Lecture 26: Leapers and Creepers

Size: px
Start display at page:

Download "Lecture 26: Leapers and Creepers"

Transcription

1 Lecue 6: Leape and Ceepe Scibe: Geain Jone (and Main Z. Bazan) Depamen of Economic, MIT May, 5 Inoducion Thi lecue conide he analyi of he non-epaable CTRW in which he diibuion of ep ize and ime beween ep ae dependen. Wih uch walk he iue aie of decibing he walk in beween uning poin, which fom a e of meaue zeo of he enie ime-pah of he poce. We will fi decibe he geneal heoy of uch walk, following he noaion of Hughe, befoe conideing wo pecial cae: leape, which ae aumed o emain a he uning poin of he walk unil he nex ep i aken, a which poin he walke move inananeouly o he nex uch poin; and ceepe, which ae aumed o move wih conan velociy beween uning poin. Non-epaable CTRW Define χ (,) he join pdf fo a ep of ize ha ake ime. Wecan wiehiinem of he condiional diibuion χ (,) = p ( ) ψ () = ψ ( ) p ( ) and he maginal diibuion ae defined a: Z ψ () = χ (,) d Z p ( ) = χ, d The dicee poin definedbyhe equence of dawfom χ ae called he uning poin of he andom walk pah, and he queion aie wha do we obeve if we obeve he walk a a ime ohe han he occuence of a uning poin. We define he deniy q (,, ),which inepolae ochaically beween he cuen locaion and he nex ep, a he poiion-ime

2 M. Z. Bazan Random Walk and Diffuion Lecue 6 deniy of he inemediae incemen of he andom walk condiional on he nex uning poin being a (, ). Thu beween uning poin we aume ha he andom walk follow a ochaic ajecoy owad he nex uning poin. Once i eache hee he nex uning poin in pace and ime i eleced and he walke follow he pah defined by q o ge hee. χ (,) and q define he andom walk. Ou goal i o wie down he analog of he Bachelie equaion o define he poiion-ime deniy of he walke. Define he pdf Ψ of he incemenal diplacemen (,) fom he peviou uning poin, wihou eaching he nex uning poin whee he inegal i aken ove all poibiliie fo he nex uning poin, in boh pace and ime, muliplied by he condiional deniy q fo he inemediae incemen in beween uning poin. The inegal in inegae ove all uning poin ha occu lae han ime. Z Z Ψ (,)= q,, χ, d d The genealizaion of he Bachelie equaion fo he non-epaable CTRW i hen: Z Z P, = Ψ (,)+ P, χ, d d The fi em in hi equaion i he deniy condiional on no uning poin having been eached, and he econd em inegae ove all he poible poible locaion of he fi uning poin and ubequen poiion of he walke. Taking he Fouie-Laplace anfom (wheee denoe he Laplace anfom andb he Fouie anfom) we deive a genealizaion of he Monoll-Wei equaion: e P b k, = ebψ k, e k, () bχ Leape Leape ae a pecial cae of he above andom walk in which he walke emain a each uning poin unil he nex incemen occu, and hen immediaely leap o he nex uning poin. We can decibe he walk hough: q,, = δ ( ) fo << Thi i ill moe geneal han peviou lecue, even hough i doe no feaue inemediae dynamic in beween uning poin, ince we allow (,) non-epaable.

3 M. Z. Bazan Random Walk and Diffuion Lecue 6 3 Then A in peviou lecue Z Z Ψ (,) = q,, χ, d d Z µz = δ ( ) χ, d d Z = δ ( ) ψ d Z Ψ e (,) = δ ( ) Z = = e ψ d d Z Z = δ ( ) ψ e dd = = Z h i = δ ( ) ψ e d = = δ ( ) ψe () and aking he Fouie anfom of he dela funcion Thu b eψ ( ψ e (),)= P e b k, = ψe () () b e χ k, Thi geneal expeion fo he Fouie-Laplace anfom of he deniy of he non-epaable CTRW wa fi deived by Sche-Lax (97). The non-epaabiliy manife ielf in he em χ e b k, which fo a epaable walk faco ino pb(k) ψ e (). Example: Polyme Suface Adopion (coninued) Coninuing he example fom Lecue 5, we can now igoouly deemine he caling of he adopion ie, and almo compleely olve he poblem fo he deniy of he andom walke, up o he inveion of a Fouie anfom. Recal ha ime coepond o he numbe of ep aken and he diffuion coefficien D = 6 a τ whee a i he peience lengh and τ i heimecale whichwecan ake a τ =. The faco aie becaue in d dimenion, he diffuion coefficien i elaed o he vaiance of he individual 6 ep hough σ d. In he peviou lecue we agued ha he waiing ime diibuion i he Sminov deniy

4 M. Z. Bazan Random Walk and Diffuion Lecue 6 4 a ψ () =p 4πD 3 e 4D a whee D i he diffuion coefficien of he pependicula componen of he andom walk. D a =3D = τ ince one hid of he vaiance i aibued o ha dimenion. Taking τ =,oha a D =we can implify: ψ () = e π 3 The Laplace anfom of he waiing ime deniy i e )=e a ψ ( /D = e To poceed we need he condiional pdf of he he locaion of eun o z =, given ha he eun ime i a. p ( /4D e ) = 4πD Thi i ju dimenional diffuion, once we condiion on he eun ime and o deniy i given immediaely a he fundamenal oluion o he diffuion equaion. The diffuion coefficien D denoe diffuioninadiecionpaallelohe z =plane, and aguing a above D = a 4τ. The Fouie anfom of p i k k, = e D pb Then, ince we can ake he Fouie anfom in he pace coodinae, we can find he Fouie anfom of he join poiion-ime ep deniy: χ (,) = p ( ) ψ () χb (,) = k e D ψ () Noe he non-epaabily ince appea in boh em. Taking he Laplace anfom: Z e e e D π 3 e d χb k k, = Bu we can evaluae hi immediaely ince i i ju he Laplace anfom of he Sminov evaluaed a + D k inead of : k, = e q e (+D k ) χb

5 M. Z. Bazan Random Walk and Diffuion Lecue 6 5 Thu applying he genealized Monol-Wei equaion () e P b e k, = µ q e ( +D k ) We can udy he long-ime behavio in he "cenal egion" by conideing he limi k and. Expanding he exponenial aound = P e b k, q + D k Noing ha he anfom can be wien: P e b k, + D k D k and noing ha Le α f () () =f e ( + α) P b k, e D k L D k Bu hi Laplace anfom can be inveed in em of he modified Beel funcion of he fi kind I (x) - ee Appendix fo deivaion. Ã! D k P b k, D k e I Inveing he Fouie anfom in pace we deive an inegal expeion fo he deniy: Z k Z D i k. dk P ( e D kk k,) e I (π) Thi i clealy no a Gauian diibuion, bu he caling i ill quae oo, i.e. <> non degeneae limiing pdf. Thi can be een if we change he vaiable o ς = we can wie ς,)=p (,) and defining κ = k κ d P ( o ha d = k

6 M. Z. Bazan Random Walk and Diffuion Lecue 6 6 Z Z P ( i ς,) e κ. ς e D kκ k I Ã! D k κk d κ (π) and i i clea ha ς ha a non-degeneae limiing diibuion which i nevehele no Gauian. 3 Ceepe The econd pecial cae ha we conide i ha of ceepe, which move wih conan velociy beween uning poin. We can define he ceepe in em of he q diibuion: q,, µ = δ fo << Thu a ceepe move non-ochaically beween uning poin. and Z µ Ψ (,)= χ, d χ (,) = p ( ) ψ ( ) = p ( ) δ ( τ ( )) whee v ( )= τ ( ) i he conan velociy fo ep ize ha will occu afe ime τ ( ). τ ( )=v/c fo a ingle conan peed c If p ( ) ha a Lévy diibuion hi i called a Lévy walk, alhough Hughe dicouage he eminology. µ Ψ (,)=p τ ( ) Hughe advocae he ue of Mellin anfom o analyze hi ype of andom walk, hough which i i poible o how:

7 M. Z. Bazan Random Walk and Diffuion Lecue 6 7 If p ( A ) d+α whee α > and α < i a Lévy diibuion and α > ha finie vaiance and τ ( ) β o ha velociy v ( ) β whee β = coepond o a ingle conan velociy c and β = i a dicee RW wih a a conan ime ep. Then he mean-quae diplacemen i ν a whee ν = α>max (, β) α β <α<β + α β β<α< β α<min (, β) 3. Applicaion: Schleinge, We, Klafe Ceepe povide a micocopic model of ubulence. Richadon (96) obeved ha in ubulen flow he mean-quae poiion of a paicle obey he following law: < > 3. Thiiaupediffuion ha i even fae han balliic moion fo a ingle ypical velociy, in which < >. Tubulen flow doe no have a ingle chaaceiic velociy, bu he queion emain, wha kind of andom walk could a micocopic paicle be pefoming ha would be conien wih hi empiical obevaion? Accoding o Richadon obevaion, he andom walk mu aify τ ( ) /3, which ugge β = in he ceepe model, and fom he eul above, if α < we deive ν 3 3 a whee ν = β =3 a equied. The ep diibuion wih α < 3 i a Lévy fligh wih ail which ae even boade han he Cauchy diibuion. Thi model alo coecly pedic he Kolmogoov enegy pecum, which i eenially he Fouie anfom of he velociy pecum: A fequency k 3 E (k) v 5/3 = = = k 5/3 /

8 M. Z. Bazan Random Walk and Diffuion Lecue Refeence Shleinge, We, Klafe. Phyic Review Lee, Appendix: Laplace Tanfom of he Modified Beel Funcion of he Fi Kind I (x) The modified Beel funcion I (x) can be defined a: I (x) = X n= (x/) n (n!) An alenaive inegal definiion i: I (x) = Z π π e x co θ dθ Conide he anfom of I (α) and change he vaiable o y = in he inegal X (α/) n Z [LI (α)] () = e n d (n!) n= X (α/) n n Z = e y y n dy n= (n!) X (α/) n n = (n)! n= (n!) X (α/) n (n )!! = n! n n= µ α = = α

PHYS GENERAL RELATIVITY AND COSMOLOGY PROBLEM SET 7 - SOLUTIONS

PHYS GENERAL RELATIVITY AND COSMOLOGY PROBLEM SET 7 - SOLUTIONS PHYS 54 - GENERAL RELATIVITY AND COSMOLOGY - 07 - PROBLEM SET 7 - SOLUTIONS TA: Jeome Quinin Mach, 07 Noe ha houghou hee oluion, we wok in uni whee c, and we chooe he meic ignaue (,,, ) a ou convenion..

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signal & Syem Prof. Mark Fowler Noe Se #27 C-T Syem: Laplace Tranform Power Tool for yem analyi Reading Aignmen: Secion 6.1 6.3 of Kamen and Heck 1/18 Coure Flow Diagram The arrow here how concepual

More information

Molecular Evolution and Phylogeny. Based on: Durbin et al Chapter 8

Molecular Evolution and Phylogeny. Based on: Durbin et al Chapter 8 Molecula Evoluion and hylogeny Baed on: Dubin e al Chape 8. hylogeneic Tee umpion banch inenal node leaf Topology T : bifucaing Leave - N Inenal node N+ N- Lengh { i } fo each banch hylogeneic ee Topology

More information

Lecture 17: Kinetics of Phase Growth in a Two-component System:

Lecture 17: Kinetics of Phase Growth in a Two-component System: Lecue 17: Kineics of Phase Gowh in a Two-componen Sysem: descipion of diffusion flux acoss he α/ ineface Today s opics Majo asks of oday s Lecue: how o deive he diffusion flux of aoms. Once an incipien

More information

Lecture 18: Kinetics of Phase Growth in a Two-component System: general kinetics analysis based on the dilute-solution approximation

Lecture 18: Kinetics of Phase Growth in a Two-component System: general kinetics analysis based on the dilute-solution approximation Lecue 8: Kineics of Phase Gowh in a Two-componen Sysem: geneal kineics analysis based on he dilue-soluion appoximaion Today s opics: In he las Lecues, we leaned hee diffeen ways o descibe he diffusion

More information

7 Wave Equation in Higher Dimensions

7 Wave Equation in Higher Dimensions 7 Wave Equaion in Highe Dimensions We now conside he iniial-value poblem fo he wave equaion in n dimensions, u c u x R n u(x, φ(x u (x, ψ(x whee u n i u x i x i. (7. 7. Mehod of Spheical Means Ref: Evans,

More information

Variance and Covariance Processes

Variance and Covariance Processes Vaiance and Covaiance Pocesses Pakash Balachandan Depamen of Mahemaics Duke Univesiy May 26, 2008 These noes ae based on Due s Sochasic Calculus, Revuz and Yo s Coninuous Maingales and Bownian Moion, Kaazas

More information

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain Lecue-V Sochasic Pocesses and he Basic Tem-Sucue Equaion 1 Sochasic Pocesses Any vaiable whose value changes ove ime in an unceain way is called a Sochasic Pocess. Sochasic Pocesses can be classied as

More information

Lecture 26: Leapers and Creepers

Lecture 26: Leapers and Creepers Lcur 6: Lapr and Crpr Scrib: Grain Jon (and Marin Z. Bazan) Dparmn of Economic, MIT May, 005 Inroducion Thi lcur conidr h analyi of h non-parabl CTRW in which h diribuion of p iz and im bwn p ar dpndn.

More information

The Production of Polarization

The Production of Polarization Physics 36: Waves Lecue 13 3/31/211 The Poducion of Polaizaion Today we will alk abou he poducion of polaized ligh. We aleady inoduced he concep of he polaizaion of ligh, a ansvese EM wave. To biefly eview

More information

Exponential Sawtooth

Exponential Sawtooth ECPE 36 HOMEWORK 3: PROPERTIES OF THE FOURIER TRANSFORM SOLUTION. Exponenial Sawooh: The eaie way o do hi problem i o look a he Fourier ranform of a ingle exponenial funcion, () = exp( )u(). From he able

More information

, the. L and the L. x x. max. i n. It is easy to show that these two norms satisfy the following relation: x x n x = (17.3) max

, the. L and the L. x x. max. i n. It is easy to show that these two norms satisfy the following relation: x x n x = (17.3) max ecure 8 7. Sabiliy Analyi For an n dimenional vecor R n, he and he vecor norm are defined a: = T = i n i (7.) I i eay o how ha hee wo norm aify he following relaion: n (7.) If a vecor i ime-dependen, hen

More information

Discussion Session 2 Constant Acceleration/Relative Motion Week 03

Discussion Session 2 Constant Acceleration/Relative Motion Week 03 PHYS 100 Dicuion Seion Conan Acceleraion/Relaive Moion Week 03 The Plan Today you will work wih your group explore he idea of reference frame (i.e. relaive moion) and moion wih conan acceleraion. You ll

More information

Lecture 22 Electromagnetic Waves

Lecture 22 Electromagnetic Waves Lecue Elecomagneic Waves Pogam: 1. Enegy caied by he wave (Poyning veco).. Maxwell s equaions and Bounday condiions a inefaces. 3. Maeials boundaies: eflecion and efacion. Snell s Law. Quesions you should

More information

MEEN 617 Handout #11 MODAL ANALYSIS OF MDOF Systems with VISCOUS DAMPING

MEEN 617 Handout #11 MODAL ANALYSIS OF MDOF Systems with VISCOUS DAMPING MEEN 67 Handou # MODAL ANALYSIS OF MDOF Sysems wih VISCOS DAMPING ^ Symmeic Moion of a n-dof linea sysem is descibed by he second ode diffeenial equaions M+C+K=F whee () and F () ae n ows vecos of displacemens

More information

Combinatorial Approach to M/M/1 Queues. Using Hypergeometric Functions

Combinatorial Approach to M/M/1 Queues. Using Hypergeometric Functions Inenaional Mahemaical Foum, Vol 8, 03, no 0, 463-47 HIKARI Ld, wwwm-hikaicom Combinaoial Appoach o M/M/ Queues Using Hypegeomeic Funcions Jagdish Saan and Kamal Nain Depamen of Saisics, Univesiy of Delhi,

More information

Support Vector Machines

Support Vector Machines Suppo Veco Machine CSL 3 ARIFICIAL INELLIGENCE SPRING 4 Suppo Veco Machine O, Kenel Machine Diciminan-baed mehod olean cla boundaie Suppo veco coni of eample cloe o bounday Kenel compue imilaiy beeen eample

More information

The distribution of the interval of the Cox process with shot noise intensity for insurance claims and its moments

The distribution of the interval of the Cox process with shot noise intensity for insurance claims and its moments The diibuion of he ineval of he Cox poce wih ho noie ineniy fo inuance claim and i momen Angelo Daio, Ji-Wook Jang Depamen of Saiic, London School of Economic and Poliical Science, Houghon See, London

More information

General Non-Arbitrage Model. I. Partial Differential Equation for Pricing A. Traded Underlying Security

General Non-Arbitrage Model. I. Partial Differential Equation for Pricing A. Traded Underlying Security 1 Geneal Non-Abiage Model I. Paial Diffeenial Equaion fo Picing A. aded Undelying Secuiy 1. Dynamics of he Asse Given by: a. ds = µ (S, )d + σ (S, )dz b. he asse can be eihe a sock, o a cuency, an index,

More information

Laplace Transform. Inverse Laplace Transform. e st f(t)dt. (2)

Laplace Transform. Inverse Laplace Transform. e st f(t)dt. (2) Laplace Tranform Maoud Malek The Laplace ranform i an inegral ranform named in honor of mahemaician and aronomer Pierre-Simon Laplace, who ued he ranform in hi work on probabiliy heory. I i a powerful

More information

Motion In One Dimension. Graphing Constant Speed

Motion In One Dimension. Graphing Constant Speed Moion In One Dimenion PLATO AND ARISTOTLE GALILEO GALILEI LEANING TOWER OF PISA Graphing Conan Speed Diance v. Time for Toy Car (0-5 ec.) be-fi line (from TI calculaor) d = 207.7 12.6 Diance (cm) 1000

More information

2. VECTORS. R Vectors are denoted by bold-face characters such as R, V, etc. The magnitude of a vector, such as R, is denoted as R, R, V

2. VECTORS. R Vectors are denoted by bold-face characters such as R, V, etc. The magnitude of a vector, such as R, is denoted as R, R, V ME 352 VETS 2. VETS Vecor algebra form he mahemaical foundaion for kinemaic and dnamic. Geomer of moion i a he hear of boh he kinemaic and dnamic of mechanical em. Vecor anali i he imehonored ool for decribing

More information

Circular Motion. Radians. One revolution is equivalent to which is also equivalent to 2π radians. Therefore we can.

Circular Motion. Radians. One revolution is equivalent to which is also equivalent to 2π radians. Therefore we can. 1 Cicula Moion Radians One evoluion is equivalen o 360 0 which is also equivalen o 2π adians. Theefoe we can say ha 360 = 2π adians, 180 = π adians, 90 = π 2 adians. Hence 1 adian = 360 2π Convesions Rule

More information

Randomized Perfect Bipartite Matching

Randomized Perfect Bipartite Matching Inenive Algorihm Lecure 24 Randomized Perfec Biparie Maching Lecurer: Daniel A. Spielman April 9, 208 24. Inroducion We explain a randomized algorihm by Ahih Goel, Michael Kapralov and Sanjeev Khanna for

More information

L. Yaroslavsky. Image data fusion. Processing system. Input scene. Output images

L. Yaroslavsky. Image data fusion. Processing system. Input scene. Output images L. Yaolavk Image daa fuion Poceing em Inpu cene Oupu image Muli componen imaging and eoaion model Conide an M componen imaging em and aume ha each image of M componen can be decibed in a domain of a ceain

More information

18.03SC Unit 3 Practice Exam and Solutions

18.03SC Unit 3 Practice Exam and Solutions Sudy Guide on Sep, Dela, Convoluion, Laplace You can hink of he ep funcion u() a any nice mooh funcion which i for < a and for > a, where a i a poiive number which i much maller han any ime cale we care

More information

Lecture 5 Emission and Low-NOx Combustors

Lecture 5 Emission and Low-NOx Combustors Lecue 5 Emiion and Low-NOx Combuo Emiion: CO, Nox, UHC, Soo Modeling equiemen vay due o diffeence in ime and lengh cale, a well a pocee In geneal, finie-ae ineic i needed o pedic emiion Flamele appoach

More information

Introduction to Congestion Games

Introduction to Congestion Games Algorihmic Game Theory, Summer 2017 Inroducion o Congeion Game Lecure 1 (5 page) Inrucor: Thoma Keelheim In hi lecure, we ge o know congeion game, which will be our running example for many concep in game

More information

r P + '% 2 r v(r) End pressures P 1 (high) and P 2 (low) P 1 , which must be independent of z, so # dz dz = P 2 " P 1 = " #P L L,

r P + '% 2 r v(r) End pressures P 1 (high) and P 2 (low) P 1 , which must be independent of z, so # dz dz = P 2  P 1 =  #P L L, Lecue 36 Pipe Flow and Low-eynolds numbe hydodynamics 36.1 eading fo Lecues 34-35: PKT Chape 12. Will y fo Monday?: new daa shee and daf fomula shee fo final exam. Ou saing poin fo hydodynamics ae wo equaions:

More information

EE Control Systems LECTURE 2

EE Control Systems LECTURE 2 Copyrigh F.L. Lewi 999 All righ reerved EE 434 - Conrol Syem LECTURE REVIEW OF LAPLACE TRANSFORM LAPLACE TRANSFORM The Laplace ranform i very ueful in analyi and deign for yem ha are linear and ime-invarian

More information

Linear Motion, Speed & Velocity

Linear Motion, Speed & Velocity Add Iporan Linear Moion, Speed & Velociy Page: 136 Linear Moion, Speed & Velociy NGSS Sandard: N/A MA Curriculu Fraework (2006): 1.1, 1.2 AP Phyic 1 Learning Objecive: 3.A.1.1, 3.A.1.3 Knowledge/Underanding

More information

Degree of Approximation of a Class of Function by (C, 1) (E, q) Means of Fourier Series

Degree of Approximation of a Class of Function by (C, 1) (E, q) Means of Fourier Series IAENG Inenaional Jounal of Applied Mahemaic, 4:, IJAM_4 7 Degee of Appoximaion of a Cla of Funcion by C, E, q Mean of Fouie Seie Hae Kihna Nigam and Kuum Shama Abac In hi pape, fo he fi ime, we inoduce

More information

5.2 GRAPHICAL VELOCITY ANALYSIS Polygon Method

5.2 GRAPHICAL VELOCITY ANALYSIS Polygon Method ME 352 GRHICL VELCITY NLYSIS 52 GRHICL VELCITY NLYSIS olygon Mehod Velociy analyi form he hear of kinemaic and dynamic of mechanical yem Velociy analyi i uually performed following a poiion analyi; ie,

More information

6.8 Laplace Transform: General Formulas

6.8 Laplace Transform: General Formulas 48 HAP. 6 Laplace Tranform 6.8 Laplace Tranform: General Formula Formula Name, ommen Sec. F() l{ f ()} e f () d f () l {F()} Definiion of Tranform Invere Tranform 6. l{af () bg()} al{f ()} bl{g()} Lineariy

More information

EE202 Circuit Theory II

EE202 Circuit Theory II EE202 Circui Theory II 2017-2018, Spring Dr. Yılmaz KALKAN I. Inroducion & eview of Fir Order Circui (Chaper 7 of Nilon - 3 Hr. Inroducion, C and L Circui, Naural and Sep epone of Serie and Parallel L/C

More information

Problem Set If all directed edges in a network have distinct capacities, then there is a unique maximum flow.

Problem Set If all directed edges in a network have distinct capacities, then there is a unique maximum flow. CSE 202: Deign and Analyi of Algorihm Winer 2013 Problem Se 3 Inrucor: Kamalika Chaudhuri Due on: Tue. Feb 26, 2013 Inrucion For your proof, you may ue any lower bound, algorihm or daa rucure from he ex

More information

Fractional Ornstein-Uhlenbeck Bridge

Fractional Ornstein-Uhlenbeck Bridge WDS'1 Proceeding of Conribued Paper, Par I, 21 26, 21. ISBN 978-8-7378-139-2 MATFYZPRESS Fracional Ornein-Uhlenbeck Bridge J. Janák Charle Univeriy, Faculy of Mahemaic and Phyic, Prague, Czech Republic.

More information

Introduction to SLE Lecture Notes

Introduction to SLE Lecture Notes Inroducion o SLE Lecure Noe May 13, 16 - The goal of hi ecion i o find a ufficien condiion of λ for he hull K o be generaed by a imple cure. I urn ou if λ 1 < 4 hen K i generaed by a imple curve. We will

More information

MATHEMATICAL FOUNDATIONS FOR APPROXIMATING PARTICLE BEHAVIOUR AT RADIUS OF THE PLANCK LENGTH

MATHEMATICAL FOUNDATIONS FOR APPROXIMATING PARTICLE BEHAVIOUR AT RADIUS OF THE PLANCK LENGTH Fundamenal Jounal of Mahemaical Phsics Vol 3 Issue 013 Pages 55-6 Published online a hp://wwwfdincom/ MATHEMATICAL FOUNDATIONS FOR APPROXIMATING PARTICLE BEHAVIOUR AT RADIUS OF THE PLANCK LENGTH Univesias

More information

KINEMATICS OF RIGID BODIES

KINEMATICS OF RIGID BODIES KINEMTICS OF RIGID ODIES In igid body kinemaics, we use he elaionships govening he displacemen, velociy and acceleaion, bu mus also accoun fo he oaional moion of he body. Descipion of he moion of igid

More information

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay)

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay) Secions 3.1 and 3.4 Eponenial Funcions (Gowh and Decay) Chape 3. Secions 1 and 4 Page 1 of 5 Wha Would You Rahe Have... $1million, o double you money evey day fo 31 days saing wih 1cen? Day Cens Day Cens

More information

The International Diversification Puzzle when Goods Prices are Sticky: It s Really about Exchange-Rate Hedging, not Equity Portfolios

The International Diversification Puzzle when Goods Prices are Sticky: It s Really about Exchange-Rate Hedging, not Equity Portfolios The Inenaional Diveificaion Puzzle when Good Pice ae Sicky: I eally abou Exchange-ae edging, no Equiy Pofolio by CALES ENGEL AND AKITO MATSUMOTO Appendix A. Soluion of he Dynamic Model An equilibium aifie

More information

FI 2201 Electromagnetism

FI 2201 Electromagnetism FI Electomagnetim Aleande A. Ikanda, Ph.D. Phyic of Magnetim and Photonic Reeach Goup ecto Analyi CURILINEAR COORDINAES, DIRAC DELA FUNCION AND HEORY OF ECOR FIELDS Cuvilinea Coodinate Sytem Cateian coodinate:

More information

The shortest path between two truths in the real domain passes through the complex domain. J. Hadamard

The shortest path between two truths in the real domain passes through the complex domain. J. Hadamard Complex Analysis R.G. Halbud R.Halbud@ucl.ac.uk Depamen of Mahemaics Univesiy College London 202 The shoes pah beween wo uhs in he eal domain passes hough he complex domain. J. Hadamad Chape The fis fundamenal

More information

CHAPTER 7: SECOND-ORDER CIRCUITS

CHAPTER 7: SECOND-ORDER CIRCUITS EEE5: CI RCUI T THEORY CHAPTER 7: SECOND-ORDER CIRCUITS 7. Inroducion Thi chaper conider circui wih wo orage elemen. Known a econd-order circui becaue heir repone are decribed by differenial equaion ha

More information

To become more mathematically correct, Circuit equations are Algebraic Differential equations. from KVL, KCL from the constitutive relationship

To become more mathematically correct, Circuit equations are Algebraic Differential equations. from KVL, KCL from the constitutive relationship Laplace Tranform (Lin & DeCarlo: Ch 3) ENSC30 Elecric Circui II The Laplace ranform i an inegral ranformaion. I ranform: f ( ) F( ) ime variable complex variable From Euler > Lagrange > Laplace. Hence,

More information

Notes on cointegration of real interest rates and real exchange rates. ρ (2)

Notes on cointegration of real interest rates and real exchange rates. ρ (2) Noe on coinegraion of real inere rae and real exchange rae Charle ngel, Univeriy of Wiconin Le me ar wih he obervaion ha while he lieraure (mo prominenly Meee and Rogoff (988) and dion and Paul (993))

More information

Graphs III - Network Flow

Graphs III - Network Flow Graph III - Nework Flow Flow nework eup graph G=(V,E) edge capaciy w(u,v) 0 - if edge doe no exi, hen w(u,v)=0 pecial verice: ource verex ; ink verex - no edge ino and no edge ou of Aume every verex v

More information

Reinforcement learning

Reinforcement learning Lecue 3 Reinfocemen leaning Milos Hauskech milos@cs.pi.edu 539 Senno Squae Reinfocemen leaning We wan o lean he conol policy: : X A We see examples of x (bu oupus a ae no given) Insead of a we ge a feedback

More information

Two-dimensional Effects on the CSR Interaction Forces for an Energy-Chirped Bunch. Rui Li, J. Bisognano, R. Legg, and R. Bosch

Two-dimensional Effects on the CSR Interaction Forces for an Energy-Chirped Bunch. Rui Li, J. Bisognano, R. Legg, and R. Bosch Two-dimensional Effecs on he CS Ineacion Foces fo an Enegy-Chiped Bunch ui Li, J. Bisognano,. Legg, and. Bosch Ouline 1. Inoducion 2. Pevious 1D and 2D esuls fo Effecive CS Foce 3. Bunch Disibuion Vaiaion

More information

Sample Final Exam (finals03) Covering Chapters 1-9 of Fundamentals of Signals & Systems

Sample Final Exam (finals03) Covering Chapters 1-9 of Fundamentals of Signals & Systems Sample Final Exam Covering Chaper 9 (final04) Sample Final Exam (final03) Covering Chaper 9 of Fundamenal of Signal & Syem Problem (0 mar) Conider he caual opamp circui iniially a re depiced below. I LI

More information

ÖRNEK 1: THE LINEAR IMPULSE-MOMENTUM RELATION Calculate the linear momentum of a particle of mass m=10 kg which has a. kg m s

ÖRNEK 1: THE LINEAR IMPULSE-MOMENTUM RELATION Calculate the linear momentum of a particle of mass m=10 kg which has a. kg m s MÜHENDİSLİK MEKANİĞİ. HAFTA İMPULS- MMENTUM-ÇARPIŞMA Linea oenu of a paicle: The sybol L denoes he linea oenu and is defined as he ass ies he elociy of a paicle. L ÖRNEK : THE LINEAR IMPULSE-MMENTUM RELATIN

More information

Sharif University of Technology - CEDRA By: Professor Ali Meghdari

Sharif University of Technology - CEDRA By: Professor Ali Meghdari Shaif Univesiy of echnology - CEDRA By: Pofesso Ali Meghai Pupose: o exen he Enegy appoach in eiving euaions of oion i.e. Lagange s Meho fo Mechanical Syses. opics: Genealize Cooinaes Lagangian Euaion

More information

Inference for A One Way Factorial Experiment. By Ed Stanek and Elaine Puleo

Inference for A One Way Factorial Experiment. By Ed Stanek and Elaine Puleo Infeence fo A One Way Factoial Expeiment By Ed Stanek and Elaine Puleo. Intoduction We develop etimating equation fo Facto Level mean in a completely andomized one way factoial expeiment. Thi development

More information

The Residual Graph. 12 Augmenting Path Algorithms. Augmenting Path Algorithm. Augmenting Path Algorithm

The Residual Graph. 12 Augmenting Path Algorithms. Augmenting Path Algorithm. Augmenting Path Algorithm Augmening Pah Algorihm Greedy-algorihm: ar wih f (e) = everywhere find an - pah wih f (e) < c(e) on every edge augmen flow along he pah repea a long a poible The Reidual Graph From he graph G = (V, E,

More information

13.1 Accelerating Objects

13.1 Accelerating Objects 13.1 Acceleraing Objec A you learned in Chaper 12, when you are ravelling a a conan peed in a raigh line, you have uniform moion. However, mo objec do no ravel a conan peed in a raigh line o hey do no

More information

Explicit form of global solution to stochastic logistic differential equation and related topics

Explicit form of global solution to stochastic logistic differential equation and related topics SAISICS, OPIMIZAION AND INFOMAION COMPUING Sa., Opim. Inf. Compu., Vol. 5, March 17, pp 58 64. Publihed online in Inernaional Academic Pre (www.iapre.org) Explici form of global oluion o ochaic logiic

More information

Physics 240: Worksheet 16 Name

Physics 240: Worksheet 16 Name Phyic 4: Workhee 16 Nae Non-unifor circular oion Each of hee proble involve non-unifor circular oion wih a conan α. (1) Obain each of he equaion of oion for non-unifor circular oion under a conan acceleraion,

More information

Estimation and Confidence Intervals: Additional Topics

Estimation and Confidence Intervals: Additional Topics Chapte 8 Etimation and Confidence Inteval: Additional Topic Thi chapte imply follow the method in Chapte 7 fo foming confidence inteval The text i a bit dioganized hee o hopefully we can implify Etimation:

More information

Additional Methods for Solving DSGE Models

Additional Methods for Solving DSGE Models Addiional Mehod for Solving DSGE Model Karel Meren, Cornell Univeriy Reference King, R. G., Ploer, C. I. & Rebelo, S. T. (1988), Producion, growh and buine cycle: I. he baic neoclaical model, Journal of

More information

The Residual Graph. 11 Augmenting Path Algorithms. Augmenting Path Algorithm. Augmenting Path Algorithm

The Residual Graph. 11 Augmenting Path Algorithms. Augmenting Path Algorithm. Augmenting Path Algorithm Augmening Pah Algorihm Greedy-algorihm: ar wih f (e) = everywhere find an - pah wih f (e) < c(e) on every edge augmen flow along he pah repea a long a poible The Reidual Graph From he graph G = (V, E,

More information

The Production of Well-Being: Conventional Goods, Relational Goods and Status Goods

The Production of Well-Being: Conventional Goods, Relational Goods and Status Goods The Poducion of Well-Bein: Convenional Good, Relaional Good and Sau Good Aloy Pinz Iniue of Public Economic II Univeiy of Müne, Gemany New Diecion in Welfae II, OECD Pai July 06 08, 2011 Conen 1. Inoducion

More information

CS4445/9544 Analysis of Algorithms II Solution for Assignment 1

CS4445/9544 Analysis of Algorithms II Solution for Assignment 1 Conider he following flow nework CS444/944 Analyi of Algorihm II Soluion for Aignmen (0 mark) In he following nework a minimum cu ha capaciy 0 Eiher prove ha hi aemen i rue, or how ha i i fale Uing he

More information

CHAPTER. Forced Equations and Systems { } ( ) ( ) 8.1 The Laplace Transform and Its Inverse. Transforms from the Definition.

CHAPTER. Forced Equations and Systems { } ( ) ( ) 8.1 The Laplace Transform and Its Inverse. Transforms from the Definition. CHAPTER 8 Forced Equaion and Syem 8 The aplace Tranform and I Invere Tranform from he Definiion 5 5 = b b {} 5 = 5e d = lim5 e = ( ) b {} = e d = lim e + e d b = (inegraion by par) = = = = b b ( ) ( )

More information

Mon Apr 2: Laplace transform and initial value problems like we studied in Chapter 5

Mon Apr 2: Laplace transform and initial value problems like we studied in Chapter 5 Mah 225-4 Week 2 April 2-6 coninue.-.3; alo cover par of.4-.5, EP 7.6 Mon Apr 2:.-.3 Laplace ranform and iniial value problem like we udied in Chaper 5 Announcemen: Warm-up Exercie: Recall, The Laplace

More information

On The Estimation of Two Missing Values in Randomized Complete Block Designs

On The Estimation of Two Missing Values in Randomized Complete Block Designs Mahemaical Theoy and Modeling ISSN 45804 (Pape ISSN 505 (Online Vol.6, No.7, 06 www.iise.og On The Esimaion of Two Missing Values in Randomized Complee Bloc Designs EFFANGA, EFFANGA OKON AND BASSE, E.

More information

Consider a Binary antipodal system which produces data of δ (t)

Consider a Binary antipodal system which produces data of δ (t) Modulaion Polem PSK: (inay Phae-hi keying) Conide a inay anipodal yem whih podue daa o δ ( o + δ ( o inay and epeively. Thi daa i paed o pule haping ile and he oupu o he pule haping ile i muliplied y o(

More information

How to Solve System Dynamic s Problems

How to Solve System Dynamic s Problems How o Solve Sye Dynaic Proble A ye dynaic proble involve wo or ore bodie (objec) under he influence of everal exernal force. The objec ay uliaely re, ove wih conan velociy, conan acceleraion or oe cobinaion

More information

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example C 188: Aificial Inelligence Fall 2007 epesening Knowledge ecue 17: ayes Nes III 10/25/2007 an Klein UC ekeley Popeies of Ns Independence? ayes nes: pecify complex join disibuions using simple local condiional

More information

Let. x y. denote a bivariate time series with zero mean.

Let. x y. denote a bivariate time series with zero mean. Linear Filer Le x y : T denoe a bivariae ime erie wih zero mean. Suppoe ha he ime erie {y : T} i conruced a follow: y a x The ime erie {y : T} i aid o be conruced from {x : T} by mean of a Linear Filer.

More information

CONTROL SYSTEMS. Chapter 10 : State Space Response

CONTROL SYSTEMS. Chapter 10 : State Space Response CONTROL SYSTEMS Chaper : Sae Space Repone GATE Objecive & Numerical Type Soluion Queion 5 [GATE EE 99 IIT-Bombay : Mark] Conider a econd order yem whoe ae pace repreenaion i of he form A Bu. If () (),

More information

Network Flows: Introduction & Maximum Flow

Network Flows: Introduction & Maximum Flow CSC 373 - lgorihm Deign, nalyi, and Complexiy Summer 2016 Lalla Mouaadid Nework Flow: Inroducion & Maximum Flow We now urn our aenion o anoher powerful algorihmic echnique: Local Search. In a local earch

More information

arxiv: v2 [math.st] 27 Jan 2016

arxiv: v2 [math.st] 27 Jan 2016 STATISTICAL IFERECE VERSUS MEA FIELD LIMIT FOR HAWKES PROCESSES axiv:157.2887v2 mah.st] 27 Jan 216 SYLVAI DELATTRE AD ICOLAS FOURIER Abac. We conide a populaion of individual, of which we obeve he numbe

More information

Chapter 7: Inverse-Response Systems

Chapter 7: Inverse-Response Systems Chaper 7: Invere-Repone Syem Normal Syem Invere-Repone Syem Baic Sar ou in he wrong direcion End up in he original eady-ae gain value Two or more yem wih differen magniude and cale in parallel Main yem

More information

Mathematische Annalen

Mathematische Annalen Mah. Ann. 39, 33 339 (997) Mahemaiche Annalen c Springer-Verlag 997 Inegraion by par in loop pace Elon P. Hu Deparmen of Mahemaic, Norhweern Univeriy, Evanon, IL 628, USA (e-mail: elon@@mah.nwu.edu) Received:

More information

Computer Propagation Analysis Tools

Computer Propagation Analysis Tools Compue Popagaion Analysis Tools. Compue Popagaion Analysis Tools Inoducion By now you ae pobably geing he idea ha pedicing eceived signal sengh is a eally impoan as in he design of a wieless communicaion

More information

u(t) Figure 1. Open loop control system

u(t) Figure 1. Open loop control system Open loop conrol v cloed loop feedbac conrol The nex wo figure preen he rucure of open loop and feedbac conrol yem Figure how an open loop conrol yem whoe funcion i o caue he oupu y o follow he reference

More information

Algorithmic Discrete Mathematics 6. Exercise Sheet

Algorithmic Discrete Mathematics 6. Exercise Sheet Algorihmic Dicree Mahemaic. Exercie Shee Deparmen of Mahemaic SS 0 PD Dr. Ulf Lorenz 7. and 8. Juni 0 Dipl.-Mah. David Meffer Verion of June, 0 Groupwork Exercie G (Heap-Sor) Ue Heap-Sor wih a min-heap

More information

CSC 364S Notes University of Toronto, Spring, The networks we will consider are directed graphs, where each edge has associated with it

CSC 364S Notes University of Toronto, Spring, The networks we will consider are directed graphs, where each edge has associated with it CSC 36S Noe Univeriy of Torono, Spring, 2003 Flow Algorihm The nework we will conider are direced graph, where each edge ha aociaed wih i a nonnegaive capaciy. The inuiion i ha if edge (u; v) ha capaciy

More information

8.5 Circles and Lengths of Segments

8.5 Circles and Lengths of Segments LenghofSegmen20052006.nb 1 8.5 Cicle and Lengh of Segmen In hi ecion we will how (and in ome cae pove) ha lengh of chod, ecan, and angen ae elaed in ome nal way. We will look a hee heoem ha ae hee elaionhip

More information

Simulation of Spatially Correlated Large-Scale Parameters and Obtaining Model Parameters from Measurements

Simulation of Spatially Correlated Large-Scale Parameters and Obtaining Model Parameters from Measurements Simulation of Spatially Coelated Lage-Scale Paamete and Obtaining Model Paamete fom PER ZETTERBERG Stockholm Septembe 8 TRITA EE 8:49 Simulation of Spatially Coelated Lage-Scale Paamete and Obtaining Model

More information

Algorithms and Data Structures 2011/12 Week 9 Solutions (Tues 15th - Fri 18th Nov)

Algorithms and Data Structures 2011/12 Week 9 Solutions (Tues 15th - Fri 18th Nov) Algorihm and Daa Srucure 2011/ Week Soluion (Tue 15h - Fri 18h No) 1. Queion: e are gien 11/16 / 15/20 8/13 0/ 1/ / 11/1 / / To queion: (a) Find a pair of ube X, Y V uch ha f(x, Y) = f(v X, Y). (b) Find

More information

Chapter 19 Webassign Help Problems

Chapter 19 Webassign Help Problems Chapte 9 Webaign Help Poblem 4 5 6 7 8 9 0 Poblem 4: The pictue fo thi poblem i a bit mileading. They eally jut give you the pictue fo Pat b. So let fix that. Hee i the pictue fo Pat (a): Pat (a) imply

More information

CS 188: Artificial Intelligence Fall Probabilistic Models

CS 188: Artificial Intelligence Fall Probabilistic Models CS 188: Aificial Inelligence Fall 2007 Lecue 15: Bayes Nes 10/18/2007 Dan Klein UC Bekeley Pobabilisic Models A pobabilisic model is a join disibuion ove a se of vaiables Given a join disibuion, we can

More information

The sudden release of a large amount of energy E into a background fluid of density

The sudden release of a large amount of energy E into a background fluid of density 10 Poin explosion The sudden elease of a lage amoun of enegy E ino a backgound fluid of densiy ceaes a song explosion, chaaceized by a song shock wave (a blas wave ) emanaing fom he poin whee he enegy

More information

Gravity. David Barwacz 7778 Thornapple Bayou SE, Grand Rapids, MI David Barwacz 12/03/2003

Gravity. David Barwacz 7778 Thornapple Bayou SE, Grand Rapids, MI David Barwacz 12/03/2003 avity David Bawacz 7778 Thonapple Bayou, and Rapid, MI 495 David Bawacz /3/3 http://membe.titon.net/daveb Uing the concept dicued in the peceding pape ( http://membe.titon.net/daveb ), I will now deive

More information

Flow networks. Flow Networks. A flow on a network. Flow networks. The maximum-flow problem. Introduction to Algorithms, Lecture 22 December 5, 2001

Flow networks. Flow Networks. A flow on a network. Flow networks. The maximum-flow problem. Introduction to Algorithms, Lecture 22 December 5, 2001 CS 545 Flow Nework lon Efra Slide courey of Charle Leieron wih mall change by Carola Wenk Flow nework Definiion. flow nework i a direced graph G = (V, E) wih wo diinguihed verice: a ource and a ink. Each

More information

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 9 Solutions [Theorems of Gauss and Stokes]

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 9 Solutions [Theorems of Gauss and Stokes] ENGI 44 Avance alculus fo Engineeing Faculy of Engineeing an Applie cience Poblem e 9 oluions [Theoems of Gauss an okes]. A fla aea A is boune by he iangle whose veices ae he poins P(,, ), Q(,, ) an R(,,

More information

Main Reference: Sections in CLRS.

Main Reference: Sections in CLRS. Maximum Flow Reied 09/09/200 Main Reference: Secion 26.-26. in CLRS. Inroducion Definiion Muli-Source Muli-Sink The Ford-Fulkeron Mehod Reidual Nework Augmening Pah The Max-Flow Min-Cu Theorem The Edmond-Karp

More information

A GEOMETRIC BROWNIAN MOTION MODEL WITH COMPOUND POISSON PROCESS AND FRACTIONAL STOCHASTIC VOLATILITY

A GEOMETRIC BROWNIAN MOTION MODEL WITH COMPOUND POISSON PROCESS AND FRACTIONAL STOCHASTIC VOLATILITY Adance and Alicaion in Saiic Volume 6, Numbe,, Page 5-47 Thi ae i aailable online a h://hmj.com/jounal/ada.hm Puha Publihing Houe A GEOMETRIC ROWNIAN MOTION MODEL WITH COMPOUND POISSON PROCESS AND FRACTIONAL

More information

15. Vector Valued Functions

15. Vector Valued Functions 1. Vecor Valued Funcions Up o his poin, we have presened vecors wih consan componens, for example, 1, and,,4. However, we can allow he componens of a vecor o be funcions of a common variable. For example,

More information

Math 2142 Exam 1 Review Problems. x 2 + f (0) 3! for the 3rd Taylor polynomial at x = 0. To calculate the various quantities:

Math 2142 Exam 1 Review Problems. x 2 + f (0) 3! for the 3rd Taylor polynomial at x = 0. To calculate the various quantities: Mah 4 Eam Review Problems Problem. Calculae he 3rd Taylor polynomial for arcsin a =. Soluion. Le f() = arcsin. For his problem, we use he formula f() + f () + f ()! + f () 3! for he 3rd Taylor polynomial

More information

UT Austin, ECE Department VLSI Design 5. CMOS Gate Characteristics

UT Austin, ECE Department VLSI Design 5. CMOS Gate Characteristics La moule: CMOS Tranior heory Thi moule: DC epone Logic Level an Noie Margin Tranien epone Delay Eimaion Tranior ehavior 1) If he wih of a ranior increae, he curren will ) If he lengh of a ranior increae,

More information

Rectilinear Kinematics

Rectilinear Kinematics Recilinear Kinemaic Coninuou Moion Sir Iaac Newon Leonard Euler Oeriew Kinemaic Coninuou Moion Erraic Moion Michael Schumacher. 7-ime Formula 1 World Champion Kinemaic The objecie of kinemaic i o characerize

More information

The Arcsine Distribution

The Arcsine Distribution The Arcsine Disribuion Chris H. Rycrof Ocober 6, 006 A common heme of he class has been ha he saisics of single walker are ofen very differen from hose of an ensemble of walkers. On he firs homework, we

More information

Chapter 6. Laplace Transforms

Chapter 6. Laplace Transforms Chaper 6. Laplace Tranform Kreyzig by YHLee;45; 6- An ODE i reduced o an algebraic problem by operaional calculu. The equaion i olved by algebraic manipulaion. The reul i ranformed back for he oluion of

More information

The continuous time random walk (CTRW) was introduced by Montroll and Weiss 1.

The continuous time random walk (CTRW) was introduced by Montroll and Weiss 1. 1 I. CONTINUOUS TIME RANDOM WALK The continuou time random walk (CTRW) wa introduced by Montroll and Wei 1. Unlike dicrete time random walk treated o far, in the CTRW the number of jump n made by the walker

More information

DERIVATION OF LORENTZ TRANSFORMATION EQUATIONS AND THE EXACT EQUATION OF PLANETARY MOTION FROM MAXWELL AND NEWTON Sankar Hajra

DERIVATION OF LORENTZ TRANSFORMATION EQUATIONS AND THE EXACT EQUATION OF PLANETARY MOTION FROM MAXWELL AND NEWTON Sankar Hajra PHYSICAL INTERPRETATION OF RELATIVITY THEORY CONFERENCE, LONDON, 4 DERIVATION OF LORENTZ TRANSFORMATION EQUATIONS AND THE EXACT EQUATION OF PLANETARY MOTION FROM MAXWELL AND NEWTON Sanka Haja CALCUTTA

More information

WORK POWER AND ENERGY Consevaive foce a) A foce is said o be consevaive if he wok done by i is independen of pah followed by he body b) Wok done by a consevaive foce fo a closed pah is zeo c) Wok done

More information

, on the power of the transmitter P t fed to it, and on the distance R between the antenna and the observation point as. r r t

, on the power of the transmitter P t fed to it, and on the distance R between the antenna and the observation point as. r r t Lecue 6: Fiis Tansmission Equaion and Rada Range Equaion (Fiis equaion. Maximum ange of a wieless link. Rada coss secion. Rada equaion. Maximum ange of a ada. 1. Fiis ansmission equaion Fiis ansmission

More information