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DS1KES - Signals Classification and Estimation

Course specification
Course title Signals Classification and Estimation
Acronym DS1KES
Study programme Electrical Engineering and Computing
Module System Control and Signal Processing
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status elective
      Condition none
      The goal Course objective is for students to be able to use the techniques for signals classification and estimation. They are expected to be able to design systems for real signals acquisition, to make their normalization, pre-filtration, digital processing in time or frequency domain, parameters estimation, as well as to make signal classification if the problem of multi-class space is properly defined.
      The outcome Learning outcomes of the course are following: design of systems for acquisition of video, audio and other physical signals, design of systems for digital processing of these signals, signals estimation and design of appropriate classifiers.
      Contents
      Contents of lectures Probability concept; Distribution of random variables and vectors; Stochastic processes; Statistical decision theory; Parameters estimation; Filtering; Signals representation; Detection and signal estimation; Hypothesis testing approach; Design of parametric and non-parametric classifiers.
      Contents of exercises none
      Literature
      1. M. Barkat, Signal Detection and Estimation, Artech House, 2005 (Original title)
      2. B. Anderson, J. Moore, Optimal Filtering, Prentice Hall, 1979. (Original title)
      3. K. Fukunaga, Introduction to statistical pattern recognition, Academic Press, 1992. (Original title)
      4. S. Kay, Modern Spectral Estimation, Prentice Hall, 1988. (Original title)
      5. J. Benesty, Y. Huang, Adaptive Signal Processing, Springer, 2003. (Original title)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      6
      Methods of teaching lectures
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures 0 Test paper 0
      Practical lessons 0 Oral examination 70
      Projects 30
      Colloquia 0
      Seminars 0