EFFECTS OF MULTIPATH ANGULAR SPREAD ON THE SPATIAL CROss -correlation OF RECEIVED VOLTAGE ENVELOPES

Size: px
Start display at page:

Download "EFFECTS OF MULTIPATH ANGULAR SPREAD ON THE SPATIAL CROss -correlation OF RECEIVED VOLTAGE ENVELOPES"

Transcription

1 IEEE Vehcular Technology Conference, Houston,TX, May 16-19, 1999 pp EFFECTS OF MULTIPATH ANGULAR SPREAD ON THE SPATIAL CROss -correlation OF RECEIVED VOLTAGE ENVELOPES Gregory D. Durgn and Theodore S. Rappaport Moble and Portable Rado Research Group Bradley Department of Electrcal and Computer Engneerng 432 New Engneerng Buldng Vrgna Polytechnc Insttute and State Unversty. Blacksburg, VA e-mal: gdurgncvt. edu Abstract - Ths paper presents a smple formula relatng multpath angular spread to small-scale fadng statstcs. Ths formula s then appled to fnd an approxmate spatal cross-correlaton functon for receved voltage envelopes. The analytcal approach s compared to 67 cross-correlaton functons smulated wth non-omndrectonal multpath. The equatons n ths paper provde nsght for applyng spatal d. versty technques to recevers operatng n the presence of non-omndrectonal multpath. I. Introducton The effects of multpath angular spread on smallscale fadng statstcs are mportant to consder n the desgn and operaton of almost any wreless system. Ths paper derves fadng rate and correlaton statstcs for Raylegh fadng envelopes based on the mult path angular spread defnton presented n [1]. The key results are Eqn (6) and Eqn (7), whch show that the spatal cross-correlaton behavor of any angular dstrbuton of mult path power - regardless of spatal complexty- may be characterzed by a sngle angular spread parameter. In the past, the analyss of these statstcs n the lterature have been lmted mostly to the dealzed case of omndrectonal multpath propagaton (2]. Ths work may assst the desgn of recevers that operate n the presence of realstc, nonomndrectonal multpath. For example, the results quanttatvely show how a spatal dversty desgn s affected by multpath of arbtrary spatal complexty and random recever orentaton. We show that usng the classcal, omndrectonal expressons for de- sgnng spatal dversty recevers wll always lead to nadequate separaton dstances for the dversty antennas whenever a bas exsts n the angle-of-arrval of multpath power. II. Overvew of Angular Spread and Fadng Statstcs For typcal terrestral propagaton, rado waves arrve at a wreless recever from azmuthal drectons about the horzon (3]. Ths dstrbuton of multpath power s convenently descrbed by the functon, p( ), where s the azmuthal angle. Angular spread, A, s an mportant propagaton parameter whch determnes how spread out multpath power s about the horzon and s defned as A= Fn = j p(o) exp(jnj)dj (1) where Fn s the nth complex Fourer coeffcent of p(o) [1]. Angular spread, A, ranges from to 1, wth denotng the case of a sngle multpath component from a sngle drecton and 1 denotng no clear bas n the angular dstrbuton of receved power. Ths defnton of angular spread, A, s partcularly useful because t s drectly related to the average rate at whch a sgnal fades n a local area [1]. Appendx A shows that the mean-square rate-of-change of a receved narrowband voltage envelope, R, n a Raylegh fadng channel s related to the angular spread, A: 27r Ths work s sponsored by a Bradley Fellowshp a.t Vrgna. Tech and an NSF Presdental Faculty Fellowshp.

2 where k s the wavenumber of propagaton (27r dvded by wavelength,.-), r s change n poston, and E { R 2 } s the mean-square receved voltage of the local area. Eqn (2) states that the average rate of envelope change decreases as the mpngng multpath power becomes concentrated n a sngle drecton. Spatal Representaton of Arrvng Power Rx a -c::c -Q. Angular Dstrbuton of Power 9, Azmuthal Angle 21t Fgure 1: Angular dstrbuton of power, p(o), for a sector of arrvng multpath components. As just one example, consder a stuaton where multpath power s arrvng unformly over a contnuous range of azmuthal angles. The functon p( ) for ths type of channel s () = { : Oo =5S Bo+a p : elsewhere (3) The angle a ndcates the wdth of the sector (n radans) of arrvng mult path power and the angle (J s any arbtrary offset angle, as llustrated by Fgure 1. Followng Eqn (2), the average envelope fadng for ths sectored multpath power s A = V a co a 2et (4) The lmtng cases of Eqn ( 4) provde deeper understandng of ths defnton of angular spread. The lmtng case of a sngle multpath arrvng from precsely one drecton corresponds to a=, whch results n A =. The other lmtng case of unform llumnaton n all drectons corresponds to a = 271', whch results n the maxmum angular spread of 1. III. Spatal Cross-Correlaton The spatal cross-correlaton functon, p( r), determnes the correlaton between voltage envelopes separated n space by a dstance r. A defnton for normalzed spatal cross-correlaton s gven below [2, 4]: (r) = E {R(ro)R(ro + rz)}- (E {R} ) 2 p E {R}- (E {R}) 2 (5) where R s the stochastc voltage envelope as a functon of two-dmensonal poston vector, r Eqn (5) assumes that the dstance r s suffcently small, re manng wthn a local area so that R s approxmately wde-sense statonary (WSS) [2]. The unt drecton vector, z, ponts n the drecton of travel. Motvaton for Spatal Dversty The spatal cross-correlaton for recever antennas at dfferent postons wthn a local area s an mportant parameter for desgnng recevers that employ spatal dversty [5, 6].. The spatal cross-correlaton functon, p(r), determnes how far dversty antennas must be separated before the fadng of ther receved voltage envelopes becomes decorrelated [7]. The exact calculaton of p(r) for a realstc angular dstrbuton ofmultpath power s extremely dffcult and often non-sotropc, dependng on the drecton of z n Eqn (5). (a) (b) (c) Fgure 2: Examples of spatal dversty wth random antenna orentatons. Non-sotropc p(r) complcates the spacng of dversty antennas, partcularly for recevers wth random orentatons. Fgure 2 shows several examples of dversty systems that may have random azmuthal orentaton. The random orentaton of examples (a) and (b) s due to moblty whle the random orentaton of example (c) s due to the multple dversty branches taken n dfferent azmuthal drectons. Cross-Correlaton Functon Appendx B derves an approxmate crosscorrelaton functon for a drectve channel, averaged over all possble azmuthal orentatons: p(r) exp [-23A 2 Gr] (6) Eqn ( 6) states that the cross-correlaton functon wll broaden as the channel becomes more drectve (angular spread, A, decreases). Furthermore, Eqn (6) s accurate for small r, but does not model the hgher-order behavor for larger r. However, many applcatons of the spatal cross-correlaton functon do not requre hgher-order behavor.

3 Voltage envelopes are consdered suffcently decorrelated when p( r) drops below.4, whch s roughly the statstcal defnton of a correlaton length [5]. The correlaton length, le, of a cross-correlaton functon s the value that satsfes the followng equaton: p(/e) = exp(-1). Applyng ths defnton to Eqn (6), the approxmate correlaton length for fadng envelopes produced by any spatal dstrbuton of mult path power s lc = A.y23 (7) " Eqn (7) analytcally demonstrates how correlaton length ncreases wth decreasng angular spread, A. In an omndrectonal multpath channel (A = 1), Eqn (7) predcts a correlaton length of lc =.21A, whch s confrmed by the classcal analyss (5]. Smulaton Eqn (6) s tested aganst smulated p(r) that are calculated from known angular dstrbutons of multpath power. The basc method of smulaton represents p( fj) as a sum of N mult path powers that ar ve from evenly-spaced, dscrete drectons n space: p(o) = L: P; 6 -..!!!_ =t N ( 2 ') N (8) From ths representaton, t s possble to generate arbtrary realzatons of envelope, R, as a functon of two-dmensonal space from N R(:c, y) = L: exp(21tu) x (9) =l exp [- 2 1r (cos + ysn )] I where U s a random varable unformly dstrbuted over the nterval [, 1). Eqn (9) s smply the envelope of a superposton of N plane waves wth constant ampltudes, Vf{. A computer smulaton was used to calculate p( r) for 67 dfferent angular dstrbutons of power, p(o). Each case of p(o) was characterzed by Eqn (3) - mult path arrvng over a contnuous, azmuthal sector of a radans. The 67 cases correspond to dfferent sector wdths, a, that ranged fron f to 21T n :S ncrements (3 to 36 n 5 ncrements). In terms of Eqn {8), the P that fall wthn the azmuthal sector a are set to a constant and the P that fall outsde of the sector are set to zero. For each smulate case of a, 72 envelope realzatons of Eqn (9) wer generated. Each realzaton s a collecton of envelope values for a 6, by 6.. area, generated by Eqn (9) usng a new draw of random phases (21TU) From these 72 realzatons, p(r) was tabulated usng the defnton of Eqn (5) for each U draw wth a vector, z, that ponted n 72 azmuthal drectons (meant to smulate random orentaton) !!1 o.a.!!! o.a.4.2 Correlaton Lengths tor Multpatt Sectors Result trom Smulaton - - Analytcal Method,, Sector Wdth, a (Degrees) Fgure 3: Comparson of correlatn lengths usng emprcal and approxmate analytcal results. Fgure 3 presents a plot between the approxmate correlaton lengths of Eqn (7) and the correlaton lengths resultng from smulaton. Note that they are n near-perfect agreement except for low values of angular spread. The slght devaton at lower angles s due to smulaton courseness rather than the valdty of Eqn (7). For N = 72 n Eqn (9), only a few terms n the summaton of Eqn (8) are nonzero for an angular dstrbuton of power wth a low angular spread. Wthout a large number of nonzero components, the frst-order statstcs no longer follow a pure Raylegh dstrbuton, as noted n [8]. Graphs of smulated vs. approxmated functons for 4 of the 67 cases s shown n Fgure 4. IV. Summary The spatal cross-correlaton behavor of any angular dstrbuton of multpath, regardless of complexty, may be approxmately characterzed by Eqn (6). Furthermore, Fgure 3 shows that correlaton lengths ncrease as angular spread decreases. Therefore, as angular spread decreases, effectve spatal dversty at the recever requres a larger separaton of dver-

4 Cross-correlaton Functon tor a = 5 Cross-correlaton Functon for a = 1 s Q. c.5.g.!!! e o Smulaton Analytcal p=e-1 A= "'""'"'"'""''''''"'''''"'"'"''"'"'"'""'''''''''"''''''''''''''""' 'C' : Smulaton Analytcal p=e-1 A=.4785 r:..5 '"'"'"'"'"'"''"'''""'"''"''''"''""'"'""'"'""''''''""'''''''''"''"'"'"' 8 I ' Dstance (rfa) 6 2 Dstance (r/'j..) 3 4 Cross-correlaton Functon for a = 2 Cross-correlaton Functon for a = 36 'C' a: g-.5 Smulaton Analytlcat p-e-1 A=.8255 f..... e o --..._,, ' s Q. Smulaton Analytcal p-e-1.5 A=1 '1a... e o.5 1 Dstance (rfa) Dstance (r/'j..) Fgure 4: Smulated vs. analytc spatal cross-correlaton functons, p(r), for sectors of ncomng multpath power for a = 5, 1, 2, and 36. sty antennas. If the spatal dversty were desgned usng the classcal, omndrectonal analyss and the recever was operated n realstc envronments wth lower angular spreads, then the envelope correlaton between the dversty antennas ncreases and the recever becomes vulnerable to fadng. References (1) G.D. Durgn and T.S. Rappaport, "A Basc Relatonshp Between Multpath Angular Spread and Narrowband Fadng n a Wreless Channel," lee Electroncs Letter-s, vol. 34, no. 25, pp , 1 Dec (2) W.C. Jakes (ed), Mcrowave Moble Communcatons, IEEE Press, New York, (3) M.J. Gans, "A Power-Spectral Theory of Propagaton n the Moble Rado Envronment," IEEE Transactons on Vehcular Technology, vol. VT-21, no. 1, pp , Feb ( 4) A.M.D. Turkman, A.A. Arowojolu, P.A. Jefford, and C.J. Kellett, "An Expermental Evaluaton of the Performance of Two-Branch Space and Polarzaton Dversty Schemes at 18 MHz," IEEE Transactons on Vehcular Technology, vol. 44, no. 2, pp , May (5) D.O. Reudnk, "Propertes of Moble Rado Propagaton Above 4 MHz," IEEE Transactons on Vehcular Technology, Nov (6) W.C. Jakes, "A Comparson of Specfc Space Dversty Technques for Reducton of Fast

5 Fadng n UHF Moble Rado Systems," IEEE Transactons on Vehcular Technology, vol. VT-2, no. 4, pp , Nov (7) R.G. Vaughn and N.1. Scott, "Closely Spaced Monopoles for Moble Communcatons," Rado Scence, vol. 28, no. 6, pp , Nov Dec (8) Matthas Patzold and Frank Lane, "Statstcal Propertes of Jakes' Fadng Channel Smulator," n IEEE 48th Vehcular Technology Conference, Ottawa, CA, May 1998, pp (9) S.O. Rce, "Mathematcal Analyss of Random Nose," Bell System Techncal Journal, vol. 23, pp , July (1) H. Stark and J.W. Woods, Probablty, Random Processes, and Estmaton Theory for Engneers, Prentce Hall, New Jersey, 2nd edton, A. Raylegh Fadng Rate as a Functon of Angular Spread Based on the power relatonshp P = R 2, t s possble to wrte the followng: whch s vald for a Raylegh fadng process snce R and ts dervatve are ndependent [6, 9]. In [1], the authors show that the average mean-square rate of change of power, P, n a local area s where A s angular spread, as defned n Eqn (1). Settng Eqn (1) equal to Eqn (11) produces the mean-square fadng rate result for a Raylegh-fadng voltage envelope n Eqn (2). B. Cross-Correlaton Dervaton To develop an approxmate expresson for the crosscorrelaton of mult path felds, frst expand the functon, p(r), nto a Mclaurn seres: - r2n d2.np( r') I p( r) - L...J (2n)! dr'2n (12) n=o r 1 = Eqn. ( 12) contans only even powers of r snce any cross-correlaton functon wll be symmetrc about r =. The dfferentaton of a WSS cross-correlaton satsfes the followng relatonshp for n ;:=: 1 (1]: d2np(r') I = j.;.e{r(f.)r(r. + r'z)}l-o dr'2n r'=o E {R2}- (E {R})2 E{(d")2} ( l)n dr = - E {R2}- (E {R})2 (13) and s useful for re-expressng the Mclaurn seres: p(r) Now consder p(r) approxmated by an arbtrary Gaussan functon and ts Mclaurn expanson: p(r) exp [-a Gr] f: (-l)an ut n=o n. A 1-a(x)2+... (15) A Gaussan functon s chosen as a generc approxmaton to the true cross-correlaton snce t s a convenent and well-behaved correlaton functon. The approprate constant a s chosen by settng equal the second terms of Eqn (14) and Eqn (15), ensurng that the behavor of both cross-correlaton functo;j.s s dentcal for small r. The soluton for a follows by calculatng the second term n Eqn (14) usng the Raylegh statstcs from Eqn (2) and the followng relatonshp: (E {R}) 2 = E { R 2 } (16) Now the constant a may be solved: a= Ul:21r] A2 (17) "-...--' 23. Therefore, the envelope spatal cross-correlaton functon n a drectve channel wth random oren taton s Eqn ( 6) whch now depends only on A. Eqn (6) shows how ncreased clusterng of multpath about a sngle drecton wll broaden p( r) by decreasng the angular spread, A.

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Research Article Green s Theorem for Sign Data

Research Article Green s Theorem for Sign Data Internatonal Scholarly Research Network ISRN Appled Mathematcs Volume 2012, Artcle ID 539359, 10 pages do:10.5402/2012/539359 Research Artcle Green s Theorem for Sgn Data Lous M. Houston The Unversty of

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

Global Sensitivity. Tuesday 20 th February, 2018

Global Sensitivity. Tuesday 20 th February, 2018 Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values

More information

Multipath richness a measure of MIMO capacity in an environment

Multipath richness a measure of MIMO capacity in an environment EUROEA COOERATIO I THE FIELD OF SCIETIFIC AD TECHICAL RESEARCH EURO-COST SOURCE: Aalborg Unversty, Denmark COST 73 TD 04) 57 Dusburg, Germany 004/Sep/0- ultpath rchness a measure of IO capacty n an envronment

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

arxiv:cs.cv/ Jun 2000

arxiv:cs.cv/ Jun 2000 Correlaton over Decomposed Sgnals: A Non-Lnear Approach to Fast and Effectve Sequences Comparson Lucano da Fontoura Costa arxv:cs.cv/0006040 28 Jun 2000 Cybernetc Vson Research Group IFSC Unversty of São

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

Error Probability for M Signals

Error Probability for M Signals Chapter 3 rror Probablty for M Sgnals In ths chapter we dscuss the error probablty n decdng whch of M sgnals was transmtted over an arbtrary channel. We assume the sgnals are represented by a set of orthonormal

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

DUE: WEDS FEB 21ST 2018

DUE: WEDS FEB 21ST 2018 HOMEWORK # 1: FINITE DIFFERENCES IN ONE DIMENSION DUE: WEDS FEB 21ST 2018 1. Theory Beam bendng s a classcal engneerng analyss. The tradtonal soluton technque makes smplfyng assumptons such as a constant

More information

Chapter 11: Simple Linear Regression and Correlation

Chapter 11: Simple Linear Regression and Correlation Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests

More information

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis Statstcal analyss usng matlab HY 439 Presented by: George Fortetsanaks Roadmap Probablty dstrbutons Statstcal estmaton Fttng data to probablty dstrbutons Contnuous dstrbutons Contnuous random varable X

More information

The Geometry of Logit and Probit

The Geometry of Logit and Probit The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.

More information

Thermal-Fluids I. Chapter 18 Transient heat conduction. Dr. Primal Fernando Ph: (850)

Thermal-Fluids I. Chapter 18 Transient heat conduction. Dr. Primal Fernando Ph: (850) hermal-fluds I Chapter 18 ransent heat conducton Dr. Prmal Fernando prmal@eng.fsu.edu Ph: (850) 410-6323 1 ransent heat conducton In general, he temperature of a body vares wth tme as well as poston. In

More information

THE SUMMATION NOTATION Ʃ

THE SUMMATION NOTATION Ʃ Sngle Subscrpt otaton THE SUMMATIO OTATIO Ʃ Most of the calculatons we perform n statstcs are repettve operatons on lsts of numbers. For example, we compute the sum of a set of numbers, or the sum of the

More information

Lecture 16 Statistical Analysis in Biomaterials Research (Part II)

Lecture 16 Statistical Analysis in Biomaterials Research (Part II) 3.051J/0.340J 1 Lecture 16 Statstcal Analyss n Bomaterals Research (Part II) C. F Dstrbuton Allows comparson of varablty of behavor between populatons usng test of hypothess: σ x = σ x amed for Brtsh statstcan

More information

U-Pb Geochronology Practical: Background

U-Pb Geochronology Practical: Background U-Pb Geochronology Practcal: Background Basc Concepts: accuracy: measure of the dfference between an expermental measurement and the true value precson: measure of the reproducblty of the expermental result

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look

More information

This column is a continuation of our previous column

This column is a continuation of our previous column Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard

More information

Inductance Calculation for Conductors of Arbitrary Shape

Inductance Calculation for Conductors of Arbitrary Shape CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors

More information

Linear Regression Analysis: Terminology and Notation

Linear Regression Analysis: Terminology and Notation ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented

More information

Comparison of Regression Lines

Comparison of Regression Lines STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence

More information

1 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations

1 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations Physcs 171/271 -Davd Klenfeld - Fall 2005 (revsed Wnter 2011) 1 Dervaton of Rate Equatons from Sngle-Cell Conductance (Hodgkn-Huxley-lke) Equatons We consder a network of many neurons, each of whch obeys

More information

ONE DIMENSIONAL TRIANGULAR FIN EXPERIMENT. Technical Advisor: Dr. D.C. Look, Jr. Version: 11/03/00

ONE DIMENSIONAL TRIANGULAR FIN EXPERIMENT. Technical Advisor: Dr. D.C. Look, Jr. Version: 11/03/00 ONE IMENSIONAL TRIANGULAR FIN EXPERIMENT Techncal Advsor: r..c. Look, Jr. Verson: /3/ 7. GENERAL OJECTIVES a) To understand a one-dmensonal epermental appromaton. b) To understand the art of epermental

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

FUZZY FINITE ELEMENT METHOD

FUZZY FINITE ELEMENT METHOD FUZZY FINITE ELEMENT METHOD RELIABILITY TRUCTURE ANALYI UING PROBABILITY 3.. Maxmum Normal tress Internal force s the shear force, V has a magntude equal to the load P and bendng moment, M. Bendng moments

More information

Chapter 6. Wideband channels. Slides for Wireless Communications Edfors, Molisch, Tufvesson

Chapter 6. Wideband channels. Slides for Wireless Communications Edfors, Molisch, Tufvesson Chapter 6 Wdeband channels 128 Delay (tme) dsperson A smple case Transmtted mpulse h h a a a 1 1 2 2 3 3 Receved sgnal (channel mpulse response) 1 a 1 2 a 2 a 3 3 129 Delay (tme) dsperson One reflecton/path,

More information

TLCOM 612 Advanced Telecommunications Engineering II

TLCOM 612 Advanced Telecommunications Engineering II TLCOM 62 Advanced Telecommuncatons Engneerng II Wnter 2 Outlne Presentatons The moble rado sgnal envronment Combned fadng effects and nose Delay spread and Coherence bandwdth Doppler Shft Fast vs. Slow

More information

Chapter 13: Multiple Regression

Chapter 13: Multiple Regression Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to

More information

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD Ákos Jósef Lengyel, István Ecsed Assstant Lecturer, Professor of Mechancs, Insttute of Appled Mechancs, Unversty of Mskolc, Mskolc-Egyetemváros,

More information

Numerical Solution of Ordinary Differential Equations

Numerical Solution of Ordinary Differential Equations Numercal Methods (CENG 00) CHAPTER-VI Numercal Soluton of Ordnar Dfferental Equatons 6 Introducton Dfferental equatons are equatons composed of an unknown functon and ts dervatves The followng are examples

More information

Note 10. Modeling and Simulation of Dynamic Systems

Note 10. Modeling and Simulation of Dynamic Systems Lecture Notes of ME 475: Introducton to Mechatroncs Note 0 Modelng and Smulaton of Dynamc Systems Department of Mechancal Engneerng, Unversty Of Saskatchewan, 57 Campus Drve, Saskatoon, SK S7N 5A9, Canada

More information

Fuzzy Boundaries of Sample Selection Model

Fuzzy Boundaries of Sample Selection Model Proceedngs of the 9th WSES Internatonal Conference on ppled Mathematcs, Istanbul, Turkey, May 7-9, 006 (pp309-34) Fuzzy Boundares of Sample Selecton Model L. MUHMD SFIIH, NTON BDULBSH KMIL, M. T. BU OSMN

More information

System in Weibull Distribution

System in Weibull Distribution Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co

More information

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant

More information

Multiple Sound Source Location in 3D Space with a Synchronized Neural System

Multiple Sound Source Location in 3D Space with a Synchronized Neural System Multple Sound Source Locaton n D Space wth a Synchronzed Neural System Yum Takzawa and Atsush Fukasawa Insttute of Statstcal Mathematcs Research Organzaton of Informaton and Systems 0- Mdor-cho, Tachkawa,

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

Robert Eisberg Second edition CH 09 Multielectron atoms ground states and x-ray excitations

Robert Eisberg Second edition CH 09 Multielectron atoms ground states and x-ray excitations Quantum Physcs 量 理 Robert Esberg Second edton CH 09 Multelectron atoms ground states and x-ray exctatons 9-01 By gong through the procedure ndcated n the text, develop the tme-ndependent Schroednger equaton

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

THEOREMS OF QUANTUM MECHANICS

THEOREMS OF QUANTUM MECHANICS THEOREMS OF QUANTUM MECHANICS In order to develop methods to treat many-electron systems (atoms & molecules), many of the theorems of quantum mechancs are useful. Useful Notaton The matrx element A mn

More information

Convergence of random processes

Convergence of random processes DS-GA 12 Lecture notes 6 Fall 216 Convergence of random processes 1 Introducton In these notes we study convergence of dscrete random processes. Ths allows to characterze phenomena such as the law of large

More information

Probability Theory. The nth coefficient of the Taylor series of f(k), expanded around k = 0, gives the nth moment of x as ( ik) n n!

Probability Theory. The nth coefficient of the Taylor series of f(k), expanded around k = 0, gives the nth moment of x as ( ik) n n! 8333: Statstcal Mechancs I Problem Set # 3 Solutons Fall 3 Characterstc Functons: Probablty Theory The characterstc functon s defned by fk ep k = ep kpd The nth coeffcent of the Taylor seres of fk epanded

More information

Uncertainty as the Overlap of Alternate Conditional Distributions

Uncertainty as the Overlap of Alternate Conditional Distributions Uncertanty as the Overlap of Alternate Condtonal Dstrbutons Olena Babak and Clayton V. Deutsch Centre for Computatonal Geostatstcs Department of Cvl & Envronmental Engneerng Unversty of Alberta An mportant

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Basic Statistical Analysis and Yield Calculations

Basic Statistical Analysis and Yield Calculations October 17, 007 Basc Statstcal Analyss and Yeld Calculatons Dr. José Ernesto Rayas Sánchez 1 Outlne Sources of desgn-performance uncertanty Desgn and development processes Desgn for manufacturablty A general

More information

Non-gaussianity in axion N-flation models

Non-gaussianity in axion N-flation models Non-gaussanty n axon N-flaton models Soo A Km Kyung Hee Unversty Based on arxv:1005.4410 by SAK, Andrew R. Lddle and Davd Seery (Sussex), and earler papers by SAK and Lddle. COSMO/CosPA 2010 @ Unversty

More information

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information

Chapter - 2. Distribution System Power Flow Analysis

Chapter - 2. Distribution System Power Flow Analysis Chapter - 2 Dstrbuton System Power Flow Analyss CHAPTER - 2 Radal Dstrbuton System Load Flow 2.1 Introducton Load flow s an mportant tool [66] for analyzng electrcal power system network performance. Load

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model EXACT OE-DIMESIOAL ISIG MODEL The one-dmensonal Isng model conssts of a chan of spns, each spn nteractng only wth ts two nearest neghbors. The smple Isng problem n one dmenson can be solved drectly n several

More information

Introduction to Statistical Methods

Introduction to Statistical Methods Introducton to Statstcal Methods Physcs 4362, Lecture #3 hermodynamcs Classcal Statstcal Knetc heory Classcal hermodynamcs Macroscopc approach General propertes of the system Macroscopc varables 1 hermodynamc

More information

Lecture 4. Macrostates and Microstates (Ch. 2 )

Lecture 4. Macrostates and Microstates (Ch. 2 ) Lecture 4. Macrostates and Mcrostates (Ch. ) The past three lectures: we have learned about thermal energy, how t s stored at the mcroscopc level, and how t can be transferred from one system to another.

More information

Feb 14: Spatial analysis of data fields

Feb 14: Spatial analysis of data fields Feb 4: Spatal analyss of data felds Mappng rregularly sampled data onto a regular grd Many analyss technques for geophyscal data requre the data be located at regular ntervals n space and/or tme. hs s

More information

Indeterminate pin-jointed frames (trusses)

Indeterminate pin-jointed frames (trusses) Indetermnate pn-jonted frames (trusses) Calculaton of member forces usng force method I. Statcal determnacy. The degree of freedom of any truss can be derved as: w= k d a =, where k s the number of all

More information

Color Rendering Uncertainty

Color Rendering Uncertainty Australan Journal of Basc and Appled Scences 4(10): 4601-4608 010 ISSN 1991-8178 Color Renderng Uncertanty 1 A.el Bally M.M. El-Ganany 3 A. Al-amel 1 Physcs Department Photometry department- NIS Abstract:

More information

CS-433: Simulation and Modeling Modeling and Probability Review

CS-433: Simulation and Modeling Modeling and Probability Review CS-433: Smulaton and Modelng Modelng and Probablty Revew Exercse 1. (Probablty of Smple Events) Exercse 1.1 The owner of a camera shop receves a shpment of fve cameras from a camera manufacturer. Unknown

More information

Statistical Evaluation of WATFLOOD

Statistical Evaluation of WATFLOOD tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth

More information

Introduction to Regression

Introduction to Regression Introducton to Regresson Dr Tom Ilvento Department of Food and Resource Economcs Overvew The last part of the course wll focus on Regresson Analyss Ths s one of the more powerful statstcal technques Provdes

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications Durban Watson for Testng the Lack-of-Ft of Polynomal Regresson Models wthout Replcatons Ruba A. Alyaf, Maha A. Omar, Abdullah A. Al-Shha ralyaf@ksu.edu.sa, maomar@ksu.edu.sa, aalshha@ksu.edu.sa Department

More information

The Concept of Beamforming

The Concept of Beamforming ELG513 Smart Antennas S.Loyka he Concept of Beamformng Generc representaton of the array output sgnal, 1 where w y N 1 * = 1 = w x = w x (4.1) complex weghts, control the array pattern; y and x - narrowband

More information

Outage Probability of Macrodiversity Reception in the Presence of Fading and Weibull Co- Channel Interference

Outage Probability of Macrodiversity Reception in the Presence of Fading and Weibull Co- Channel Interference ISSN 33-365 (Prnt, ISSN 848-6339 (Onlne https://do.org/.7559/tv-67847 Orgnal scentfc paper Outage Probablty of Macrodversty Recepton n the Presence of Fadng and Webull Co- Channel Interference Mloš PERIĆ,

More information

Simulation and Probability Distribution

Simulation and Probability Distribution CHAPTER Probablty, Statstcs, and Relablty for Engneers and Scentsts Second Edton PROBABILIT DISTRIBUTION FOR CONTINUOUS RANDOM VARIABLES A. J. Clark School of Engneerng Department of Cvl and Envronmental

More information

), it produces a response (output function g (x)

), it produces a response (output function g (x) Lnear Systems Revew Notes adapted from notes by Mchael Braun Typcally n electrcal engneerng, one s concerned wth functons of tme, such as a voltage waveform System descrpton s therefore defned n the domans

More information

9 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations

9 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations Physcs 171/271 - Chapter 9R -Davd Klenfeld - Fall 2005 9 Dervaton of Rate Equatons from Sngle-Cell Conductance (Hodgkn-Huxley-lke) Equatons We consder a network of many neurons, each of whch obeys a set

More information

2016 Wiley. Study Session 2: Ethical and Professional Standards Application

2016 Wiley. Study Session 2: Ethical and Professional Standards Application 6 Wley Study Sesson : Ethcal and Professonal Standards Applcaton LESSON : CORRECTION ANALYSIS Readng 9: Correlaton and Regresson LOS 9a: Calculate and nterpret a sample covarance and a sample correlaton

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

ECE559VV Project Report

ECE559VV Project Report ECE559VV Project Report (Supplementary Notes Loc Xuan Bu I. MAX SUM-RATE SCHEDULING: THE UPLINK CASE We have seen (n the presentaton that, for downlnk (broadcast channels, the strategy maxmzng the sum-rate

More information

Section 8.3 Polar Form of Complex Numbers

Section 8.3 Polar Form of Complex Numbers 80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the

More information

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential Open Systems: Chemcal Potental and Partal Molar Quanttes Chemcal Potental For closed systems, we have derved the followng relatonshps: du = TdS pdv dh = TdS + Vdp da = SdT pdv dg = VdP SdT For open systems,

More information

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient 58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),

More information

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University PHYS 45 Sprng semester 7 Lecture : Dealng wth Expermental Uncertantes Ron Refenberger Brck anotechnology Center Purdue Unversty Lecture Introductory Comments Expermental errors (really expermental uncertantes)

More information

Notes on Frequency Estimation in Data Streams

Notes on Frequency Estimation in Data Streams Notes on Frequency Estmaton n Data Streams In (one of) the data streamng model(s), the data s a sequence of arrvals a 1, a 2,..., a m of the form a j = (, v) where s the dentty of the tem and belongs to

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

An Improved multiple fractal algorithm

An Improved multiple fractal algorithm Advanced Scence and Technology Letters Vol.31 (MulGraB 213), pp.184-188 http://dx.do.org/1.1427/astl.213.31.41 An Improved multple fractal algorthm Yun Ln, Xaochu Xu, Jnfeng Pang College of Informaton

More information

Georgia Tech PHYS 6124 Mathematical Methods of Physics I

Georgia Tech PHYS 6124 Mathematical Methods of Physics I Georga Tech PHYS 624 Mathematcal Methods of Physcs I Instructor: Predrag Cvtanovć Fall semester 202 Homework Set #7 due October 30 202 == show all your work for maxmum credt == put labels ttle legends

More information

The Two-scale Finite Element Errors Analysis for One Class of Thermoelastic Problem in Periodic Composites

The Two-scale Finite Element Errors Analysis for One Class of Thermoelastic Problem in Periodic Composites 7 Asa-Pacfc Engneerng Technology Conference (APETC 7) ISBN: 978--6595-443- The Two-scale Fnte Element Errors Analyss for One Class of Thermoelastc Problem n Perodc Compostes Xaoun Deng Mngxang Deng ABSTRACT

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

829. An adaptive method for inertia force identification in cantilever under moving mass

829. An adaptive method for inertia force identification in cantilever under moving mass 89. An adaptve method for nerta force dentfcaton n cantlever under movng mass Qang Chen 1, Mnzhuo Wang, Hao Yan 3, Haonan Ye 4, Guola Yang 5 1,, 3, 4 Department of Control and System Engneerng, Nanng Unversty,

More information

Solution Thermodynamics

Solution Thermodynamics Soluton hermodynamcs usng Wagner Notaton by Stanley. Howard Department of aterals and etallurgcal Engneerng South Dakota School of nes and echnology Rapd Cty, SD 57701 January 7, 001 Soluton hermodynamcs

More information

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve

More information

/ n ) are compared. The logic is: if the two

/ n ) are compared. The logic is: if the two STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence

More information

Computing MLE Bias Empirically

Computing MLE Bias Empirically Computng MLE Bas Emprcally Kar Wa Lm Australan atonal Unversty January 3, 27 Abstract Ths note studes the bas arses from the MLE estmate of the rate parameter and the mean parameter of an exponental dstrbuton.

More information

Cokriging Partial Grades - Application to Block Modeling of Copper Deposits

Cokriging Partial Grades - Application to Block Modeling of Copper Deposits Cokrgng Partal Grades - Applcaton to Block Modelng of Copper Deposts Serge Séguret 1, Julo Benscell 2 and Pablo Carrasco 2 Abstract Ths work concerns mneral deposts made of geologcal bodes such as breccas

More information

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1 Random varables Measure of central tendences and varablty (means and varances) Jont densty functons and ndependence Measures of assocaton (covarance and correlaton) Interestng result Condtonal dstrbutons

More information

Average Decision Threshold of CA CFAR and excision CFAR Detectors in the Presence of Strong Pulse Jamming 1

Average Decision Threshold of CA CFAR and excision CFAR Detectors in the Presence of Strong Pulse Jamming 1 Average Decson hreshold of CA CFAR and excson CFAR Detectors n the Presence of Strong Pulse Jammng Ivan G. Garvanov and Chrsto A. Kabachev Insttute of Informaton echnologes Bulgaran Academy of Scences

More information

Another converse of Jensen s inequality

Another converse of Jensen s inequality Another converse of Jensen s nequalty Slavko Smc Abstract. We gve the best possble global bounds for a form of dscrete Jensen s nequalty. By some examples ts frutfulness s shown. 1. Introducton Throughout

More information

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

VQ widely used in coding speech, image, and video

VQ widely used in coding speech, image, and video at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng

More information

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1 C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned

More information

( ) = ( ) + ( 0) ) ( )

( ) = ( ) + ( 0) ) ( ) EETOMAGNETI OMPATIBIITY HANDBOOK 1 hapter 9: Transent Behavor n the Tme Doman 9.1 Desgn a crcut usng reasonable values for the components that s capable of provdng a tme delay of 100 ms to a dgtal sgnal.

More information

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0 MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector

More information

Laboratory 1c: Method of Least Squares

Laboratory 1c: Method of Least Squares Lab 1c, Least Squares Laboratory 1c: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly

More information

ESCI 341 Atmospheric Thermodynamics Lesson 10 The Physical Meaning of Entropy

ESCI 341 Atmospheric Thermodynamics Lesson 10 The Physical Meaning of Entropy ESCI 341 Atmospherc Thermodynamcs Lesson 10 The Physcal Meanng of Entropy References: An Introducton to Statstcal Thermodynamcs, T.L. Hll An Introducton to Thermodynamcs and Thermostatstcs, H.B. Callen

More information