Lecture 01 Signal and System Muhammad Umair Muhammad Umair, Lecturer (CS), KICSIT.

Slides:



Advertisements
Similar presentations
Lecture 3: Signals & Systems Concepts
Advertisements

Learning Objectives Static and Dynamic Characteristics of Signals
Introduction to Signals Hany Ferdinando Dept. of Electrical Engineering Petra Christian University.
Review of Frequency Domain
September 2, 2009ECE 366, Fall 2009 Introduction to ECE 366 Selin Aviyente Associate Professor.
Lecture 1: Signals & Systems Concepts
1 بسم الله الرحمن الرحيم Islamic University of Gaza Electrical & Computer Engineering Department.
DSP for Engineering Aplications DSP for Engineering Aplications ECI Semester /2010 Department of Engineering and Design London South Bank University.
Lecture 2: Signals Concepts & Properties
EE-2027 SaS, L11/7 EE-2027 Signals and Systems Dr Martin Brown E1k, Main Building
Signals Systems For example ...
EE-2027 SaS, L11 1/13 Lecture 11: Discrete Fourier Transform 4 Sampling Discrete-time systems (2 lectures): Sampling theorem, discrete Fourier transform.
Lecture 6: Linear Systems and Convolution
Introduction to signals
ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: Examples of Signals Functional Forms Continuous vs. Discrete Symmetry and Periodicity.
Discrete-Time and System (A Review)
ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: Definition of a System Examples Causality Linearity Time Invariance.
Introduction Signals is a cornerstone of an electrical or computer engineering education and relevant to most engineering disciplines. The concepts described.
Ch. 1 Fundamental Concepts Kamen and Heck. 1.1 Continuous Time Signals x(t) –a signal that is real-valued or scalar- valued function of the time variable.
Course Outline (Tentative)
1 Chapter 1 Fundamental Concepts. 2 signalpattern of variation of a physical quantity,A signal is a pattern of variation of a physical quantity, often.
Time-Domain Representations of LTI Systems
Signal and Systems Prof. H. Sameti
ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: Useful Building Blocks Time-Shifting of Signals Derivatives Sampling (Introduction)
Signals and Systems M. Rahmati Computer Engineering and Information Technology Department Amir Kabir University of Technology (Tehran Polytechnic)
Time-Domain Representations of LTI Systems
CISE315 SaS, L171/16 Lecture 8: Basis Functions & Fourier Series 3. Basis functions: Concept of basis function. Fourier series representation of time functions.
Lecturer: Dr. Peter Tsang Room: G6505 Phone: Website: Files: SIG01.ppt, SIG02.ppt,
EE3010_Lecture2 Al-Dhaifallah_Term Introduction to Signal and Systems Dr. Mujahed Al-Dhaifallah EE3010: Signals and Systems Analysis Term 332.
Module 2 SPECTRAL ANALYSIS OF COMMUNICATION SIGNAL.
1 Signals and Systems Fall 2003 Lecture #1 Prof. Alan S. Willsky 4 September ) Administrative details 2) Signals 3) Systems 4) For examples... “Figures.
1 Chapter 1 Fundamental Concepts. 2 signalpattern of variation of a physical quantity,A signal is a pattern of variation of a physical quantity, often.
ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: Definition of a System Examples Causality Linearity Time Invariance Resources:
Signal and System I The unit step response of an LTI system.
EE 64 Linear System Theory M. R. Gustafson II Adjunct Assistant Professor Duke University.
Signals and Systems Dr. Mohamed Bingabr University of Central Oklahoma
Course Outline (Tentative) Fundamental Concepts of Signals and Systems Signals Systems Linear Time-Invariant (LTI) Systems Convolution integral and sum.
SIGNALS & SYSTEMS LECTURER: MUZAMIR ISA MUHAMMAD HATTA HUSSEIN
Input Function x(t) Output Function y(t) T[ ]. Consider The following Input/Output relations.
Signals and Systems 1 Lecture 7 Dr. Ali. A. Jalali September 4, 2002
EEE 503 Digital Signal Processing Lecture #2 : EEE 503 Digital Signal Processing Lecture #2 : Discrete-Time Signals & Systems Dr. Panuthat Boonpramuk Department.
2006 Fall Welcome to Signals and Systems
EE3010_Lecture3 Al-Dhaifallah_Term Introduction to Signal and Systems Dr. Mujahed Al-Dhaifallah EE3010: Signals and Systems Analysis Term 332.
Instructor: Mian Shahzad Iqbal
4. Introduction to Signal and Systems
Geology 6600/7600 Signal Analysis 21 Sep 2015 © A.R. Lowry 2015 Last time: The Cross-Power Spectrum relating two random processes x and y is given by:
(Part one: Continuous)
1 “Figures and images used in these lecture notes by permission, copyright 1997 by Alan V. Oppenheim and Alan S. Willsky” Signals and Systems Spring 2003.
Overview of Signals and Systems  Overview of Overview Administrative details Administrative details Syllabus, attendance, report, notebookSyllabus, attendance,
Signals and Systems Lecture #6 EE3010_Lecture6Al-Dhaifallah_Term3321.
Chapter 2. Signals and Linear Systems
Discrete Time Signal Processing Chu-Song Chen (陳祝嵩) Institute of Information Science Academia Sinica 中央研究院 資訊科學研究所.
Signal & Linear Systems (EELE 3310)
Instructor: Mian Shahzad Iqbal
Introduction Signals and Systems is a cornerstone of an electrical or computer engineering education and relevant to most engineering disciplines. The.
SIGNALS & SYSTEMS.
Signals & Systems.
What is System? Systems process input signals to produce output signals A system is combination of elements that manipulates one or more signals to accomplish.
Signals and systems By Dr. Amin Danial Asham.
Lecture 1: Signals & Systems Concepts
CEN340 Signals and Systems
Chapter 1 Fundamental Concepts
Signals and Systems Networks and Communication Department Chapter (1)
Signal & Linear Systems (EELE 3310)
1.1 Continuous-time and discrete-time signals
Lecture 1: Signals & Systems Concepts
Signals and Systems Lecture 2
Lecture 2: Signals Concepts & Properties
Lecture 3: Signals & Systems Concepts
Presentation transcript:

Lecture 01 Signal and System Muhammad Umair Muhammad Umair, Lecturer (CS), KICSIT

Objective By the end of the course, you would have understood: Basic signal analysis Basic system analysis Time-domain analysis Laplace Transform and transfer functions Fourier Series (revision) and Fourier Transform Sampling Theorem and Signal Reconstructions Muhammad Umair, Lecturer (CS), KICSIT

Recommended Reading Material Signals and Systems, Oppenheim & Willsky Signals and Systems, Haykin & Van Veen Mastering Matlab 6

Grading  Mid Term Exam 20%  Final Term Exam 60%  3 Written Assignments20%  6 Quizzes 15%  Attendance 5% Muhammad Umair, Lecturer (CS), KICSIT

Lecture 1: Signals & Systems Concepts Specific Objectives: Introduce, using examples, what is a signal and what is a system Mathematical models What are continuous-time and discrete-time representations and how are they related

Muhammad Umair, Lecturer (CS), KICSIT What is a Signal? A signal is a pattern of variation of some form Signals are variables that carry information Examples of signal include: Electrical signals –Voltages and currents in a circuit Acoustic signals –Acoustic pressure (sound) over time Mechanical signals –Velocity of a car over time Video signals –Intensity level of a pixel (camera, video) over time

Muhammad Umair, Lecturer (CS), KICSIT How is a Signal Represented? Mathematically, signals are represented as a function of one or more independent variables. For instance a black & white video signal intensity is dependent on x, y coordinates and time t f(x,y,t) On this course, we shall be exclusively concerned with signals that are a function of a single variable: time t f(t)f(t)

Muhammad Umair, Lecturer (CS), KICSIT Example: Signals in an Electrical Circuit The signals v c and v s are patterns of variation over time Note, we could also have considered the voltage across the resistor or the current as signals +-+- i vcvc vsvs R C Step (signal) v s at t=1 RC = 1 First order (exponential) response for v c v s, v c t

Muhammad Umair, Lecturer (CS), KICSIT Continuous & Discrete-Time Signals Continuous-Time Signals Most signals in the real world are continuous time, as the scale is infinitesimally fine. Eg voltage, velocity, Denote by x(t), where the time interval may be bounded (finite) or infinite Discrete-Time Signals Some real world and many digital signals are discrete time, as they are sampled E.g. pixels, daily stock price (anything that a digital computer processes) Denote by x[n], where n is an integer value that varies discretely Sampled continuous signal x[n] =x(nk) – k is sample time x(t)x(t) t x[n]x[n] n

Muhammad Umair, Lecturer (CS), KICSIT Signal Properties On this course, we shall be particularly interested in signals with certain properties: Periodic signals: a signal is periodic if it repeats itself after a fixed period T, i.e. x(t) = x(t+T) for all t. A sin(t) signal is periodic. Even and odd signals: a signal is even if x(-t) = x(t) (i.e. it can be reflected in the axis at zero). A signal is odd if x(-t) = -x(t). Examples are cos(t) and sin(t) signals, respectively. Exponential signals: a signal is (real) exponential if it can be represented as x(t) = Ce at. A signal is (complex) exponential if it can be represented in the same form but C and a are complex numbers. Step and pulse signals: A pulse signal is one which is nearly completely zero, apart from a short spike,  (t). A step signal is zero up to a certain time, and then a constant value after that time, u(t). These properties define a large class of tractable, useful signals and will be further considered in the coming lectures

11 Transformation of Independent Variables Reflection Shifting

12 Scaling Example

Muhammad Umair, Lecturer (CS), KICSIT What is a System? Systems process input signals to produce output signals Examples: –A circuit involving a capacitor can be viewed as a system that transforms the source voltage (signal) to the voltage (signal) across the capacitor –A CD player takes the signal on the CD and transforms it into a signal sent to the loud speaker –A communication system is generally composed of three sub-systems, the transmitter, the channel and the receiver. The channel typically attenuates and adds noise to the transmitted signal which must be processed by the receiver

Muhammad Umair, Lecturer (CS), KICSIT How is a System Represented? A system takes a signal as an input and transforms it into another signal In a very broad sense, a system can be represented as the ratio of the output signal over the input signal That way, when we “multiply” the system by the input signal, we get the output signal This concept will be firmed up in the coming weeks System Input signal x(t) Output signal y(t)

Muhammad Umair, Lecturer (CS), KICSIT Example: An Electrical Circuit System Simulink representation of the electrical circuit +-+- i vcvc vsvs R C vs(t)vs(t) vc(t)vc(t) first order system v s, v c t

Muhammad Umair, Lecturer (CS), KICSIT Continuous & Discrete-Time Mathematical Models of Systems Continuous-Time Systems Most continuous time systems represent how continuous signals are transformed via differential equations. E.g. circuit, car velocity Discrete-Time Systems Most discrete time systems represent how discrete signals are transformed via difference equations E.g. bank account, discrete car velocity system First order differential equations First order difference equations

Muhammad Umair, Lecturer (CS), KICSIT Properties of a System On this course, we shall be particularly interested in signals with certain properties: Causal: a system is causal if the output at a time, only depends on input values up to that time. Linear: a system is linear if the output of the scaled sum of two input signals is the equivalent scaled sum of outputs Time-invariance: a system is time invariant if the system’s output is the same, given the same input signal, regardless of time. These properties define a large class of tractable, useful systems and will be further considered in the coming lectures

Muhammad Umair, Lecturer (CS), KICSIT Lecture 1: Exercises Read SaS OW, Chapter 1. This contains most of the material in the first three lectures, a bit of pre-reading will be extremely useful!