Multiphase Shift Keying (MPSK) Lecture 8. Constellation. Decision Regions. s i. 2 T cos 2π f c t iφ 0 t As iφ 1 t. t As. A c i.

Size: px
Start display at page:

Download "Multiphase Shift Keying (MPSK) Lecture 8. Constellation. Decision Regions. s i. 2 T cos 2π f c t iφ 0 t As iφ 1 t. t As. A c i."

Transcription

1 π fc uliphase Shif Keying (PSK) Goals Lecure 8 Be able o analyze PSK modualion s i Ac i Ac Pcos π f c cos π f c iφ As iφ π i p p As i sin π f c p Be able o analyze QA modualion Be able o quanify he radeoff beween daa rae and energy. for i φ sin, where φ π f p. cos p and A c i A s i Ecos πi Esin πi VIII- VIII- Consellaion φ Decision Regions φ s s s s 4 s φ φ s 5 s 7 s 6 VIII- VIII-4

2 ψ ψ φ Noes on PSK For his modulaion scheme we should use Gray coding o map bis ino signals. BPSK 4 QPSK his ype of modulaion has he properies ha all signals have he same power hus he use of nonlinear amplifiers (class C amplifiers) affecs each signal in he same manner. Furhermore if we are resriced o wo dimensions and every signal mus have he same power hen his signal se minimizes he error probabiliy of all such signal ses. φ QPSK and BPSK are special cases of his modulaion. VIII-5 VIII-6 Symbol Error Probabiliy for PSK he error probabiliy for PSK can be deermined as follows. Consider a signal ransmied where wih he consellaion shown above. he probabiliy of error given signal ransmied is he probabiliy ha he noise brings he received signal ouside he region R where he decision is ha signal was ransmied. his is given as π ψ π ψ π ψ π π R r R u π exp r σ exp R ψ σ exp σ dψ r σ drdψ u dudψ P e Rc Rc Rc π ψ f Z H z πσ exp E R r H f Z H σ πσ exp z z σ r π σ exp E H dz dz z z z r σ dz dz dz dz drdψ where R c is he complemen of R and R is he disance from he signal poin s o he line wih slope π R. sin π E sin E sin sin ψ ψ π π π π VIII-7 VIII-8

3 π π hus R σ E sin N sin ψ π In he derivaion of he error probabiliy he las line follows from a change of variables where he poin z axis wih reference poin s and a magniude r from he poin s. he symbol error probabiliy is hus φ z is mapped o an angle φ from he horizonal φ P e s π ψ π exp E sin N sin ψ π dψ E b E log VIII-9 VIII- φ Consellaion (E b N db).5.5 Quadraure Phase.5.5 π ψ R E φ VIII- VIII-

4 Consellaion (E b N db) Consellaion (E b N 4dB) Quadraure Phase.5.5 Quadraure Phase VIII- VIII-4 Consellaion (E b N 6dB) Consellaion (E b N 8dB) Quadraure Phase.5.5 Quadraure Phase VIII-5 VIII-6

5 Consellaion (E b N db) Consellaion (E b N db) Quadraure Phase.5.5 Quadraure Phase VIII-7 VIII-8 Consellaion (E b N 4dB) Consellaion (E b N 6dB) Quadraure Phase.5.5 Quadraure Phase VIII-9 VIII-

6 Sym bol Error Probabiliy Consellaion (E b N db) Consellaion (E b N db) Quadraure Phase.5.5 Quadraure Phase VIII- VIII- Consellaion (E b N 5dB) Symbol Error Probabiliy.5 Perform ance of PSK odulaion Quadraure Phase = = 4 = 8 = 6 = E b /N (db) Figure 9: Symbol Error Probabiliy for PSK Signaling VIII- VIII-4

7 Bi Error Probabiliy A Bi Error Probabiliy -ary Pulse Ampliude odulaion (PA) Bi Error Rae for PSK s i Ai φ - where A i i Ai - - s s E i Ai φ s s -4 =,4 = 8 = 6 = A A A φ Eb/N (db) Figure 4: Bi Error Probabiliy for PSK Signaling (wih Gray coding) E E i i A i i A VIII-5 VIII-6 Error Probabiliy he error probabiliy (for 4-ary) is P e P e Pe Pe Q Q A N A N he average error probabiliy (for 4-ary PA) is In general he error probabiliy is P e s Q Q 6E 6E b log N N P e 4 P e 4 P e 4 P e 4 P e Q A N Q Ē 5N VIII-7 VIII-8

8 Symbol Error Probabiliy P e,s 4 5 = =4 =8 =6 = E b /N (db) P e,b =4 =8 = E b /N (db) Figure 4: Symbol Error Probabiliy for PA Signaling Figure 4: Bi Error Probabiliy for PA Signaling VIII-9 VIII- Quadraure Ampliude odulaion Consellaion φ For i s i s i Ai cosπ fc Bi sinπ fc Ai φ Bi φ φ VIII- VIII-

9 Decision Regions Error Probabiliies for QA φ Since his is wo PA sysems in quadraure. P e wih signals P e for PA φ he applicaion of QA is o bandwidh consrained channels. We can consider as a baseline a wo dimensional modulaion sysem ransmiing a 4 symbols per second. If each symbol represens 4 bis of informaion hen he daa rae is 96 bis per second. So we would like o have more signals per dimension in order o increase he daa rae. However, we mus ry o keep he signals as far apar from each oher as possible (in oder o keep he error rae low). So an increase of he size of he signal consellaion for fixed minimum disance would likely increase he oal signal energy ransmied. VIII- VIII-4 -ary signal ses Consider a -ary QA signal se shown below. he average energy is. he minimum disance is and he rae is 5 bis/dimension ary signal ses Consider a 64-ary QA signal se shown below. he average energy is VIII-5 VIII-6

10 odified QA (used in Paradyne 4.4kbi modem). his has average energy of VIII-7 he following hexagonal consellaion has energy 5.5 bu each inerior poin now has 6 neighbors compared o he four neighbors for he recangular srucures VIII-8 Figure 4: Consellaion used in v4 8.8kbps modems (96 poins) VIII-9 Capaciy E b N (db) R W BPSK QPSK 8PSK 6PSK PSK VIII-4

11 Capaciy Eb N (db) BPSK R W 8PSK QPSK PSK 6PSK VIII-4 VIII-4

Solutions to the Exam Digital Communications I given on the 11th of June = 111 and g 2. c 2

Solutions to the Exam Digital Communications I given on the 11th of June = 111 and g 2. c 2 Soluions o he Exam Digial Communicaions I given on he 11h of June 2007 Quesion 1 (14p) a) (2p) If X and Y are independen Gaussian variables, hen E [ XY ]=0 always. (Answer wih RUE or FALSE) ANSWER: False.

More information

Lecture 4. Goals: Be able to determine bandwidth of digital signals. Be able to convert a signal from baseband to passband and back IV-1

Lecture 4. Goals: Be able to determine bandwidth of digital signals. Be able to convert a signal from baseband to passband and back IV-1 Lecure 4 Goals: Be able o deermine bandwidh o digial signals Be able o conver a signal rom baseband o passband and back IV-1 Bandwidh o Digial Daa Signals A digial daa signal is modeled as a random process

More information

Lecture 8. Digital Communications Part III. Digital Demodulation

Lecture 8. Digital Communications Part III. Digital Demodulation Lecure 8. Digial Communicaions Par III. Digial Demodulaion Binary Deecion M-ary Deecion Lin Dai (Ciy Universiy of Hong Kong) EE38 Principles of Communicaions Lecure 8 Analog Signal Source SOURCE A-D Conversion

More information

EE456 Digital Communications

EE456 Digital Communications EE456 Digial Communicaions Professor Ha Nguyen Sepember 6 EE456 Digial Communicaions Inroducion o Basic Digial Passband Modulaion Baseband ransmission is conduced a low frequencies. Passband ransmission

More information

UNIVERSITY OF CALIFORNIA College of Engineering Department of Electrical Engineering and Computer Sciences EECS 121 FINAL EXAM

UNIVERSITY OF CALIFORNIA College of Engineering Department of Electrical Engineering and Computer Sciences EECS 121 FINAL EXAM Name: UNIVERSIY OF CALIFORNIA College of Engineering Deparmen of Elecrical Engineering and Compuer Sciences Professor David se EECS 121 FINAL EXAM 21 May 1997, 5:00-8:00 p.m. Please wrie answers on blank

More information

EE3723 : Digital Communications

EE3723 : Digital Communications EE373 : Digial Communicaions Week 6-7: Deecion Error Probabiliy Signal Space Orhogonal Signal Space MAJU-Digial Comm.-Week-6-7 Deecion Mached filer reduces he received signal o a single variable zt, afer

More information

Block Diagram of a DCS in 411

Block Diagram of a DCS in 411 Informaion source Forma A/D From oher sources Pulse modu. Muliplex Bandpass modu. X M h: channel impulse response m i g i s i Digial inpu Digial oupu iming and synchronizaion Digial baseband/ bandpass

More information

Coherent PSK. The functional model of passband data transmission system is. Signal transmission encoder. x Signal. decoder.

Coherent PSK. The functional model of passband data transmission system is. Signal transmission encoder. x Signal. decoder. Cheren PSK he funcinal mdel f passand daa ransmissin sysem is m i Signal ransmissin encder si s i Signal Mdular Channel Deecr ransmissin decder mˆ Carrier signal m i is a sequence f syml emied frm a message

More information

Section 7.4 Modeling Changing Amplitude and Midline

Section 7.4 Modeling Changing Amplitude and Midline 488 Chaper 7 Secion 7.4 Modeling Changing Ampliude and Midline While sinusoidal funcions can model a variey of behaviors, i is ofen necessary o combine sinusoidal funcions wih linear and exponenial curves

More information

EE 315 Notes. Gürdal Arslan CLASS 1. (Sections ) What is a signal?

EE 315 Notes. Gürdal Arslan CLASS 1. (Sections ) What is a signal? EE 35 Noes Gürdal Arslan CLASS (Secions.-.2) Wha is a signal? In his class, a signal is some funcion of ime and i represens how some physical quaniy changes over some window of ime. Examples: velociy of

More information

6.003 Homework #9 Solutions

6.003 Homework #9 Solutions 6.00 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () 0 0 a 0 5 a k sin πk 5 sin πk 5 πk for k 0 a k 0 πk j

More information

6.003 Homework #9 Solutions

6.003 Homework #9 Solutions 6.003 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () 0 3 0 a 0 5 a k a k 0 πk j3 e 0 e j πk 0 jπk πk e 0

More information

KEY. Math 334 Midterm III Winter 2008 section 002 Instructor: Scott Glasgow

KEY. Math 334 Midterm III Winter 2008 section 002 Instructor: Scott Glasgow KEY Mah 334 Miderm III Winer 008 secion 00 Insrucor: Sco Glasgow Please do NOT wrie on his exam. No credi will be given for such work. Raher wrie in a blue book, or on your own paper, preferably engineering

More information

Lecture 4 Kinetics of a particle Part 3: Impulse and Momentum

Lecture 4 Kinetics of a particle Part 3: Impulse and Momentum MEE Engineering Mechanics II Lecure 4 Lecure 4 Kineics of a paricle Par 3: Impulse and Momenum Linear impulse and momenum Saring from he equaion of moion for a paricle of mass m which is subjeced o an

More information

( ) ( ) if t = t. It must satisfy the identity. So, bulkiness of the unit impulse (hyper)function is equal to 1. The defining characteristic is

( ) ( ) if t = t. It must satisfy the identity. So, bulkiness of the unit impulse (hyper)function is equal to 1. The defining characteristic is UNIT IMPULSE RESPONSE, UNIT STEP RESPONSE, STABILITY. Uni impulse funcion (Dirac dela funcion, dela funcion) rigorously defined is no sricly a funcion, bu disribuion (or measure), precise reamen requires

More information

AP Calculus BC Chapter 10 Part 1 AP Exam Problems

AP Calculus BC Chapter 10 Part 1 AP Exam Problems AP Calculus BC Chaper Par AP Eam Problems All problems are NO CALCULATOR unless oherwise indicaed Parameric Curves and Derivaives In he y plane, he graph of he parameric equaions = 5 + and y= for, is a

More information

CHAPTER 2 Signals And Spectra

CHAPTER 2 Signals And Spectra CHAPER Signals And Specra Properies of Signals and Noise In communicaion sysems he received waveform is usually caegorized ino he desired par conaining he informaion, and he undesired par. he desired par

More information

6.003: Signals and Systems Lecture 20 November 17, 2011

6.003: Signals and Systems Lecture 20 November 17, 2011 6.3: Signals and Sysems Lecure November 7, 6.3: Signals and Sysems Applicaions of Fourier ransforms Filering Noion of a filer. LI sysems canno creae new frequencies. can only scale magniudes and shif phases

More information

A First Course in Digital Communications

A First Course in Digital Communications A Firs Course in Digial Communicaions Ha H. Nguyen and E. Shwedyk February 9 A Firs Course in Digial Communicaions /58 Block Diagram of Binary Communicaion Sysems m { b k } bk = s b = s k m ˆ { bˆ } k

More information

DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING EXAMINATIONS 2008

DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING EXAMINATIONS 2008 [E5] IMPERIAL COLLEGE LONDON DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING EXAMINATIONS 008 EEE/ISE PART II MEng BEng and ACGI SIGNALS AND LINEAR SYSTEMS Time allowed: :00 hours There are FOUR quesions

More information

Chapter 3: Signal Transmission and Filtering. A. Bruce Carlson Paul B. Crilly 2010 The McGraw-Hill Companies

Chapter 3: Signal Transmission and Filtering. A. Bruce Carlson Paul B. Crilly 2010 The McGraw-Hill Companies Communicaion Sysems, 5e Chaper 3: Signal Transmission and Filering A. Bruce Carlson Paul B. Crilly 00 The McGraw-Hill Companies Chaper 3: Signal Transmission and Filering Response of LTI sysems Signal

More information

Phys 221 Fall Chapter 2. Motion in One Dimension. 2014, 2005 A. Dzyubenko Brooks/Cole

Phys 221 Fall Chapter 2. Motion in One Dimension. 2014, 2005 A. Dzyubenko Brooks/Cole Phys 221 Fall 2014 Chaper 2 Moion in One Dimension 2014, 2005 A. Dzyubenko 2004 Brooks/Cole 1 Kinemaics Kinemaics, a par of classical mechanics: Describes moion in erms of space and ime Ignores he agen

More information

Overview. COMP14112: Artificial Intelligence Fundamentals. Lecture 0 Very Brief Overview. Structure of this course

Overview. COMP14112: Artificial Intelligence Fundamentals. Lecture 0 Very Brief Overview. Structure of this course OMP: Arificial Inelligence Fundamenals Lecure 0 Very Brief Overview Lecurer: Email: Xiao-Jun Zeng x.zeng@mancheser.ac.uk Overview This course will focus mainly on probabilisic mehods in AI We shall presen

More information

Transform Techniques. Moment Generating Function

Transform Techniques. Moment Generating Function Transform Techniques A convenien way of finding he momens of a random variable is he momen generaing funcion (MGF). Oher ransform echniques are characerisic funcion, z-ransform, and Laplace ransform. Momen

More information

Answers to 1 Homework

Answers to 1 Homework Answers o Homework. x + and y x 5 y To eliminae he parameer, solve for x. Subsiue ino y s equaion o ge y x.. x and y, x y x To eliminae he parameer, solve for. Subsiue ino y s equaion o ge x y, x. (Noe:

More information

28. Narrowband Noise Representation

28. Narrowband Noise Representation Narrowband Noise Represenaion on Mac 8. Narrowband Noise Represenaion In mos communicaion sysems, we are ofen dealing wih band-pass filering of signals. Wideband noise will be shaped ino bandlimied noise.

More information

control properties under both Gaussian and burst noise conditions. In the ~isappointing in comparison with convolutional code systems designed

control properties under both Gaussian and burst noise conditions. In the ~isappointing in comparison with convolutional code systems designed 535 SOFT-DECSON THRESHOLD DECODNG OF CONVOLUTONAL CODES R.M.F. Goodman*, B.Sc., Ph.D. W.H. Ng*, M.S.E.E. Sunnnary Exising majoriy-decision hreshold decoders have so far been limied o his paper a new mehod

More information

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon 3..3 INRODUCION O DYNAMIC OPIMIZAION: DISCREE IME PROBLEMS A. he Hamilonian and Firs-Order Condiions in a Finie ime Horizon Define a new funcion, he Hamilonian funcion, H. H he change in he oal value of

More information

Math 111 Midterm I, Lecture A, version 1 -- Solutions January 30 th, 2007

Math 111 Midterm I, Lecture A, version 1 -- Solutions January 30 th, 2007 NAME: Suden ID #: QUIZ SECTION: Mah 111 Miderm I, Lecure A, version 1 -- Soluions January 30 h, 2007 Problem 1 4 Problem 2 6 Problem 3 20 Problem 4 20 Toal: 50 You are allowed o use a calculaor, a ruler,

More information

BEng (Hons) Telecommunications. Examinations for / Semester 2

BEng (Hons) Telecommunications. Examinations for / Semester 2 BEng (Hons) Telecommunicaions Cohor: BTEL/14/FT Examinaions for 2015-2016 / Semeser 2 MODULE: ELECTROMAGNETIC THEORY MODULE CODE: ASE2103 Duraion: 2 ½ Hours Insrucions o Candidaes: 1. Answer ALL 4 (FOUR)

More information

Signals and Systems Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin

Signals and Systems Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE 345S Real-Time Digial Signal Processing Lab Spring 26 Signals and Sysems Prof. Brian L. Evans Dep. of Elecrical and Compuer Engineering The Universiy of Texas a Ausin Review Signals As Funcions of Time

More information

KINEMATICS IN ONE DIMENSION

KINEMATICS IN ONE DIMENSION KINEMATICS IN ONE DIMENSION PREVIEW Kinemaics is he sudy of how hings move how far (disance and displacemen), how fas (speed and velociy), and how fas ha how fas changes (acceleraion). We say ha an objec

More information

Ground Rules. PC1221 Fundamentals of Physics I. Kinematics. Position. Lectures 3 and 4 Motion in One Dimension. A/Prof Tay Seng Chuan

Ground Rules. PC1221 Fundamentals of Physics I. Kinematics. Position. Lectures 3 and 4 Motion in One Dimension. A/Prof Tay Seng Chuan Ground Rules PC11 Fundamenals of Physics I Lecures 3 and 4 Moion in One Dimension A/Prof Tay Seng Chuan 1 Swich off your handphone and pager Swich off your lapop compuer and keep i No alking while lecure

More information

Traveling Waves. Chapter Introduction

Traveling Waves. Chapter Introduction Chaper 4 Traveling Waves 4.1 Inroducion To dae, we have considered oscillaions, i.e., periodic, ofen harmonic, variaions of a physical characerisic of a sysem. The sysem a one ime is indisinguishable from

More information

Lecture 4 Notes (Little s Theorem)

Lecture 4 Notes (Little s Theorem) Lecure 4 Noes (Lile s Theorem) This lecure concerns one of he mos imporan (and simples) heorems in Queuing Theory, Lile s Theorem. More informaion can be found in he course book, Bersekas & Gallagher,

More information

For example, the comb filter generated from. ( ) has a transfer function. e ) has L notches at ω = (2k+1)π/L and L peaks at ω = 2π k/l,

For example, the comb filter generated from. ( ) has a transfer function. e ) has L notches at ω = (2k+1)π/L and L peaks at ω = 2π k/l, Comb Filers The simple filers discussed so far are characeried eiher by a single passband and/or a single sopband There are applicaions where filers wih muliple passbands and sopbands are required The

More information

Experimental Buck Converter

Experimental Buck Converter Experimenal Buck Converer Inpu Filer Cap MOSFET Schoky Diode Inducor Conroller Block Proecion Conroller ASIC Experimenal Synchronous Buck Converer SoC Buck Converer Basic Sysem S 1 u D 1 r r C C R R X

More information

6.003: Signals and Systems

6.003: Signals and Systems 6.3: Signals and Sysems Lecure 7 April 8, 6.3: Signals and Sysems C Fourier ransform C Fourier ransform Represening signals by heir frequency conen. X(j)= x()e j d ( analysis equaion) x()= π X(j)e j d

More information

EE 435. Lecture 31. Absolute and Relative Accuracy DAC Design. The String DAC

EE 435. Lecture 31. Absolute and Relative Accuracy DAC Design. The String DAC EE 435 Lecure 3 Absolue and Relaive Accuracy DAC Design The Sring DAC . Review from las lecure. DFT Simulaion from Malab Quanizaion Noise DACs and ADCs generally quanize boh ampliude and ime If convering

More information

Proposal of atomic clock in motion: Time in moving clock

Proposal of atomic clock in motion: Time in moving clock Proposal of aomic clock in moion: Time in moving clock Masanori Sao Honda Elecronics Co., d., 0 Oyamazuka, Oiwa-cho, Toyohashi, ichi 441-3193, Japan E-mail: msao@honda-el.co.jp bsrac: The ime in an aomic

More information

1. VELOCITY AND ACCELERATION

1. VELOCITY AND ACCELERATION 1. VELOCITY AND ACCELERATION 1.1 Kinemaics Equaions s = u + 1 a and s = v 1 a s = 1 (u + v) v = u + as 1. Displacemen-Time Graph Gradien = speed 1.3 Velociy-Time Graph Gradien = acceleraion Area under

More information

Vehicle Arrival Models : Headway

Vehicle Arrival Models : Headway Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where

More information

Impacts of both Tx and Rx IQ Imbalances on OFDM Systems - Analytical Approach

Impacts of both Tx and Rx IQ Imbalances on OFDM Systems - Analytical Approach mpacs of boh Tx and Rx Q mbalances on OFD Sysems - Analyical Approach Hassan Zareian, Vahid Tabaaba Vakili ran Universiy of Science and Technology UST, Tehran, ran Faculy of he slamic Republic of ran Broadcasing

More information

Random variables. A random variable X is a function that assigns a real number, X(ζ), to each outcome ζ in the sample space of a random experiment.

Random variables. A random variable X is a function that assigns a real number, X(ζ), to each outcome ζ in the sample space of a random experiment. Random variables Some random eperimens may yield a sample space whose elemens evens are numbers, bu some do no or mahemaical purposes, i is desirable o have numbers associaed wih he oucomes A random variable

More information

The Quantum Theory of Atoms and Molecules: The Schrodinger equation. Hilary Term 2008 Dr Grant Ritchie

The Quantum Theory of Atoms and Molecules: The Schrodinger equation. Hilary Term 2008 Dr Grant Ritchie e Quanum eory of Aoms and Molecules: e Scrodinger equaion Hilary erm 008 Dr Gran Ricie An equaion for maer waves? De Broglie posulaed a every paricles as an associaed wave of waveleng: / p Wave naure of

More information

From Complex Fourier Series to Fourier Transforms

From Complex Fourier Series to Fourier Transforms Topic From Complex Fourier Series o Fourier Transforms. Inroducion In he previous lecure you saw ha complex Fourier Series and is coeciens were dened by as f ( = n= C ne in! where C n = T T = T = f (e

More information

Lecture 12: Multiple Hypothesis Testing

Lecture 12: Multiple Hypothesis Testing ECE 830 Fall 00 Saisical Signal Processing insrucor: R. Nowak, scribe: Xinjue Yu Lecure : Muliple Hypohesis Tesing Inroducion In many applicaions we consider muliple hypohesis es a he same ime. Example

More information

3 at MAC 1140 TEST 3 NOTES. 5.1 and 5.2. Exponential Functions. Form I: P is the y-intercept. (0, P) When a > 1: a = growth factor = 1 + growth rate

3 at MAC 1140 TEST 3 NOTES. 5.1 and 5.2. Exponential Functions. Form I: P is the y-intercept. (0, P) When a > 1: a = growth factor = 1 + growth rate 1 5.1 and 5. Eponenial Funcions Form I: Y Pa, a 1, a > 0 P is he y-inercep. (0, P) When a > 1: a = growh facor = 1 + growh rae The equaion can be wrien as The larger a is, he seeper he graph is. Y P( 1

More information

KEY. Math 334 Midterm III Fall 2008 sections 001 and 003 Instructor: Scott Glasgow

KEY. Math 334 Midterm III Fall 2008 sections 001 and 003 Instructor: Scott Glasgow KEY Mah 334 Miderm III Fall 28 secions and 3 Insrucor: Sco Glasgow Please do NOT wrie on his exam. No credi will be given for such work. Raher wrie in a blue book, or on your own paper, preferably engineering

More information

MEMS 0031 Electric Circuits

MEMS 0031 Electric Circuits MEMS 0031 Elecric Circuis Chaper 1 Circui variables Chaper/Lecure Learning Objecives A he end of his lecure and chaper, you should able o: Represen he curren and volage of an elecric circui elemen, paying

More information

NMR Spectroscopy: Principles and Applications. Nagarajan Murali 1D - Methods Lecture 5

NMR Spectroscopy: Principles and Applications. Nagarajan Murali 1D - Methods Lecture 5 NMR pecroscop: Principles and Applicaions Nagarajan Murali D - Mehods Lecure 5 D-NMR To full appreciae he workings of D NMR eperimens we need o a leas consider wo coupled spins. omeimes we need o go up

More information

THE 2-BODY PROBLEM. FIGURE 1. A pair of ellipses sharing a common focus. (c,b) c+a ROBERT J. VANDERBEI

THE 2-BODY PROBLEM. FIGURE 1. A pair of ellipses sharing a common focus. (c,b) c+a ROBERT J. VANDERBEI THE 2-BODY PROBLEM ROBERT J. VANDERBEI ABSTRACT. In his shor noe, we show ha a pair of ellipses wih a common focus is a soluion o he 2-body problem. INTRODUCTION. Solving he 2-body problem from scrach

More information

Let us start with a two dimensional case. We consider a vector ( x,

Let us start with a two dimensional case. We consider a vector ( x, Roaion marices We consider now roaion marices in wo and hree dimensions. We sar wih wo dimensions since wo dimensions are easier han hree o undersand, and one dimension is a lile oo simple. However, our

More information

Lecture #6: Continuous-Time Signals

Lecture #6: Continuous-Time Signals EEL5: Discree-Time Signals and Sysems Lecure #6: Coninuous-Time Signals Lecure #6: Coninuous-Time Signals. Inroducion In his lecure, we discussed he ollowing opics:. Mahemaical represenaion and ransormaions

More information

Guest Lecturer Friday! Symbolic reasoning. Symbolic reasoning. Practice Problem day A. 2 B. 3 C. 4 D. 8 E. 16 Q25. Will Armentrout.

Guest Lecturer Friday! Symbolic reasoning. Symbolic reasoning. Practice Problem day A. 2 B. 3 C. 4 D. 8 E. 16 Q25. Will Armentrout. Pracice Problem day Gues Lecurer Friday! Will Armenrou. He d welcome your feedback! Anonymously: wrie somehing and pu i in my mailbox a 111 Whie Hall. Email me: sarah.spolaor@mail.wvu.edu Symbolic reasoning

More information

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H.

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H. ACE 564 Spring 2006 Lecure 7 Exensions of The Muliple Regression Model: Dumm Independen Variables b Professor Sco H. Irwin Readings: Griffihs, Hill and Judge. "Dumm Variables and Varing Coefficien Models

More information

Problem Set 11(Practice Problems) Spring 2014

Problem Set 11(Practice Problems) Spring 2014 Issued: Wednesday April, ECE Signals and Sysems I Problem Se (Pracice Problems) Spring Due: For pracice only. No due. Reading in Oppenheim/Willsky/Nawab /9/ Secions 6.-6. // Secions 6.-6. See revised schedule

More information

Some Basic Information about M-S-D Systems

Some Basic Information about M-S-D Systems Some Basic Informaion abou M-S-D Sysems 1 Inroducion We wan o give some summary of he facs concerning unforced (homogeneous) and forced (non-homogeneous) models for linear oscillaors governed by second-order,

More information

We just finished the Erdős-Stone Theorem, and ex(n, F ) (1 1/(χ(F ) 1)) ( n

We just finished the Erdős-Stone Theorem, and ex(n, F ) (1 1/(χ(F ) 1)) ( n Lecure 3 - Kövari-Sós-Turán Theorem Jacques Versraëe jacques@ucsd.edu We jus finished he Erdős-Sone Theorem, and ex(n, F ) ( /(χ(f ) )) ( n 2). So we have asympoics when χ(f ) 3 bu no when χ(f ) = 2 i.e.

More information

6.01: Introduction to EECS I Lecture 8 March 29, 2011

6.01: Introduction to EECS I Lecture 8 March 29, 2011 6.01: Inroducion o EES I Lecure 8 March 29, 2011 6.01: Inroducion o EES I Op-Amps Las Time: The ircui Absracion ircuis represen sysems as connecions of elemens hrough which currens (hrough variables) flow

More information

Parametrics and Vectors (BC Only)

Parametrics and Vectors (BC Only) Paramerics and Vecors (BC Only) The following relaionships should be learned and memorized. The paricle s posiion vecor is r() x(), y(). The velociy vecor is v(),. The speed is he magniude of he velociy

More information

SOLUTIONS TO ECE 3084

SOLUTIONS TO ECE 3084 SOLUTIONS TO ECE 384 PROBLEM 2.. For each sysem below, specify wheher or no i is: (i) memoryless; (ii) causal; (iii) inverible; (iv) linear; (v) ime invarian; Explain your reasoning. If he propery is no

More information

Explore 2 Proving the Vertical Angles Theorem

Explore 2 Proving the Vertical Angles Theorem Explore 2 Proving he Verical Angles Theorem The conjecure from he Explore abou verical angles can be proven so i can be saed as a heorem. The Verical Angles Theorem If wo angles are verical angles, hen

More information

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle Chaper 2 Newonian Mechanics Single Paricle In his Chaper we will review wha Newon s laws of mechanics ell us abou he moion of a single paricle. Newon s laws are only valid in suiable reference frames,

More information

EGR 544 Communication Theory

EGR 544 Communication Theory EGR 544 Commuicaio heory 7. Represeaio of Digially Modulaed Sigals II Z. Aliyazicioglu Elecrical ad Compuer Egieerig Deparme Cal Poly Pomoa Represeaio of Digial Modulaio wih Memory Liear Digial Modulaio

More information

Lecture 2-1 Kinematics in One Dimension Displacement, Velocity and Acceleration Everything in the world is moving. Nothing stays still.

Lecture 2-1 Kinematics in One Dimension Displacement, Velocity and Acceleration Everything in the world is moving. Nothing stays still. Lecure - Kinemaics in One Dimension Displacemen, Velociy and Acceleraion Everyhing in he world is moving. Nohing says sill. Moion occurs a all scales of he universe, saring from he moion of elecrons in

More information

72 Calculus and Structures

72 Calculus and Structures 72 Calculus and Srucures CHAPTER 5 DISTANCE AND ACCUMULATED CHANGE Calculus and Srucures 73 Copyrigh Chaper 5 DISTANCE AND ACCUMULATED CHANGE 5. DISTANCE a. Consan velociy Le s ake anoher look a Mary s

More information

Chapter 2 Summary. Carnegie Learning

Chapter 2 Summary. Carnegie Learning Chaper Summary Key Terms inducion (.1) deducion (.1) counerexample (.1) condiional saemen (.1) proposiional form (.1) proposiional variables (.1) hypohesis (.1) conclusion (.1) ruh value (.1) ruh able

More information

236 CHAPTER 3 Torsion. Strain Energy in Torsion

236 CHAPTER 3 Torsion. Strain Energy in Torsion 36 CHAPER 3 orsion Srain Energy in orsion Problem 3.9-1 A solid circular bar of seel (G 11. 1 6 psi) wih lengh 3 in. and diameer d 1.75 in. is subjeced o pure orsion by orques acing a he ends (see figure).

More information

Computing Probability of Symbol Error

Computing Probability of Symbol Error Computing Probability of Symbol Error I When decision boundaries intersect at right angles, then it is possible to compute the error probability exactly in closed form. I The result will be in terms of

More information

Equations of motion for constant acceleration

Equations of motion for constant acceleration Lecure 3 Chaper 2 Physics I 01.29.2014 Equaions of moion for consan acceleraion Course websie: hp://faculy.uml.edu/andriy_danylo/teaching/physicsi Lecure Capure: hp://echo360.uml.edu/danylo2013/physics1spring.hml

More information

A Bayesian Approach to Spectral Analysis

A Bayesian Approach to Spectral Analysis Chirped Signals A Bayesian Approach o Specral Analysis Chirped signals are oscillaing signals wih ime variable frequencies, usually wih a linear variaion of frequency wih ime. E.g. f() = A cos(ω + α 2

More information

ADDITIONAL PROBLEMS (a) Find the Fourier transform of the half-cosine pulse shown in Fig. 2.40(a). Additional Problems 91

ADDITIONAL PROBLEMS (a) Find the Fourier transform of the half-cosine pulse shown in Fig. 2.40(a). Additional Problems 91 ddiional Problems 9 n inverse relaionship exiss beween he ime-domain and freuency-domain descripions of a signal. Whenever an operaion is performed on he waveform of a signal in he ime domain, a corresponding

More information

Demodulation of Digitally Modulated Signals

Demodulation of Digitally Modulated Signals Addiional maerial for TSKS1 Digial Communicaion and TSKS2 Telecommunicaion Demodulaion of Digially Modulaed Signals Mikael Olofsson Insiuionen för sysemeknik Linköpings universie, 581 83 Linköping November

More information

INTRODUCTION TO MACHINE LEARNING 3RD EDITION

INTRODUCTION TO MACHINE LEARNING 3RD EDITION ETHEM ALPAYDIN The MIT Press, 2014 Lecure Slides for INTRODUCTION TO MACHINE LEARNING 3RD EDITION alpaydin@boun.edu.r hp://www.cmpe.boun.edu.r/~ehem/i2ml3e CHAPTER 2: SUPERVISED LEARNING Learning a Class

More information

INDEX. Transient analysis 1 Initial Conditions 1

INDEX. Transient analysis 1 Initial Conditions 1 INDEX Secion Page Transien analysis 1 Iniial Condiions 1 Please inform me of your opinion of he relaive emphasis of he review maerial by simply making commens on his page and sending i o me a: Frank Mera

More information

Notes 04 largely plagiarized by %khc

Notes 04 largely plagiarized by %khc Noes 04 largely plagiarized by %khc Convoluion Recap Some ricks: x() () =x() x() (, 0 )=x(, 0 ) R ț x() u() = x( )d x() () =ẋ() This hen ells us ha an inegraor has impulse response h() =u(), and ha a differeniaor

More information

Lecture #7. EECS490: Digital Image Processing. Image Processing Example Fuzzy logic. Fourier Transform. Basics Image processing examples

Lecture #7. EECS490: Digital Image Processing. Image Processing Example Fuzzy logic. Fourier Transform. Basics Image processing examples Lecure #7 Image Processing Example Fuzzy logic Basics Image processing examples Fourier Transorm Inner produc, basis uncions Fourier series Image Processing Example original image Laplacian o image (c)

More information

EELE Lecture 8 Example of Fourier Series for a Triangle from the Fourier Transform. Homework password is: 14445

EELE Lecture 8 Example of Fourier Series for a Triangle from the Fourier Transform. Homework password is: 14445 EELE445-4 Lecure 8 Eample o Fourier Series or a riangle rom he Fourier ransorm Homework password is: 4445 3 4 EELE445-4 Lecure 8 LI Sysems and Filers 5 LI Sysem 6 3 Linear ime-invarian Sysem Deiniion o

More information

EE 435. Lecture 35. Absolute and Relative Accuracy DAC Design. The String DAC

EE 435. Lecture 35. Absolute and Relative Accuracy DAC Design. The String DAC EE 435 Lecure 35 Absolue and Relaive Accuracy DAC Design The Sring DAC Makekup Lecures Rm 6 Sweeney 5:00 Rm 06 Coover 6:00 o 8:00 . Review from las lecure. Summary of ime and ampliude quanizaion assessmen

More information

1. Kinematics I: Position and Velocity

1. Kinematics I: Position and Velocity 1. Kinemaics I: Posiion and Velociy Inroducion The purpose of his eperimen is o undersand and describe moion. We describe he moion of an objec by specifying is posiion, velociy, and acceleraion. In his

More information

Spectral Analysis. Joseph Fourier The two representations of a signal are connected via the Fourier transform. Z x(t)exp( j2πft)dt

Spectral Analysis. Joseph Fourier The two representations of a signal are connected via the Fourier transform. Z x(t)exp( j2πft)dt Specral Analysis Asignalx may be represened as a funcion of ime as x() or as a funcion of frequency X(f). This is due o relaionships developed by a French mahemaician, physicis, and Egypologis, Joseph

More information

Kinematics Vocabulary. Kinematics and One Dimensional Motion. Position. Coordinate System in One Dimension. Kinema means movement 8.

Kinematics Vocabulary. Kinematics and One Dimensional Motion. Position. Coordinate System in One Dimension. Kinema means movement 8. Kinemaics Vocabulary Kinemaics and One Dimensional Moion 8.1 WD1 Kinema means movemen Mahemaical descripion of moion Posiion Time Inerval Displacemen Velociy; absolue value: speed Acceleraion Averages

More information

Zhihan Xu, Matt Proctor, Ilia Voloh

Zhihan Xu, Matt Proctor, Ilia Voloh Zhihan Xu, Ma rocor, lia Voloh - GE Digial Energy Mike Lara - SNC-Lavalin resened by: Terrence Smih GE Digial Energy CT fundamenals Circui model, exciaion curve, simulaion model CT sauraion AC sauraion,

More information

Circuit Variables. AP 1.1 Use a product of ratios to convert two-thirds the speed of light from meters per second to miles per second: 1 ft 12 in

Circuit Variables. AP 1.1 Use a product of ratios to convert two-thirds the speed of light from meters per second to miles per second: 1 ft 12 in Circui Variables 1 Assessmen Problems AP 1.1 Use a produc of raios o conver wo-hirds he speed of ligh from meers per second o miles per second: ( ) 2 3 1 8 m 3 1 s 1 cm 1 m 1 in 2.54 cm 1 f 12 in 1 mile

More information

T L. t=1. Proof of Lemma 1. Using the marginal cost accounting in Equation(4) and standard arguments. t )+Π RB. t )+K 1(Q RB

T L. t=1. Proof of Lemma 1. Using the marginal cost accounting in Equation(4) and standard arguments. t )+Π RB. t )+K 1(Q RB Elecronic Companion EC.1. Proofs of Technical Lemmas and Theorems LEMMA 1. Le C(RB) be he oal cos incurred by he RB policy. Then we have, T L E[C(RB)] 3 E[Z RB ]. (EC.1) Proof of Lemma 1. Using he marginal

More information

8 PAM BER/SER Monte Carlo Simulation

8 PAM BER/SER Monte Carlo Simulation xercise.1 8 PAM BR/SR Monte Carlo Simulation - Simulate a 8 level PAM communication system and calculate bit and symbol error ratios (BR/SR). - Plot the calculated and simulated SR and BR curves. - Plot

More information

Random Walk with Anti-Correlated Steps

Random Walk with Anti-Correlated Steps Random Walk wih Ani-Correlaed Seps John Noga Dirk Wagner 2 Absrac We conjecure he expeced value of random walks wih ani-correlaed seps o be exacly. We suppor his conjecure wih 2 plausibiliy argumens and

More information

Sensors, Signals and Noise

Sensors, Signals and Noise Sensors, Signals and Noise COURSE OUTLINE Inroducion Signals and Noise: 1) Descripion Filering Sensors and associaed elecronics rv 2017/02/08 1 Noise Descripion Noise Waveforms and Samples Saisics of Noise

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Linear Response Theory: The connecion beween QFT and experimens 3.1. Basic conceps and ideas Q: How do we measure he conduciviy of a meal? A: we firs inroduce a weak elecric field E, and

More information

Lecture 20: Riccati Equations and Least Squares Feedback Control

Lecture 20: Riccati Equations and Least Squares Feedback Control 34-5 LINEAR SYSTEMS Lecure : Riccai Equaions and Leas Squares Feedback Conrol 5.6.4 Sae Feedback via Riccai Equaions A recursive approach in generaing he marix-valued funcion W ( ) equaion for i for he

More information

Voltage/current relationship Stored Energy. RL / RC circuits Steady State / Transient response Natural / Step response

Voltage/current relationship Stored Energy. RL / RC circuits Steady State / Transient response Natural / Step response Review Capaciors/Inducors Volage/curren relaionship Sored Energy s Order Circuis RL / RC circuis Seady Sae / Transien response Naural / Sep response EE4 Summer 5: Lecure 5 Insrucor: Ocavian Florescu Lecure

More information

An Introduction to Backward Stochastic Differential Equations (BSDEs) PIMS Summer School 2016 in Mathematical Finance.

An Introduction to Backward Stochastic Differential Equations (BSDEs) PIMS Summer School 2016 in Mathematical Finance. 1 An Inroducion o Backward Sochasic Differenial Equaions (BSDEs) PIMS Summer School 2016 in Mahemaical Finance June 25, 2016 Chrisoph Frei cfrei@ualbera.ca This inroducion is based on Touzi [14], Bouchard

More information

UNIVERSITY OF CALIFORNIA AT BERKELEY

UNIVERSITY OF CALIFORNIA AT BERKELEY Homework #10 Soluions EECS 40, Fall 2006 Prof. Chang-Hasnain Due a 6 pm in 240 Cory on Wednesday, 04/18/07 oal Poins: 100 Pu (1) your name and (2) discussion secion number on your homework. You need o

More information

Theory of! Partial Differential Equations!

Theory of! Partial Differential Equations! hp://www.nd.edu/~gryggva/cfd-course/! Ouline! Theory o! Parial Dierenial Equaions! Gréar Tryggvason! Spring 011! Basic Properies o PDE!! Quasi-linear Firs Order Equaions! - Characerisics! - Linear and

More information

Theory of! Partial Differential Equations-I!

Theory of! Partial Differential Equations-I! hp://users.wpi.edu/~grear/me61.hml! Ouline! Theory o! Parial Dierenial Equaions-I! Gréar Tryggvason! Spring 010! Basic Properies o PDE!! Quasi-linear Firs Order Equaions! - Characerisics! - Linear and

More information

Resource Allocation in Visible Light Communication Networks NOMA vs. OFDMA Transmission Techniques

Resource Allocation in Visible Light Communication Networks NOMA vs. OFDMA Transmission Techniques Resource Allocaion in Visible Ligh Communicaion Neworks NOMA vs. OFDMA Transmission Techniques Eirini Eleni Tsiropoulou, Iakovos Gialagkolidis, Panagiois Vamvakas, and Symeon Papavassiliou Insiue of Communicaions

More information

Physics 180A Fall 2008 Test points. Provide the best answer to the following questions and problems. Watch your sig figs.

Physics 180A Fall 2008 Test points. Provide the best answer to the following questions and problems. Watch your sig figs. Physics 180A Fall 2008 Tes 1-120 poins Name Provide he bes answer o he following quesions and problems. Wach your sig figs. 1) The number of meaningful digis in a number is called he number of. When numbers

More information

Different assumptions in the literature: Wages/prices set one period in advance and last for one period

Different assumptions in the literature: Wages/prices set one period in advance and last for one period Øisein Røisland, 5.3.7 Lecure 8: Moneary policy in New Keynesian models: Inroducing nominal rigidiies Differen assumpions in he lieraure: Wages/prices se one period in advance and las for one period Saggering

More information