Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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General Information Programs
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  • Depart. of Electrical and Electronics Engineering
  • Doctorate Program in Electronics and Electric Eng.
  • Course Structure Diagram with Credits
  • Advanced Digital Signal Processing
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title EEM623 - Advanced Digital Signal Processing
Course Type Elective Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) PROFESÖR DOKTOR MEHMET TANKUT ÖZGEN
Mode of Delivery Face to face.
Prerequisites Prerequisites: Digital Signal Processing course, EEM 504: Random Variables and Processes.
Courses Recomended Statistical Signal Processing, EEM 547: Detection and Estimation Fundamentals.
Required or Recommended Resources Alan V. Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing, Prentice-Hall International, 2010, Third Edition. Chapters 10, 11.Proakis, et. al., Advanced Digital Signal Processing, Macmillan Publishing Company, New York, 1992. Chapter 6.
Recommended Reading List Related sources may be added in addition to above book chapters.
Assessment methods and criteria MT-1: 25%, MT-2: 30%, Final: 45%. All exams are classical. Some exams may include computer simulation assignments.
Work Placement None.
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Oppenheim, et. al., Ch. 10: Fourier Analysis of Signals Using the Discrete Fourier Transform: Spectral leakage.
Week - 2 Oppenheim, et. al., Ch. 10: Fourier Analysis of Signals Using the Discrete Fourier Transform: Spectral sampling issues.
Week - 3 Short-time Fourier transform, and its application in analysis of speech and radar signals.
Week - 4 Nonparametric spectral analysis of stationary signals: Periodogram.
Week - 5 Nonparametric spectral analysis of stationary signals: Correlogram.
Week - 6 Ch. 11: Parametric Signal Modeling: All-pole (autoregressive (AR)) modeling of deterministic and random signals; linear prediction.
Week - 7 Estimation of correlation functions; model order selection; all-pole spectrum analysis.
Week - 8 Solution of autocorrelation normal equations: QR factorization.
Week - 9 Levinson-Durbin algorithm, lattice filters.
Week - 10 Proakis, et. al., Ch. 6: Adaptive Filters: Applications of adaptive filters.
Week - 11 EN: Proakis, et. al., Ch. 6: Adaptive Filters: Applications of adaptive filters.
Week - 12 Adaptive direct-form FIR filters: LMS (least-mean-squares) algorithm and its properties.
Week - 13 Adaptive direct-form FIR filters: LMS (least-mean-squares) algorithm and its properties.
Week - 14 Direct-form RLS (recursive least-squares) algorithm and its properties.

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Drill - Practise
  • Problem Solving
  • Report Preparation and/or Presentation
  • Competences
  • Productive
  • True to core values
  • Rational
  • Questoning
  • Entrepreneur
  • Follow ethical and moral rules
  • Use time effectively
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Applying theoretical knowledge into practice
  • To work autonomously
  • Elementary computing skills

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
Toplam (%) 0
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