M-ary Detection Problem. Lecture Notes 2: Detection Theory. Example 1: Additve White Gaussian Noise

Size: px
Start display at page:

Download "M-ary Detection Problem. Lecture Notes 2: Detection Theory. Example 1: Additve White Gaussian Noise"

Transcription

1 Hi ue Hi ue -ay Deecio Pole Coide he ole of decidig which of hyohei i ue aed o oevig a ado vaiale (veco). he efoace cieia we coide i he aveage eo oailiy. ha i he oailiy of decidig ayhig ece hyohei H whe hyohei H i ue. Lecue oe : Deecio heoy he udelyig odel i ha hee i a codiioal oailiy deiy (a) fucio of he oevaio give each hyohei H. Goal: Oiu Deecio i AWG Oiu Deecio wih uiace (Uwaed) Paaee Pe P i decide H P decide H i i Ri i i Ri i πid πid II- II- he deciio ule ha iiize aveage co aig o R i if i a πi Le e a aiay deiy fucio ha i ozeo eveywhee i i ozeo he a equivale deciio ule i o aig o R i if i a hu fo hyohee he deciio ule ha iiize aveage eo oailiy i o chooe i o ha i i. Le whee πi π, Λ i Chooe i if Λ i i π. he he oial deciio ule i: π fo all i We will uually aue i. (If o we hould do ouce ecodig o educe he eoy (ae)). Fo hi cae he oial deciio ule i Chooe i if Λ i i π. ale : Addive Whie Gauia oie Coide hee igal i addiive whie Gauia oie. Fo addiive whie Gauia oie K δ. Leϕi e ay colee ohooal e o. Coide he cae of 3 igal. Fid he deciio ule o iiize aveage eo oailiy. Fi ad he oie uig ohooal e of fucio ad ado vaiale. i ϕ i whee i ad Va i ad i i a ideede ideically diiued (i.i.d.) equece of ado vaiale wih Gauia deiy fucio. Le ϕ ϕ oe ha he eegy of each of he hee igal i he ae, i.e. ϕ ϕ ϕ ϕ i d i 5. he II-3 II-4

2 we have a hee hyohei eig ole. H : H : H : i i i i i i he deciio ule o iiize he aveage eo oailiy i give a follow Decide H i if i a π Fie u oalize each ide y he deiy fucio fo he oie aloe. he oie deiy fucio fo vaiale i π he he oial deciio ule i equivale o i Decide H i if a π i ϕi ϕi ϕi A uual aue. he π π i 4 5 i i i i i i i ow ice he aove doe deed o we ca le adhe eul i he ae, i.e. Siilaly li i II-5 II-6 φ Deciio Regio φ II-7 II-8

3 ale : Oiu Deecio of -ay ohogoal igal fo iiu i eo oailiy I hi ecio we coide he ole of deecio wih uwaed aaee. o illuae coide he ole of iiizig he i eo oailiy i a -ay ohogoal igal e. Le e ohogoal igal. he eceive coi of a a of ached file (coelao) ha geeae a ufficie aiic. If igal i aied he δ δ η η φ φ δ η φ Le e he equece of i deeiig which of he igal i aied. Aue he i ae ideede ad equally liely. Coide he deecio of daa i. ha i, we ae ieeed i iiizig he oailiy of eo fo daa i. Le H e he eve ha ad H e he eve ha. Le. he he oial eceive u coae he wo aoeioi oailiie H π H H H π II-9 II- o calculae H we oceed a follow. H Siilaly π π πσ πσ πσ πσ π πσ πσ σ σ σ σ σ σ l l l l δl δl l l lδl δl σ σ σ l δ l σ H π πσ π oice ha ay of he faco i aio fo i he log-lielihood aio i log H H π π hi ca e aoiaed y log H H π π H H π π H log a σ l π ad σ σ H σ σ π ae he ae. hu he lielihood σ σ log a σ σ II- II-

4 ale 3: Oiu Deecio of iay igal i fadig chael Coide a ye wih L aea. Aue ha he eceive ow eacly he faded aliude o each aea. he deciio aiic ae he give y l z l η l l whee l ae Rayleigh, η l i Gauia ad eee he daa i aied which i eihe + o -. he ado vaiale l eee he fadig fo he aie o he l deiy l σ e σ L h aea ad ha We aue he fadig o each aea i ideede. he oial ehod o coie he deodulao ouu ca e deived a follow. Le z z L L e he codiioal deiy fucio of z z L give he aied i i + ad he fadig aliude i L. he ucodiioal deiy i z z L L z z L he codiioal deiy of z give ad, i Gauia wih ea l. he oi diiuio of z z L i he oduc of he agial deiy fucio. he oial coiig ule i deived fo he aio Λ z z z z z z z L z L z L z L z L z L L l z l 4 L l L l L L L L L L L l zl l z l l L L L ad vaiace II-3 II-4 he oiu deciio ule i o coae Λ wih o ae a deciio. hu he oial ule i L l l z l oe ha we do o eed o ow he deiy of he aliude fo hi deciio ule. hi deciio ule i called aiu aio coiig (RC). I he ecial cae whee hee i u oe aea he oiu eceive educe o z hu he oiu eceive fo u oe aea (ad BPSK) doe o eed he ifoaio aou he eceived aliude o ae a (had) deciio. Howeve, he efoace deed ciically o he diiuio of he fadig aliude. Fo he Rayleigh faded cae he eo oailiy ecoe P e Ē Ē z Lielihood Raio fo Real Sigal i AG Aue wo igal i Gauia oie. H : H : Goal: Fid deciio ule o iiize he aveage eo oailiy. Le have covaiace K eigefucio ϕi wih ad eigevalue. We aue ha i a zeo ea Gauia ado oce. he eige fucio ϕ i ae ohooal fucio ad eal ue uch ha (ee Aedi) K d ϕi λiϕi II-5 II-6

5 d d By Kahue-Loeve aio Kahue-Loeve aio i i ϕ i whee i i Gauia ea i vaiace. i i i π πλi i i i i whee i ae Gauia ado vaiale wih ea vaiace ad i i ideede i eal). Sice ϕ i ae a colee ohooal e ad we aue ha fiie eegy we have hu Defie H : Λ i iϕ i. Λ i i i i i i i li ϕi i Λ i Le Λ l l li πλi πλi i i i ϕ i i i i l i i i i iϕ i i i l i i i i i l i II-7 II-8 he hu Λ l li Λ l i i l ϕ l i l i i ϕ i d l iϕ i ϕ l d ϕ i ql ϕl d ql So K d i ϕ i K K d If he oie i whie, he he oie owe i each diecio i coa (ay λ) ad hu he oial eceive he ecoe o equivalely Λ l λ i λ ϕ i λ oe: i oluio of he iegal equaio Λ l λ II-9 II-

6 Lielihood Raio fo Cole Sigal Fo equal eegy igal hi aou o icig he igal wih he lage coelaio wih he eceived igal. he oial eceive i owhie Gauia oie ca e ileeed i a iila fahio a how elow. hu Λ l K K K K K K K K K K K I i clea he ha hi iu he oial file fo igal K whe eceived i addiive whie Gauia oie. hi aoach i called whieig ecaue K will e a whie Gauia oie oce. I hi ecio we edeive he lielihood aio fo cole igal eceived i cole oie. We aue ha he igal ae he lowa eeeaio of ada igal ad he oie i he lowa eeeaio of a aowad ado oce. Le H : H : whee ha covaiace K, wih eigefucio ϕ i, eigevalue. Uig Kahue-Loeve aio we have i πλ e l e π H i : l l l i l i l l λl λl i l il ϕ λl II- II- Le i ϕ i he l l l l l i l l Re Re i l l a l l ϕ l λl l l l i l λl a l l l ϕ l λ l ϕ ϕ d d i l Re l i l So Λ i li H Λ i i i oe: Sice we ae dealig wih oie ha i deived fo a aowad ado oce we ca o ue he eul deived fo eal ado ocee we u ue he lielihood aio fo cole ado oce give aove. Fo eal ado oce he lielihood aio i Λ i Fo addiive whie Gauia oie (eal) q i i ϕ λ i Re i i ϕ q II-3 II-4

7 So he lielihood aio (fo eal igal) ecoe Λ lli l H H l ale: ohogoal igal i addiive whie Gauia oie I hi ecio we coide he oiu eceive fo -ay ohogoal igal ad he aociaed eo oailiy. Aue he igal ae equieegy igal ad equioale. he deciio ule deived eviouly fo AWG i Decide H i if i i Aue π. he α. A equivale deciio ule he i H l H H l H he oiu deciio ule fo addiive whie Gauia oie i he o chooe i if i i ow ice he igal ae ohogoal ad equieegy we ca wie hi a he fi e aove i coa fo each a i he la e. hu fidig he iiu i equivale o fidig he aiu of hu he eceive hould coue he ie oduc ewee he diffee igal ad fid he lage uch coelaio. If he igal ae all of duaio, i.e. zeo ouide he ieval he hi i alo equivale o fileig he eceived igal wih a file wih ilue eoe, alig he ouu of he file a ie ad chooig he lage a how elow. II-5 II-6

The Non-Truncated Bulk Arrival Queue M x /M/1 with Reneging, Balking, State-Dependent and an Additional Server for Longer Queues

The Non-Truncated Bulk Arrival Queue M x /M/1 with Reneging, Balking, State-Dependent and an Additional Server for Longer Queues Alied Maheaical Sciece Vol. 8 o. 5 747-75 The No-Tucaed Bul Aival Queue M x /M/ wih Reei Bali Sae-Deede ad a Addiioal Seve fo Loe Queue A. A. EL Shebiy aculy of Sciece Meofia Uiveiy Ey elhebiy@yahoo.co

More information

Lecture 25 Outline: LTI Systems: Causality, Stability, Feedback

Lecture 25 Outline: LTI Systems: Causality, Stability, Feedback Lecure 5 Oulie: LTI Sye: Caualiy, Sabiliy, Feebac oucee: Reaig: 6: Lalace Trafor. 37-49.5, 53-63.5, 73; 7: 7: Feebac. -4.5, 8-7. W 8 oe, ue oay. Free -ay eeio W 9 will be oe oay, ue e Friay (o lae W) Fial

More information

Physics 232 Exam I Feb. 13, 2006

Physics 232 Exam I Feb. 13, 2006 Phsics I Fe. 6 oc. ec # Ne..5 g ss is ched o hoizol spig d is eecuig siple hoic oio. The oio hs peiod o.59 secods. iiil ie i is oud o e 8.66 c o he igh o he equiliiu posiio d oig o he le wih eloci o sec.

More information

BINOMIAL THEOREM OBJECTIVE PROBLEMS in the expansion of ( 3 +kx ) are equal. Then k =

BINOMIAL THEOREM OBJECTIVE PROBLEMS in the expansion of ( 3 +kx ) are equal. Then k = wwwskshieduciocom BINOMIAL HEOREM OBJEIVE PROBLEMS he coefficies of, i e esio of k e equl he k /7 If e coefficie of, d ems i e i AP, e e vlue of is he coefficies i e,, 7 ems i e esio of e i AP he 7 7 em

More information

Lecture 4. Electrons and Holes in Semiconductors

Lecture 4. Electrons and Holes in Semiconductors ecue 4 lec ad Hle i Semicduc I hi lecue yu will lea: eeai-ecmbiai i emicduc i me deail The baic e f euai gveig he behavi f elec ad hle i emicduc Shcley uai Quai-eualiy i cducive maeial C 35 Sig 2005 Faha

More information

Cameras and World Geometry

Cameras and World Geometry Caeas ad Wold Geoe How all is his woa? How high is he caea? Wha is he caea oaio w. wold? Which ball is close? Jaes Has Thigs o eebe Has Pihole caea odel ad caea (pojecio) ai Hoogeeous coodiaes allow pojecio

More information

Physics 232 Exam I Feb. 14, 2005

Physics 232 Exam I Feb. 14, 2005 Phsics I Fe., 5 oc. ec # Ne..5 g ss is ched o hoizol spig d is eecuig siple hoic oio wih gul eloci o dissec. gie is i ie i is oud o e 8 c o he igh o he equiliiu posiio d oig o he le wih eloci o.5 sec..

More information

Two-Pion Exchange Currents in Photodisintegration of the Deuteron

Two-Pion Exchange Currents in Photodisintegration of the Deuteron Two-Pion Exchange Cuens in Phoodisinegaion of he Deueon Dagaa Rozędzik and Jacek Goak Jagieonian Univesiy Kaków MENU00 3 May 00 Wiiasbug Conen Chia Effecive Fied Theoy ChEFT Eecoagneic cuen oeaos wihin

More information

Optical flow equation

Optical flow equation Opical Flow Sall oio: ( ad ae le ha piel) H() I(++) Be foce o poible ppoe we ake he Talo eie epaio of I: (Sei) Opical flow eqaio Cobiig hee wo eqaio I he lii a ad go o eo hi becoe eac (Sei) Opical flow

More information

Lecture 4. Electrons and Holes in Semiconductors

Lecture 4. Electrons and Holes in Semiconductors Lecue 4 lec ad Hle i Semicduc I hi lecue yu will lea: Geeai-ecmbiai i emicduc i me deail The baic e f euai gveig he behavi f elec ad hle i emicduc Shckley uai Quai-eualiy i cducive maeial C 35 Sig 2005

More information

Angle Modulation: NB (Sinusoid)

Angle Modulation: NB (Sinusoid) gle Moulaio: NB Siuoi I uay, i he eage igal i a pue iuoi, ha i, a a i o o PM o FM The, i whee a p a o PM o FM : pea equey eviaio Noe ha i ow a oulaio ie o agle oulaio a i he aiu value o phae eviaio o boh

More information

Comparing Different Estimators for Parameters of Kumaraswamy Distribution

Comparing Different Estimators for Parameters of Kumaraswamy Distribution Compaig Diffee Esimaos fo Paamees of Kumaaswamy Disibuio ا.م.د نذير عباس ابراهيم الشمري جامعة النهرين/بغداد-العراق أ.م.د نشات جاسم محمد الجامعة التقنية الوسطى/بغداد- العراق Absac: This pape deals wih compaig

More information

Lesson 5. Chapter 7. Wiener Filters. Bengt Mandersson. r x k We assume uncorrelated noise v(n). LTH. September 2010

Lesson 5. Chapter 7. Wiener Filters. Bengt Mandersson. r x k We assume uncorrelated noise v(n). LTH. September 2010 Optimal Sigal Poceig Leo 5 Chapte 7 Wiee Filte I thi chapte we will ue the model how below. The igal ito the eceive i ( ( iga. Nomally, thi igal i ditubed by additive white oie v(. The ifomatio i i (.

More information

Strong Result for Level Crossings of Random Polynomials. Dipty Rani Dhal, Dr. P. K. Mishra. Department of Mathematics, CET, BPUT, BBSR, ODISHA, INDIA

Strong Result for Level Crossings of Random Polynomials. Dipty Rani Dhal, Dr. P. K. Mishra. Department of Mathematics, CET, BPUT, BBSR, ODISHA, INDIA Iteatioal Joual of Reseach i Egieeig ad aageet Techology (IJRET) olue Issue July 5 Available at http://wwwijetco/ Stog Result fo Level Cossigs of Rado olyoials Dipty Rai Dhal D K isha Depatet of atheatics

More information

CODING & MODULATION Prof. Ing. Anton Čižmár, PhD. also from Digital Communications 4th Ed., J. G. Proakis, McGraw-Hill Int. Ed.

CODING & MODULATION Prof. Ing. Anton Čižmár, PhD. also from Digital Communications 4th Ed., J. G. Proakis, McGraw-Hill Int. Ed. CODING & MODULATION Pof. Ig. Ato Čižá, PhD. alo fo Digital Couicatio 4th Ed., J. G. Poai, McGaw-Hill It. Ed. 5 Optiu Receive fo the AWGN Chael I the peviou chapte, we decied vaiou type of odulatio ethod

More information

ABSOLUTE INDEXED SUMMABILITY FACTOR OF AN INFINITE SERIES USING QUASI-F-POWER INCREASING SEQUENCES

ABSOLUTE INDEXED SUMMABILITY FACTOR OF AN INFINITE SERIES USING QUASI-F-POWER INCREASING SEQUENCES Available olie a h://sciog Egieeig Maheaics Lees 2 (23) No 56-66 ISSN 249-9337 ABSLUE INDEED SUMMABILIY FACR F AN INFINIE SERIES USING QUASI-F-WER INCREASING SEQUENCES SKAIKRAY * RKJAI 2 UKMISRA 3 NCSAH

More information

Meromorphic Functions Sharing Three Values *

Meromorphic Functions Sharing Three Values * Alied Maheaic 11 718-74 doi:1436/a11695 Pulihed Olie Jue 11 (h://wwwscirporg/joural/a) Meroorhic Fucio Sharig Three Value * Arac Chagju Li Liei Wag School o Maheaical Sciece Ocea Uiveriy o Chia Qigdao

More information

Exercise: Show that. Remarks: (i) Fc(l) is not continuous at l=c. (ii) In general, we have. yn ¾¾. Solution:

Exercise: Show that. Remarks: (i) Fc(l) is not continuous at l=c. (ii) In general, we have. yn ¾¾. Solution: Exercie: Show ha Soluio: y ¾ y ¾¾ L c Þ y ¾¾ p c. ¾ L c Þ F y (l Fc (l I[c,(l "l¹c Þ P( y c

More information

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

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

More information

Strong Result for Level Crossings of Random Polynomials

Strong Result for Level Crossings of Random Polynomials IOSR Joual of haacy ad Biological Scieces (IOSR-JBS) e-issn:78-8, p-issn:19-7676 Volue 11, Issue Ve III (ay - Ju16), 1-18 wwwiosjoualsog Stog Result fo Level Cossigs of Rado olyoials 1 DKisha, AK asigh

More information

Physics 232 Exam II Mar. 28, 2005

Physics 232 Exam II Mar. 28, 2005 Phi 3 M. 8, 5 So. Se # Ne. A piee o gl, ide o eio.5, h hi oig o oil o i. The oil h ide o eio.4.d hike o. Fo wh welegh, i he iile egio, do ou ge o eleio? The ol phe dieee i gie δ Tol δ PhDieee δ i,il δ

More information

Outline. Review Homework Problem. Review Homework Problem II. Review Dimensionless Problem. Review Convection Problem

Outline. Review Homework Problem. Review Homework Problem II. Review Dimensionless Problem. Review Convection Problem adial diffsio eqaio Febay 4 9 Diffsio Eqaios i ylidical oodiaes ay aeo Mechaical Egieeig 5B Seia i Egieeig Aalysis Febay 4, 9 Olie eview las class Gadie ad covecio boday codiio Diffsio eqaio i adial coodiaes

More information

Introduction to Hypothesis Testing

Introduction to Hypothesis Testing Noe for Seember, Iroducio o Hyohei Teig Scieific Mehod. Sae a reearch hyohei or oe a queio.. Gaher daa or evidece (obervaioal or eerimeal) o awer he queio. 3. Summarize daa ad e he hyohei. 4. Draw a cocluio.

More information

New Results on Oscillation of even Order Neutral Differential Equations with Deviating Arguments

New Results on Oscillation of even Order Neutral Differential Equations with Deviating Arguments Advace i Pue Maheaic 9-53 doi: 36/ap3 Pubihed Oie May (hp://wwwscirpog/oua/ap) New Reu o Ociaio of eve Ode Neua Diffeeia Equaio wih Deviaig Ague Abac Liahog Li Fawei Meg Schoo of Maheaica Sye Sciece aiha

More information

Available online at J. Math. Comput. Sci. 2 (2012), No. 4, ISSN:

Available online at   J. Math. Comput. Sci. 2 (2012), No. 4, ISSN: Available olie a h://scik.og J. Mah. Comu. Sci. 2 (22), No. 4, 83-835 ISSN: 927-537 UNBIASED ESTIMATION IN BURR DISTRIBUTION YASHBIR SINGH * Deame of Saisics, School of Mahemaics, Saisics ad Comuaioal

More information

The Nehari Manifold for a Class of Elliptic Equations of P-laplacian Type. S. Khademloo and H. Mohammadnia. afrouzi

The Nehari Manifold for a Class of Elliptic Equations of P-laplacian Type. S. Khademloo and H. Mohammadnia. afrouzi Wold Alied cieces Joal (8): 898-95 IN 88-495 IDOI Pblicaios = h x g x x = x N i W whee is a eal aamee is a boded domai wih smooh boday i R N 3 ad< < INTRODUCTION Whee s ha is s = I his ae we ove he exisece

More information

X-Ray Notes, Part III

X-Ray Notes, Part III oll 6 X-y oe 3: Pe X-Ry oe, P III oe Deeo Coe oupu o x-y ye h look lke h: We efe ue of que lhly ffee efo h ue y ovk: Co: C ΔS S Sl o oe Ro: SR S Co o oe Ro: CR ΔS C SR Pevouly, we ee he SR fo ye hv pxel

More information

State-Space Model. In general, the dynamic equations of a lumped-parameter continuous system may be represented by

State-Space Model. In general, the dynamic equations of a lumped-parameter continuous system may be represented by Sae-Space Model I geeral, he dyaic equaio of a luped-paraeer coiuou ye ay be repreeed by x & f x, u, y g x, u, ae equaio oupu equaio where f ad g are oliear vecor-valued fucio Uig a liearized echique,

More information

AB for hydrogen in steel is What is the molar flux of the hydrogen through the steel? Δx Wall. s kmole

AB for hydrogen in steel is What is the molar flux of the hydrogen through the steel? Δx Wall. s kmole ignen 6 Soluion - Hydogen ga i oed a high peue in a ecangula conaine (--hick wall). Hydogen concenaion a he inide wall i kole / and eenially negligible on he ouide wall. The B fo hydogen in eel i.6 / ec

More information

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

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

More information

Then the number of elements of S of weight n is exactly the number of compositions of n into k parts.

Then the number of elements of S of weight n is exactly the number of compositions of n into k parts. Geneating Function In a geneal combinatoial poblem, we have a univee S of object, and we want to count the numbe of object with a cetain popety. Fo example, if S i the et of all gaph, we might want to

More information

Mathematical Models and the Soil Hydraulic Properties

Mathematical Models and the Soil Hydraulic Properties Bullei UASVM Hoiculue 66/9 Pi ISSN 843-554; Elecoic ISSN 843-5394 Maeaical Model ad e Soil Hydaulic Popeie Floica MATEI Macel IRJA Ioaa POP Vioel BUIU Maia MICULA Faculy of Hoiculue Uiveiy of Agiculual

More information

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing Commet o Dicuio Sheet 18 ad Workheet 18 ( 9.5-9.7) A Itroductio to Hypothei Tetig Dicuio Sheet 18 A Itroductio to Hypothei Tetig We have tudied cofidece iterval for a while ow. Thee are method that allow

More information

ME 321 Kinematics and Dynamics of Machines S. Lambert Winter 2002

ME 321 Kinematics and Dynamics of Machines S. Lambert Winter 2002 ME 31 Kiemaic ad Dyamic o Machie S. Lamber Wier 6.. Forced Vibraio wih Dampig Coider ow he cae o orced vibraio wih dampig. Recall ha he goverig diereial equaio i: m && c& k F() ad ha we will aume ha he

More information

t = s D Overview of Tests Two-Sample t-test: Independent Samples Independent Samples t-test Difference between Means in a Two-sample Experiment

t = s D Overview of Tests Two-Sample t-test: Independent Samples Independent Samples t-test Difference between Means in a Two-sample Experiment Overview of Te Two-Sample -Te: Idepede Sample Chaper 4 z-te Oe Sample -Te Relaed Sample -Te Idepede Sample -Te Compare oe ample o a populaio Compare wo ample Differece bewee Mea i a Two-ample Experime

More information

Supplementary Information

Supplementary Information Supplemeay Ifomaio No-ivasive, asie deemiaio of he coe empeaue of a hea-geeaig solid body Dea Ahoy, Daipaya Saka, Aku Jai * Mechaical ad Aeospace Egieeig Depame Uivesiy of Texas a Aligo, Aligo, TX, USA.

More information

CHATTERJEA CONTRACTION MAPPING THEOREM IN CONE HEPTAGONAL METRIC SPACE

CHATTERJEA CONTRACTION MAPPING THEOREM IN CONE HEPTAGONAL METRIC SPACE Fameal Joal of Mahemaic a Mahemaical Sciece Vol. 7 Ie 07 Page 5- Thi pape i aailable olie a hp://.fi.com/ Pblihe olie Jaa 0 07 CHATTERJEA CONTRACTION MAPPING THEOREM IN CONE HEPTAGONAL METRIC SPACE Caolo

More information

Review - Week 10. There are two types of errors one can make when performing significance tests:

Review - Week 10. There are two types of errors one can make when performing significance tests: Review - Week Read: Chaper -3 Review: There are wo ype of error oe ca make whe performig igificace e: Type I error The ull hypohei i rue, bu we miakely rejec i (Fale poiive) Type II error The ull hypohei

More information

Summary of Grade 1 and 2 Braille

Summary of Grade 1 and 2 Braille Sa of Gade 1 ad 2 Baie Wiia Pa Seebe 1998, Ai 1999 1 Baie Aabe Te fooig i i of TEX aco ad Baie bo coaied i baie Te e coad \baie{} cove eece of ag o Baie bo A ag ca be oe caace ic aea a i, o i caace ic

More information

CHAPTER 2 Quadratic diophantine equations with two unknowns

CHAPTER 2 Quadratic diophantine equations with two unknowns CHAPTER - QUADRATIC DIOPHANTINE EQUATIONS WITH TWO UNKNOWNS 3 CHAPTER Quadraic diophaie equaio wih wo ukow Thi chaper coi of hree ecio. I ecio (A), o rivial iegral oluio of he biar quadraic diophaie equaio

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Mau Lkelhood aon Beln Chen Depaen of Copue Scence & Infoaon ngneeng aonal Tawan oal Unvey Refeence:. he Alpaydn, Inoducon o Machne Leanng, Chape 4, MIT Pe, 4 Saple Sac and Populaon Paaee A Scheac Depcon

More information

Chapter 2: Descriptive Statistics

Chapter 2: Descriptive Statistics Chapte : Decptve Stattc Peequte: Chapte. Revew of Uvaate Stattc The cetal teecy of a oe o le yetc tbuto of a et of teval, o hghe, cale coe, ofte uaze by the athetc ea, whch efe a We ca ue the ea to ceate

More information

S.E. Sem. III [EXTC] Applied Mathematics - III

S.E. Sem. III [EXTC] Applied Mathematics - III S.E. Sem. III [EXTC] Applied Mhemic - III Time : 3 Hr.] Prelim Pper Soluio [Mrk : 8 Q.() Fid Lplce rform of e 3 co. [5] A.: L{co }, L{ co } d ( ) d () L{ co } y F.S.T. 3 ( 3) Le co 3 Q.() Prove h : f (

More information

ECE 350 Matlab-Based Project #3

ECE 350 Matlab-Based Project #3 ECE 350 Malab-Based Projec #3 Due Dae: Nov. 26, 2008 Read he aached Malab uorial ad read he help files abou fucio i, subs, sem, bar, sum, aa2. he wrie a sigle Malab M file o complee he followig ask for

More information

. Since P-U I= P+ (p-l)} Aap Since pn for every GF(pn) we have A pn A Therefore. As As. A,Ap. (Zp,+,.) ON FUNDAMENTAL SETS OVER A FINITE FIELD

. Since P-U I= P+ (p-l)} Aap Since pn for every GF(pn) we have A pn A Therefore. As As. A,Ap. (Zp,+,.) ON FUNDAMENTAL SETS OVER A FINITE FIELD Ie J Mh & Mh Sci Vol 8 No 2 (1985) 373-388 373 ON FUNDAMENTAL SETS OVER A FINITE FIELD YOUSEF ABBAS d JOSEH J LIANG Dee of Mheic Uiveiy of Souh Floid T, Floid 33620 USA (Received Mch 3, 1983) ABSTRACT

More information

Last time: Completed solution to the optimum linear filter in real-time operation

Last time: Completed solution to the optimum linear filter in real-time operation 6.3 tochatic Etimatio ad Cotrol, Fall 4 ecture at time: Completed olutio to the oimum liear filter i real-time operatio emi-free cofiguratio: t D( p) F( p) i( p) dte dp e π F( ) F( ) ( ) F( p) ( p) 4444443

More information

Revision of Lecture Eight

Revision of Lecture Eight Revision of Lectue Eight Baseband equivalent system and equiements of optimal tansmit and eceive filteing: (1) achieve zeo ISI, and () maximise the eceive SNR Thee detection schemes: Theshold detection

More information

ON THE EXTENSION OF WEAK ARMENDARIZ RINGS RELATIVE TO A MONOID

ON THE EXTENSION OF WEAK ARMENDARIZ RINGS RELATIVE TO A MONOID wwweo/voue/vo9iue/ijas_9 9f ON THE EXTENSION OF WEAK AENDAIZ INGS ELATIVE TO A ONOID Eye A & Ayou Eoy Dee of e Nowe No Uvey Lzou 77 C Dee of e Uvey of Kou Ou Su E-: eye76@o; you975@yooo ABSTACT Fo oo we

More information

Clicks, concurrency and Khoisan

Clicks, concurrency and Khoisan Poooy 31 (2014). Sueey ei Cic, cocuecy Koi Jui Bie Uiveiy o Eiu Sueey ei Aeix: Tciio Ti Aeix y ou e coex ei ioy o oio ue o e ou o!xóõ i e iy ouce. 1 Iii o-cic Te o-cic iii e oy ii o oe ue, o ee i ie couio

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

FI 2201 Electromagnetism

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

More information

Congruences for sequences similar to Euler numbers

Congruences for sequences similar to Euler numbers Coguece fo equece iila to Eule ube Zhi-Hog Su School of Matheatical Sciece, Huaiyi Noal Uiveity, Huaia, Jiagu 00, Peole Reublic of Chia Received July 00 Revied 5 Augut 0 Couicated by David Go Abtact a

More information

Lesson 5. Chapter 7. Wiener Filters. Bengt Mandersson. r k s r x LTH. September Prediction Error Filter PEF (second order) from chapter 4

Lesson 5. Chapter 7. Wiener Filters. Bengt Mandersson. r k s r x LTH. September Prediction Error Filter PEF (second order) from chapter 4 Optimal Sigal oceig Leo 5 Capte 7 Wiee Filte I ti capte we will ue te model ow below. Te igal ito te eceie i ( ( iga. Nomally, ti igal i ditubed by additie wite oie (. Te ifomatio i i (. Alo, we ofte ued

More information

EQUATION SHEET Principles of Finance Exam 1

EQUATION SHEET Principles of Finance Exam 1 EQUATION SHEET Piciple of iace Exa INANCIAL STATEMENT ANALYSIS Ne cah flow Ne icoe + Depeciaio ad aoizaio DuPo equaio: ROANe pofi agi Toal ae uove Ne icoe Sale Sale Toal ae DuPo equaio: ROE ROA Equiy uliplie

More information

rad / sec min rev 60sec. 2* rad / sec s

rad / sec min rev 60sec. 2* rad / sec s EE 559, Exa 2, Spig 26, D. McCalley, 75 iute allowed. Cloed Book, Cloed Note, Calculato Peitted, No Couicatio Device. (6 pt) Coide a.5 MW, 69 v, 5 Hz, 75 p DFG wid eegy yt. he paaete o the geeato ae give

More information

TRAVELING WAVES. Chapter Simple Wave Motion. Waves in which the disturbance is parallel to the direction of propagation are called the

TRAVELING WAVES. Chapter Simple Wave Motion. Waves in which the disturbance is parallel to the direction of propagation are called the Chapte 15 RAVELING WAVES 15.1 Simple Wave Motion Wave in which the ditubance i pependicula to the diection of popagation ae called the tanvee wave. Wave in which the ditubance i paallel to the diection

More information

10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP)

10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP) ENGG450 Probabiliy ad Saisics for Egieers Iroducio 3 Probabiliy 4 Probabiliy disribuios 5 Probabiliy Desiies Orgaizaio ad descripio of daa 6 Samplig disribuios 7 Ifereces cocerig a mea 8 Comparig wo reames

More information

Chapter 9. Key Ideas Hypothesis Test (Two Populations)

Chapter 9. Key Ideas Hypothesis Test (Two Populations) Chapter 9 Key Idea Hypothei Tet (Two Populatio) Sectio 9-: Overview I Chapter 8, dicuio cetered aroud hypothei tet for the proportio, mea, ad tadard deviatio/variace of a igle populatio. However, ofte

More information

Moment Generating Function

Moment Generating Function 1 Mome Geeraig Fucio m h mome m m m E[ ] x f ( x) dx m h ceral mome m m m E[( ) ] ( ) ( x ) f ( x) dx Mome Geeraig Fucio For a real, M () E[ e ] e k x k e p ( x ) discree x k e f ( x) dx coiuous Example

More information

Neutron Slowing Down Distances and Times in Hydrogenous Materials. Erin Boyd May 10, 2005

Neutron Slowing Down Distances and Times in Hydrogenous Materials. Erin Boyd May 10, 2005 Neu Slwig Dw Disaces ad Times i Hydgeus Maeials i Byd May 0 005 Oulie Backgud / Lecue Maeial Neu Slwig Dw quai Flux behavi i hydgeus medium Femi eame f calculaig slwig dw disaces ad imes. Bief deivai f

More information

Secure Chaotic Spread Spectrum Systems

Secure Chaotic Spread Spectrum Systems Seue Chaoi Sea Seum Sysems Ji Yu WSEAB ECE Deame Seves siue of Tehology Hoboke J 73 Oulie ouio Chaoi SS sigals Seuiy/ efomae ee eeives Biay oelaig eeio Mismah oblem aile-fileig base aoah Dual-aea aoah

More information

Lecture 15: Three-tank Mixing and Lead Poisoning

Lecture 15: Three-tank Mixing and Lead Poisoning Lecure 15: Three-ak Miig ad Lead Poisoig Eigevalues ad eigevecors will be used o fid he soluio of a sysem for ukow fucios ha saisfy differeial equaios The ukow fucios will be wrie as a 1 colum vecor [

More information

Construction of Malliavin differentiable strong solutions of SDEs under an integrability condition on the drift without the Yamada-Watanabe principle

Construction of Malliavin differentiable strong solutions of SDEs under an integrability condition on the drift without the Yamada-Watanabe principle Coucio of Malliavi diffeeiable og oluio of SD ude a iegabiliy codiio o he dif wihou he Yamada-Waaabe icile David R. Baño e-mail: davidu@mah.uio.o Side Duedahl e-mail: ided@mah.uio.o Thilo Meye-Badi e-mail:

More information

10-716: Advanced Machine Learning Spring Lecture 13: March 5

10-716: Advanced Machine Learning Spring Lecture 13: March 5 10-716: Advaced Machie Learig Sprig 019 Lecture 13: March 5 Lecturer: Pradeep Ravikumar Scribe: Charvi Ratogi, Hele Zhou, Nicholay opi Note: Lae template courtey of UC Berkeley EECS dept. Diclaimer: hee

More information

Low-complexity Algorithms for MIMO Multiplexing Systems

Low-complexity Algorithms for MIMO Multiplexing Systems Low-complexiy Algoihms fo MIMO Muliplexing Sysems Ouline Inoducion QRD-M M algoihm Algoihm I: : o educe he numbe of suviving pahs. Algoihm II: : o educe he numbe of candidaes fo each ansmied signal. :

More information

th m m m m central moment : E[( X X) ] ( X X) ( x X) f ( x)

th m m m m central moment : E[( X X) ] ( X X) ( x X) f ( x) 1 Trasform Techiques h m m m m mome : E[ ] x f ( x) dx h m m m m ceral mome : E[( ) ] ( ) ( x) f ( x) dx A coveie wa of fidig he momes of a radom variable is he mome geeraig fucio (MGF). Oher rasform echiques

More information

LIPSCHITZ ESTIMATES FOR MULTILINEAR COMMUTATOR OF MARCINKIEWICZ OPERATOR

LIPSCHITZ ESTIMATES FOR MULTILINEAR COMMUTATOR OF MARCINKIEWICZ OPERATOR Reseh d ouiios i heis d hei Siees Vo. Issue Pges -46 ISSN 9-699 Puished Oie o Deee 7 Joi Adei Pess h://oideiess.e IPSHITZ ESTIATES FOR UTIINEAR OUTATOR OF ARINKIEWIZ OPERATOR DAZHAO HEN Dee o Siee d Ioio

More information

Hadamard matrices from the Multiplication Table of the Finite Fields

Hadamard matrices from the Multiplication Table of the Finite Fields adamard marice from he Muliplicaio Table of he Fiie Field 신민호 송홍엽 노종선 * Iroducio adamard mari biary m-equece New Corucio Coe Theorem. Corucio wih caoical bai Theorem. Corucio wih ay bai Remark adamard

More information

Hidden Markov Model Parameters

Hidden Markov Model Parameters .PPT 5/04/00 Lecture 6 HMM Traiig Traiig Hidde Markov Model Iitial model etimate Viterbi traiig Baum-Welch traiig 8.7.PPT 5/04/00 8.8 Hidde Markov Model Parameter c c c 3 a a a 3 t t t 3 c a t A Hidde

More information

8.6 Order-Recursive LS s[n]

8.6 Order-Recursive LS s[n] 8.6 Order-Recurive LS [] Motivate ti idea wit Curve Fittig Give data: 0,,,..., - [0], [],..., [-] Wat to fit a polyomial to data.., but wic oe i te rigt model?! Cotat! Quadratic! Liear! Cubic, Etc. ry

More information

Lecture 5. Chapter 3. Electromagnetic Theory, Photons, and Light

Lecture 5. Chapter 3. Electromagnetic Theory, Photons, and Light Lecue 5 Chape 3 lecomagneic Theo, Phoons, and Ligh Gauss s Gauss s Faada s Ampèe- Mawell s + Loen foce: S C ds ds S C F dl dl q Mawell equaions d d qv A q A J ds ds In mae fields ae defined hough ineacion

More information

Valley Forge Middle School Fencing Project Facilities Committee Meeting February 2016

Valley Forge Middle School Fencing Project Facilities Committee Meeting February 2016 Valley Forge iddle chool Fencing roject Facilities ommittee eeting February 2016 ummer of 2014 Installation of Fencing at all five istrict lementary chools October 2014 Facilities ommittee and

More information

EEC 483 Computer Organization

EEC 483 Computer Organization EEC 8 Compuer Orgaizaio Chaper. Overview of Pipeliig Chau Yu Laudry Example Laudry Example A, Bria, Cahy, Dave each have oe load of clohe o wah, dry, ad fold Waher ake 0 miue A B C D Dryer ake 0 miue Folder

More information

Topics in MMSE Estimation for Sparse Approximation

Topics in MMSE Estimation for Sparse Approximation for Spare Approximatio * Michael Elad The Computer Sciece Departmet The Techio Irael Ititute of techology Haifa 3, Irael Workhop: Sparity ad Computatio Haudorff ceter of Mathematic Uiverity of Bo Jue 7-,

More information

Construction of Malliavin differentiable strong solutions of SDEs under an integrability condition on the drift without the Yamada-Watanabe principle

Construction of Malliavin differentiable strong solutions of SDEs under an integrability condition on the drift without the Yamada-Watanabe principle Coucio of Malliavi diffeeiable og oluio of SD ude a iegabiliy codiio o he dif wihou he Yamada-Waaabe icile David R. Baño e-mail: davidu@mah.uio.o Side Duedahl e-mail: ided@mah.uio.o Thilo Meye-Badi e-mail:

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

Two Implicit Runge-Kutta Methods for Stochastic Differential Equation

Two Implicit Runge-Kutta Methods for Stochastic Differential Equation Alied Mahemaic, 0, 3, 03-08 h://dx.doi.org/0.436/am.0.306 Publihed Olie Ocober 0 (h://www.scirp.org/oural/am) wo mlici Ruge-Kua Mehod for Sochaic Differeial quaio Fuwe Lu, Zhiyog Wag * Dearme of Mahemaic,

More information

S n. = n. Sum of first n terms of an A. P is

S n. = n. Sum of first n terms of an A. P is PROGREION I his secio we discuss hree impora series amely ) Arihmeic Progressio (A.P), ) Geomeric Progressio (G.P), ad 3) Harmoic Progressio (H.P) Which are very widely used i biological scieces ad humaiies.

More information

Pattern Distributions of Legendre Sequences

Pattern Distributions of Legendre Sequences IEEE TRASACTIOS O IFORMATIO THEORY, VOL., O., JULY 1998 1693 [9] J. E. Savage, Some imle elf-ychoizig digial daa camble, Bell Sy. Tech. J., vol., o.,. 9 87, Feb. 1967. [10] A. Paouli, Pobabiliy, Radom

More information

هقارنت طرائق تقذير هعلواث توزيع كاها ري املعلوتني

هقارنت طرائق تقذير هعلواث توزيع كاها ري املعلوتني هقارنت طرائق تقذير هعلواث توزيع كاها ري املعلوتني يف حالت البياناث املفقودة باستخذام احملاكاة د أ. الباحثة ظافر حسين رشيد جامعة بغداد- كمية االدارة واالقتصاد قسم االحصاء آوات سردار وادي املستخلص Maxiu

More information

Dividing Algebraic Fractions

Dividing Algebraic Fractions Leig Eheme Tem Model Awe: Mlilig d Diidig Algei Fio Mlilig d Diidig Algei Fio d gide ) Yo e he me mehod o mlil lgei io o wold o mlil meil io. To id he meo o he we o mlil he meo o he io i he eio. Simill

More information

Processamento Digital de Sinal

Processamento Digital de Sinal Deparaeo de Elecróica e Telecouicações da Uiversidade de Aveiro Processaeo Digial de ial Processos Esocásicos uar ado Processes aioar ad ergodic Correlaio auo ad cross Fucio Covariace Fucio Esiaes of he

More information

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad

More information

Degree of Approximation of Fourier Series

Degree of Approximation of Fourier Series Ieaioal Mahemaical Foum Vol. 9 4 o. 9 49-47 HIARI Ld www.m-hiai.com h://d.doi.og/.988/im.4.49 Degee o Aoimaio o Fouie Seies by N E Meas B. P. Padhy U.. Misa Maheda Misa 3 ad Saosh uma Naya 4 Deame o Mahemaics

More information

Spectrum of The Direct Sum of Operators. 1. Introduction

Spectrum of The Direct Sum of Operators. 1. Introduction Specu of The Diec Su of Opeaos by E.OTKUN ÇEVİK ad Z.I.ISMILOV Kaadeiz Techical Uivesiy, Faculy of Scieces, Depae of Maheaics 6080 Tabzo, TURKEY e-ail adess : zaeddi@yahoo.co bsac: I his wok, a coecio

More information

Addition & Subtraction of Polynomials

Addition & Subtraction of Polynomials Addiion & Sucion of Polynomil Addiion of Polynomil: Adding wo o moe olynomil i imly me of dding like em. The following ocedue hould e ued o dd olynomil 1. Remove enhee if hee e enhee. Add imil em. Wie

More information

Test 2 phy a) How is the velocity of a particle defined? b) What is an inertial reference frame? c) Describe friction.

Test 2 phy a) How is the velocity of a particle defined? b) What is an inertial reference frame? c) Describe friction. Tet phy 40 1. a) How i the velocity of a paticle defined? b) What i an inetial efeence fae? c) Decibe fiction. phyic dealt otly with falling bodie. d) Copae the acceleation of a paticle in efeence fae

More information

LECTURE 13 SIMULTANEOUS EQUATIONS

LECTURE 13 SIMULTANEOUS EQUATIONS NOVEMBER 5, 26 Demad-upply ytem LETURE 3 SIMULTNEOUS EQUTIONS I thi lecture, we dicu edogeeity problem that arie due to imultaeity, i.e. the left-had ide variable ad ome of the right-had ide variable are

More information

An interesting result about subset sums. Nitu Kitchloo. Lior Pachter. November 27, Abstract

An interesting result about subset sums. Nitu Kitchloo. Lior Pachter. November 27, Abstract A ieresig resul abou subse sums Niu Kichloo Lior Pacher November 27, 1993 Absrac We cosider he problem of deermiig he umber of subses B f1; 2; : : :; g such ha P b2b b k mod, where k is a residue class

More information

Stat 3411 Spring 2011 Assignment 6 Answers

Stat 3411 Spring 2011 Assignment 6 Answers Stat 3411 Sprig 2011 Aigmet 6 Awer (A) Awer are give i 10 3 cycle (a) 149.1 to 187.5 Sice 150 i i the 90% 2-ided cofidece iterval, we do ot reject H 0 : µ 150 v i favor of the 2-ided alterative H a : µ

More information

TIME RESPONSE Introduction

TIME RESPONSE Introduction TIME RESPONSE Iroducio Time repoe of a corol yem i a udy o how he oupu variable chage whe a ypical e ipu igal i give o he yem. The commoly e ipu igal are hoe of ep fucio, impule fucio, ramp fucio ad iuoidal

More information

Physics 201 Lecture 15

Physics 201 Lecture 15 Phscs 0 Lecue 5 l Goals Lecue 5 v Elo consevaon of oenu n D & D v Inouce oenu an Iulse Coens on oenu Consevaon l oe geneal han consevaon of echancal eneg l oenu Consevaon occus n sses wh no ne eenal foces

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 30 Sigal & Sytem Prof. Mark Fowler Note Set #8 C-T Sytem: Laplace Traform Solvig Differetial Equatio Readig Aigmet: Sectio 6.4 of Kame ad Heck / Coure Flow Diagram The arrow here how coceptual flow

More information

u t u 0 ( 7) Intuitively, the maximum principles can be explained by the following observation. Recall

u t u 0 ( 7) Intuitively, the maximum principles can be explained by the following observation. Recall Oct. Heat Equatio M aximum priciple I thi lecture we will dicu the maximum priciple ad uiquee of olutio for the heat equatio.. Maximum priciple. The heat equatio alo ejoy maximum priciple a the Laplace

More information

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

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

More information

ASTR 3740 Relativity & Cosmology Spring Answers to Problem Set 4.

ASTR 3740 Relativity & Cosmology Spring Answers to Problem Set 4. ASTR 3740 Relativity & Comology Sping 019. Anwe to Poblem Set 4. 1. Tajectoie of paticle in the Schwazchild geomety The equation of motion fo a maive paticle feely falling in the Schwazchild geomety ae

More information

Math 213b (Spring 2005) Yum-Tong Siu 1. Explicit Formula for Logarithmic Derivative of Riemann Zeta Function

Math 213b (Spring 2005) Yum-Tong Siu 1. Explicit Formula for Logarithmic Derivative of Riemann Zeta Function Math 3b Sprig 005 Yum-og Siu Expliit Formula for Logarithmi Derivative of Riema Zeta Futio he expliit formula for the logarithmi derivative of the Riema zeta futio i the appliatio to it of the Perro formula

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

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY VOL. 8, NO. 7, JULY 03 ISSN 89-6608 ARPN Jourl of Egieerig d Applied Sciece 006-03 Ai Reerch Publihig Nework (ARPN). All righ reerved. www.rpjourl.com SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO

More information