INFORMATION BOOKLET

Transkrypt

INFORMATION BOOKLET

Faculty of Electrical Engineering, Computer
Science and Telecommunications
University of Zielona Góra
INFORMATION BOOKLET
Subject Area: COMPUTER SCIENCE (INFORMATICS)
Second-cycle Level Studies
(Full-time, Part-time)
Academic Year 2010/2011
European Credit Transfer System ECTS
Part II.B
ECTS COURSE CATALOGUE
COMPUTER SCIENCE (INFORMATICS)
SECOND-CYCLE LEVEL STUDY (M.Sc.Degree)
T ABLE
OF CONTENTS
Numerical methods
3
Security Engineering (Information Security)
5
Operational research
7
Digital processing and data compression
9
Data Warehouses
11
Neural and neuro-fuzzy networks
13
Digital system design
15
Computer-aided design
17
Virtual Reality Systems
19
Hardware/software co-design
21
Digital signal processing
23
Network programming
25
Visualisation systems
27
Evolutionary Computation
29
Expert systems
31
Data mining
33
Mobile applications
35
Pattern recognition
37
i
S P E C I AL I S T S U B J E C T S
ECTS Course Catalogue Computer Science – second-cycle level
N
NU
UM
ME
ER
RIIC
CA
ALL M
ME
ETTH
HO
OD
DS
S
Co ur s e c o de : 11.9-WE-I-MN-PK1_S2S
T yp e of c o ur s e: Compulsory
E ntr y r e q u ir em e nts : La n gu a ge of i ns tr uc t io n: Polish
Dir ec tor of s t ud i es : Prof. dr hab. inŜ. Krzysztof Gałkowski
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er :
Prof. dr hab. inŜ. Krzysztof Gałkowski, mgr
inŜ. Łukasz Hładowski
F o r m o f r e c e i vi n g a c r e d i t
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
I
Laboratory
30
2
Exam
Grade
7
Part-time studies
Lecture
18
2
I
Laboratory
18
2
Exam
Grade
COURSE CONTENTS:
Mathematics basics. Basic notions and theorems used in numerical analysis. Taylor series.
Numbers and Errors. Decimal, binary and hexadecimal numbers, floating point representations. Error
definitions and most commonly seen error types. Ill-conditioning and numerical stability.
Rootfinding. Bisection, Newton and Secant methods. Errors estimation. Extrapolation. Ill-conditioning
and numerical stability of solutions.
Interpolation. Aims and characterization, Lagrange metod. Newton metod. Errors. Splinem. Hermie
interpolation.
Approximation. Sum-of-squares error minimization. Ortogonal polynomials. Min-max error
minimization. Chebyshev polynomials.
Numerical integration. Trapezoidal and Simpson method. Gauss metod. Terror estimation. Richardson
ekstrapolation.
Solving of the linear algebraic equations set. Gauss elimination method; LU factorization and Doolittle
method. Errors estimation and correction. Numerical stability of solutions and conditional number.
Iterative methods: Jacobi and Gauss-Seidel method.
Basics of solving differential equations. Euler and Runge-Kutta methods.
3
Specialist subjects
LEARNING OUTCOMES:
Experience in computer solving of basic computational problems in Engineering with regard limitations
of floating point arithmetic.
ASSESSMENT CRITERIA:
Lecture – obtaining a positive grade in written or oral exam.
Laboratory – the main condition to get a pass are sufficient marks for all exercises and tests conducted
during the semester.
.
RECOMMENDED READING:
[1] Lloyd N. Trefethen and David Bau, III: Numerical Linear Algebra, SIAM, 1997,
[2] H.M. Antia: Numerical Methods for Scientists and Engineers, Birkhauser, 2000,
[3] Richard L. Burden, J. Douglas Faires, Numerical analysis, Brooks /Cole Publishing Company, ITP An
International Thomson Publishing Company, sixth edition, 1997
[4] Kendall Atkinson, Elementary numerical anlysis, John Wiley & Sons, Inc., second edition, 1993
OPTIONAL READING:
[1] –
4
S
SE
EC
CU
UR
RIITTY
Y E
EN
NG
GIIN
NE
EE
ER
RIIN
NG
G ((IIN
NFFO
OR
RM
MA
ATTIIO
ON
N S
SE
EC
CU
UR
RIITTY
Y))
Co ur s e c o de : 11.9-WE-I-IB-PK3_S2S
Dir ec tor of s t ud i es :
Prof dr hab inŜ. Eugeniusz Kuriata, Dr inŜ.
Bartosz Sulikowski
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : Dr inŜ. Bartosz Sulikowski
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
I
Laboratory
30
2
Grade
Grade
5
Part-time studies
Lecture
30
2
I
Laboratory
30
2
Grade
Grade
COURSE CONTENTS:
Information security. Introduction. Definitions. Security infrastructure. Security models.
Legal status. The classified information protection act (in Polish). Secret chambers. Classifications.
System access. System access supervision. User access management. User responsibility.
Systems and telecommunication networks security. Types of attacks. Firewalls. Physical protection
methods.
Security policy. Information security administrator role and tasks.
Cryptography. Symmetric and asymmetric methods. DES, AES standards.
Public key cryptography. RSA algorithm. Application of hash functions in cryptography.
Digital signature. PKI servers.
LEARNING OUTCOMES:
Destructive actions protection of information and applications. Legal status, laws and regulations in the
field of data protection. Computer crimes survey and analysis. Active and passive defense against
threats. Ways to handle with risks and their effects minimization.
Lecture – the main condition to get a pass are sufficient marks in written or oral tests conducted at least
once per semester.
5
Specialist subjects
1.
2.
3.
4.
Denning D. E. R.: Cryptography and Data Security, Addison-Wesley, New York, 1982
Allen J. H.: The CERT Guide to System and Network Security Practices. Boston, MA: AddisonWesley, 2001
Mochnacki W.: Kody korekcyjne i kryptografia, Oficyna Wydawnicza Politechniki Wrocławskiej,
Wrocław, 1997 (in Polish)
Menezes A. J., van Oorschot P. C.: Handbook of Applied Cryptography, CRC Press, 1996
OPTIONAL READING:
[1] –
6
O
OP
PE
ER
RA
ATTIIO
ON
NA
ALL R
RE
ES
SE
EA
AR
RC
CH
H
Co ur s e c o de : 11.9-WE-I-BO-PK4_S2S
Dir ec tor of s t ud i es : Dr inŜ. Maciej Patan
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Dr hab. inŜ. Krzysztof Patan, Dr inŜ. Maciej
Patan
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
I
Laboratory
30
2
Exam
Grade
Part-time studies
Lecture
18
2
I
Laboratory
18
2
6
Exam
Grade
COURSE CONTENTS:
Linear programming tasks (LPT). Standard formulation of LPT. Method of elementary solutions and
simplex algorithm. Optimal choice for production assortment. Mixture problem. Technological process
choice. Rational programming. Transportation and assignment problems. Two-person zero sum games
and games with nature.
Network programming. Network models with determined logical structure. CPM and PERT methods.
Time-cost analysis. CPM_COST and PERT-COST methods.
Non-linear programming tasks (NPT) – optimality conditions. Convex sets and functions. Necessary and
sufficient conditions for the solution existence in the case without constraints. Lagrange multiplayers
method. Extrema of the function with equality and inequality constraints. Kuhn-Tucker conditions.
Constraints regularity. Conditions of an equilibrium point existence. Least squares method. Quadratic
programming.
Computational methods for solving NPT. Directional search methods: Fibonacci, golden search, Kiefer,
Powell and Davidon. Method of basic search: Hooke-Jeeves and Nelder-Mead. Continuous and discrete
gradient algorithm. Newton method. Gauss-Newton and Levenberg-Marquardt algorithms. Elementary
methods of feasible direction: Gauss-Seidel, steepest decent, conjugate gradient of Fletcher-Reeves,
variable metric of Davidon-Fletcher-Powell. Searching for minimum in the case of constraints: internal,
external and mixed penalty functions, projected gradient, sequential quadratic programming and
admissible directions method. Elements of dynamic programming.
Practical issues. Simplification and elimination of constraints. Discontinuity elimination.
Scaling. Numerical approximation of gradient. Usage of numerical packages. Presentation of
methods implemented in popular environments for symbolic and numerical processing.
7
Specialist subjects
LEARNING OUTCOMES:
Skills and competences in: formulating mathematical programming tasks; constructing models for optimization
problems; solving linear and non-linear programming tasks with constraints; application of optimality conditions; timecost analysis of logistic problems; algorithmic approach to determine optimal solutions; creative usage of existing
numerical packages.
once per semester.
Project – the main condition to get a pass are sufficient marks for all exercises and tests conducted
1.
2.
3.
Barkalov, M. Węgrzyn.: Design of Control Units with Programmable Logic, University of Zielona
Góra Press, Zielona Góra, 2006
M. Adamski, A Barkalov: Architectural and sequential synthesis of digital devices, University of
Zielona Góra Press, Zielona Góra, 2006
A. Barkalov, L. Titarenko.: Logic synthesis for compositional microprogram control units, Lectures
Notes Electrical Engineering, V.22, Springer, 2008.
OPTIONAL READING:
[1] –
8
D
DIIG
GIITTA
ALL P
PR
RO
OC
CE
ES
SS
SIIN
NG
G A
AN
ND
D D
DA
ATTA
A C
CO
OM
MP
PR
RE
ES
SS
SIIO
ON
N
Co ur s e c o de : 11.9-WE-I-CPKD-PSW_A6_S2S
Dir ec tor of s t ud i es : Dr inŜ. Andrzej Popławski
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : Dr inŜ. Wojciech Zając
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
II
Laboratory
30
2
Exam
Grade
Part-time studies
Lecture
18
2
II
Laboratory
18
2
7
Exam
Grade
COURSE CONTENTS:
Conversion AC of signal. Image and video acquisition.
Filtration, convolution, Fourier transform.
Discrete cosine transform.
Discrete wavelet transform.
Algorithms of entropy coding.
Lossless and lossy data compression, significance of compression.
Image quality measurements.
Image coding standards.
Video coding standards.
LEARNING OUTCOMES:
Abilities and competence in programming of application to process the digital images and video
sequences.
9
Specialist subjects
1.
Lyons R.G.: Introduction to Digital Signal Processing. Warszawa, WKŁ, 2003 (in Polish)
2.
Zieliński T.P.: Digital Signal Processing. From theory to application, Warszawa, WKŁ, 2007 (in
Polish)
3.
Sayood K.: Introduction to Data Compression, READ ME, 2002 (in Polish)
4.
Domański M.: Advanced compression techniques of pictures and video sequences, Poznań, WPP,
1998 (in Polish)
Ohm J. R.: Multimedia Communication Technology, Springer, 2004
Skarbek W.: Multimedia. Algorithms and compression standards, PLJ, 1998 (in Polish)
5.
6.
OPTIONAL READING:
[1] –
10
D
DA
ATTA
A W
WA
AR
RE
EH
HO
OU
US
SE
ES
S
Co ur s e c o de : 11.3-WE-I-HD-PSW_A6_S2S
T yp e of c o ur s e: Optional
Dir ec tor of s t ud i es : Dr hab. inŜ. Wiesław Miczulski, prof. UZ
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Dr hab. inŜ. Wiesław Miczulski, prof. UZ , Dr
inŜ. Robert Szulim
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
II
Laboratory
30
2
Exam
Grade
7
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Exam
Grade
COURSE CONTENTS:
Introduction. Decision support systems. Operational processing versus analytical processing.
Data warehouses. Definition of Data Warehouse. Features of Data Warehouse. Exemplary applications.
Architectures of Data Warehouses. Layered structure of the Warehouse: data sources, extraction layer,
cleaning, transformation and data loading, data access layer. Tools for designing, building, maintaining
and administering of the Data Warehouse.
Multidimensional data models. Models: MOLAP, ROLAP, HOLAP. Building of exemplary data cube.
Data Mining. Data preparation process. Selected Data Mining methods: classification,
grouping, regression, discovering association and sequences, time series. Knowledge
representation forms: logical rules, decision trees, neural nets, Exemplary Data Mining
applications.
LEARNING OUTCOMES:
Skills and competences in: designing and maintaining of data warehouses, designing and
conducting data analysis based on OLAP technology and selected data mining techniques.
11
Specialist subjects
1.
2.
3.
4.
5.
Hand D., Mannila H., Smyth P.: Principles of Data Mining. Massachusetts Institute of Technology,
2001.
Jarke M., Lenzerini M., Vassiliou Y., Vassiliadis P.: Fundamentals of Data Warehouses. SpringerVerlag, Berlin, 2002.
Larose D.T.: Discovering Knowledge in Data. An Introduction to Data Mining. John Wiley & Sonc,
Inc., 2005.
Larose D.T.: Data Mining Methods and Models. John Wiley & Sonc, Inc., 2006.
Poe V., Klauer P., Brobst S.: Building a Data Warehouse for Decision Support. Prentice-Hall, Inc., a
Simon & Schuster Company, 1999.
OPTIONAL READING:
[1] –
12
N
NE
EU
UR
RA
ALL A
AN
ND
D N
NE
EU
UR
RO
O--FFU
UZZZZY
Y N
NE
ETTW
WO
OR
RK
KS
S
Co ur s e c o de : 11.9-WE-I-SNSR-PSW_A6_S2S
Dir ec tor of s t ud i es : Prof. dr hab. inŜ. Józef Korbicz
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : Prof. dr hab. inŜ. Józef Korbicz
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
II
Laboratory
30
2
Exam
Grade
7
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Exam
Grade
COURSE CONTENTS:
Introduction to neural networks. History and development of neural networks. Structure of biological
neuron. Mathematical model of artificial neuron. Neuron activation functions. Perceptron. Learning
algorithm for perceptron. Adaline and Madaline structures. Supervised and unsupervised learning
methods. Classical XOR problem.
Feedforward neural networks. Fundamentals of multilayer neural networks. Backpropagation algorithm
for neural network learning. Issues and limitations of gradient descent learning algorithms. Adaptive
learning rate. Momentum. Example applications of neural networks. Review of advanced learning
algorithms. Evolutionary algorithms for neural network design and learning. GMDH type networks.
Recurrent neural networks. Dynamic-feedback neural networks. Learning algorithms for feedback neural
networks. Mathematical model of dynamic neuron. Locally recurrent globally feedforward neural
networks. Hopfield networks. Learning algorithms for Hopfield network.
Self-organizing neural networks. Kohonen self-organizing feature maps. Competitive learning. Neural
gas algorithm. Example applications of Kohonen network.
Neuro-fuzzy systems. Fuzzy sets and fuzzy logic. Fuzzy inference. Mamadani type neurofuzzy networks. Takagi-Sugeno neuro-fuzzy networks. Learning algorithms for neuro-fuzzy
networks.
LEARNING OUTCOMES:
Skills and competences in: using and implementing neural networks and neuro-fuzzy
networks, properties of different structures of neural and neuro-fuzzy networks,
understanding mathematical principles of learning algorithms, using and
implementing learning algorithms, knowledge about limitations of learning
13
Specialist subjects
algorithms, applying neural network and neuro-fuzzy networks to model nonlinear
systems or pattern recognition .
[1] Haykin S.: Neural Networks: A Comprehensive Foundation (2nd Edition), Prentice Hall, 1998
[2] Rutkowska D.: Neuro-Fuzzy Architectures and Hybrid Learning, Physica-Verlag, 2002
[3] Bishop M.: Neural Networks for Pattern Recognition, Oxford University Press, 1996
[4] Rutkowski L.: Computational Intelligence, Springer-Verlag, 2008
[5] Nauck D., Kruse R., Klawonn F.: Foundations of Neuro-Fuzzy Systems, John Wiley & Sons, 1997.
OPTIONAL READING:
[1] –
14
D
DIIG
GIITTA
ALL S
SY
YS
STTE
EM
M D
DE
ES
SIIG
GN
N
Co ur s e c o de : 11.9-WE-I-PSSI-PSW_B7_S2S
Dir ec tor of s t ud i es : Prof. dr hab. inŜ. Alexander Barkalov
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Dr inŜ. Grzegorz Łabiak, dr inŜ. Remigiusz
Wiśniewski
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
15
1
Laboratory
30
2
Project
15
1
Grade
II
Grade
Grade
6
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Grade
Grade
COURSE CONTENTS:
Basic principles for control units’organization. Methods for presentation and interpretation of control
algorithms; Methods of control units’ organization for programmable logic devices.
Systems-on-Programmable-Chip:analysis and characteristics. Evolution of programmable logic;
Foundations of System-on-Programmable-Chip; Analysis of control units as the parts of SoPC.
Design of Moore control unit. Design of Moore FSM with trivial state encoding; Design of Moore FSM
with optimal state encoding; Design of Moore FSM with transformation of the code sates; Design of
Moore FSM with multilevel structure.
Design of microprogram control units I. Basic principles for organization and design of microprogram
control units; Design of microprogram control units with natural addressing of microinstructions; Design
of microprogram control units with combined addressing of microinstructions.
Design of microprogram control units II. Design of compositional microprogram control units
with a base structure; Design of compositional microprogram control units with common
memory; Design of compositional microprogram control units with address transformation;
Design of compositional microprogram control units code sharing.
LEARNING OUTCOMES:
Skills in design of control units; synthesis and analysis of control units with different
types; choice of the proper model of control unit based on analysis of the particular
project requirements .
15
Specialist subjects
once per semester.
[6] Haykin S.: Neural Networks: A Comprehensive Foundation (2nd Edition), Prentice Hall, 1998
[7] Rutkowska D.: Neuro-Fuzzy Architectures and Hybrid Learning, Physica-Verlag, 2002
[8] Bishop M.: Neural Networks for Pattern Recognition, Oxford University Press, 1996
[9] Rutkowski L.: Computational Intelligence, Springer-Verlag, 2008
[10]Nauck D., Kruse R., Klawonn F.: Foundations of Neuro-Fuzzy Systems, John Wiley & Sons, 1997.
OPTIONAL READING:
[2] –
16
C
CO
OM
MP
PU
UTTE
ER
R--A
AIID
DE
ED
D D
DE
ES
SIIG
GN
N
Co ur s e c o de : 11.9-WE-I-KWP-PSW_B7_S2S
Dir ec tor of s t ud i es : dr inŜ. Janusz Kaczmarek
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr inŜ. Janusz Kaczmarek
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
15
1
Laboratory
30
2
Project
15
1
Grade
II
Grade
Grade
6
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Grade
Grade
COURSE CONTENTS:
Introduction to the computer-aided design of electronic circuits. Historical outline. Overview of Electronic
Design Automation systems. Basic notions and definitions. Imperial and metric system of units.
Methodology of designing an electronic circuit using EDA system. Basic concepts on capturing a circuit
as a schematic diagram: netlist, wires and buses. Component library structure: part, symbol, package
and padstack. Creating schematic diagrams with hierarchical and multipage techniques. Printed Circuit
Board designing using layout editor. Methods of placing components and routing traces. Designing one,
two and multilayer PCB. Automatic routing of PCB traces with an autorouter tool. Design rule check in
EDA systems.
Printed Circuit Board designing for EMC requirements. Basic knowledge of RF emissions and
susceptibility of electronic circuits. PCB EMC techniques: circuit zoning, suppressing interfaces between
circuit zones, ground system, power routing and decoupling, signal routing and line termination. Signal
integrity and transmission lines on PCB.
Computer simulation of electronic circuits. SPICE simulation fundamentals. Types of simulation analysis:
nonlinear dc, small signal ac, transient, sensitivity and distortion. Models of electronic devices.
Schematic-level simulation of embedded microprocessor systems. Analysis of simulation results.
Computer simulation of thermal and electromagnetic properties of printed circuit boards.
Producing design documentation and CAM files in EDA system.
17
Specialist subjects
LEARNING OUTCOMES:
Know-how and competences in the field of applying Electronic Design Automation software
supporting the process of designing electronic circuits with emphasis on embedded
microprocessor systems.
once per semester
1.
2.
3.
4.
5.
Rymarski Z.: Materials technology and construction of electronic circuits. Designing and production
of electronic circuits, Wydawnictwo Politechniki Śląskiej, Gliwice, 2000 (in Polish)
Williams T.: The Circuit Designer's Companion, Newnes, 2005
Michalski J.: Technology and Assembly of Printed Circuit Boards, WNT, Warszawa, 1992 (in Polish)
Dobrowolski A.: Under the mask of SPICE, BTC, Warszawa, 2004 (in Polish)
Sidor T.: Computer analysis of electronic measurement circuits, Uczelniane Wydawnictwa
Naukowo-Dydaktyczne AGH, Kraków, 2006 (in Polish).
OPTIONAL READING:
[1] –
18
V
VIIR
RTTU
UA
ALL R
RE
EA
ALLIITTY
Y S
SY
YS
STTE
EM
MS
S
Co ur s e c o de : 11.3-WE-I-SWR-PSW_B7_S2S
Dir ec tor of s t ud i es : dr hab inŜ. Sławomir Nikiel
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr hab inŜ. Sławomir Nikiel
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
15
1
Laboratory
30
2
Project
15
1
Grade
II
Grade
Grade
6
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Grade
Grade
COURSE CONTENTS:
Human factors. Human perception, definition of human senses. Content creation process: authoring,
distribution and viewing. Interaction modalities, sense of ‘presence’ in virtual environments.
Introduction to virtual reality-related technologies: Introduction to Virtual Environments (VE), historical
background, classification, technological demands, enabling technologies, VE applications. 3D game
programming environments. Application case studies in education, entertainment, architecture, industry
and healthcare.
Input/Output interfaces.VE hardware and software: visual, audio, multimodal, haptic and olfactory
interfacing. Brain-Computer Interfaces (BCI).
3D computer graphics. Geometric modeling, transformations in 3D space, navigation and scene
viewing. Virtual reality as a real-time computer graphics. World construction, scene graphs, building
elements for interactive environments. Object representation and transformations/deformations, terrain
models. Shading and shadows. Texture models and materials.
Animation and interactions in VR. Animation of position, orientation and scaling. Key-frame animations,
physical-based simulations, morphing and warping. Concepts of sensors and triggers. Collision
detection. Interaction with user.
Web-based VR. Introduction to Virtual Reality Modeling Language (VRML) and eXtensible 3D (X3D).
Modeling distributed VR environments (background, objects, actions)
VR modeling tools. Efficiency of geometrical modeling. 3D sound. Level of detail, normal
mapping and progressive meshes. Scripting and PROTO-typing. XNA and shaders.
19
Specialist subjects
LEARNING OUTCOMES:
Analysis and design of real-time computer graphics systems, design of virtual reality systems
based on X3D and XNA technologies; Preparation of media components for virtual reality
applications and 3D games.
once per semester
[1] Vince J.: Virtual Reality Systems, Addison Wesley, Cambridge, 1995
[2] Ames A. et al: VRML Sourcebook, Wiley, 1997
[3] Arnaud R., Barnes M.C.: Collada, sailing the gulf of 3D digital content creation, A.K. Peters, 2006
[4] Sarris N., Strintzis M.G.: 3D Modeling and Animation Synthesis and Analysis Techniques for the
Human Body, IRM Press, 2005
[5] Vince J.: Interacting with virtual environments, Wiley, 1994
[6] Materials of PEACHbit consortium.
OPTIONAL READING:
[1] –
20
H
HA
AR
RD
DW
WA
AR
RE
E//S
SO
OFFTTW
WA
AR
RE
E C
CO
O--D
DE
ES
SIIG
GN
N
Co ur s e c o de : 06.0-WE-I-ZPSS-PSW_C8_S2S
Dir ec tor of s t ud i es : dr inŜ. Zbigniew Skowroński
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr inŜ. Zbigniew Skowroński
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
II
Laboratory
30
2
Exam
Grade
7
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Exam
Grade
COURSE CONTENTS:
Introduction to Co-design. Co-design definition. Motivation for co-design. Categories of systems and the
co-design problem. Embedded systems.
Co-design methodology. System specification (models and languages). Allocation and partitioning.
Scheduling. Communication synthesis. Analysis and validation flow.
Models and architectures of system design. Models (Finite State Machines, Dataflow Graph, Finite State
Machines with Datapath, Hierarchical Concurrent Finite State Machines, Programming languages,
Program State Machines, Petri Nets). Architectures (Controller architecture, Datapath architecture,
FSMD architecture and others).
Hardware/software co-synthesis algorithms. Architectural models. Hardware/software partitioning
(architectural models, performance estimation, partitioning systems: Vulcan and Cosyma). Distributed
system co-synthesis (performance analysis, heuristic algorithms, system partitioning, reactive system cosynthesis, communication modeling and co-synthesis).
Prototyping and emulation. Prototyping and emulation techniques. Prototyping and emulation
environments. Target architecture (architecture specialization techniques, architecture for controldominated systems and architecture for data-dominated systems).
Architecture for embedded systems. Single processor – coprocessor architecture, multiprocessor
architecture. System on chip: IP and PIP core based architecture, bus standard.
Design specification and verification. Co-design computational models. Classification of high-level
languages. Verification (design verification and implementation verification).
Languages for system-level specification and design. System-level specification (homogeneous and
heterogeneous specification). Design representation for system-level synthesis. System-level
specification languages. Heterogeneous specification and multi-language co-simulation.
Hardware/software co-design systems: Cosyma, LYCOS, Cosmos, Ptolemy, POLIS (Codesign Finite State Machines) and Chinook.
21
Specialist subjects
LEARNING OUTCOMES:
The student shall be able to:
•
Design simple hardware/software systems using microprocessors, digital functional blocks and
programmable logic structures
•
Design embedded systems using modern system level description languages and CAD
systems.
1.
2.
3.
4.
5.
6.
nd
Rushton: VHDL for Logic Synthesis, 2 Edition, John Wiley & Sons Ltd., 1998
F. A. Scarpino: VHDL and AHDL Digital System Implementation, Prentice-Hall, 1998
K. Skahill: VHDL for Programmable Logic, Addison-Wesley Publishing, 1996
nd
M. Zwolinski: Digital System Design with VHDL, 2 Edition, Prentice-Hall, 2003
P. J. Ashenden: The Designer’s Guide to VHDL, Morgan Kaufmann Publishers, 1996
F. Balarin et al.: Hardware-Software Co-Design of Embedded Systems, Kluwer Academic
Publishers, 1997
7. J. Staunstrup et al.: Hardware/Software Co-Design: Principles and Practice, Kluwer Academic
Publishers, 1997
8. Jarraya et al.: System-Level Synthesis, Kluwer Academic Publishers, 1999
9. J. Van den Hurk et al.: Hardware/Software Co-Design: An Industrial Approach, Kluwer Academic
Publishers, 1998
10. P. Eles et al.: System Synthesis with VHDL, Kluwer Academic Publishers, 1998
11. M. D. Ciletti: Modeling, Synthesis, and Rapid Prototyping with the Verilog HDL, Prentice-Hall, 1999
12. Proceedings of the IEEE, Special issue on Hardware/Software Codesign, vol. 85, No. 3, March
1997.
OPTIONAL READING:
[1] –
22
D
DIIG
GIITTA
ALL S
SIIG
GN
NA
ALL P
PR
RO
OC
CE
ES
SS
SIIN
NG
G
Co ur s e c o de : 11.9-WE-I-CPS-PSW_C8_S2S
Dir ec tor of s t ud i es :
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
dr hab inŜ. Ryszard Rybski, dr inŜ. Mirosław
Kozioł
dr inŜ. Mirosław Kozioł, mgr inŜ. Segiusz
Sienkowski
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
II
Laboratory
30
2
Exam
Grade
7
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Exam
Grade
COURSE CONTENTS:
Introduction. Applications of digital signal processing (DSP). DSP advantages and disadvantages.
Fundamentals of signal theory. Notion of signal. Classifications of signals: analog, discrete and digital
signals, deterministic and random signals. Mathematical models of selected signals.
Fourier series and Fourier transform for continuous time signals. Fourier series representation of
continuous-time signals with examples. Synthesis of continuous-time signals on the basis of the Fourier
coefficients. Gibbs phenomenon. Dirichlet conditions of the Fourier series representation of signals.
Fourier series properties. The Fourier transform. Dirichlet conditions for Fourier transform. Fourier
transform properties. An influence of a signal observation in finite time interval on its spectrum.
Analog-to-digital and digital-to-analog conversion. Path of analog-to-digital and digital-to-analog
processing. Sampling, quantization and coding. Quantization error. Signal-to-noise ratio. Sampling
theorem. Spectrum of a sampled signal. Aliasing. Anti-aliasing filter. Recovery of an analog signal.
Discrete Fourier transform (DFT). Derivation of Fourier transform for discrete-time signals. DFT
properties. Derivation of amplitude and phase spectrum. Leakage. Parametric and non-parametric
spectral windows. Spectrum resolution improvement by zero padding. Examples of spectral analysis of
discrete-time signals and their interpretation.
Fast Fourier transform (FFT). Basic butterfly computation in radix-2 FFT algorithm. Computational profit.
Different aspects of practical implementation of radix-2 FFT. Computation of inverse DFT using FFT.
Real-valued FFT.
Linear causal time-invariant (LTI) systems. Definitions of discrete, linear and time-invariant system.
Convolution. Stability of LTI systems in BIBO sense. Causal systems. Difference equation.
Z-transform. The z-transform definition. Region of convergence for z-transform. The inverse z-transform
and methods of its evaluation. Z-transform properties. The transfer function. Poles and zeros of transfer
function. Pole locus and stability of system.
23
Specialist subjects
Digital filters. Finite and infinite impulse response systems. Processing discrete-time signals by digital
filters. Basic structures of filters. Determination and interpretation of the frequency response of digital
filters. An influence of zeroes and poles locus on the system frequency response. Filters with linear
phase response. Group delay.
IIR digital filter design. Bilinear transformation method.
FIR digital filter design. Method based on windowed Fourier series.
Digital filters and finite wordlength effects. Filter coefficients quantization. Quantization of input signal
samples. Round-off and truncation errors. Limit cycles.
Convolution and deconvolution. Linear and circular convolution. Convolution in frequency
domain. Block convolution. Deconvolution in frequency and z-transform domain. Inverse
systems.
LEARNING OUTCOMES:
Skills and competences: theoretical background of analog-to-digital and digital-to-analog
processing, transform analysis of linear time-invariant systems, spectrum analysis of
discrete-time signals, mathematical description of discrete-time systems, implementation of
digital filtering, digital filter design.
[1] Izydorczyk J., Konopacki J.: Analog and digital filters, Wydawnictwo Pracowni Komputerowej Jacka
Skalmierskiego, Gliwice, 2003 (in Polish)
[2] Lyons R.G.: Understanding Digital Signal Processing, Prentice Hall, 2004
[3] Mitra S.: Digital Signal Processing: A Computer-Based Approach, McGraw-Hill, 2005
[4] Oppenheim A.V., Schafer R.W., Buck J.R.: Discrete-Time Signal Processing, Prentice Hall, 1999
[5] Oppenheim A.V., Willsky A.S., Nawab H.: Signals & Systems, Prentice Hall, 1997
[6] Smith S.W.: Digital Signal Processing: A Practical Guide for Engineers and Scientists, Newnes, 2002
[7] Szabatin, J.: Fundamentals of signals theory, WKŁ, Warsaw, 2003 (in Polish)
[8] Zieliński T.P.: Digital signal processing: From theory to applications, WKŁ, Warszawa, 2005 (in
Polish).
OPTIONAL READING:
[1] –
24
N
NE
ETTW
WO
OR
RK
K P
PR
RO
OG
GR
RA
AM
MM
MIIN
NG
G
Co ur s e c o de : 11.3-WE-I-PS-PSW_D9_S2S
Dir ec tor of s t ud i es : dr inŜ. Tomasz Gratkowski
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr inŜ. Tomasz Gratkowski
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
15
1
Laboratory
30
2
Project
15
1
Grade
II
Grade
Grade
6
Part-time studies
Lecture
9
1
Laboratory
18
2
Project
9
1
Grade
III
Grade
Grade
COURSE CONTENTS:
High level mechanism of access to the global network – Internet. WWW applications. Java applets. Java
Servlets. Working with Uniform Resource Locator. Creating Content and Protocol Handlers in Java.
Model client-server. Stream Sockets - TCP and Datagram Socket (connectionless sockets) - UDP. IP
multicast addressing. Programming services for Internet. Network Time Protocol. Interactive using
remote machines.
Remote processing in Java - Remote Method Invocation (RMI). Network requirements for
communications in distributed systems. Models of network file systems. Review of network
file systems. Review name services. Distributed coordination function. Distributed
transactions. Creating distributed applications with the CORBA technology. Using dedicated
Java packages for construction of distributed systems.
LEARNING OUTCOMES:
The goal of the lectures is to present the programming network application in Java language.
once per semester
25
Specialist subjects
[1] Horstmann C. S., Cornell G.: Core Java 2, Volume 1: Fundamentals (8th Edition), Prentice Hall PTR,
2007.
[2] Horstmann C. S., Cornell G.: Core Java, Vol. 2: Advanced Features, (8th Edition), Prentice Hall
PTR, 2008.
[3] The Java Tutorial SUN.
[4] Stevens W.R.: Unix Network Programming, Volume 1: The Sockets Networking API, AddisonWesley Professional, 2003.
[5] Stevens W.R: TCP/IP Illustrated, Volume 1: The Protocols, Addison-Wesley Professional, 1994.
[6] Coulouris G., Dollimore J., Kindberg T.: Distributed Systems: Concepts and Design, Addison
Wesley, 2005.
[7] Tanenbaum A.S., Van Steen M.: Distributed Systems: Principles and Paradigms, Prentice Hall,
2006.
[8] Rosenberger J. L.: Teach Yourself CORBA in 14 Days, Sams Publishing 1998.
OPTIONAL READING:
[1] –
26
V
VIIS
SU
UA
ALLIIS
SA
ATTIIO
ON
N S
SY
YS
STTE
EM
MS
S
Co ur s e c o de : 60.0/11.9-WE-I-SW-PSW_D9_S2S
Dir ec tor of s t ud i es : dr inŜ. Adam Markowski
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr inŜ. Adam Markowski
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
15
1
Laboratory
30
2
Project
15
1
Grade
II
Grade
Grade
6
Part-time studies
Lecture
9
1
Laboratory
18
2
Project
9
1
Grade
III
Grade
Grade
COURSE CONTENTS:
Introduction. Monitoring and visualisation of industrial processes. Division and functions of visualisation
systems - MMI, HMI, SCADA, EMS. Requirements put forward for visualisation systems. Visualisation
systems in the information structure of an enterprise.
Elements of visualisation systems. Intelligent measuring-controlling devices in visualisation systems.
Architecture of a communication layer of visualisation systems. Communication protocols in visualisation
systems.
Information technologies in visualisation systems. Utility programs and dedicated solutions. Database,
synoptic screen, report and alarm editors. Archiving. Software platforms for visualisation systems.
Visualisation systems operating in computer networks.
Applications of visualisation systems. Configuring visualisation systems. Transparency of visualisation
systems. Object technologies in visualisation systems. Integration of visualisation systems with expert
systems. Using internet technologies in visualisation systems. Examples of utility programs for creating
visualisation systems: GENIE, PRO-2000, FIX Dynamics, FactorySuite, Modicon FactoryLink, Wizcon.
Example applications of visualisation systems.
LEARNING OUTCOMES:
Skills and competences in: creating simple applications visualising industrial process,
creating synoptic images, alerting to variables, tracing varying values in real time, handling
archived variables, reporting variables, using advanced tools to create recipes and statistic
process control.
27
Specialist subjects
once per semester
[1] Winiecki W., Nowak J., Stanik S.: Graphic integrated software environments for designing measuring
– controlling systems, Mikom, Warszawa, 2001 (in Polish)
[2] Jakuszewski R: Programming SCADA systems, The Jacek Skalmierski Computer Workshop,
Gliwice, 2006 (in Polish)
[3] InTouch 7.0 User manual, Astor, Kraków, 1999 (in Polish)
[4] InTouch 7.0 Description of system fields and variables. Astor, Kraków, 1999 (in Polish)
[5] InTouch 7.0 Recipe Manager, Astor, Kraków, 1997 (in Polish)
[6] InTouch7.0 SQL Access Module, Astor, Kraków, 1997 (in Polish)
[7] InTouch 7.0 SPC PRO Module, Astor, Kraków, 1997 (in Polish).
OPTIONAL READING:
[1] –
28
E
EV
VO
OLLU
UTTIIO
ON
NA
AR
RY
Y C
CO
OM
MP
PU
UTTA
ATTIIO
ON
N
Co ur s e c o de : 11.3/11.9-WE-I-OE-PSW_D9_S2S
Dir ec tor of s t ud i es : dr hab inŜ. Andrzej Obuchowicz, prof.UZ
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr hab inŜ. Andrzej Obuchowicz, prof.UZ
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
15
1
Laboratory
30
2
Project
15
1
Grade
II
Grade
Grade
6
Part-time studies
Lecture
9
1
Laboratory
18
2
Project
9
1
Grade
III
Grade
Grade
COURSE CONTENTS:
Introduction. Repetition of nonlinear and convex optimization methods. Constrain optimization.
Classification of the optimization methods.
Non-deterministic methods. Methods of the Monte Carlo class, adaptive random search, simulated
annealing and their modifications.
Foundations of evolutionary algorithms (EAs). Basic concepts, the general EA scheme. Classes of EAs
and standard EAs.
Genotypic evolution. Simple Genetic Algorithm (SGA), problems with coding, Holland’s theory of
schemes, premature convergence of the SGA and techniques of avoiding it, theoretical aspects of a
stochastic genetic process. Genetic Programming (GP), tree as an individual representation, samples of
the GP applications.
Phenotypic evolution. Evolutionary Programming (EP) and Evolution Strategies (ES) algorithms,
theoretical aspects. Evolutionary Search with Soft election (ESSS), remarks from simulation
experiments.
n
Evolutionary operators. Breakdown of the selection mechanisms, mutations in R .
Adaptation in non-stationary environments. Characteristics of non-stationary environments, quality
measures of optimization and adaptation in non-stationary environments. Evolution in dynamic
landscapes.
Multi-objective optimization. Problem formulation. Pareto-optimality. Evolutoinary algorithms
dedicated to multi-objective optimalization.
29
Specialist subjects
LEARNING OUTCOMES:
Basic knowledge of formulation of optimization and adaptation problems for stationary, nonstationary and multi-objective conditions; engineering skills in designing and implementing
stochastic algorithms for global optimization and adaptation, especially evolutionary
algorithms.
once per semester
Project – the main condition is to complete a project containing a design and implementation of an
evolutionary algorithm to a commissioned optimization-adaptation problem.
[1] Arabas J.: Lectures on Evolutionary Algorithms, WNT, Warsaw, 2000 (in Polish).
[2] Michalewicz Z.: Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin
Heidelberg, 1996
[3] Beck T., Fogel D. B., Michalewicz Z.: Handbook of Evolutionary Computation, Institute of Physics
and Oxford University Press, New York, 1997 and later.
[4] Horst R., Tuy H.: Global Optimization: Deterministic Approaches, Springer, Berlin, 1996.
[5] Obuchowicz A.: Evolutionary Algorithms for Global Optimization and Dynamic System Diagnosis,
Lubuskie Scientific Society, Zielona Góra, 2003.
OPTIONAL READING:
[1] –
30
E
EX
XP
PE
ER
RTT S
SY
YS
STTE
EM
MS
S
Co ur s e c o de : 11.9-WE-I-SE-PSW_E10_S2S
Dir ec tor of s t ud i es : Prof. dr hab inŜ. Jan Jagielski
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : Prof. dr hab inŜ. Jan Jagielski
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
III
Project
30
2
Grade
Grade
6
Part-time studies
Lecture
18
2
III
Project
18
2
Grade
Grade
COURSE CONTENTS:
Ideas of the modelling of intellectual acts of the man. Intelligent systems and their differentiation.
Artificial intelligence streams. Interpretation of notions information, knowledge.
Expert systems. Structure of expert system. Categories of expert systems.
Proprieties of expert systems.
Expert systems design. Methods of the expert system design. Tools of expert system design.
Knowledge acquisition. Knowledge acquisition from experts. Knowledge acquisition from databases.
Knowledge base of expert system. Rule representation of the knowledge.
Knowledge base design. Knowledge base verification.
Exact knowledge transformation in expert systems. Forward reasoning. Backward reasoning. Cases
based reasoning.
Machine learning. Notions and definitions. Strategies of machine learning
The interface of the communication the user-the system Graphic user interface.
Dialogue design. Explanations system
Approximate representation of the knowledge. Forms of knowledge uncertainty. Fuzzy sets elements.
Approximate knowledge processing. The fuzzyfication and defuzzyfication.
Fuzzy reasoning.
Other forms of artificial intelligence General characterization of artificial neural networks General
characterization of genetic algorithm.
The evolution of systems of artificial intelligence. Hybrid structures.
Development tendencies.
31
Specialist subjects
LEARNING OUTCOMES:
Skills and competences within the range: knowledge acquisition for knowledge bases, expert
systems design and services.
once per semester
[1] Cichosz P.: Learning systems. WNT, Warsaw, 2000 (in Polish)
[2] Hand D., Mannila H., Smyth P.: Principles of Data Mining. WNT, Warsaw 2005 (in Polish)
[3] Jagielski J.: Knowledge engineering. Zielona Góra University of Technology Press, Zielona Góra, ,
2005 (In Polish)
[4] Larase D.: Discovering Knowledge in Data. An Introduction to Data Mining. PWN, Warsaw, 20006
(in Polish)
[5] Mulawka J.: Expert Systems. WNT, Warsaw, 1996 (in Polish)
[6] Osowski S.: Neural networks to information processing. Warsaw University of Technology Press,
Warsaw, 2006 (in Polish)
[7] Rutkowski L., Rutkowska D., Piliński M.: Neural networks, genetic algorithms and fuzzy systems.
WNT, Warsaw, 1997 (in Polish)
[8] Rutkowski L.: Methods and techniques of artificial intelligence. PWN, Warsaw, 2005 (in Polish)
[9] Zieliński Z.: Intelligent systems in management. PWN, Warsaw, 2000 (in Polish)
OPTIONAL READING:
[1] –
32
D
DA
ATTA
A M
MIIN
NIIN
NG
G
Co ur s e c o de : 11.9-WE-I-OWD-PSW_E10_S2S
Dir ec tor of s t ud i es : Prof. dr hab inŜ. Dariusz Uciński
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr inŜ. Marek Kowal
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
III
Laboratory
30
2
Grade
Grade
6
Part-time studies
Lecture
18
2
III
Laboratory
18
2
Grade
Grade
COURSE CONTENTS:
Data mining concepts. Data warehouses. Preparing the data: transformation of raw data, missing data,
time-dependent data, outlier analysis. Data reduction: features reduction, entropy measure for ranking
features, principal component analysis, values reduction, feature discretization, cases reduction.
Learning from data: learning machine, model estimation. Statistical methods: statistical inference,
Bayesian inference, predictive regression, analysis of variance, linear discriminant analysis. Cluster
analysis: similarity measures, agglomerative hierarchical clustering, partitional clustering, incremental
clustering. Decision trees and decision rules: C4.5 Algorithm, unknown attribute value, pruning decision
tree, associative-classification method. Association rules: Market-Basket Analysis, algorithm Apriori,
frequent pattern-growth method, multidimensional association-rules mining, Web mining, HITS and
LOGSOM algorithms, text mining. Artificial neural networks. Genetic Algorithms. Fuzzy sets and fuzzy
logic. Visualization methods. Commercially and publicly available data-mining tools. SAS Enterprise
Miner, SAS Webhound, SAS Text Miner and the concept of the SEMMA.
LEARNING OUTCOMES:
Basic knowledge and competence in using models and techniques of discovering information
hidden in data, as well as practical data mining in large data sets.
once per semester
33
Specialist subjects
[1] Larose D. T.: Discovering knowledge in data: An introduction to data mining, Wiley, New York, 2004
[2] Kantardzic M.: Data mining. Concepts, models, methods, and algorithms, IEEE Press, New York,
2003
[3] Cerrito P.: Introduction to data mining using SAS Enterprise Miner, SAS Press, Cary, 2006
OPTIONAL READING:
[1] –
34
M
MO
OB
BIILLE
E A
AP
PP
PLLIIC
CA
ATTIIO
ON
NS
S
Co ur s e c o de : 11.9-WE-I-AM-PSW_F11_S2S
Dir ec tor of s t ud i es : dr inŜ. Jacek Tkacz
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr inŜ. Jacek Tkacz
for a course
Number of
ECTS
credits
allocated
Full-time studies
Laboratory
30
2
III
Grade
3
Part-time studies
Laboratory
18
2
III
Grade
COURSE CONTENTS:
Introduction to mobile application design: configuration of MS VISUAL STUDIO; using of emulator of
mobile devices
User interface. Design and implementation of GUI for mobile systems.
Data access. Databases for mobile applications; communications using: bluetooth, IrDA, XML; Wireless
network; GPS; NMEA-0183 standard.
LEARNING OUTCOMES:
Skills and competence in: design and implementation of mobile applications using .NET.
[1] Shekhar S., Chwala S.: Spatial database A TOUR, Prentice Hall, 1983
[2] Clark M.: Wireless Access Networks, Wiley, 2002
[3] http://www.pckuritr.pl/archiwum/art0.asp?ID=206
[4] http://www.blutooth.com
35
Specialist subjects
[5] Baddeley G.: NMEA sentence information
[6] http://home.mira.net/~gnb/gps/nmea.html
[7] Nakamura K.: The Global Positioning System FAQ
[8] http://www.gpsy.com/gpsinfo/gps-faq.txt
OPTIONAL READING:
[1] –
36
P
PA
ATTTTE
ER
RN
N R
RE
EC
CO
OG
GN
NIITTIIO
ON
N
Co ur s e c o de : 11.9-WE-I-RO-PSW_F11_S2S
Dir ec tor of s t ud i es : dr inŜ. Andrzej Marciniak
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr inŜ. Andrzej Marciniak
for a course
Number of
ECTS
credits
allocated
Full-time studies
Laboratory
30
2
III
Grade
3
Part-time studies
Laboratory
18
2
III
Grade
COURSE CONTENTS:
Introduction. Real world examples of pattern recognition appplications. Formulation of basic problem,
notions and terms. Fundamentals of decision theory.
Reception and structure of feature space. Feature extraction and mapping for representation.
Dimensionality reduction, data reduction. Feature selection and searching strategies: sequential and
branch & bound approaches.
Estimation of missclassification. Generalization and substitutions. Assessment and validation methods:
bootstrapping, crossvalidation, VC criterion.
Linear discriminant analysis. Fisher’s discriminant, separability in feature space, regions and decision
boundaries, scatter plot.
Bayes decision theory and Bayes classifier. Decision rules, the Bayes decision rule for minimum error,
the Bayes decision rule for minimum cost, error probability in hypothesis testing, upper bounds on the
Bayes error. Bayes linear classifier, naïve approach, design of: linear classifier, quadratic classifier,
piecewise classifier.
Nonparametric classifiers. Nearest neighbor decision rules: editing, condensing and efficient nearest
neighbor search. KNN Density Estimation, Parzen Density Estimation.
Cluster analysis. Decision-directed learning, graph-theoretic methods, agglomerative and divisive
methods, ISODATA.
Syntactic and structural classification. String methods, Freeman chain coding, Shaw description
language.
Applications. Biometrics: face detection and recognition. Image processing: segmentation and
compression. Medical diagnosis: classification of breast cancer.
LEARNING OUTCOMES:
This course is an introduction to statistical pattern recognition. The students are provided
with sufficient knowledge for designing and evaluating classifiers using proper methods for
the problem at hand. Students will be able to implement a set of practical methods, given
37
Specialist subjects
example algorithms in MATLAB, and be able to program solutions to some given real world
problems in medical diagnosis, biometrics and image processing .
[1] Bishop C.: Neural Networks for Pattern Recognition, Oxford University Press, 1995.
[2] Duda P., Hart R. and Stork O.: Pattern Classification, 2nd edition, Wiley, 2000.
[3] Fukunaga K.:, Statistical Pattern Recognition, 2nd edition, Morgan Kaufmann, 1990.
[4] Mitchell T.M.: Machine Learning, WCB/McGraw-Hill, 1997.
[5] Vapnik V.N.: The Nature of Statistical Learning Theory, 2nd edition, Springer, 2000.
OPTIONAL READING:
[1] –
38

INFORMATION BOOKLET

Transkrypt

Podobne dokumenty

Semester Course of Polish Language

Paper 3301- Functional Polish

Opis modułu kształcenia / przedmiotu (sylabus) Rok akademicki

klub byłych więźniów politycznych obozu koncentracyjnego

Zajęcia w językach obcych - i

Miło mi panią poznać – język polski w sytuacjach komunikacyjnych

SEM. DYPLOMOWE - AMERYKANISTYKA Dr Ewelina Gutowska