VANDERMONDE INVARIANCE TRANSFORMATION
- 格式:pdf
- 大小:112.86 KB
- 文档页数:4
数学专业英语词汇(G)数学专业英语词汇(G)数学专业英语词汇(G)g space g空间g surface g曲面galerkin equations 加勒金方程galerkin method 加勒金法galois algebra 伽罗瓦代数galois cohomology 伽罗瓦上同调galois extension 伽罗瓦扩张galois field 伽罗瓦域galois group 伽罗瓦群galois theory 伽罗瓦理论galton watson process 高尔顿沃森过程game 对策game in normalized form 标准型对策game in partition function form 分拆函数形对策game of chance 机会对策game of hex 六角形对策game of pursuit 追逐对策game theoretic 对策论的game theoretic model 对策论模型game theory 对策论game with infinitely many players 无限局中人对策gamma distribution 分布gamma function 函数gamma rays 射线gap 间隙gap series 间隙级数gap theorem 间隙定理gateaux differential 加特微分gauge group 规范群gauge surface 规范面gauge transformation 度规变换gaugeinvariance 度规不变性gauss curvature 高斯曲率gauss distribution 高斯分布gauss elimination method 高斯消去法gauss equations 高斯方程gauss formula 高斯公式gauss integral 高斯积分gauss markov theorem 高斯马尔可夫定理gauss seidel method 高斯赛得尔方法gauss transformation 高斯变换gaussian algorithm 高斯消去法gaussian bell shaped curve 高斯钟形曲线gaussian curvature of surface 曲面的高斯曲率gaussian curve 误差曲线gaussian distribution 高斯分布gaussian elimination 高斯消去法gaussian integer 高斯整数gaussian interpolation formula 高斯插值公式gaussian number field 高斯数域gaussian plane 复数平面gaussian process 高斯过程gaussian quadrature formula 高斯求积公式gaussian sum 高斯和gegenbauer polynomial 格根包尔多项式general algebra 一般代数general algebraic equation 一般方程general associative law 一般结合律general dirichlet series 一般狄利克雷级数general distributive law 一般分配律general distributivity 无限分配性general equation 一般方程general factor 一般因子general integral 通积分general laplace transform 一般拉普拉斯变换general linear equation 一般线性方程general linear group 全线性群general point 普通点general polynomial 一般多项式general position 一般位置general proposition 一般命题general purpose computer 通用计算机general reciprocal 广义逆矩阵general recursive function 一般递归函数general recursive predicate 一般递归谓词general recursive relation 一般递归关系general set theory 一般集合论general solution 通积分general term 通项general topology 集论拓扑general uniformization theorem 一般单值化定理general validity 一般有效性general valuation 广义赋值generalization 一般化generalize 普遍化generalized almost periodic function 广义殆周期函数generalized boolean algebra 广义布尔代数generalized continuum hypothesis 广义连续统假设generalized coordinates 广义坐标generalized derivative 广义导数generalized distance 广义距离generalized eigenspace 广义特照间generalized fourier series 广义傅里叶级数generalized function 广义函数generalized green function 广义格林函数generalized inverse 广义逆矩阵generalized limit 广义极限generalized mean 广义平均generalized sequence 有向系generalized simplex method 推广的单形法generalized solution 弱解generalized sum 广义级数的和generalized symmetric group 广义对称群generalized vandermonde determinant 广义范得蒙弟行列式generate 生成generated group 生成群generated subspace 生成子空间generating circle 母圆generating cone 母锥generating element 生成元generating function 母函数generating line 母线generating line of surface 曲面的母线generating routine 生成程序generating series 生成级数generating subspace 生成子空间generation 生成generator 母线generator of a surface 曲面的母线generic 一般的generic point 一般点generic zero 一般零点genus 狂genus of a surface 曲面的狂geodesic 测地线geodesic coordinates 测地坐标geodesic curvature 测地曲率geodesic deviation 测地偏差geodesic distance 测地距geodesic line 测地线geodesic parameter 测地参数geodesic torsion 测地挠率geodesy 测地学geoid 地球体geometric average 比例中项geometric boundary condition 本质边界条件geometric complex 几何复形geometric cross section 几何截面geometric difference equation 几何差分方程geometric distribution 几何分布geometric figure 几何图形geometric genus 几何狂geometric interpretation 几何解释geometric mean 比例中项geometric meaning 几何意义geometric multiplicity 几何重数geometric optics 几何光学geometric probability 几何概率geometric progression 等比级数geometric representation 几何表示geometric sequence 等比级数geometric series 几何级数geometric simplex 几何单形geometric sum 几何和geometrical element 几何元素geometrical locus 几何轨迹geometrical optics 几何光学geometrical vector 几何向量geometrization 几何化geometry 几何geometry of numbers 数的几何学geometry of spheres 球几何学geometry of the circle 圆几何germ 芽global analysis 整体分析global convergence 整体收敛global differential geometry 整体微分几何global existence 整体存在global limit theorem 整体极限定理global lipschitz condition 整体利普希茨条件global lipschitz constant 全局利普希茨常数global mapping 整体映射global property 整体性质globe 球globular 球的gluing theorem 胶合定理gnomon 磬折形gnomonic projection 心射图法godel number 哥德尔数golden cut algorithm 黄金分割算法golden section 黄金分割goniometer 量角计goniometry 测角术good reduction 好约化goodness of fit 拟合良度gorenstein ring 戈伦斯坦环grade 百分度gradient 梯度gradient method 梯度法gradient of scalar field 纯量场的梯度graduation 修均法gram schmidt orthogonalization 格兰姆施密特正交化法gramian 格兰姆行列式gramian matrix 格兰姆矩阵grand average 总平均grand total 总计graph 图graph coloring 图色graph of an equation 方程的图graph of function 函数的图graph of operator 算子的图graph theory 图论graphic integration 图解积分法graphic method 图示法graphic representation 图示graphic solution 图解graphical calculation 图解计算法graphical differentiation 图解微分法graphical solution 图解法gravitation 引力gravitational constant 引力常数gravitational field 引力场gravity 重力great circle 大圆greater than or equal to 大于或等于greatest common divisor 最大公因子greatest common submodule 最大公共子模greatest element 最大元greatest lower bound 最大下界greek numerals 希腊数字green function 格林函数green operator 格林算子green space 格林空间green theorem 格林公式grid size 网格大小gross error 过失误差gross profit 总利润grothendieck category 格罗坦狄克范畴grothendieck group 格罗坦狄克群ground field 基域group 群group algebra 群代数group axioms 群公理group comparison 群比较group determinant 群行列式group element 群元素group extension 群扩张group factor 群因子group factor model 群因子模型group frequency 群频率group mean 群平均group object 群对象group of automorphisms 自同构群group of coefficients 系数群group of homomorphisms 同态群group of isotropy 迷向群group of linear transformations 线性变换群group of motions 运动群group of movements 运动群group of n cycles n循环群group of points 点群group of quotients 商群group of similarity transformations 相似变换群group operation 群运算group scheme 群概型group space 群空间group theory 群论group variety 群簇group velocity 群速度group without torsion 非挠群grouped data 分类资料grouped sample unit 分类样本单位grouping 分类groupoid 广群grouptheoretical 群论的growth 增长growth curve 增长曲线growth function 生长函数growth law 增长律growth rate 增长率gudermannian 古得曼行列式guldin rule 古尔丁法则gyration radius 回转半径数学专业英语词汇(G) 相关内容:。
(0,2) 插值||(0,2) interpolation0#||zero-sharp; 读作零井或零开。
0+||zero-dagger; 读作零正。
1-因子||1-factor3-流形||3-manifold; 又称“三维流形”。
AIC准则||AIC criterion, Akaike information criterionAp 权||Ap-weightA稳定性||A-stability, absolute stabilityA最优设计||A-optimal designBCH 码||BCH code, Bose-Chaudhuri-Hocquenghem codeBIC准则||BIC criterion, Bayesian modification of the AICBMOA函数||analytic function of bounded mean oscillation; 全称“有界平均振动解析函数”。
BMO鞅||BMO martingaleBSD猜想||Birch and Swinnerton-Dyer conjecture; 全称“伯奇与斯温纳顿-戴尔猜想”。
B样条||B-splineC*代数||C*-algebra; 读作“C星代数”。
C0 类函数||function of class C0; 又称“连续函数类”。
CA T准则||CAT criterion, criterion for autoregressiveCM域||CM fieldCN 群||CN-groupCW 复形的同调||homology of CW complexCW复形||CW complexCW复形的同伦群||homotopy group of CW complexesCW剖分||CW decompositionCn 类函数||function of class Cn; 又称“n次连续可微函数类”。
Cp统计量||Cp-statisticC。
一类带移民超brown运动的极限定理
移民超贝尔定理(Berry-Lane Theorem)是一类可以用来描述移
民流动的极限定理。
这个定理认为,当一个人或一群人离开所在的国
家时,他们会创造出一个“极限数量”的新改变,其中包括文化,政
治和社会表述。
换句话说,当一个人或一群人被迫离开家园的时候,
他们会产生一种不可避免的文化多样性,就像巴拿马运河一样,会有
许多流向不同的地方,也会有不少改变,从而形成一种新的文化。
移民超贝尔定理的另一个重要的特征就是识别群体之间的多样性。
每一个移民群体都有自己特有的文化,他们会影响当地居民的文化,
因此由此证明移民超Paul内在的多样性。
同时,移民群体也会和当地
居民有一些共同的感知——比如贫富差距,他们在语言、节日以及文
化上也会有一些相同之处。
由此可以看出,移民超brown运动的极限
定理清楚地指出,移民群体多元性并不等于“无序”,而是一种秩序
的多元性。
移民超贝尔定理也会有一些专业的文化,比如移民群体中有一种
灵活性和可调整性,而且他们也会将自身的文化技能,习惯和价值观
带到当地社会,从而影响当地文化,成为当地文化的一部分。
移民超
贝尔定理也强调,移民群体不仅仅是独立的,而是存在着内在的社会
网络,这种社会网络会形成新的社会模式,新的社会表述,从而影响
当地社会的发展。
总之,移民超贝尔定理将多元文化作为一种特殊的
社会结构,它综合了政治、文化和社会变化,使每一个社会群体都能
够更好地发展并融入更广阔的社会环境。
Vandermonde 矩阵及其变形矩阵的快速求逆格式 (修改稿)叶贻才(福建师大 数计学院)摘 要导出在实算中颇具实用的关于-阵及其变形矩阵的一种快速求逆格式,.算术运算量为,,算法格式紧凑、简便,V )(2nO 并给出具体算例。
关键词-阵,变形矩阵,逆阵,反递推关系V Vandermonde 矩阵(简称-阵)以及它的变形矩阵是实际计算中一类结构特殊的著名的常见矩阵,关于-阵求逆V V 问题的探讨,早为人们所关注。
50年代以来,有关这方面的研究文章相继出现(如文〔1~3〕等)。
如所知,矩阵求逆,按一般计算是很繁难的(尤其是高阶矩阵)。
本文借助于V-阵与多项式之间的密切联系,直观地导出V-阵的一种简易快速求逆算式。
其运算量为(这里记号[2]M 和A 分别代表一次乘(除)法和一次加(减)法),较通常求)57)(1(21A M n n +-逆算法所需运算量低了一个数量级。
本文利用导出的V 之逆阵的分解阵及阵中各未知元的逐个推算,从而]4[3)(n O 1-V求得,本算法无论用于人工手算(极具可操作性)或编程机算都很简便。
1-V1 算法的构成任取一组互异实数{},可构造一个实系数首1多项式:n x x x ,,,21 (1)∏∑==-=+=ni i jnj x x x j ax f 1)(1)()1(1=+n a 将视为实变量的函数,并记其偏导数为),,1,0(1n j a j =+ ,,21x x n x,),...,2,1,(,n s j x a a sjjs =∂∂=有(s=1,2,…,).(2),)(10,1∏∑≠==+--=nsi i i nj jsj x x x an 注意到有,11=+n a(3)⎪⎩⎪⎨⎧=--≠=∏∑≠==-n li i i l nj j l jss l x x s l x a111.......),........(..........................,.........0其中s,=1,2,…,.l n 引入记号(4)n J ,212221212111⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡=nn nnn n a a a a a aa a a⎥⎥⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎢⎢⎣⎡=---112112222121111n n n n n n x x x x x x x x x V 及(4)′)).(',),('),('(21n n x x x diag D ωωω---= 这里(=1,2,…,),)()('1∏≠=-=nij j j i i x x x ωi n 是在处的导数。
1范德蒙恒等式(Vandermonde's identity)是指Cna+b=∑iCiaCn−ib=∑iCn−iaCib(1)(1)Ca+bn=∑iCaiCbn−i=∑iCan−iCbi其中求和是对所有使得表达式有意义的非负整数ii 进行的,即max{0,n−b}⩽i⩽min{a,n}(2)(2)max{0,n−b}⩽i⩽min{a,n}当a,b⩾na,b⩾n 时化简为0⩽i⩽n(3)(3)0⩽i⩽n1. 证明 1假设有编了号的a+ba+b 个小球。
不分顺序抓取nn 个,求总共有几种情况(用NN 表示)。
方法 1:根据定义,有N=Cna+bN=Ca+bn 种情况。
方法 2:先把球分成AA,BB 两组,分别有aa 个和bb 个。
如果在AA 组中抽取ii 个球(有CiaCai 种情况),在BB 组中只能抽取n−in−i 个(有Cn −ibCbn−i 种情况),所以一个ii 对应CiaCn−ibCaiCbn−i 种情况。
所有可能的ii 一共有N=∑iCiaCn−ibN=∑iCaiCbn−i 种情况。
由于这个问题只有一个答案,所以有Cna+b=∑iCiaCn−ibCa+bn=∑iCaiCbn−i。
但ii 的范围具体从多少取到多少,由aa,bb 是否大于nn 来决定。
当a,ba,b 都大于nn 时,ii 可以从 0 取到nn,如果其中至少有一个小于nn,那么ii 的取值不能使CC 的上标大于下标。
证毕。
2. 证明 2我们也可以通过二项式定理来证明(1+x)a+b=a+b∑n=0Cna+bxn=(1+x)a(1+x)b=a∑i=0Ciaxib∑j=0Cjbxj=a+b∑n=0∑iCiaCn−ibxn(4)(4)(1+x)a+b=∑n=0a+bCa+bnxn=(1+x)a(1+x)b=∑i=0aCaixi∑j=0bCbjxj=∑n=0a+b ∑iCaiCbn−ixn最后两部可以看作对一个表格求和,第ii 行第jj 列的值为CiaCjbxi+jCaiCbjxi+j,求和的方式有两种,一种是每行每列求和,另一种是延斜线求和,每条斜线n=i+jn=i+j 的值相同。
高阶刘维尔定理
高阶刘维尔定理(Liouville's theorem)是复变函数理论中一个重要的基本定理,它以法国数学家约瑟夫·刘维尔(Joseph Liouville)命名。
这个定理的内容可以简单描述为“一个有界的整函数必是常函数”。
具体来说,假设f(z)是一个在复平面C上有界的整函数,即对于所有满足|z|≤R 的点z,我们有|f(z)|≤M(其中M是一个正常数)。
那么根据高阶刘维尔定理,这个函数f(z)在C上必为常数。
高阶刘维尔定理在许多数学领域都有广泛的应用,包括解析函数理论、微分方程、复几何等。
在物理学中,以刘维尔命名的刘维尔定理也是经典统计和哈密顿力学中的一个关键定理。
它断言相空间分布函数沿着系统的轨迹是恒定的,也就是说,在穿过相空间的给定系统点附近的系统点的密度随时间是恒定的。
这种与时间无关的密度在统计力学中被称为经典先验概率。
需要指出的是,虽然高阶刘维尔定理的证明相对复杂,但是它对于理解许多数学和物理问题起到了关键作用。
同时,这个定理也为进一步研究复变函数、数学物理等领域提供了有力的工具。
1。
V ANDERMONDE INV ARIANCE TRANSFORMATION Tobias P.Kurpjuhn Michel T.Ivrlaˇc Josef A.Nossek kurpjuhn@nws.ei.tum.de ivrlac@nws.ei.tum.de nossek@nws.ei.tum.de Institute for Circuit Theory and Signal ProcessingTechnische Universit¨a t M¨u nchen,Munich,GermanyABSTRACTIn this article we introduce a novel multiple-input-multiple-output (MIMO)spatialfilter(SF)which can be applied as a preprocess-ing scheme to uniform linear arrays,preserving the V andermonde structure of the steering vectors while changing the amplitude andthe phase gradient of the steering vector in a nonlinear fashion. The new scheme is therefore titled V andermonde Invariance Trans-formation.The introduced degrees of freedom due to this preprocessing transformation can be used to beneficially influence the propertiesof the channel to achieve an enhanced performance of the subse-quent signal processing algorithm.1.INTRODUCTORY MOTIV ATIONSome years ago the application of antenna arrays has been pro-posed for mobile communication systems to attain an increase in capacity and interference reduction by additionally exploiting the spatial separation of the mobile users.Using more then one antenna for the receiver and/or sender pro-vokes a noticeable increase of the system performance by achiev-ing antenna gain,enhanced interference cancellation,and also transmit-receive diversity.Exploiting these properties requires the knowl-edge of the channel.Therefore channel estimation has to be ap-plied to determine the parameters of the channel.These channel estimation schemes work better for higher signal-to-noise ratios (SNR).To this end preprocessing schemes can be applied to the antenna output to amplify the user signal over the noise and inter-ference.The most commonly used structure of an antenna array is the Uniform Linear Array(ULA).In this case antennas are ar-ranged in a line with equal distance to their neighboring antennas. Assuming different propagation paths impinging at a ULA with antennas under the influence of additive,possibly colored noiseproduces the data model(1)where,,,and denote the complex amplitude of path,the steering vector of path,the arriving signal of path, and complex,possibly colored noise,respectively.Under the assumption of discrete wavefronts[1],the steering vec-tors are parameterized only by one angle between the prop-agation path and the ULA.The steering vector can be written ase j e j T(2)where T denotes transposition and is the spatial frequency with the antenna spacing in fractions of the wavelength.Rewriting eq.(1)in vector-matrix notation leads todiag(3)In the following we will derive a transformation matrix which is applied to the data vector.The new output reads asdiag(4) Thereby,we design such,that the matrix is again a steering matrix of a ULA having V andermonde structure.The vector can be regarded as the output of a virtual ULA with noise.Fig.1.Scheme of the proposed preprocessing transformation at the receiving antenna array.2.V ANDERMONDE INV ARIANCE TRANSFORMATION We start with two V andermonde vectorsTTwhere and are unimodular complex numberse j e j(5) and is the amplitude function,that is usually nonlinear with respect to.In the sequel we will investigate matrices that map to:(6)Matrices having this property will be called V andermonde Invari-ant Matrices,the transformation from to V andermonde Invari-ance Transformation(VIT).After the vector-matrix multiplication the n-th component of vector is a polynomial in,that we will write in sum and product form(7) where are the complex roots of the polynomialsand is a scaling factor.For to be a V andermonde invari-ant matrix,it must provide the vector with the propertye j(9) As the total number of zeros in(7)is,H(x)must be an all-pass function offirst order(10) where and denotes the mirror operator on the unit circle.This leads toforelse(11)For convenience we require,that(6)maps an all-ones vector again onto an all-ones vector,which translates to(12) Plugging(12),(11)and(5)into(7)gives the transformed vector from(6)in its compact form ase j(14) SeeFig.2.Plot of the phase transformation for and K=.Note that is mapped to.At this point the second derivative of with respect to vanishes.This indicates,that the nonlinear relation(14)may be linearly approximated in the vicinity of aslin(15) with phase amplification.3.2.Amplitude amplificationWhile the norm of the input vector is always constant tominmaxand is therefore exponential in but only polynomial in or(20)The expression.Notethat is a value of second order.The optimum value settles tolargely independent of the ULA size.4.4.Multiple-Input Multiple-Output Spatial FilterIn contrast to the classical form of a spatialfilter,which maps avector input to a scalar output,the VIT maps vectors onto vec-tors,and therefore is a MIMO spatialfilter.The VIT can also bethought of as a bank of spatialfilters,that have tuned phase andamplitude relationships to preserve the V andermonde structure ofthe input signal.Note that the VIT is linear in terms of its inputand output V andermonde vectors,but nonlinear in terms of theirspatial frequencies.SNR 2.5 5.1 6.765.APPLICATION EXAMPLE5.1.V AP-DOA AlgorithmIn the conventional setup,samples of the ULA output are measured at successive time instants and collected into a data matrix[2].The measured data is then fed into one of the well known high resolution DOA estimation algorithms like MUSIC or ESPRIT[3,2]that returns a set of estimated di-rections of arrival1.The quality of estimation de-pends on the reliability of the measured array output,i.e.the SNR, and also on the number of snapshots that can be obtained dur-ing the coherence time of the channel.By introducing a VIT based preprocessing scheme we can achieve the same accuracy at a lower SNR level and/or with fewer snapshots.The second property en-ables us to track DOAs of faster changing channels.This idea of Virtual Array Processing(V AP)is to start with raw estimates of directions of arrival and then sequentially apply a set of VITs that are focused on these estimated directions,followed by subsequent DOA estimations based on the transformed data set. Due to the noise shaping effect of VIT this will lead to a more ac-curate estimate,for the price of a times higher computational load.5.2.Simulation ResultsWe assume one wavefront impinging from at an-ULA with spacing in spatially white noise and being estimated with the Standard ESPRIT algorithm.In the sequel we will com-pare the performance of the ESPRIT algorithm to its V AP variant. Figure4shows the RMSE of Standard ESPRIT as a function of the SNR.The upper line corresponds to the estimation without prepro-cessing and the lower line to the case of V AP enhanced estimation for.The simulation validated the previous results,that a choice of is optimal in terms of lowest RMSE.For this value of the V AP enhanced estimation achieves a gain in SNR of approximately dB for a reasonable SNR range.If we plot the RMSE as a function of the number of samples,。