8.1 Circuit Parameters

Size: px
Start display at page:

Download "8.1 Circuit Parameters"

Transcription

1 8.1 Circuit Parameters definition of decibels using decibels transfer functions impulse response rise time analysis Gaussian amplifier transfer function RC circuit transfer function analog-to-digital conversion software transfer functions averaging transfer function multi-step transfer functions 8.1 : 1/13

2 Amplification/Attenuation The gain or loss of a circuit is often given in decibels (db). P in P out circuit A ( f ) db ( f ) ( f ) Pout = 10log Pin A positive value of A db is an amplification, a negative value an attenuation: 10 (W) = 10 db; 100 (W) = 20 db; (W) = -30 db; 0.5 (W) = -3 db. V in V out circuit A ( f ) db ( ) ( ) ( ) ( f ) 2 Vout f R Vout f = 10log = 20log 2 V V in f R in When voltage is measured, the multiplier is 20: 10 (V) = 20 db, 100 (V) = 40 db; (V) = -60 db; 0.5 (V) = -6dB. 8.1 : 2/13

3 Decibels Add Decibels are convenient when evaluating the performance of connected circuits. P in circuit 1 circuit P out G = G G AT ( f ) = A1( f ) + A2( f ) T The ability to add or subtract instead of multiplying and dividing makes it easier to graph the frequency response A A 2-10 = A T log log log 8.1 : 3/13

4 Transfer Functions A transfer function is a mathematical or graphic depiction of how a circuit transforms the input into the output. The transfer function can be described in time, F(t), or frequency, Φ. V in (t) circuit F(t) V out (t) out ( ) = ( ) ( ) V t V t F t in the temporal transfer function is convolved with the input signal V in circuit Φ V out out ( ) = ( ) Φ( ) V f V f f in the spectral transfer function is multiplied with the input signal By expressing the voltages and transfer function in decibels, the output spectrum can be obtained by addition: A out = A in + A Φ 8.1 : 4/13

5 Impulse Response The temporal transfer function can be obtained directly by sending a voltage impulse, δ(t), into the circuit (assume it occurs at t = 0). δ(t) circuit F(t) F(t) Since convolution with an impulse is a replication, V out (t) = F(t). The spectral transfer function is then obtained by a Fourier transform, Φ F(t). This approach doesn't always work because of practical limitations. (1) It is often difficult to find a suitable approximation to an impulse function. (2) Circuits often cannot handle impulses much narrower than F(t). This is because stray capacitance will shunt the impulse to ground, and stray inductance will severely attenuate the high frequencies in an impulse. 8.1 : 5/13

6 Rise Time Analysis The practical difficulties with obtaining and using an impulse input can often be circumvented by using a waveform with a sharp edge. V in (t) circuit F(t) τ r 90% 10% V out (t) The rise time, τ r, of the output edge is due to convolution of V in (t) with F(t). The rising portion of V out (t) is the integral of F(t). The functional form of F(t) can be recovered by numeric differentiation of V out (t). When the functional form of F(t) is known, the rise time can often be used to characterize F(t) and, via Fourier transformation, Φ. A rising edge of V in (t) that is 3 times faster than F(t) will only cause a ~10% error in the determination of the rise time. An edge that is 10 times faster will cause a ~1% error. 8.1 : 6/13

7 Gaussian Amplifier (1) Most amplifiers designed to handle pulsed inputs have a Gaussian transfer function. With an impulse input, F(t) can be obtained directly by measuring its FWHM, Γ t. Φ will be Gaussian with Γ f = (4 ln2)/πγ t. V V Γ t Γ f t f If a rise time analysis is used, the integral of the Gaussian transfer function will be identical to a Gaussian cdf. The 10-90% rise time is obtained from the cdf tabular values, V 90% 10% 8.1 : 7/13 t r = cdf(0.9) - cdf(0.1) = 2.58σ t t r t

8 Gaussian Amplifier (2) The frequency FWHM can be computed using the rise time, and the relationship between Γ and σ, Γ = 2 (2 ln2) 1/2 σ Γ 2 2ln 2 4ln ln 2 t = 2 2ln2σ t = tr f ftr Γ = πγ = πt Γ = Two changes need to be made to the Γ f t r relationship: (1) The value of Γ f is for a voltage transfer function. We need the FWHM for the power transfer function: Γ power = Γ f /2 1/2. t r Γ power Γ f (2) For a Gaussian centered at f = 0, the 3dB frequency is given by Γ power /2. f 3dB With these changes we can relate the measured rise time to the 3dB frequency: f 3dB t r = : 8/13

9 RC Circuit The transfer function of an RC circuit can be measured with equal ease using either an impulse or step function input. V V 1/e 1/e τ RC t τ RC t Although the frequency voltage transfer function is complex, the power function is a real Lorentzian, where f 3dB τ RC = 1/2π. By defining a 10-90% rise time, t r = τ RC ln0.9 - τ RC ln0.1 = 2.2τ RC W f : 9/13 f 3dB t r = 0.35 f 3dB f

10 Analog-to-Digital Conversion The process of analog-to-digital conversion multiplies V in (t) by a comb function, comb(δt). This is the only temporal transfer function we have seen so far that is a multiplication. = In the frequency domain, the spectrum of V out is V in convolved with comb(δf ). V in (t) comb(δt) V in comb(δf ) As long as the frequency span (width) of V in is less than the comb spacing, the comb acts as a replicator at the fundamental, Δf, and every harmonic, nδf. 8.1 : 10/13

11 Software Modules Increasingly scientific instrumentation is constructed around an analog-to-digital converter followed by software processing of data. It is often advantageous to think of software modules as having transfer functions. As an example, a digital filter might be used to remove an interference. In this case the transfer function is the "missing one frequency" waveform discussed under Fourier transform examples (slide 7.5-5). Example modules: averaging least-square smoothing digital filters apodization of frequency data curve fitting software autocorrelation software lock-in amplifiers software multi-channel analyzers 8.1 : 11/13

12 Averaging Averaging can be represented by a convolution of the signal with a rectangle. V t 0 f 0 f The relationship between t 0 and f 0 is given by t 0 f 0 = 1. This relationship is true for both power and voltage. W 8.1 : 12/13 To be consistent with other transfer functions, the 3dB frequency with power can be used, where f 3dB = 0.44f 0. Thus, t 0 f 3dB = f 3dB f 0 f

13 Multiple Step Transfer Functions P in Gaussian Amplifier RC Low Pass Filter ADC Running Average P out instrument circuit computer software P out (t) = [[P in (t) gauss(t 0 ) exp(t')] comb(δt)] rect(t) P out (f ) = [[P in gauss(f 0 ) lorentz(f')] comb(δf )] sinc(f) Because of replication by the comb function, the spectrum at each harmonic, nδf, can be given by a sum of decibels. A out (nδf) = A in (nδf) + A gauss (nδf) + A lorentz (nδf) + A sinc (nδf) 8.1 : 13/13

9.4 Enhancing the SNR of Digitized Signals

9.4 Enhancing the SNR of Digitized Signals 9.4 Enhancing the SNR of Digitized Signals stepping and averaging compared to ensemble averaging creating and using Fourier transform digital filters removal of Johnson noise and signal distortion using

More information

The (Fast) Fourier Transform

The (Fast) Fourier Transform The (Fast) Fourier Transform The Fourier transform (FT) is the analog, for non-periodic functions, of the Fourier series for periodic functions can be considered as a Fourier series in the limit that the

More information

EE303: Communication Systems

EE303: Communication Systems EE303: Communication Systems Professor A. Manikas Chair of Communications and Array Processing Imperial College London Introductory Concepts Prof. A. Manikas (Imperial College) EE303: Introductory Concepts

More information

Lecture 9. PMTs and Laser Noise. Lecture 9. Photon Counting. Photomultiplier Tubes (PMTs) Laser Phase Noise. Relative Intensity

Lecture 9. PMTs and Laser Noise. Lecture 9. Photon Counting. Photomultiplier Tubes (PMTs) Laser Phase Noise. Relative Intensity s and Laser Phase Phase Density ECE 185 Lasers and Modulators Lab - Spring 2018 1 Detectors Continuous Output Internal Photoelectron Flux Thermal Filtered External Current w(t) Sensor i(t) External System

More information

Sampling. Alejandro Ribeiro. February 8, 2018

Sampling. Alejandro Ribeiro. February 8, 2018 Sampling Alejandro Ribeiro February 8, 2018 Signals exist in continuous time but it is not unusual for us to process them in discrete time. When we work in discrete time we say that we are doing discrete

More information

Lab 5 AC Concepts and Measurements II: Capacitors and RC Time-Constant

Lab 5 AC Concepts and Measurements II: Capacitors and RC Time-Constant EE110 Laboratory Introduction to Engineering & Laboratory Experience Lab 5 AC Concepts and Measurements II: Capacitors and RC Time-Constant Capacitors Capacitors are devices that can store electric charge

More information

The structure of laser pulses

The structure of laser pulses 1 The structure of laser pulses 2 The structure of laser pulses Pulse characteristics Temporal and spectral representation Fourier transforms Temporal and spectral widths Instantaneous frequency Chirped

More information

3.2 Complex Sinusoids and Frequency Response of LTI Systems

3.2 Complex Sinusoids and Frequency Response of LTI Systems 3. Introduction. A signal can be represented as a weighted superposition of complex sinusoids. x(t) or x[n]. LTI system: LTI System Output = A weighted superposition of the system response to each complex

More information

PY3107 Experimental Physics II

PY3107 Experimental Physics II PY3107 Experimental Physics II ock-in Amplifiers MP aughan and F Peters Related Experiments ock-in ab ignal processing and phase sensitive detection using a lock-in amplifier The problem The signal to

More information

Vibration Testing. Typically either instrumented hammers or shakers are used.

Vibration Testing. Typically either instrumented hammers or shakers are used. Vibration Testing Vibration Testing Equipment For vibration testing, you need an excitation source a device to measure the response a digital signal processor to analyze the system response Excitation

More information

The Discrete Fourier Transform. Signal Processing PSYCH 711/712 Lecture 3

The Discrete Fourier Transform. Signal Processing PSYCH 711/712 Lecture 3 The Discrete Fourier Transform Signal Processing PSYCH 711/712 Lecture 3 DFT Properties symmetry linearity shifting scaling Symmetry x(n) -1.0-0.5 0.0 0.5 1.0 X(m) -10-5 0 5 10 0 5 10 15 0 5 10 15 n m

More information

VID3: Sampling and Quantization

VID3: Sampling and Quantization Video Transmission VID3: Sampling and Quantization By Prof. Gregory D. Durgin copyright 2009 all rights reserved Claude E. Shannon (1916-2001) Mathematician and Electrical Engineer Worked for Bell Labs

More information

EE401: Advanced Communication Theory

EE401: Advanced Communication Theory EE401: Advanced Communication Theory Professor A. Manikas Chair of Communications and Array Processing Imperial College London Introductory Concepts Prof. A. Manikas (Imperial College) EE.401: Introductory

More information

Basic Electronics. Introductory Lecture Course for. Technology and Instrumentation in Particle Physics Chicago, Illinois June 9-14, 2011

Basic Electronics. Introductory Lecture Course for. Technology and Instrumentation in Particle Physics Chicago, Illinois June 9-14, 2011 Basic Electronics Introductory Lecture Course for Technology and Instrumentation in Particle Physics 2011 Chicago, Illinois June 9-14, 2011 Presented By Gary Drake Argonne National Laboratory Session 2

More information

Lecture 8 ELE 301: Signals and Systems

Lecture 8 ELE 301: Signals and Systems Lecture 8 ELE 30: Signals and Systems Prof. Paul Cuff Princeton University Fall 20-2 Cuff (Lecture 7) ELE 30: Signals and Systems Fall 20-2 / 37 Properties of the Fourier Transform Properties of the Fourier

More information

1-D MATH REVIEW CONTINUOUS 1-D FUNCTIONS. Kronecker delta function and its relatives. x 0 = 0

1-D MATH REVIEW CONTINUOUS 1-D FUNCTIONS. Kronecker delta function and its relatives. x 0 = 0 -D MATH REVIEW CONTINUOUS -D FUNCTIONS Kronecker delta function and its relatives delta function δ ( 0 ) 0 = 0 NOTE: The delta function s amplitude is infinite and its area is. The amplitude is shown as

More information

Patrick F. Dunn 107 Hessert Laboratory Department of Aerospace and Mechanical Engineering University of Notre Dame Notre Dame, IN 46556

Patrick F. Dunn 107 Hessert Laboratory Department of Aerospace and Mechanical Engineering University of Notre Dame Notre Dame, IN 46556 Learning Objectives to Accompany MEASUREMENT AND DATA ANALYSIS FOR ENGINEERING AND SCIENCE Second Edition, Taylor and Francis/CRC Press, c 2010 ISBN: 9781439825686 Patrick F. Dunn pdunn@nd.edu 107 Hessert

More information

Fourier transform. XE31EO2 - Pavel Máša. EO2 Lecture 2. XE31EO2 - Pavel Máša - Fourier Transform

Fourier transform. XE31EO2 - Pavel Máša. EO2 Lecture 2. XE31EO2 - Pavel Máša - Fourier Transform Fourier transform EO2 Lecture 2 Pavel Máša - Fourier Transform INTRODUCTION We already know complex form of Fourier series f(t) = 1X k= 1 A k e jk! t A k = 1 T Series frequency spectra is discrete Circuits

More information

-Digital Signal Processing- FIR Filter Design. Lecture May-16

-Digital Signal Processing- FIR Filter Design. Lecture May-16 -Digital Signal Processing- FIR Filter Design Lecture-17 24-May-16 FIR Filter Design! FIR filters can also be designed from a frequency response specification.! The equivalent sampled impulse response

More information

Figure 1.1 (a) Model of a communication system, and (b) signal processing functions.

Figure 1.1 (a) Model of a communication system, and (b) signal processing functions. . Introduction to Signals and Operations Model of a Communication System [] Figure. (a) Model of a communication system, and (b) signal processing functions. Classification of Signals. Continuous-time

More information

SEISMIC WAVE PROPAGATION. Lecture 2: Fourier Analysis

SEISMIC WAVE PROPAGATION. Lecture 2: Fourier Analysis SEISMIC WAVE PROPAGATION Lecture 2: Fourier Analysis Fourier Series & Fourier Transforms Fourier Series Review of trigonometric identities Analysing the square wave Fourier Transform Transforms of some

More information

Lecture 27 Frequency Response 2

Lecture 27 Frequency Response 2 Lecture 27 Frequency Response 2 Fundamentals of Digital Signal Processing Spring, 2012 Wei-Ta Chu 2012/6/12 1 Application of Ideal Filters Suppose we can generate a square wave with a fundamental period

More information

6.003: Signal Processing

6.003: Signal Processing 6.003: Signal Processing Discrete Fourier Transform Discrete Fourier Transform (DFT) Relations to Discrete-Time Fourier Transform (DTFT) Relations to Discrete-Time Fourier Series (DTFS) October 16, 2018

More information

Probability and Statistics for Final Year Engineering Students

Probability and Statistics for Final Year Engineering Students Probability and Statistics for Final Year Engineering Students By Yoni Nazarathy, Last Updated: May 24, 2011. Lecture 6p: Spectral Density, Passing Random Processes through LTI Systems, Filtering Terms

More information

80% of all excitatory synapses - at the dendritic spines.

80% of all excitatory synapses - at the dendritic spines. Dendritic Modelling Dendrites (from Greek dendron, tree ) are the branched projections of a neuron that act to conduct the electrical stimulation received from other cells to and from the cell body, or

More information

ECG782: Multidimensional Digital Signal Processing

ECG782: Multidimensional Digital Signal Processing Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Filtering in the Frequency Domain http://www.ee.unlv.edu/~b1morris/ecg782/ 2 Outline Background

More information

Therefore the new Fourier coefficients are. Module 2 : Signals in Frequency Domain Problem Set 2. Problem 1

Therefore the new Fourier coefficients are. Module 2 : Signals in Frequency Domain Problem Set 2. Problem 1 Module 2 : Signals in Frequency Domain Problem Set 2 Problem 1 Let be a periodic signal with fundamental period T and Fourier series coefficients. Derive the Fourier series coefficients of each of the

More information

Ver 3808 E1.10 Fourier Series and Transforms (2014) E1.10 Fourier Series and Transforms. Problem Sheet 1 (Lecture 1)

Ver 3808 E1.10 Fourier Series and Transforms (2014) E1.10 Fourier Series and Transforms. Problem Sheet 1 (Lecture 1) Ver 88 E. Fourier Series and Transforms 4 Key: [A] easy... [E]hard Questions from RBH textbook: 4., 4.8. E. Fourier Series and Transforms Problem Sheet Lecture. [B] Using the geometric progression formula,

More information

0 t < 0 1 t 1. u(t) =

0 t < 0 1 t 1. u(t) = A. M. Niknejad University of California, Berkeley EE 100 / 42 Lecture 13 p. 22/33 Step Response A unit step function is described by u(t) = ( 0 t < 0 1 t 1 While the waveform has an artificial jump (difficult

More information

Advanced Analog Building Blocks. Prof. Dr. Peter Fischer, Dr. Wei Shen, Dr. Albert Comerma, Dr. Johannes Schemmel, etc

Advanced Analog Building Blocks. Prof. Dr. Peter Fischer, Dr. Wei Shen, Dr. Albert Comerma, Dr. Johannes Schemmel, etc Advanced Analog Building Blocks Prof. Dr. Peter Fischer, Dr. Wei Shen, Dr. Albert Comerma, Dr. Johannes Schemmel, etc 1 Topics 1. S domain and Laplace Transform Zeros and Poles 2. Basic and Advanced current

More information

Fundamentals of Engineering Exam Review Electromagnetic Physics

Fundamentals of Engineering Exam Review Electromagnetic Physics Dr. Gregory J. Mazzaro Spring 2018 Fundamentals of Engineering Exam Review Electromagnetic Physics (currently 5-7% of FE exam) THE CITADEL, THE MILITARY COLLEGE OF SOUTH CAROLINA 171 Moultrie Street, Charleston,

More information

The Hilbert Transform

The Hilbert Transform The Hilbert Transform Frank R. Kschischang The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto October 22, 2006; updated March 0, 205 Definition The Hilbert

More information

L6: Short-time Fourier analysis and synthesis

L6: Short-time Fourier analysis and synthesis L6: Short-time Fourier analysis and synthesis Overview Analysis: Fourier-transform view Analysis: filtering view Synthesis: filter bank summation (FBS) method Synthesis: overlap-add (OLA) method STFT magnitude

More information

Modeling Buck Converter by Using Fourier Analysis

Modeling Buck Converter by Using Fourier Analysis PIERS ONLINE, VOL. 6, NO. 8, 2010 705 Modeling Buck Converter by Using Fourier Analysis Mao Zhang 1, Weiping Zhang 2, and Zheng Zhang 2 1 School of Computing, Engineering and Physical Sciences, University

More information

Analog Digital Sampling & Discrete Time Discrete Values & Noise Digital-to-Analog Conversion Analog-to-Digital Conversion

Analog Digital Sampling & Discrete Time Discrete Values & Noise Digital-to-Analog Conversion Analog-to-Digital Conversion Analog Digital Sampling & Discrete Time Discrete Values & Noise Digital-to-Analog Conversion Analog-to-Digital Conversion 6.082 Fall 2006 Analog Digital, Slide Plan: Mixed Signal Architecture volts bits

More information

IB Paper 6: Signal and Data Analysis

IB Paper 6: Signal and Data Analysis IB Paper 6: Signal and Data Analysis Handout 5: Sampling Theory S Godsill Signal Processing and Communications Group, Engineering Department, Cambridge, UK Lent 2015 1 / 85 Sampling and Aliasing All of

More information

Fourier Series. Fourier Transform

Fourier Series. Fourier Transform Math Methods I Lia Vas Fourier Series. Fourier ransform Fourier Series. Recall that a function differentiable any number of times at x = a can be represented as a power series n= a n (x a) n where the

More information

Communication Theory Summary of Important Definitions and Results

Communication Theory Summary of Important Definitions and Results Signal and system theory Convolution of signals x(t) h(t) = y(t): Fourier Transform: Communication Theory Summary of Important Definitions and Results X(ω) = X(ω) = y(t) = X(ω) = j x(t) e jωt dt, 0 Properties

More information

Digital Signal Processing Prof. T. K. Basu Department of Electrical Engineering Indian Institute of Technology, Kharagpur

Digital Signal Processing Prof. T. K. Basu Department of Electrical Engineering Indian Institute of Technology, Kharagpur Digital Signal Processing Prof. T. K. Basu Department of Electrical Engineering Indian Institute of Technology, Kharagpur Lecture - 6 Z-Transform (Contd.) Discussing about inverse inverse Z transform,

More information

2A1H Time-Frequency Analysis II Bugs/queries to HT 2011 For hints and answers visit dwm/courses/2tf

2A1H Time-Frequency Analysis II Bugs/queries to HT 2011 For hints and answers visit   dwm/courses/2tf Time-Frequency Analysis II (HT 20) 2AH 2AH Time-Frequency Analysis II Bugs/queries to david.murray@eng.ox.ac.uk HT 20 For hints and answers visit www.robots.ox.ac.uk/ dwm/courses/2tf David Murray. A periodic

More information

Square Root Raised Cosine Filter

Square Root Raised Cosine Filter Wireless Information Transmission System Lab. Square Root Raised Cosine Filter Institute of Communications Engineering National Sun Yat-sen University Introduction We consider the problem of signal design

More information

Signals, Instruments, and Systems W5. Introduction to Signal Processing Sampling, Reconstruction, and Filters

Signals, Instruments, and Systems W5. Introduction to Signal Processing Sampling, Reconstruction, and Filters Signals, Instruments, and Systems W5 Introduction to Signal Processing Sampling, Reconstruction, and Filters Acknowledgments Recapitulation of Key Concepts from the Last Lecture Dirac delta function (

More information

DIGITAL COMMUNICATIONS. IAGlover and P M Grant. Prentice Hall 1997 PROBLEM SOLUTIONS CHAPTER 6

DIGITAL COMMUNICATIONS. IAGlover and P M Grant. Prentice Hall 1997 PROBLEM SOLUTIONS CHAPTER 6 DIGITAL COMMUNICATIONS IAGlover and P M Grant Prentice Hall 997 PROBLEM SOLUTIONS CHAPTER 6 6. P e erf V σ erf. 5 +. 5 0.705 [ erf (. 009)] [ 0. 999 979 ]. 0 0 5 The optimum DC level is zero. For equiprobable

More information

In addition to resistors that we have considered to date, there are two other basic electronic components that can be found everywhere: the capacitor

In addition to resistors that we have considered to date, there are two other basic electronic components that can be found everywhere: the capacitor In addition to resistors that we have considered to date, there are two other basic electronic components that can be found everywhere: the capacitor and the inductor. We will consider these two types

More information

Sistemas de Aquisição de Dados. Mestrado Integrado em Eng. Física Tecnológica 2016/17 Aula 3, 3rd September

Sistemas de Aquisição de Dados. Mestrado Integrado em Eng. Física Tecnológica 2016/17 Aula 3, 3rd September Sistemas de Aquisição de Dados Mestrado Integrado em Eng. Física Tecnológica 2016/17 Aula 3, 3rd September The Data Converter Interface Analog Media and Transducers Signal Conditioning Signal Conditioning

More information

2.161 Signal Processing: Continuous and Discrete Fall 2008

2.161 Signal Processing: Continuous and Discrete Fall 2008 MIT OpenCourseWare http://ocw.mit.edu 2.161 Signal Processing: Continuous and Discrete Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Massachusetts

More information

Now identified: Noise sources Amplifier components and basic design How to achieve best signal to noise?

Now identified: Noise sources Amplifier components and basic design How to achieve best signal to noise? Signal processing Now identified: Noise sources Amplifier components and basic design How to achieve best signal to noise? Possible constraints power consumption ability to provide power & extract heat,

More information

Summary of Fourier Transform Properties

Summary of Fourier Transform Properties Summary of Fourier ransform Properties Frank R. Kschischang he Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of oronto January 7, 207 Definition and Some echnicalities

More information

Fourier Series Representation of

Fourier Series Representation of Fourier Series Representation of Periodic Signals Rui Wang, Assistant professor Dept. of Information and Communication Tongji University it Email: ruiwang@tongji.edu.cn Outline The response of LIT system

More information

Signal types. Signal characteristics: RMS, power, db Probability Density Function (PDF). Analogue-to-Digital Conversion (ADC).

Signal types. Signal characteristics: RMS, power, db Probability Density Function (PDF). Analogue-to-Digital Conversion (ADC). Signal types. Signal characteristics:, power, db Probability Density Function (PDF). Analogue-to-Digital Conversion (ADC). Signal types Stationary (average properties don t vary with time) Deterministic

More information

2A1H Time-Frequency Analysis II

2A1H Time-Frequency Analysis II 2AH Time-Frequency Analysis II Bugs/queries to david.murray@eng.ox.ac.uk HT 209 For any corrections see the course page DW Murray at www.robots.ox.ac.uk/ dwm/courses/2tf. (a) A signal g(t) with period

More information

Optics for Engineers Chapter 11

Optics for Engineers Chapter 11 Optics for Engineers Chapter 11 Charles A. DiMarzio Northeastern University Nov. 212 Fourier Optics Terminology Field Plane Fourier Plane C Field Amplitude, E(x, y) Ẽ(f x, f y ) Amplitude Point Spread

More information

Fourier transform. Stefano Ferrari. Università degli Studi di Milano Methods for Image Processing. academic year

Fourier transform. Stefano Ferrari. Università degli Studi di Milano Methods for Image Processing. academic year Fourier transform Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Methods for Image Processing academic year 27 28 Function transforms Sometimes, operating on a class of functions

More information

Roundoff Noise in Digital Feedback Control Systems

Roundoff Noise in Digital Feedback Control Systems Chapter 7 Roundoff Noise in Digital Feedback Control Systems Digital control systems are generally feedback systems. Within their feedback loops are parts that are analog and parts that are digital. At

More information

Introduction to Image Processing #5/7

Introduction to Image Processing #5/7 Outline Introduction to Image Processing #5/7 Thierry Géraud EPITA Research and Development Laboratory (LRDE) 2006 Outline Outline 1 Introduction 2 About the Dirac Delta Function Some Useful Functions

More information

THE FOURIER TRANSFORM (Fourier series for a function whose period is very, very long) Reading: Main 11.3

THE FOURIER TRANSFORM (Fourier series for a function whose period is very, very long) Reading: Main 11.3 THE FOURIER TRANSFORM (Fourier series for a function whose period is very, very long) Reading: Main 11.3 Any periodic function f(t) can be written as a Fourier Series a 0 2 + a n cos( nωt) + b n sin n

More information

2.1 Basic Concepts Basic operations on signals Classication of signals

2.1 Basic Concepts Basic operations on signals Classication of signals Haberle³me Sistemlerine Giri³ (ELE 361) 9 Eylül 2017 TOBB Ekonomi ve Teknoloji Üniversitesi, Güz 2017-18 Dr. A. Melda Yüksel Turgut & Tolga Girici Lecture Notes Chapter 2 Signals and Linear Systems 2.1

More information

ADAPTIVE EQUALIZATION AT MULTI-GHZ DATARATES

ADAPTIVE EQUALIZATION AT MULTI-GHZ DATARATES ADAPTIVE EQUALIZATION AT MULTI-GHZ DATARATES Department of Electrical Engineering Indian Institute of Technology, Madras 1st February 2007 Outline Introduction. Approaches to electronic mitigation - ADC

More information

PART 1. Review of DSP. f (t)e iωt dt. F(ω) = f (t) = 1 2π. F(ω)e iωt dω. f (t) F (ω) The Fourier Transform. Fourier Transform.

PART 1. Review of DSP. f (t)e iωt dt. F(ω) = f (t) = 1 2π. F(ω)e iωt dω. f (t) F (ω) The Fourier Transform. Fourier Transform. PART 1 Review of DSP Mauricio Sacchi University of Alberta, Edmonton, AB, Canada The Fourier Transform F() = f (t) = 1 2π f (t)e it dt F()e it d Fourier Transform Inverse Transform f (t) F () Part 1 Review

More information

Lecture 7 ELE 301: Signals and Systems

Lecture 7 ELE 301: Signals and Systems Lecture 7 ELE 30: Signals and Systems Prof. Paul Cuff Princeton University Fall 20-2 Cuff (Lecture 7) ELE 30: Signals and Systems Fall 20-2 / 22 Introduction to Fourier Transforms Fourier transform as

More information

Homework 4. May An LTI system has an input, x(t) and output y(t) related through the equation y(t) = t e (t t ) x(t 2)dt

Homework 4. May An LTI system has an input, x(t) and output y(t) related through the equation y(t) = t e (t t ) x(t 2)dt Homework 4 May 2017 1. An LTI system has an input, x(t) and output y(t) related through the equation y(t) = t e (t t ) x(t 2)dt Determine the impulse response of the system. Rewriting as y(t) = t e (t

More information

MATHEMATICAL TOOLS FOR DIGITAL TRANSMISSION ANALYSIS

MATHEMATICAL TOOLS FOR DIGITAL TRANSMISSION ANALYSIS ch03.qxd 1/9/03 09:14 AM Page 35 CHAPTER 3 MATHEMATICAL TOOLS FOR DIGITAL TRANSMISSION ANALYSIS 3.1 INTRODUCTION The study of digital wireless transmission is in large measure the study of (a) the conversion

More information

Digital Band-pass Modulation PROF. MICHAEL TSAI 2011/11/10

Digital Band-pass Modulation PROF. MICHAEL TSAI 2011/11/10 Digital Band-pass Modulation PROF. MICHAEL TSAI 211/11/1 Band-pass Signal Representation a t g t General form: 2πf c t + φ t g t = a t cos 2πf c t + φ t Envelope Phase Envelope is always non-negative,

More information

ESS Finite Impulse Response Filters and the Z-transform

ESS Finite Impulse Response Filters and the Z-transform 9. Finite Impulse Response Filters and the Z-transform We are going to have two lectures on filters you can find much more material in Bob Crosson s notes. In the first lecture we will focus on some of

More information

Introduction to Fourier Transforms. Lecture 7 ELE 301: Signals and Systems. Fourier Series. Rect Example

Introduction to Fourier Transforms. Lecture 7 ELE 301: Signals and Systems. Fourier Series. Rect Example Introduction to Fourier ransforms Lecture 7 ELE 3: Signals and Systems Fourier transform as a limit of the Fourier series Inverse Fourier transform: he Fourier integral theorem Prof. Paul Cuff Princeton

More information

Stability. X(s) Y(s) = (s + 2) 2 (s 2) System has 2 poles: points where Y(s) -> at s = +2 and s = -2. Y(s) 8X(s) G 1 G 2

Stability. X(s) Y(s) = (s + 2) 2 (s 2) System has 2 poles: points where Y(s) -> at s = +2 and s = -2. Y(s) 8X(s) G 1 G 2 Stability 8X(s) X(s) Y(s) = (s 2) 2 (s 2) System has 2 poles: points where Y(s) -> at s = 2 and s = -2 If all poles are in region where s < 0, system is stable in Fourier language s = jω G 0 - x3 x7 Y(s)

More information

Chapter 9. Estimating circuit speed. 9.1 Counting gate delays

Chapter 9. Estimating circuit speed. 9.1 Counting gate delays Chapter 9 Estimating circuit speed 9.1 Counting gate delays The simplest method for estimating the speed of a VLSI circuit is to count the number of VLSI logic gates that the input signals must propagate

More information

Digital Communications

Digital Communications Digital Communications Chapter 9 Digital Communications Through Band-Limited Channels Po-Ning Chen, Professor Institute of Communications Engineering National Chiao-Tung University, Taiwan Digital Communications:

More information

Chapter 6 THE SAMPLING PROCESS 6.1 Introduction 6.2 Fourier Transform Revisited

Chapter 6 THE SAMPLING PROCESS 6.1 Introduction 6.2 Fourier Transform Revisited Chapter 6 THE SAMPLING PROCESS 6.1 Introduction 6.2 Fourier Transform Revisited Copyright c 2005 Andreas Antoniou Victoria, BC, Canada Email: aantoniou@ieee.org July 14, 2018 Frame # 1 Slide # 1 A. Antoniou

More information

8: Correlation. E1.10 Fourier Series and Transforms ( ) Fourier Transform - Correlation: 8 1 / 11. 8: Correlation

8: Correlation. E1.10 Fourier Series and Transforms ( ) Fourier Transform - Correlation: 8 1 / 11. 8: Correlation E. Fourier Series and Transforms (25-5585) - Correlation: 8 / The cross-correlation between two signals u(t) and v(t) is w(t) = u(t) v(t) u (τ)v(τ +t)dτ = u (τ t)v(τ)dτ [sub: τ τ t] The complex conjugate,

More information

Module 3. Convolution. Aim

Module 3. Convolution. Aim Module Convolution Digital Signal Processing. Slide 4. Aim How to perform convolution in real-time systems efficiently? Is convolution in time domain equivalent to multiplication of the transformed sequence?

More information

Sound & Vibration Magazine March, Fundamentals of the Discrete Fourier Transform

Sound & Vibration Magazine March, Fundamentals of the Discrete Fourier Transform Fundamentals of the Discrete Fourier Transform Mark H. Richardson Hewlett Packard Corporation Santa Clara, California The Fourier transform is a mathematical procedure that was discovered by a French mathematician

More information

Time Varying Circuit Analysis

Time Varying Circuit Analysis MAS.836 Sensor Systems for Interactive Environments th Distributed: Tuesday February 16, 2010 Due: Tuesday February 23, 2010 Problem Set # 2 Time Varying Circuit Analysis The purpose of this problem set

More information

Convolution and Linear Systems

Convolution and Linear Systems CS 450: Introduction to Digital Signal and Image Processing Bryan Morse BYU Computer Science Introduction Analyzing Systems Goal: analyze a device that turns one signal into another. Notation: f (t) g(t)

More information

Fourier transform representation of CT aperiodic signals Section 4.1

Fourier transform representation of CT aperiodic signals Section 4.1 Fourier transform representation of CT aperiodic signals Section 4. A large class of aperiodic CT signals can be represented by the CT Fourier transform (CTFT). The (CT) Fourier transform (or spectrum)

More information

Gabor Deconvolution. Gary Margrave and Michael Lamoureux

Gabor Deconvolution. Gary Margrave and Michael Lamoureux Gabor Deconvolution Gary Margrave and Michael Lamoureux = Outline The Gabor idea The continuous Gabor transform The discrete Gabor transform A nonstationary trace model Gabor deconvolution Examples = Gabor

More information

Modelling the Temperature Changes of a Hot Plate and Water in a Proportional, Integral, Derivative Control System

Modelling the Temperature Changes of a Hot Plate and Water in a Proportional, Integral, Derivative Control System Modelling the Temperature Changes of a Hot Plate and Water in a Proportional, Integral, Derivative Control System Grant Hutchins 1. Introduction Temperature changes in a hot plate heating system can be

More information

EA2.3 - Electronics 2 1

EA2.3 - Electronics 2 1 In the previous lecture, I talked about the idea of complex frequency s, where s = σ + jω. Using such concept of complex frequency allows us to analyse signals and systems with better generality. In this

More information

Efficient Algorithms for Pulse Parameter Estimation, Pulse Peak Localization And Pileup Reduction in Gamma Ray Spectroscopy M.W.Raad 1, L.

Efficient Algorithms for Pulse Parameter Estimation, Pulse Peak Localization And Pileup Reduction in Gamma Ray Spectroscopy M.W.Raad 1, L. Efficient Algorithms for Pulse Parameter Estimation, Pulse Peak Localization And Pileup Reduction in Gamma Ray Spectroscopy M.W.Raad 1, L. Cheded 2 1 Computer Engineering Department, 2 Systems Engineering

More information

Chapter 5 Frequency Domain Analysis of Systems

Chapter 5 Frequency Domain Analysis of Systems Chapter 5 Frequency Domain Analysis of Systems CT, LTI Systems Consider the following CT LTI system: xt () ht () yt () Assumption: the impulse response h(t) is absolutely integrable, i.e., ht ( ) dt< (this

More information

FILTERING IN THE FREQUENCY DOMAIN

FILTERING IN THE FREQUENCY DOMAIN 1 FILTERING IN THE FREQUENCY DOMAIN Lecture 4 Spatial Vs Frequency domain 2 Spatial Domain (I) Normal image space Changes in pixel positions correspond to changes in the scene Distances in I correspond

More information

NIH Public Access Author Manuscript Ultrason Imaging. Author manuscript; available in PMC 2013 November 21.

NIH Public Access Author Manuscript Ultrason Imaging. Author manuscript; available in PMC 2013 November 21. NIH Public Access Author Manuscript Published in final edited form as: Ultrason Imaging. 2012 October ; 34(4):. doi:10.1177/0161734612463847. Rapid Transient Pressure Field Computations in the Nearfield

More information

Signals & Systems interaction in the Time Domain. (Systems will be LTI from now on unless otherwise stated)

Signals & Systems interaction in the Time Domain. (Systems will be LTI from now on unless otherwise stated) Signals & Systems interaction in the Time Domain (Systems will be LTI from now on unless otherwise stated) Course Objectives Specific Course Topics: -Basic test signals and their properties -Basic system

More information

ENSC327 Communications Systems 2: Fourier Representations. School of Engineering Science Simon Fraser University

ENSC327 Communications Systems 2: Fourier Representations. School of Engineering Science Simon Fraser University ENSC37 Communications Systems : Fourier Representations School o Engineering Science Simon Fraser University Outline Chap..5: Signal Classiications Fourier Transorm Dirac Delta Function Unit Impulse Fourier

More information

Cast of Characters. Some Symbols, Functions, and Variables Used in the Book

Cast of Characters. Some Symbols, Functions, and Variables Used in the Book Page 1 of 6 Cast of Characters Some s, Functions, and Variables Used in the Book Digital Signal Processing and the Microcontroller by Dale Grover and John R. Deller ISBN 0-13-081348-6 Prentice Hall, 1998

More information

Lecture 2. Introduction to Systems (Lathi )

Lecture 2. Introduction to Systems (Lathi ) Lecture 2 Introduction to Systems (Lathi 1.6-1.8) Pier Luigi Dragotti Department of Electrical & Electronic Engineering Imperial College London URL: www.commsp.ee.ic.ac.uk/~pld/teaching/ E-mail: p.dragotti@imperial.ac.uk

More information

1 otherwise. Note that the area of the pulse is one. The Dirac delta function (a.k.a. the impulse) can be defined using the pulse as follows:

1 otherwise. Note that the area of the pulse is one. The Dirac delta function (a.k.a. the impulse) can be defined using the pulse as follows: The Dirac delta function There is a function called the pulse: { if t > Π(t) = 2 otherwise. Note that the area of the pulse is one. The Dirac delta function (a.k.a. the impulse) can be defined using the

More information

Single-Time-Constant (STC) Circuits This lecture is given as a background that will be needed to determine the frequency response of the amplifiers.

Single-Time-Constant (STC) Circuits This lecture is given as a background that will be needed to determine the frequency response of the amplifiers. Single-Time-Constant (STC) Circuits This lecture is given as a background that will be needed to determine the frequency response of the amplifiers. Objectives To analyze and understand STC circuits with

More information

Principles of Communications Lecture 8: Baseband Communication Systems. Chih-Wei Liu 劉志尉 National Chiao Tung University

Principles of Communications Lecture 8: Baseband Communication Systems. Chih-Wei Liu 劉志尉 National Chiao Tung University Principles of Communications Lecture 8: Baseband Communication Systems Chih-Wei Liu 劉志尉 National Chiao Tung University cwliu@twins.ee.nctu.edu.tw Outlines Introduction Line codes Effects of filtering Pulse

More information

Review of Linear Time-Invariant Network Analysis

Review of Linear Time-Invariant Network Analysis D1 APPENDIX D Review of Linear Time-Invariant Network Analysis Consider a network with input x(t) and output y(t) as shown in Figure D-1. If an input x 1 (t) produces an output y 1 (t), and an input x

More information

Empirical Mean and Variance!

Empirical Mean and Variance! Global Image Properties! Global image properties refer to an image as a whole rather than components. Computation of global image properties is often required for image enhancement, preceding image analysis.!

More information

Spatial-Domain Convolution Filters

Spatial-Domain Convolution Filters Spatial-Domain Filtering 9 Spatial-Domain Convolution Filters Consider a linear space-invariant (LSI) system as shown: The two separate inputs to the LSI system, x 1 (m) and x 2 (m), and their corresponding

More information

Chapter 8. Conclusions and Further Work Overview of Findings

Chapter 8. Conclusions and Further Work Overview of Findings 8.1. Overview of Findings The aim of this thesis was to analyse, theoretically and experimentally, the signal and noise aspects of digital magnetic recording on longitudinal thin-film disk media. This

More information

1 Understanding Sampling

1 Understanding Sampling 1 Understanding Sampling Summary. In Part I, we consider the analysis of discrete-time signals. In Chapter 1, we consider how discretizing a signal affects the signal s Fourier transform. We derive the

More information

Feature extraction 1

Feature extraction 1 Centre for Vision Speech & Signal Processing University of Surrey, Guildford GU2 7XH. Feature extraction 1 Dr Philip Jackson Cepstral analysis - Real & complex cepstra - Homomorphic decomposition Filter

More information

Today. 1/25/11 Physics 262 Lecture 2 Filters. Active Components and Filters. Homework. Lab 2 this week

Today. 1/25/11 Physics 262 Lecture 2 Filters. Active Components and Filters. Homework. Lab 2 this week /5/ Physics 6 Lecture Filters Today Basics: Analog versus Digital; Passive versus Active Basic concepts and types of filters Passband, Stopband, Cut-off, Slope, Knee, Decibels, and Bode plots Active Components

More information

Signal Design for Band-Limited Channels

Signal Design for Band-Limited Channels Wireless Information Transmission System Lab. Signal Design for Band-Limited Channels Institute of Communications Engineering National Sun Yat-sen University Introduction We consider the problem of signal

More information

8 PAM BER/SER Monte Carlo Simulation

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

More information

Introduction to Digital Signal Processing

Introduction to Digital Signal Processing Introduction to Digital Signal Processing What is DSP? DSP, or Digital Signal Processing, as the term suggests, is the processing of signals by digital means. A signal in this context can mean a number

More information

UCSD ECE153 Handout #40 Prof. Young-Han Kim Thursday, May 29, Homework Set #8 Due: Thursday, June 5, 2011

UCSD ECE153 Handout #40 Prof. Young-Han Kim Thursday, May 29, Homework Set #8 Due: Thursday, June 5, 2011 UCSD ECE53 Handout #40 Prof. Young-Han Kim Thursday, May 9, 04 Homework Set #8 Due: Thursday, June 5, 0. Discrete-time Wiener process. Let Z n, n 0 be a discrete time white Gaussian noise (WGN) process,

More information