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EE102 – SYSTEMS & SIGNALS

EE102 – SYSTEMS & SIGNALS. Fall Quarter, 2001. Instructor: Fernando Paganini. Signal : Function that describes the evolution of a variable with time. Examples: Voltage across an electrical component. Position of a moving object. +. V(t). _. x(t).

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EE102 – SYSTEMS & SIGNALS

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  1. EE102 – SYSTEMS & SIGNALS Fall Quarter, 2001. Instructor: Fernando Paganini.

  2. Signal: Function that describes the evolution of a variable with time. • Examples: • Voltage across an electrical component. • Position of a moving object. + V(t) _ x(t)

  3. Sound = pressure of the air outside your ear p t • Information lies in the time evolution. • The signal can be converted to and from other domains: • Electrical (in a stereo), electro-chemical (in your brain). • What matters is the mathematical structure.

  4. Signal examples (cont): • Population of a species over time (decades) • Daily value of the Nasdaq Time can be continuous (a real number) or discrete (an integer). This course focuses on continuous time.

  5. Is R3 V1 R1 Io Vs R2 System: component that establishes a relationship between signals • Example: circuit Relationship between voltages and currents.

  6. Systems: examples • Car: Relationship between signals: • Throttle/brake position • Motor speed • Fuel concentration in chamber • Vehicle speed. • … • Ecosystem: relates populations, … • The economy: relates GDP, inflation, interest rates, stock prices,… • The universe…

  7. Math needed to study signals & systems? Example 1: static system Vo R1 I Vs R2 • Not much math there… • Time does not enter in a fundamental way.

  8. Example 2: dynamical system Vo R Vs C I • Switch closes at t=0. • For t >= 0, we have the differential equation (ODE) Solution: Time is essential here.

  9. Dynamic, differential equation models appear in many systems • Mechanical system, e.g. the mass-spring system • Chemical reactions • Population dynamics • Economic models

  10. The issue of complexity • Consider modeling the dynamic behavior of • An IC with millions of transistors • A biological organism • “Reductionist” method: zoom in a component, write a differential equation model, combine them into an overall model. • Difficulty: solving those ODE’s is impossible; even numerical simulation is prohibitive. • Even harder: design the differential equation (e.g., the circuit) so that it has a desired solution.

  11. The “black box” concept x y • Idea: describe a portion of a system by a input-output (cause-effect) relationship. • Derive a mathematical model of this relationship. This can involve ODEs, or other methods we will study. Make reasonable approximations. • Interconnect these boxes to describe a more complex system.

  12. or Definition: Input-Output System y x • The input function x(t) belongs to a space X, and can be freely manipulated from outside. • The output function y(t) varies in a space Y, and is uniquely determined by the input function. • The relationship between input and output is described by a transformation T between X and Y. Notation:

  13. Example: RC circuit as an input-output system + + R x(t) y(t) C _ _ • We assume here that time starts at t=0, y(0) = 0 • To represent the mapping from x to y explicitly, we • must solve the differential equation Here

  14. Solution of • First, solve homogeneous equation Solution: • Next, look for a solution of the non-homogeneous • equation. One method: “Variation of constants”, try • a solution of the form

  15. Using initial condition y(0)=0, Input-output representation, y = T[x] Solution of

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