Bài giảng Xử lý tín hiệu số - Introduction - Hà Hoàng Kha

Chapter 0
Introduction
Click to edit Master subtitle styleHa Hoang Kha, Ph.D.
Ho Chi Minh City University of Technology
Email: hhkha@hcmut.edu.vn
1. Signal and Systems
™ A signal is defined as any physical quantity that varies with time, 
space or an other independent ariables, y v . 
‰ Speech, image, video and electrocardiogram signals are information-bearing 
signals.
™Mathematically, we describe a signal as a function of one or more 
independent variables.
‰ Examples: ( ) 110sin(2 50 )x t tπ= ⋅
2( , ) 3 2 10I x y x xy y= + +
™ A system is defined as a physical device that performs any operation 
on a signal.
‰ A filter is used to reduce noise and interference corrupting a desired 
information-bearing signal.
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1. Signal and Systems
™ Signal processing is to pass a signal 
h ht roug a system. 
™ A digital system can be 
implemented as a digital computer 
or digital hardware (logic circuits).
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2. Classification of Signal
Multichannels and Multidimensional signals
™ Signals which are generated by multiple sources or multiple sensors 
can be represented in a vector form. Such a vector of signals is 
referred to as a m ltichannel signals u 
‰ Ex: 3-lead and 12-lead electrocardiograms (ECG) are often used in practice, 
which results in 3-channel and 12-channel signals . 
™ A signal is called M-dimensional if its value is a function of M 
d d bin epen ent varia le 
‰ Picture: the intensity or brightness I(x,y) at each point is a function of 2 
independent variables 
‰ Color TV picture is 3-dimensional signals I(x,y,t)
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2. Classification of Signal
Continous-time versus discrete-time signal
™ Signals can be classified into four different categories depending on 
the characteristics of the time variable and the values they take.
Time 
Amplitue
Continuous Discrete
(t) ( )
Continuous t
x
n
x n
Analog signal Discrete time signal
Discrete
xQ(n)
101110
111xQ(t)
Quantized signal Digital signal
n
000001
010011
100
t
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3. Basic elements of a DSP system
™Most of the signals encountered in science and engineering are 
analog in nat re To perform the processing digitall there is a need u . y, 
for an interface between the analog signal and the digital processor
Fig: Analog signal processing 
Fi Di i l i l ig: g ta s gna process ng 
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4. DSP applications-Communications
™ Telephony: transmission of information in 
digital form via telephone lines, modem 
technology, mobile phone.
™ Encoding and decoding of the 
information sent over physical 
h nn l (t ptimizc a e s o o e 
transmission, to detect or 
correct errors in transmission) 
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4. DSP applications-Radar
Radar and sonar:
™ Target detection: 
position and 
velocity estimation
™ Tracking 
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4. DSP applications-Biomedical
™ Analysis of biomedical signals, diagnosis, patient monotoring, 
pre enti e health care artificial organsv v , .
™ Examples: 
™ l di ( CG) i l idE ectrocar ogram E s gna prov es 
information about the condition of the 
patient’s heart .
™ Electroencephhalogram (EEG) signal 
pro ides information abo t thev u 
activity of the brain.
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4. DSP applications-Speech
™Noise reduction: reducing 
backgro nd noise in the seq enceu u 
produced by a sensing device (a 
microphone).
™ Speech recognition: differentiating 
between various speech sounds
™ Synthesis of artificial speech : 
text to speech systems
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4. DSP applications-Image Processing
™ Content based image retrieval-
bro sing searching and retrie ingw , v 
images from database.
™ Image enhancement
™ Compression: reducing the 
redundancy in the image data to 
optimize transmission/storage
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4. DSP applications-Multimedia
™ Generation storage and transmission 
of so nd still images motion u , , 
pictures.
™ Digital TV
™ Video conference
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The Journey
“ L i di i l i l i i hi earn ng g ta s gna process ng s not somet ng 
you accomplish; it’s a journey you take”.
R.G. Lyons, Understanding Digital Signal Processing
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5. Advantages of digital 
over analog signal processing
™ A digital programmable system allows flexibility in reconfiguring the 
DSP operations simply by changing the program.
™ A digital system provides much better control of accuracy 
requirements.
™ Digital signals are easily stored.
™ DSP methods allow for implementation of more sophisticated signal 
processing algorithms.
™ Li it ti P ti l li it ti f DSP th ti tim a on: rac ca m a ons o are e quan za on errors 
and the speed of A/D converters and digital signal processors -> not 
suitable for analog signals with large bandwidths. 
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Course overview
™ Introduction to Digital Signal Processing (3 periods)
™ Sampling and reconstruction, quantization (6 periods)
™ Analysis of linear time invariant systems (LTI)(3 periods)
™ Finite Impulse Response (FIR) of LTI systems (3 periods)
™ Z-transform and its applications to the analysis of linear systems (6 
Mid-term Exam
periods)
™ Fourier transform & FFT Algorithm (9 periods)
™ Digital filter realization(3 periods)
™ FIR and IIR filter designs (9 periods)
Final Exam 
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References
™ T t b kex oo s:
[1]  S. J. Orfanidis, Introduction to Signal Processing, Prentice –Hall 
Publisher 2010. 
[2]  J. Proakis, D. Manolakis, Introduction to Digital Signal 
Processing, Macmillan Publishing Company, 1989.
™ Reference books:
[3] V K Ingle J Proakis Digital Signal Processing Using Matlab  .  .  ,  .  ,          , 
Cengage Learning, 3 Edt, 2011. 
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Learning outcomes
™ Understand how to convert the analog to digital signal 
™ Have a thorough grasp of signal processing in linear time invariant                  ‐  
systems.
™ Understand the z‐transform and Fourier transforms in analyzing 
the signal and systems      .
™ Be able to design and implement FIR and IIR filters.
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Assessment
™ Mid‐term exam:  30%
™ Final exam:  70%
™ Bonus:  0.5 mark/solving a problem in the class.
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