UNIVERSITATEA 'POLITEHNICA' TIMIªOARA -...

PROGRAMEANALITICE

MASTERCOMPUTER ENGINEERING

UNIVERSITATEA "POLITEHNICA" DIN TIMIŞOARAFACULTATEA DE AUTOMATICĂ ŞI CALCULATOAREDOMENIUL CALCULATOARE ŞI TEHNOLOGIA INFORMAŢIEISPECIALIZAREA COMPUTER ENGINEERING (MASTER)

SEMESTRUL 1

„POLITEHNICA” UNIVERSITY FROM TIMIŞOARA

SYLLABUSfor the discipline:

“TESTING OF COMPUTER SYSTEMS”

FACULTY: AUTOMATION AND COMPUTERSDOMAIN / SPECIALIZATION: COMPUTER SYSTEMS ENGINEERING

Year of studies: I or II (MASTER)Semester: 1

Course instructor: professor Mircea Vlăduţiu, PhDApplications instructor: assistant professor Lucian Prodan PhD, teaching assistant Versavia Ancuşa, teaching assistant Alexandru Amaricăi, teaching assistant Oana BoncaloNumber of hours/week/Evaluation/Credits

Course Seminar Laboratory Project Evaluation Credits2 0 2 0 Exam 9

A. COURSE OBJECTIVES

This course introduces fundamentals of theory and practice of detecting failures in complex digital systems, fault analysis, test generation, and design for testability for digital ICs and systems. Covered topics include circuit and system modeling; fault sources and types, fault simulation methods, automatic test pattern generation (ATPG) algorithms for combinational and sequential circuits, testability measures, design-for-testability, VLSI testing issues and processor and memory testing. Fundamental techniques of detecting failures in complex digital systems will be introduces along with algorithms for automatic test generation, schemes for designing systems to be easily testable and with self test capability; hands-on experience with state-of-the-art computer-aided test tools in the laboratory.

B. COURSE SUBJECTS

Chapter 1. Introduction1.1. Dependability attributes and scope. Reliability and Fault tolerance1.2. Applications. Systems with high reliability, high availability, safety critical 1.3. Fault tolerance as a design target1.4. Fault modeling. Fault causability chain1.5. Physical and logical defects. Types of faults1.6. Fault recovery techniques1.7. Redundancy and reconfiguration1.8. Defects specific to digital computing

Chapter 2. Automatic Test Pattern Generation2.1. One path sensitization algorithm2.2. D-algorithm2.3. Poage-McClusky

Chapter 3. Error Checking and Correcting Codes3.1. Fault detection and correction concepts3.2. Detecting and correcting a single error3.3. Hamming codes3.4. Hamming modified codes3.5. Correction of a single error with detection of all double errors3.6. Correction of a double error3.7. Error coding for memory chips

Chapter 4. Self-Testing Techniques4.1. Introduction4.2. Self-checking ALUs 4.3. Parity checked ALU4.4. Checksum codes

4.5. Combination codes4.6. Residue codes4.7. Self checking PLAs4.8. Berger codes

C. APPLICATIONS SUBJECTS (laboratory, seminar, project)The project can comprise of

1. a thorough survey of a topic, with original insight 2. a development of a new scheme, or a fresh implementation of an existing scheme 3. or modeling and analysis of an existing scheme.

A project proposal, a progress report and a final report will be due at the beginning, in the middle and at the end of the semester. The proposal should include a title, name, one paragraph abstract, motivation, proposed approach and applicable references The progress report should indicate that at least half of work has been finished, with a summary of the findings, any refinements of the objectives as a result of the past study, what the final report will contain and the applicable references. An oral presentation is required. The final report should follow the structure and formatting of a research paper.

D. REFERENCES

1. Parag K. Lala. Digital Circuit Testing and Testability. Academic Press, 1997.2. A.J. van de Goor. Testing Semiconductor Memories. Theory and Practice. John Wiley and Sons, 1991.3. M. Abramovici, M.A. Breuer and A.D. Friedman. Digital Systems Testing and Testable Design. Computer Science Press, New York, NY, 1990.

E. EVALUATION PROCEDURE

Course ends with a 2 hour written exam accounting for 50%. Candidates will have to cover several subjects (3-4) based on course topics. Projects are based on a one page proposal that should include motivation, brief scope of study and some specific references. The progress report and the final report will account for 20% and 30% of the final mark.

F. INTERNATIONAL COMPATIBILITY

1. Carnegie-Mellon University, 18765 – Digital Systems Testing and Testable Design2. University of Illinois at Urbana-Champaign, ECE 543 – Digital System Testing & Design for Testability3. Stanford University, EE488: Testing Aspects of Computer Systems

Date: HEAD OF DEPARTMENT COURSE INSTRUCTOR,

Prof. Dr. Eng. Vladimir CREŢU Prof. Dr. Eng. Mircea VLADUTIU

http://www.uiuc.edu/


SYLLABUSFor the discipline:

“IMAGE PROCESSING AND RECOGNITION”



Course instructor: Associate professor Horaţiu Moldovan, PhDApplications instructor: ing. Ronald WagmannNumber of hours/week/Evaluation/Credits



The course was originally designed to present processing and recognition techniques of digital images.

B. COURSE SUBJECTS

1. Introduction: generalities, numerical images representation, fundamental steps in image processing2. Basic notions: elements of human visual perception, image model, pixels relations 3. Images transformation: Fourier transformation – introduction, description, properties4. Image improvement: generalities, image improvement by points processing, spatial filters and image improvement regarding frequency5. Restoration of images6. Compression of images7. Segmentation of images8. Representation and description: representation schemes, regional descriptors, morphology.

C. APPLICATIONS SUBJECTS (laboratory, seminar, project)1. Image representation2. Fourier transformation3. Properties of Fourier transformation4. Uniform filter5. Image improvement by point processing6. Spatial filters7. Increasing image quality. Filters for front detection8. Frequency filters9. Dilatation and erosion10. Restoration of images with a band stop filter

D. REFERENCES

1. R.C. Gonzalez, P. Wintz - "Digital Image Processing. Addison Wesley, 19872. R.C. Gonzales, R.E. Woods - "Digital Image Processing", Addison Wesley, 1992.3. F.C.A. Groen, R.J. Ekkers, R. de Vries - "Image processing with personal computers", Signal Processing 15,1988

E. EVALUATION PROCEDUREExaminer 2/3, applications 1/3


1. Wien Institute of Technology, Austria2. University of Karlsruhe, Germany3. University of Dortmund, Germany

Date: 10.09.2007

HEAD OF DEPARTMENT COURSE INSTRUCTOR,

Prof. Dr. ing. Vladimir CREŢU Conf. Dr. Ing. Horaţiu MOLDOVAN



“SMART SENSORS AND SENSOR NETWORKS”



Course instructor: professor Mircea Popa, PhDApplications instructor: Number of hours/week/Evaluation/Credits



- to understand the Smart sensor term; to know its characteristics, architecture, software level and applications;- to understand the Sensor networks; to know the topics for the sensor networks: communication protocols, data gathering and processing, energy management, security, reliability and foult tolerance;- to know examples, standards, platforms and tools for sensor networks;- to be able to choose among different smart sensors and to establish the topics for designing a sensor network.

B. COURSE SUBJECTS

Smart sensors fundamentals: Basic sensor technolog Sensor systems; Smart sensors definitions; Smart sensors: Characteristics; Smart sensors architectures; Smart sensors buses and interfaces; Smart sensors software; Data acquisition methods for smart sensors; Virtual sensor systems; Smart sensors for electrical and non-electrical variables; Sensor networks architectures: Single node architecture; Multi node architectures; Design principles; Energy efficient topologies; Wired sensor networks and wireless sensor networks; Applications; Communication protocols: Phisical layer; MAC protocols; Link layer protocols; Localization and positioning; Routing protocols; Transport layer; Data gathering and processing: Protocols for gather information; Data processing techniques; Energy management: Energy consumption of sensor nodes; Techniques for reducing consumption and communication energy; Energy aware routing; Security, reliability and fault-tolerance: Security and privacy protection; Reliability support; Fault-tolerance; Sensor networks standards; platforms and tools: IEEE 802.15.4 and IEEE 802.11; Berkeley motes; Operating systems.

C. APPLICATIONS SUBJECTS (laboratory; seminar; project)Projects:1. Smart sensors for electrical variables2. Smart sensors for non-electrical variables3. Study of Berkeley Motes4. Study of Smart Dusts5. Microsensors and MEMS6. Operating systems for sensor networks;7. Time synchronization and calibration in sensor networks;8. Distributed sensor networks9. Coverage in sensor networks10. Localization in sensor networks11. Data centric protocols in sensor networks12. Routing and information aggregation in sensor networks13. Dynamic sensor networks14. Low power design in sensor networks15. Minimization of consumption in sensor networks

D. REFERENCES

1. N. V. Kirianaki, S. Y. Yurish, N. O. Shpak V. P. Deynega: Data Acquisition and Signal Processing for Smart Sensors, John Wiley, 2004

2. H. Karl, A. Willig: Protocols and Architectures for Wireless Sensor Networks, John Wiley, 20053. M. Ilyas, I. Mahgoub (ed.): Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, CRC,

2004


Exam and project each of them with 50% of the final note.


1. Stanford University: Sensor Networks2. Lancaster Engineering University: Smart Sensors3. Harvard University: Wireless Communications and Sensor Networks

Date: 26.03.2007 HEAD OF DEPARTMENT COURSE INSTRUCTOR;

Prof. dr. ing. Vladimir CREŢU Prof. dr. ing. Mircea POPA

"POLITEHNICA" UNIVERSITY OF TIMISOARA


"DATA TRANSMISSION, CODING AND COMPRESSION"


Year of Studies: I or II (MASTER)Semester: 1

Course instructor: associate professor Mihai V. Micea, PhDApplications instructor: teaching assistant Bogdan CiubotaruNumber of hours/week/Evaluation/Credits



The course provides a detailed view of underlying support techniques employed in data communication networks. The students attending this course will gain detailed knowledge about data transmission techniques, data processing for enhancing communication efficiency and security. The students will also gain specific skills and abilities in analyzing, designing and implementing communication solutions for various types of networks and distributed applications.

B. COURSE SUBJECTS

Introduction: General description of the field; Data communication principles, issues and solutions; Fundamental aspects of data transmission: Signal transmission; Signal representation; Modulation techniques; Data transmission protocols: OSI Layer 1 and 2 implementations for different network architectures; Coding: Notion of channel coding; Block coding; Convolution coding; Mobile networking: Mobile networks characteristics; Mobile networks specific design issues; Mobile network specific protocols; Mobile network location and mobility management; Ad-hoc mobile networks: Characteristics of ad-hoc mobile networks; Specific issues; Ad-hoc mobile communication protocols; Wireless sensor networks; Data compression techniques: Huffman coding, MNP5, V.42; Security in data communication: General issues; Security in traditional networks and Internet; Security in wireless networks; Encryption and Authentication Techniques: Symmetric key encryption, RSA, Diffie-Hellman, Digital signature, SSL/TSL, PGP.

C. APPLICATIONS SUBJECTS (laboratory, seminar, project)

1. Evaluation and testing of network protocols using NS-2 (Network Simulator).2. Evaluation and testing of wireless network protocols using NS-2 (Network Simulator).3. Implementation of a simple protocol stack for data transmission in wireless sensor networks.4. Protocol evaluation metrics.5. Wireless sensor networks SPIN protocol implementation in NS-2.6. Wireless sensor networks Directed Diffusion protocol implementation in NS-2.7. Wireless sensor networks SPIN protocol implementation on a 5-node network platform.8. Wireless sensor networks Directed Diffusion protocol implementation on a 5-node network platform.9. Data encryption library implementation.10. Data compression library implementation.

D. BIBLIOGRAPHY

1. J.G. Proakis, "Digital Communications", 4-th Edition, Prentice-Hall, 2001.2. J. Schiller, "Mobile Communications", Addison Wesley, 2000. 3. Pahlavan, Krishnamurthy, "Principles of Wireless Networks", Prentice Hall, 2002.


The final grade is composed of the evaluation during the semester at the project workshops and at the course classes (50%), and of the final written exam evaluation (3 hours, 50%).


1. Carnegie Mellon University, SUA, Computer Scinece Department, Undergraduate Program: “Computer Networks” (15-441) , Masters Program: “Networking” (15-744)

2. Johns Hopkins University, SUA, Whiting School of Engineering, Undergraduate Program: “Introduction to Information Theory and Coding” (520.447), “Basic Communications” (520.401), “Digital Communications I ” (520.465), “Digital Communication II ” (520.466)

3. Stony Brooks State University of New York, SUA, Computer Science Department, Undergraduate Program: “Data Communications and Networks” (CSE/ISE 310), “Wireless and Mobile Networking“ (CSE370)

26.03.2007

HEAD OF DEPARTMENT COURSE INSTRUCTOR,Prof. Dr. Eng. Vladimir CRETU Assoc.Prof. Dr. Eng. Mihai V. MICEA

http://www.cs.sunysb.edu/~cse370



“EMERGING SYSTEMS”



Course instructor: lecturer Mihai Udrescu, PhDApplications instructor: teaching assistant Oana BoncaloNumber of hours/week/Evaluation/Credits



This course approaches an essential aspect regarding the engineering training process of the master program: understanding and employing the emerging computing technologies and techniques. The need for these new technologies and the benefits that come along are thoroughly explained, as well as the difficulties in implementing them. The advent of the first commercial quantum computer (built by the D-Wave Systems Company) and its presentation at Stanford University has transformed quantum computation into a high priority trend within the emerging computing technology field. This course is presenting the fundamental features that make quantum computation able to solve in polynomial time (efficiently) some problems that have exponential (inefficient) solutions on a classical computer.

B. COURSE SUBJECTS

Introduction: ITRS (International Technology Roadmap for Semiconductors) guidelines; The ERD – Emerging Research Devices Document; Emerging technologies comparison; Introduction to quantum mechanics; Computer science basics; Quantum Circuits: Quantum algorithms; One-qubit operations; Controlled operations; Measurement; Universal gate sets; The circuit model of the quantum computer; Quantum systems simulation; Quantum Fourier transform and its applications: Quantum Fourier transform; Phase estimation; Factoring problem – Shor’s algorithm; General applications; Quantum search algorithms: Grover’s algorithm; Quantum simulation; Quantum counting; NP-complete problems solution speedup; Search algorithms performance; Black-box algorithms limitations; Quantum computer physical implementation: Basic principles; Conditions for quantum computation; Harmonic oscillator quantum computer; Optical photon quantum computer; Nuclear Magnetic Resonance; Ion Trap technology, Optical cavity quantum electrodynamics; Quantum computer reliability: Classical noise and Markov models; Quantum noise; Quantum information measures; Quantum error correction; Quantum error detection and correction codes; Entropy and information: Entanglement; Quantum cryptography.


Representing quantum states and transformations with procedural programming languages;Quantum program algorithmic emulation. QPL (Quantum Programming Language) scripts;Describing quantum circuits with hardware description languages: QHDL and VHDL;Automated synthesis of quantum circuits with genetic algorithms;Template-based automated synthesis of quantum circuits;Quantum circuit reliability assessment: partitioning and reliability graphs;Simulation-based quantum fault injection in order to evaluate the accuracy threshold as main reliability measure;Gate-level quantum algorithm simulation by FPGA board configuration;

D. REFERENCES

1. Michael A. Nielsen, Isaac L. Chuang. Quantum Computation and Quantum Information. Cambridge University Press, 2000.2. Sandeer K. Shukla, R.. Iris Bahar (Eds.) Nano, Quantum and Molecular Computing: Implications to High Level Design and Validation. Springer Verlag, 2005.3. Dan C. Marinescu, Gabriela M. Marinescu. Approaching Quantum Computing. Prentice Hall, 2006.


The evaluation process output consists of two grades: one for the laboratory activity, which is yielded by evaluating the implementation of the lab projects, and one for the final exam grade. The relative weight of these grades in the final grade outcome is 66% for the exam and 33% for the lab projects assessment. The final exam lasts for 120 minutes, and consists of a written response to 10 short theoretical questions, plus 2 problems (quantum circuit synthesis and quantum algorithm assessment).


1. Massachusetts Institute of Technology (MIT)2. University of California Berkeley3. California Institute of Technology (CALTECH)

Date: 27 March 2007


Prof. Dr. ing. Vladimir CREŢU Lecturer dr. Mihai UDRESCU-MILOSAV



“HIGH-END INTERFACES AND EQUIPMENTS”



Course instructor: professor Mircea Stratulat, PhDApplications instructor: Number of hours/week/Evaluation/Credits



The course was originally designed to introduce optical recording systems to undergraduates and postgraduates in science and engineering

B. COURSE SUBJECTS

1. Introduction to optical recording, Basic optics, Lasers, photoreceptors, media substrates2. Optical recording drive systems3. Magneto–optic recording4. Magneto–optic recording, drive systems5. DVD recording, HDVD, Ray-blue DVD. Holographic memories, other memoriesC. APPLICATIONS SUBJECTS (laboratory, seminar, project)Optical recording, Basic optics, Lasers, photodectectors, media substrates, Optical recording drive systems, Magneto–optic recording, Magneto–optic recording, drive systems, DVD recording, HDVD, Ray-blue DVD. Holographic memories, other memories

D. REFERENCES

1. E.W.Williams – The CD-ROM and Optical Disc Recording Systems. Oxford, 19952. S.O.Kasap – Optoelectronics and photonics. -”, Prentice Hall, New Jersey, 20013. Linda G. Shapiro, George C: Stockman – Computer Vision - Prentice Hall, New Jersey, 2001


Examiner 2/3, applications 1/3


1. 1. 1. University of. California. Berkeley,2. Linkoping Institute of Technology, 3. Massachusetts Institute of Technology

Date: 20.03.2007 HEAD OF DEPARTMENT COURSE INSTRUCTOR,

Prof. Dr. ing. Vladimir CREŢU prof.dr.ing.Mircea Stratulat

„POLITEHNICA” UNIVERSITY OF TIMIŞOARA


“RESEARCH TOPICS IN COMPUTER SYSTEMS”


Year of studies: I (MASTER)Semester: 1

Course instructor: associate professor Marius Minea, PhD, lecturer Radu Marinescu, PhDApplications instructor:Number of hours/week/Evaluation/Credits

Course Seminar Laboratory Project Evaluation Credits2 0 0 0 Distributed 3


To present research papers that have been influential in shaping the field of software systems. Students will learn to appreciate past and future developments in the field, contents and structure of a good research paper, make a critical analysis, a clear and consistent presentation, and participate actively in discussions. The papers will address topics that are essential both for researchers and practitioners. Thus, students will gain insight into the essence of the most widely used software techniques, practices and paradigms. By understanding the foundations of current approaches, they will form solid reference points for taking future competent decisions on adopting novel techniques, practices or paradigms.

B. COURSE SUBJECTS

Programming language paradigms; software system architecture and design; program semantics, specification, modeling; program analysis techniques; managing software development

C. APPLICATION SUBJECTS (laboratory, seminar, project)

D. REFERENCES

Donald D. Chamberlin, Raymond F. Boyce - SEQUEL: A Structured English Query Language, 1974

Frederick P. Brooks, Jr: The Mythical Man-Month, 1975

Walter Tichy: Software development control based on module interconnection, ICSE-4, 1979

Mark Weiser: Program Slicing, ICSE-5, 1981.

D. L. Parnas, P. C. Clements, D. M. Weiss: The Modular Structure of Complex Systems, ICSE-7, 1984

Frederick Brooks, Jr.: No Silver Bullet: Essence and Accidents of Software Engineering. Computer, 1987

Lee Osterweil: Software processes are software too, ICSE-9, 1987.

David Ungar, Randall B. Smith - Self: The Power of Simplicity. OOPSLA, 1987

David Harel, H. Lachover, A. Naamad, Amir Pnueli, Michal Politi, Rivi Sherman, Aharon Shtul-Trauring: Statemate: A

Working Environment for the Development of Complex Reactive Systems, ICSE-10, 1988

David S. Rosenblum: Towards a Method of Programming with Assertions, ICSE-14, 1992

W. Harrison and H. Ossher - Subject-Oriented Programming (A Critique of Pure Objects) OOPSLA, 1993

Michael Jackson, Pamela Zave: Deriving Specifications from Requirements: An Example, ICSE-17, 1995

J. Dean, C. Chambers, and D. Grove: Selective Specialization for Object-Oriented Languages, PLDI, 1995

Bjarne Steensgaard, Points-to Analysis in Almost Linear Time, POPL, 1996

George Necula: Proof-Carrying Code, POPL, 1997


Students will each present one paper and participate in the discussion of other papers, both activities contributing to the final grade.


1. What Makes Good Research in Software Engineering, course 17-939A, Carnegie Mellon Universityhttp://spoke.compose.cs.cmu.edu/ser04/course-info.htm

2. Research Topics in Software Engineering, course P02102, University of Edinburghhttp://www.drps.ed.ac.uk/06-07/course.php?code=P02012

3. Research inSoftware Engineering, course CMSC 838p, University of Marylandhttp://www.cs.umd.edu/~vibha/838p/


Prof. Dr. ing. Vladimir CREŢU

SEMESTRUL 2



“EMBEDDED SYSTEMS”






- to understand the design goals and methodologies for high performance embedded computing;- to learn the main and specific topics for high performance embedded systems;- to understand the modeling process for embedded systems;- to obtain a consistent overview of the specific problems of the embedded systems;- to be able to outline the adequate embedded system architecture for a type of applications.

B. COURSE SUBJECTS

High-Performance embedded computing: Design goals; Design methodologies; Models of computation; Typical architectures; CPUs: Microprocessors vs. Microcontrollers vs. DSPs; Parallel execution mechanisms; Processor memory hierarchy; CPU simulation; Automated CPU design; Programs: Code generation; Program performance analysis; Models of Computation and Programming; Processes and operating systems: Real-time process scheduling; Operating system design; Verification; Modeling embedded systems: System level modeling; Models of computation; Represntations of different models; Specific software: The GNU toolchain; The GNU assembler; The GNU linker; Converting files; The GNU debugger; Specific topics: Design and validation of embedded systems; Design and verification languages; Operating systems; Timing and performance analysis; Memory organization techniques for low power computing; Power aware computing; Security; Applications

C. APPLICATIONS SUBJECTS (laboratory; seminar; project)Projects:1. Modeling embedded systems2. The GNU operating systems3. Operating systems for embedded systems4. Programming languages for embedded systems5. System-on-chip’s (SoC’s)6. Network-on-chip’s (NoC’s)7. Debugging embedded systems 8. Solutions for minimizing the consumption in embedded systems

D. REFERENCES

1. W. Wolf: High-Performance Embedded Computing, Elsevier, 2006

2. A. Jantsch: Modeling Embedded Systems and SoC’s: Concurrency and Time in Models of Computation, Morgan Kaufmann, 2004

3. R. Zurawski: Embedded Systems Handbook (Industrial Technology), CRC, 2005




1. University of Essex: Advanced Embedded System Design2. University of Utah School of Computing: Advanced Embedded Systems3. University of Leicester: Embedded Systems for condition monitoring and control.

Date: 26.03.2007 HEAD OF DEPARTMENT COURSE INSTRUCTOR;




"ROBOTIC SYSTEMS"


Year of Studies: I (MASTER)Semester: 2

Course instructor: associate professor Mihai V. Micea, PhDApplications instructor: teaching assistant Dan ChiciudeanNumber of hours/week/Evaluation/Credits



The course objective is to learn the theory and practice of mobile robotic systems and applications. The students will gain advanced knowledge on designing and implementing relative complex applications which involve real world problems like robot movement control, navigation and interaction, obstacle avoidance using robotic modular perception equipment and techniques. The students will apply the learned knowledge using real-life systems during course projects.

B. COURSE SUBJECTS

Introduction: General description of the field; Overview and preliminaries; Application of robotic systems. General architecture: Description of the main blocks and subsystems. Power supply block: Battery types, Charging modules, Power control and management. Locomotion subsystem: Locomotion types (Tricycle method, Differential system, Ackerman principle, Syncronous locomotion systems); Electric motors (DC, steppers, brushless); Electric motor driving techniques (H-bridge, PWM). Perception block: Transducers and sensors (proximity and collision, ultrasonic sensors, optic and video sensors); Data acquisition and processing. Control subsystem: Central processing unit (embedded systems, microcontrollers); Data communication block (wireless communication: 802.11 standard, 802.15.4 standard, bluetooth, GSM and GPRS modules). Robot operating modes: Remote control (tele-operation); Autonomous operation techniques. Navigation and localisation techniques: Obstacle avoidance algorithms, GPS and ultrasonic localisation, 3D perception. Collaborative robotic systems: Robot swarms; Distributed sensing and control; Cooperative behavior.

C. APPLICATION SUBJECTS (projects)

1. Maze solving project: the mobile robot must find autonomously its way out from a predetermined starting position next to the central area of the maze.

2. Line following robot: the robot must follow a light-reflective stripe.3. Autonomous mini-vehicle using ultrasonic Sonar. Desing and implementation.4. Autonomous orientation and obstacle avoidance techniques implemented on the Sonar-based robot. 5. Soccer player robotic team: development of a robotic team to play a simplified soccer game.

D. BIBLIOGRAPHY

4. U. Nehmzow, "Mobile Robotics: A practical introduction", 2nd Edition, Springer-Verlag London, 2003.5. G. Dudek, M. Jenkin, "Computational Principles of Mobile Robotics", Cambridge University Press, 2000.6. G. Branwyan, "Absolute Beginner’s Guide to Building Robots", Que, 2003.


The final grade is composed of the evaluation during the semester, at the project workshops and at the course classes (50%), and of the final written exam evaluation (3 hours, 50%).


4. University of Ottawa, Canada, Computer Engineering, Undergraduate Program: “Computer Control in Robotics”, “Robotics: Intelligent Connection of the Perception to Action”

5. Carnegie Mellon University, SUA, The Robotics Institute, Undergraduate Minor in Robotics: "Introduction to Robotics " (16-311), "Introduction to Mobile Robot Programming" (16-362 / 16-862)

6. University of Cambridge, UK, Department of Engineering, Undergraduate Program: "Computer Vision and Robotics " (4F12)

26.03.2007




“OPTIC FIBER TRANSMISSIONS”



Course instructor: professor Mircea Stratulat, PhDApplications instructor:Number of hours/week/Evaluation/Credits



If you hope to become a professional in components field and concentrate in the area of optical fiber, transmitters, receivers, amplifiers, and active and passive components, you will need understanding of the technology.

B. COURSE SUBJECTS

1. Introduction to Telecommunications and Fiber Optics2. Optical Fibers – Basics2.1 Attenuation, 2.2 Intermodal and Chromatic Dispersion, 2.3 Bit Rate and Bandwidth3. Multimode Fibers4. Singlemode Fibers5 Fabrication, Cabling, and Installation6. Receivers, Light sources and Transmitters7. Components of Fiber – Optic Networks 7.1. Passive Components, Switches, and Functional Modules of Fiber Optic Network8. An Introduction to Fiber Optic Networks

C. APPLICATIONS SUBJECTS (laboratory, seminar, project)Optical Fibers Basics, Attenuation, Intermodal and Chromatic Dispersion, Bit Rate and Bandwidth, .Multimode Fibers, Single mode Fibers, Fabrication, Cabling, and Installation, Receivers, Light sources and Transmitters, Components of Fiber – Optic Networks, Passive Components, Switches, and Functional Modules of Fiber Optic Network, An Introduction to Fiber Optic Networks

D. REFERENCES

1. Jeff Hecht – Understanding Fiber optics – Howard w. Sams&Company. Prentice Hall, New Jersey, 20012. D.Mynbaev, L.L. Scheiner – Fiber Optic Communication Technology - . Prentice Hall, New Jersey, 20013. Leon W. Couch – Digital and analog Communication Systems, sixth edition, Prentice Hall, New Jersey, 2000


Examiner 2/3, applications 1/3F. INTERNATIONAL COMPATIBILITY

1. 1.University of. California. Berkeley,2. Linkoping Institute of Technology, 3. Massachusetts Institute of Technology

Date: HEAD OF DEPARTMENT COURSE INSTRUCTOR, Prof. Dr. ing. Vladimir CREŢU prof.dr.ing.Mircea Stratulat„POLITEHNICA” UNIVERSITY FROM TIMIŞOARA


“CELLULAR DATA NETWORKS”



Course instructor: lecturer Doru Todincă, PhDApplications instructor: lecturer Doru Todincă, PhDNumber of hours/week/Evaluation/Credits



The goals of the course are to give first an overview of mobile communication systems, then to give more detail about cellular data network, with an emphasis on mobile data networks.

B. COURSE SUBJECTS

1. Introduction: applications, history, market vision2. Wireless Transmission: frequencies & regulations, signals, antennas, signal propagation, multiplexing, modulation, spread spectrum, cellular system 3. Medium Access Control: motivation, SDMA, FDMA, TDMA (fixed, Aloha, CSMA, DAMA, PRMA, MACA, collision avoidance, polling), CDMA4. Telecommunication Systems: GSM, UMTS/IMT-20005. GPRS/EGPRS: main concepts, system architecture, protocol architecture, Quality of Service (QoS)6. Wireless LAN: IR vs. radio, infrastructure/ad-hoc, IEEE 802.11 (a, b, g, h), HiperLAN2, mobile QoS, Bluetooth, IEEE 802.157. Mobile Network Layer: Mobile IP, DHCP, ad-hoc networks, routing optimization8. Research issues concerning cellular data networks: QoS and QoS differentiation; applications of artificial intelligence techniques in cellular data networks C. APPLICATIONS SUBJECTS (laboratory, seminar, project)Students can choose one of two types of projects:

- Select a particular topic of component mobile communication and analyse it in detail, writing a technical report

- Design a simulation model of a mobile data network

D. REFERENCES Se pastreaza indicatiile din modelul in lb. romana

1. Jochen H. Schiller, Mobile Communications, second edition, Addison-Wesley, 2003, ISBN 0 32112381 62. William Stallings, Wireless Communications and Networking, Prentice Hall, 20053. T. Halonen, J. Romero, J. Molero (editors), GSM, GPRS and EDGE Performance, John Wiley & Sons LTD, 2002


50% written examination and 50% evaluation of assignments

F. INTERNATIONAL COMPATIBILITY Se pastreaza indicatiile din modelul in lb. romana

1. University of California, Irvine, U.S.2. Dublin City University, Ireland3. Freie Universität Berlin, Germany


Prof. Dr. ing. Vladimir CREŢU Conf. Dr. Ing. Doru TODINCA



“AUTOMATIC DESIGN AND OPTIMIZATION OF VLSI CIRCUITS”



Course instructor: professor Marius Crişan, PhDApplications instructor: Number of hours/week/Evaluation/Credits

Course Seminar Laboratory Project Evaluation Credits2 0 2 0 E 9


Upon completion of the course, students will have an understanding of the characteristics of CMOS circuit construction and emerging nanometer-scale electronic circuit technologies and processes. Stuents will be able to use mathematical methods and circuit analysis models in analysis of VLSI circuits, including logic components and their interconnect. Students will be able to complete a significant VLSI design project having a set of objective criteria and design constraints.

B. COURSE SUBJECTS

Introduction, Objectives: VLSI concept. VLSI design methodology. Transistors and layout.CMOS Process and Layout: Basic CMOS device topology and fabrication. Construction of digital logic gates in CMOS.Advanced interconnect models: Delay bottlenecks. Reducing crosstalk. Modeling copper interconnect.Power Estimation and Optimization: Advanced low-power design techniques. Minimizing power utilization.CAD Systems and Algorithms: Overview of HDLs. Practical IP-based design solutions


1. Designing and simulating CMOS gates 2. Designing a simple sequential circuit (e.g. one-bit accumulator)3. Designing a comparator of two n-bit numbers.4. Designing a 4-bit Parallel Full Adder with carry-look-ahead.5. Designing “latch” and memory circuits.6. Designing a A/D and D/A converter.


1. Neil Weste, David Harris, CMOS VLSI Design: A Circuits and Systems Perspective, 3rd Edition, , Addison Wesley, 2003

2. Wayne Wolf, Modern VLSI Design, 3rd edition, Prentice Hall, 2002.3. Rabaey, J., et al., Digital Integrated Circuits: A Design Perspective, 2nd. ed., Prentice Hall Publishers, Inc., 2003.


There will be a final examination from lecture notes and project paper. The paper will be graded on technical content, format, and writing skill. 40% lab assignments and 60% final exam and project paper.


1. University of Massachusetts: VLSI Design Principles

2. University of South Carolina: Design of VLSI Circuits

3. Princeton University: Design of VLSI Sytems


Prof. Dr. ing. Vladimir CREŢU Prof.dr.ing. Marius Crisan



“VIRTUAL MEASUREMENT SYSTEMS”






- to understand the Virtual Instrumentation (VI) concept, its features, advantages and implementation;- to learn about a classical VI software, the National Instruments’ LabVIEW, the programming techniques and concepts from labVIEW;- to learn about the hardware needed for instrumentation;- to be able to design a LabVIEW based virtual instrument.

B. COURSE SUBJECTS

Overview of virtual instrumentation: Evolution of virtual instrumentation (VI); Architecture of a virtual instrument; Software level; Advantages of VI; LabVIEW; VI in engineering; Programming techniques: LabVIEW environment; Dataflow programming; G programming; Programming concepts: Control structures; Selection structures; Arrays; Clusters; Strings; Waveform charts and graphs; Tables; File input/ output; Inputs and outputs: Transducers; Sensors; Signal operations; A/D and D/A control; Interfaces for instrumentation: Current loops; RS232; RS422; RS485; GBIP; VISA; Input/ output buses: USB; PCI; PCI Express; PXI; PCMCIA; VXI; LXI; Data acquisition: Data acquisition in LabVIEW; Hardware installation and configuration; Data acquisition hardware; Data acquisition software; Data transmission: Pulse codes; A/D modulation techniques; Wireless communications; Distributed automation and control systems; SCADA; Applications

C. APPLICATIONS SUBJECTS (laboratory, seminar, project)Projects:1. Remote data acquisition using the ADVANTECH PCI 1710 board and LabVIEW2. Remote data analysis and control using the ADVANTECH PCI 1712 board and LabVIEW3. Remote data acquisition using the ADVANTECH PCL 816 board and 4. Study of the IEEE 1488 GPIB interface5. Client – Server applications in LabVIEW6. Distributed virtual instrumentation with LabVIEW7. LabVIEW based robot control system8. LabVIEW tools

D. REFERENCES

1. J. Travis: LabVIEW for Everyone (2nd Edition), CRC, 20022. S. Sumathi, P. Surehka: LabVIEW based Advanced Instrumentation Systems, Springer-Verlag, 20073. R. Bitter, T. Mohiuddin, M. Nawrocki: LabVIEW Advanced Programming Techniques, 2006




1. University of Glasgow: MSc Virtual Instrumentation2. Cornell University: Computer Instrumentation Design3. New Jersey’s Science & Technology University: Virtual Instrumentation





“INTRODUCTION TO RESEARCH”



Course instructor: each dissertation thesis coordinator for his master students groupApplications instructor: Number of hours/week/Evaluation/Credits



The main objective of the discipline “Introduction to research activity” is the creation of support for the student-centered research activities. The coordinators introduce, based on their experience, the main specific aspects of the research activity.

B. COURSE SUBJECTS

Research methodology, bibliographical synthesis elaboration, practical aspects of research activity. Establishing the dissertation thesis domain.

C. APPLICATIONS SUBJECTS Each master student will elaborate a state-of the-art report concerning his dissertation thesis domain as starting point for the dissertation thesis elaboration.

D. REFERENCES

Specific domain bibliography and references


The state-of-the-art report will be presented and defended by each master student in a special presentation session, and will be evaluated by a commission


Date: 27-th of March 2007 HEAD OF DEPARTMENT COURSE INSTRUCTOR,


SEMESTRUL 3



"ADVANCED DIGITAL SIGNAL PROCESSING"



Course instructor: associate professor Mihai V. Micea, PhDApplications instructor: teaching assistant Dan ChiciudeanNumber of hours/week/Evaluation/Credits



The course focuses on the advanced techniques and algorithms used in digital signal processing. Students will learn the main design and analysis techniques of advanced types of digital filters such as: efficient FIR and IIR filters, adaptive filters, linear prediction and optimum filters. Students will also gain abilities of designing and implementing advanced digital signal processing algorithms and systems using generic and specialized digital devices (Digital Signal Processors – DSPs).

B. COURSE SUBJECTS

Implementation of discrete-time systems: Structures for FIR and IIR systems; State-space system analysis and structures; Quantization of filter coefficients; Round-off effects in digital filters. Design of digital filters: Generic model for digital filter design; Design of FIR and IIR filters; Frequency transformations; Design of digital filters based on Least-Squares Method. Multirate digital signal processing: Decimation and interpolation; Sampling rate conversion; Filter design and implementation for sampling-rate conversion; Multistage implementation of sampling-rate conversion; Applications of multirate signal processing. Design of adaptive digital filters: Goal of adaptive digital filtering; General concepts; Theory of Wiener-type digital filters; Adaptive LMS algorithm; Least-Squares recursive algorithm; Applications of adaptive digital filtering. Elements of spectral analysis and estimation: Principle and traditional methods of spectral estimation; Modern methods of parametric estimation; Applications of spectral analysis. Linear prediction and optimum linear filters: Forward and backward linear prediction; Properties of the linear prediction – error filters; Applications of linear prediction.


11. Architecture and programming of the Freescale StarCore DSP system.12. FFT implementation and evaluation on a PC and on the StarCore DSP.13. Implementation and evaluation of FIR digital filters on a PC and on the StarCore DSP.14. Implementation and evaluation of IIR digital filters on a PC and on the StarCore DSP.15. Design and implementation of a digital audio effects processor.16. Design and implementation of a digital audio equalizer on specialized devices (DSP).17. Design, implementation and analysis of multirate systems.18. Adaptive digital filters: design, implementation and evaluation.19. Linear prediction filters: design, implementation and evaluation.

D. BIBLIOGRAPHY

7. G. Proakis, D. G. Manolakis, "Digital Signal Processing. Principles, Algorithms and Applications", 3rd Edition, Prentice-Hall, 1996.

8. A. V. Oppenheim, R. W. Schafer, "Digital Signal Processing", Prentice-Hall, 1996.9. S. Stergiopoulos (editor), "Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar,

and Medical Imaging Real-Time Systems", CRC Press LLC, 2001.


The final grade is composed of the evaluation during the semester at the project workshops and at the course classes (50%), and of the final written exam evaluation (3 hours, 50%).


7. Massachusetts Institute of Technology, SUA, Department of Electrical Engineering and Computer Science, Basic Undergraduate Program: "Discrete-Time Signal Processing" (6.341, A. V. Oppenheim, V. K. Goyal), "Digital Speech Processing" (6.343, T. F. Quatieri), "Introduction to Communication, Control, and Signal Processing" (6.011, A. V. Oppenheim, G. C. Verghese)

8. Carnegie Mellon University, SUA, Department of Electrical and Computer Engineering, Undergraduate Program: "Advanced Digital Signal Processing" (18-792), "Digital Signal Processing" (18-491), "Digital Communication and Signal Processing Systems Design" (18-551)

9. University of Cambridge, UK, Department of Engineering, Undergraduate Program: "Digital Filters and Spectrum Estimation" (4F7, S. J. Godsill, S. Singh), "Signals and Systems" (3F1, J.M. Goncalves, N.G. Kingsbury), "Signal and Pattern Processing" (3F3, S. J. Godsill)

G. OBSERVATIONS

1. The course is presented using PowerPoint slides with a video projector. Students can receive the course support (course slides) in electronic or printed format.

2. The project and application workshops will be hosted by the Digital Signal Processing Laboratories – DSPLabs, which provides latest generation equipments and specialized modules through various partnerships with top-ranking companies in the domain, such as: Motorola/Freescale, Alcatel-Lucent, etc.

3. Interested students have the opportunity to collaborate at various projects and research contracts, developed within the DSPLabs. Such participation replaces the applicative activities required by the syllabus.

26.03.2007




“INTEGRATED INFORMATION SYSTEMS”



Course instructor: Associate professor Horaţiu Moldovan, PhDApplications instructor: ing. Ronald WagmannNumber of hours/week/Evaluation/Credits



The course was originally designed to understand what is the role of an ERP (Enterprise Resource Planning) system for an enterprise, to be able to define and modify database structure, to show all the functions and modules of an ERP system and to present implementation methodologies for an ERP system.

B. COURSE SUBJECTS

1. Introduction to information systems, defining and classifying integrated information systems2. Enterprise organization 3. Life cycle of an ERP system4. Enterprise business analysis 5. Technologies. Architectures. Design of ERP systems 6. Business processes7. ERP implementation8. Developing an ERP system

C. APPLICATIONS SUBJECTS (laboratory, seminar, project)We will demonstrate all the functionality of an ERP system. This includes an overview of the basis modules of an ERP such as Accounting, Controlling, Material Management, Sales and Distribution and Production Planning. We will create scenarios to simulate the main activities that take place in an enterprise.

D. REFERENCES

1. Travis Anderegg – ERP: A-Z Implementer’s Guide for Success. Resource Pub, SUA, 20002. Doina Fotache, Luminiţa Hurbean – Integrated information systems for managing businesses – ERP, Ed.Economică, Bucureşti, 19993. ***, SAP Documentation; SAP Germany 2006 online


Examiner 2/3, applications 1/3


1. University of Southern California, USA2. Wien Institute of Technology, Austria3. University of Missouri, USA

Date: 10.09.2007


Prof. Dr. ing. Vladimir CREŢU Conf. Dr. Ing. Horaţiu MOLDOVAN



"EMERGENT AND COLLECTIVE INTELLIGENCE SYSTEMS"



Course instructor: associate professor Mihai V. Micea, PhDApplications instructor: teaching assistant Răzvan-Dorel CioargăNumber of hours/week/Evaluation/Credits



The primary objective is to learn some of the main advanced techniques and architectures underlying distributed systems and collaborative environments. The course is targeted at understanding and obtaining hands-on experience with distributed, collaborative environments such as wireless sensor networks or robotic collectives, which are often composed using relatively inexpensive nodes requiring low power consumption, processing power and bandwidth. The students will apply the learned knowledge in working with real systems in course projects.

B. COURSE SUBJECTS

Introduction: Introduction to Distributed Systems; Examples and Applications of Distributed Systems; Introduction to Emergent Behavior; Examples and Applications of Emergent Behavior. Distributed Embedded and Robotic Systems: Real-time Operation; Fault Tolerance; Distributed Communications; Programming Paradigms. Emergent Behavior: Principles of Emergent Behavior; Formalization of Emergent Behavior Patterns; Programming Paradigms; Collaborative Environments: Scalable Networks; Discovery; Auto-configuration; Collaboration.


20. Introduction to the architecture of a collaborative system node.21. Basic programming of the collaborative system node.22. Introduction to the eBML (Emergent Behavioral Modeling Language). Applications.23. Solving a specific task for a collaborative system using distributed systems paradigms. Formal modeling using

eBML.24. Solving a specific task for a collaborative system using emergent behavior patterns systems paradigms. Formal

modeling using eBML.25. Implementing the “distributed” solution. 26. Implementing the “emergent” solution.27. Comparison between solutions.28. Presentation of final report

D. BIBLIOGRAPHY

10. S. Russell, P. Norvig, "Artificial Intelligence: A Modern Approach", 2nd Ed., Prentice Hall, 2002.11. M. Wooldridge, "An Introduction to MultiAgent Systems", Wiley & Sons, 2002.12. R.C. Arkin, "Behavior-Based Robotics (Intelligent Robotics and Autonomous Agents)", MIT Press, 1998.


The final grade is composed of the evaluation during the semester at the project workshops and at the course classes (33%), and of the final exam evaluation. The final exam is composed of a theory module (1.5 hours, 33%) and a problem solving module (1.5 hours, 34%). Each of the final exam modules can be attended independently.


10. Carnegie Mellon University, USA, Department of Electrical Engineering and Computer Science, Undergraduate Program, “Wireless Sensor Networks” (18748), “Distributed Systems” (18842), “Special Topics in Systems and Control: Multi-Agent Systems” (18879N)

11. California Institute of Technology, USA, Computer Science, Undergraduate and Graduate Programs, “CS 141 abc. Distributed Computation Laboratory”

12. Université de Lausanne, Switzerland, HEC (Ecole des Hautes Etudes Commerciales), “Intelligent Agents for Collaborative Information System” (“Agents Intelligents pour Système d'information Collaboratif”), Min-Jung YOO.

26.03.2007




“EVOLVABLE HARDWARE”



Course instructor: assistant professor Lucian Prodan, PhDApplications instructor: teaching assistant Versavia Ancuşa, teaching assistant Alexandru Amaricăi, teaching assistant Oana BoncaloNumber of hours/week/Evaluation/Credits



This course is a graduate level complement to Reconfigurable Computing (8L1.1.1) and Fault Tolerant Computing (8L2.5). It introduces a range of computational models that appeared at the crosspoints between reconfigurable hardware and evolutionary algorithms in the recent years as an attempt to solve traditionally hard computational problems. The course surveys relevant aspects of means to evolve reconfigurable architectures by using evolutionary techniques inspired from biological processes pertaining to phylogeny, ontogeny and epigenesis. In particular, the following topics will be discussed: evolvability characteristics, evolutionary computation and design, traditional and novel reconfigurable architectures, evolvable hardware. Typical applications will illustrate the mentioned approaches.

B. COURSE SUBJECTS

Chapter 1. Introduction1.1. Characteristics of Evolvable Circuits and Systems1.2. The Good and The Bad of Evolvable Hardware1.3. Technology and Applications1.4. Evolvable VS Evolved Hardware1.5. Intrinsic VS Extrinsic Evolution. Online VS Offline Evolution

Chapter 2. Fundamentals of Evolutionary Computation2.1. Definition of Evolutionary Algorithms2.2. Components of an EA: Representation, Variation, Evaluation, Selection, Population and Termination Criteria2.3. Peramenter Tuning and Selection of Best EA

Chapter 3. Reconfigurable Digital Devices3.1. Basic Architectures: PLDs and FPGAs3.2. Using Reconfigurable Hardware. Design Phase. Execution Phase3.3. Experimental Results3.4. The POEtic Architecture. Organic Subsystem. Molecules. Routing Layer

Chapter 4. Using Evolvable Hardware (EHW)4.1. Synthesis VS Adaptation4.2. Design of Self-Adaptive Systems. Fault Tolerant and Real Time Systems4.3. Online Reconfigurable Systems4.4. Examples of EHW-based Fault Recovery

C. APPLICATIONS SUBJECTS (laboratory, seminar, project)The project can comprise of

4. a thorough survey of a topic, with original insight 5. a development of a new scheme, or a fresh implementation of an existing scheme 6. or modeling and analysis of an existing scheme.

A project proposal, a progress report and a final report will be due at the beginning, in the middle and at the end of the semester. The proposal should include a title, name, one paragraph abstract, motivation, proposed approach and applicable references The progress report should indicate that at least half of work has been finished, with a summary of the findings, any refinements of the objectives as a result of the past study, what the final report will contain and the applicable references. An oral presentation is required. The final report should follow the structure and formatting of a research paper.

D. REFERENCES

1. D. Mange, M. Tomassini (eds.). Bio-Inspired Computing Machines. Towards Novel Computational Architectures. Presse Polytechniques et Universitaires Romandes, 1998.2. G.W. Greenwood, A.M.Tyrrell. Introduction to Evolvable Hardware. A Practical Guide for Designing Self-Adaptive Systems. IEEE Press Series on Computational Intelligence, 2007.3. R.S. Zebulum et al. (eds.). Proceedings of the NASA/DoD Conference on Evolvable Hardware (EH’05). IEEE Computer Society, Los Alamitos CA, 2003-2005.


Course ends with a 2 hour written exam accounting for 50%. Candidates will have to cover several subjects (3-4) based on course topics. Projects are based on a one page proposal that should include motivation, brief scope of study and some specific references. The progress report and the final report will account for 20% and 30% of the final mark.


1. University of Missouri-Rolla, CpE/EE/SysEng 301 – Evolvable Hardware (Adaptive Devices, Circuits and Systems)2. Brno University of Technology, EUD – Evolutionary and Unconventional Hardware3. University of Oslo, INF-EHW – Evolutionary Computation and Evolvable Hardware


Prof. Dr. eng. Vladimir CREŢU Assist. Prof. Dr. Lucian PRODAN

„POLITEHNICA” UNIVERSITY OF TIMIŞOARA


“ADVANCED ARTIFICIAL INTELLIGENCE”



Course instructor: professor Marius Crişan, PhDApplications instructor: Number of hours/week/Evaluation/Credits



The course starts with a review of the theory and practice of the most advanced strategies in AI and how to utilize the various techniques in knowledge-based systems. Then, the cognitive processes (perception, memory, language and thought) are investigated. Finally, the main research approaches are presented that may lead to valid intelligent techniques suitable for various applications. Upon completion of this course students will be able to provide solutions for solving real human-like problems and develop their own research approaches.

B. COURSE SUBJECTS

1. Computational approaches in mental function modeling. 42. Models of vision and aural perception. 43. Memory systems. 44. Natural language processing 45. Reasoning and thinking models. 46. Learning and development. 47. Quantum model of brain. 4 Total 28


1. Sensory system modeling based on self-organizing neural networks. 42. Models of perception and memory. 43. Complexity and information content analysis of cognitive systems. 44. Models of natural language understanding. 45. Approaches in Q & A systems. 46. Learning by simulating evolution. 47. Development of a mind/brain model based on quantum computing. 4 Total 28


1. S. Russell and P. Norvig. “Artificial Intelligence- A Modern Approach.” Prentice Hall, 2003.2. T. Malim: “Cognitive Processes”, Macmillan Press Ltd, 1994 (translated Ro. Ed. Tehnica 1999)3. Speech and Language Processing: An Introduction to Natural Language Processing, Computational

Linguistics, and Speech Recognition. By Daniel Jurafsky and James H. Martin. Prentice-Hall, 2000.


There will be a final examination from lecture notes weighting 60% of the final grade. The lab assignments weight 40% of the final grade.


1. University of Houston: Advanced Artificial Intelligence

2. New York University: Advanced Artificial Intelligence

3. University of Adelaide: Advanced Artificial Intelligence

Date: 20.03.2008


Prof. Dr. ing. Vladimir CREŢU Prof.dr.ing. Marius Crisan



“DIRECTED THESIS RESEARCH”


Year of studies: II (MASTER)Semester: 1

Course instructor: each dissertation thesis coordinator for his master students Applications instructor: Number of hours/week/Evaluation/Credits



Directed thesis research activity has as main objective to create the support for the student-coordinator collaboration. The main objective of directed thesis research activity is the elaboration of the dissertation thesis proposal by the master student.

B. COURSE SUBJECTS

Starting from the state-of–the-art report and focusing on thesis topic, the master student will elaborate the dissertation thesis proposal, under surveillance of the coordinator

C. APPLICATIONS SUBJECTS Elaboration of the dissertation thesis proposal document

D. REFERENCES


The dissertation thesis proposal will be presented and defended by each master student in a special presentation session, and will be evaluated by a commission.


Date: 27-th of March 2007 HEAD OF DEPARTMENT COURSE INSTRUCTOR,


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