Modeling of the linear time-variant channel. Sven-Gustav Häggman

Size: px
Start display at page:

Download "Modeling of the linear time-variant channel. Sven-Gustav Häggman"

Transcription

1 Moelg of he lear me-vara chael Sve-Gusav Häggma

2 2

3 1. Characerzao of he lear me-vara chael 3 The rasmsso chael (rao pah) of a rao commucao sysem s mos cases a mulpah chael. Whe chages ae place he propagao evrome e.g. he rao saos are moble, reflecors a scaerers are movg, or he meum self (roposphere, oosphere) s chagg, he he chael respose wll also chage as a fuco of me, he rao pah s fag. If he chages are slow so ha he chael s almos cosa uer he urao of a sgle aa symbol or eve uer he urao of a frame coag hures of symbols, he chael ca be characerze as quasvara, a he chael respose o oe symbol or oe frame ca be calculae usg he formulas of me vara sysems. I he rasmsso of a log message he chael s, however, graually chagg a he chages ca be represee e.g. wh he jo esy fuco of he mulpah chael ap pah amplues a elays a her correlao fucos. Whe he chael s sgfcaly chagg urg he rasmsso of a aa frame ew chael represeaos mus be use. The feaures of hese mus also be ow, so ca be ece whe he chael ca be moele as quas-vara. The mpulse respose h() or he rasfer fuco H(f), whch s he Fourer rasform of he mpulse respose, fully escrbes he lear mevara sysem. I he characerzao of lear me-vara sysems several ew sysem fucos are rouce. Ther physcal erpreao s o always easy, a her cocepo s raher laborous. Hereafer we wll use he abbrevaos LTV-chael for he Lear Tme-Vara chael a LTI-chael for he Lear Tme-Ivara chael. Bello has evelope he heory of LTV-sysem alreay 1963 [1] for characerzao of he roposcaer chael. Goo escrpos are also clue refereces [2] a [3]. I Fg. 1 he approach use here s presee. 1 [ 1] P.A.Bello: Characerzao of raomly me-vara lear chaels. IEEE Tras., Vol CS- 11, No. 4, Dec. 1963, pp [2] R.Seele (eor): Moble rao commucaos. Loo 1992, Peech Press, 779p.

4 LTV-chael approach 4 Trasmsso chaels LTI-chaels LTV-chaels Raom LTV-chaels Deermsc LTV-chaels Sysem fucos of he eermsc LTV-chael Aeuao srbuos of he fla fag chael 4-mesoal auocorrelao fucos of he raom LTVchael 2-mesoal auocorrelao fucos of he WSSUSchael 1-mesoal specral fucos Scalar parameer represeaos Fg. 1 [3] D.Parsos: The moble rao propagao chael. Loo 1992, Peech Press, 316p.

5 1.1 Calculao of LTV-chael sgal respose. 5 The calculao of he sgal respose s frs presee for he scree LTV-mulpah chael. The resul s he geeralze for a arbrary LTVchael. The sgal aalyss s performe for complex low-pass sgals. Fg. 2 shows he behavor of he LTV-mulpah chael a ealze suao. The ealsao meas e.g. ha he sgal passes usore over all pahs. The sgal urao s so shor ha he sgals over ffere pah o o overlap a he recever. The pu sgal s a arrow pulse, whch s rasme a he me sas 1, 2, 3, a 4. The chael has of course some mmum elay, before he respose sars. O he frs me sa 1 he chael s a hree-pah chael a he pulse s aeuae vually o each pah bu he pulse shape remas uchage. The me-vara aure of he chael ca be see from he varyg umber, aeuao, a elay of he pahs a ffere me sas. From hs ca be coclue ha he mulpah chael respose epes boh o he me of arrval of he pu pulse (me) a of he me pas sce ha (elay). The mpulse respose s a fuco of boh me a elay, whle he me-vara chael s oly a fuco of elay. The pu a oupu sgals are real he fgure bu geerally he oupu sgal s complex whe low-pass represeao of he sysem s use. Also he pu sgal ca be complex. If he elay fferece bewee he pahs s less ha he sgal urao, he pah resposes wll overlap a he respose wll be very complex a he mulpah srucure cao be recly observe. Complex low-pass sysem represeao of he sgals s use. The physcal ba-pass pu a oupu sgals are hus: L NM = R ST U VW 1L = 2NM + R s () = Re ST ze j 2π f cu () VW 1 ze j 2π () f c = + z () e j 2π f c 2 r we j 2π f c we j 2π f c () Re () () w() e j 2π f c O QP O QP (1) (2) where fc s he carrer frequecy use.

6 6 The me-vara chael pulse respose a four ffere me sas z(- ) 1 w() z(- 2 ) w() z(- ) 3 w() z(- ) 4 w() M() 1 α = j f 2π cτ() w () = () e z τ () c h Fg. 2

7 7 From Fg. 2 ca be see ha he physcal oupu sgal of he LTVmulpah chael ca be expresse as M () 1 α = r () = () s τ () (3) where - α() s he ga of he :h propagao pah as fuco of me, - τ() s he propagao elay of he :h propagao pah as fuco of me, - M() s he umber of propagao pahs as fuco of me. Isero of Eq. (1) Eq. (3) gves: M () 1 r () = α ()Re z τ () e = R S M () 1 2π c τ () = Re α() z τ() e T = RM () 1 j2πf j Re () e cτ() 2π = α z τ () e S T = R S T j2πf c τ () j f U V W U V W fcu V W (4) I he las form of Eq. (4) he complex low-pass oupu sgal ca be recogze: M () 1 α = j f 2π w e cτ () () () = z τ () (5) Eq. (5) ca also be wre as: L N M M () 1 w () = h () δλ τ () P z( λ) = O Q (6) where eoes covoluo a j2πf h () () e cτ () = α (7)

8 From hs follows he mpulse respose of he LTV-mulpah chael: 8 M () h( λ, ) = h ( ) δ λ τ ( ) = (8) where h(λ,) s he respose a me o a mpulse arrve λ secos earler. Eqs. (5) - (8) are val for a scree mulpah chael. I some rao chaels e.g. he roposcaer chael a me-couous mpulse respose s a more aequae moel. The he sgal respose s calculae wh a geeralze covoluo egral: z w () = h( λ,)( z λ ) λ (9) The respose of he mulpah chael o a sewave z e j 2π ()= f z (1) s obae by serg he pu sgal Eq. (1) Eq. (5), whch gves M () 1 2π τ 2π τ w () = α () e e = M () 1 = = α () e j f j f c () z () j2 fc+ fz j2 fz π τ () π e (11) A smple example wll show ha he pah elay varao a o he amplue varao wll maly eerme he yamc characerscs of he LTV-chael. Example 1. I Fg. 3 s assume ha he refleco coeffce s -1. The geomercal legh of he propagao pahs s calculae usg he rgh-agle ragles a he mage prcple. The pah amplue gas a elays follow recly from he legh of he propagao pahs. The logarhmc amplue of he receve sgal as a fuco of me s calculae assumg he carrer frequecy 2 GHz a he spee of he recever 15 m/s. The geomercal values are gve Fg. 3.

9 The graph Fg. 3 shows he logarmc sgal amplu as fuco of me assumg frs ha oly he elays epe o me, a he ha boh elays a gas are chagg. I hs smple suao he me epeece s very regular, whch s early equal uer boh assumpos. From hs ca be coclue ha he behavor s eerme almos solely by he elay chages a he chages pah gas oly have mor effecs. Ths ca be geeralse o almos all rao chaels. The reaso for hs behavour s of course ha elay chages are mulple by he carrer frequecy he expoeal fuco Eq. (11). 9 Example 2. The moble chael ca a small area be moele wh he mpulse respose M 1 j2 = πν h( λ, ) = h e δ λ τ (12) Ths s a scree M-pah chael where he complex pah gas h a he elays τ o o chage wh me. As a cosequece of he cosa spee each pah has s ow cosa Doppler-shf ν = v α c f cos (13) c where v o spee of he moble sao, c s he spee of he rao wave, fc s he carrer frequecy, a α s he agle bewee he :h propagao pah a he moble sao velocy vecor. I realy h, τ, α, a M are fucos of me bu he small rego where he moble sao moves uer oe rasmsso frame hey are approxmaely cosa. The raomess of hs chael appears so ha he moel parameers afer some few frames have obae ew values.

10 1 x v y ()= c + h+ 2 2 ()= c + h+ c2 h r x v y o 2 2 r x v y 1 c α o () = τ r () () = r o () α 1 () = r 1 () τ c () = 1 r 1 () 1 B 5 5,4 B 5,2-5 5, 1,8 1,9 /s ,5 B -17, -2,2,4,6,8 1, 1,2 1,4 /s 1,6 1,8 2, Fg. 3-17,5 1,945 1,95 /s 1,955

11 3.2.2 The sysem fucos of a LTV-chael 11 The basc sysem fuco of a eermsc LTV-chael a of a sample fuco of a raom LTV-chael s me vara mpulse respose h(λ,) escrbe above, also ow as he chael pu elay sprea fuco. Oher frequely use sysem fucos are: he me-vara chael rasfer fuco: p a f H( f, ) = λ h( λ, ) = zh( λ, )exp j2 πfλ λ (14) whch s obae by Fourer-rasformg he mpulse respose wh respec o he elay varable λ. I escrbes he complex evelope of he oupu sgal whe he pu sgal s ej2πf. he chael oupu Doppler sprea fuco: p a f D( f, ν) = H( f, ) = zh( f, )exp j2 πν (15) whch s obae by Fourer-rasformg he rasfer fuco wh respec o he me varable, a whch escrbes he chael frequecy respose o he frequecy f + ν, whe he pu sgal s ej2πf. he elay-doppler sprea fuco p a f S( λν, ) = h( λ, ) = zh( λ, )exp j2 πν (16) s obae by Fourer-rasformg h(λ,) wh respec o he me varable or by ag he verse Fourer-rasform of he Doppler-sprea fuco wh respec o frequecy f. I gves he complex ga of he chael o he elay erval τ + τ a he Doppler-shf erval ν + ν. The Fourer-rasform relaos of hese four sysem fucos are show Fg. 4. I ao four oher ual sysem fucos ca be efe.

12 REPRESENTATION OF THE LTV-CHANNEL 12 SYSTEM FUNCTIONS OF A DETERMINISTIC CHANNEL z() h(λ,) w() λ a f λ a f a ν f aλν f λ w () = zh( λ,)( z λ) λ j2πf = H( f, ) Z( f) e f u j = z u 2πνλ S( λν, ) z( λ) e νλ j f = z u 2π D( f νν, ) Z( f ν) e νf Fg. 4

13 Example 2 coues The saaeous sysem fucos of hs chael moel are: he me-vara mpulse respose (moel efo): 13 M 1 j2 = πν h( λ, ) = h e δ λ τ he me-vara rasfer fuco: (17) M 1 p j2 j2πfτ = πν H( f, ) = h(, ) = h e λ λ e (18) he oupu Doppler-sprea fuco: M 1 p = D( f, ν) = H( f, ) = h δ ν ν e j2πτ f (19) he elay-doppler-sprea fuco: M 1 p = S( λν, ) = h( λ, ) = h δν ν δλ τ (2) I Fg. 5 he mpulse respose, amplue frequecy respose, a he elay- Doppler-sprea fucos are show for a 2-pah chael wh he parameer values: h1 = 1, h2 = 9, τ = 1µ s, τ = 5µ s 1 2 ν = 1Hz, ν = 5Hz 1 2 From he fgure or from Eqs. (17) - (2) appears ha he chael mpulse respose epeece of me ca be see oly from he phase behavour of he complex pah ga, whch s a lear fuco of me (I he fgure raw moulo 2π). I he rasfer fuco ca be see as a glg of he rasmsso mmum hrough he sgal bawh. he elay-doppler-sprea fucos coas mpulses whch ell he elay a Doppler-shf of each pah.

14 14 h(λ,) 1,5 λ/µs 5,1,2 /s Arg{h(λ,)} 2 ra -2 ra λ/µs 5,1,2 /s H(f,) 1 f/hz /ms 25 1 S(λ,ν) -1 ν/hz 1 λ/µs 5 Fg. 5

Continuous Time Markov Chains

Continuous Time Markov Chains Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,

More information

14. Poisson Processes

14. Poisson Processes 4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur

More information

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits. ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh

More information

FORCED VIBRATION of MDOF SYSTEMS

FORCED VIBRATION of MDOF SYSTEMS FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

More information

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3. C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)

More information

The Poisson Process Properties of the Poisson Process

The Poisson Process Properties of the Poisson Process Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad

More information

Topology Optimization of Structures under Constraints on First Passage Probability

Topology Optimization of Structures under Constraints on First Passage Probability Topology Opmzao of Srucures uer Cosras o Frs Passage Probably Juho Chu Docoral Sue, Dep. of Cvl a Evromeal Egeerg, Uv. of Illos, Urbaa-Champag, USA Juho Sog Assocae Professor, Dep. of Cvl a Evromeal Egeerg,

More information

(1) Cov(, ) E[( E( ))( E( ))]

(1) Cov(, ) E[( E( ))( E( ))] Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )

More information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae

More information

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral

More information

The Signal, Variable System, and Transformation: A Personal Perspective

The Signal, Variable System, and Transformation: A Personal Perspective The Sgal Varable Syem ad Traformao: A Peroal Perpecve Sherv Erfa 35 Eex Hall Faculy of Egeerg Oule Of he Talk Iroduco Mahemacal Repreeao of yem Operaor Calculu Traformao Obervao O Laplace Traform SSB A

More information

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state) Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm Exam. Tuesday, September hour, 15 minutes Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.

More information

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad

More information

Fresnel Equations cont.

Fresnel Equations cont. Lecure 12 Chaper 4 Fresel quaos co. Toal eral refleco ad evaesce waves Opcal properes of meals Laer: Famlar aspecs of he eraco of lgh ad maer Fresel quaos r 2 Usg Sell s law, we ca re-wre: r s s r a a

More information

The Linear Regression Of Weighted Segments

The Linear Regression Of Weighted Segments The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed

More information

C.11 Bang-bang Control

C.11 Bang-bang Control Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of

More information

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions: Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log

More information

Density estimation III. Linear regression.

Density estimation III. Linear regression. Lecure 6 Mlos Hauskrec mlos@cs.p.eu 539 Seo Square Des esmao III. Lear regresso. Daa: Des esmao D { D D.. D} D a vecor of arbue values Obecve: r o esmae e uerlg rue probabl srbuo over varables X px usg

More information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

Chapter 8. Simple Linear Regression

Chapter 8. Simple Linear Regression Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple

More information

4. THE DENSITY MATRIX

4. THE DENSITY MATRIX 4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o

More information

Reliability Analysis of Sparsely Connected Consecutive-k Systems: GERT Approach

Reliability Analysis of Sparsely Connected Consecutive-k Systems: GERT Approach Relably Aalyss of Sparsely Coece Cosecuve- Sysems: GERT Approach Pooa Moha RMSI Pv. L Noa-2131 poalovely@yahoo.com Mau Agarwal Deparme of Operaoal Research Uversy of Delh Delh-117, Ia Agarwal_maulaa@yahoo.com

More information

Stat 6863-Handout 5 Fundamentals of Interest July 2010, Maurice A. Geraghty

Stat 6863-Handout 5 Fundamentals of Interest July 2010, Maurice A. Geraghty S 6863-Hou 5 Fuels of Ieres July 00, Murce A. Gerghy The pror hous resse beef cl occurreces, ous, ol cls e-ulero s ro rbles. The fl copoe of he curl oel oles he ecooc ssupos such s re of reur o sses flo.

More information

Cyclone. Anti-cyclone

Cyclone. Anti-cyclone Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme

More information

Queuing Theory: Memory Buffer Limits on Superscalar Processing

Queuing Theory: Memory Buffer Limits on Superscalar Processing Cle/ Model of I/O Queug Theory: Memory Buffer Lms o Superscalar Processg Cle reques respose Devce Fas CPU s cle for slower I/O servces Buffer sores cle requess ad s a slower server respose rae Laecy Tme

More information

Solution set Stat 471/Spring 06. Homework 2

Solution set Stat 471/Spring 06. Homework 2 oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o

More information

Quantitative Portfolio Theory & Performance Analysis

Quantitative Portfolio Theory & Performance Analysis 550.447 Quaave Porfolo heory & Performace Aalyss Week February 4 203 Coceps. Assgme For February 4 (hs Week) ead: A&L Chaper Iroduco & Chaper (PF Maageme Evrome) Chaper 2 ( Coceps) Seco (Basc eur Calculaos)

More information

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.

More information

Partial Molar Properties of solutions

Partial Molar Properties of solutions Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a

More information

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below. Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50

More information

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare

More information

Mathematical Formulation

Mathematical Formulation Mahemacal Formulao The purpose of a fe fferece equao s o appromae he paral ffereal equao (PE) whle maag he physcal meag. Eample PE: p c k FEs are usually formulae by Taylor Seres Epaso abou a po a eglecg

More information

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body. The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe

More information

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem

More information

Square law expression is non linear between I D and V GS. Need to operate in appropriate region for linear behaviour. W L

Square law expression is non linear between I D and V GS. Need to operate in appropriate region for linear behaviour. W L MOS Feld-Effec Trassrs (MOSFETs ecure # 4 MOSFET as a Amplfer k ( S Square law express s lear bewee ad. Need perae apprprae reg fr lear behaur. Cpyrgh 004 by Oxfrd Uersy Press, c. MOSFET as a Amplfer S

More information

EE 6885 Statistical Pattern Recognition

EE 6885 Statistical Pattern Recognition EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://.ee.columba.edu/~sfchag Lecure 8 (/8/05 8- Readg Feaure Dmeso Reduco PCA, ICA, LDA, Chaper 3.8, 0.3 ICA Tuoral: Fal Exam Aapo Hyväre ad Erkk Oja,

More information

Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks

Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks Sochasc Power Corol for Tme-Varyg Log-Term Fadg Wreless Neworks ohammed Olama Deparme of Elecrcal ad Compuer Egeerg, Uversy of Teessee, Koxvlle, TN 37996, USA. Emal: molama@uk.edu Seddk. Djouad Deparme

More information

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables Joural of Mahemacs ad Sascs 6 (4): 442-448, 200 SSN 549-3644 200 Scece Publcaos Momes of Order Sascs from Nodecally Dsrbued Three Parameers Bea ype ad Erlag Trucaed Expoeal Varables A.A. Jamoom ad Z.A.

More information

EMA5001 Lecture 3 Steady State & Nonsteady State Diffusion - Fick s 2 nd Law & Solutions

EMA5001 Lecture 3 Steady State & Nonsteady State Diffusion - Fick s 2 nd Law & Solutions EMA5 Lecue 3 Seady Sae & Noseady Sae ffuso - Fck s d Law & Soluos EMA 5 Physcal Popees of Maeals Zhe heg (6) 3 Noseady Sae ff Fck s d Law Seady-Sae ffuso Seady Sae Seady Sae = Equlbum? No! Smlay: Sae fuco

More information

Upper Bound For Matrix Operators On Some Sequence Spaces

Upper Bound For Matrix Operators On Some Sequence Spaces Suama Uer Bou formar Oeraors Uer Bou For Mar Oeraors O Some Sequece Saces Suama Dearme of Mahemacs Gaah Maa Uersy Yogyaara 558 INDONESIA Emal: suama@ugmac masomo@yahoocom Isar D alam aer aa susa masalah

More information

Available online Journal of Scientific and Engineering Research, 2014, 1(1): Research Article

Available online  Journal of Scientific and Engineering Research, 2014, 1(1): Research Article Avalable ole wwwjsaercom Joural o Scec ad Egeerg Research, 0, ():0-9 Research Arcle ISSN: 39-630 CODEN(USA): JSERBR NEW INFORMATION INEUALITIES ON DIFFERENCE OF GENERALIZED DIVERGENCES AND ITS APPLICATION

More information

Geometric Modeling

Geometric Modeling Geomerc Modelg 9.58. Crves coed Cc Bezer ad B-Sle Crves Far Chaers 4-5 8 Moreso Chaers 4 5 4 Tycal Tyes of Paramerc Crves Corol os flece crve shae. Ierolag Crve asses hrogh all corol os. Herme Defed y

More information

Common MidPoint (CMP) Records and Stacking

Common MidPoint (CMP) Records and Stacking Evromeal ad Explorao Geophyscs II Commo MdPo (CMP) Records ad Sackg om.h.wlso om.wlso@mal.wvu.edu Deparme of Geology ad Geography Wes rga Uversy Morgaow, W Commo Mdpo (CMP) gaher, also ofe referred o as

More information

SHOCK AND VIBRATION RESPONSE SPECTRA COURSE Unit 21 Base Excitation Shock: Classical Pulse

SHOCK AND VIBRATION RESPONSE SPECTRA COURSE Unit 21 Base Excitation Shock: Classical Pulse SHOCK AND VIBRAION RESPONSE SPECRA COURSE Ui 1 Base Exciaio Shock: Classical Pulse By om Irvie Email: omirvie@aol.com Iroucio Cosier a srucure subjece o a base exciaio shock pulse. Base exciaio is also

More information

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad

More information

Linear Regression Linear Regression with Shrinkage

Linear Regression Linear Regression with Shrinkage Lear Regresso Lear Regresso h Shrkage Iroduco Regresso meas predcg a couous (usuall scalar oupu from a vecor of couous pus (feaures x. Example: Predcg vehcle fuel effcec (mpg from 8 arbues: Lear Regresso

More information

RATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA

RATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Absrac RATIO ESTIMATORS USING HARATERISTIS OF POISSON ISTRIBUTION WITH APPLIATION TO EARTHQUAKE ATA Gamze Özel Naural pulaos bolog geecs educao

More information

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period. ublc Affars 974 Meze D. Ch Fall Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he Effce Markes Hypohess (rev d //) The rese Value Model Approach o Asse rcg The exbook expresses he sock prce

More information

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period. coomcs 435 Meze. Ch Fall 07 Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he ffce Markes Hypohess The rese Value Model Approach o Asse rcg The exbook expresses he sock prce as he prese dscoued

More information

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview Probably 1/19/ CS 53 Probablsc mehods: overvew Yashwa K. Malaya Colorado Sae Uversy 1 Probablsc Mehods: Overvew Cocree umbers presece of uceray Probably Dsjo eves Sascal depedece Radom varables ad dsrbuos

More information

General Complex Fuzzy Transformation Semigroups in Automata

General Complex Fuzzy Transformation Semigroups in Automata Joural of Advaces Compuer Research Quarerly pissn: 345-606x eissn: 345-6078 Sar Brach Islamc Azad Uversy Sar IRIra Vol 7 No May 06 Pages: 7-37 wwwacrausaracr Geeral Complex uzzy Trasformao Semgroups Auomaa

More information

Redundancy System Fault Sampling Under Imperfect Maintenance

Redundancy System Fault Sampling Under Imperfect Maintenance A publcao of CHEMICAL EGIEERIG TRASACTIOS VOL. 33, 03 Gues Edors: Erco Zo, Pero Barald Copyrgh 03, AIDIC Servz S.r.l., ISB 978-88-95608-4-; ISS 974-979 The Iala Assocao of Chemcal Egeerg Ole a: www.adc./ce

More information

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3

More information

Continuous Indexed Variable Systems

Continuous Indexed Variable Systems Ieraoal Joural o Compuaoal cece ad Mahemacs. IN 0974-389 Volume 3, Number 4 (20), pp. 40-409 Ieraoal Research Publcao House hp://www.rphouse.com Couous Idexed Varable ysems. Pouhassa ad F. Mohammad ghjeh

More information

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China,

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China, Mahemacal ad Compuaoal Applcaos Vol. 5 No. 5 pp. 834-839. Assocao for Scefc Research VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS Hoglag Lu Aguo Xao Yogxag Zhao School of Mahemacs

More information

NUMERICAL EVALUATION of DYNAMIC RESPONSE

NUMERICAL EVALUATION of DYNAMIC RESPONSE NUMERICAL EVALUATION of YNAMIC RESPONSE Aalycal solo of he eqao of oo for a sgle degree of freedo syse s sally o ossble f he excao aled force or grod accelerao ü g -vares arbrarly h e or f he syse s olear.

More information

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No. www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra

More information

Introduction to Matrices and Matrix Approach to Simple Linear Regression

Introduction to Matrices and Matrix Approach to Simple Linear Regression Itroducto to Matrces ad Matrx Approach to Smple Lear Regresso Matrces Defto: A matrx s a rectagular array of umbers or symbolc elemets I may applcatos, the rows of a matrx wll represet dvduals cases (people,

More information

The algebraic immunity of a class of correlation immune H Boolean functions

The algebraic immunity of a class of correlation immune H Boolean functions Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales

More information

-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for

-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for Assgme Sepha Brumme Ocober 8h, 003 9 h semeser, 70544 PREFACE I 004, I ed o sped wo semesers o a sudy abroad as a posgraduae exchage sude a he Uversy of Techology Sydey, Ausrala. Each opporuy o ehace my

More information

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &

More information

Turbo Coded MIMO Multiplexing with Iterative Adaptive Soft Parallel Interference Cancellation

Turbo Coded MIMO Multiplexing with Iterative Adaptive Soft Parallel Interference Cancellation Turbo Coe MIMO Mulplexg wh Ierave Aapve Sof Parallel Ierferece Cacellao Akor akaja, eepshkha Garg, a Fuyuk Aach ep. of Elecrcal a Coucaos Egeerg Tohoku Uversy, Sea, Japa akaja@oble.ece.ohoku.ac.jp Absrac

More information

Chapter 14 Logistic Regression Models

Chapter 14 Logistic Regression Models Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as

More information

Optimal Eye Movement Strategies in Visual Search (Supplement)

Optimal Eye Movement Strategies in Visual Search (Supplement) Opmal Eye Moveme Sraeges Vsual Search (Suppleme) Jr Naemk ad Wlso S. Gesler Ceer for Percepual Sysems ad Deparme of Psychology, Uversy of exas a Aus, Aus X 787 Here we derve he deal searcher for he case

More information

Section 8 Convolution and Deconvolution

Section 8 Convolution and Deconvolution APPLICATIONS IN SIGNAL PROCESSING Secio 8 Covoluio ad Decovoluio This docume illusraes several echiques for carryig ou covoluio ad decovoluio i Mahcad. There are several operaors available for hese fucios:

More information

Fresnel Dragging Explained

Fresnel Dragging Explained Fresel Draggig Explaied 07/05/008 Decla Traill Decla@espace.e.au The Fresel Draggig Coefficie required o explai he resul of he Fizeau experime ca be easily explaied by usig he priciples of Eergy Field

More information

To Estimate or to Predict

To Estimate or to Predict Raer Schwabe o Esmae or o Predc Implcaos o he esg or Lear Mxed Models o Esmae or o Predc - Implcaos o he esg or Lear Mxed Models Raer Schwabe, Marya Prus raer.schwabe@ovgu.de suppored by SKAVOE Germa ederal

More information

Solving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision

Solving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision Frs Jo Cogress o Fuzzy ad Iellge Sysems Ferdows Uversy of Mashhad Ira 9-3 Aug 7 Iellge Sysems Scefc Socey of Ira Solvg fuzzy lear programmg problems wh pecewse lear membershp fucos by he deermao of a crsp

More information

Solving Fuzzy Equations Using Neural Nets with a New Learning Algorithm

Solving Fuzzy Equations Using Neural Nets with a New Learning Algorithm Joural of Advaces Compuer Research Quarerly ISSN: 28-6148 Sar Brach, Islamc Azad Uversy, Sar, I.R.Ira (Vol. 3, No. 4, November 212), Pages: 33-45 www.jacr.ausar.ac.r Solvg Fuzzy Equaos Usg Neural Nes wh

More information

PubH 7440 Spring 2010 Midterm 2 April

PubH 7440 Spring 2010 Midterm 2 April ubh 7440 Sprg 00 Mderm Aprl roblem a: Because \hea^_ s a lear combao of ormal radom arables wll also be ormal. Thus he mea ad arace compleel characerze he dsrbuo. We also use ha he Z ad \hea^{-}_ are depede.

More information

Brownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus

Brownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus Browa Moo Sochasc Calculus Xogzh Che Uversy of Hawa a Maoa earme of Mahemacs Seember, 8 Absrac Ths oe s abou oob decomoso he bascs of Suare egrable margales Coes oob-meyer ecomoso Suare Iegrable Margales

More information

Efficient Estimators for Population Variance using Auxiliary Information

Efficient Estimators for Population Variance using Auxiliary Information Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav

More information

Signal,autocorrelation -0.6

Signal,autocorrelation -0.6 Sgal,autocorrelato Phase ose p/.9.3.7. -.5 5 5 5 Tme Sgal,autocorrelato Phase ose p/.5..7.3 -. -.5 5 5 5 Tme Sgal,autocorrelato. Phase ose p/.9.3.7. -.5 5 5 5 Tme Sgal,autocorrelato. Phase ose p/.8..6.

More information

A note on Turán number Tk ( 1, kn, )

A note on Turán number Tk ( 1, kn, ) A oe o Turá umber T (,, ) L A-Pg Beg 00085, P.R. Cha apl000@sa.com Absrac: Turá umber s oe of prmary opcs he combaorcs of fe ses, hs paper, we wll prese a ew upper boud for Turá umber T (,, ). . Iroduco

More information

Fully Fuzzy Linear Systems Solving Using MOLP

Fully Fuzzy Linear Systems Solving Using MOLP World Appled Sceces Joural 12 (12): 2268-2273, 2011 ISSN 1818-4952 IDOSI Publcaos, 2011 Fully Fuzzy Lear Sysems Solvg Usg MOLP Tofgh Allahvraloo ad Nasser Mkaelvad Deparme of Mahemacs, Islamc Azad Uversy,

More information

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model . Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces

More information

Integral Equations and their Relationship to Differential Equations with Initial Conditions

Integral Equations and their Relationship to Differential Equations with Initial Conditions Scece Refleco SR Vol 6 wwwscecereflecocom Geerl Leers Mhemcs GLM 6 3-3 Geerl Leers Mhemcs GLM Wese: hp://wwwscecereflecocom/geerl-leers--mhemcs/ Geerl Leers Mhemcs Scece Refleco Iegrl Equos d her Reloshp

More information

Bianchi Type II Stiff Fluid Tilted Cosmological Model in General Relativity

Bianchi Type II Stiff Fluid Tilted Cosmological Model in General Relativity Ieraoal Joural of Mahemacs esearch. IN 0976-50 Volume 6, Number (0), pp. 6-7 Ieraoal esearch Publcao House hp://www.rphouse.com Bach ype II ff Flud led Cosmologcal Model Geeral elay B. L. Meea Deparme

More information

Pricing Asian Options with Fourier Convolution

Pricing Asian Options with Fourier Convolution Prcg Asa Opos wh Fourer Covoluo Cheg-Hsug Shu Deparme of Compuer Scece ad Iformao Egeerg Naoal Tawa Uversy Coes. Iroduco. Backgroud 3. The Fourer Covoluo Mehod 3. Seward ad Hodges facorzao 3. Re-ceerg

More information

SOLUTION OF PARABOLA EQUATION BY USING REGULAR,BOUNDARY AND CORNER FUNCTIONS

SOLUTION OF PARABOLA EQUATION BY USING REGULAR,BOUNDARY AND CORNER FUNCTIONS SOLUTION OF PAABOLA EQUATION BY USING EGULA,BOUNDAY AND CONE FUNCTIONS Dr. Hayder Jabbar Abood, Dr. Ifchar Mdhar Talb Deparme of Mahemacs, College of Edcao, Babylo Uversy. Absrac:- we solve coverge seqece

More information

EECE 301 Signals & Systems

EECE 301 Signals & Systems EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel

More information

Stabilization of LTI Switched Systems with Input Time Delay. Engineering Letters, 14:2, EL_14_2_14 (Advance online publication: 16 May 2007) Lin Lin

Stabilization of LTI Switched Systems with Input Time Delay. Engineering Letters, 14:2, EL_14_2_14 (Advance online publication: 16 May 2007) Lin Lin Egeerg Leers, 4:2, EL_4_2_4 (Advace ole publcao: 6 May 27) Sablzao of LTI Swched Sysems wh Ipu Tme Delay L L Absrac Ths paper deals wh sablzao of LTI swched sysems wh pu me delay. A descrpo of sysems sablzao

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

Mixed Integral Equation of Contact Problem in Position and Time

Mixed Integral Equation of Contact Problem in Position and Time Ieraoal Joural of Basc & Appled Sceces IJBAS-IJENS Vol: No: 3 ed Iegral Equao of Coac Problem Poso ad me. A. Abdou S. J. oaquel Deparme of ahemacs Faculy of Educao Aleadra Uversy Egyp Deparme of ahemacs

More information

CHAPTER 5. Shallow water theory

CHAPTER 5. Shallow water theory Damcal Oceaograph CAPTER 5 Shallow waer heor I he shallow waer he characersc eph s much smaller ha he horoal scale o moos (5) I s cosere ha he low oes o epe o eph Ths s eacl rue or baroropc ( cos ) o-rcoal

More information

DIFFUSION MAPS FOR PLDA-BASED SPEAKER VERIFICATION

DIFFUSION MAPS FOR PLDA-BASED SPEAKER VERIFICATION DIFFUSION MAPS FOR PLDA-BASED SPEAKER VERIFICATION Ore Barka,, Haga Aroowz IBM Research Hafa, Israel School of Compuer Scece, Tel Avv Uversy, Israel oreba@l.bm.com, hagaa@l.bm.com ABSTRACT Durg he las

More information

Voltage Sensitivity Analysis in MV Distribution Networks

Voltage Sensitivity Analysis in MV Distribution Networks Proceedgs of he 6h WSEAS/IASME I. Cof. o Elecrc Power Sysems, Hgh olages, Elecrc Maches, Teerfe, Spa, December 6-8, 2006 34 olage Sesvy Aalyss M Dsrbuo Neworks S. CONTI, A.M. GRECO, S. RAITI Dparmeo d

More information

A Fuzzy Weight Representation for Inner Dependence Method AHP

A Fuzzy Weight Representation for Inner Dependence Method AHP A Fuzzy Wegh Represeao for Ier Depeece Meho AHP Sh-ch Ohsh 2 Taahro Yamao Heyu Ima 2 Faculy of Egeerg, Hoa-Gaue Uversy Sapporo, 0640926 JAPAN 2 Grauae School of Iformao Scece a Techology, Hoao Uversy Sapporo,

More information

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc

More information

Analyticity of Semigroups Generated by Singular Differential Matrix Operators

Analyticity of Semigroups Generated by Singular Differential Matrix Operators pple Mahemacs,,, 83-87 o:.436/am..436 Publshe Ole Ocober (hp://www.scrp.org/joural/am) alycy of Semgroups Geerae by Sgular Dffereal Mar Operaors Oul hme Mahmou S hme, el Sa Deparme of Mahemacs, College

More information

1 Notes on Little s Law (l = λw)

1 Notes on Little s Law (l = λw) Copyrigh c 26 by Karl Sigma Noes o Lile s Law (l λw) We cosider here a famous ad very useful law i queueig heory called Lile s Law, also kow as l λw, which assers ha he ime average umber of cusomers i

More information

EDUCATION COMMITTEE OF THE SOCIETY OF ACTUARIES ADVANCED TOPICS IN GENERAL INSURANCE STUDY NOTE CREDIBILITY WITH SHIFTING RISK PARAMETERS

EDUCATION COMMITTEE OF THE SOCIETY OF ACTUARIES ADVANCED TOPICS IN GENERAL INSURANCE STUDY NOTE CREDIBILITY WITH SHIFTING RISK PARAMETERS EDUCATION COMMITTEE OF THE SOCIETY OF ACTUARIES ADVANCED TOPICS IN GENERAL INSURANCE STUDY NOTE CREDIBILITY WITH SHIFTING RISK PARAMETERS Suar Klugma, FSA, CERA, PhD Copyrgh 04 Socey of Acuares The Educao

More information

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable

More information

( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices.

( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices. 4.3 - Modal Aalyss Physcal coordates are ot always the easest to work Egevectors provde a coveet trasformato to modal coordates Modal coordates are lear combato of physcal coordates Say we have physcal

More information

On the maintenance of a proper reference frame for VLBI and GPS global networks

On the maintenance of a proper reference frame for VLBI and GPS global networks O he maeace of a proper referece frame for VLB a GPS global ewors Ahaasos Dermas. rouco he use of a approprae erresral referece frame orer o escrbe he poso of pos o he earh, as well as s emporal varao,

More information

EE 6885 Statistical Pattern Recognition

EE 6885 Statistical Pattern Recognition EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://www.ee.columba.edu/~sfchag Lecure 5 (9//05 4- Readg Model Parameer Esmao ML Esmao, Chap. 3. Mure of Gaussa ad EM Referece Boo, HTF Chap. 8.5 Teboo,

More information

CONTROLLABILITY OF A CLASS OF SINGULAR SYSTEMS

CONTROLLABILITY OF A CLASS OF SINGULAR SYSTEMS 44 Asa Joural o Corol Vol 8 No 4 pp 44-43 December 6 -re Paper- CONTROLLAILITY OF A CLASS OF SINGULAR SYSTEMS Guagmg Xe ad Log Wag ASTRACT I hs paper several dere coceps o corollably are vesgaed or a class

More information