STATISTICAL SIGNAL PROCESSING - TIME SERIES ANALYSIS

 Title ΣΤΑΤΙΣΤΙΚΗ ΕΠΕΞΡΓΑΣΙΑ ΣΗΜΑΤΩΝ-ΧΡΟΝΟΣΕΙΡΕΣ / STATISTICAL SIGNAL PROCESSING - TIME SERIES ANALYSIS Code IWW-02-18 Faculty Sciences School Informatics Cycle / Level 2nd / Postgraduate Teaching Period Spring Coordinator Konstantinos(constantine) Kotropoulos Common No Status Active Course ID 600000910

Programme of Study: Internet and World Wide Web

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses217.5

 Academic Year 2017 – 2018 Class Period Spring Faculty Instructors Weekly Hours 3 Class ID 600111873
Course Type 2016-2020
• Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
• Face to face
Digital Course Content
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 spectal 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 foun-dations 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
• 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
wo sets of slides (Petre Stoica - Randolf Moses, Jain Li) and MATLAB demos.
Course Organization
Lectures1053.5
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