STOCHASTIC SIGNAL PROCESSING

Course Information
TitleΕΠΕΞΕΡΓΑΣΙΑ ΣΤΟΧΑΣΤΙΚΟΥ ΣΗΜΑΤΟΣ / STOCHASTIC SIGNAL PROCESSING
CodeNDM-06-02
FacultySciences
SchoolInformatics
Cycle / Level1st / Undergraduate
Teaching PeriodSpring
CoordinatorKonstantinos(constantine) Kotropoulos
CommonNo
StatusActive
Course ID40002963

Programme of Study: Undergradute Studies - School of Informatics (2015-today)

Registered students: 15
OrientationAttendance TypeSemesterYearECTS
Information SystemsElective Courses635
Digital MediaCompulsory Course belonging to the selected specialization (Compulsory Specialization Course)635
Communication, Networks And Systems ArchitectureElective Courses635
Information And Communication Technologies In EducationElective Courses635
General Common DirectionElective Courses635

Class Information
Academic Year2015 – 2016
Class PeriodSpring
Faculty Instructors
Weekly Hours4
Class ID
600004976
Type of the Course
  • Scientific Area
Course Category
Specific Foundation / Core
Mode of Delivery
  • Face to face
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Prerequisites
General Prerequisites
Prior exposition to an introductory course on probabilities and statistics, signals and systems, and digital signal processing facilitates creating insight and faster grasping of the concepts introduced.
Learning Outcomes
Cognitive: Acquaintance with randomness and its impact in signal transmission; image, speech, and audio processing; language processing. Thorough grasp of the concepts of random variable, random vector, stochastic signal, moments of random variables with emphasis on the autocorrelation. Augmenting and revising linear system theory so that it allows for the analysis of linear systems excited by stochastic signals. Skills: Promoting analytical and programming skills in signal processing. Building the foundations for undertaking advanced studies in image, speech, audio, and biomedical signal processing as well as further reading in pattern recognition and statistical learning theory. Programming applications to speech, music, and telecommunications in MATLAB.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Generate new research ideas
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Probability theory. Repeated trials. Random variables. Functions of random variables. Joint statistics. Moments and conditional statistics. Stochastic signals. Basic categories of stochastic signals (Gaussian, Markov, Stationary, Ergodic) . Statistical auto-correlation and cross-correlation function. Input-output relationship of linear systems with stochastic excitation. Theory of optimal linear systems. Mean square estimation.
Keywords
Probabilities, Random Variables, Stochastic Processes
Educational Material Types
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Description
Slides and MATLAB demos.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures602
Reading Assigment150.5
Tutorial150.5
Project301
Written assigments150.5
Exams150.5
Total1505
Student Assessment
Description
Students are graded taking into account their achievements in the compulsory home-work assigned to them, their attendance and active participation in the lectures and tutorials, and the assessment of the mid-term and final progress exams. Compulsory homework includes solving two problems per chapter of Papoulis’ textbook taught and working out two computer-based projects in MATLAB assigned to each student during the semester. During the mid-term and final progress exams, students are requested to provide short answers in 10-15 questions/problems covering the topics taught in the course. Homework assignment and deadlines are announced in the course web page at http://pileas.csd.auth.gr. Students pass the course, if their total grade is greater than on equal to five (5). Details on the grading procedure are announced in the course web page, which supersede any prior arrangement.
Student Assessment methods
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
  • Performance / Staging (Formative, Summative)
  • Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
Papoulis A., Pillai S. (translated in Greek) «Πιθανότητες, Τυχαίες Μεταβλητές και Στοχαστικές Διαδικασίες», 4η Έκδοση, Εκδόσεις Τζιόλα, Θεσσαλονίκη, 2005. Πανάς Σ. «Ανάλυση Στοχαστικού Σήματος», University Studio Press, Θεσσαλονίκη, 1985.      
Additional bibliography for study
S. M. Kay, Intuitive Probability and Random Processes Using MATLAB}. New York, N.Y.: Springer 2006. (e-book accessible through www.lib.auth.gr) T. Dutoit and F. Marques, Applied Signal Processing. A MATLAB-Based Proof of Concept. New York, N.Y.: Springer, 2009 (e-book accessible through www.lib.auth.gr) R. E. Ziemer, Elements of Engineering Probability and Statistics. Upper Saddle River, N.J.: Prentice-Hall, 1997. D. P. Bertsekas and J. N. Tsitsiklis, Introduction to Probability. Belmont, MA: Athena Scientific, 2002. C. S. Burrus, J. H. McClellan, A. V. Oppenheim, T. W. Parks, R. W. Schafer, and H. W. Schuessler, Computer-Based Exercises for Signal Processing. Englewood Cliffs, N.J.: Prentice-Hall, 1994.
Last Update
15-02-2016