ECE 145A / 218 C, notes set 13: Very Short Summary of Noise

Size: px
Start display at page:

Download "ECE 145A / 218 C, notes set 13: Very Short Summary of Noise"

Transcription

1 class otes, M. odwell, copyrighted 009 C 45A / 8 C, otes set 3: Very hort ummary o Noise Mark odwell Uiversity o Calioria, ata Barbara rodwell@ece.ucsb.edu , ax

2 Backgroud / tet class otes, M. odwell, copyrighted 009 Noise is a complex subject Mth Math Physics Device oise models dl Circuit oise aalysis - port oise represetatios ti ystems oise aalysis ystem sesitivity calculatios l We ca oly touch upo a very ew poits here. More dtil details i other classes.

3 class otes, M. odwell, copyrighted 009 Probability Distributio Fuctio: adom Variable Durig a experimet, a radom variable The probability that x lies P{ x < x < x} X ( x dx X ( x is x x betwee X x ad x the probability distributio uctio. X (x takes o a particular value x. is x x x

4 Mea value, Variace, tadard Deviatio class otes, M. odwell, copyrighted 009 Mea Value o X : [ X ] X X x X ( x dx xpected value o : X [ X ] X x X ( x dx The variace σ x o rom its average value σ X σ X ( X x ( X x X ( x The stadard deviatio σ. X is its root - mea - square deviatio [ ] ( x x ( x x o X dx X is simply the square root o the variace

5 The Gaussia Distributio class otes, M. odwell, copyrighted 009 The Gaussia distributio : X ( x πσ The mea ( x x exp ( x x σ ( x x ad the stadard deviatio ( σ are as deied previously. Because o the * cetral limit theorem*, physical radom processes arisig rom the sum o may small eects have probability distributios close to that o the Gaussia. X (x σ x x x

6 Pair o adom Variables class otes, M. odwell, copyrighted 009 a experimet, a pair o radom variables takes o speciic particular values x ad y. X ad Y Their joit behavior is described by the joit distributio P { A < x < B ad C < y < D} ( x, y dxdy D C B A XY ( x, y XY

7 Correlatio ad Covariace class otes, M. odwell, copyrighted 009 The correlatio o XY X ad Y is [ XY ] xy ( x, y dxdy XY The covariace o C XY X ad Y is [ ( X x ( Y y ] XY x y Note that correlatio ad covariace are i either X or Y have zero mea values. the same we are dealig with AC oise sigals (subtractig o the mea values are zero. DC bias,

8 adom Processes class otes, M. odwell, copyrighted 009 A voltagev(t varyig (i some sese radomly with time. Measured at times t ad t, V ( t has valuesv ( t adv ( t. v(t v(t v(t t t t

9 Autocorrelatio Fuctio o A adom Process class otes, M. odwell, copyrighted 009 Write or simplicity V V ( t, V V ( t ( t, t [ V, V ] the process is statioary, the time origi does ot matter, ad [ V ( t, V ( t ] [ V ( t 3, V ( t 3 t t ] [ V ( t, V ( t τ ] The ( τ [ V ( t, V ( t τ ]

10 Power pectral Desity o a adom Process class otes, M. odwell, copyrighted 009 Power spectral desity ( ω ( τ exp( jωτ Fourier trasorm o dτ ( τ ad ( ω are a trasorm pair, so ( τ ( ω exp( jωτ ( τ : π dω The power delivered to a load isv so the average value o But [ V o, ] (0 the oise power is π ( ω itegratig the power spectral gives us the oise power. / dω load, [ V ]/ load desity (& dividig by Load

11 igle-ided Hz-based pectral Desities class otes, M. odwell, copyrighted 009 Double - ided pectral Desities V ( t V ( t τ π ( τ [ ] ( jω exp( jωτ dω ( jω ( τ exp( jωτ dτ igle - ided Hz - based pectral Desities ( τ [ V ( t V ( t τ ] ( j exp( jπ τ ( j ( τ exp( j π τ dτ d

12 class otes, M. odwell, copyrighted 009 igle-ided Hz-based pectral Desities- Why? Why this otatio? The sigal Power power i the badwidth low {, } low high high ( j d ( j d ( j Load high Load ( j / Load is directly the Watts o sigal power per Hz o sigal badwidth at requecies ear to. low Load high low d

13 Thermal Noise: esistaces class otes, M. odwell, copyrighted 009 For h << kt, resistors produce oise voltage. ( j 4 kt By a Thevei - 4kT ( j Norto trasormatio, the oise curret is

14 Available Thermal Noise Power class otes, M. odwell, copyrighted 009 Maximum power traser : load matched to erator. With matched load, voltage across load is With matched hdload, curret through h loadis N N / / Give that ( j 4kT or ( j 4kT load d P d kt P load is the maximum (the available oise power, hece i a badwidth Δ, P, kt Δ available oise All resistors have equal available oise power. Ay compooet uder thermal equilibrium (o bias ollows this law.

15 Noise rom a Atea class otes, M. odwell, copyrighted 009 d P available, oise d kt ( j 4kT e( Z The atea has both Ohmic ad radiatio resistaces. rad The Ohmic resistace has a oise voltage o spectral desity 4 kt ambiet Ohmic, where T ambiet is the physical atea temperature The radiatio resistace has a oise voltage o spectral desity 4kT o ield rad, where T ield is the average temperature the regio rom which the atea receives sigal power,rad loss,loss ter - galactic space is at.7 Kelvi (Big Bag

16 hot oise: PN juctios class otes, M. odwell, copyrighted 009 * *, give a DC curret DC, the arrival idepedet o every other electro, o each electro is statistically * the * the DC curret has a oise luctuatio ti o power spectral desity ( j q DC DC currets i resistors are * ot * a statistically idepedet low o ad *do ot * erate short oise. electros, Currets i PN juctios * DO*exhibit shot oise.

17 class otes, M. odwell, copyrighted 009 Device Noise Models: Descriptive, No xplaatio Bipolar Trasistor thermal oise shot oise FT

18 Two-Port Noise Descriptio class otes, M. odwell, copyrighted 009 Through the methods o circuit aalysis, the iteral oise erators o a circuit ca be summed ad represeted by two oise erators ad. The spectral desities o ad must be calculated ad speciied. The cross spectral desity * must also be calculated ad speciied. * Cross spectral desity : V ( j V ( τ exp( jπ τ dτ Where V ( τ [ V ( t ( t τ ] is the * cross - correllatio uctio o V ad.

19 Calculatig Total Noise class otes, M. odwell, copyrighted 009 the erator just has 4 kt N 4, thermal oise, epreset the combiatio o ampliier voltage ad curret oise by a sigle source Total N N Z We ca ow calculate the spectraldesity o { } * Z g e Z, total, ampliier g Z e jx g this { ( } total oise :

20 igal / Noise atio o Geerator class otes, M. odwell, copyrighted 009 V sigal,, total ad, are i series ad see the same load impedace. The ratios o powers delivered by these will ot deped upo the load. Thereore cosider the aviable oise powers. The sigal power available rom the erator is Psigal available Vsigal M / 4,, we cosider a arrow badwidth betwee( the the available oise power rom N, is P [ ] ( j Δ / 4 oise, available, erator, sigal Δ / ad ( sigal Δ /, The sigal/oise ratio o the erator is the N P P sigal, available V oise, available, erator, sigal, M ( j Δ / 4 / 4 V sigal, M kt / 4 Δ

21 igal / Noise atio o GeeratorAmpliier class otes, M. odwell, copyrighted 009 igal power available rom the erator : P V / 4 Ni Noise power available rom erator : Poise, av, sigal, available sigal, M ( j Δ / 4 kt Δ Noise power available rom ampliier : P 4, total, ampliier oise, av, Amp ( j Δ / igal/oise ratio icludig ampliier oise: N P P sigal, available P ( j Δ V sigal, M / 4 / 4 oise, avail, oise, avail, amp total, total V sigal, M / 4 ( j Δ / 4 kt Δ ( j Δ / 4

22 class otes, M. odwell, copyrighted 009 Noise Figure: igal / Noise atio Degradatio Noise igure sigal/oise ratio beore addig ampliier sigal/oise ratio beore addig ampliier igal/oise ratio beore addig ampliier : N V sigal, M kt Δ / 4 igal/oise ratio ater addig ampliier : N total ( V sigal, M / 4 j Δ / 4 kt Δ Noise igure ( j Δ / 4 total Δ kt kt Δ total ( j / 4 Noise igure kt ampliier available iput oise kt power

23 Calculatig Noise Figure class otes, M. odwell, copyrighted 009 Noise igure total ( j / 4 kt We also kow that : Z e { Z * },, g total ampliier g We ca calculate l rom this a expressio or oise igure : F * Z e( Z s 4kT s

24 Miimum Noise Figure class otes, M. odwell, copyrighted 009 Noise igure varies as a uctio o Z jx : F Z s ( * e Zs 4kT Ater some calculus, we ca id a mimimum oise igure ad a erator impedace which gives us this miimum : F Z mi opt opt 4kT jx ( [ ] m e[ ] opt m [ ] m[ ] j Pit Poits a optimum to remember are ( F Z varies with Z, hece ( which gives a miimum F(3. there is

25 Noise Figure i Wave Notatio class otes, M. odwell, copyrighted 009 Writte F F mi istead i terms o wave parameters, 4 r Γ Γ [ ] Γ [ Γ ] s s opt opt These describe cotous i the Γs plae o costat oise igure :" oise igure circles", i.e. a descriptio o with source the variatio o relectio coeiciet. oise igure

26 Low-Noise Ampliier Desig Yue & odwell, CC hort Course, Nov. 006 Desig steps are i-bad stabilizatio: this is best doe at output port to avoid degradig oise iput tuig or F mi 3 output tuig (match 4 out-o-bad stabilizatio Note that tuig or miimum oise igure requires a *mismatch* o the ampliier iput; ampliier gai thereore must lie below the trasistor MAG/MG. Note that tuig or miimum oise igure implies that ampliier iput is mismatched: iput relectio coeiciet is thereore ot zero! Discrepacy i iput oise-match & gai-match ca be reduced by addig source iductace Z s Z o Z s oise match V

27 xample LNA Desig: 60 GHz, 30 m ige BJT Yue & odwell, CC hort Course, Nov. 006 gai & oise circles ater iput matchig gai & oise circles ater iput matchig ote compromise betwee gai & oise tuig

28 Noise Figure o Cascaded tages Yue & odwell, CC hort Course, Nov. 006 F cascade F F F3 F4 G G G G G G A A A A A A3... This is the Friis oise igure ormula. G Ai are the available power gais. The relatioship applies whether or ot iter - stages are matched.

ECE594I Notes set 13: Two-port Noise Parameters

ECE594I Notes set 13: Two-port Noise Parameters C594 otes, M. Rodwell, copyrighted C594 Notes set 13: Two-port Noise Parameters Mark Rodwell Uiversity of Califoria, Sata Barbara rodwell@ece.ucsb.edu 805-893-3244, 805-893-3262 fax Refereces ad Citatios:

More information

Mixed Signal IC Design Notes set 7: Electrical device noise models.

Mixed Signal IC Design Notes set 7: Electrical device noise models. C145C /18C otes, M. owell, copyrighte 007 Mixe Sigal C Desig Notes set 7: lectrical evice oise moels. Mark owell Uiversity of Califoria, Sata Barbara rowell@ece.ucsb.eu 805-893-344, 805-893-36 fax Topics

More information

High Speed Mixed Signal IC Design notes set 8. Noise in Electrical Circuits: circuit noise analysis

High Speed Mixed Signal IC Design notes set 8. Noise in Electrical Circuits: circuit noise analysis C145C /18C otes, M. odwell, copyrihted 007 Hih peed Mixed ial C Desi otes set 8 Noise i lectrical Circuits: circuit oise aalysis Mark odwell Uiversity of Califoria, ata Barbara rodwell@ece.ucsb.edu 805-893-344,

More information

ECE 145B / 218B, notes set 3: Electrical device noise models.

ECE 145B / 218B, notes set 3: Electrical device noise models. class otes, M. owell, copyrighte 2012 C 145B / 218B, otes set 3: lectrical evice oise moels. Mark owell Uiversity of Califoria, Sata Barbara rowell@ece.ucsb.eu 805-893-3244, 805-893-3262 fax class otes,

More information

EE 505 Lecture 9. Statistical Circuit Modeling

EE 505 Lecture 9. Statistical Circuit Modeling EE 505 Lecture 9 Statistical Circuit Modelig eview from previous lecture: Statistical Aalysis Strategy Will first focus o statistical characterizatio of resistors, the exted to capacitors ad trasistors

More information

Voltage controlled oscillator (VCO)

Voltage controlled oscillator (VCO) Voltage cotrolled oscillator (VO) Oscillatio frequecy jl Z L(V) jl[ L(V)] [L L (V)] L L (V) T VO gai / Logf Log 4 L (V) f f 4 L(V) Logf / L(V) f 4 L (V) f (V) 3 Lf 3 VO gai / (V) j V / V Bi (V) / V Bi

More information

Some Basic Probability Concepts. 2.1 Experiments, Outcomes and Random Variables

Some Basic Probability Concepts. 2.1 Experiments, Outcomes and Random Variables Some Basic Probability Cocepts 2. Experimets, Outcomes ad Radom Variables A radom variable is a variable whose value is ukow util it is observed. The value of a radom variable results from a experimet;

More information

Chapter 9 - CD companion 1. A Generic Implementation; The Common-Merge Amplifier. 1 τ is. ω ch. τ io

Chapter 9 - CD companion 1. A Generic Implementation; The Common-Merge Amplifier. 1 τ is. ω ch. τ io Chapter 9 - CD compaio CHAPTER NINE CD-9.2 CD-9.2. Stages With Voltage ad Curret Gai A Geeric Implemetatio; The Commo-Merge Amplifier The advaced method preseted i the text for approximatig cutoff frequecies

More information

ECE 145B / 218B, notes set 5: Two-port Noise Parameters

ECE 145B / 218B, notes set 5: Two-port Noise Parameters class oes, M. odwell, copyrihed 01 C 145B 18B, oes se 5: Two-por Noise Parameers Mark odwell Uiersiy of Califoria, aa Barbara rodwell@ece.ucsb.edu 805-893-344, 805-893-36 fax efereces ad Ciaios: class

More information

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering CEE 5 Autum 005 Ucertaity Cocepts for Geotechical Egieerig Basic Termiology Set A set is a collectio of (mutually exclusive) objects or evets. The sample space is the (collectively exhaustive) collectio

More information

Probability and Statistics

Probability and Statistics Probability ad Statistics Cotets. Multi-dimesioal Gaussia radom variable. Gaussia radom process 3. Wieer process Why we eed to discuss Gaussia Process The most commo Accordig to the cetral limit theorem,

More information

Last time: Moments of the Poisson distribution from its generating function. Example: Using telescope to measure intensity of an object

Last time: Moments of the Poisson distribution from its generating function. Example: Using telescope to measure intensity of an object 6.3 Stochastic Estimatio ad Cotrol, Fall 004 Lecture 7 Last time: Momets of the Poisso distributio from its geeratig fuctio. Gs () e dg µ e ds dg µ ( s) µ ( s) µ ( s) µ e ds dg X µ ds X s dg dg + ds ds

More information

From deterministic regular waves to a random field. From a determinstic regular wave to a deterministic irregular solution

From deterministic regular waves to a random field. From a determinstic regular wave to a deterministic irregular solution Classiicatio: Iteral Status: Drat z ξ(x,y, w& x w u u& h Particle ositio From determiistic regular waves to a radom ield Sverre Haver, StatoilHydro, Jauary 8 From a determistic regular wave to a determiistic

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

Mixed Signal IC Design Notes set 6: Mathematics of Electrical Noise

Mixed Signal IC Design Notes set 6: Mathematics of Electrical Noise ECE45C /8C notes, M. odwell, copyrighted 007 Mied Signal IC Design Notes set 6: Mathematics o Electrical Noise Mark odwell University o Caliornia, Santa Barbara rodwell@ece.ucsb.edu 805-893-344, 805-893-36

More information

NOISE IN SC CIRCUITS. NLCOTD: Gain Booster CMFB. Highlights (i.e. What you will learn today) Review. Course Goals. ECE1371 Advanced Analog Circuits

NOISE IN SC CIRCUITS. NLCOTD: Gain Booster CMFB. Highlights (i.e. What you will learn today) Review. Course Goals. ECE1371 Advanced Analog Circuits EE37 Advaced Aalog ircuits Lecture 0 NIE IRUIT Richard chreier richard.schreier@aalog.com Trevor aldwell trevor.caldwell@utoroto.ca ourse Goals Deepe Uderstadig o M aalog circuit desig through a top-dow

More information

THE SYSTEMATIC AND THE RANDOM. ERRORS - DUE TO ELEMENT TOLERANCES OF ELECTRICAL NETWORKS

THE SYSTEMATIC AND THE RANDOM. ERRORS - DUE TO ELEMENT TOLERANCES OF ELECTRICAL NETWORKS R775 Philips Res. Repts 26,414-423, 1971' THE SYSTEMATIC AND THE RANDOM. ERRORS - DUE TO ELEMENT TOLERANCES OF ELECTRICAL NETWORKS by H. W. HANNEMAN Abstract Usig the law of propagatio of errors, approximated

More information

Statistical Noise Models and Diagnostics

Statistical Noise Models and Diagnostics L. Yaroslavsky: Advaced Image Processig Lab: A Tutorial, EUSIPCO2 LECTURE 2 Statistical oise Models ad Diagostics 2. Statistical models of radom iterfereces: (i) Additive sigal idepedet oise model: r =

More information

Random Variables, Sampling and Estimation

Random Variables, Sampling and Estimation Chapter 1 Radom Variables, Samplig ad Estimatio 1.1 Itroductio This chapter will cover the most importat basic statistical theory you eed i order to uderstad the ecoometric material that will be comig

More information

Crash course part 2. Frequency compensation

Crash course part 2. Frequency compensation Crash course part Frequecy compesatio Ageda Frequecy depedace Feedback amplifiers Frequecy depedace of the Trasistor Frequecy Compesatio Phatom Zero Examples Crash course part poles ad zeros I geeral a

More information

Random Signals and Noise Winter Semester 2017 Problem Set 12 Wiener Filter Continuation

Random Signals and Noise Winter Semester 2017 Problem Set 12 Wiener Filter Continuation Radom Sigals ad Noise Witer Semester 7 Problem Set Wieer Filter Cotiuatio Problem (Sprig, Exam A) Give is the sigal W t, which is a Gaussia white oise with expectatio zero ad power spectral desity fuctio

More information

Consider the circuit below. We have seen this one already. As before, assume that the BJT is on and in forward active operation.

Consider the circuit below. We have seen this one already. As before, assume that the BJT is on and in forward active operation. Saturatio Cosider the circuit below. We have see this oe already. As before, assume that the BJT is o ad i forward active operatio. VCC 0 V VBB ib RC 0 k! RB 3V 47 k! vbe ic vce βf 00. ( )( µ µ ). (. )(!!

More information

Lecture 2: Poisson Sta*s*cs Probability Density Func*ons Expecta*on and Variance Es*mators

Lecture 2: Poisson Sta*s*cs Probability Density Func*ons Expecta*on and Variance Es*mators Lecture 2: Poisso Sta*s*cs Probability Desity Fuc*os Expecta*o ad Variace Es*mators Biomial Distribu*o: P (k successes i attempts) =! k!( k)! p k s( p s ) k prob of each success Poisso Distributio Note

More information

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense,

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense, 3. Z Trasform Referece: Etire Chapter 3 of text. Recall that the Fourier trasform (FT) of a DT sigal x [ ] is ω ( ) [ ] X e = j jω k = xe I order for the FT to exist i the fiite magitude sese, S = x [

More information

Chapter 2 The Monte Carlo Method

Chapter 2 The Monte Carlo Method Chapter 2 The Mote Carlo Method The Mote Carlo Method stads for a broad class of computatioal algorithms that rely o radom sampligs. It is ofte used i physical ad mathematical problems ad is most useful

More information

FIR Filter Design: Part I

FIR Filter Design: Part I EEL3: Discrete-Time Sigals ad Systems FIR Filter Desig: Part I. Itroductio FIR Filter Desig: Part I I this set o otes, we cotiue our exploratio o the requecy respose o FIR ilters. First, we cosider some

More information

EE C245 - ME C218 Introduction to MEMS Design Fall Today s Lecture

EE C245 - ME C218 Introduction to MEMS Design Fall Today s Lecture EE C45 ME C8 Itroductio to MEMS Desig Fall 003 Roger Howe ad Thara Sriiasa Lecture 3 Capacitie Positio Sesig: Electroic ad Mechaical Noise EE C45 ME C8 Fall 003 Lecture 3 Today s Lecture Basic CMOS buffer

More information

Wind Energy Explained, 2 nd Edition Errata

Wind Energy Explained, 2 nd Edition Errata Wid Eergy Explaied, d Editio Errata This summarizes the ko errata i Wid Eergy Explaied, d Editio. The errata ere origially compiled o July 6, 0. Where possible, the chage or locatio of the chage is oted

More information

Some illustrations of possibilistic correlation

Some illustrations of possibilistic correlation Some illustratios of possibilistic correlatio Robert Fullér IAMSR, Åbo Akademi Uiversity, Joukahaisekatu -5 A, FIN-252 Turku e-mail: rfuller@abofi József Mezei Turku Cetre for Computer Sciece, Joukahaisekatu

More information

The Scattering Matrix

The Scattering Matrix 2/23/7 The Scatterig Matrix 723 1/13 The Scatterig Matrix At low frequecies, we ca completely characterize a liear device or etwork usig a impedace matrix, which relates the currets ad voltages at each

More information

STAT 516 Answers Homework 6 April 2, 2008 Solutions by Mark Daniel Ward PROBLEMS

STAT 516 Answers Homework 6 April 2, 2008 Solutions by Mark Daniel Ward PROBLEMS STAT 56 Aswers Homework 6 April 2, 28 Solutios by Mark Daiel Ward PROBLEMS Chapter 6 Problems 2a. The mass p(, correspods to either o the irst two balls beig white, so p(, 8 7 4/39. The mass p(, correspods

More information

The Discrete-Time Fourier Transform (DTFT)

The Discrete-Time Fourier Transform (DTFT) EEL: Discrete-Time Sigals ad Systems The Discrete-Time Fourier Trasorm (DTFT) The Discrete-Time Fourier Trasorm (DTFT). Itroductio I these otes, we itroduce the discrete-time Fourier trasorm (DTFT) ad

More information

TMA4245 Statistics. Corrected 30 May and 4 June Norwegian University of Science and Technology Department of Mathematical Sciences.

TMA4245 Statistics. Corrected 30 May and 4 June Norwegian University of Science and Technology Department of Mathematical Sciences. Norwegia Uiversity of Sciece ad Techology Departmet of Mathematical Scieces Corrected 3 May ad 4 Jue Solutios TMA445 Statistics Saturday 6 May 9: 3: Problem Sow desity a The probability is.9.5 6x x dx

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science. BACKGROUND EXAM September 30, 2004.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science. BACKGROUND EXAM September 30, 2004. MASSACHUSETTS INSTITUTE OF TECHNOLOGY Departmet of Electrical Egieerig ad Computer Sciece 6.34 Discrete Time Sigal Processig Fall 24 BACKGROUND EXAM September 3, 24. Full Name: Note: This exam is closed

More information

Probability 2 - Notes 10. Lemma. If X is a random variable and g(x) 0 for all x in the support of f X, then P(g(X) 1) E[g(X)].

Probability 2 - Notes 10. Lemma. If X is a random variable and g(x) 0 for all x in the support of f X, then P(g(X) 1) E[g(X)]. Probability 2 - Notes 0 Some Useful Iequalities. Lemma. If X is a radom variable ad g(x 0 for all x i the support of f X, the P(g(X E[g(X]. Proof. (cotiuous case P(g(X Corollaries x:g(x f X (xdx x:g(x

More information

[ ] ( ) ( ) [ ] ( ) 1 [ ] [ ] Sums of Random Variables Y = a 1 X 1 + a 2 X 2 + +a n X n The expected value of Y is:

[ ] ( ) ( ) [ ] ( ) 1 [ ] [ ] Sums of Random Variables Y = a 1 X 1 + a 2 X 2 + +a n X n The expected value of Y is: PROBABILITY FUNCTIONS A radom variable X has a probabilit associated with each of its possible values. The probabilit is termed a discrete probabilit if X ca assume ol discrete values, or X = x, x, x 3,,

More information

Bipolar Junction Transistors

Bipolar Junction Transistors ipolar Juctio Trasistors ipolar juctio trasistor (JT) was iveted i 948 at ell Telephoe Laboratories Sice 97, the high desity ad low power advatage of the MOS techology steadily eroded the JT s early domiace.

More information

577. Estimation of surface roughness using high frequency vibrations

577. Estimation of surface roughness using high frequency vibrations 577. Estimatio of surface roughess usig high frequecy vibratios V. Augutis, M. Sauoris, Kauas Uiversity of Techology Electroics ad Measuremets Systems Departmet Studetu str. 5-443, LT-5368 Kauas, Lithuaia

More information

Matrix Representation of Data in Experiment

Matrix Representation of Data in Experiment Matrix Represetatio of Data i Experimet Cosider a very simple model for resposes y ij : y ij i ij, i 1,; j 1,,..., (ote that for simplicity we are assumig the two () groups are of equal sample size ) Y

More information

Mixtures of Gaussians and the EM Algorithm

Mixtures of Gaussians and the EM Algorithm Mixtures of Gaussias ad the EM Algorithm CSE 6363 Machie Learig Vassilis Athitsos Computer Sciece ad Egieerig Departmet Uiversity of Texas at Arligto 1 Gaussias A popular way to estimate probability desity

More information

ECON 3150/4150, Spring term Lecture 3

ECON 3150/4150, Spring term Lecture 3 Itroductio Fidig the best fit by regressio Residuals ad R-sq Regressio ad causality Summary ad ext step ECON 3150/4150, Sprig term 2014. Lecture 3 Ragar Nymoe Uiversity of Oslo 21 Jauary 2014 1 / 30 Itroductio

More information

Chapter 10 Advanced Topics in Random Processes

Chapter 10 Advanced Topics in Random Processes ery Stark ad Joh W. Woods, Probability, Statistics, ad Radom Variables for Egieers, 4th ed., Pearso Educatio Ic.,. ISBN 978--3-33-6 Chapter Advaced opics i Radom Processes Sectios. Mea-Square (m.s.) Calculus

More information

Chapter 12 EM algorithms The Expectation-Maximization (EM) algorithm is a maximum likelihood method for models that have hidden variables eg. Gaussian

Chapter 12 EM algorithms The Expectation-Maximization (EM) algorithm is a maximum likelihood method for models that have hidden variables eg. Gaussian Chapter 2 EM algorithms The Expectatio-Maximizatio (EM) algorithm is a maximum likelihood method for models that have hidde variables eg. Gaussia Mixture Models (GMMs), Liear Dyamic Systems (LDSs) ad Hidde

More information

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter Time Respose & Frequecy Respose d -Order Dyamic System -Pole, Low-Pass, Active Filter R 4 R 7 C 5 e i R 1 C R 3 - + R 6 - + e out Assigmet: Perform a Complete Dyamic System Ivestigatio of the Two-Pole,

More information

Lecture 3. Properties of Summary Statistics: Sampling Distribution

Lecture 3. Properties of Summary Statistics: Sampling Distribution Lecture 3 Properties of Summary Statistics: Samplig Distributio Mai Theme How ca we use math to justify that our umerical summaries from the sample are good summaries of the populatio? Lecture Summary

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 5

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 5 CS434a/54a: Patter Recogitio Prof. Olga Veksler Lecture 5 Today Itroductio to parameter estimatio Two methods for parameter estimatio Maimum Likelihood Estimatio Bayesia Estimatio Itroducto Bayesia Decisio

More information

TAMS24: Notations and Formulas

TAMS24: Notations and Formulas TAMS4: Notatios ad Formulas Basic otatios ad defiitios X: radom variable stokastiska variabel Mea Vätevärde: µ = X = by Xiagfeg Yag kpx k, if X is discrete, xf Xxdx, if X is cotiuous Variace Varias: =

More information

ELE B7 Power Systems Engineering. Symmetrical Components

ELE B7 Power Systems Engineering. Symmetrical Components ELE B7 Power Systems Egieerig Symmetrical Compoets Aalysis of Ubalaced Systems Except for the balaced three-phase fault, faults result i a ubalaced system. The most commo types of faults are sigle liegroud

More information

The Maximum-Likelihood Decoding Performance of Error-Correcting Codes

The Maximum-Likelihood Decoding Performance of Error-Correcting Codes The Maximum-Lielihood Decodig Performace of Error-Correctig Codes Hery D. Pfister ECE Departmet Texas A&M Uiversity August 27th, 2007 (rev. 0) November 2st, 203 (rev. ) Performace of Codes. Notatio X,

More information

Signal Processing in Mechatronics. Lecture 3, Convolution, Fourier Series and Fourier Transform

Signal Processing in Mechatronics. Lecture 3, Convolution, Fourier Series and Fourier Transform Sigal Processig i Mechatroics Summer semester, 1 Lecture 3, Covolutio, Fourier Series ad Fourier rasform Dr. Zhu K.P. AIS, UM 1 1. Covolutio Covolutio Descriptio of LI Systems he mai premise is that the

More information

Probability and statistics: basic terms

Probability and statistics: basic terms Probability ad statistics: basic terms M. Veeraraghava August 203 A radom variable is a rule that assigs a umerical value to each possible outcome of a experimet. Outcomes of a experimet form the sample

More information

Linear Regression Models

Linear Regression Models Liear Regressio Models Dr. Joh Mellor-Crummey Departmet of Computer Sciece Rice Uiversity johmc@cs.rice.edu COMP 528 Lecture 9 15 February 2005 Goals for Today Uderstad how to Use scatter diagrams to ispect

More information

Outline. L7: Probability Basics. Probability. Probability Theory. Bayes Law for Diagnosis. Which Hypothesis To Prefer? p(a,b) = p(b A) " p(a)

Outline. L7: Probability Basics. Probability. Probability Theory. Bayes Law for Diagnosis. Which Hypothesis To Prefer? p(a,b) = p(b A)  p(a) Outlie L7: Probability Basics CS 344R/393R: Robotics Bejami Kuipers. Bayes Law 2. Probability distributios 3. Decisios uder ucertaity Probability For a propositio A, the probability p(a is your degree

More information

Bayesian Methods: Introduction to Multi-parameter Models

Bayesian Methods: Introduction to Multi-parameter Models Bayesia Methods: Itroductio to Multi-parameter Models Parameter: θ = ( θ, θ) Give Likelihood p(y θ) ad prior p(θ ), the posterior p proportioal to p(y θ) x p(θ ) Margial posterior ( θ, θ y) is Iterested

More information

Lecture 11 and 12: Basic estimation theory

Lecture 11 and 12: Basic estimation theory Lecture ad 2: Basic estimatio theory Sprig 202 - EE 94 Networked estimatio ad cotrol Prof. Kha March 2 202 I. MAXIMUM-LIKELIHOOD ESTIMATORS The maximum likelihood priciple is deceptively simple. Louis

More information

Shadowgraph, Schlieren and Interferometry Part - 01

Shadowgraph, Schlieren and Interferometry Part - 01 AerE 545 class otes #8 Shadowgraph, Schliere ad tererometry Part - Hui Hu Departmet o Aerospace Egieerig, owa State Uiversity Ames, owa 5, U.S.A dex o reractio: λ c / v > λ Deped o variatio o idex o reractio

More information

Quick Review of Probability

Quick Review of Probability Quick Review of Probability Berli Che Departmet of Computer Sciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Refereces: 1. W. Navidi. Statistics for Egieerig ad Scietists. Chapter 2 & Teachig

More information

ECE 564/645 - Digital Communication Systems (Spring 2014) Final Exam Friday, May 2nd, 8:00-10:00am, Marston 220

ECE 564/645 - Digital Communication Systems (Spring 2014) Final Exam Friday, May 2nd, 8:00-10:00am, Marston 220 ECE 564/645 - Digital Commuicatio Systems (Sprig 014) Fial Exam Friday, May d, 8:00-10:00am, Marsto 0 Overview The exam cosists of four (or five) problems for 100 (or 10) poits. The poits for each part

More information

ENGI 4421 Confidence Intervals (Two Samples) Page 12-01

ENGI 4421 Confidence Intervals (Two Samples) Page 12-01 ENGI 44 Cofidece Itervals (Two Samples) Page -0 Two Sample Cofidece Iterval for a Differece i Populatio Meas [Navidi sectios 5.4-5.7; Devore chapter 9] From the cetral limit theorem, we kow that, for sufficietly

More information

Axis Aligned Ellipsoid

Axis Aligned Ellipsoid Machie Learig for Data Sciece CS 4786) Lecture 6,7 & 8: Ellipsoidal Clusterig, Gaussia Mixture Models ad Geeral Mixture Models The text i black outlies high level ideas. The text i blue provides simple

More information

Chapter If n is odd, the median is the exact middle number If n is even, the median is the average of the two middle numbers

Chapter If n is odd, the median is the exact middle number If n is even, the median is the average of the two middle numbers Chapter 4 4-1 orth Seattle Commuity College BUS10 Busiess Statistics Chapter 4 Descriptive Statistics Summary Defiitios Cetral tedecy: The extet to which the data values group aroud a cetral value. Variatio:

More information

Charles Moore, Avago Technologies. Singapore, March 2011

Charles Moore, Avago Technologies. Singapore, March 2011 A method or evaluatig chaels Charles Moore, Avago Techologies Adam ealey, LSI Corporatio 00 Gb/s Backplae ad Copper Study Group 00 Gb/s ac p a e a d Coppe S udy G oup Sigapore, March 0 Supporters Bria

More information

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course Sigal-EE Postal Correspodece Course 1 SAMPLE STUDY MATERIAL Electrical Egieerig EE / EEE Postal Correspodece Course GATE, IES & PSUs Sigal System Sigal-EE Postal Correspodece Course CONTENTS 1. SIGNAL

More information

Statistical Theory MT 2008 Problems 1: Solution sketches

Statistical Theory MT 2008 Problems 1: Solution sketches Statistical Theory MT 008 Problems : Solutio sketches. Which of the followig desities are withi a expoetial family? Explai your reasoig. a) Let 0 < θ < ad put fx, θ) = θ)θ x ; x = 0,,,... b) c) where α

More information

Lecture 9: Diffusion, Electrostatics review, and Capacitors. Context

Lecture 9: Diffusion, Electrostatics review, and Capacitors. Context EECS 5 Sprig 4, Lecture 9 Lecture 9: Diffusio, Electrostatics review, ad Capacitors EECS 5 Sprig 4, Lecture 9 Cotext I the last lecture, we looked at the carriers i a eutral semicoductor, ad drift currets

More information

CS537. Numerical Analysis and Computing

CS537. Numerical Analysis and Computing CS57 Numerical Aalysis ad Computig Lecture Locatig Roots o Equatios Proessor Ju Zhag Departmet o Computer Sciece Uiversity o Ketucky Leigto KY 456-6 Jauary 9 9 What is the Root May physical system ca be

More information

BHW #13 1/ Cooper. ENGR 323 Probabilistic Analysis Beautiful Homework # 13

BHW #13 1/ Cooper. ENGR 323 Probabilistic Analysis Beautiful Homework # 13 BHW # /5 ENGR Probabilistic Aalysis Beautiful Homework # Three differet roads feed ito a particular freeway etrace. Suppose that durig a fixed time period, the umber of cars comig from each road oto the

More information

Lecture 7: Properties of Random Samples

Lecture 7: Properties of Random Samples Lecture 7: Properties of Radom Samples 1 Cotiued From Last Class Theorem 1.1. Let X 1, X,...X be a radom sample from a populatio with mea µ ad variace σ

More information

FREQUENCY STABILITY. 6.1 Frequency Stability of a Passive Atomic Frequency Standard

FREQUENCY STABILITY. 6.1 Frequency Stability of a Passive Atomic Frequency Standard Chapter 6 9 FRQUNCY TBILITY 6. Frequec tabilit o a Passive tomic Frequec tadard This sectio calculates the requec stabilit o a passive atomic requec stadard, which depeds o the width o the atomic resoace

More information

ECE 901 Lecture 14: Maximum Likelihood Estimation and Complexity Regularization

ECE 901 Lecture 14: Maximum Likelihood Estimation and Complexity Regularization ECE 90 Lecture 4: Maximum Likelihood Estimatio ad Complexity Regularizatio R Nowak 5/7/009 Review : Maximum Likelihood Estimatio We have iid observatios draw from a ukow distributio Y i iid p θ, i,, where

More information

A Slight Extension of Coherent Integration Loss Due to White Gaussian Phase Noise Mark A. Richards

A Slight Extension of Coherent Integration Loss Due to White Gaussian Phase Noise Mark A. Richards A Slight Extesio of Coheret Itegratio Loss Due to White Gaussia Phase oise Mark A. Richards March 3, Goal I [], the itegratio loss L i computig the coheret sum of samples x with weights a is cosidered.

More information

Continuous Random Variables: Conditioning, Expectation and Independence

Continuous Random Variables: Conditioning, Expectation and Independence Cotiuous Radom Variables: Coditioig, Expectatio ad Idepedece Berli Che Departmet o Computer ciece & Iormatio Egieerig Natioal Taiwa Normal Uiversit Reerece: - D.. Bertsekas, J. N. Tsitsiklis, Itroductio

More information

Department of Civil Engineering-I.I.T. Delhi CEL 899: Environmental Risk Assessment HW5 Solution

Department of Civil Engineering-I.I.T. Delhi CEL 899: Environmental Risk Assessment HW5 Solution Departmet of Civil Egieerig-I.I.T. Delhi CEL 899: Evirometal Risk Assessmet HW5 Solutio Note: Assume missig data (if ay) ad metio the same. Q. Suppose X has a ormal distributio defied as N (mea=5, variace=

More information

Session 5. (1) Principal component analysis and Karhunen-Loève transformation

Session 5. (1) Principal component analysis and Karhunen-Loève transformation 200 Autum semester Patter Iformatio Processig Topic 2 Image compressio by orthogoal trasformatio Sessio 5 () Pricipal compoet aalysis ad Karhue-Loève trasformatio Topic 2 of this course explais the image

More information

Unbiased Estimation. February 7-12, 2008

Unbiased Estimation. February 7-12, 2008 Ubiased Estimatio February 7-2, 2008 We begi with a sample X = (X,..., X ) of radom variables chose accordig to oe of a family of probabilities P θ where θ is elemet from the parameter space Θ. For radom

More information

Name Solutions to Test 2 October 14, 2015

Name Solutions to Test 2 October 14, 2015 Name Solutios to Test October 4, 05 This test cosists of three parts. Please ote that i parts II ad III, you ca skip oe questio of those offered. The equatios below may be helpful with some problems. Costats

More information

MCT242: Electronic Instrumentation Lecture 2: Instrumentation Definitions

MCT242: Electronic Instrumentation Lecture 2: Instrumentation Definitions Faculty of Egieerig MCT242: Electroic Istrumetatio Lecture 2: Istrumetatio Defiitios Overview Measuremet Error Accuracy Precisio ad Mea Resolutio Mea Variace ad Stadard deviatio Fiesse Sesitivity Rage

More information

Quick Review of Probability

Quick Review of Probability Quick Review of Probability Berli Che Departmet of Computer Sciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Refereces: 1. W. Navidi. Statistics for Egieerig ad Scietists. Chapter & Teachig Material.

More information

PRACTICE PROBLEMS FOR THE FINAL

PRACTICE PROBLEMS FOR THE FINAL PRACTICE PROBLEMS FOR THE FINAL Math 36Q Fall 25 Professor Hoh Below is a list of practice questios for the Fial Exam. I would suggest also goig over the practice problems ad exams for Exam ad Exam 2 to

More information

Parameter, Statistic and Random Samples

Parameter, Statistic and Random Samples Parameter, Statistic ad Radom Samples A parameter is a umber that describes the populatio. It is a fixed umber, but i practice we do ot kow its value. A statistic is a fuctio of the sample data, i.e.,

More information

Math 176 Calculus Sec. 5.1: Areas and Distances (Using Finite Sums)

Math 176 Calculus Sec. 5.1: Areas and Distances (Using Finite Sums) Math 176 Calculus Sec. 5.1: Areas ad Distaces (Usig Fiite Sums) I. Area A. Cosider the problem of fidig the area uder the curve o the f y=-x 2 +5 over the domai [0, 2]. We ca approximate this area by usig

More information

Chapter 11 Output Analysis for a Single Model. Banks, Carson, Nelson & Nicol Discrete-Event System Simulation

Chapter 11 Output Analysis for a Single Model. Banks, Carson, Nelson & Nicol Discrete-Event System Simulation Chapter Output Aalysis for a Sigle Model Baks, Carso, Nelso & Nicol Discrete-Evet System Simulatio Error Estimatio If {,, } are ot statistically idepedet, the S / is a biased estimator of the true variace.

More information

ECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015

ECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015 ECE 8527: Itroductio to Machie Learig ad Patter Recogitio Midterm # 1 Vaishali Ami Fall, 2015 tue39624@temple.edu Problem No. 1: Cosider a two-class discrete distributio problem: ω 1 :{[0,0], [2,0], [2,2],

More information

Lecture 4. Hw 1 and 2 will be reoped after class for every body. New deadline 4/20 Hw 3 and 4 online (Nima is lead)

Lecture 4. Hw 1 and 2 will be reoped after class for every body. New deadline 4/20 Hw 3 and 4 online (Nima is lead) Lecture 4 Homework Hw 1 ad 2 will be reoped after class for every body. New deadlie 4/20 Hw 3 ad 4 olie (Nima is lead) Pod-cast lecture o-lie Fial projects Nima will register groups ext week. Email/tell

More information

Statistical Theory MT 2009 Problems 1: Solution sketches

Statistical Theory MT 2009 Problems 1: Solution sketches Statistical Theory MT 009 Problems : Solutio sketches. Which of the followig desities are withi a expoetial family? Explai your reasoig. (a) Let 0 < θ < ad put f(x, θ) = ( θ)θ x ; x = 0,,,... (b) (c) where

More information

ST 305: Exam 3 ( ) = P(A)P(B A) ( ) = P(A) + P(B) ( ) = 1 P( A) ( ) = P(A) P(B) σ X 2 = σ a+bx. σ ˆp. σ X +Y. σ X Y. σ X. σ Y. σ n.

ST 305: Exam 3 ( ) = P(A)P(B A) ( ) = P(A) + P(B) ( ) = 1 P( A) ( ) = P(A) P(B) σ X 2 = σ a+bx. σ ˆp. σ X +Y. σ X Y. σ X. σ Y. σ n. ST 305: Exam 3 By hadig i this completed exam, I state that I have either give or received assistace from aother perso durig the exam period. I have used o resources other tha the exam itself ad the basic

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

Statistical Properties of OLS estimators

Statistical Properties of OLS estimators 1 Statistical Properties of OLS estimators Liear Model: Y i = β 0 + β 1 X i + u i OLS estimators: β 0 = Y β 1X β 1 = Best Liear Ubiased Estimator (BLUE) Liear Estimator: β 0 ad β 1 are liear fuctio of

More information

TIME-CORRELATION FUNCTIONS

TIME-CORRELATION FUNCTIONS p. 8 TIME-CORRELATION FUNCTIONS Time-correlatio fuctios are a effective way of represetig the dyamics of a system. They provide a statistical descriptio of the time-evolutio of a variable for a esemble

More information

Final Review for MATH 3510

Final Review for MATH 3510 Fial Review for MATH 50 Calculatio 5 Give a fairly simple probability mass fuctio or probability desity fuctio of a radom variable, you should be able to compute the expected value ad variace of the variable

More information

Chapter 6 Sampling Distributions

Chapter 6 Sampling Distributions Chapter 6 Samplig Distributios 1 I most experimets, we have more tha oe measuremet for ay give variable, each measuremet beig associated with oe radomly selected a member of a populatio. Hece we eed to

More information

Formula Sheet. December 8, 2011

Formula Sheet. December 8, 2011 Formula Sheet December 8, 2011 Abtract I type thi for your coveice. There may be error. Ue at your ow rik. It i your repoible to check it i correct or ot before uig it. 1 Decriptive Statitic 1.1 Cetral

More information

Correlation and Covariance

Correlation and Covariance Correlatio ad Covariace Tom Ilveto FREC 9 What is Next? Correlatio ad Regressio Regressio We specify a depedet variable as a liear fuctio of oe or more idepedet variables, based o co-variace Regressio

More information

Lecture 27. Capacity of additive Gaussian noise channel and the sphere packing bound

Lecture 27. Capacity of additive Gaussian noise channel and the sphere packing bound Lecture 7 Ageda for the lecture Gaussia chael with average power costraits Capacity of additive Gaussia oise chael ad the sphere packig boud 7. Additive Gaussia oise chael Up to this poit, we have bee

More information

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 5 STATISTICS II. Mea ad stadard error of sample data. Biomial distributio. Normal distributio 4. Samplig 5. Cofidece itervals

More information

Analysis of Experimental Data

Analysis of Experimental Data Aalysis of Experimetal Data 6544597.0479 ± 0.000005 g Quatitative Ucertaity Accuracy vs. Precisio Whe we make a measuremet i the laboratory, we eed to kow how good it is. We wat our measuremets to be both

More information

THE KALMAN FILTER RAUL ROJAS

THE KALMAN FILTER RAUL ROJAS THE KALMAN FILTER RAUL ROJAS Abstract. This paper provides a getle itroductio to the Kalma filter, a umerical method that ca be used for sesor fusio or for calculatio of trajectories. First, we cosider

More information

SINGLE-CHANNEL QUEUING PROBLEMS APPROACH

SINGLE-CHANNEL QUEUING PROBLEMS APPROACH SINGLE-CHANNEL QUEUING ROBLEMS AROACH Abdurrzzag TAMTAM, Doctoral Degree rogramme () Dept. of Telecommuicatios, FEEC, BUT E-mail: xtamta@stud.feec.vutbr.cz Supervised by: Dr. Karol Molár ABSTRACT The paper

More information

Exponential Moving Average Pieter P

Exponential Moving Average Pieter P Expoetial Movig Average Pieter P Differece equatio The Differece equatio of a expoetial movig average lter is very simple: y[] x[] + (1 )y[ 1] I this equatio, y[] is the curret output, y[ 1] is the previous

More information

Properties and Hypothesis Testing

Properties and Hypothesis Testing Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.

More information