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Signals and Systems

Signals and Systems. Dr. Mohamed Bingabr University of Central Oklahoma Some of the Slides For Lathi’s Textbook Provided by Dr. Peter Cheung. Course Objectives. • Signal analysis (continuous-time ) • System analysis (mostly continuous systems)

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Signals and Systems

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  1. Signals and Systems Dr. Mohamed Bingabr University of Central Oklahoma Some of the Slides For Lathi’s Textbook Provided by Dr. Peter Cheung

  2. Course Objectives • Signal analysis (continuous-time) • System analysis (mostly continuous systems) • Time-domain analysis (including convolution) • Laplace Transform and transfer functions • Fourier Series analysis of periodic signal • Fourier Transform analysis of aperiodic signal • Sampling Theorem and signal reconstructions

  3. Outline • Size of a signal • Useful signal operations • Classification of Signals • Signal Models • Systems • Classification of Systems • System Model: Input-Output Description • Internal and External Description of a System

  4. Size of Signal-Energy Signal • Signal: is a set of data or information collected over time. • Measured by signal energy Ex: • Generalize for a complex valued signal to: • Energy must be finite, which means

  5. Size of Signal-Power Signal • If amplitude of x(t) does not  0 when t ", need to measure power Px instead: • Again, generalize for a complex valued signal to:

  6. Useful Signal Operation-Time Delay Find x(t-2) and x(t+2) for the signal x(t) 2 t 1 4

  7. Useful Signal Operation-Time Delay Signal may be delayed by time T: (t) = x (t – T) or advanced by time T: (t) = x (t + T)

  8. Useful Signal Operation-Time Scaling Find x(2t) and x(t/2) for the signal x(t) 2 t 1 4

  9. Useful Signal Operation-Time Scaling Signal may be compressed in time (by a factor of 2): (t) = x (2t) or expanded in time (by a factor of 2): (t) = x (t/2) Same as recording played back at twice and half the speed respectively

  10. Useful Signal Operation-Time Reversal Signal may be reflected about the vertical axis (i.e. time reversed): (t) = x (-t)

  11. Useful Signal Operation-Example We can combine these three operations. For example, the signal x(2t - 6) can be obtained in two ways; • Delay x(t) by 6 to obtain x(t - 6), and then time-compress this signal by factor 2 (replace t with 2t) to obtain x(2t - 6). • Alternately, time-compress x(t) by factor 2 to obtain x(2t), then delay this signal by 3 (replace t with t - 3) to obtain x(2t - 6).

  12. Signal Classification Signals may be classified into: 1. Continuous-time and discrete-time signals 2. Analogue and digital signals 3. Periodic and aperiodic signals 4. Energy and power signals 5. Deterministic and probabilistic signals 6. Causal and non-causal 7. Even and Odd signals

  13. Signal Classification- Continuous vs Discrete Continuous-time Discrete-time

  14. Signal Classification- Analogue vs Digital Analogue, continuous Digital, continuous Analogue, discrete Digital, discrete

  15. Signal Classification- Periodic vsAperiodic A signal x(t) is said to be periodic if for some positive constant To x(t) = x (t+To) for all t The smallest value of To that satisfies the periodicity condition of this equation is the fundamental period of x(t).

  16. Signal Classification- Deterministic vs Random Deterministic Random

  17. Signal Classification- Causal vs Non-causal

  18. Signal Classification- Even vs Odd

  19. Signal Models – Unit Step Function u(t) Step function defined by: Useful to describe a signal that begins at t = 0 (i.e. causal signal). For example, the signal e-at represents an everlasting exponential that starts at t = -. The causal for of this exponential e-atu(t)

  20. Signal Models – Pulse Signal A pulse signal can be presented by two step functions: x(t) = u(t-2) – u(t-4)

  21. Signal Models – Unit Impulse Function δ(t) First defined by Dirac as:

  22. Multiplying Function  (t) by an Impulse Since impulse is non-zero only at t = 0, and (t) at t = 0 is (0), we get: We can generalize this for t = T:

  23. Sampling Property of Unit Impulse Function Since we have: It follows that: This is the same as “sampling” (t) at t = 0. If we want to sample (t) at t = T, we just multiple (t) with This is called the “sampling or sifting property” of the impulse.

  24. Examples Simplify the following expression Evaluate the following Find dx/dt for the following signal x(t) = u(t-2) – 3u(t-4)

  25. The Exponential Function est This exponential function is very important in signals & systems, and the parameter s is a complex variable given by:

  26. The Exponential Function est If  = 0, then we have the function ejωt, which has a real frequency of ω Therefore the complex variable s =  +jωis the complex frequency The function est can be used to describe a very large class of signals and functions. Here are a number of example:

  27. The Exponential Function est

  28. The Complex Frequency Plane s= + jω A real function xe(t) is said to be an even function of t if A real function xo(t) is said to be an odd function of t if HW1_Ch1: 1.1-3, 1.1-4, 1.2-2(a,b,d), 1.2-5, 1.4-3, 1.4-4, 1.4-5, 1.4-10 (b, f)

  29. Even and Odd Function Even and odd functions have the following properties: • Even x Odd = Odd • Odd x Odd = Even • Even x Even = Even Every signal x(t) can be expressed as a sum of even and odd components because:

  30. Even and Odd Function Consider the causal exponential function

  31. What are Systems? • Systems are used to process signals to modify or extract information • Physical system – characterized by their input-output relationships • E.g. electrical systems are characterized by voltage-current relationships • From this, we derive a mathematical model of the system • “Black box” model of a system:

  32. Classification of Systems Systems may be classified into: Linear and non-linear systems Constant parameter and time-varying-parameter systems Instantaneous (memoryless) and dynamic (with memory) systems Causal and non-causal systems Continuous-time and discrete-time systems Analogue and digital systems Invertible and noninvertible systems Stable and unstable systems

  33. Linear Systems (1) • A linear system exhibits the additivity property: • if and then • It also must satisfy the homogeneity or scaling property: • if then • These can be combined into the property of superposition: • if and then • A non-linear system is one that is NOT linear (i.e. does not obey the principle of superposition)

  34. Linear Systems (2)

  35. Linear Systems (3)

  36. Linear Systems (4) Is the system y = x2 linear?

  37. Linear Systems (5) A complex input can be represented as a sum of simpler inputs (pulse, step, sinusoidal), and then use linearity to find the response to this simple inputs to find the system output to the complex input.

  38. Time-Invariant System Which of the system is time-invariant? (a) y(t) = 3x(t) (b) y(t) = t x(t)

  39. Instantaneous and Dynamic Systems

  40. Causal and Noncausal Systems Which of the two systems is causal? a) y(t) = 3 x(t) + x(t-2) b) y(t) = 3x(t) + x(t+2)

  41. Analogue and Digital Systems

  42. Invertible and Noninvertible Which of the two systems is invertible? y(t) = x2 y= 2x

  43. System External Stability

  44. Electrical System + v(t) - i(t) R + v(t) - i(t) i(t) + v(t) -

  45. Mechanical System

  46. Linear Differential Systems (1)

  47. Linear Differential Systems (2) Find the input-output relationship for the transational mechanical system shown below. The input is the force x(t), and the output is the mass position y(t)

  48. Linear Differential Systems (3)

  49. Linear Differential Systems (4) HW2_Ch1: 1.7-1 (a, b, d), 1.7-2 (a, b, c), 1.7-7, 1.7-13, 1.8-1, 1.8-3

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