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## Signals and Systems

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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

• 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

- 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

- 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

- If amplitude of x(t) does not 0 when t ", need to measure power Px instead:
- Again, generalize for a complex valued signal to:

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)

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

Useful Signal Operation-Time Reversal

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

(t) = x (-t)

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).

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

Signal Classification- Analogue vs Digital

Analogue, continuous

Digital, continuous

Analogue, discrete

Digital, discrete

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).

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)

A pulse signal can be presented by two step functions:

x(t) = u(t-2) – u(t-4)

Signal Models – Unit Impulse Function δ(t)

First defined by Dirac as:

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:

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.

Simplify the following expression

Evaluate the following

Find dx/dt for the following signal

x(t) = u(t-2) – 3u(t-4)

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

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:

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)

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:

Consider the causal exponential function

- 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:

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

- 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)

Is the system y = x2 linear?

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.

Which of the two systems is causal?

a) y(t) = 3 x(t) + x(t-2)

b) y(t) = 3x(t) + x(t+2)

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)

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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