Angle Modulation (Ch.6 in Textbook)

Size: px
Start display at page:

Download "Angle Modulation (Ch.6 in Textbook)"

Transcription

1 Objeves: gle Mdula (Ch.6 Texbk T sudy requey dula (FM T sudy phase dula (PM T sudy he sruure dular ad dedular plude dula (M: The aplude he arrer vares ardae wh he essage sgal (. gle dula (FM, PM: The agle he arrer s vared ardae wh he essage sgal whle he aplude he arrer s sa. a s M FM, PM 144

2 plude Mdula vs. gle Mdula plude Mdula: -- The speru he dulaed sgal s shed/saled vers he essage sgal speru. -- Trasss badwdh Message badwdh -- SNR u a be reased ly reasg he rased sgal pwer. gle Mdula: -- The speru he dulaed sgal s NOT sply relaed essage sgal speru. -- Trasss badwdh >>Message badwdh. -- Iprvee SNR u whu reasg he rased sgal pwer 145

3 gle Mdulaed Sgal s s Phasr Represea: Sgal s represeed by a rag ver plex plae. Re : phasr agude j j e e Re : saaeus phase agle (deeres he ps rag ver a e relaed essage sgal 146

4 gular vely: (requey 1 = s de average requey r dura daes hw as/slw agle hages de l l d d saeus requey a e als kw as agular vely d d d d vared wh essage sgal Reall X=v X=dsae, v=vely, = e gle: d 147

5 ( Phase Mdula (PM Phase Mdular PM s geer r s k p k p : phase deva sa s Isaeus agle Isaeus requey k p d d k p d d Phase ( PM ax k p ax Maxu (peak phase deva 148

6 Frequey Mdula (FM s geer r ( Frequey Mdular s FM s k d k : requey deva sa Isaeus agle Isaeus requey k d d k Frequey ( d ax k ax Maxu (peak requey deva FM 149

7 Exaple FM ad PM sgals Phase hages Phase hages Frequey hages 15

8 PM FM Rela bewee FM ad PM Sgals (I s k p s k d ( Phase Mdular PM s ( Derear d ( d Frequey Mdular PM s 151

9 PM FM Rela bewee FM ad PM Sgals (II s k p s k d ( Frequey Mdular FM s ( Iegrar d Phase Mdular FM s 15

10 Narrwbad FM (NBFM s s s s s s a b sasb s asb NBFM assup s 1 s 1 s s NBFM sgal 153

11 ssue Case Sudy: NBFM Sgle Te Mdula a s s k d Isaeus requey d d Sgle Te Mdula k ak s s ak k ax Peak requey deva Isaeus agle k d ak s d de a k ak s Mdula dex FM sgal 154

12 Case Sudy: NBFM Sgle Te Mdula (C d as s k d s s s s s s s s s a b sasb s asb s s 1 1 s 1 s s s NBFM s s s NBFM sgal r sgle e dula 155

13 Case Sudy: NBFM Sgle Te Mdula (C d s s s Rearragg as k s k a 1 1 s s 1 1 s s a s dulaes he aplude he evelpe suers dsr, uless β s very sall 156

14 Case Sudy: NBFM Sgle Te Mdula (C d The ls β r NBFM apprxa 1 1 s s a s a 1 s d s? d 1 s s s s 1 s 1 s 1 s s reasable bud r he arrwbad apprxa 157

15 Wdebad FM (WBFM Fr a geeral FM sgal, s pssble evaluae he Furer rasr. Here, we aga us sgle e dula. as s k d s ak s d s s ak Hw a we wre he abve express ers s?, 1,,

16 s s Re WBFM (C d j e s j Ree x x j s e x X e j X T / 1 j T x T / e d X X T / 1 j s j T e T / e d 1 j s T e d Varable hage Ths egral a be evaluaed uerally ers he paraeers ad β ad abulaed exesvely. I s deed by J (β ad alled Bessel u he rs kd. They are real-valued. 159

17 WBFM (C d x j s j e J e X where J de 1 j s e d j Ree x j s Re e Ф (= Φ j j Re e J e [(] J s s s J s J 16

18 Bas Prperes Bessel Fus J 1 J J J J,, s s eve dd 1 Fr J 1 J 1 J r J 1 161

19 Pls Bessel u he rs kd J J 1 1 J r

20 WBFM Sgal J s Hw a we ba NBFM sgal r WBFM express? Fr 1 J r J J s s 1 J s 1 J 1 J 1 J 1 s s s 163

21 Badwdh FM Sgals J s Φ J ( as a arrer pe ad a e se sde requees laed syerally eher sde arrer requey (.e. ±ω. These requees are eger ulples ω, deed as hars. = =1 J =-1 = J 1 J1 J =- J Φ = =-1 J =1 =- J 1 J1 J = J I geeral, Fr 1 B rad depeds de sga sdebads B rad, ly =,±1 ers exs. 164

22 Sga Sdebads De r Badwdh FM Sgals Hw ay sdebads are pra he FM rasss a sgal? Ths depeds he eded appla requrees ad essage sgal. rule ly adped s ha a sdebad s sga s agude s equal r exeeds 1% he udulaed arrer,.e. J. 1 J (β dshes rapdly ad he ra /β 1 as β bees large. β>>1 J. 1 J. 1 Therere, he badwdh r large β a be apprxaed as B rad Reall ak 165

23 Cars s Rule r Badwdh FM Sgals B rad 1 rad B Fr very sall, Brad 1 Fr very large, B rad 1 a be egleed a be egleed Cars s Rule agrees wh ur prevus bservas r lg ases, baed r he speal ase dulag sgal he r a susdal. Cars s Rule als hlds r geeral dulag sgals ha are bad-led ad have e pwer. Cars s Rule gves less badwdh ha ur de sga sdebads. ( rue r he lg ases 166

24 verage Pwer FM Sgals s s P lerave represea ( ers Bessel u J s Tal average pwer J J Φ 4 1 J d S P =1 J S Pwer speral desy Tal average pwer 167

25 FM Sgal Uder NB assups Geera NBFM Sgals s s s s s k ( = a k s( s( egrar + s( _ + ( NBFM -9 phase sh Iegrar pu Iegrar upu k (= a k s( = s( (/ s( = s( s( 168

26 Geera WBFM Sgals Idre Mehd ( Narrwbad Freq. Mdular Frequey Mulpler Frequey Cverer NBFM sgal WBFM sgal Frequey Mulpler Frequey Cverer Nlear Deve Badpass Fler s 1 Badpass Fler 169

27 Idre Mehd (C d Frequey Mulpler (x Nlear Deve Badpass Nlear deve desged ulply he k requees he pu sgal by a gve ar. eu ke ssue NBFM has d. dex β ad desred WBFM has d. dex β We eed use = Ipu Oupu e e s s u s s s s k 1 1 s s s e u e e s Reved by he ler 1 Ca be adjused by he ler ga Mulpled by 17

28 Idre Mehd (C d Csderg he requrees, requey ulpler gh eed be pleeed usg ulple seps. The upu he requey ulpler: s s + addal ers B 1 1 B BPF Lg d ( verlappg B 1 B B 1 B 171

29 Frequey ulpler upu: Idre Mehd (C d s s Frequey ulpler reases he dula dex by a ar as desred. Ths als resuls a rease he arrer requey, whh gh be sue ay ases. The arrer requey eeds be shed he desred requey by requey verer. Frequey Cverer Badpass Fler Frequey verer s used sh he speru he sgal by a gve au. I des hage s speral e. s 1 Oe he upu ers shuld gve desred requey ad he her wll be reved by he prper he BPF

30 Geera WBFM Sgals Dre Mehd Vlage-rlled sllar (VCO s ay sllar whse requey s rlled by he dulag-sgal vlage. Osllar C L Oslla requey Vlage-varable apaae C C k 1 LC k 1 LC 1 k / C LC C Reall: 1 x 1/ 1 x / The requey s prpral he essage sgal (. The lg-er requey-sably s as gd as he dre ehd. 173

31 k FM FM dfm d Dedula FM Sgals Dre Mehd: Dsrar Derear s k d k s k d Evelpe Deer The abve express has he r DSB-LC sgal. Evelpe he sgal a he upu k 1 derear There s a slgh vara he requey. Hwever, he evelpe deer a be sll used dee he (. The deal derear a be apprxaed by ay deve whse agude raser u s reasably lear wh he rage requees eres. 174

32 Exaple 6.1: Sple Derear v C R v u v s s H V V u V R jrc R 1 j C 1 j RC RC 1 H jrc v h RC d d Reall: V=IR R: ressr dv u RC RC 1 s s s d V=I(1/jwC C: apar V=I(jwL L: dur evelpe 175

33 Idre Mehd: Phase Lked Lp (PLL LPF Lp ler v u Phase parar r Vlage rlled sllar Ipu sgal s s k d The vlage-rlled sllar (VCO prdues a saaeus requey whh s prpral v u r r u k v d ssue VCO upu as r r s kr vu d r s r 176

34 PLL ( d Lw pass ler upu r r r s s Oly hs er passes he ler r Fr sall s r r r r I he learzed reg, hs shee a be used as a phase parar LPF 177

35 PLL ( d --- Whe he pu sgal s appled, phase pars wh VCO geeraes errr vlage. I ur, hs res VCO syhrze sel he pu requey. --- I he lk ps (.e. VCO s syhrzed VCO requey bees deal pu requey. --- s he pu requey vares slwly wh he essage sgal, PLL s able rak pu requey hrugh hages errr vlage. r k d k r v u d v u 178

36 Exaple 6.: 1MHz sgal s requey-dulaed by a susdal sgal suh ha he peak requey deva s 5kHz. Deere he badwdh he FM sgal he requey he dulag susd s a 5kHz b 5 Hz 1kHz. a 51 1 MHz Narrwbad sgal B B MHz. 1 b Wdebad sgal B 1 khz B 1 11 khz B B 1 1 khz khz Usg de sga sdebads 179

37 Exaple 6.3 Carrer sgal 1s Message sgal s The essage s used requey dulae he arrer wh k =1π. Fd he express r he dulaed sgal ad deere hw ay hars shuld be seleed a 99% he dulaed sgal pwer. 1s k d s 1s 5s 1 J 5s P 5 k k J 5 J J 5 J k 1 By ral ad errr, k=6 18

38 5 Exaple 6.4 NBFM s 1 5 x d 8 3 WBFM s x d ax 1 x Frequey ulpler a be pleeed wh e sep! NBFM Mulpler 5 6 Mulpler 6 BPF 8 8 s 1 WBFM Frs ulpla Sed ulpla Carrer requey eeds be adjused. 181

39 Sgal--Nse Ra (SNR Frequey Dsrar ( + Fr-ed ler aplude ler derear (+( s( -( (+ ( Evelpe deer LPF s (+ ( w( Ipu BPF: (1 ( s[ k d ] ( w( s whe se wh PSD S (ω w η/ r - ω. S w ( / whe se Oupu sgalr BPF, whse badwdh equals he badwdh ( B (1 N hage ( w( (, whse PSD S. η /, r -B ω B (ω., herwse. S ( / : Bad-led se 18

40 SNR Frequey Dsrar ( d ( + Fr-ed ler plude ler Derear (+( s( -( (+ ( Evelpe deer LPF s (+ ( w( plude Ler reves ay udesred evelpe vara (.e.keepg sa ( ( Derear upu d d k s k d Evelpe deer' s upu(aer revghe er Sgaler s ( k ( wh pwer S s ( k ( 183

41 SNR Frequey Dsrar ( d Hw d The bad- led se ( s( ω he speral desy ( he s( ω se press? presee ( s( ω a udulaed ( s( ω ( s( ω s( s( ω r ( s[ ω ( ] aplude se phase se s arrer s( ω : Se r he 1 ( a s( ( dealg ( wh phase- se: ( requey dula, 1 s( a ( d ( 1 d s( d d s ( gre aplude- se r( a S 1 s( (? s ( s( bu us 184

42 SNR Frequey Dsrar ( d S s ( he LPF's S ( N Derear ( ( H s upu: S S s ( ( S ( S ( ( S LPF ( s LPF d See leure es se 3 h( 3 1 d d 1 H j S S ( S s - ( - / ( PSD s squarelaw depedee 185

43 ( pwer wh ( Sgaler k ( s S k ( s pwer wh er Nse N ( 3 3 ( 3 3 ( k k N S SNR Frequey Dsrar ( d 3 a k N S a a 3 ( ( s ( I 3 3 B rad Fr wdebad FM, SNR reases wh reasg badwdh hrugh k depedee. ω s prpral k 186

44 Cpars Bewee FM & DSB-LC Csder DSB-LC wh arrer sgal s ad essage sgal a s FM N S a 3 s ( essage sgal Fr he LC DSB d N ( LC DSB a N S ssue uy dula dex, =1,.e. =a. Ths s he s avrable ds r DSB-LC ers pwer requrees. FM N LC DSB FM N S a N S 3 LC DSB FM N S N S wll I LC DSB FM N S N S 3 187

45 Cpars Bewee FM & M The prevus argue hlds r he pars bewee FM ad DSB-LC. We have earler dsussed ha DSB-LC ad SSB has he deal upu SNR he presee whe se. S N FM 3 S N M B 1 FM prves he SNR a he s reased badwdh. The exhage badwdh r SNR FM a NOT be ued deely. The se pwer reases wh he reased badwdh ad resuls very pr syse perrae. Ths s kw as hreshld ee ad he abve aalyss des hld ayre. 188

46 s s s ω ω ω ( ω ( ( ω r ω ( ω ( ω ( ω ω ( s( s( s( s( The,. respe wh sa as ed apprxa are ad Suppse ] ( s[ ( s( s( s( s( prevusly deled as: was s( arrer d udulae a presee he se Bad- led se phase se aplude FM s Phase-Nse s Threshld Ee (Sreler Chaper 6.9 b b b s b s b b b B B B C ω C ω B ω B ω B ω s s a, s a, B where s( s( s( s( s( s( s(

47 wll ex us : a 1 FM s Phase-Nse s Threshld Ee ( d s requey dula ers prarly he requey/ phase, he aalyss Bs( b Bs( b 1 1 Bs( a b Bs( b a be uerall y evaluaed. ad s are parable, apprxas d b Ne /B a be regarded as a vlage sgal--se ra. B s 3dB Mea-square phase se reases rapdly whe he ra /B s saller ha abu 3dB. Ths s kw as hreshld ee. 19

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

Reference: Communication systems-simon Haykin (2001)

Reference: Communication systems-simon Haykin (2001) Reeree: ouao syses-so Hayk haper: I haper, we esgaed he way o odulag a susodal arrer wae usg AM ehque. There s aoher way o odulag a, susodal arrer wae, aely, agle odulao whh he agle o he arrer wae s ared

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

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

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

Corrupt the signal waveform Degrade the performance of communication systems

Corrupt the signal waveform Degrade the performance of communication systems Nie Nie : rd luui pwer i ye Crrup he igl wver Degrde he perre uii ye ure Nie: rd wderig ree eler i reir herl ie, rd lw hrge i eidur jui h ie, e. ddiive ie Zer-e Whie Gui-diribued Nie, pwer perl deiy /

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

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

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

Modeling Micromixing Effects in a CSTR

Modeling Micromixing Effects in a CSTR delig irixig Effes i a STR STR, f all well behaved rears, has he wides RTD i.e. This eas ha large differees i perfrae a exis bewee segregaed flw ad perais a axiu ixedess diis. The easies hig rea is he

More information

dm dt = 1 V The number of moles in any volume is M = CV, where C = concentration in M/L V = liters. dcv v

dm dt = 1 V The number of moles in any volume is M = CV, where C = concentration in M/L V = liters. dcv v Mg: Pcess Aalyss: Reac ae s defed as whee eac ae elcy lue M les ( ccea) e. dm he ube f les ay lue s M, whee ccea M/L les. he he eac ae beces f a hgeeus eac, ( ) d Usually s csa aqueus eeal pcesses eac,

More information

The automatic optimal control process for the operation changeover of heat exchangers

The automatic optimal control process for the operation changeover of heat exchangers Te aua pal rl pre fr e pera agever f ea exager K. L. Lu B. eeyer 4 & M. L very f e Feeral Are Fre Haburg Geray very f Saga fr See & Telgy P. R. Ca Tg J very P. R. Ca 4 GKSS Reear Cere Geray Abra Crl prble

More information

Analyzing Control Structures

Analyzing Control Structures Aalyzg Cotrol Strutures sequeg P, P : two fragmets of a algo. t, t : the tme they tae the tme requred to ompute P ;P s t t Θmaxt,t For loops for to m do P t: the tme requred to ompute P total tme requred

More information

On Metric Dimension of Two Constructed Families from Antiprism Graph

On Metric Dimension of Two Constructed Families from Antiprism Graph Mah S Le 2, No, -7 203) Mahemaal Sees Leers A Ieraoal Joural @ 203 NSP Naural Sees Publhg Cor O Mer Dmeso of Two Cosrued Famles from Aprm Graph M Al,2, G Al,2 ad M T Rahm 2 Cere for Mahemaal Imagg Tehques

More information

A Mean- maximum Deviation Portfolio Optimization Model

A Mean- maximum Deviation Portfolio Optimization Model A Mea- mamum Devato Portfolo Optmzato Model Wu Jwe Shool of Eoom ad Maagemet, South Cha Normal Uversty Guagzhou 56, Cha Tel: 86-8-99-6 E-mal: wujwe@9om Abstrat The essay maes a thorough ad systemat study

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

The Simple Linear Regression Model: Theory

The Simple Linear Regression Model: Theory Chapter 3 The mple Lear Regress Mdel: Ther 3. The mdel 3.. The data bservats respse varable eplaatr varable : : Plttg the data.. Fgure 3.: Dsplag the cable data csdered b Che at al (993). There are 79

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

Communication Systems Lecture 25. Dong In Kim School of Info/Comm Engineering Sungkyunkwan University

Communication Systems Lecture 25. Dong In Kim School of Info/Comm Engineering Sungkyunkwan University Commuiaio Sysems Leure 5 Dog I Kim Shool o Io/Comm Egieerig Sugkyukwa Uiversiy 1 Oulie Noise i Agle Modulaio Phase deviaio Large SNR Small SNR Oupu SNR PM FM Review o Agle Modulaio Geeral orm o agle modulaed

More information

T h e C S E T I P r o j e c t

T h e C S E T I P r o j e c t T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T

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

The Buck Resonant Converter

The Buck Resonant Converter EE646 Pwer Elecrnics Chaper 6 ecure Dr. Sam Abdel-Rahman The Buck Resnan Cnverer Replacg he swich by he resnan-ype swich, ba a quasi-resnan PWM buck cnverer can be shwn ha here are fur mdes f pera under

More information

Consumer Theory - Expenditure Function & Compensated Demand

Consumer Theory - Expenditure Function & Compensated Demand Csmer Thery - pedre F & Cmpesaed Demad pedre F -, M s U ad 0; pmzed vale f f he dal he ly mamza prblem.e., ryg mmze wha smer wld have sped a gve pres rder aheve a spef vale f ly sepedre Les - level rves

More information

Learning of Graphical Models Parameter Estimation and Structure Learning

Learning of Graphical Models Parameter Estimation and Structure Learning Learg of Grahal Models Parameer Esmao ad Sruure Learg e Fukumzu he Isue of Sasal Mahemas Comuaoal Mehodology Sasal Iferee II Work wh Grahal Models Deermg sruure Sruure gve by modelg d e.g. Mxure model

More information

The ZCS Boost Converter

The ZCS Boost Converter EEL646 Pwer Elernis II Chaper 6 Leure Dr. Sam Abdel-Rahman The ZCS Bs Cnverer The bs-quasi-resnan nverer wih an M-ype swih as shwn Fig. 6.(a, wih is equivalen irui shwn Fig. 6.(b. (a (b Fig 6. (a ZCS bs

More information

Dr. Kasra Etemadi February 20, 2007

Dr. Kasra Etemadi February 20, 2007 Dr. Kasra Eeadi February, 7 Seady-Sae Sinusidal Analysis Sinusidal Surces: Elecric pwer disribued fr residences and businesses Radi cunicain All signal f pracical ineres are cpsed f sinusidal cpnens Furier

More information

P a g e 3 6 of R e p o r t P B 4 / 0 9

P a g e 3 6 of R e p o r t P B 4 / 0 9 P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J

More information

PY3101 Optics. Learning objectives. Wave propagation in anisotropic media Poynting walk-off The index ellipsoid Birefringence. The Index Ellipsoid

PY3101 Optics. Learning objectives. Wave propagation in anisotropic media Poynting walk-off The index ellipsoid Birefringence. The Index Ellipsoid The Ide Ellpsd M.P. Vaugha Learg bjectves Wave prpagat astrpc meda Ptg walk-ff The de ellpsd Brefrgece 1 Wave prpagat astrpc meda The wave equat Relatve permttvt I E. Assumg free charges r currets E. Substtutg

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

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

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

Integrated Optical Waveguides

Integrated Optical Waveguides Su Opls Faha Raa Cll Uvs Chap 8 Ia Opal Wavus 7 Dl Slab Wavus 7 Iu: A va f ff a pal wavus a us f a u lh a hp Th s bas pal wavu s a slab wavus shw blw Th suu s uf h - Lh s u s h b al al fl a h -la fas Cla

More information

β A Constant-G m Biasing

β A Constant-G m Biasing p 2002 EE 532 Anal IC Des II Pae 73 Cnsan-G Bas ecall ha us a PTAT cuen efeence (see p f p. 66 he nes) bas a bpla anss pes cnsan anscnucance e epeaue (an als epenen f supply lae an pcess). Hw h we achee

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

Chapter Simpson s 1/3 Rule of Integration. ( x)

Chapter Simpson s 1/3 Rule of Integration. ( x) Cper 7. Smpso s / Rule o Iegro Aer redg s per, you sould e le o. derve e ormul or Smpso s / rule o egro,. use Smpso s / rule o solve egrls,. develop e ormul or mulple-segme Smpso s / rule o egro,. use

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

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace

More information

ERASMUS Application form for entry Please use BLOCK CAPITAL letters.

ERASMUS Application form for entry Please use BLOCK CAPITAL letters. ERSMUS ppl fr fr 2018-19 ery Plee e BLOCK CPITL leer. Plee re ll he fr he he re reflly efre pleg h fr. Frher fr he ppl pre vlle hp://f.le..k/rre-e/erve/er/fr-fr-g-e I el 1. He 2. H epre LSE 3. e f prgre

More information

The Periodic Table of Elements

The Periodic Table of Elements The Periodic Table of Elements 8 Uuo Uus Uuh (9) Uup (88) Uuq (89) Uut (8) Uub (8) Rg () 0 Ds (9) 09 Mt (8) 08 Hs (9) 0 h () 0 Sg () 0 Db () 0 Rf () 0 Lr () 88 Ra () 8 Fr () 8 Rn () 8 At (0) 8 Po (09)

More information

Uniform DFT Filter Banks 1/27

Uniform DFT Filter Banks 1/27 .. Ufor FT Flter Baks /27 Ufor FT Flter Baks We ll look at 5 versos of FT-based flter baks all but the last two have serous ltatos ad are t practcal. But they gve a ce trasto to the last two versos whch

More information

Laplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as.

Laplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as. Lplce Trfor The Lplce Trfor oe of he hecl ool for olvg ordry ler dfferel equo. - The hoogeeou equo d he prculr Iegrl re olved oe opero. - The Lplce rfor cover he ODE o lgerc eq. σ j ple do. I he pole o

More information

Unit-I (Feedback amplifiers) Features of feedback amplifiers. Presentation by: S.Karthie, Lecturer/ECE SSN College of Engineering

Unit-I (Feedback amplifiers) Features of feedback amplifiers. Presentation by: S.Karthie, Lecturer/ECE SSN College of Engineering Uni-I Feedback ampliiers Feaures eedback ampliiers Presenain by: S.Karhie, Lecurer/ECE SSN Cllege Engineering OBJECTIVES T make he sudens undersand he eec negaive eedback n he llwing ampliier characerisics:

More information

LM A F LABL Y H FRMA H P UBLCA B LV B ACCURA ALL R PC H WVR W C A AU M RP BLY FR AY C QUC RUL G F RM H U HR F H FRMA C A HR UBJC CHA G WHU C R V R W H

LM A F LABL Y H FRMA H P UBLCA B LV B ACCURA ALL R PC H WVR W C A AU M RP BLY FR AY C QUC RUL G F RM H U HR F H FRMA C A HR UBJC CHA G WHU C R V R W H H R & C C M RX700-2 Bx C LM A F LABL Y H FRMA H P UBLCA B LV B ACCURA ALL R PC H WVR W C A AU M RP BLY FR AY C QUC RUL G F RM H U HR F H FRMA C A HR UBJC CHA G WHU C R V R W H PUBLCA M AY B U CRP RA UCH

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

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of

More information

H STO RY OF TH E SA NT

H STO RY OF TH E SA NT O RY OF E N G L R R VER ritten for the entennial of th e Foundin g of t lair oun t y on ay 8 82 Y EEL N E JEN K RP O N! R ENJ F ] jun E 3 1 92! Ph in t ed b y h e t l a i r R ep u b l i c a n O 4 1922

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

Some Chain Type Estimators for Population Variance in Two Phase Sampling

Some Chain Type Estimators for Population Variance in Two Phase Sampling Ieraal Jural Rece ad Iva Treds Cmpu ad Cmmuca I: 3-869 me Cha Tpe Esmars fr Ppula Varace Tw Phase ampl A. Badpadha, P. Parchha ad Pambar Das. Deparme f Mahemacs, Asasl Eeer Cllee, Asasl 7335, Ida. Emal:

More information

02/05/09 Last 4 Digits of USC ID: Dr. Jessica Parr

02/05/09 Last 4 Digits of USC ID: Dr. Jessica Parr Chemistry 05 B First Letter of PLEASE PRINT YOUR NAME IN BLOCK LETTERS Exam last Name Name: 02/05/09 Last 4 Digits of USC ID: Dr. Jessica Parr Lab TA s Name: Question Points Score Grader 2 2 9 3 9 4 2

More information

Control Systems. Controllability and Observability (Chapter 6)

Control Systems. Controllability and Observability (Chapter 6) 6.53 trl Systems trllaility ad Oservaility (hapter 6) Geeral Framewrk i State-Spae pprah Give a LTI system: x x u; y x (*) The system might e ustale r des t meet the required perfrmae spe. Hw a we imprve

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

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

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e

More information

Section 3. Measurement Errors

Section 3. Measurement Errors eto 3 Measuremet Errors Egeerg Measuremets 3 Types of Errors Itrs errors develops durg the data aqusto proess. Extrs errors foud durg data trasfer ad storage ad are due to the orrupto of the sgal y ose.

More information

PERIODIC TABLE OF THE ELEMENTS

PERIODIC TABLE OF THE ELEMENTS Useful Constants and equations: K = o C + 273 Avogadro's number = 6.022 x 10 23 d = density = mass/volume R H = 2.178 x 10-18 J c = E = h = hc/ h = 6.626 x 10-34 J s c = 2.998 x 10 8 m/s E n = -R H Z 2

More information

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps

More information

Principles of Communications Lecture 12: Noise in Modulation Systems. Chih-Wei Liu 劉志尉 National Chiao Tung University

Principles of Communications Lecture 12: Noise in Modulation Systems. Chih-Wei Liu 劉志尉 National Chiao Tung University Priiples of Commuiatios Leture 1: Noise i Modulatio Systems Chih-Wei Liu 劉志尉 Natioal Chiao ug Uiversity wliu@twis.ee.tu.edu.tw Outlies Sigal-to-Noise Ratio Noise ad Phase Errors i Coheret Systems Noise

More information

Energy Storage Devices

Energy Storage Devices Energy Srage Deces Objece f ecure Descrbe The cnsrucn f an nducr Hw energy s sred n an nducr The elecrcal prperes f an nducr Relanshp beween lage, curren, and nducance; pwer; and energy Equalen nducance

More information

N! AND THE GAMMA FUNCTION

N! AND THE GAMMA FUNCTION N! AND THE GAMMA FUNCTION Cosider he produc of he firs posiive iegers- 3 4 5 6 (-) =! Oe calls his produc he facorial ad has ha produc of he firs five iegers equals 5!=0. Direcly relaed o he discree! fucio

More information

Basics of heteroskedasticity

Basics of heteroskedasticity Sect 8 Heterskedastcty ascs f heterskedastcty We have assumed up t w ( ur SR ad MR assumpts) that the varace f the errr term was cstat acrss bservats Ths s urealstc may r mst ecmetrc applcats, especally

More information

RAMIFICATIONS of POSITION SERVO LOOP COMPENSATION

RAMIFICATIONS of POSITION SERVO LOOP COMPENSATION RAMIFICATIONS f POSITION SERO LOOP COMPENSATION Gerge W. Yunk, P.E. Lfe Fellw IEEE Indural Cnrl Cnulg, Inc. Fnd du Lac, Wcn Fr many year dural pg er dre dd n ue er cmpena he frward p lp. Th wa referred

More information

Last 4 Digits of USC ID:

Last 4 Digits of USC ID: Chemistry 05 B Practice Exam Dr. Jessica Parr First Letter of last Name PLEASE PRINT YOUR NAME IN BLOCK LETTERS Name: Last 4 Digits of USC ID: Lab TA s Name: Question Points Score Grader 8 2 4 3 9 4 0

More information

ACTIVE FILTERS EXPERIMENT 2 (EXPERIMENTAL)

ACTIVE FILTERS EXPERIMENT 2 (EXPERIMENTAL) EXPERIMENT ATIVE FILTERS (EXPERIMENTAL) OBJETIVE T desig secd-rder lw pass ilters usig the Salle & Key (iite psitive- gai) ad iiite-gai apliier dels. Oe circuit will exhibit a Butterwrth respse ad the

More information

Lecture 02: Bounding tail distributions of a random variable

Lecture 02: Bounding tail distributions of a random variable CSCI-B609: A Theorst s Toolkt, Fall 206 Aug 25 Lecture 02: Boudg tal dstrbutos of a radom varable Lecturer: Yua Zhou Scrbe: Yua Xe & Yua Zhou Let us cosder the ubased co flps aga. I.e. let the outcome

More information

Lecture 2: The Simple Regression Model

Lecture 2: The Simple Regression Model Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.

More information

Chapter 5 Solutions. Problem 5.1

Chapter 5 Solutions. Problem 5.1 Chapter 5 Solutions Problem 5. Since X = X X 2 X 3 takes on the values,,, with equal probability, it follows that P Xi () = P Xi () = /2, i =, 2, 3. Furthermore, P X X 2 () = P X X 2 () = P X X 2 () =

More information

Atoms and the Periodic Table

Atoms and the Periodic Table Atoms and the Periodic Table Parts of the Atom Proton Found in the nucleus Number of protons defines the element Charge +1, mass 1 Parts of the Atom Neutron Found in the nucleus Stabilizes the nucleus

More information

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu

More information

Radiometric Dating (tap anywhere)

Radiometric Dating (tap anywhere) Radiometric Dating (tap anywhere) Protons Neutrons Electrons Elements on the periodic table are STABLE Elements can have radioactive versions of itself called ISOTOPES!! Page 1 in your ESRT has your list!

More information

Diodes Waveform shaping Circuits. Sedra & Smith (6 th Ed): Sec. 4.5 & 4.6 Sedra & Smith (5 th Ed): Sec. 3.5 & 3.6

Diodes Waveform shaping Circuits. Sedra & Smith (6 th Ed): Sec. 4.5 & 4.6 Sedra & Smith (5 th Ed): Sec. 3.5 & 3.6 des Waefrm shapng Cruts Sedra & Smth (6 th Ed): Se. 4.5 & 4.6 Sedra & Smth (5 th Ed): Se. 3.5 & 3.6 Tw-prt netwrks as buldng blks Reall: Transfer funtn f a tw-prt netwrk an be fund by slng ths rut ne.

More information

CHEM 10113, Quiz 5 October 26, 2011

CHEM 10113, Quiz 5 October 26, 2011 CHEM 10113, Quiz 5 October 26, 2011 Name (please print) All equations must be balanced and show phases for full credit. Significant figures count, show charges as appropriate, and please box your answers!

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

Advanced Placement. Chemistry. Integrated Rates

Advanced Placement. Chemistry. Integrated Rates Advanced Placement Chemistry Integrated Rates 204 47.90 9.22 78.49 (26) 50.94 92.9 80.95 (262) 52.00 93.94 83.85 (263) 54.938 (98) 86.2 (262) 55.85 0. 90.2 (265) 58.93 02.9 92.2 (266) H Li Na K Rb Cs Fr

More information

(please print) (1) (18) H IIA IIIA IVA VA VIA VIIA He (2) (13) (14) (15) (16) (17)

(please print) (1) (18) H IIA IIIA IVA VA VIA VIIA He (2) (13) (14) (15) (16) (17) CHEM 10113, Quiz 3 September 28, 2011 Name (please print) All equations must be balanced and show phases for full credit. Significant figures count, show charges as appropriate, and please box your answers!

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

Guide to the Extended Step-Pyramid Periodic Table

Guide to the Extended Step-Pyramid Periodic Table Guide to the Extended Step-Pyramid Periodic Table William B. Jensen Department of Chemistry University of Cincinnati Cincinnati, OH 452201-0172 The extended step-pyramid table recognizes that elements

More information

b) Choose one o f the graphs in part a that did b) is the atomic number o f

b) Choose one o f the graphs in part a that did b) is the atomic number o f REVIEW ad f^l^h^s. Ths table shows soe Northwest Coast artsts ad ther cultural hertage. Artst Hertage Bob Depse Tlgt Doroth Grat Hada Bll Hel Tssha Joh Joseph Squash Judth P. Morga Gtxsa Bll Red Hada a)

More information

single-layer transition metal dichalcogenides MC2

single-layer transition metal dichalcogenides MC2 single-layer transition metal dichalcogenides MC2 Period 1 1 H 18 He 2 Group 1 2 Li Be Group 13 14 15 16 17 18 B C N O F Ne 3 4 Na K Mg Ca Group 3 4 5 6 7 8 9 10 11 12 Sc Ti V Cr Mn Fe Co Ni Cu Zn Al Ga

More information

ELG3175 Introduction to Communication Systems. Angle Modulation Continued

ELG3175 Introduction to Communication Systems. Angle Modulation Continued ELG3175 Iroduio o Couiaio Sye gle Modulaio Coiued Le araériique de igaux odulé e agle PM Sigal M Sigal Iaaeou phae i Iaaeou requey Maxiu phae deviaio D ax Maxiu requey deviaio D ax Power p p p x où 0 d

More information

Diodes Waveform shaping Circuits

Diodes Waveform shaping Circuits des Waefrm shapng Cruts Leture ntes: page 2-2 t 2-31 Sedra & Smth (6 th Ed): Se. 4.5 & 4.6 Sedra & Smth (5 th Ed): Se. 3.5 & 3.6 F. Najmabad, ECE65, Wnter 212 Tw-prt netwrks as buldng blks Reall: Transfer

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

Analytical Solution Describing the Periodicity of the Elements in the Periodic System

Analytical Solution Describing the Periodicity of the Elements in the Periodic System Aalytical Solutio Describig the Periodicity of the Elemets i the Periodic System Jozsef Garai Departmet of Earth Scieces, Florida Iteratioal Uiversity, Uiversity Park PC, Miami, FL 199 E-mail: jozsef.garai@fiu.edu

More information

PEGN 513 Reservoir Simulation I Fall 2009

PEGN 513 Reservoir Simulation I Fall 2009 Hmer #3 l The smples rm r aerld a lear cre ally saraed h l ad a resdal aer sara h gravy r capllary eecs s represeed by he -dmesal Bcley-Levere maeral balace eqa () Eplc sl Csderg he space dscreza sh Fgre

More information

Chemistry 431 Practice Final Exam Fall Hours

Chemistry 431 Practice Final Exam Fall Hours Chemistry 431 Practice Final Exam Fall 2018 3 Hours R =8.3144 J mol 1 K 1 R=.0821 L atm mol 1 K 1 R=.08314 L bar mol 1 K 1 k=1.381 10 23 J molecule 1 K 1 h=6.626 10 34 Js N A = 6.022 10 23 molecules mol

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

Chemistry 2 Exam Roane State Academic Festival. Name (print neatly) School

Chemistry 2 Exam Roane State Academic Festival. Name (print neatly) School Name (print neatly) School There are fifteen question on this exam. Each question is weighted equally. n the answer sheet, write your name in the space provided and your answers in the blanks provided.

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

Some Different Perspectives on Linear Least Squares

Some Different Perspectives on Linear Least Squares Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,

More information

Thabet Abdeljawad 1. Çankaya Üniversitesi Fen-Edebiyat Fakültesi, Journal of Arts and Sciences Say : 9 / May s 2008

Thabet Abdeljawad 1. Çankaya Üniversitesi Fen-Edebiyat Fakültesi, Journal of Arts and Sciences Say : 9 / May s 2008 Çaaya Üiversiesi Fe-Edebiya Faülesi, Jural Ars ad Scieces Say : 9 / May s 008 A Ne e Cai Rule ime Scales abe Abdeljawad Absrac I is w, i eeral, a e cai rule eeral ime scale derivaives des beave well as

More information

P-Convexity Property in Musielak-Orlicz Function Space of Bohner Type

P-Convexity Property in Musielak-Orlicz Function Space of Bohner Type J N Sce & Mh Res Vol 3 No (7) -7 Alble ole h://orlwlsogocd/deh/sr P-Coey Proery Msel-Orlcz Fco Sce o Boher ye Yl Rodsr Mhecs Edco Deree Fcly o Ss d echology Uerss sl Neger Wlsogo Cerl Jdoes Absrcs Corresodg

More information

9/20/2017. Elements are Pure Substances that cannot be broken down into simpler substances by chemical change (contain Only One Type of Atom)

9/20/2017. Elements are Pure Substances that cannot be broken down into simpler substances by chemical change (contain Only One Type of Atom) CAPTER 6: TE PERIODIC TABLE Elements are Pure Substances that cannot be broken down into simpler substances by chemical change (contain Only One Type of Atom) The Periodic Table (Mendeleev) In 1872, Dmitri

More information

Communications II Lecture 4: Effects of Noise on AM. Professor Kin K. Leung EEE and Computing Departments Imperial College London Copyright reserved

Communications II Lecture 4: Effects of Noise on AM. Professor Kin K. Leung EEE and Computing Departments Imperial College London Copyright reserved Commuiaio II Leure 4: Effe of Noie o M Profeor Ki K. Leug EEE ad Compuig Deparme Imperial College Lodo Copyrigh reerved Noie i alog Commuiaio Syem How do variou aalog modulaio heme perform i he preee of

More information

THIS PAGE DECLASSIFIED IAW EO IRIS u blic Record. Key I fo mation. Ma n: AIR MATERIEL COMM ND. Adm ni trative Mar ings.

THIS PAGE DECLASSIFIED IAW EO IRIS u blic Record. Key I fo mation. Ma n: AIR MATERIEL COMM ND. Adm ni trative Mar ings. T H S PA G E D E CLA SSFED AW E O 2958 RS u blc Recod Key fo maon Ma n AR MATEREL COMM ND D cumen Type Call N u b e 03 V 7 Rcvd Rel 98 / 0 ndexe D 38 Eneed Dae RS l umbe 0 0 4 2 3 5 6 C D QC d Dac A cesson

More information

Decompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files)

Decompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files) . Iroduco Probblsc oe-moh forecs gudce s mde b 50 esemble members mproved b Model Oupu scs (MO). scl equo s mde b usg hdcs d d observo d. We selec some prmeers for modfg forecs o use mulple regresso formul.

More information

Faculty of Natural and Agricultural Sciences Chemistry Department. Semester Test 1 MEMO. Analytical Chemistry CMY 283

Faculty of Natural and Agricultural Sciences Chemistry Department. Semester Test 1 MEMO. Analytical Chemistry CMY 283 Faculty of Natural and Agricultural Sciences Chemistry Department Semester Test 1 MEMO Analytical Chemistry CMY 283 Date: 5 September 2016 Lecturers : Prof P Forbes, Dr Laurens, Mr SA Nsibande Time: 90

More information

Parts Manual. EPIC II Critical Care Bed REF 2031

Parts Manual. EPIC II Critical Care Bed REF 2031 EPIC II Critical Care Bed REF 2031 Parts Manual For parts or technical assistance call: USA: 1-800-327-0770 2013/05 B.0 2031-109-006 REV B www.stryker.com Table of Contents English Product Labels... 4

More information

EE 232 Lightwave Devices. Photodiodes

EE 232 Lightwave Devices. Photodiodes EE 3 Lgwav Dvcs Lcur 8: oocoucors a p-- ooos Rag: Cuag, Cap. 4 Isrucor: Mg C. Wu Uvrsy of Calfora, Brkly Elcrcal Egrg a Compur Sccs Dp. EE3 Lcur 8-8. Uvrsy of Calfora oocoucors ω + - x Ara w L Euval Crcu

More information

Chapter 15: Fourier Series

Chapter 15: Fourier Series Chapter 5: Fourier Series Ex. 5.3- Ex. 5.3- Ex. 5.- f(t) K is a Fourier Series. he coefficiets are a K; a b for. f(t) AcosZ t is a Fourier Series. a A ad all other coefficiets are zero. Set origi at t,

More information

Faculty of Natural and Agricultural Sciences Chemistry Department. Semester Test 1. Analytical Chemistry CMY 283. Time: 120 min Marks: 100 Pages: 6

Faculty of Natural and Agricultural Sciences Chemistry Department. Semester Test 1. Analytical Chemistry CMY 283. Time: 120 min Marks: 100 Pages: 6 Faculty of Natural and Agricultural Sciences Chemistry Department Semester Test 1 Analytical Chemistry CMY 283 Date: 5 September 2016 Lecturers : Prof P Forbes, Dr Laurens, Mr SA Nsibande Time: 120 min

More information

CHM 101 PRACTICE TEST 1 Page 1 of 4

CHM 101 PRACTICE TEST 1 Page 1 of 4 CHM 101 PRACTICE TEST 1 Page 1 of 4 Please show calculations (stuffed equations) on all mathematical problems!! On the actual test, "naked answers, with no work shown, will receive no credit even if correct.

More information