EEM401 Digital Signal Processing

Transkript

EEM401 Digital Signal Processing
Contents
Contents
EEM401 Digital Signal Processing
∼usezen/eem401/
http://www.ee.hacettepe.edu.tr/
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Dr. Umut Sezen
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Department of Electrical and Electronic Engineering,
Hacettepe University
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Introduction: Signals and Signal Processing
Discrete-Time Signals in the Time and Frequency Domain
Discrete-Time Fourier Transform (DTFT)
Discrete-Time Systems and Transforms
Z -transform
Transform Analysis of LTI Systems
Digital Filters and Filter Design
Applications of Digital Signal Processing
These lecture slides are based on "Digital Signal Processing: A Computer-Based Approach,
4th ed." textbook by S.K. Mitra and its instructor materials. U.Sezen
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Umut Sezen (Hacettepe University)
Contents
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Introduction: Signals and Signal Processing
Main textbook:
I S.K. Mitra, Digital Signal Processing: A Computer-Based
McGraw-Hill, 4th Ed., 2011 (or 3rd Ed., 2006).
Supplementary textbook:
I A.V. Oppenheim and R.W. Schafer,
Prentice Hall, 2nd Ed., 1998.
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Approach
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Discrete-Time Signal Processing
EEM401 Digital Signal Processing
Introduction: Signals and Signal Processing
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I Speech and music signals
time at a point in space
Signals play an important role in our daily life.
A signal is a function of independent variables such as time, distance,
position, temperature and pressure.
Some examples of typical signals are shown on the next slides.
Umut Sezen (Hacettepe University)
Examples of Typical Signals
EEM401 Digital Signal Processing
Introduction: Signals and Signal Processing
Examples of Typical Signals - Speech
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Introduction: Signals and Signal Processing
Textbook
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Examples of Typical Signals
Examples of Typical Signals - ECG
- Represent air pressure as a function of
I Electrocardiography (ECG) Signal
activity of the heart
Waveform of the speech signal "I like digital signal processing" is
shown below.
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- Represents the electrical
A typical ECG signal is shown below
An ECG signal
Play Sound
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Introduction: Signals and Signal Processing
Examples of Typical Signals
Introduction: Signals and Signal Processing
Examples of Typical Signals - ECG
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Examples of Typical Signals - EEG
The ECG trace is a periodic waveform
- Represent the electrical
activity caused by the random rings of billions of neurons in the brain
I Electroencephalogram (EEG) Signals
One period of the waveform shown below represents one cycle of the
blood transfer process from the heart to the arteries
An EEG signal
One period of an ECG signal
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Examples of Typical Signals
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Umut Sezen (Hacettepe University)
Examples of Typical Signals
EEM401 Digital Signal Processing
Introduction: Signals and Signal Processing
Examples of Typical Signals - Seismic
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Examples of Typical Signals
Examples of Typical Signals - Seismic
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Typical seismograph record
- Caused by the movement of rocks resulting from
an earthquake, a volcanic eruption, or an underground explosion
I Seismic Signals
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The ground movement generates 3 types of elastic waves that
propagate through the body of the earth in all directions from the
source of movement
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Examples of Typical Signals
Umut Sezen (Hacettepe University)
Introduction: Signals and Signal Processing
Examples of Typical Signals - Image
- Represents light intensity, I(x, y) as a
function of two spatial coordinates, x and y .
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Examples of Typical Signals
- Consists of a sequence of images, called frames, and
is a function of 3 variables, I(x, y, t): two spatial coordinates, x and y
and time, t
I Video signals
Play Movie
A grayscale image
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Examples of Typical Signals - Video
I Black-and-white picture
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Introduction: Signals and Signal Processing
Signals and Signal Processing
Introduction: Signals and Signal Processing
Signals and Signal Processing
Signals and Signal Processing
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A signal carries information
I Objective of signal processing:
carried by the signal
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Most signals we encounter are generated naturally
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However, a signal can also be generated synthetically or by a computer
Depends on the type of signal and
the nature of the information being carried by the signal
I Method information extraction:
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Depends on the nature of the independent variables
and the value of the function dening the signal
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For example, the independent variables can be continuous or discrete,
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Likewise, the signal can be a continuous or discrete function of the
independent variables
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A signal generated by a single source is called a scalar signal
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A signal generated by multiple sources is called a vector signal or a
multichannel signal
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Characterization and Classication of Signals
Characterization and Classication of Signals Video
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EEM401 Digital Signal Processing
Characterization and Classication of Signals
EEM401 Digital Signal Processing
Introduction: Signals and Signal Processing
The 3 color components of a color image and the full color image
obtained by displaying the previous 3 color components are shown
below
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A one-dimensional (1-D) signal is a function of a single
independent variable
A multidimensional (M-D) signal is a function of more than one
independent variables
The speech signal is an example of a 1-D signal where the
independent variable is time
Moreover, the signal can be either a real-valued function or a
complex-valued function
An image signal, such as a photograph, is an example of a 2-D
signal where the 2 independent variables are the 2 spatial variables
A color image signal is composed of three 2-D signals representing
the three primary colors: red, green and blue (RGB)
Umut Sezen (Hacettepe University)
Characterization and Classication of Signals
Characterization and Classication of Signals Image
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Characterization and Classication of Signals
Moreover, the signal can be either a real-valued function or a
complex-valued function
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EEM401 Digital Signal Processing
Introduction: Signals and Signal Processing
I Types of signal:
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This course is concerned with the discrete-time representation of
signals and their discrete-time processing
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Characterization and Classication of Signals
Characterization and Classication of Signals
Extract the useful information
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Each frame of a black-and-white digital video signal is a 2-D image
signal that is a function of 2 discrete spatial variables, with each
frame occurring at discrete instants of time
Hence, black-and-white digital video signal can be considered as an
example of a 3-D signal where the 3 independent variables are the 2
spatial variables and time
A color video signal is a 3-channel signal composed of three 3-D
signals representing the three primary colors: red, green and blue
(RGB)
For transmission purposes, the RGB television signal is transformed
into another type of 3-channel signal composed of a luminance
component and 2 chrominance components
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Introduction: Signals and Signal Processing
Characterization and Classication of Signals
Introduction: Signals and Signal Processing
Characterization and Classication of Signals
Characterization and Classication of Signals
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For a 1-D signal, the independent variable is usually labeled as time
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If the independent variable is continuous, the signal is called a
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continuous-time signal
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Characterization and Classication of Signals
If the independent variable is discrete, the signal is called a
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discrete-time signal
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A continuous-time signal is dened at every instant of time
A discrete-time signal is dened at discrete instants of time, and
hence, it is a sequence of numbers
A continuous-time signal with a continuous amplitude is usually called
an analog signal
I A speech signal is an example of an analog signal
A discrete-time signal with discrete-valued amplitudes represented by a
nite number of digits is referred to as the digital signal
I An example of a digital signal is the digitized music signal stored
in a CD-ROM disk
A discrete-time signal with continuous valued amplitudes is called a
sampled-data signal
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Characterization and Classication of Signals
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Characterization and Classication of Signals
(a) a continuous-time signal
(b) a sampled-data signal
(c) a digital signal
(d) a quantized boxcar signal
The gure on the next slide illustrates the 4 types of signals
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Umut Sezen (Hacettepe University)
Characterization and Classication of Signals
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For a continuous-time 1-D signal, the continuous independent
variable is usually denoted by t
I For example, u(t) represents a continuous-time 1-D signal
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For a discrete-time 1-D signal, the discrete independent variable is
usually denoted by n
I For example, {v[n]} represents a discrete-time 1-D signal
I Each member, v[n], of a discrete-time signal is called a sample
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Characterization and Classication of Signals
Characterization and Classication of Signals
The functional dependence of a signal in its mathematical
representation is often explicitly shown
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Introduction: Signals and Signal Processing
Characterization and Classication of Signals
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Characterization and Classication of Signals
A continuous-time signal with discrete-value amplitudes is usually
called a quantized boxcar signal
Introduction: Signals and Signal Processing
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A digital signal is thus a quantized sampled-data signal
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EEM401 Digital Signal Processing
Introduction: Signals and Signal Processing
Characterization and Classication of Signals
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In many applications, a discrete-time signal is generated by sampling
a parent continuous-time signal at uniform intervals of time
If the discrete instants of time at which a discrete-time signal is
dened are uniformly spaced, the independent discrete variable n
can be normalized to assume integer values
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Introduction: Signals and Signal Processing
Characterization and Classication of Signals
Introduction: Signals and Signal Processing
Characterization and Classication of Signals
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Characterization and Classication of Signals
In the case of a continuous-time 2-D signal, the 2 independent
variables are the spatial coordinates, usually denoted by x and y
I For example, the intensity of a black-and white image at
location (x, y) can be expressed as u(x, y)
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A continuous-time black-and-white video signal is a 3-D signal and
can be represented as u(x, y, t)
A color video signal is a vector signal composed of 3 signals
representing the 3 primary colors: red, green and blue
On the other hand, a digitized image is a 2-D discrete-time signal,
and its 2 independent variables are discretized spatial variables,
often denoted by m and n
I Thus, a digitized image can be represented as v[m, n]
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EEM401 Digital Signal Processing
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Characterization and Classication of Signals
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r(x, y, t)
u(x, y, t) = g(x, y, t)
b(x, y, t)
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