3.6 Limiting and Clamping Circuits

Size: px
Start display at page:

Download "3.6 Limiting and Clamping Circuits"

Transcription

1 3/10/2008 secton_3_6_lmtng_and_clampng_crcuts 1/1 3.6 Lmtng and Clampng Crcuts Readng Assgnment: pp (.e., neglect secton 3.6.2) Another applcaton of juncton dodes Q: What s a lmter? A: A 2-port devce that restrcts (.e., lmts) the voltage across a devce to some specfed regon. HO: ode Lmters Q: A: HO: Steps for Analyzng Lmter Crcuts Example: A ode Lmter Jm Stles The Unv. of Kansas ept. of EECS

2 3/10/2008 secton_3_6_lmtng_and_clampng_crcuts 1/1 3.6 Lmtng and Clampng Crcuts Readng Assgnment: pp (.e., neglect secton 3.6.2) Another applcaton of juncton dodes Lmters Q: What s a lmter? A: A 2-port devce that restrcts (.e., lmts) the voltage across a devce to some specfed regon. HO: ode Lmters Q: A: HO: Steps for Analyzng Lmter Crcuts Example: A ode Lmter Jm Stles The Unv. of Kansas ept. of EECS

3 3/10/2008 secton_3_6_lmtng_and_clampng_crcuts 1/1 3.6 Lmtng and Clampng Crcuts Readng Assgnment: pp (.e., neglect secton 3.6.2) Another applcaton of juncton dodes Lmters Q: What s a lmter? A: A 2-port devce that restrcts (.e., lmts) the voltage across a devce to some specfed regon. A lmter s a protecton devce! HO: ode Lmters Q: A: HO: Steps for Analyzng Lmter Crcuts Example: A ode Lmter Jm Stles The Unv. of Kansas ept. of EECS

4 3/10/2008 secton_3_6_lmtng_and_clampng_crcuts 1/1 3.6 Lmtng and Clampng Crcuts Readng Assgnment: pp (.e., neglect secton 3.6.2) Another applcaton of juncton dodes Lmters Q: What s a lmter? A: A 2-port devce that restrcts (.e., lmts) the voltage across a devce to some specfed regon. A lmter s a protecton devce! HO: ode Lmters Q: So how do we determne the transfer functon of a lmter? A: HO: Steps for Analyzng Lmter Crcuts Example: A ode Lmter Jm Stles The Unv. of Kansas ept. of EECS

5 3/10/2008 ode Lmters 1/4 ode Lmters Often, a voltage source (ether C or AC) s used to supply an electronc devce that s very expensve and/or very senstve. n ths case, we may choose nsert a dode lmter between the source and the devce ths lmter wll provde over-voltage protecton! To see how, we should frst consder a typcal transfer functon for a juncton dode lmter: + v ( t ) Juncton - ode v ( t) Lmter + O Senstve evce v O L + K v L K L+ K L - Jm Stles The Unv. of Kansas ept. of EECS

6 3/10/2008 ode Lmters 2/4 Note that ths transfer functon ndcates that the output voltage v O can never be more than a maxmum voltage L +, nor less than a mnmum voltage L -. * Thus, the devce places some lmts on the value of the output voltage: L < v < L for any v O + * The lmts L - and L + provde a safe operatng value for v O, the voltage across our senstve electronc devce. * Presumably, f no lmter were present, we mght fnd that vo > L + or v O < L, resultng n damage to the devce! * Note althoughl+ > L, the values of L - and L + may be both postve, both negatve, or even zero. For example, a lmter wth L - =0 (L + >0) would prevent the voltage from ever becomng negatve (postve). We fnd that for many devces, the wrong voltage polarty can be destructve! To llustrate, let s consder an example nput voltage v (t), and the resultng output voltage when passed through a lmter wth values L - =0 and L + =20 V (K=1)..E.: 0 f v < 0 vo = v f 0 < v < f v > 20 Jm Stles The Unv. of Kansas ept. of EECS

7 3/10/2008 ode Lmters 3/4 v L + =20 v (t) v O (t) L - =0 t Note there are a couple of hccups n the nput voltage that take the voltage value outsde the safety range of the senstve devce. However, the lmter does n fact lmt these excursons, such that the voltage across the senstve devce always remans between 0 and 20 Volts. Q: Why would these hccups occur? A: There are many possble reasons, ncludng: 1. A power surge (e.g., lghtnng strke) 2. Statc dscharge 3. Swtchng transents (e.g., at power up or down). Jm Stles The Unv. of Kansas ept. of EECS

8 3/10/2008 ode Lmters 4/4 Perhaps the most prevalent reason, however, s operator error. Someone connects the wrong source to the senstve devce! Thus, lmters are often used on expensve/senstve devces to make them fool-proof. Your book has many examples of lmter crcuts, ncludng: Jm Stles The Unv. of Kansas ept. of EECS

9 3/10/2008 Steps for Analyzng Lmter Crcuts 1/4 Steps for Analyzng Lmter Crcuts The juncton dodes n most lmter crcuts can/wll be n forward bas, or reverse bas, or breakdown modes! Thus, the dstncton between a Zener dode and a normal juncton dode s essentally meanngless. But, ths presents us wth a bg problem what dode model do we use to analyze a lmter? Recall that none of the dode models that we studed wll provde accurate estmates for all three juncton dode modes! The soluton we wll use s to change the dode model we mplement, as we consder each of the possble juncton dode modes. Specfcally: Juncton ode Mode Forward Bas Reverse Bas Breakdown Juncton ode Model CV model wth deal dode f.b. deal dode model wth deal dode r.b Zener CV model wth deal dode f.b. Jm Stles The Unv. of Kansas ept. of EECS

10 3/10/2008 Steps for Analyzng Lmter Crcuts 2/4 Step 1: Assume that the lmter dode s forward based, so replace wth a CV model, where the deal dode s forward based: or A C C A A V Now, usng ths model, determne: C 1. The output voltage v O n terms of nput voltage v. 2. The deal dode current n terms of nput voltage v. Fnally, we solve the nequalty > 0 for v, thus determnng when (.e., for what values of v ) ths assumpton, and thus the derved expresson for output voltage v O, s true. Step 2: Assume that the lmter dode s n breakdown, so replace A C or A C Jm Stles The Unv. of Kansas ept. of EECS

11 3/10/2008 Steps for Analyzng Lmter Crcuts 3/4 wth a Zener CV model, where the deal dode s forward based: A V ZK + C Now, usng ths model, determne: 1. The output voltage v O n terms of nput voltage v. 2. The deal dode current n terms of nput voltage v. Fnally, we solve the nequalty > 0 for v, thus determnng when (.e., for what values of v ) ths assumpton, and thus the derved expresson for output voltage v O, s true. Step 3: Assume that the lmter dode s reverse based, so replace A C or A C wth an deal ode model, where the deal dode s reversed based: A + v C Jm Stles The Unv. of Kansas ept. of EECS

12 3/10/2008 Steps for Analyzng Lmter Crcuts 4/4 Now, usng ths model, determne the output voltage v O n terms of nput voltage v. Q: What about v? on t we need to lkewse determne ts value, and then determne when v < 0? A: Actually, no. f the juncton dode s not forward based and t s not n breakdown, then t must be reverse based! As obvous as ths statement s, we can use t determne when the juncton dode s reverse based t s when the juncton dode s not n forward bas and when t s not n reverse bas. For example, say that we fnd that the juncton dode s forward based when: v > 20 V, and that the juncton dode s n breakdown when: v < 15 V. We can thus conclude that the juncton dode s reverse based when: 15V < < 20 V v Step 4: We take the result of the prevous 3 steps and form a contnuous, pecewse lnear transfer functon (make sure t s contnuous, and that t s a functon!). Jm Stles The Unv. of Kansas ept. of EECS

13 3/10/2008 Example A ode Lmter 1/7 Example: A ode Lmter Consder the followng juncton dode crcut: +5V 1K V ZK =10V open crcut v v O 1K Ths crcut s a juncton dode lmter! Perhaps that would be clearer f we redrew ths crcut as: 1K v V - V ZK =10V 1K + v O - Ths s the same crcut as above! Jm Stles The Unv. of Kansas ept. of EECS

14 3/10/2008 Example A ode Lmter 2/7 Now, let s determne the transfer functon of ths lmter. To do ths, we must follow the 4 steps detaled n the prevous handout! Step1: Assume juncton dode s forward based Replace the juncton dode wth a CV model. ASSUME the deal dode s forward based, ENFORCE v = 0. v 1K 1 +5V 1K - 0.7V + 2 v O We fnd that the output voltage s smply: v = = 57V. O whle the deal dode current s more dffcult to determne. From KCL: where from Ohm s Law: = Jm Stles The Unv. of Kansas ept. of EECS

15 3/10/2008 Example A ode Lmter 3/7 v = = v and: = = Thus, the deal dode current s: = = v = v Now, for our assumpton to be correct, ths current must be postve (.e., > 0 ). Thus, we solve ths nequalty to determne when our assumpton s true: So, from ths step we fnd: v > 0 v > V v = 57V. when v > 114V. O Step2: Assume the juncton dode s n breakdown Replace the juncton dode wth a Zener CV model. ASSUME the deal dode s forward based, ENFORCE v = 0. Jm Stles The Unv. of Kansas ept. of EECS

16 3/10/2008 Example A ode Lmter 4/7 +5V v 1K V - v O 1 1K 2 We fnd that the output voltage s smply: v = 5 10 = 5 0V. O whle the deal dode current s more dffcult to determne. From KCL: where from Ohm s Law: and: = V 1 = = v = = 50V. 1 Thus, the deal dode current s: Jm Stles The Unv. of Kansas ept. of EECS

17 3/10/2008 Example A ode Lmter 5/7 = = v = v Now, for our assumpton to be correct, ths current must be postve (.e., > 0 ). Thus, we solve ths nequalty to determne when our assumpton s true: So, from ths step we fnd: v > 0 v > V v < V v = 50V. when v < 100V. O Step 3: Assume the juncton dode s reverse based Replace the juncton dode wth the deal ode model. ASSUME the deal dode s reverse based, ENFORCE = 0. v 1K +5V + v v O 1K A voltage dvder! Jm Stles The Unv. of Kansas ept. of EECS

18 3/10/2008 Example A ode Lmter 6/7 Thus the output voltage s: v O ( 1) v = 1+ 1 v = 2 Ths output voltage s true when the juncton dode s nether forward based nor n breakdown. Thus, usng the results from the frst two steps, we can nfer that t s true when: < v < Step 4: etermne the contnuous transfer functon Combnng the results of the prevous 3 steps, we get the followng contnuous, pece-wse lnear transfer functon: 57V. f v > 114V. vo = v 2 f 100. < v < 114V. 50.V f v < 100V. Jm Stles The Unv. of Kansas ept. of EECS

19 3/10/2008 Example A ode Lmter 7/7 v O v Note that at v = 10 : and at v = : v 10 v O = = = 50V. 2 2 v v O = = = 57V. 2 2 Thus, ths functon s contnuous! Jm Stles The Unv. of Kansas ept. of EECS

3.2 Terminal Characteristics of Junction Diodes (pp )

3.2 Terminal Characteristics of Junction Diodes (pp ) /9/008 secton3_termnal_characterstcs_of_juncton_odes.doc /6 3. Termnal Characterstcs of Juncton odes (pp.47-53) A Juncton ode I.E., A real dode! Smlar to an deal dode, ts crcut symbol s: HO: The Juncton

More information

3.5 Rectifier Circuits

3.5 Rectifier Circuits 9/24/2004 3_5 Rectfer Crcuts empty.doc 1/2 3.5 Rectfer Crcuts A. Juncton ode 2-Port Networks - ( t ) Juncton ode Crcut ( t ) H: The Transfer Functon of ode Crcuts Q: A: H: teps for fndng a Juncton ode

More information

Driving your LED s. LED Driver. The question then is: how do we use this square wave to turn on and turn off the LED?

Driving your LED s. LED Driver. The question then is: how do we use this square wave to turn on and turn off the LED? 0//00 rng your LE.doc / rng your LE s As we hae preously learned, n optcal communcaton crcuts, a dgtal sgnal wth a frequency n the tens or hundreds of khz s used to ampltude modulate (on and off) the emssons

More information

4.1 The Ideal Diode. Reading Assignment: pp Before we get started with ideal diodes, let s first recall linear device behavior!

4.1 The Ideal Diode. Reading Assignment: pp Before we get started with ideal diodes, let s first recall linear device behavior! 1/25/2012 secton3_1the_ideal_ode 1/2 4.1 The Ideal ode Readng Assgnment: pp.165-172 Before we get started wth deal dodes, let s frst recall lnear dece behaor! HO: LINEAR EVICE BEHAVIOR Now, the deal dode

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

More information

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg prnceton unv. F 17 cos 521: Advanced Algorthm Desgn Lecture 7: LP Dualty Lecturer: Matt Wenberg Scrbe: LP Dualty s an extremely useful tool for analyzng structural propertes of lnear programs. Whle there

More information

Chapter Newton s Method

Chapter Newton s Method Chapter 9. Newton s Method After readng ths chapter, you should be able to:. Understand how Newton s method s dfferent from the Golden Secton Search method. Understand how Newton s method works 3. Solve

More information

( ) = ( ) + ( 0) ) ( )

( ) = ( ) + ( 0) ) ( ) EETOMAGNETI OMPATIBIITY HANDBOOK 1 hapter 9: Transent Behavor n the Tme Doman 9.1 Desgn a crcut usng reasonable values for the components that s capable of provdng a tme delay of 100 ms to a dgtal sgnal.

More information

MAE140 - Linear Circuits - Winter 16 Midterm, February 5

MAE140 - Linear Circuits - Winter 16 Midterm, February 5 Instructons ME140 - Lnear Crcuts - Wnter 16 Mdterm, February 5 () Ths exam s open book. You may use whatever wrtten materals you choose, ncludng your class notes and textbook. You may use a hand calculator

More information

Graphical Analysis of a BJT Amplifier

Graphical Analysis of a BJT Amplifier 4/6/2011 A Graphcal Analyss of a BJT Amplfer lecture 1/18 Graphcal Analyss of a BJT Amplfer onsder agan ths smple BJT amplfer: ( t) = + ( t) O O o B + We note that for ths amplfer, the output oltage s

More information

FE REVIEW OPERATIONAL AMPLIFIERS (OP-AMPS)( ) 8/25/2010

FE REVIEW OPERATIONAL AMPLIFIERS (OP-AMPS)( ) 8/25/2010 FE REVEW OPERATONAL AMPLFERS (OP-AMPS)( ) 1 The Op-amp 2 An op-amp has two nputs and one output. Note the op-amp below. The termnal labeled l wth the (-) sgn s the nvertng nput and the nput labeled wth

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

Copyright 2004 by Oxford University Press, Inc.

Copyright 2004 by Oxford University Press, Inc. JT as an Amplfer &a Swtch, Large Sgnal Operaton, Graphcal Analyss, JT at D, asng JT, Small Sgnal Operaton Model, Hybrd P-Model, TModel. Lecture # 7 1 Drecton of urrent Flow & Operaton for Amplfer Applcaton

More information

Lecture 4: Universal Hash Functions/Streaming Cont d

Lecture 4: Universal Hash Functions/Streaming Cont d CSE 5: Desgn and Analyss of Algorthms I Sprng 06 Lecture 4: Unversal Hash Functons/Streamng Cont d Lecturer: Shayan Oves Gharan Aprl 6th Scrbe: Jacob Schreber Dsclamer: These notes have not been subjected

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

Math1110 (Spring 2009) Prelim 3 - Solutions

Math1110 (Spring 2009) Prelim 3 - Solutions Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

ELE B7 Power Systems Engineering. Power Flow- Introduction

ELE B7 Power Systems Engineering. Power Flow- Introduction ELE B7 Power Systems Engneerng Power Flow- Introducton Introducton to Load Flow Analyss The power flow s the backbone of the power system operaton, analyss and desgn. It s necessary for plannng, operaton,

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

Over-Temperature protection for IGBT modules

Over-Temperature protection for IGBT modules Over-Temperature protecton for IGBT modules Ke Wang 1, Yongjun Lao 2, Gaosheng Song 1, Xanku Ma 1 1 Mtsubsh Electrc & Electroncs (Shangha) Co., Ltd., Chna Room2202, Tower 3, Kerry Plaza, No.1-1 Zhongxns

More information

The exam is closed book, closed notes except your one-page cheat sheet.

The exam is closed book, closed notes except your one-page cheat sheet. CS 89 Fall 206 Introducton to Machne Learnng Fnal Do not open the exam before you are nstructed to do so The exam s closed book, closed notes except your one-page cheat sheet Usage of electronc devces

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Exercises of Chapter 2

Exercises of Chapter 2 Exercses of Chapter Chuang-Cheh Ln Department of Computer Scence and Informaton Engneerng, Natonal Chung Cheng Unversty, Mng-Hsung, Chay 61, Tawan. Exercse.6. Suppose that we ndependently roll two standard

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016 U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and

More information

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA. 1. Matrices in Mathematica

DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA. 1. Matrices in Mathematica demo8.nb 1 DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA Obectves: - defne matrces n Mathematca - format the output of matrces - appl lnear algebra to solve a real problem - Use Mathematca to perform

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The Order Relation and Trace Inequalities for. Hermitian Operators Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence

More information

Solutions HW #2. minimize. Ax = b. Give the dual problem, and make the implicit equality constraints explicit. Solution.

Solutions HW #2. minimize. Ax = b. Give the dual problem, and make the implicit equality constraints explicit. Solution. Solutons HW #2 Dual of general LP. Fnd the dual functon of the LP mnmze subject to c T x Gx h Ax = b. Gve the dual problem, and make the mplct equalty constrants explct. Soluton. 1. The Lagrangan s L(x,

More information

ELECTRONICS. EE 42/100 Lecture 4: Resistive Networks and Nodal Analysis. Rev B 1/25/2012 (9:49PM) Prof. Ali M. Niknejad

ELECTRONICS. EE 42/100 Lecture 4: Resistive Networks and Nodal Analysis. Rev B 1/25/2012 (9:49PM) Prof. Ali M. Niknejad A. M. Nknejad Unversty of Calforna, Berkeley EE 100 / 42 Lecture 4 p. 1/14 EE 42/100 Lecture 4: Resstve Networks and Nodal Analyss ELECTRONICS Rev B 1/25/2012 (9:49PM) Prof. Al M. Nknejad Unversty of Calforna,

More information

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton

More information

Section 8.3 Polar Form of Complex Numbers

Section 8.3 Polar Form of Complex Numbers 80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the

More information

Key component in Operational Amplifiers

Key component in Operational Amplifiers Key component n Operatonal Amplfers Objectve of Lecture Descrbe how dependent voltage and current sources functon. Chapter.6 Electrcal Engneerng: Prncples and Applcatons Chapter.6 Fundamentals of Electrc

More information

E40M Device Models, Resistors, Voltage and Current Sources, Diodes, Solar Cells. M. Horowitz, J. Plummer, R. Howe 1

E40M Device Models, Resistors, Voltage and Current Sources, Diodes, Solar Cells. M. Horowitz, J. Plummer, R. Howe 1 E40M Devce Models, Resstors, Voltage and Current Sources, Dodes, Solar Cells M. Horowtz, J. Plummer, R. Howe 1 Understandng the Solar Charger Lab Project #1 We need to understand how: 1. Current, voltage

More information

MAE140 - Linear Circuits - Fall 13 Midterm, October 31

MAE140 - Linear Circuits - Fall 13 Midterm, October 31 Instructons ME140 - Lnear Crcuts - Fall 13 Mdterm, October 31 () Ths exam s open book. You may use whatever wrtten materals you choose, ncludng your class notes and textbook. You may use a hand calculator

More information

Hila Etzion. Min-Seok Pang

Hila Etzion. Min-Seok Pang RESERCH RTICLE COPLEENTRY ONLINE SERVICES IN COPETITIVE RKETS: INTINING PROFITILITY IN THE PRESENCE OF NETWORK EFFECTS Hla Etzon Department of Technology and Operatons, Stephen. Ross School of usness,

More information

Solution Thermodynamics

Solution Thermodynamics Soluton hermodynamcs usng Wagner Notaton by Stanley. Howard Department of aterals and etallurgcal Engneerng South Dakota School of nes and echnology Rapd Cty, SD 57701 January 7, 001 Soluton hermodynamcs

More information

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results.

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results. Neural Networks : Dervaton compled by Alvn Wan from Professor Jtendra Malk s lecture Ths type of computaton s called deep learnng and s the most popular method for many problems, such as computer vson

More information

Module 9. Lecture 6. Duality in Assignment Problems

Module 9. Lecture 6. Duality in Assignment Problems Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data

More information

Electrical Circuits 2.1 INTRODUCTION CHAPTER

Electrical Circuits 2.1 INTRODUCTION CHAPTER CHAPTE Electrcal Crcuts. INTODUCTION In ths chapter, we brefly revew the three types of basc passve electrcal elements: resstor, nductor and capactor. esstance Elements: Ohm s Law: The voltage drop across

More information

Econ Statistical Properties of the OLS estimator. Sanjaya DeSilva

Econ Statistical Properties of the OLS estimator. Sanjaya DeSilva Econ 39 - Statstcal Propertes of the OLS estmator Sanjaya DeSlva September, 008 1 Overvew Recall that the true regresson model s Y = β 0 + β 1 X + u (1) Applyng the OLS method to a sample of data, we estmate

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Canonical transformations

Canonical transformations Canoncal transformatons November 23, 2014 Recall that we have defned a symplectc transformaton to be any lnear transformaton M A B leavng the symplectc form nvarant, Ω AB M A CM B DΩ CD Coordnate transformatons,

More information

Department of Electrical and Computer Engineering FEEDBACK AMPLIFIERS

Department of Electrical and Computer Engineering FEEDBACK AMPLIFIERS Department o Electrcal and Computer Engneerng UNIT I EII FEEDBCK MPLIFIES porton the output sgnal s ed back to the nput o the ampler s called Feedback mpler. Feedback Concept: block dagram o an ampler

More information

A Robust Method for Calculating the Correlation Coefficient

A Robust Method for Calculating the Correlation Coefficient A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal

More information

First day August 1, Problems and Solutions

First day August 1, Problems and Solutions FOURTH INTERNATIONAL COMPETITION FOR UNIVERSITY STUDENTS IN MATHEMATICS July 30 August 4, 997, Plovdv, BULGARIA Frst day August, 997 Problems and Solutons Problem. Let {ε n } n= be a sequence of postve

More information

FE REVIEW OPERATIONAL AMPLIFIERS (OP-AMPS)

FE REVIEW OPERATIONAL AMPLIFIERS (OP-AMPS) FE EIEW OPEATIONAL AMPLIFIES (OPAMPS) 1 The Opamp An opamp has two nputs and one output. Note the opamp below. The termnal labeled wth the () sgn s the nvertng nput and the nput labeled wth the () sgn

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

Physics 114 Exam 2 Spring Name:

Physics 114 Exam 2 Spring Name: Physcs 114 Exam Sprng 013 Name: For gradng purposes (do not wrte here): Queston 1. 1... 3. 3. Problem Answer each of the followng questons. Ponts for each queston are ndcated n red wth the amount beng

More information

Homework Assignment 3 Due in class, Thursday October 15

Homework Assignment 3 Due in class, Thursday October 15 Homework Assgnment 3 Due n class, Thursday October 15 SDS 383C Statstcal Modelng I 1 Rdge regresson and Lasso 1. Get the Prostrate cancer data from http://statweb.stanford.edu/~tbs/elemstatlearn/ datasets/prostate.data.

More information

Unit 1. Current and Voltage U 1 VOLTAGE AND CURRENT. Circuit Basics KVL, KCL, Ohm's Law LED Outputs Buttons/Switch Inputs. Current / Voltage Analogy

Unit 1. Current and Voltage U 1 VOLTAGE AND CURRENT. Circuit Basics KVL, KCL, Ohm's Law LED Outputs Buttons/Switch Inputs. Current / Voltage Analogy ..2 nt Crcut Bascs KVL, KCL, Ohm's Law LED Outputs Buttons/Swtch Inputs VOLTAGE AND CRRENT..4 Current and Voltage Current / Voltage Analogy Charge s measured n unts of Coulombs Current Amount of charge

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

AN EXTENDED CLASS OF TIME-CONTINUOUS BRANCHING PROCESSES. Rong-Rong Chen. ( University of Illinois at Urbana-Champaign)

AN EXTENDED CLASS OF TIME-CONTINUOUS BRANCHING PROCESSES. Rong-Rong Chen. ( University of Illinois at Urbana-Champaign) AN EXTENDED CLASS OF TIME-CONTINUOUS BRANCHING PROCESSES Rong-Rong Chen ( Unversty of Illnos at Urbana-Champagn Abstract. Ths paper s devoted to studyng an extended class of tme-contnuous branchng processes,

More information

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA 4 Analyss of Varance (ANOVA) 5 ANOVA 51 Introducton ANOVA ANOVA s a way to estmate and test the means of multple populatons We wll start wth one-way ANOVA If the populatons ncluded n the study are selected

More information

Odd/Even Scroll Generation with Inductorless Chua s and Wien Bridge Oscillator Circuits

Odd/Even Scroll Generation with Inductorless Chua s and Wien Bridge Oscillator Circuits Watcharn Jantanate, Peter A. Chayasena, Sarawut Sutorn Odd/Even Scroll Generaton wth Inductorless Chua s and Wen Brdge Oscllator Crcuts Watcharn Jantanate, Peter A. Chayasena, and Sarawut Sutorn * School

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

Math 426: Probability MWF 1pm, Gasson 310 Homework 4 Selected Solutions

Math 426: Probability MWF 1pm, Gasson 310 Homework 4 Selected Solutions Exercses from Ross, 3, : Math 26: Probablty MWF pm, Gasson 30 Homework Selected Solutons 3, p. 05 Problems 76, 86 3, p. 06 Theoretcal exercses 3, 6, p. 63 Problems 5, 0, 20, p. 69 Theoretcal exercses 2,

More information

CHAPTER 13. Exercises. E13.1 The emitter current is given by the Shockley equation:

CHAPTER 13. Exercises. E13.1 The emitter current is given by the Shockley equation: HPT 3 xercses 3. The emtter current s gen by the Shockley equaton: S exp VT For operaton wth, we hae exp >> S >>, and we can wrte VT S exp VT Solng for, we hae 3. 0 6ln 78.4 mv 0 0.784 5 4.86 V VT ln 4

More information

Math 261 Exercise sheet 2

Math 261 Exercise sheet 2 Math 261 Exercse sheet 2 http://staff.aub.edu.lb/~nm116/teachng/2017/math261/ndex.html Verson: September 25, 2017 Answers are due for Monday 25 September, 11AM. The use of calculators s allowed. Exercse

More information

IV. Diodes. 4.1 Energy Bands in Solids

IV. Diodes. 4.1 Energy Bands in Solids I. Dodes We start our study of nonlnear crcut elements. These elements (dodes and transstors) are made of semconductors. A bref descrpton of how semconductor devces work s frst gven to understand ther

More information

A Simple Inventory System

A Simple Inventory System A Smple Inventory System Lawrence M. Leems and Stephen K. Park, Dscrete-Event Smulaton: A Frst Course, Prentce Hall, 2006 Hu Chen Computer Scence Vrgna State Unversty Petersburg, Vrgna February 8, 2017

More information

Using the estimated penetrances to determine the range of the underlying genetic model in casecontrol

Using the estimated penetrances to determine the range of the underlying genetic model in casecontrol Georgetown Unversty From the SelectedWorks of Mark J Meyer 8 Usng the estmated penetrances to determne the range of the underlyng genetc model n casecontrol desgn Mark J Meyer Neal Jeffres Gang Zheng Avalable

More information

IV. Diodes. 4.1 Energy Bands in Solids

IV. Diodes. 4.1 Energy Bands in Solids I. Dodes We start our study of nonlnear crcut elements. These elements (dodes and transstors) are made of semconductors. A bref descrpton of how semconductor devces work s frst gven to understand ther

More information

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

Tracking with Kalman Filter

Tracking with Kalman Filter Trackng wth Kalman Flter Scott T. Acton Vrgna Image and Vdeo Analyss (VIVA), Charles L. Brown Department of Electrcal and Computer Engneerng Department of Bomedcal Engneerng Unversty of Vrgna, Charlottesvlle,

More information

Maximizing the number of nonnegative subsets

Maximizing the number of nonnegative subsets Maxmzng the number of nonnegatve subsets Noga Alon Hao Huang December 1, 213 Abstract Gven a set of n real numbers, f the sum of elements of every subset of sze larger than k s negatve, what s the maxmum

More information

Notes on Frequency Estimation in Data Streams

Notes on Frequency Estimation in Data Streams Notes on Frequency Estmaton n Data Streams In (one of) the data streamng model(s), the data s a sequence of arrvals a 1, a 2,..., a m of the form a j = (, v) where s the dentty of the tem and belongs to

More information

1 Matrix representations of canonical matrices

1 Matrix representations of canonical matrices 1 Matrx representatons of canoncal matrces 2-d rotaton around the orgn: ( ) cos θ sn θ R 0 = sn θ cos θ 3-d rotaton around the x-axs: R x = 1 0 0 0 cos θ sn θ 0 sn θ cos θ 3-d rotaton around the y-axs:

More information

Generalized Linear Methods

Generalized Linear Methods Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

DUE: WEDS FEB 21ST 2018

DUE: WEDS FEB 21ST 2018 HOMEWORK # 1: FINITE DIFFERENCES IN ONE DIMENSION DUE: WEDS FEB 21ST 2018 1. Theory Beam bendng s a classcal engneerng analyss. The tradtonal soluton technque makes smplfyng assumptons such as a constant

More information

#64. ΔS for Isothermal Mixing of Ideal Gases

#64. ΔS for Isothermal Mixing of Ideal Gases #64 Carnot Heat Engne ΔS for Isothermal Mxng of Ideal Gases ds = S dt + S T V V S = P V T T V PV = nrt, P T ds = v T = nr V dv V nr V V = nrln V V = - nrln V V ΔS ΔS ΔS for Isothermal Mxng for Ideal Gases

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information

find (x): given element x, return the canonical element of the set containing x;

find (x): given element x, return the canonical element of the set containing x; COS 43 Sprng, 009 Dsjont Set Unon Problem: Mantan a collecton of dsjont sets. Two operatons: fnd the set contanng a gven element; unte two sets nto one (destructvely). Approach: Canoncal element method:

More information

1 Convex Optimization

1 Convex Optimization Convex Optmzaton We wll consder convex optmzaton problems. Namely, mnmzaton problems where the objectve s convex (we assume no constrants for now). Such problems often arse n machne learnng. For example,

More information

Note 10. Modeling and Simulation of Dynamic Systems

Note 10. Modeling and Simulation of Dynamic Systems Lecture Notes of ME 475: Introducton to Mechatroncs Note 0 Modelng and Smulaton of Dynamc Systems Department of Mechancal Engneerng, Unversty Of Saskatchewan, 57 Campus Drve, Saskatoon, SK S7N 5A9, Canada

More information

CS 229, Public Course Problem Set #3 Solutions: Learning Theory and Unsupervised Learning

CS 229, Public Course Problem Set #3 Solutions: Learning Theory and Unsupervised Learning CS9 Problem Set #3 Solutons CS 9, Publc Course Problem Set #3 Solutons: Learnng Theory and Unsupervsed Learnng. Unform convergence and Model Selecton In ths problem, we wll prove a bound on the error of

More information

e - c o m p a n i o n

e - c o m p a n i o n OPERATIONS RESEARCH http://dxdoorg/0287/opre007ec e - c o m p a n o n ONLY AVAILABLE IN ELECTRONIC FORM 202 INFORMS Electronc Companon Generalzed Quantty Competton for Multple Products and Loss of Effcency

More information

MAE140 - Linear Circuits - Winter 16 Final, March 16, 2016

MAE140 - Linear Circuits - Winter 16 Final, March 16, 2016 ME140 - Lnear rcuts - Wnter 16 Fnal, March 16, 2016 Instructons () The exam s open book. You may use your class notes and textbook. You may use a hand calculator wth no communcaton capabltes. () You have

More information

8.6 The Complex Number System

8.6 The Complex Number System 8.6 The Complex Number System Earler n the chapter, we mentoned that we cannot have a negatve under a square root, snce the square of any postve or negatve number s always postve. In ths secton we want

More information

MAE140 - Linear Circuits - Fall 10 Midterm, October 28

MAE140 - Linear Circuits - Fall 10 Midterm, October 28 M140 - Lnear rcuts - Fall 10 Mdterm, October 28 nstructons () Ths exam s open book. You may use whatever wrtten materals you choose, ncludng your class notes and textbook. You may use a hand calculator

More information

Exercise Solutions to Real Analysis

Exercise Solutions to Real Analysis xercse Solutons to Real Analyss Note: References refer to H. L. Royden, Real Analyss xersze 1. Gven any set A any ɛ > 0, there s an open set O such that A O m O m A + ɛ. Soluton 1. If m A =, then there

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information

Designing Information Devices and Systems II Spring 2018 J. Roychowdhury and M. Maharbiz Discussion 3A

Designing Information Devices and Systems II Spring 2018 J. Roychowdhury and M. Maharbiz Discussion 3A EECS 16B Desgnng Informaton Devces and Systems II Sprng 018 J. Roychowdhury and M. Maharbz Dscusson 3A 1 Phasors We consder snusodal voltages and currents of a specfc form: where, Voltage vt) = V 0 cosωt

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model EXACT OE-DIMESIOAL ISIG MODEL The one-dmensonal Isng model conssts of a chan of spns, each spn nteractng only wth ts two nearest neghbors. The smple Isng problem n one dmenson can be solved drectly n several

More information

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1 C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned

More information

Chapter 6. Operational Amplifier. inputs can be defined as the average of the sum of the two signals.

Chapter 6. Operational Amplifier.  inputs can be defined as the average of the sum of the two signals. 6 Operatonal mpler Chapter 6 Operatonal mpler CC Symbol: nput nput Output EE () Non-nvertng termnal, () nvertng termnal nput mpedance : Few mega (ery hgh), Output mpedance : Less than (ery low) Derental

More information

Feature Selection: Part 1

Feature Selection: Part 1 CSE 546: Machne Learnng Lecture 5 Feature Selecton: Part 1 Instructor: Sham Kakade 1 Regresson n the hgh dmensonal settng How do we learn when the number of features d s greater than the sample sze n?

More information

Advanced Circuits Topics - Part 1 by Dr. Colton (Fall 2017)

Advanced Circuits Topics - Part 1 by Dr. Colton (Fall 2017) Advanced rcuts Topcs - Part by Dr. olton (Fall 07) Part : Some thngs you should already know from Physcs 0 and 45 These are all thngs that you should have learned n Physcs 0 and/or 45. Ths secton s organzed

More information

Solution Set #1

Solution Set #1 05-78-0 Soluton Set #. Fnd epressons and setch the results of the followng operatons: (a) COMB RECT The spacng of the elements of the COMB functon matches the wdth of the rectangle; we can do ths n ether

More information

ACTM State Calculus Competition Saturday April 30, 2011

ACTM State Calculus Competition Saturday April 30, 2011 ACTM State Calculus Competton Saturday Aprl 30, 2011 ACTM State Calculus Competton Sprng 2011 Page 1 Instructons: For questons 1 through 25, mark the best answer choce on the answer sheet provde Afterward

More information

Integrals and Invariants of Euler-Lagrange Equations

Integrals and Invariants of Euler-Lagrange Equations Lecture 16 Integrals and Invarants of Euler-Lagrange Equatons ME 256 at the Indan Insttute of Scence, Bengaluru Varatonal Methods and Structural Optmzaton G. K. Ananthasuresh Professor, Mechancal Engneerng,

More information