1. Signals and Systems. CHAPTER 1
School of Electrical System Engineering, UniMAP
NORSHAFINASH BT SAUDIN
EKT 230

2. Chapter Overview.
The objective of this chapter is to understand the signals and their classifications, basic operation of the signal, the systems and their properties.
Operation of the Signal.
Classification of Signals
Elementary Signals.
Systems & Properties of the Systems

3. Signals and Systems. 1.1 What is a Signal ?
1.2 Classification of a Signals.
Continuous-Time and Discrete-Time Signals
Even and Odd Signals.
Periodic and Non-periodic Signals.
Deterministic and Random Signals.
Energy and Power Signals.
1.3 Basic Operation of the Signal.
1.4 Elementary Signals.
1.4.1 Exponential Signals.
1.4.2 Sinusoidal Signal.
1.4.3 Sinusoidal and Complex Exponential Signals.
1.4.4 Exponential Damped Sinusoidal Signals.
1.4.5 Step Function.
1.4.6 Impulse Function.
1.4.7 Ramped Function.

4. Cont’d… 1.5 What is a System ? 1.5.1 System Block Diagram.
1.6 Properties of the System.
Stability.
Memory.
Causality.
Inevitability.
1.6.5 Time Invariance.
Linearity.

5. 1.1 What is a Signal ? A common form of human communication;
(i) use of speech signal, face to face or telephone channel.
(ii) use of visual, signal taking the form of images of people or objects around us.
Real life example of signals;
(i) Doctor listening to the heartbeat, blood pressure and temperature of the patient. These indicate the state of health of the patient.
(ii) Daily fluctuations in the price of stock market will convey an information on the how the share for a company is doing.
(iii) Weather forecast provides information on the temperature, humidity, and the speed and direction of the prevailing wind.

6. Cont’d…
By definition, signal is a function of one or more variable, which conveys information on the nature of a physical phenomenon.
A function of time representing a physical or mathematical quantities.
e.g. : Velocity, acceleration of a car, voltage/current of a circuit.
An example of signal; the electrical activity of the heart recorded with electrodes on the surface of the chest — the electrocardiogram (ECG or EKG) in the figure below.

7. Cont’d…
Figure 1.1 (A) Left: (a) Snapshot of Pathfinder exploring the surface of Mars. (b) The 70-meter (230-foot) diameter antenna located at Canberra, Australia. The surface of the 70-meter reflector must remain accurate within a fraction of the signal’s wavelength. (Courtesy of Jet Propulsion Laboratory.)
Figure 1.1 (B) Right: Perspectival view of Mount Shasta (California), derived from a pair of stereo radar images acquired from orbit with the shuttle Imaging Radar (SIR-B). (Courtesy of Jet Propulsion Laboratory.)

8. 1.2 Classifications of a Signal.
There are five types of signals;
(i) Continuous-Time and Discrete-Time Signals
(ii) Even and Odd Signals.
(iii) Periodic and Non-periodic Signals.
(iv) Deterministic and Random Signals.
(v) Energy and Power Signals.

9. Figure 1.1: Continuous-Time Signal.
1.2.1 Continuous-Time and Discrete-Time Signals.
Continuous-Time (CT) Signals
Continuous-Time (CT) Signals are functions whose amplitude or value varies continuously with time, x(t).
The symbol t denotes time for continuous-time signal and (. ) used to denote continuous-time value quantities.
Example, speed of car, converting acoustic or light wave into electrical signal and microphone converts variation in sound pressure into correspond variation in voltage and current.
Figure 1.1: Continuous-Time Signal.

10. Figure 1.3: Discrete-Time Signal.
Cont’d…
Discrete-Time Signals
Discrete-Time Signals are function of discrete variable, i.e. they are defined only at discrete instants of time.
It is often derived from continuous-time signal by sampling at uniform rate. Ts denotes sampling period and n denotes integer.
The symbol n denotes time for discrete time signal and [. ] is used to denote discrete-value quantities.
Example: the value of stock at the end of the month.
Figure 1.3: Discrete-Time Signal.

11. Figure 1.4: Even Signal Figure 1.5: Odd Signal.
1.2.2 Even and Odd Signals.
A continuous-time signal x(t) is said to be an even signal if
The signal x(t) is said to be an odd signal if
In summary, an even signal are symmetric about the vertical axis (time origin) whereas an odd signal are antisymetric about the origin.
Figure 1.4: Even Signal Figure 1.5: Odd Signal.

12. Cont’d…

13. Example 1.1: Even and Odd Signals.
Find the even and odd components of each of the following signals:
x(t) = Cos(t) + Sin(t) + Cos(t)Sin(t)
x(t) = 1 + t + 3t2 + 5t3 +9t4
Solution:
(In Class)

14. Figure 1.6: Aperiodic Signal. Figure 1.7: Periodic Signal.
1.2.3 Periodic and Non-Periodic Signals.
Periodic Signal.
A periodic signal x(t) is a function of time that satisfies the condition
where T is a positive constant.
The smallest value of T that satisfy the definition is called a period.
Figure 1.6: Aperiodic Signal. Figure 1.7: Periodic Signal.

15. Figure 1.8: Deterministic Signal; Square Wave.
1.2.4 Deterministic and Random Signals.
Deterministic Signal.
A deterministic signal is a signal that is no uncertainty with respect to its value at any time.
The deterministic signal can be modeled as completely specified function of time.
Figure 1.8: Deterministic Signal; Square Wave.

16. Cont’d…
Random Signal.
A random signal is a signal about which there is uncertainty before it occurs. The signal may be viewed as belonging to an ensemble or a group of signals which each signal in the ensemble having a different waveform.
The signal amplitude fluctuates between positive and negative in a randomly fashion.
Example; noise generated by amplifier of a radio or television.
Figure 1.9: Random Signal

17. 1.2.5 Energy Signal and Power Signals.
A signal is refer to energy signal if and only if the total energy satisfy the condition;
Power Signal.
A signal is refer to as power signal if and only if the average power satisfy the condition;

18. Figure 1.10: Bounded and Unbounded Signal
1.2.6 Bounded and Unbounded Signals.
Figure 1.10: Bounded and Unbounded Signal

19. 1.3 Basic Operation of the Signals.
1.3.1 Time Scaling.
1.3.2 Reflection and Folding.
1.3.3 Time Shifting.
1.3.4 Precedence Rule for Time Shifting and Time Scaling.

20. 1.3.1 Time Scaling.
Time scaling refers to the multiplication of the variable by a real positive constant.
If a > 1 the signal y(t) is a compressed version of x(t).
If 0 < a < 1 the signal y(t) is an expanded version of x(t).
Example:
Figure 1.11: Time-scaling operation; continuous-time signal x(t),
(b) version of x(t) compressed by a factor of 2, and
(c) version of x(t) expanded by a factor of 2.

21. Figure 1.12: Effect of time scaling on a discrete-time signal:
Cont’d…
In the discrete time,
It is defined for integer value of k, k > 1. Figure below for k = 2, sample for n = +-1,
Figure 1.12: Effect of time scaling on a discrete-time signal:
(a) discrete-time signal x[n] and (b) version of x[n] compressed by a factor of 2, with some values of the original x[n] lost as a result of the compression.

22. 1.3.2 Reflection and Folding.
Let x(t) denote a continuous-time signal and y(t) is the signal obtained by replacing time t with –t;
y(t) is the signal represents a refracted version of x(t) about t = 0.
Two special cases for continuous and discrete-time signal;
(i) Even signal; x(-t) = x(t) an even signal is same as reflected version.
(ii) Odd signal; x(-t) = -x(t) an odd signal is the negative of its reflected version.

23. (b) reflected version of x(t) about the origin
Example 1.2: Reflection.
Given the triangular pulse x(t), find the reflected version of x(t) about the amplitude axis (origin).
Solution:
Replace the variable t with –t, so we get y(t) = x(-t) as in figure below.
Figure 1.13: Operation of reflection: (a) continuous-time signal x(t) and
(b) reflected version of x(t) about the origin
x(t) = 0 for t < -T1 and t > T2.
y(t) = 0 for t > T1 and t < -T2. .

24. Figure 1.14: Shift to the Left. Figure 1.15: Shift to the Right.
1.3.3 Time Shifting.
A time shift delay or advances the signal in time by a time interval +t0 or –t0, without changing its shape.
y(t) = x(t-t0)
If t0 > 0 the waveform of y(t) is obtained by shifting x(t) toward the right, relative to the tie axis.
If t0 < 0, x(t) is shifted to the left.
Example:
Figure 1.14: Shift to the Left. Figure 1.15: Shift to the Right.
Q: How does the x(t) signal looks like?

25. Example 1.3: Time Shifting.
Given the rectangular pulse x(t) of unit amplitude and unit duration. Find y(t)=x (t-2)
Solution:
t0 is equal to 2 time units. Shift x(t) to the right by 2 time units.
Figure 1.16: Time-shifting operation:
continuous-time signal in the form of a rectangular pulse of amplitude 1.0 and duration 1.0, symmetric about the origin; and
(b) time-shifted version of x(t) by 2 time shifts. .

26. 1.3.4 Precedence Rule for Time Shifting and Time Scaling.
Time shifting operation is performed first on x(t), which results in
Time shift has replace t in x(t) by t - b.
Time scaling operation is performed on v(t), replacing t by at and resulting in,
Example in real-life: Voice signal recorded on a tape recorder;
(a > 1) tape is played faster than the recording rate, resulted in compression.
(a < 1) tape is played slower than the recording rate, resulted in expansion.

27. Example 1.4: Continuous Signal.
A CT signal is shown in Figure 1.17 below, sketch and label each of this signal;
a) x(t -1)
b) x(2t)
c) x(-t)
Figure 1.17 -1 3 2 t
x(t)

28. Solution: (a) x(t -1) (b) x(2t) (c) x(-t) 2 x(t) x(t-1) 2 t t 4 -1/24
-1/2
3/2 -3 1 2 t
x(-t)

29. Example 1.5: Discrete Time Signal.
A discrete-time signal x[n] is shown below,
Sketch and label each of the following signal.
(a) x[n – 2] (b) x[2n]
(c.) x[-n+2] (d) x[-n]
x[n] n 4 2

30. (a) A discrete-time signal, x[n-2].
Cont’d…
A delay by 2 4 2 n
x(n-2)

31. (b) A discrete-time signal, x[2n].
Cont’d…
Down-sampling by a factor of 2. 4 2 n
x(2n)

32. (c) A discrete-time signal, x[-n+2].
Cont’d…
Time reversal and shifting 4 2 n
x(-n+2)

33. (d) A discrete-time signal, x[-n].
Cont’d…
Time reversal 4 2 n
x(-n)

34. In Class Exercises .
A continuous-time signal x(t) is shown below, Sketch and label each of the following signal
(a) x(t – 2) (b) x(2t) (c.) x(t/2) (d) x(-t)
x(t) t 4

35. 1.4 Elementary Signals.
There are many types of signals prominently used in the study of signals and systems.
1.4.1 Exponential Signals.
1.4.2 Exponential Damped Sinusoidal Signals.
1.4.3 Step Function.
1.4.4 Impulse Function.
1.4.5 Ramp Function.

36. 1.4.1 Exponential Signals.
A real exponential signal, is written as x(t) = Beat.
Where both B and a are real parameters. B is the amplitude of the exponential signal measured at time t = 0.
(i) Decaying exponential, for which a < 0.
(ii) Growing exponential, for which a > 0.
Figure 1.18: (a) Decaying exponential form of continuous-time signal. (b) Growing exponential form of continuous-time signal.
Figure 1.19: (a) Decaying exponential form of discrete-time signal.
(b) Growing exponential form of discrete-time signal.

37. Cont’d… Continuous-Time. Case a = 0: Constant signal x(t) =C.
Case a > 0: The exponential tends to infinity as t infinity.
Case a > Case a < 0
Case a < 0: The exponential tend to zero as t infinity (here C > 0).

38. Cont’d… Discrete-Time. where B and a are real.
There are six cases to consider apart from a = 0.
Case 1 (a = 0): Constant signal x[n]=B.
Case 2 (a > 1): positive signal that grows exponentially.
Case 3 (0 < a < 1): The signal is positive and decays exponentially.

39. Cont’d…
Case 4 (a < 1): The signal alternates between positive and negative values and grows exponentially.
Case 5 (a = -1): The signal alternates between +C and -C.
Case 6 (-1 < a <0): The signal alternates between positive and negative values and decays exponentially.

40. Figure 1.20: Continuous-Time Sinusoidal signal A cos(ωt + Φ).
1.4.2 Sinusoidal Signals.
A general form of sinusoidal signal is
where A is the amplitude, wo is the frequency in radian per second, and q is the phase angle in radians.
Figure 1.20: Continuous-Time Sinusoidal signal A cos(ωt + Φ).

41. Figure 1.21: Discrete-Time Sinusoidal Signal A cos(ωt + Φ).
Cont’d…
Discrete time version of sinusoidal signal, written as
Figure 1.21: Discrete-Time Sinusoidal Signal A cos(ωt + Φ).

42. 1.4.3 Sinusoidal and Complex Exponential Signals.
Continuous time sinusoidal signals,
In the discrete time case,

43. Cont’d…
Figure 1.22: Complex plane, showing eight points uniformly distributed on the unit circle.

44. 1.4.4 Exponential Damped Sinusoidal Signals.
Multiplication of a sinusoidal signal by a real-value decaying exponential signal result in an exponential damped sinusoidal signal.
Where ASin(wt + f) is the continuous signal and e-at is the exponential
Figure 1.23: Exponentially damped
sinusoidal signal Ae-at sin(ωt), with
A = 60 and α = 6.
Observe that in Figure 1.23, an increased in time t, the amplitude of the sinusoidal oscillation decrease in an exponential fashion and finally approaching zero for infinite time.

45. Figure 1.24: Discrete–time of Step Function of Unit Amplitude.
The discrete-time version of the unit-step function is defined by,
Figure 1.24: Discrete–time of Step Function of Unit Amplitude.

46. Figure 1.25: Continuous-time of step function of unit amplitude.
Cont’d…
The continuous-time version of the unit-step function is defined by,
Figure 1.25: Continuous-time of step function of unit amplitude.
The discontinuity exhibit at t = 0 and the value of u(t) changes instantaneously from 0 to 1 when t = 0. That is the reason why u(0) is undefined.

47. 1.4.6 Impulse Function.
The discrete-time version of the unit impulse is defined by,
Figure 1.26: Discrete-Time form of Impulse.
Figure 1.41 is a graphical description of the unit impulse d(t).
The continuous-time version of the unit impulse is defined by the following pair,
The d(t) is also refer as the Dirac Delta function.

48. Cont’d…
Figure 1.27 is a graphical description of the continuous-time unit impulse d(t).
Figure 1.27: (a) Evolution of a rectangular pulse of unit area into an impulse of unit strength (i.e., unit impulse). (b) Graphical symbol for unit impulse. (c) Representation of an impulse of strength a that results from allowing the duration Δ of a rectangular pulse of area a to approach zero.
The duration of the pulse (t) decreased and its amplitude is increased. The area under the pulse is maintained constant at unity.

49. Cont’d…

50. Cont’d…
Institutive Impulse definition;
Application of unit impulse;
Impulse of current in time delivers a unit charge instantaneous to the network.
Impulse of force in time delivers an instantaneous momentum to a mechanical system.

51. 1.4.7 Ramp Function.
The integral of the step function u(t) is a ramp function of unit slope. or
Figure 1.28: Ramp Function of Unite Slope.
The discrete-time version of the ramp function,
Figure 1.29: Discrete-Time Version of the Ramp Function.

52. Successive Integration of Unit Impulse Function.

53. 1.5 What is a System ?
A system can be viewed as an interconnection of operation that transfer an input signal into an output signal with properties different from those of the input signal.
y(t) is the impulse response of the continuous-time system and y[n] is the impulse response of the discrete-time system.

54. Cont’d… Real life example of system;
(i) In automatic speaker recognition system; the system is to extract the information from an incoming speech signal for the purpose of recognizing and identifying the speaker.
(ii) In communication system; the system will transport the information contained in the message over a communication channel and deliver that information to the destination.
Figure 1.30: Elements of a communication system.
Figure 1.31: Block diagram representation of a system.

55. Cont’d…
By definition, a system is an entity that manipulates one or more signals to accomplish a function, thereby yielding new signals.
A physical process or a mathematical model of the physical process that relates a set of input signals to yield another set of output signal.
Process input signals to produce output signals
System representation of the systems.

56. (I) Dynamic Analogies. Mechanical free-body diagram.
Physically divergent systems can have similar dynamic properties.

57. (II) Circuit Sum Element Current.
The mechanical and electrical systems are dynamically analogous.
Thus, understanding one of these systems gives insights into the other.

58. Electronic synthesis of block diagram
(III) Block Diagram Using Integrators, Adders and Gain.
Electronic synthesis of block diagram
The integrator, adder, and gain blocks are other examples of functional descriptions of systems. We can produce a structural model of each of these blocks. For example, the gain block is easily synthesized with an op-amp circuit.

59. 1.5.1 System Block Diagram.
System may be interconnections of other system.
Cascade interconnection.
Parallel interconnection.
Feedback interconnection.
Eg. Car cruise control system.

60. 1.6 Properties of Systems.
The properties of a system describe the characteristics of the operator H representing the system.
Basic properties of the system;
1.6.1 Stability.
1.6.2 Memory.
1.6.3 Causality.
1.6.4 Invertibility.
1.6.5 Time Invariance.
1.6.6 Linearity.

61. 1.6.1 Stability.
A system is said to be bounded-input bounded-output (BIBO) stable if and only if all bounded inputs result in bounded outputs. The output of the system does not diverge if the input does not diverge.
For the resistor, if i(t) is bounded then so is v(t), but for the capacitance this is not true. Consider i(t) = u(t) then v(t) = tu(t) which is unbounded.

62. 1.6.2 Memory.
A system is said to possess memory if its output signal depend on pass or future values of the input signal.
Note that v(t) depends not just on i(t) at one point in time t. Therefore, the system that relates v to i exhibits memory.
The system is said to be memoryless if its output signal depends only on the present value of the input signal.
Example: The resistive divider network
Therefore, vo(to) depends upon the value of vi(to) and not on vi(t) for t = to.

63. Example 1.6: Memory and Memoryless System.
Below is the moving-average system described by the input-output relation. Does it has memory or not?
(a)
(b)
Solution:
It has memory, the value of the output signal y[n] at time n depends on the present and two pass values of x[n].
It is memoryless, because the value of the output signal y[n] depends only on the present value of the input signal x[n]. .

64. 1.6.3 Causality.
Causal.
A system is said to be casual if the present value of the output signal depends only on the present or the past values of the input signal. The system cannot anticipate the input.
Noncausal.
In contrast, the output signal of a noncausal system depends on one or more future values of the input signal.

65. Example 1.7: Causal and Noncausal.
Causal or noncausal?
Solution:
Noncausal; the output signal y[n] depends on a future value of the input signal, x[n+1]
Causality is required for a system to be capable of operating in real time. .

66. 1.6.4 Invertibility.
A system is said to be invertible if the input of the system can be recovered from the output.
Figure 1.32: The notion of system inevitability. The second operator Hinv is the inverse of the first operator H. Hence, the input x(t) is passed through the cascade correction of H and H-1 completely unchanged.

67. 1.6.5 Time Invariance.
A system is said to be time invariant if the time delay or time advance of the input signal leads to an identical time shift in the output signal.
The Time invariance system responds identically no mater when the input signal is applied.
Figure 1.33: (a) Time-shift operator St0 preceding operator H. (b) Time-shift operator St0 following operator H. These two situations are equivalent, provided that H is time invariant

68. 1.6.6 Linearity.
A system is said to be linear in term of the system input (excitation) x(t) and the system output (response) y(t) if it satisfies the following two properties.
1. Superposition
The system is initially at rest. The input is x(t)=x1(t), the output y(t)=y1(t). So x(t)=x1(t)+x2(t) the corresponding output y(t)=y1(t)+y2(t).
2. Homogeneity.
The system is initially at rest. Input x(t) result in y(t). The system exhibit the property of homogeneity if x(t) scaled by constant factor a result in output y(t) is scaled by exact constant a.

69. Cont’d…
Figure 1.34: The linearity property of a system. (a) The combined operation of amplitude scaling and summation precedes the operator H for multiple inputs.
(b) The operator H precedes amplitude scaling for each input; the resulting outputs are summed to produce the overall output y(t). If these two configurations produce the same output y(t), the operator H is linear.
If the system violates either of the properties the system is said to be nonlinear.

70. Cont’d…
Example 1.8: Linearity.

71. Cont’d…
Solution:

72. Cont’d…