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Faculty of Electrical
Engineering and Information Technologies
Skopje, Macedonia
in partnership with
Faculty of Electrical
Engineering and Computer Science
Maribor, Slovenia
and
Faculty of Electronics
and Telecommunications
Poznan, Poland |
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Structure
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Core courses:
Elective courses:
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Statistical signal processing
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Course Objective
The
goal of this course is to provide a comprehensive
coverage of statistical signal processing methods
and tools, including the algorithms for various
applications.
Course prerequisite:
This course requires basic knowledge of probability.
Texts:
[1]
M. Hayes,
“Statistical Digital Signal Processing and
Modeling”, Wiley, 1996.
[2]
D. G. Manolakis,
V. K. Ingle, S. M. Kogon, “Statistical and Adaptive
Signal Processing: Spectral Estimation, Signal
Modeling, Adaptive Filtering and Array Processing”,
Artech House, 2005.
Course Outline: (Outline syllabus)
Random vectors: definition, moments, characteristic
functions, multidimensional Gaussian distribution.
Discrete time random processes: definition,
stationarity and ergodicity, autocorrelation and
power spectral density, Markov and Gauss-Markov
processes. Estimation of unknown parameters: MVUE,
ML, LS. Estimation of random parameters: MAP, MMSE,
orthogonality principle. Optimal estimation of
discrete time random processes: Wiener and Kalman
filters. Parametric models of discrete time random
processes: AR, MA, ARMA models. Spectral analysis of
discrete time random processes: periodogram,
correlogram, methods based on parametric models,
high resolution methods. Adaptive signal processing:
steepest descent, LMS, RLS algorithms. Array signal
processing: beam forming, high resolution methods.
Applications of statistical signal processing
methods and algorithms.
Learning Outcomes
Upon completion of this course, students will be
able to:
-understand and know how to implement the methods
and algorithms of statistical signal processing:
parameter estimation, random parameter estimation
and estimation of random processes, and adaptive
signal processing
-identify the engineering problems that can be put
into the frame of statistical signal processing,
-solve the identified problems using the techniques
learned through this course, and
-apply the fundamental ideas of statistical signal
processing to study further and make significant
contributions to the theory and the practice of
statistical signal processing.
Course methodology:
The
course concepts will be taught by the instructor.
Students will have to prepare and teach certain
lectures in class. A research project will be
assigned to each student/group of students. At the
end of the semester, each student/group will present
their project. There will be a final exam covering
the principal methods and algorithms learned in the
course.
Grading Method:
The
grading will be based on the exam and research
project. Specifically:
Exam: 50%
In/class presentation of lecture: 10%
Project: 40% |
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DSP
in transform domain
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Course Aims
Digital Signal Processing (DSP) is a
fascinating blend of mathematics, computation and
practical implementation. It is the bedrock upon
which all modern digital communication systems are
built.
This course mainly focuses on the
basic concepts and techniques for processing one and
two dimensional signals in transform domain
and includes in-depth treatment of
different discrete transforms and various fast
algorithms giving the necessary mathematical
background for practical
implementations of the theoretical concepts .
The final part of the course examines
several typical transform domain application as
filtering, spectral estimation,
coding, adaptive filtering, multirate signal
processing.
By the end of the course, students
will be familiar with the most important methods and
algorithms in DSP transform-domain processing.
Course prerequisite:
Basic knowledge of signals, systems
and signal processing.
Texts:
[1] D.
F. Elliot and K. Ramamohan Rao, Fast Transforms:
Algorithms, Analyses, Applications, Academic
Press. Orlando FL, 1982.
[2] V.
K. Madisetti and D. B. Williams, Digital Signal
Processing, CRC Press, 1998.
[3] H.
S. Malvar, Signal
Processing
with Lapped Transform,
Artech House, Boston MA, 1.
Course Outline: (Outline syllabus)
Brief revue of signal models.
Definitions and basic properties of discrete
transforms. Orthogonal discrete transforms:
Fourier(DFT), Hartley(DHT), Karhunen-Loeve (KLT),
Cosine (DCT), Lapped (LOT), wavelet(WLT),
Walsh-Hadamard (WHT). Two- dimensional transforms.
Fast algorithms: concept and selected examples.
Applications in signal processing: filtering,
spectral estimation, coding, adaptive filtering,
multirate signal processing.
Learning Outcomes
A student who has met the objectives
of the course will be able to:
· The
students should be able to achieve the above goals
in a group effort while maintaining individual
accountability
· The
student should be able to communicate his results in
a clear and precise manner
Course methodology:
The course concepts will be taught by
the instructor. A research project will be assigned
to group of students. They will be expected to do a bit of
independent reading, a presentation in class, and a
short report.
There will be a final exam covering the principal
methods and algorithms learned in the course.
Grading Method:
The grade will be determined from
class attendance, homeworks, and a final project.
Homeworks will be issued periodically (5 or 6 during
the semester). The students are encouraged to
collaborate on them. |
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Filter banks and wavelets
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Various transform-domain techniques are proposed in
order to analyze the signals with aim to identify
their nature and most important characteristics.
Probably the most famous and most used is Fourier
transform. However in some
application the use of the Fourier transform entails
a difficulty, namely any time-local information
about the signal is lost. The wavelet transform, its
natural framework of multiresolution analysis and
its discrete-time counterpart (the discrete wavelet
transform performed by multirate filter banks) allow
for better analysis of signals, by looking at them
at various scales or resolutions. As results of this
property, there is increasing number of applications
where wavelet transform is used. For instance, in
image coding, wavelet based coding methods already
outperform current JPEG standards.
This course will provide both a mathematical
background in wavelets and an introduction to their
discrete-time implementation (multirate filter
banks).
Pre-requisite:
Digital Signal Processing course
Literature:
[1] G. Strang and T. Nguyen, Wavelets and filter
banks, Wellesley-Cambridge Press
[2] P.P. Vaidyanathan, Multirate Systems and Filters
Banks, Prentice Hall, 1993
[3] M. Vetterli and J. Kovacevic, Wavelets and
Subband coding, Prentice Hall, 1995
Outline syllabus
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Introduction to the
fundamental theory of multirate signal
processing: decimation, interpolation, and
sampling rate conversion
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Two-channel filter banks:
quadrature-mirror filter banks (QMF), perfectly
reconstructing, paraunitary, biorthogonal and
linear phase filter banks,
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M-channel filter banks used
as subband coding or transmultiplexing filter
banks.
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Polyphase structures for
two-channel and M-channel filter banks.
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Lattice structures for Linear
Phase PR QMF Banks
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Discrete
Wavelet Transform
(DWT) and its relations to multirate filter
banks. The Short-Time Fourier Transform. The
Wavelet Transform. Discrete-Time Orthonormal
Wavelets. Continuous-Time Orthonormal Wavelet
Bases.
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Lifting implementations of wavelet transform
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Applications of wavelets for
signal analysis and compression.
Learning Outcomes
Having successfully completed this course, student
will be able to:
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Understand
the basics of multirate signal processing
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understand the idea of a
different types of filter banks
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gain an overview of their design
methods
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understand how to design perfect
reconstruction filter banks
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understand the fundamentals of
wavelet theory
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become familiar with the most
commonly used wavelets
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understand the link between
design of filter banks and construction of discrete
and continuous-time bases for efficient signal
analysis.
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analyse and compress a signal
using wavelets.
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work in team
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research, present, and report a
selected project within a specified time.
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think critically, ask questions,
and apply problem-solving techniques.
Course methodology:
Collaborative learning model will be implemented in
this course. Accent will be given to the team work.
For efficient implementation of the proposed
collaborative model it is essential that student
come to class familiar with the material that will
be discussed that day. The class is split to short
lectures for teaching and for group work of the
student. The lectures will focus on the most
important or most difficult concepts in the reading.
Students will spend most of their class time working
in groups on problems.
After each class homework will be assigned. In most
of the homeworks the concepts discussed in the class
should be implemented in Matlab. One project will be
assigned to each group of students. The project will
be presented by the group members of the end of the
semester.
Grading Method:
Main course activities will be evaluated and grading
will be as follows:
Exam: 60 %
Homework: 20 %
Project: 20 % |
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Real time DSP lab
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Course
Aims
With the
widespread usage of DSPs as part of real-time
embedded systems, the breadth of applications in
which DSPs can be used is large. These applications
include, but are not limited to, modems, faxes, data
transmission, data encryption, speech and image
processing and compression, vehicle navigation,
automotive control, seismic and spectral analysis,
radar and sonar, ECG monitoring, digital audio and
music, hearing aids, digital cellular phones, and
video telephones.
The
objectives of the proposed course are to provide the
students with knowledge and hands-on experience in
translating DSP concepts into real-time software for
embedded systems using DSP boards. The proposed
course emphasizes designing and implementing
real-time software for embedded systems through the
programming of a special type of micro-processor,
the DSP, or Digital Signal Processor. Laboratory
exercises will be based on writing C and assembly
language software for selected DSP boards that are
used in current consumer products, and interfacing
the DSPs to external devices for test and
measurement.
Course
prerequisite:
This
course requires basic knowledge of: C programming,
Signals and systems
Texts:
[1] Sen M.
Kuo, Bob H. Lee, Wenshun Tian: "Real-Time Digital
Signal Processing: Implementations and Applications",
Wiley, Prentice Hall
[2] Sen
M_Kuo,Woon-Seng S_Gan: "Digital Signal Processors -
Architectures, Implementations, and Applications",
(Amazon)
[3] DSP
manufacturer manuals
Course
Outline: (Outline syllabus)
Introduction to digital signal processors, general
characteristics and real-time applications, DSP
families, general rules for selecting a DSP.
Detailed
architecture of a selected DSP, instruction set,
development environment.
Integer
and floating point arithmetic overview with emphasis
on DSP arithmetic characteristics.
Overview
of DSP peripherals.
Application examples, libraries, real-time operation
optimization.
DSP/BIOS
RT Kernel
Learning
Outcomes
Having
completed this unit, students will be able to
demonstrate:
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Knowledge of a digital signal processor architecture
and it’s influence on real-time digital signal
processing
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Experience using equipment and tools commonly used
in industry and/or experimental laboratory
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Understanding of the process of converting ideas and
algorithms into real-time working hardware with
real-time signals
Also,
upon
completion of this course, student should be able
to:
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work in
team and individually
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prepare
for, perform and report experimental work
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self-organize to acquire knowledge, implement it and
track the progress through a real-life project(s)
Course
methodology:
The
course will be given by lectures, readings,
discussions, laboratory experiments and
mini-project(s). After the general introductory
lectures given by the instructor, students will be
studying chapters from the text-book(s), datasheet
and manuals and preparing written work plans for
performing the assigned experiments. At the same
time they will be exposed to certain examples and
problems where real-time signal processing is needed
to choose from for their project assignment. The
work plans will consist of homework assignment as
theoretical preparation and practical work
preparation, and will be discussed and graded in
class prior lab. experiment. After the lab. each
student will prepare a report. After achieving the
basic level of mastering the required tools and
techniques, students will start working on their
projects and will be giving regular reports on their
progress that will be discussed and graded in class.
The course will end with a final report,
presentation and demonstration of their work.
Grading
Method:
The
grading will be based on student’s performance in
written work plans and discussions, laboratory work,
reports preparation and discussion and project
report presentation and demonstration. Specifically:
Work
plans: 20%
Reports:
20%
Laboratory: 20%
Project: 40% |
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Biomedical signal analysis and processing
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Course
Description:
Nowadays, signal processing come in widely in all
areas of technical and technological development.
Parallel with the new achievements in signal
processing, a row of disciplines develop too. So, on
the demand for developing new techniques adequate to
the signal processing characteristic is paid more
and more attention.
Biomedical signal analysis and processing is focused
on acquisition and processing of signals that come
from living systems, and allow us to find out about
their state, therefore its accurate interpretation
has significant value for clinicians and researches.
The
aim of this course is to demonstrate the biomedical
signals, their origins and sources, use of
techniques in processing, according to their
characteristics, as noise reduction techniques
spectral analysis and filtering, digital biomedical
signal acquisition and processing, their
compression, use of wavelet analysis. Special
attention is focused on characteristics and analysis
of ECG, EEG and EMG signals.
Course
prerequisite:
Basic
knowledge of signal processing is required for the
course.
Texts:
[1] K.
Najarian, R. Splinter, “ Medical Signal and Image
Processing”, CRC Press, 2006
[2] J. D. Enderle, S. M. Blanchard, J. D. Bronzino,
„Introduction to Biomedical Engineering“, Elsevier
Press
[3] J. D. Bronzino, editor, “Biomedical Engineering
Fundamentals”, CRC Press, 2006
Course
Outline: (Outline syllabus)
Signals,
introduction, definitions
Use of
techniques in processing
– Fourier transform, wavelet transform, other methods
Clustering and classification of signals
Modulation and demodulation
Signal
acquisition
Noise
reduction techniques,
time averaging, spectral analysis and filtering,
optimal filtering
Biomedical signals, origins, sources, and properties
Characteristics of cell and tissue from electric
aspect
Analysis
of biosignals with low level in presence of noise
Digital
biomedical signal acquisition and processing
Compression of digital biomedical signals
Wavelet
analysis
in digital
biomedical signals
Characteristics and analysis of ECG, EEG and EMG
signals
Other
biomedical signals
Learning
Outcomes
With
successfully completed a learning process, the
student will be able to demonstrate knowledge and
understanding of:
·
Signal definition and understanding of their
characteristics
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Techniques for signal processing
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Their analysis
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The characteristics of biomedical signals
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Use of techniques for analysis and processing of
biomedical signals according their characteristics
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Special overview and analysis of ECG, EEG and EMG
signals
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Another biomedical signals
Also, the student
should
be capable to:
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Identify
and formulate different problems connected with the
material
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Use the
theoretical knowledge and techniques during research
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Analyze
critically the results and bring appropriate
conclusions
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Research, present, and report a
selected project within a specified time
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Work
either individually or in team
Course
methodology:
The
course methodology contains the ways of the
presentations of material. It will be combined from
lectures and paper reading. The lectures are taught
by the instructor, but some of them can be given by
the students too. In the same time, students should
read articles, it is necessary each of the student
to have read several papers. So, organized in teams,
they can select adequate project topics. During the
semester, several seminars will be organized, so the
students can present read articles in front of the
class, and their current work on their projects. The
students are expected to take active part either in
the lecture or paper discussions. All the projects
are presented at the end of semester by the student
or team.
Grading
Method:
The
grading method will be according to the student's
activities in class participation, lecture and paper
presentation, and the making and presentation of
research project. Specifically:
Class
participation: 10%
Lectures
and paper presentation: 30%
Project:
60%. |
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Biomedical image processing
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Course
Description
This
course will offer the students the basics of
biomedical image processing through the coverage of
the physical principles of biomedical imaging and
the image processing basics and techniques for image
enhancement, compression, segmentation, registration
and motion analysis. Students will learn the
fundamentals behind image processing methods and
algorithms with an emphasis on biomedical
applications.
Course
Prerequisite:
This
course requires basic knowledge of signal
processing.
Texts:
[1]
Kayvan Najarian, Robert Splinter, Biomedical
Signal and Image Processing, CRC Press, 2006
[2]
Rafael C. Gonzalez, Richard E. Woods,
Digital Image Processing,
Prentice Hall,
3rd ed.,
2007
[3] Selected articles will be put on reserve
Course
Outline: (Outline Syllabus)
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Basic
concepts in digital image processing: Image
representation in the spatial and frequency
domains; Digitization; Visual perception; Noise
and image quality; Components of an image
processing system
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Biomedical imaging modalities: physics of
X-rays, the Fourier slice theorem, x-ray and CAT
imaging; Physical and physiological principles
of magnetic resonance and MR imaging; Physical
and physiological principles of ultrasound and
ultrasound imaging
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Basics
of morphological image processing: Introduction;
Logic operations on digital images; Dilation and
erosion; Openning and closing
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Filtering, enhancement and restoration:
Intensity modifications; Mask processing;
Spatial frequency processing; Wavelet denoising
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Motion: Introduction; Motion quantification from
image sequences; Application for measuring
dynamic biological phenomena
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Basics
of image compression: Introduction; Lossless
compression; Lossy compression
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Edge
detection and image segmentation: Edge
detection; Thresholding; Region-based
segmentation; Segmentation by morphological
watersheds; Application of motion for
segmentation
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Rigid
and non-rigid image registration: Introduction
and transformations; Match metrices;
Optimization and interpolation; Robustness
Learning
Outcomes
Having
successfully completed this course, you will be able
to:
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explain the fundamentals of: image formation and
acquisition, image representation in the spatial
and frequency domains, the respective roles of
sampling, quantization, transformation and the
HVS
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explain the basic mechanisms and modalities of
biomedical imaging
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understand the characteristics of images
obtained via different mechanisms and modalities
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roughly understand the applicability of
individual modalities to medical
diagnostics/therapy
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demonstrate practical basic image processing
skills (filtering, enhancement, restoration,
motion, compression, segmentation, registration)
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discuss applications of image processing
techniques to biomedical image processing and
choose what types kinds of image processing are
suitable for given biomedical applications
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evaluate whether an image processing system is a
good candidate for given biomedical information
system
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keep up with recent advances in image processing
and identify possible biomedical applications
Course
Methodology:
The
course will consist of lectures, homework
assignments
and final projects. Homeworks will include Matlab
programming assignments and articles to be read and
then discussed in class. Individual students will
lead discussions on selected articles. Final
projects will be extrapolations of topics discussed
within the course (pre-approved
by the instructor),
with simulations illustrating the results. A final
project will include a (journal styled) paper and a
15 minute oral presentation describing the methods
and the results.
Grading
Method:
Main
course activities will be evaluated and grading will
be as follows:
Class
participation: 10% (according to participation
in interactive discussion based on assigned
articles)
Homework: 50% (according to
results and written analysis of topic and app. in
prog. assignments)
Final project: 40%: (according to
quality of simulation, paper and oral presentation) |
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Data hiding
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Course
Aims
The
advancement in technology offers new solutions, but
in the other way, creates new problems as well.
Digital media (e.g. audio, images, video, etc.)
offers many benefits: it can be stored, duplicated
and distributed everywhere in the world, with no
loss of fidelity, but in contrary, it can also be
manipulated and modified easily, often only with
personal computer and appropriate software, and
sometimes even unintentionally. While these
properties are desirable in general, they can create
problems for parties who own digital media and want
to distribute it, but at the same time, want to
protect it from illegal multiplication and
distribution. Digital
watermarking has been proposed as a solution for the
copyright protection
The
goal of the course is to
give an
introduction to watermarking and hiding messages
in audio, image, and video signals, together
some
techniques
for various applications: copyright protection,
intellectual property,
user identification, and authentication.
Course
prerequisite:
This
course requires basic knowledge of signal
processing,
Texts:
[1] I. Cox, M. Miller, and J.
Bloom, Digital Watermarking, Morgan Kaufmann
Publishers, Inc., San Francisco, 2001.
[2] S. Katzenbeisser and F.
Petitcolas (editors), Information Hiding
Techniques for Steganography and Digital
Watermarking, Artech House, 2000
Course
Outline: (Outline syllabus)
Introduction to the problem of data hiding.
Digital
watermarking. History of watermarking. Applications
and Properties. Models of Watermarking. Methods
classification and evaluation. Perceptual Models in
digital watermarking. Robustness and security.
Digital
watermarking techniques. Content Authentication
Steganography and Steganalysis. History of
steganography. General concepts and applications of
steganography. Multimedia data steganography
techniques. Steganalysis.
Learning
Outcomes
Having
successfully completed this unit, you will be able
to demonstrate knowledge and understanding of:
·
the basics of digital watermarking
· the various applications of robust invisible digital
watermarking such as copyright protection, image
fingerprinting, image authentication, etc.
· the technical principles of the various
spatial-domain and transform-domain techniques
employed for the robust invisible watermarking of
images and video
·
the
attacks of the watermarking systems
as well as methods to
countermeasure these attacks.
·
The benchmarks used to evaluate the performance of
robust watermarking techniques.
·
the technical description of fragile and
semi-fragile watermarking techniques and how they
can be used for image authentication
·
the
general concepts and applications of steganography
·
the technical principles of steganography techniques
·
state-of-the-art steganalysis techniques that can
detect if an image contains an invisible watermark.
Also,
upon
completion of this course, student should be able
to:
·
work in
team
·
research, present, and report a
selected project within a specified time.
·
think
critically, ask questions, and apply problem-solving
techniques.
Course
methodology:
The
course concepts will be given by lectures, readings,
seminar-like presentations, and projects. Regular
lectures will be given in the first half of the
semester by the instructor. During this time,
students will be reading articles and selecting
project topics. In the second half, students will be
required to present papers in class and to give
progress reports on their projects. There will be
several research papers that each student is
expected to read, and students will be expected to
participate actively in both the lecture and paper
discussions. At the end of the semester, each
student/team will present their project.
Grading
Method:
The
grading will be based on the student's performance
in class participation, paper presentation, and the
research project. Specifically:
Class
participation: %
Paper
presentation: %
Project: % |
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Biometrics and video surveillance
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Course Aims
Biometrics is the study of automated
methods for the identification or authentication of
individuals using biological characteristics (e.g.
fingerprint, face, and iris images). Biometrics
technology as well as
video surveillance
methods has the potential to solve
key security challenges associated with information
systems. This course explores the underlying
technology, use, issues, and potential of biometrics
and
video surveillance
for identification and authentication. The selected
topics provide an overview of the use of biometrics
for identification with an emphasis on its role in
information security.
Trough the course the students should
develop:
- a knowledge of the most important biometric
approaches.
- the capability to select a suitable algorithm /
system for a given application context (e.g.
physical access control)
- a good understanding of the complex relationships
between biometric systems and environmental
conditions and their impact on biometric
performance.
- the capability to assess the security properties
of a biometric system.
Course prerequisite:
Basic knowledge of signal processing,
Texts:
[1]
John D.
Woodward Jr., et al
Biometrics,
McGrow-Hill Osborne Media 1st edition, 2002
[2]
A. K. Jain, P. Flynn, A. Ross, "
Handbook of Biometrics", Springer,
2007.
[3] A. Ross, K. Nandakumar and A.K.
Jain,
Handbook of
Multibiometrics, Springer Verlag,
2006.
[4] A.K. Jain, R. Bolle and S.
Pankanti (Eds.),
BIOMETRICS: Personal
Identification in Networked society,
Kluwer Academic Publishers,1999.
Course Outline: (Outline syllabus)
Introduction to biometrics. Biometric
systems based on fingerprint recognition. Biometric
systems based on iris recognition. Face
identification and localization in images. Retina
recognition methods. Human identification based on
gait. Fusion in biometrics (multibiometrics). New
trends in biometrics.
Video surveillance – introduction.
Video acquisition systems and video quality
improvement. Motion detection techniques and object
tracking. Recognition humans and their activities in
video. Intelligent surveillance techniques.
Barcode and RFID
Learning Outcomes
A student who has met the objectives
of the course will be able to:
· Explain concrete biometric models
introduced in the course
· Analyze the suitability of biometric
models for a given scenario
· Use the biometric ingredients of
existing systems to obtain a given security goal
· Indicate the potential limitations
biometric ingredients in existing systems for
achieving a given security goal
· Judge the appropriateness of
proposals in research papers and text books for a
given application
·
Design a biometric solution for a given application
scenario
· The students should be able to
achieve the above goals in a group effort while
maintaining individual accountability
· The student should be able to
communicate his/her results in a clear and precise
manner
Course methodology:
The course concepts consist of two
parts: the first one when regular lectures will be
given by lectures, and in the second one consisting
of readings, seminar- presentations, and projects. .
In the second half, students will be required to
present papers in class and to give progress reports
on their projects. There will be several research
papers that each student is expected to read, and
students will be expected to participate actively in
both the lecture and paper discussions. At the end
of the semester, each student/team will present
their project.
Grading Method:
The grading will be based on the
student's performance in class participation, paper
presentation, and the research project.
Specifically:
Class partisipation:30 %
Paper presentation:20 %
Project:50 % |
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Image processing
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Course
Description
Image
processing and analysis represents an exciting and
dynamic part of the science of digital signal
processing with a significant growth of active
applications (remote sensing, technical diagnostics,
autonomous vehicle guidance, medical imaging,
automatic surveillance etc.), and with a swift
progress demonstrated through an increasing number
of image processing and analysis software and
hardware products on the market.
This
course will offer the students the basics of image
processing through the coverage of image
enhancement, restoration, and segmentation, and of
the basics of image analysis through the coverage of
shape/texture representation and description,
object/texture recognition and motion analysis. The
areas of image understanding and 3D-vision will also
be briefly introduced.
Course
Prerequisite:
This
course requires basic knowledge of signal
processing.
Texts:
[1]
Milan Sonka, Vaclav Hlavac, Roger Boyle, Image
Processing, Analysis and Machine Vision, Chapman
& Hall, 3rd ed., 2008
[2]
Rafael C. Gonzalez, Richard E. Woods,
Digital Image Processing,
Prentice Hall,
3rd ed.,
2007
Course
Outline: (Outline Syllabus)
·-Digitized
image and its properties
·-Data structures for image analysis
·-Image
pre-processing: Pixel brightness transformations;
Geometric transformations: Local pre-processing;
Restoration
·-Advanced
segmentation: Thresholding; Border detection;
Advanced
optimal border and surface detection approaches
·-Shape
representation and description: Region
identification; Contour-based shape representation
and description; Region-based shape representation
and description; Shape classes
·-Object
recognition: Knowledge representation; Statistical
and syntactic pattern recognition; Recognition as
graph matching; Optimization techniques in
recognition
·-Matematical morphology: Basic principles and
morphological transformations; Topological
processing
·-Texture:
Statistical texture description; Syntactic texture
descriptions; Hybrid texture description methods;
Texture recognition methods applications
·-Image understanding: Image understanding control
strategies; Active contour models;
Point distribution models;
Pattern recognition methods in image understanding;
Scene labelling and constraint propagation; Semantic
image segmentation and understanding
·-Motion analysis:
Differential motion analysis methods; Optical flow;
Motion analysis based on detection of interest
points; Kalman filters
·-3D-vision:
3D-visioin tasks; Geometry of 3D-vision; Radiometry
and 3D-vision
Learning
Outcomes
Having
successfully completed this course, you will be able
to:
-
explain the fundamentals of: image formation and
acquisition, image representation in the spatial
and frequency domains, the respective roles of
sampling, quantization, transformation and of
the HVS
-
explain the basics of image processing and
demonstrate practical skills in: image
pre-processing, advanced segmentation
techniques, shape and texture
representation/description techniques
-
understand shape/texture recognition, and object
tracking techniques
-
understand the very basics of 3D-vision and
image understanding
-
discuss the applications of image processing,
pattern recognition and image analysis to
various problem-solving tasks
-
keep up with recent advances in image
processing/recognition and identify possible
applications
-
evaluate whether an image processing/recognition
system is a good candidate for a given
information system
-
describe evaluation criteria for image
processing/recognition systems
Course
Methodology:
The
course will consist of lectures, homework
assignments
and final projects. Homework will include Matlab
programming assignments and articles to be read and
then discussed in class. Individual students will
lead discussions on selected articles Final
projects will be extrapolations of topics discussed
within the course (pre-approved
by the instructor),
with simulations illustrating the results. A final
project will include a (journal styled) paper and a
15 minute oral presentation describing the methods
and the results.
Grading
Method:
Main
course activities will be evaluated and grading will
be as follows:
Class
participation: 10% (according to participation in
interactive discussion based on assigned articles)
Homework: 50% (according to results
and written analysis of topic and approach in
programming assignments)
Final project: 40%: (according to
quality of simulation, paper and oral presentation) |
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Image and video compression
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Course
Description
Due to
the widespread application of digital images and
video, enhanced quality high-resolution images
and/or video are becoming desirable in all cases,
and in certain applications they are even essential.
Recording, storage, distribution and usage of such
images and video are made possible by applying
efficient compression techniques.
In this
course students will be introduced to basic theory
of image and video compression and to a variety of
image and video compression techniques and
algorithms, as well as compression standards that
have widespread use in different fields of
application of digital images and video today.
Course
prerequisite:
This
course requires basic knowledge of signal
processing.
Texts:
[1]
V.
Bhaskaran, K. Konstantinides, Image and Video
Compression Standards, Kluwer Academic
Publishers, 1997.
[2]
Y. Wang,
J. Ostermann, Y-Q. Zhang, Video Processing and
Communications, Prentice Hall, 2002.
[3] J.D. Gibson, T.
Berger, T. Lookabaugh, D. Lindbergh, R.L. Baker,
Digital Compression for Multimedia, Morgan
Kaufmann Publishers Inc., San Francisco, CA, 1998.
[4]
J. Watkinson, The
MPEG Handbook, 2nd Edition, Elsevier, 2004.
Course
Outline: (Outline syllabus)
-
Compression fundamentals, Entropy coding
-
Lossless coding:
Application of Huffman coding in image
compression, JPEG-LS standard
-
Measures of distortion and video quality:
Mean squared error
(MSE),
mean absolute error
(MAE),
signal-to-noise ratio (SNR), Subjective visual
quality, mean opinion score (MOS)
-
Lossy image and video compression:
Fundamentals of Rate-Distortion theory, Scalar
and vector quantization, Transform coding,
Predictive coding,
Hybrid coding
-
Content-based video coding: Shape coding,
Texture coding, Object-based coding,
Knowledge-based coding, MPEG-4 standard
-
Post-processing
techniques for compression artifacts
suppression: Compression artifacts detection and
estimation, Compression artifacts suppression
techniques, H.264 AVC standard
-
Scalable video coding: Quality scalability,
Spatial scalability, Temporal scalability,
Object-based scalability
Learning
Outcomes
Having
successfully completed this course, the student will
be able to demonstrate knowledge and understanding
of:
-
basics principles of digital image and video
compression
-
contemporary techniques for lossless image
compression
-
most
frequently used distortion measures and
subjective image quality measures
-
the
general concept of lossy image and video
compression
-
the
fundamentals of rate-distortion theory and
scalar and vector quantization
-
most
frequently used transform coding techniques for
image compression
-
the
basics of spatial and temporal prediction and
prediction techniques used in image and video
coding standards
-
hybrid coding
-
the
genesis of video coding standards
-
contemporary video coding standards
-
negative effects of compression and
state-of-the-art techniques for compression
artifacts suppression
-
basics of context-based video coding
-
the
concept of scalability and basic techniques for
scalable video coding
Also,
upon completion of this course, the student should
be able to:
-
cooperate with team members of the same or
different professional profiles connected to
digital image and video storage and
distribution, like network and broadcast systems
experts;
-
understand problems, do research and present the
results of the research in a given timeframe.
Course
methodology:
The
course material will be covered by lectures,
readings, presentations, and projects. Regular
lectures will be held in the first two thirds of the
semester by the instructor. During this period,
while following the lectures, students will be
requested to read selected articles and prepare
presentations and discussions of the papers read. At
the beginning of the last third of the semester the
students will choose their project topics. During
the last third they will work on their projects and
will be requested to give short presentations about
project progress. The complete presentation and
project report are due to the semester’s end. The
students will be expected to actively participate in
all activities during the semester.
Grading
Method:
The
grading will be based on the student's performance
in class participation, paper presentation, and
research project. Specifically:
Class
participation: 20%
Paper
presentation: 30%
Project: 50% |
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DSP
microcontrollers
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Course
Aims
Low-end
and midrange microcontrollers are nowadays
ubiquitous in electronic equipment. They provide
versatility and “intelligence” to the equipment
performing simple control tasks easing the user’s
life. They can also perform the necessary signal
processing calculations and/or process control
calculations but are limited to relatively slow
processes. When high dynamic processes are
controlled, such as vector motor control, or high
throughput calculations are needed, such as with
sensorless or spectral data based control, DSP
architectures appear more suitable. DSP
microcontrollers are aimed to suit both functions.
Since their price is comparable with “classical”
midrange microcontrollers, they are expected to
penetrate the midrange microcontroller application
field.
The
objectives of the proposed course are to provide the
students with knowledge and hands-on experience in
translating DSP and control concepts into real-life
software for embedded systems using DSP boards. The
proposed course emphasizes designing and
implementing software for embedded systems through
the programming of a special type of
microcontroller, the DSP microcontroller. Laboratory
exercises will be based on writing C and assembly
language software for selected DSP microcontroller
boards that are used in current consumer products,
and interfacing them to external devices for test
and measurement.
Course
prerequisite:
This
course requires basic knowledge of C programming
Texts:
[1] Magazine articles, DSP
microcontroller manufacturer’s manuals
[2] (TI instead of Freescale) Sen
M_Kuo,Woon-Seng S_Gan: "Digital Signal Processors -
Architectures, Implementations, and Applications",
Prentice Hall, 2004, ISBN: 0-13-035214-4
[3] Sen M.
Kuo, Dennis R. Morgan,
“Active Noise Control Systems: Algorithms and DSP
Implementations”, Willey, ISBN: 978-0-471-13424-4
[4]
Other Control Topics…
Course
Outline: (Outline syllabus)
Introduction to DSP microcontrollers, general
characteristics, examples of DSP microcontroller
typical applications and families.
Detailed
architecture of a selected DSP microcontroller,
instruction set, development environment.
Overview
of peripherals.
New
advances in configurable microcontrollers.
Topics
in control, optimal digital control, high
performance
digital control, active noise control (…).
Application examples, libraries; control (…) and DSP
capabilities.
Learning
Outcomes
Having
completed this unit, students will be able to
demonstrate:
·
Knowledge of a DSP microcontroller architecture and
it’s influence on control and digital signal
processing functions
·
Experience using equipment and tools commonly used
in industry and/or experimental laboratory
·
Understanding of the process of converting ideas and
algorithms into real-life working hardware with
real-life signals
Also,
upon
completion of this course, students should be able
to:
·
work in
team and individually
·
prepare
for, perform and report experimental work
·
self-organize to acquire knowledge, implement it and
track the progress through a real-life project(s)
Course
methodology:
The
course will be given by lectures, readings,
discussions, laboratory experiments and
mini-project(s). After the general introductory
lectures given by the instructor, students will be
studying chapters from the text-book(s), datasheet
and manuals and preparing written work plans for
performing the assigned experiments. At the same
time they will be exposed to certain examples and
problems where control and sensor signal processing
is needed to choose from for their project
assignment. The work plans will consist of homework
assignment as theoretical preparation and practical
work preparation, and will be discussed and graded
in class prior lab. experiment. After the lab. each
student will prepare a report. After achieving the
basic level of mastering the required tools and
techniques, students will start working on their
projects and will be giving regular reports on their
progress that will be discussed and graded in class.
The course will end with a final report,
presentation and demonstration of their work.
Grading
Method:
The
grading will be based on student’s performance in
written work plans and discussions, laboratory work,
reports preparation and discussion and project
report presentation and demonstration. Specifically:
Work
plans: 20%
Reports:
20%
Laboratory: 20%
Project:
40% |
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Digital audio and speech analysis and processing
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Course
Description
Progress in digital audio and speech processing has
been phenomenally rapid. It is employed in recording
and storing music and speech signals, for sound
mixing and production of digital programs, in
digital audio broadcasting and digital television,
in cellular telephony as well as in consumer
products like CDs, DVDs and PCs.
This
course provides the basis of an advanced course in
digital audio signal processing and is directed at
students studying engineering, computer science,
physics but also for professionals looking solutions
to problems in audio signal processing in studio
engineering, consumer electronics and multimedia.
Course
prerequisite:
Prior
basic knowledge of acoustics, systems theory and
digital signal processing are taken as a
prerequisite.
Texts:
Basic:
[1] Udo Zolzer,
Digital Audio Signal Processing,
John Wiley & Sons Ltd.
, 1997.
[2] R. Rabiner and R.W. Schafer,
Digital Processing Of Speech Signals, Prentice
Hall, 1978
[3] Sadaoki Furui,
digital speech processing, synthesis
and recognition,
Marcel Dekker Inc.,
2001
Additional:
[1] Mark Kahrs, Karlheinz
Brandenburg ed., Applications Of Digital Signal
Processing To Audio And Acoustics, Kluwer
Academic Publishers, 2002
[2]
Udo Zolzer, DAFX - Digital
Audio Effects, John Wiley & Sons Ltd., 2002
[3] Saeed V. Vaseghi, Advanced
Digital Signal Processing And Noise Reduction,
Second Edition. (chs.1, 2, 5, 8.5, 11.4, 12.6, 14),
John Wiley & Sons Ltd , 2000
[4]
Chou, Juang, Pattern
Recognition In Speech And Language Processing,
(chs.1, 4, 5, 7, 8), CRC Press, 2003
Course
Outline: (Outline syllabus)
Introduction to digital audio transmission system
and storage, sampling rate audio conversion,
sigma/delta modulation, dither, noise shaping. Data
compression fundamentals. Principal characteristics
of audio and speech. Compression techniques.
Psychoacoustic models. Speech analysis and coding
techniques.
Audio
and speech synthesis: Waveform synthesis, TTS.
Principles of speech (speaker) recognition. Digital
audio effects and synthetic music.
Learning Outcomes
Having
successfully completed this unit, you will be able
to demonstrate knowledge and understanding of:
·
the basics of digital audio signal processing
hardware and algorithms for processing digital audio
and speech signals.
·
the various coding algorithms applied in digital
audio recording and storage.
·
the technical principles of the various techniques
employed in analysis and synthesis of audio and
speech, digital audio effects and synthetic music.
·
the
general concepts and applications of
speech-to-speech synthesis.
·
the technical principles of
speech
and speaker recognition
techniques.
Also,
upon
completion of this course, student should be able
to:
·
work in
team
·
explore
technical papers, write reports and present and a
selected project within a specified time.
·
think
critically, ask questions, and apply problem-solving
techniques.
Course
methodology:
The
course concepts will be given by lectures, readings,
seminar-like presentations and individual (or team)
projects. Regular lectures will be given in the
first half of the semester by the instructor. During
this time, students will be reading articles and
selecting project topics. In the second half,
students will be required to present papers in class
and to give progress reports on their own projects.
There will be several research papers that each
student is expected to read, and students will be
expected to participate actively in both the lecture
and paper discussions. At the end of the semester,
each student/team will present their project report.
Grading
Method:
The
grading will be based on the student's performance
in class participation, specific paper presentation,
and the research project report. Specifically:
Class
participation: 10%
Paper
presentation: 30%
Project: 60% |
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Application of neural networks in signal processing
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Multimedia technologies
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Course
Description
Multimedia has become a widespread and indispensable
part of modern computer technology.
The goal
of this course is to introduce students to the
principles and technologies of multimedia systems,
as well as to provide them with hands-on experience
in this area. Issues on effective ways to represent,
process, communicate and retrieve multimedia data,
such as computer graphics, digital audio, image and
video, will be addressed.
Course
prerequisite:
This
course requires basic knowledge of signal
processing.
Texts:
[1]
Ze-Nian
Li, Mark S. Drew, FUNDAMENTALS OF MULTIMEDIA,
Prentice Hall, 2003.
[2]
Y.
Wang, J. Ostermann, Y-Q.
Zhang,
VIDEO PROCESSING AND COMMUNICATIONS, Prentice Hall,
2002.
[3]
Ralf Steinmetz,
Klara Nahrstedt,
MULTIMEDIA SYSTEMS, Springer-Verlag, 2004.
Course
Outline: (Outline syllabus)
-
Introduction to multimedia: What is multimedia,
Historical overview
-
Fundamentals of computer graphics
-
Fundamentals of digital audio processing: Audio
compression
-
Fundamentals of digital image and video
processing:
Image analysis and segmentation, Image and video
coding
-
Multimedia communications: Computer and
multimedia networks, Error control, Multimedia
streaming, Quality of service, Media-on-Demand
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Content-based retrieval in digital libraries:
Retrieving multimedia data (sound, music,
graphics, image and video), Principles of image
and video databases, MPEG-7 standard
-
Advanced multimedia techniques
-
Sound, speech and gesture recognition
Learning
Outcomes
Having
successfully completed this course, the student will
be able to demonstrate knowledge and understanding
of:
-
the
basics of computer graphics, digital audio
processing and digital image and video
processing;
-
technical principles of multimedia
communications;
-
information transfer in computer and multimedia
networks;
-
methods and techniques for error control in
multimedia systems;
-
establishing quality of service in multimedia
networks;
-
interactivity in multimedia systems and
media-on-demand;
-
the
general concept of content-based retrieval;
-
techniques and algorithms for multimedia data
retrieval;
-
basic principles of video databases and
supporting standards;
-
state-of-the-art multimedia techniques mainly
for sound, speech and gesture recognition.
Also,
upon
completion of this course, student should be able
to:
-
work
in a team consisting of experts of different
professional profiles like DSP experts, network
and database experts;
-
understand the problems concerning the
interoperability of different systems and the
effects of their interconnection;
·
conduct
research in an interdisciplinary environment and
present the results of the research in a given
timeframe.
Course
methodology:
In this
course the students will attend lectures during
first two thirds of the semester. In the same period
they will be assigned selected articles for reading,
for which they will prepare presentations and
discussions. All students will be required to attend
presentations and participate in discussions. At the
beginning of the last third of the semester the
students will choose their project topics. During
the last third they will work on the project and
will be requested to give short presentations about
project progress. Team work will be supported, and
the project can be assigned to teams of students.
The complete presentation and project report are due
to semester’s end. The students will be expected to
actively participate in all activities during the
semester.
Grading
Method:
The
grading will be based on the student's performance
in class participation, paper presentation, and
research project. Specifically:
Class
participation: 20%
Paper
presentation: 30%
Project:
50% |
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