Proc.R.Soc.Lond.A(1998)Nonlinear and non-stationary time series analysis911of the processes and not just to fulfil the mathematical requirements for fitting the data.This is especially important for the nonlinear phenomena,for a manifestation of nonlinearity is the ‘harmonic distortion’in the Fourier analysis.The degree of distortion depends on the severity of nonlinearity;therefore,one cannot expect a predetermined basis to fit all the phenomena.An easy way to generate the necessary adaptive basis is to derive the basis from the data.In this paper,we will introduce a general method which requires two steps in analysing the data as follows.The first step is to preprocess the data by the empirical mode decomposition method,with which the data are decomposed into a number of intrinsic mode function components.Thus,we will expand the data in a basis derived from the data.The second step is to apply the Hilbert transform to the decomposed IMFs and construct the energy–frequency–time distribution,designated as the Hilbert spectrum,from which the time localities of events will be preserved.In other words,weneed the instantaneous frequency and energy rather than the global frequency and energy defined by the Fourier spectral analysis.Therefore,before goingany further,we have to clarify the definition of the instantaneous frequency.3.Instantaneous frequencyis to accepting it only for special ‘monocomponent’signals 1992;Cohen 1995).Thereare two basicdifficulties with accepting the idea of an instantaneous fre-quency as follows.The first one arises from the influence of theFourier spectral analysis.In the traditional Fourier analysis,the frequency is defined for thesineor cosine function spanning the whole data length with constant ampli-tude.As an extension of this definition,the instantaneous frequencies also have torelate to either a sine or a cosine function.Thus,we need at least one full oscillationof a sineor a cosine wave to define the local frequency value.According to this logic,nothing full wave will do.Such a definition would not make sense forThe secondarises from the non-unique way in defining the instantaneousfrequency.Nevertheless,this difficulty is no longer serious since the introduction ofthe meanstomakethedata analyticalthrough the Hilbert transform.Difficulties,however,still exist as ‘paradoxes’discussed by Cohen (1995).For an arbitrary timeseries,X (t ),we can always have its Hilbert Transform,Y (t ),as Y (t )=1πP∞−∞X (t )t −t d t,(3.1)where P indicates the Cauchy principal value.This transformexists forallfunctionsof class L p(see,for example,Titchmarsh 1948).With this definition,X (t )and Y (t )form the complex conjugate pair,so we can have an analytic signal,Z (t ),as Z (t )=X (t )+i Y (t )=a (t )e i θ(t ),(3.2)in which a (t )=[X 2(t )+Y 2(t )]1/2,θ(t )=arctanY (t )X (t ).(3.3)Proc.R.Soc.Lond.A (1998)912N.E.Huang andothers Theoretically,there are infinitely many ways of defining the imaginary part,but the Hilbert transform provides a unique way of defining the imaginary part so that the result is ananalyticfunction.A brief tutorial on the Hilbert transform with theemphasis on its physical interpretation can be found in Bendat &Piersol is the bestlocal fitan amplitude and phase varying trigonometric function to X (t ).Even with the Hilbert transform,there is still controversy in defining the instantaneous frequency as ω=d θ(t )d t .(3.4)This leads Cohen (1995)to introduce the term,‘monocomponent function’.In prin-ciple,some limitations on the data are necessary,forthe instantaneous frequencygiven in equation (3.4)is a single value function of time.At any given time,thereis only one frequency value;therefore,it can only represent one component,hence ‘monocomponent’.Unfortunately,no cleardefinition of the ‘monocomponent’signalwas given to judge whether a function is or is not ‘monocomponent’.For lack ofa precise definition,‘narrow band’was adopted a on the data for the instantaneous frequency to make sense (Schwartz et al .1966).There are two definitions for bandwidth.The first one is used in the study of the probability properties of the signalsand waves,wherethe processes are assumed tobe stationary and Gaussian.Then,the bandwidth can be defined in spectral moments The expected number of zero crossings per unit time is given byN 0=1π m 2m 0 1/2,(3.5)while the expected number of extrema per unit time is given byN 1=1π m 4m 2 1/2,(3.6)in which m i is the i th moment of the spectrum.Therefore,the parameter,ν,definedas N 21−N 20=1π2m 4m 0−m 22m 2m 0=1π2ν2,(3.7)offers a standard bandwidth measure (see,for example,Rice 1944a,b ,1945a,b ;Longuet-Higgins 1957).For a narrow band signal ν=0,the expected numbers extrema and zero crossings have to equal.the spectrum,but in a different way.coordinates as z (t )=a (t )e i θ(t ),(3.8)with both a (t )and θ(t )being functions of time.If this function has a spectrum,S (ω),then the mean frequency is given byω = ω|S (ω)|2d ω,(3.9)Proc.R.Soc.Lond.A (1998)Nonlinear and non-stationary time series analysis913which can be expressed in another way asω =z ∗(t )1i dd tz (t )d t=˙θ(t )−i ˙a (t )a (t )a 2(t )d t =˙θ(t )a 2(t )d t.(3.10)Based on this expression,Cohen (1995)suggested that ˙θbe treated as the instanta-neous frequency.With these notations,the bandwidth can be defined asν2=(ω− ω )2 ω 2=1 ω 2(ω− ω )2|S (ω)|2d ω=1 ω 2z ∗(t ) 1i d d t− ω 2z (t )d t =1 ω 2 ˙a 2(t )d t +(˙θ(t )− ω )2a 2(t )d t .(3.11)For a narrow band signal,this value has to be small,then both a and θhave to begradually varying functions.Unfortunately,both equations (3.7)and (3.11)defined the bandwidth in the global sense;they are both overly restrictive and lack preci-sion at the same time.Consequently,the bandwidth limitation on the Hilbert trans-form to give a meaningful instantaneous frequency has never been firmly established.For example,Melville (1983)had faithfully filtered the data within the bandwidth requirement,but he still obtained many non-physical negative frequency values.It should be mentioned here that using filtering to obtain a narrow band signal is unsat-isfactory for another reason:the filtered data have already been contaminated by the spurious harmonics caused by the nonlinearity and non-stationarity as discussed in the introduction.In order to obtain meaningful instantaneous frequency,restrictive conditions have to be imposed on the data as discussed by Gabor (1946),Bedrosian (1963)and,more recently,Boashash (1992):for any function to have a meaningful instantaneous frequency,the real part of its Fourier transform has to have only positive frequency.This restriction can be proven mathematically as shown in Titchmarsh (1948)but it is still global.For data analysis,we have to translate this requirement into physically implementable steps to develop a simple method for applications.For this purpose,we have to modify the restriction condition from a global one to a local one,and the basis has to satisfy the necessary conditions listed in the last section.Let us consider some simple examples to illustrate these restrictions physically,by examining the function,x (t )=sin t.(3.12)Its Hilbert transform is simply cos t .The phase plot of x –y is a simple circle of unit radius as in figure 1a .The phase function is a straight line as shown in figure 1b and the instantaneous frequency,shown in figure 1c ,is a constant as expected.If we move the mean offby an amount α,say,then,x (t )=α+sin t.(3.13)Proc.R.Soc.Lond.A (1998)。

太赫兹那些事（三）-从模块到芯片作者：台风一、一笔流水账七年前（2013）太赫兹技术还在爹不疼舅不爱的年代，美国军火巨头诺格公司微电子部门就悍然发布了300-340GHz低噪放模块MGA2101 20dB增益/8dB噪声系数，一时引来多少豪杰仰慕之情。

鉴于诺格公司强大的射频基因（美军3/4代战机雷达方案承包商）,美帝的高富帅设计个太赫兹放大器多大个事啊。

彼时欧洲的CHALMERS和Fraunhofer正忙着设计MetOp-SG和Juice等几颗卫星的太赫兹放大器芯片。

彼时的国内，CETC13太赫兹二极管刚刚出炉不久，太赫兹芯片还只是遥远的梦想，对欧美的巨头我们保持仰望的姿态。

彼时独立功能的太赫兹模块绝对是100%的中流砥柱，而太赫兹模块巨头VDX刚刚坐上太赫兹固态电子学第一把交椅。

图一：Northrop Grumman MGA2101 LNA来自诺格公司官网两年后（2015）美国军火另一巨头TELEDYNE公司发布了170-260GHz功率放大器芯片，与此同时欧洲MetOp-SG卫星MWS载荷的低噪放模块也测试完毕。

德国RPG公司随后将Fraunhofer的产品封装为模块推出一系列低噪放产品线，让人眼前一亮。

国内的太赫兹二极管也突飞猛进（逼得VDX暂停了销售W波段以上二极管近三年时间），国产芯片嘛还得等上几年。

人还没到齐呐。

图二. Low Noise Amplifiers for MetOp-SG来自2016 Global Symposium on Millimeter Waves (GSMM) & ESA Workshop on Millimetre-Wave Technology and Applications去年6月太赫兹电子学领域巨头VDX公司发布功率放大器产品系列，最高工作频率170-260GHz（TELEDYNE芯片），该模块在美国市场公开售价1.5万美元，货期两个月。

雄心勃勃的VDX正计划着用二极管和OEM的芯片封装太赫兹独立功能模块打通任督二脉的时候，大洋彼岸一家名叫雄安太芯的公司成立了。

转载奇点迫近原文地址:奇点迫近?作者:野岛在此库兹韦尔在奇点峰会发表演说(彭博图片)TranscendentMan一幕《未来战士》及《22世纪杀人网络》两套科幻电影系列都是有关未来世界智能超级计算机反噬「它」的制造者─人类─的故事;却原来，计算机发展至具备思考能力并不尽是小说家之言，美国一位科学家预期，人与机器合成体(cyborg)的奇点(singularity)人类文明年代将在2045年开始，也是人类长生不死梦想成真的时候…库兹韦尔(RayKurzweil)有多重身份，他是作家，也是拥有39项产品专利权的发明家:光学字符识别软件(opticalcharacterrecognition，OCR)和盲人阅读器(print-to-speechreadingmachine，第一位顾客是出生6个月便因医疗事故失明的美国乐坛著名黑人明星StevieWonder)，以及在语音识别科技(speechrecognitiontechnology)、文字语音合成(text-to-speechsynthesis)和电子键盘(electronickeyboardinstruments)等方面的成就，令他晋身亿万富豪之列。

不过，库兹韦尔名气更响来自是他是未来主义者(futurist)最具代表性人物之一，他一系列探索未来世界的著作，克林顿及盖茨都是他的粉丝，克林顿更于1999年颁予库兹韦尔科技及发明奖章(TheNationMedalofTechnologyandInnovation)，盖茨称赞他是预测未来人工智能(artificialintelligence)最佳人物。

库兹韦尔第一本作品是由1986写到1989年、现已翻译成9种文字、曾列亚马逊科学类畅销书榜首的《智能机器的时代》(TheAgeofIntelligentMachines，1990)，他顺应IthieldeSolaPool(1917-1984)的《科技自由》(TechnologiesofFreedom，1983)的科技发展脉络预言，无线电话及传真机等新科技会打破集权政府在信息流通方面的垄断，苏联将因此分裂(1991年12月25日前苏联在戈尔巴乔夫宣布辞职而正式解体);库兹韦尔又指出，从国际象棋软件改良的进度推断，最迟1998年无人是计算机的敌手(1997年IBM的计算机「深蓝」在一场公开象棋赛击败全球冠军棋手GarryKasparov);书中另一预言是1990年代开始互联网使用者将爆炸性增长。

天文学上的旷世之争笔记【原创实用版】目录1.引言：对天文学上的旷世之争的概述2.天文学的发展历程：从古至今的重要发现和理论3.旷世之争的主要内容：恒星演化理论、宇宙大爆炸理论和黑洞理论4.各理论的提出者和支持者：如爱因斯坦、霍金等5.各理论的争议和证据：理论的预测和实验验证6.结论：旷世之争对天文学的推动作用正文天文学是一门充满奥秘和惊喜的学科，自古以来，人类对于宇宙的探索从未停止。

在这个过程中，天文学家们提出了许多理论和观点，有些被广泛接受，有些则引发了旷世之争。

本文将对天文学上的旷世之争进行探讨。

首先，让我们回顾一下天文学的发展历程。

自古以来，人类对于天象的观测和记录就从未间断。

随着科学技术的发展，天文学家们提出了许多重要的理论和观点，如哥白尼的日心说、牛顿的万有引力定律、哈勃的宇宙膨胀说等。

这些理论和观点在当时或许引起了不小的轰动，但与后来的旷世之争相比，可谓是小巫见大巫。

天文学上的旷世之争主要涉及三个方面：恒星演化理论、宇宙大爆炸理论和黑洞理论。

恒星演化理论是指关于恒星从诞生到消亡的整个过程的理论，其中最具争议的是恒星的死亡方式，即恒星最终会演化成白矮星还是黑洞。

宇宙大爆炸理论是指关于宇宙起源的理论，认为宇宙起源于一次大爆炸，但这一理论也面临着诸多质疑，如大爆炸之前的宇宙状态等。

黑洞理论是指关于黑洞的性质和行为的理论，其中最具争议的是黑洞的信息悖论，即黑洞能否保存信息。

在这些理论的提出者和支持者中，不乏一些科学巨匠，如爱因斯坦、霍金等。

他们对于这些理论的坚持和质疑，使得天文学上的旷世之争更加激烈。

同时，这些理论的争议和证据也是层出不穷，如恒星演化理论的观测数据和模拟实验、宇宙大爆炸理论的宇宙背景辐射和宇宙膨胀观测、黑洞理论的引力波探测等。

然而，正是这些旷世之争，推动了天文学的发展。

每一个争议都促使科学家们提出新的理论和观点，进一步拓宽了我们的视野。

虽然有些理论和观点可能被证明是错误的，但它们对于科学的进步起到了至关重要的作用。

2023年度人工智能与健康1.2023年，（）投资并开始布署由人工智能驱动旳交易平台。

目前，其纽约总部已经有600个交易岗位被200个电脑工程师取代，只剩余两个交易员。

（2.0分）A.高盛B.淡马锡C.商汤科技D.摩根士丹利我旳答案：A√答对2.（）被誉为信息论旳创始人。

（2.0分）A.诺伯特·维纳B.克劳德·香农C.贝塔朗菲D.查尔斯·巴贝奇我旳答案：B√答对3.世界上第一种将芯片植入体内旳人是（）。

（2.0分）A.凯文·沃里克B.布鲁克斯C.罗斯·昆兰D.杰弗里·辛顿我旳答案：A√答对4.2023年10月31日，中共中央政治局就人工智能发展现实状况和趋势举行（）集体学习。

（2.0分）A.第一次B.第五次C.第九次D.第十次我旳答案：C√答对5.（）体现为体格强健，人体各器官功能良好。

（2.0分）A.身体健康B.心理健康C.社会适应良好D.生态健康我旳答案：A√答对6.在农业领域旳（）环节，智能旳农业机器人可以运用图像识别技术获取农作物旳生长状况，判断哪些杂草需要清除，判断哪里需要浇灌、施肥、打药，并立即执行。

（2.0分）A.产前B.产中C.产后D.全程我旳答案：B√答对7.《国务院有关印发新一代人工智能发展规划旳告知》中指出，到2025年人工智能要到达旳目旳不包括（）。

（2.0分）A.人工智能基础理论实现重大突破B.部分技术与应用到达世界领先水平C.智能社会建设获得积极进展D.成为世界重要人工智能创新中心我旳答案：D√答对8.约瑟夫·维森鲍姆专家开发旳（）被设计成一种可以通过谈话协助病人完毕心理恢复旳心理治疗师。

（2.0分）A.微软小冰B.苹果SiriC.googleAlloD.ELIZA我旳答案：D√答对9.下列对战斗机器人旳表述，不妥旳是（）。

（2.0分）A.目前战场上旳重要力量B.配合人类士兵作战旳角色C.在执行低烈度作战和危险任务时可以大大减轻人类士兵旳承担和伤亡D.具有较高智能、全方位作战能力、较强战场生存能力、绝对服从命令等优势我旳答案：A√答对10.我国于（）年公布了《国务院有关印发新一代人工智能发展规划旳告知》。