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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

Signals and Systems

Dr. Mohamed Bingabr

University of Central Oklahoma

Some of the Slides For Lathi’s Textbook Provided by

Dr. Peter Cheung

slide2

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

slide3

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
slide4

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
slide5

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:
slide6

Useful Signal Operation-Time Delay

Find x(t-2) and x(t+2) for the signal

x(t)

2

t

1

4

slide7

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)

slide8

Useful Signal Operation-Time Scaling

Find x(2t) and x(t/2) for the signal

x(t)

2

t

1

4

slide9

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

slide10

Useful Signal Operation-Time Reversal

Signal may be reflected about the vertical axis (i.e. time reversed):

(t) = x (-t)

slide11

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).

slide12

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

slide14

Signal Classification- Analogue vs Digital

Analogue, continuous

Digital, continuous

Analogue, discrete

Digital, discrete

slide15

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).

slide19

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)

slide20

Signal Models – Pulse Signal

A pulse signal can be presented by two step functions:

x(t) = u(t-2) – u(t-4)

slide22

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:

slide23

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.

slide24

Examples

Simplify the following expression

Evaluate the following

Find dx/dt for the following signal

x(t) = u(t-2) – 3u(t-4)

slide25

The Exponential Function est

This exponential function is very important in signals & systems, and the parameter s is a complex variable given by:

slide26

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:

slide28

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)

slide29

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:

slide30

Even and Odd Function

Consider the causal exponential function

slide31

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:
slide32

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

slide33

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)
slide36

Linear Systems (4)

Is the system y = x2 linear?

slide37

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.

slide38

Time-Invariant System

Which of the system is time-invariant?

(a) y(t) = 3x(t) (b) y(t) = t x(t)

slide40

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)

slide42

Invertible and Noninvertible

Which of the two systems is invertible?

y(t) = x2

y= 2x

slide44

Electrical System

+ v(t) -

i(t)

R

+

v(t)

-

i(t)

i(t)

+ v(t) -

slide47

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)

slide49

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