The time series data in this example are obtained from sampling a function describing the free decay of a torsion oscillator for time t > t o

Size: px
Start display at page:

Download "The time series data in this example are obtained from sampling a function describing the free decay of a torsion oscillator for time t > t o"

Transcription

1 The Excel FFT Fucti v2 P T Debevec July 5, 28 The discrete Furier trasfrm may be used t idetify peridic structures i time series data Suppse that a physical prcess is represeted by the fucti f time, ht ( ) The fucti is sampled at times, t = Δ t where =,,2,, Frm these measuremets, h, cmplex amplitudes, H, are determied which satisfy the equatis H = 2π i = h e The sampled fucti the has the discrete Furier expasi 2π i h = He This equati ca be cast i familiar frm with ( ) ( ) = 2π = 2π T T = ωδ t= ωt h iω t He = = The right-had side is the discrete aalgue t the cmplex frm f the Furier expasi where the cmplex cefficiets, i t ht () = ce ω =, are give by c iωt c = ht () e dt T T The Excel data aalysis pacage has a Furier aalysis rutie which calculates the cmplex cefficiets, H, frm the time series data, h The rutie requires that the umber f samples i the time series data be a pwer f 2, ie = 2 m The example i this te uses = 248 The Excel fucti is t well dcumeted, but it is straightfrward t use This te describes the Excel wrsheet, Furier_examplexls, which is i the Physics 4 web site uder Tutrials ad Lectures, Experimet

2 The time series data i this example are btaied frm samplig a fucti describig the free decay f a trsi scillatr fr time t > t, () ( ) ( π ( )) at t θ t = Ae si 2 f t t The fucti is calculated i time steps f 2 s, which crrespds t samplig rate f 5 Hz The time series data are shw i the Fig belw free decay damped scillatr raw cuts time (s) Fig Plt f time series data decay f trsi scillatr The data ccupy cells B3 t B25 i the data wrsheet f the wrb Clic Tls i the Excel meu bar, ad select Data Aalysis I Data Aalysis select Furier Aalysis, ad a simple dialg bx appears The dialg bx is shw belw i Fig 2 Fig 2 Furier Aalysis dialg bx

3 Fr Iput Rage eter $B$3:$B$25, the lcati f the time series data, ad fr utput rage eter a cveiet place the wrsheet, fr example, $J$3:$J$25 After selectig the OK butt, Excel returs i clum J the cmplex cefficiets, 248 f them A prti f clum J is shw i Fig 3 belw Fig 3 Prti f Excel wrsheet shwig FFT utput Excel isists i displayig 5 digits fr bth the real ad imagiary part f the cefficiet, ad thus the utput ccupies csiderable space Examiati f the clum f cmplex umbers shws that the first etry (i cell J3) ad the 25 th etry (i cell J27) are real These cells represet the cefficiets fr the zer frequecy ad the fldig frequecy, f c Sice the data were sampled at 5 Hz, the fldig frequecy is ½ 5 Hz = 25 Hz Cells J3 thrugh J27 represet the cmplex cefficiets fr 2+ frequecies frm Hz t 25 Hz The frequecy icremet is the Δ f = 2 f c It is cveiet t mae a clum f frequecies ext t the clum f cefficiets This clum icludes cells I3 t I27 The Furier Aalysis rutie perates ly the time series data The crrespdece betwee frequecy ad cmplex cefficiet must be calculated idepedetly The 23 etries frm cell J28 t cell J25 are idetical t the 23 etries frm cell J26 t J4 These etries ctai additial ifrmati, ad they culd have bee mitted frm the display The Furier Aalysis f time series data yields the cmplex cefficiets fr ly 2+ frequecies The pwer i each frequecy bi is prprtial t the square f the mdulus f the cmplex cefficiet The cstat f prprtiality adpted i this te has the prperty that the mea squared amplitude f the time series data is equal t the ttal pwer i the frequecy dmai as shw belw Excel has a umber f fuctis fr cmplex argumets Multiplicati f tw cmplex umbers is accmplished with the fucti IMPRODUCT(z, z 2 ) Multiplicati f the cefficiets i clum J by prduces the rmalized cefficiets i clum The abslute value f a cmplex umber is accmplished with the fucti IMABS(z) Applyig the IMABS fucti t the rmalized cefficiets i clum ad multiplyig by a factr f 2 prduces the magitude f the Furier cefficiets i clum P The magitude f the Furier cefficiets as a fucti f frequecy is shw i Fig 4 belw The maximum value ccurs i the frequecy bi at 4883 Hz

4 magitude f Furier amplitude magitude f Furier amplitude versus frequecy frequecy (Hz) Fig 4 Magitude f rmalized Furier cefficiet versus frequecy Fially, the pwer at each frequecy (except zer frequecy) is the square f the magitude The pwer is calculated i clum Q ad shw i Fig 5 belw The pwer at zer frequecy is the square f the magitude divided by 2 This factr f 2 als appears i -discrete Furier aalysis This pwer distributi is characteristic f expetial decay E+5 Furier pwer versus frequecy E+3 Furier pwer E+ E- E frequecy (Hz) Fig 5 Furier pwer versus frequecy

5 The rmalizati adpted i this te has a cveiet prperty Defie the mea squared amplitude f the time series data as θ = mea squred amplitude = ( ) 2 t The square f the amplitude is calculated i clum C The mea squared amplitude is calculated i cell F9 Fr these data it has the value f A pure sie wave with a amplitude f 2, fr which a itegral umber f perids ccur i the ttal sampled time, has a mea squared amplitude f uity Thus the mea squared amplitude has the same rle as the rms value f a peridic fucti The ttal Furier pwer, the sum f the etries i clum Q, is calculated i cell G9 Fr these data it als has the value f , because f the rmalizati If ly the relative pwer i differet frequecy bis is eeded, the the rmalizati is t required The relative pwer is give by the square f the mdulus f the Excel utput directly

Section 8.3 : De Moivre s Theorem and Applications

Section 8.3 : De Moivre s Theorem and Applications The Sectio 8 : De Moivre s Theorem ad Applicatios Let z 1 ad z be complex umbers, where z 1 = r 1, z = r, arg(z 1 ) = θ 1, arg(z ) = θ z 1 = r 1 (cos θ 1 + i si θ 1 ) z = r (cos θ + i si θ ) ad z 1 z =

More information

Oscillations in Mean Arterial Blood Pressure in Conscious Dogs

Oscillations in Mean Arterial Blood Pressure in Conscious Dogs 692 Oscillatis i Mea Arterial Bld Pressure i Cscius Dgs STVN G. SHIMADA AND DONALD J. MARSH SUMMARY Oscillatis i mea arterial bld pressure (MABP) with perids ear 1.5 hurs were bserved i cscius male dgs

More information

The scattering of light may be thought of as the redirection of light that takes place when

The scattering of light may be thought of as the redirection of light that takes place when D.W.H. July 009 Itrducti Light Scatterig Thery David W. Hah Departmet f Mechaical ad Aerspace Egieerig Uiversity f Flrida (dwhah@ufl.edu) The terig f light may be thught f as the redirecti f light that

More information

A Production-Delivery Inventory System under Continuous Price Decrease and Finite Planning Horizon

A Production-Delivery Inventory System under Continuous Price Decrease and Finite Planning Horizon Prceedigs f the 008 Idustrial Egieerig esearch Cferece J. Fwler ad S. as, eds. A Prducti-elivery Ivetry System uder Ctiuus Price ecrease ad Fiite Plaig Hriz Jufag Yu epartmet f Egieerig aagemet, Ifrmati

More information

Cooley-Tukey. Tukey FFT Algorithms. FFT Algorithms. Cooley

Cooley-Tukey. Tukey FFT Algorithms. FFT Algorithms. Cooley Cooley Cooley-Tuey Tuey FFT Algorithms FFT Algorithms Cosider a legth- sequece x[ with a -poit DFT X[ where Represet the idices ad as +, +, Cooley Cooley-Tuey Tuey FFT Algorithms FFT Algorithms Usig these

More information

Elastic Conformal Transformation of Digital Images

Elastic Conformal Transformation of Digital Images Lubmír SOUKUP, Ja HAVRLANT, Odre BOHM, ad Mila TALICH, Czech Republic Key wrds: Cartgraphy, Geifrmati, Egieerig survey, Cadastre, Image Prcessig, Data quality, Accuracy aalysis, Bayesia apprach SUMMARY

More information

MOSFET Small Signal Model and Analysis

MOSFET Small Signal Model and Analysis Just as we did with the BJT, we ca csider the MOSFET amplifier aalysis i tw parts: Fid the DC peratig pit The determie the amplifier utput parameters fr ery small iput sigals. + V 1 - MOSFET Small Sigal

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

Data Validation and Iteration

Data Validation and Iteration Financial Mdeling Data Validatin and Iteratin As analysts are bmbarded with a lt ff data, ne shuld be able t check data veracity. This is where the data validatin functins cme in handy. Few excel functins

More information

Outage Probability for GPRS over GSM Voice Services

Outage Probability for GPRS over GSM Voice Services Outage Prbability fr GPRS ver GSM Vice Services Shaji Ni, Yg Liag ad Sve-Gustav Häggma Helsii Uiversity f Techlgy, Istitute f Radi Cmmuicatis, Cmmuicatis Labratry, P.O. Bx 3, Otaaari 8, 5 Es, Filad, Fax:358-9-45345,

More information

The Design of a Flash-based Linux Swap System. Yeonseung Ryu Myongji University October, 2008

The Design of a Flash-based Linux Swap System. Yeonseung Ryu Myongji University October, 2008 The Desig f a Flash-based Liux Swap System Yeseug Ryu Mygji Uiversity Octber, 2008 Ctets Overview f liux Swap System Hw des the swap system perates? What are the prblems f flash based swap system? New

More information

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies ( 3.1.1) Limitations of Experiments. Pseudocode ( 3.1.2) Theoretical Analysis

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies ( 3.1.1) Limitations of Experiments. Pseudocode ( 3.1.2) Theoretical Analysis Ruig Time ( 3.) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.

More information

Local Mobility Anchoring for Seamless Handover in Coordinated Small Cells

Local Mobility Anchoring for Seamless Handover in Coordinated Small Cells Lcal Mbility Achrig fr Seamless Hadver i Crdiated Small Cells Ravikumar Balakrisha ad Ia F Akyildiz Bradbad Wireless Netwrkig Labratry Schl f Electrical ad Cmputer Egieerig, Gergia Istitute f Techlgy,

More information

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean 1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.

More information

hp calculators HP 12C Statistics - average and standard deviation Average and standard deviation concepts HP12C average and standard deviation

hp calculators HP 12C Statistics - average and standard deviation Average and standard deviation concepts HP12C average and standard deviation HP 1C Statistics - average ad stadard deviatio Average ad stadard deviatio cocepts HP1C average ad stadard deviatio Practice calculatig averages ad stadard deviatios with oe or two variables HP 1C Statistics

More information

Basic Measurement Issues. Sampling Theory and Analog-to-Digital Conversion

Basic Measurement Issues. Sampling Theory and Analog-to-Digital Conversion Theory ad Aalog-to-Digital Coversio Itroductio/Defiitios Aalog-to-digital coversio Rate Frequecy Aalysis Basic Measuremet Issues Reliability the extet to which a measuremet procedure yields the same results

More information

Chapter 04.00E Physical Problem for Electrical Engineering Simultaneous Linear Equations

Chapter 04.00E Physical Problem for Electrical Engineering Simultaneous Linear Equations hpter 04.00E Phyicl Prblem fr Electricl Egieerig Simulteu Lie Equti Prblem Sttemet Three-phe ytem e the rm fr mt idutril pplicti. pwer i the frm f vltge d curret it delivered frm the pwer cmpy uig three-phe

More information

Introduction to Complex Numbers in Physics/Engineering

Introduction to Complex Numbers in Physics/Engineering Introduction to Complex Numbers in Physics/Engineering ference: Mary L. Boas, Mathematical Methods in the Physical Sciences Chapter 2 & 14 George Arfken, Mathematical Methods for Physicists Chapter 6 The

More information

Problem Set 2 Solution

Problem Set 2 Solution Due: April 8, 2004 Sprig 2004 ENEE 426: Cmmuicati Netwrks Dr. Naraya TA: Quag Trih Prblem Set 2 Sluti 1. (3.57) A early cde used i radi trasmissi ivlved usig cdewrds that csist biary bits ad ctai the same

More information

Sinusoidal Steady State Response of Linear Circuits. The circuit shown on Figure 1 is driven by a sinusoidal voltage source v s (t) of the form

Sinusoidal Steady State Response of Linear Circuits. The circuit shown on Figure 1 is driven by a sinusoidal voltage source v s (t) of the form Sinusidal Steady State espnse f inear Circuits The circuit shwn n Figure 1 is driven by a sinusidal ltage surce v s (t) f the frm v () t = v cs( ωt) (1.1) s i(t) + v (t) - + v (t) s v c (t) - C Figure

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

Ekkehart Schlicht: Economic Surplus and Derived Demand

Ekkehart Schlicht: Economic Surplus and Derived Demand Ekkehart Schlicht: Ecoomic Surplus ad Derived Demad Muich Discussio Paper No. 2006-17 Departmet of Ecoomics Uiversity of Muich Volkswirtschaftliche Fakultät Ludwig-Maximilias-Uiversität Müche Olie at http://epub.ub.ui-mueche.de/940/

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

More information

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number.

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number. GCSE STATISTICS You should kow: 1) How to draw a frequecy diagram: e.g. NUMBER TALLY FREQUENCY 1 3 5 ) How to draw a bar chart, a pictogram, ad a pie chart. 3) How to use averages: a) Mea - add up all

More information

Operational Amplifier Circuits Comparators and Positive Feedback

Operational Amplifier Circuits Comparators and Positive Feedback Operatinal Amplifier Circuits Cmparatrs and Psitive Feedback Cmparatrs: Open Lp Cnfiguratin The basic cmparatr circuit is an p-amp arranged in the pen-lp cnfiguratin as shwn n the circuit f Figure. The

More information

Inference on Proportion. Chapter 8 Tests of Statistical Hypotheses. Sampling Distribution of Sample Proportion. Confidence Interval

Inference on Proportion. Chapter 8 Tests of Statistical Hypotheses. Sampling Distribution of Sample Proportion. Confidence Interval Chapter 8 Tests of Statistical Hypotheses 8. Tests about Proportios HT - Iferece o Proportio Parameter: Populatio Proportio p (or π) (Percetage of people has o health isurace) x Statistic: Sample Proportio

More information

FREQUENTLY ASKED QUESTIONS-PLP PROGRAM

FREQUENTLY ASKED QUESTIONS-PLP PROGRAM FREQUENTLY ASKED QUESTIONS-PLP PROGRAM What is "PLP"? PLP is a isurace prgram that prvides Cmmercial Geeral Liability cverage fr all f Swiert's subctractrs f every tier while wrkig desigated Swiert's prjects.

More information

Sequences and Series Using the TI-89 Calculator

Sequences and Series Using the TI-89 Calculator RIT Calculator Site Sequeces ad Series Usig the TI-89 Calculator Norecursively Defied Sequeces A orecursively defied sequece is oe i which the formula for the terms of the sequece is give explicitly. For

More information

Oscillations. Vern Lindberg. June 10, 2010

Oscillations. Vern Lindberg. June 10, 2010 Oscillations Vern Lindberg June 10, 2010 You have discussed oscillations in Vibs and Waves: we will therefore touch lightly on Chapter 3, mainly trying to refresh your memory and extend the concepts. 1

More information

Case Study. Normal and t Distributions. Density Plot. Normal Distributions

Case Study. Normal and t Distributions. Density Plot. Normal Distributions Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca

More information

TaskCentre v4.5 File Transfer (FTP) Tool White Paper

TaskCentre v4.5 File Transfer (FTP) Tool White Paper TaskCentre v4.5 File Transfer (FTP) Tl White Paper Dcument Number: PD500-03-22-1_0-WP Orbis Sftware Limited 2010 Table f Cntents COPYRIGHT 1 TRADEMARKS 1 INTRODUCTION 2 Overview 2 FEATURES 2 GLOBAL CONFIGURATION

More information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

Mathematical goals. Starting points. Materials required. Time needed

Mathematical goals. Starting points. Materials required. Time needed Level A1 of challege: C A1 Mathematical goals Startig poits Materials required Time eeded Iterpretig algebraic expressios To help learers to: traslate betwee words, symbols, tables, ad area represetatios

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

Applied Radio Labs. Group Delay Explanations and Applications. Introduction. The Delay of Signals. Definition of Group Delay.

Applied Radio Labs. Group Delay Explanations and Applications. Introduction. The Delay of Signals. Definition of Group Delay. Grup Delay - Explanatins and Applicatins Applied Radi Labs Grup Delay Explanatins and Applicatins www.radi-labs.cm Design File: DN4 5 Nvember 999 Intrductin Grup delay is a cncept that radi engineers are

More information

SOLUTIONS ELECTRICAL ENGINEERING (EE) PRACTICE PROBLEMS FOR TECHNICAL MAJORS

SOLUTIONS ELECTRICAL ENGINEERING (EE) PRACTICE PROBLEMS FOR TECHNICAL MAJORS SOUTIONS EECTRICA ENGINEERING (EE) PRACTICE PROBEMS FOR TECHNICA MAJORS Nte: The text entitled Applied Engineering Principles Manual is used as the reference fr the questins and prblems belw. Althugh nly

More information

Cantilever Beam Experiment

Cantilever Beam Experiment Mechaical Egieerig Departmet Uiversity of Massachusetts Lowell Catilever Beam Experimet Backgroud A disk drive maufacturer is redesigig several disk drive armature mechaisms. This is the result of evaluatio

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

This document contains a collection of formulas and constants useful for SPC chart construction. It assumes you are already familiar with SPC.

This document contains a collection of formulas and constants useful for SPC chart construction. It assumes you are already familiar with SPC. SPC Formulas ad Tables 1 This documet cotais a collectio of formulas ad costats useful for SPC chart costructio. It assumes you are already familiar with SPC. Termiology Geerally, a bar draw over a symbol

More information

5: Introduction to Estimation

5: Introduction to Estimation 5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample

More information

Research Method (I) --Knowledge on Sampling (Simple Random Sampling)

Research Method (I) --Knowledge on Sampling (Simple Random Sampling) Research Method (I) --Kowledge o Samplig (Simple Radom Samplig) 1. Itroductio to samplig 1.1 Defiitio of samplig Samplig ca be defied as selectig part of the elemets i a populatio. It results i the fact

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

MATHEMATICS FOR ENGINEERING TRIGONOMETRY TUTORIAL 1 TRIGONOMETRIC RATIOS, TRIGONOMETRIC TECHNIQUES AND GRAPHICAL METHODS

MATHEMATICS FOR ENGINEERING TRIGONOMETRY TUTORIAL 1 TRIGONOMETRIC RATIOS, TRIGONOMETRIC TECHNIQUES AND GRAPHICAL METHODS MATHEMATICS FOR ENGINEERING TRIGONOMETRY TUTORIAL 1 TRIGONOMETRIC RATIOS, TRIGONOMETRIC TECHNIQUES AND GRAPHICAL METHODS This is the ne f a series f basic tutrials in mathematics aimed at beginners r anyne

More information

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows: Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network

More information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

MATH 083 Final Exam Review

MATH 083 Final Exam Review MATH 08 Fial Eam Review Completig the problems i this review will greatly prepare you for the fial eam Calculator use is ot required, but you are permitted to use a calculator durig the fial eam period

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

CHAPTER 3 DIGITAL CODING OF SIGNALS

CHAPTER 3 DIGITAL CODING OF SIGNALS CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity

More information

The Gibbs Free Energy and Cell Voltage

The Gibbs Free Energy and Cell Voltage The Gibbs Free Energy and Cell Vltage When an amunt f charge, Q, mves thrugh a ptential difference, E w = - Q E b/c wrk dne by the system E > 0 fr galvanic (vltaic) cells Recall, G = H TS = E + PV TS Fr

More information

Convexity, Inequalities, and Norms

Convexity, Inequalities, and Norms Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. Powers of a matrix We begi with a propositio which illustrates the usefuless of the diagoalizatio. Recall that a square matrix A is diogaalizable if

More information

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments Project Deliverables CS 361, Lecture 28 Jared Saia Uiversity of New Mexico Each Group should tur i oe group project cosistig of: About 6-12 pages of text (ca be loger with appedix) 6-12 figures (please

More information

Student Exploration: Photosynthesis Lab

Student Exploration: Photosynthesis Lab Name: Date: Student Explratin: Phtsynthesis Lab Vcabulary: carbn dixide, chlrphyll, glucse, limiting factr, nanmeter, phtsynthesis, wavelength Prir Knwledge Questins (D these BEFORE using the Gizm.) T

More information

Study on the application of the software phase-locked loop in tracking and filtering of pulse signal

Study on the application of the software phase-locked loop in tracking and filtering of pulse signal Advaced Sciece ad Techology Letters, pp.31-35 http://dx.doi.org/10.14257/astl.2014.78.06 Study o the applicatio of the software phase-locked loop i trackig ad filterig of pulse sigal Sog Wei Xia 1 (College

More information

How To Understand An Outut Stage

How To Understand An Outut Stage CHATE 3 OUTUT STAGES AND OE AMFES Chapter Outline 3. Classificatin f Output Stages 3. Class A Output Stage 3.3 Class B Output Stage 3.4 Class AB Output Stage 3.5 Biasing the Class AB Circuit 3.6 CMOS Class

More information

TaskCentre v4.5 Send Fax (Tobit) Tool White Paper

TaskCentre v4.5 Send Fax (Tobit) Tool White Paper TaskCentre v4.5 Send Fax (Tbit) Tl White Paper Dcument Number: PD500-03-19-1_0-WP Orbis Sftware Limited 2010 Table f Cntents COPYRIGHT 1 TRADEMARKS 1 INTRODUCTION 2 Overview 2 FEATURES 2 GLOBAL CONFIGURATION

More information

Some Statistical Procedures and Functions with Excel

Some Statistical Procedures and Functions with Excel Sme Statistical Prcedures and Functins with Excel Intrductry Nte: Micrsft s Excel spreadsheet prvides bth statistical prcedures and statistical functins. The prcedures are accessed by clicking n Tls in

More information

The Binomial Multi- Section Transformer

The Binomial Multi- Section Transformer 4/15/21 The Bioial Multisectio Matchig Trasforer.doc 1/17 The Bioial Multi- Sectio Trasforer Recall that a ulti-sectio atchig etwork ca be described usig the theory of sall reflectios as: where: Γ ( ω

More information

Network Theorems - Alternating Current examples - J. R. Lucas

Network Theorems - Alternating Current examples - J. R. Lucas Netwrk Therems - lternating urrent examples - J. R. Lucas n the previus chapter, we have been dealing mainly with direct current resistive circuits in rder t the principles f the varius therems clear.

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Escola Federal de Engenharia de Itajubá

Escola Federal de Engenharia de Itajubá Escola Federal de Egeharia de Itajubá Departameto de Egeharia Mecâica Pós-Graduação em Egeharia Mecâica MPF04 ANÁLISE DE SINAIS E AQUISÇÃO DE DADOS SINAIS E SISTEMAS Trabalho 02 (MATLAB) Prof. Dr. José

More information

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio

More information

APPLICATION NOTE 30 DFT or FFT? A Comparison of Fourier Transform Techniques

APPLICATION NOTE 30 DFT or FFT? A Comparison of Fourier Transform Techniques APPLICATION NOTE 30 DFT or FFT? A Compariso of Fourier Trasform Techiques This applicatio ote ivestigates differeces i performace betwee the DFT (Discrete Fourier Trasform) ad the FFT(Fast Fourier Trasform)

More information

Chapter 14 Nonparametric Statistics

Chapter 14 Nonparametric Statistics Chapter 14 Noparametric Statistics A.K.A. distributio-free statistics! Does ot deped o the populatio fittig ay particular type of distributio (e.g, ormal). Sice these methods make fewer assumptios, they

More information

Motor Calculations. Calculating Mechanical Power Requirements Torque - Speed Curves Numerical Calculation Sample Calculation Thermal Calculations

Motor Calculations. Calculating Mechanical Power Requirements Torque - Speed Curves Numerical Calculation Sample Calculation Thermal Calculations Mtr Calculatins Calculating Mechanical Pwer Requirements Trque - Speed Curves Numerical Calculatin Sample Calculatin Thermal Calculatins Calculating Mechanical Pwer Requirements Physically, pwer is defined

More information

Simple Harmonic Motion Experiment. 1 f

Simple Harmonic Motion Experiment. 1 f Simple Harmonic Motion Experiment In this experiment, a motion sensor is used to measure the position of an oscillating mass as a function of time. The frequency of oscillations will be obtained by measuring

More information

7. Beats. sin( + λ) + sin( λ) = 2 cos(λ) sin( )

7. Beats. sin( + λ) + sin( λ) = 2 cos(λ) sin( ) 34 7. Beats 7.1. What beats are. Musicians tune their instruments using beats. Beats occur when two very nearby pitches are sounded simultaneously. We ll make a mathematical study of this effect, using

More information

TRAINING GUIDE. Crystal Reports for Work

TRAINING GUIDE. Crystal Reports for Work TRAINING GUIDE Crystal Reprts fr Wrk Crystal Reprts fr Wrk Orders This guide ges ver particular steps and challenges in created reprts fr wrk rders. Mst f the fllwing items can be issues fund in creating

More information

TaskCentre v4.5 File Management Tool White Paper

TaskCentre v4.5 File Management Tool White Paper TaskCentre v4.5 File Management Tl White Paper Dcument Number: PD500-03-30-1_0-WP Orbis Sftware Limited 2010 Table f Cntents COPYRIGHT 1 TRADEMARKS 1 INTRODUCTION 2 Overview 2 FEATURES 2 TECHNICAL SUMMARY

More information

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series 8 Fourier Series Our aim is to show that uder reasoable assumptios a give -periodic fuctio f ca be represeted as coverget series f(x) = a + (a cos x + b si x). (8.) By defiitio, the covergece of the series

More information

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu>

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu> (March 16, 004) Factorig x 1: cyclotomic ad Aurifeuillia polyomials Paul Garrett Polyomials of the form x 1, x 3 1, x 4 1 have at least oe systematic factorizatio x 1 = (x 1)(x 1

More information

MATHEMATICS P2 COMMON TEST JUNE 2014 NATIONAL SENIOR CERTIFICATE

MATHEMATICS P2 COMMON TEST JUNE 2014 NATIONAL SENIOR CERTIFICATE Mathematics/P Jue 04 Cmm Test MATHEMATICS P COMMON TEST JUNE 04 NATIONAL SENIOR CERTIFICATE GRADE Marks: 5 Time: ½ hurs N.B. This questi paper csists f 9 pages, diagram sheets ad ifrmati sheet. Mathematics/P

More information

Biology 171L Environment and Ecology Lab Lab 2: Descriptive Statistics, Presenting Data and Graphing Relationships

Biology 171L Environment and Ecology Lab Lab 2: Descriptive Statistics, Presenting Data and Graphing Relationships Biology 171L Eviromet ad Ecology Lab Lab : Descriptive Statistics, Presetig Data ad Graphig Relatioships Itroductio Log lists of data are ofte ot very useful for idetifyig geeral treds i the data or the

More information

Normal Distribution.

Normal Distribution. Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued

More information

Heat (or Diffusion) equation in 1D*

Heat (or Diffusion) equation in 1D* Heat (or Diffusio) equatio i D* Derivatio of the D heat equatio Separatio of variables (refresher) Worked eamples *Kreysig, 8 th Ed, Sectios.4b Physical assumptios We cosider temperature i a log thi wire

More information

A Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design

A Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design A Combied Cotiuous/Biary Geetic Algorithm for Microstrip Atea Desig Rady L. Haupt The Pesylvaia State Uiversity Applied Research Laboratory P. O. Box 30 State College, PA 16804-0030 haupt@ieee.org Abstract:

More information

London Borough of Hounslow

London Borough of Hounslow Ld Brugh f Huslw Applicati fr a premises licece t be grated uder the Licesig Act 2003 PLEASE READ THE FOLLOWIG ISTRUCTIOS FIRST Befre cmpletig this frm please read the guidace tes at the ed f the frm.

More information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

OECD-NEA Study Cost of Nuclear Accidents-liabilities Issues and their Impact on Electricity Costs

OECD-NEA Study Cost of Nuclear Accidents-liabilities Issues and their Impact on Electricity Costs OECD-NEA Study Cst f Nuclear Accidents-liabilities Issues and their Impact n Electricity Csts Wrkshp Appraches t estimatin f the csts f nuclear accidents May 28-29, 2013 OECD/NEA Headquarters, Issy-les-Mulineaux,

More information

Dreamweaver MX 2004. Templates

Dreamweaver MX 2004. Templates Dreamweaver MX 2004 Templates Table f Cntents Dreamweaver Templates... 3 Creating a Dreamweaver template... 3 Types f template regins... 4 Inserting an editable regin... 4 Selecting editable regins...

More information

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

More information

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Istructor: Nicolas Christou Three importat distributios: Distributios related to the ormal distributio Chi-square (χ ) distributio.

More information

Multiplexers and Demultiplexers

Multiplexers and Demultiplexers I this lesso, you will lear about: Multiplexers ad Demultiplexers 1. Multiplexers 2. Combiatioal circuit implemetatio with multiplexers 3. Demultiplexers 4. Some examples Multiplexer A Multiplexer (see

More information

Domain 1 - Describe Cisco VoIP Implementations

Domain 1 - Describe Cisco VoIP Implementations Maual ONT (642-8) 1-800-418-6789 Domai 1 - Describe Cisco VoIP Implemetatios Advatages of VoIP Over Traditioal Switches Voice over IP etworks have may advatages over traditioal circuit switched voice etworks.

More information

In this chapter, you will learn to use net present value analysis in cost and price analysis.

In this chapter, you will learn to use net present value analysis in cost and price analysis. 9.0 - Chapter Intrductin In this chapter, yu will learn t use net present value analysis in cst and price analysis. Time Value f Mney. The time value f mney is prbably the single mst imprtant cncept in

More information

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets BENEIT-CST ANALYSIS iacial ad Ecoomic Appraisal usig Spreadsheets Ch. 2: Ivestmet Appraisal - Priciples Harry Campbell & Richard Brow School of Ecoomics The Uiversity of Queeslad Review of basic cocepts

More information

Chapter 04.05 System of Equations

Chapter 04.05 System of Equations hpter 04.05 System of Equtios After redig th chpter, you should be ble to:. setup simulteous lier equtios i mtrix form d vice-vers,. uderstd the cocept of the iverse of mtrix, 3. kow the differece betwee

More information

KronoDesk Migration and Integration Guide Inflectra Corporation

KronoDesk Migration and Integration Guide Inflectra Corporation / KrnDesk Migratin and Integratin Guide Inflectra Crpratin Date: September 24th, 2015 0B Intrductin... 1 1B1. Imprting frm Micrsft Excel... 2 6B1.1. Installing the Micrsft Excel Add-In... 2 7B1.1. Cnnecting

More information

THE ABRACADABRA PROBLEM

THE ABRACADABRA PROBLEM THE ABRACADABRA PROBLEM FRANCESCO CARAVENNA Abstract. We preset a detailed solutio of Exercise E0.6 i [Wil9]: i a radom sequece of letters, draw idepedetly ad uiformly from the Eglish alphabet, the expected

More information

Institute of Actuaries of India Subject CT1 Financial Mathematics

Institute of Actuaries of India Subject CT1 Financial Mathematics Istitute of Actuaries of Idia Subject CT1 Fiacial Mathematics For 2014 Examiatios Subject CT1 Fiacial Mathematics Core Techical Aim The aim of the Fiacial Mathematics subject is to provide a groudig i

More information

Lecture 4: Cheeger s Inequality

Lecture 4: Cheeger s Inequality Spectral Graph Theory ad Applicatios WS 0/0 Lecture 4: Cheeger s Iequality Lecturer: Thomas Sauerwald & He Su Statemet of Cheeger s Iequality I this lecture we assume for simplicity that G is a d-regular

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

4.3. The Integral and Comparison Tests

4.3. The Integral and Comparison Tests 4.3. THE INTEGRAL AND COMPARISON TESTS 9 4.3. The Itegral ad Compariso Tests 4.3.. The Itegral Test. Suppose f is a cotiuous, positive, decreasig fuctio o [, ), ad let a = f(). The the covergece or divergece

More information

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 [C] Commuicatio Measuremet A1. Solve problems that ivolve liear measuremet, usig: SI ad imperial uits of measure estimatio strategies measuremet strategies.

More information

Chapter 6: Continuous Probability Distributions GBS221, Class 20640 March 25, 2013 Notes Compiled by Nicolas C. Rouse, Instructor, Phoenix College

Chapter 6: Continuous Probability Distributions GBS221, Class 20640 March 25, 2013 Notes Compiled by Nicolas C. Rouse, Instructor, Phoenix College Chapter Objectives 1. Understand the difference between hw prbabilities are cmputed fr discrete and cntinuus randm variables. 2. Knw hw t cmpute prbability values fr a cntinuus unifrm prbability distributin

More information