STATISTICAL SIGNAL PROCESSING - TIME SERIES ANALYSIS

Course Information
TitleΣΤΑΤΙΣΤΙΚΗ ΕΠΕΞΕΡΓΑΣΙΑ ΣΗΜΑΤΩΝ - ΧΡΟΝΟΣΕΙΡΕΣ / STATISTICAL SIGNAL PROCESSING - TIME SERIES ANALYSIS
CodeDM02
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
CoordinatorKonstantinos(constantine) Kotropoulos
CommonNo
StatusActive
Course ID40002277

Programme of Study: PPS School of Informatics (2014-today)

Registered students: 3
OrientationAttendance TypeSemesterYearECTS
TECΗNOLOGIES GNŌSĪS DEDOMENŌN KAI LOGISMIKOUElective Courses217.5
TECΗNOLOGIES PLĪROFORIAS KAI EPIKOINŌNIŌN STĪN EKPAIDEUSĪElective Courses217.5
PSĪFIAKA MESA- YPOLOGISTIKĪ NOĪMOSYNĪElective Courses belonging to the selected specialization217.5
DIKTYAKA SYSTĪMATAElective Courses217.5

Programme of Study: PPS of School of Informatics (2013-today)

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
Information SystemsElective Courses117.5
Information And Communication Technologies In EducationElective Courses117.5
Digital MediaCompulsory Course117.5
Communication Systems and TechnologiesElective Courses117.5

Class Information
Academic Year2015 – 2016
Class PeriodSpring
Faculty Instructors
Weekly Hours3
Class ID
600011298
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 signals and systems, digital signal processing, and stochastic signal processing facilitates creating insight and faster grasping of the concepts introduced.
Learning Outcomes
Cognitive: Thorough grasp of concepts such as power spectrum, correlation, and non-parametric and parametric algorithms for spectral estimation. Critical review of concepts from estimation theory, such as bias-variance dilemma, maximum likelihood estimation (Cramer-Rao bound, efficiency, etc.) Skills: Promoting analytical and programming skills in signal processing. Building the foundations for undertaking advanced studies in speech, audio, biomedical, and financial signal processing. Programming applications in MATLAB in order to understand the various concepts and assess the performance of spectral analysis algorithms.
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
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Introduction to spectral analysis. Non-parametric spectral analysis techniques (periodogram and its refined variants). Parametric methods for rational spectra (signals AR, MA, ARMA). Parametric methods for line spectra. Filter bank methods. Spatial methods. Detection and estimation theory. Adaptive filters.
Keywords
spectral analysis, parametric techniques, non-parametric techniques, subspace techniques, spatial methods
Educational Material Types
  • Notes
  • Slide presentations
  • 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
Two sets of slides (Petre Stoica - Randolf Moses, Jain Li) and MATLAB demos.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures1053.5
Reading Assigment301
Project602
Written assigments150.5
Exams150.5
Total2257.5
Student Assessment
Description
Students are assessed with respect to the progress the make in compulsory homework assigned to them as well as the attendance and active participation in the lectures (50%) and their performance in written exams (50%). Compulsory homework includes solving two problems and working out one computer-based project in MATLAB per chapter of Stoica-Mose's textbook taught. 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 5.
Student Assessment methods
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
  • Performance / Staging (Formative, Summative)
Bibliography
Additional bibliography for study
Προτεινόμενη βιβλιογραφία P. Stoica and R. Moses, Introduction to Spectral Analysis. Upper Saddle River, N.J.: Prentice Hall, 1997. Επιπρόσθετη βιβλιογραφία J. G. Proakis, C. M. Rader, F. Ling, C. L. Nikias, M. Moonen, and I. K. Proudler, Αlgorithms for Statistical Signal Processing. Upper Saddle River, N.J.: Prentice Hall, 2001. T. Soenderstrom and P. Stoica, System Identification. London, U.K.: Prentice Hall International, 1989. L. Marple, Digital Spectral Analysis and Applications. Englewood Cliffs, N.J.: Prentice-Hall, 1987. L. Cohen, Time-Frequency Analysis. Englewood Cliffs, N.J.: Prentice-Hall, 1995. T. Chonavel, Statistical Singal Processing. N.Y.: Springer, 2002. J. M. Mendel, Lessons in Estimation Theory for Signal Processing, Communications, and Control. Englewood Cliffs, N.J.: Prentice Hall PTR, 1995. S. Kay, Fundamentals of Statistical Signal Processing, vol. 1: Estimation Theory. Englewood Cliffs, N.J.: Prentice Hall PTR, 1993. S. Kay, Fundamentals of Statistical Signal Processing, vol. 2: Detection Theory. Upper River Saddle, N.J.: Prentice Hall PTR, 1998. Γ. Β. Μουστακίδης, Βασικές Τεχνικές Ψηφιακής Επεξεργασίας Σημάτων. Θεσσαλονίκη: Εκδόσεις Τζιόλα, 2004. 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. T. Dutoit and F. Marques, Applied Signal Processing. A MATLAB-Based Proof of Concept. New York, N.Y.: Springer, 2009 (πρόσβαση στο e-book μέσω του www.lib.auth.gr)
Last Update
15-02-2016