DSP Lecture 1.pdf

Muhammad Majid (m.majid@uettaxila.edu.pk) Digital Signal Processing
DIGITAL SIGNAL PROCESSING

Lecture 1: Outline
• Course Information
• Course Syllabus
• DSP is Everywhere
• Why Signal Processing?
• Limitations of Analog Signal Processing
• Digital Signal Processing: Pros and Cons
• Basic Discrete Time Signals and Operations

Course Information
Instructor:
Dr. Muhammad Majid
Associate Professor,
Department of Computer Engineering,
University of Engineering and Technology Taxila,
Pakistan.
Email:
m.majid@uettaxila.edu.pk

Course Information
Objective
• To establish the idea of using computing techniques
to alter the properties of a signal for desired effects,
via understanding of
• Fundamentals of discrete-time, linear, shift-
invariant signals and systems
• To gain preliminary experience in computational
processing of real signal and to relate the above
understanding to real world scenario

Course Information
Nuts and Bolts
Prerequisites:
Signals and Systems
Reference Book:
1. Discrete-Time Signal Processing, Prentice Hall,
2nd Edition by Alan Oppenheim, Ronald Schafer,
John Buck

Course Information
Policies
• Grading:
• Assignments 15%
• Quizzes 10%
• Midterm Exam 25%
• Final Exam 50%
• Labs (Separate)
• Exams:
• Exams must be taken at scheduled time, no
makeup exams

Course Syllabus
• Introduction to Digital Signal Processing
• Discrete-Time Signals and System
• Discrete Time Fourier Transform
• The Z-Transform
• Sampling of Continuous-Time Signals
• Structures for Discrete-Time Systems
• Filter Design Techniques
• The Discrete Fourier Transform
• The Fast Fourier Transform

Course Learning Outcomes
CLO 1: Explain digital signal processing applications and
apply fundamentals operations on linear time invariant
systems
CLO 2: Analyze discrete linear time invariant system in
time and transform domain
CLO3: Analyze the process of Analog to Digital
Conversion and Digital to Analog Conversion
CLO 4: Design IIR and FIR filters with desired frequency
response and draw their structures

DSP is Everywhere
• Sound Applications
• Compression, enhancement, special effects,
synthesis, recognition, echo cancellation,…
• Cell Phones, MP3 Players, Movies, Dictation,
Text-to-speech,…
• Communication
• Modulation, coding, detection, equalization, echo
cancellation,…
• Cell Phones, dial-up modem, DSL modem,
Satellite Receiver,…
• Automotive
• ABS, GPS, Active Noise Cancellation, Cruise
Control, Parking,…

DSP is Everywhere
• Medical
• Magnetic Resonance, Tomography,
Electrocardiogram,…
• Military
• Radar, Sonar, Space photographs, remote
sensing,…
• Image and Video Applications
• DVD, JPEG, Movie special effects, video
conferencing,…
• Mechanical
• Motor control, process control, oil and mineral
prospecting,…

Signal Processing:
Foundation for True Intelligence
• Humans are the most advanced signal processors
• speech and pattern recognition, speech
synthesis,…
• We encounter many types of signals in various
applications
• Electrical signals: voltage, current, magnetic and
electric fields,…
• Mechanical signals: velocity, force,
displacement,…
• Acoustic signals: sound, vibration,…
• Other signals: pressure, temperature,…

• Most real-world signals are analog
• They are continuous in time and amplitude
• Convert to voltage or currents using sensors and
transducers
• Analog circuits process these signals using
• Resistors, Capacitors, Inductors, Amplifiers,…
• Analog signal processing examples
• Audio processing in FM radios
• Video processing in traditional TV sets
Signal Processing:
Foundation for True Intelligence

Limitations of Analog Signal
Processing
• Accuracy limitations due to
• Component tolerances
• Undesired nonlinearities
• Limited repeatability due to
• Tolerances
• Changes in environmental conditions
• Temperature
• Vibration
• Sensitivity to electrical noise
• Limited dynamic range for voltage and currents
• Inflexibility to changes

Limitations of Analog Signal
Processing
• Difficulty of implementing certain operations
• Nonlinear operations
• Time-varying operations
• Difficulty of storing information

Discrete-Time Signals:
Sequences
• Discrete-time signals are represented by sequence of
numbers
• The nth number in the sequence is represented
with x[n]
• Often times sequences are obtained by sampling of
continuous-time signals
• In this case x[n] is value of the analog signal at
xc(nT)
• Where T is the sampling period

Discrete-Time Signals:
Sequences
0 20 40 60 80 100
-10
0
10
t (ms)
0 10 20 30 40 50
-10
0
10
n (samples)

Digital Signal Processing
• Represent signals by a sequence of numbers
• Sampling and quantization or analog-to-digital
conversions
• Perform processing on these numbers with a digital
processor
• Digital signal processing
• Reconstruct analog signal from processed numbers
• Reconstruction or digital-to-analog conversion
A/D DSP D/A
analog
signal
analog
signal
digital
signal
digital
signal

• Analog to Digital Converter

• Digital to Analog Converter

Digital Signal Processing:
Pros and Cons
• Pros
• Accuracy can be controlled by choosing word
length
• Repeatable
• Sensitivity to electrical noise is minimal
• Dynamic range can be controlled using floating
point numbers
• Flexibility can be achieved with software
implementations
• Non-linear and time-varying operations are easier
to implement

Digital Signal Processing:
Pros and Cons
• Pros
• Digital storage is cheap
• Digital information can be encrypted for security
• Price/performance and reduced time-to-market
• Cons
• Sampling causes loss of information
• A/D and D/A requires mixed-signal hardware
• Limited speed of processors
• Quantization and round-off errors

Comparison of Chips

DSP in Real Time Embedded
Systems

Basic Operations
• Reflection (Folding) a sequence
• Delaying (Shifting) a sequence
]
n
n
[
x
]
n
[
y o


y[n]= x[-n]

Basic Sequences
• Unit sample (impulse) sequence
• Unit step sequence
• Exponential sequences







0
n
1
0
n
0
]
n
[






0
n
1
0
n
0
]
n
[
u
n
A
]
n
[
x 

-10 -5 0 5 10
0
0.5
1
1.5
-10 -5 0 5 10
0
0.5
1
1.5
-10 -5 0 5 10
0
0.5
1

Basic Sequences
• Sinusoidal Sequence
• Important class of sequences
• An exponential sequence with complex
• x[n] is a sum of weighted sinusoids
   



 n
cos
n
x o





 j
j
e
A
A
and
e o
   
     















 




n
sin
A
j
n
cos
A
n
x
e
A
e
e
A
A
n
x
o
n
o
n
n
j
n
n
j
n
j
n o
o

Basic Sequences
• Difference of continuous-time, discrete-time sinusoids
• Have ambiguity of 2k in frequency
• Periodicity
• Interpretation of high and low frequencies

DSP Lecture 1.pdf

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