Net 222: Communications and networks fundamentals (Practical Part)

Slides:



Advertisements
Similar presentations
Digital Signal Processing Solutions to Midterm Exam 2009 Edited by Shih-Ming Huang Confirmed by Prof. Jar-Ferr Yang LAB: R, TEL: ext
Advertisements

MM3FC Mathematical Modeling 3 LECTURE 3
Lecture 4: Linear Systems and Convolution
Basics of Digital Filters & Sub-band Coding Gilad Lerman Math 5467 (stealing slides from Gonzalez & Woods)
Chapter 8. Linear Systems with Random Inputs 1 0. Introduction 1. Linear system fundamentals 2. Random signal response of linear systems Spectral.
Discrete-Time Convolution Linear Systems and Signals Lecture 8 Spring 2008.
MSJ-1 Alignment Network. MSJ-2 Alignment Network ALU 32 general purpose registers 32 bits memory width − a.k.a., block size (8 bytes, in this example)
Appendix B: An Example of Back-propagation algorithm
26 Sep 2014Lecture 3 1. Last lecture: Experimental observation & prediction Cost models: Counting the number of executions of Every single kind of command.
Leo Lam © Signals and Systems EE235. Leo Lam © Pet Q: Has the biomedical imaging engineer done anything useful lately? A: No, he's.
Math – What is a Function? 1. 2 input output function.
ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: Causality Linearity Time Invariance Temporal Models Response to Periodic.
4. Introduction to Signal and Systems
Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Neural Networks Laboratory Slide 1 DISCRETE SIGNALS AND SYSTEMS.
Dr. Engr. Sami ur Rahman Assistant Professor Department of Computer Science University of Malakand Visualization in Medicine Lecture: Convolution.
(Part one: Continuous)
1 LTI Systems; Convolution September 4, 2000 EE 64, Section 1 ©Michael R. Gustafson II Pratt School of Engineering.
1 Computing the output response of LTI Systems. By breaking or decomposing and representing the input signal to the LTI system into terms of a linear combination.
Free International University of Moldova Faculty of Informatics and Engineering DIGITAL SIGNALS PROCESSING Course of lectures Theme: THE SIGNALS FUNCTIONAL.
Digital Signal Processing Lecture 3 LTI System
Computer Vision – 2D Signal & System Hanyang University Jong-Il Park.
EENG 420 Digital Signal Processing Lecture 2.
MATH10001 Project 3 Difference Equations 2
Digital Signal Processing
State Space Representation
Chapter 7: Introduction to Data Communications and Networking
EE 309 Signal and Linear System Analysis
Lecture 3: Linear Regression (with One Variable)
Signal Processing First
EE 309 Signal and Linear System Analysis
Chapter 1. -Signals and Systems
Net 222: Communications and networks fundamentals (Practical Part)
Net 222: Communications and networks fundamentals (Practical Part)
COMMUNICATION.
الفصل السادس: التعلم عن بعد (تال101ت)
Outline Linear Shift-invariant system Linear filters
Neural Networks & MPIC.
Lecture 4: Discrete-Time Systems
سیگنال ها و سیستم ها درس دوم حمیدرضا پوررضا.
فرایند تسهیلگری در مددکاری جامعه ای
Net 222: Communications and networks fundamentals (Practical Part)
برنامج قواعد البيانات (مايكروسوفت أوفيس اكسس (Microsoft Office Access
Neural Networks & MPIC.
Signal and Systems Chapter 2: LTI Systems
Signals and Systems Networks and Communication Department Chapter (1)
Modeling in the Time Domain
CS3291: "Interrogation Surprise" on Section /10/04
2 Linear Time-Invariant Systems --Analysis of Signals and Systems in time-domain An arbitrary signal can be represented as the supposition of scaled.
Net 412 (Practical Part) Networks and Communication Department LAB 1.
Function Notation “f of x” Input = x Output = f(x) = y.
State Space Analysis UNIT-V.
Lecture 9: Radix-64 Tutorial
Net 222: Communications and networks fundamentals (Practical Part)
Lecture 1: Introduction
UNIT 5. Linear Systems with Random Inputs
NET 424: REAL-TIME SYSTEMS (Practical Part)
Lecture 6: Digital Signature
NET 424: REAL-TIME SYSTEMS (Practical Part)
Net 222: Communications and networks fundamentals (Practical Part)
Signals & Systems (CNET - 221) Chapter-2 Introduction to Systems
System Properties Especially: Linear Time Invariant Systems
Signals & Systems (CNET - 221) Chapter-2
Signals & Systems (CNET - 221) Chapter-3 Linear Time Invariant System
NET 424: REAL-TIME SYSTEMS (Practical Part)
Networks. partial spectra - trivariate (M,N,O)
NET 424: REAL-TIME SYSTEMS (Practical Part)
Topics Linearity Superposition Convolution. Bioengineering 280A Principles of Biomedical Imaging Fall Quarter 2008 X-Rays Lecture 2.
Signals and Systems Lecture 18: FIR Filters.
The Chinese University of Hong Kong
Lecture 3 Discrete time systems
Presentation transcript:

Net 222: Communications and networks fundamentals (Practical Part) Networks and Communication Department Lab 5 : linear and Time invariance system

Lecture Contants Linear system Time invariance system Networks and Communication Department

Linear System If a system is linear, this means that when an input to a given system is scaled by a value, the output of the system is scaled by the same amount. Ya[n]=Yb[n] is linear system.

Linear system coding We could write a system that test if the giving input are linear or NOT Eg: yn[n]=x[n]^4 By Assuming : X[n]1=sin(3) X[n]2=sin(5) Scaling value a=4 Networks and Communication Department

Example output YA != YB Networks and Communication Department

Example Another Eg: yn[n]= 2*x[n] YA = YB X[n]1=sin(3) X[n]2=sin(5) Scaling value a=4 output YA = YB Networks and Communication Department

Time-Invariant System : X[n] Y1[n]=y[n-n0] S T Y2[n]=T{x[n-n0]} Y1[n]=Y2[n] for all n T is time invariant 24-Feb-19 Networks and Communication Department

Example We could write a system that test if the giving input are linear or NOT Eg: yn[n]=x[n]*sin(3) By Assuming : X[n]=3 Shifting value n0=4 output Networks and Communication Department

The End Any Questions ? Networks and Communication Department