1. Presented by Huanhuan Chen University of Science and Technology of China 信号与信息处理 Signal and Information Processing

2. Course information Course Homepage: http://staff.ustc.edu.cn/~hchen/signal Email address: hchen@ustc.edu.cn

3. An Introduction to Signal and Information Processing About signal Some intuitions of signals Convert analog signal to discrete signal Some elemental signals Introduction to Signal Continuous signals Discrete signals Time domain Frequency domain …… Properties of signals Convolution DFT …… The operations on the signal

4. About Signal The term signal is generally applied to something that conveys information. The independent variable in the mathematical representation of a signal may be either continuous or discrete.  Voltage across a resister  Velocity of a vehicle  Light intensity of an image  Temperature, pressure inside a system

5. 1-d signals Seismic vibrations EEG and EKG Speech Sonar Audio Music ph - o - n - e - t - i - c - ia - n

6. 2-d signals. Photographs Medical images Radar IED detection Satellite data Fax Fingerprints

7. 3-d signals. Video Sequences Motion Sensing Volumetric data sets Computed Tomography, Synthetic Aperture Radar Reconstruction)

8. An bird’s view of signal processing Processing system Information Signal Recognition - radar, sonar, seismic, … Storage Transmission Processing system Display Information Communications Storage Media

9. Continuous-Time signal Continuous-time signals are defined along a continuum of times and thus are represented by a continuous independent variable. Continuous-time signals are often referred to as analog signals.

10. Discrete-time signal Discrete-time signals are defined at discrete times, and thus, the independent variable has discrete values. Digital signals are those for both time and amplitude are discrete. Discrete-time signals are represented mathematically as sequences of numbers

11. Discrete-time signal

12. Some basic sequences The sequences shown play important roles in the analysis and representation of discrete-time signals and systems.

13. Elementary Signals Sinusoidal & Exponential Signals Sinusoids and exponentials are important in signal and system analysis because they arise naturally in the solutions of the differential equations. Sinusoidal Signals can expressed in either of two ways : cyclic frequency form- A sin 2 П f o t = A sin(2 П /T o )t radian frequency form- A sin ω o t ω o = 2 П f o = 2 П /T o T o = Time Period of the Sinusoidal Wave

14. Sinusoidal & Exponential Signals x(t) = A sin (2 П f o t+ θ ) = A sin ( ω o t+ θ ) x(t) = Ae at Real Exponential = Ae j ω ̥ t = A[cos ( ω o t) +j sin ( ω o t)] Complex Exponential θ = Phase of sinusoidal wave A = amplitude of a sinusoidal or exponential signal f o = fundamental cyclic frequency of sinusoidal signal ω o = radian frequency Sinusoidal signal

15. Unit Step Function Precise GraphCommonly-Used Graph

16. Signum Function Precise GraphCommonly-Used Graph The signum function, is closely related to the unit-step function.

17. Unit Ramp Function The unit ramp function is the integral of the unit step function. It is called the unit ramp function because for positive t, its slope is one amplitude unit per time.

18. Rectangular Pulse or Gate Function Rectangular pulse,

19. Unit Impulse Function So unit impulse function is the derivative of the unit step function or unit step is the integral of the unit impulse function Functions that approach unit step and unit impulse

20. Representation of Impulse Function The area under an impulse is called its strength or weight. It is represented graphically by a vertical arrow. An impulse with a strength of one is called a unit impulse.

21. Properties of the Impulse Function The Sampling Property The Scaling Property The Replication Property g(t) ⊗ δ (t) = g (t)

22. Unit Impulse Train The unit impulse train is a sum of infinitely uniformly- spaced impulses and is given by

23. The Unit Rectangle Function The unit rectangle or gate signal can be represented as combination of two shifted unit step signals as shown

24. The Unit Triangle Function A triangular pulse whose height and area are both one but its base width is not, is called unit triangle function. The unit triangle is related to the unit rectangle through an operation called convolution.

25. Sinc Function

26. Time Domain and Frequency Domain Many ways that information can be contained in a signal.  Manmade signals.  AM  FM  Single-sideband  Pulse-code modulation  Pulse-width modulation Only two ways that are common for information to be represented.  Information represented in the time domain,  Information represented in the frequency domain.

27. The time domain Domain describes when something occurs  What the amplitude of the occurrence was Each sample in the signal indicates  What is happening at that instant, and the  Level of the event  If something occurs at time t, the signal directly provides information on the time it occurred, the duration, and the development over time. Contains information that is interpreted without reference to any other part of the sample.

28. The frequency domain Frequency domain is considered indirect. Information is contained in the overall relationship between many points in the signal. By measuring the frequency, phase, and amplitude, information can be obtained about the system producing the motion.

29. Converting analog to digital signals Sampling is the acquisition of the values of a continuous- time signal at discrete points in time x(t) is a continuous-time signal, x[n] is a discrete-time signal

30. Signal sampling Nyquist sampling theorem. The lower bound of the rate at which we should sample a signal, in order to be guaranteed there is enough information to reconstruct the original signal is 2 times the maximum frequency. Now in its digital form, we can process the signal in some way..

31. Sampling continuous signal

33. Discrete Time Unit Step Function or Unit Sequence Function

34. Discrete Time Unit Ramp Function

35. Discrete Time Unit Impulse Function or Unit Pulse Sequence

36. Techniques for processing a signal  A system is a function that produces an output signal in response to an input signal.  An input signal can be broken down into a set of components, called an impulses.  Impulses are passed through a system resulting in output components, which are synthesized into an output signal.

37. Operations of Signals Sometime a given mathematical function may completely describe a signal. Different operations are required for different purposes of arbitrary signals. The operations on signals can be Time Shifting Time Scaling Time Inversion or Time Folding

38. Time Shifting The original signal x(t) is shifted by an amount t. X(t)  X(t-to)  Signal Delayed  Shift to the right

39. Time Shifting X(t)  X(t+to)  Signal Advanced  Shift to the left

40. Time Scaling For the given function x(t), x(at) is the time scaled version of x(t) For a ˃ 1,period of function x(t) reduces and function speeds up. Graph of the function shrinks. For a ˂ 1, the period of the x(t) increases and the function slows down. Graph of the function expands.

41. Time scaling Example: Given x(t) and we are to find y(t) = x(2t). The period of x(t) is 2 and the period of y(t) is 1,

42. Time scaling Given y(t), find w(t) = y(3t) and v(t) = y(t/3).

43. Time Reversal Time reversal is also called time folding In Time reversal signal is reversed with respect to time i.e. y(t) = x(-t) is obtained for the given function

44. Time reversal Contd.

45. Operations of Discrete Time Functions

46. Operations of Discrete Functions K an integer > 1

47. Convolution of two sequence (a)–(c) The sequences x[k] and h[n− k] as a function of k for different values of n. (Only nonzero samples are shown.) (d) Corresponding output sequence as a function of n.

48. Properties of Convolution Commutative Property Distributive Property Associative Property

49. Filtering Digital filtering: Digital filtering can be accomplished by number of different well-characterized numerical procedure (a) Fourier transformation (b) Least squares polynomial smoothing.

50. Fourier transformation Fourier transformation: In this transformation, a signal which is acquired in the time domain, is converted to a frequency domain signal in which the independent variable is frequency rather than time. This transformation is accomplished mathematically on a computer by a very fast and efficient algorithm. The frequency domain signal is then multiplied by the frequency response of a digital low pass filter which remove frequency components. The inverse Fourier transform then recovers the filtered time domain spectrum.

51. Discrete Fourier transform Given the time domain, the process of calculating the frequency domain is called DFT. Given frequency domain the process of calculating the time domain is inverse DFT. O(n 2 )

52. Discrete Fourier transform DFT for continuous signals, not for digital signals. DFT Inverse DFT Plug in angular frequency f. DFT to get frequency. Inverse DFT to get time t.

53. Discrete Fourier transform Convert continuous DFT to discrete DFT. Continuous version Discrete version Let  stand for (a primitive nth root of unity) We get

54. Smoothing of signal with DFT

55. Intuitions of DFT

56. Boxes as Sum of Cosines

57. Introduction to System

58. What is System? Systems process input signals to produce output signals A system is combination of elements that manipulates one or more signals to accomplish a function and produces some output. system output signal input signal

59. Examples of Systems A circuit involving a capacitor can be viewed as a system that transforms the source voltage (signal) to the voltage (signal) across the capacitor A communication system is generally composed of three sub- systems, the transmitter, the channel and the receiver. The channel typically attenuates and adds noise to the transmitted signal which must be processed by the receiver Biomedical system resulting in biomedical signal processing Control systems

60. System - Example Consider an RL series circuit Using a first order equation: L V(t) R

61. Mathematical Modeling of Continuous Systems Most continuous time systems represent how continuous signals are transformed via differential equations. E.g. RC circuit System indicating car velocity

62. Mathematical Modeling of Discrete Time Systems Most discrete time systems represent how discrete signals are transformed via difference equations e.g. bank account, discrete car velocity system

63. Order of System Order of the Continuous System is the highest power of the derivative associated with the output in the differential equation For example the order of the system shown is 1.

64. Order of System Order of the Discrete Time system is the highest number in the difference equation by which the output is delayed For example the order of the system shown is 1.

65. Interconnected Systems notes Parallel Serial (cascaded) Feedback L V(t) R L C

66. Interconnected System Example Consider the following systems with 4 subsystem Each subsystem transforms it input signal The result will be: y3(t)=y1(t)+y2(t)=T1[x(t)]+T2[x(t)] y4(t)=T3[y3(t)]= T3(T1[x(t)]+T2[x(t)]) y(t)= y4(t)* y5(t)= T3(T1[x(t)]+T2[x(t)])* T4[x(t)]

67. Feedback System Used in automatic control e(t)=x(t)-y3(t)= x(t)-T3[y(t)]= y(t)= T2[m(t)]=T2(T1[e(t)])  y(t)=T2(T1[x(t)-y3(t)])= T2(T1( [x(t)] - T3[y(t)] ) ) = =T2(T1([x(t)] –T3[y(t)]))

68. Types of Systems Causal & Anticausal Linear & Non Linear Time Variant & Time-invariant Stable & Unstable Static & Dynamic Invertible & Inverse Systems

69. Causal & Anticausal Systems Causal system : A system is said to be causal if the present value of the output signal depends only on the present and/or past values of the input signal. Example: y[n]=x[n]+1/2x[n-1]

70. Causal & Anticausal Systems Contd. Anticausal system : A system is said to be anticausal if the present value of the output signal depends only on the future values of the input signal. Example: y[n]=x[n+1]+1/2x[n-1]

71. Linear & Non Linear Systems A system is said to be linear if it satisfies the principle of superposition For checking the linearity of the given system, firstly we check the response due to linear combination of inputs Then we combine the two outputs linearly in the same manner as the inputs are combined and again total response is checked If response in step 2 and 3 are the same, the system is linear othewise it is non linear.

72. Time Invariant and Time Variant Systems A system is said to be time invariant if a time delay or time advance of the input signal leads to a identical time shift in the output signal.

73. Stable & Unstable Systems A system is said to be bounded-input bounded-output stable (BIBO stable) iff every bounded input results in a bounded output. i.e.

74. Stable & Unstable Systems Contd. Example - y[n]=1/3(x[n]+x[n-1]+x[n-2])

75. Stable & Unstable Systems Contd. Example: The system represented by y(t) = A x(t) is unstable ; A ˃ 1 Reason: let us assume x(t) = u(t), then at every instant u(t) will keep on multiplying with A and hence it will not be bonded.

76. Static & Dynamic Systems A static system is memoryless system It has no storage devices its output signal depends on present values of the input signal For example

77. Static & Dynamic Systems Contd. A dynamic system possesses memory It has the storage devices A system is said to possess memory if its output signal depends on past values and future values of the input signal

78. Example: Static or Dynamic?

79. Answer: The system shown above is RC circuit R is memoryless C is memory device as it stores charge because of which voltage across it can’t change immediately Hence given system is dynamic or memory system

80. Invertible & Inverse Systems If a system is invertible it has an Inverse System Example: y(t)=2x(t) System is invertible  must have inverse, that is: For any x(t) we get a distinct output y(t) Thus, the system must have an Inverse x(t)=1/2 y(t)=z(t) y(t) System Inverse System x(t) y(t)=2x(t) System (multiplier) Inverse System (divider) x(t)

81. LTI Systems LTI Systems are completely characterized by its unit sample response The output of any LTI System is a convolution of the input signal with the unit-impulse response, i.e.

82. Useful Properties of (DT) LTI Systems Causality: Stability: Bounded Input ↔ Bounded Output

83. The end Thank you