ECE
 
 
 
Teaching
   
 

 

ECE220 - Signals and Systems I - Spring 2009-2013
Core curriculum course for 2nd year ECE students: Continuous Time Signals and Systems, Linear Time Invariant Systems, Convolution, Differential Equation Models, Frequency Response and Filtering, Fourier and Laplace Transforms.
http://www.eng.ucy.ac.cy/gmitsis/ece220/

ECE429 - Introduction to Digital Signal Processing - Spring 2011, 2012

Elective course for 4th year ECE students: Discrete-time signals and systems, Sampling and digital signal reconstruction, Decimation and interpolation, Z Transform, Discrete Fourier Transform (DFT), Algorithms for DFT computation - the Fast Fourier Transform (FFT), FIR and IIR digital filters, Random discrete-time signals, Power spectral density estimation, Applications and advanced methods of DSP

http://www.eng.ucy.ac.cy/gmitsis/ece429/

ECE623 – Digital Signal Processing - Spring 2013
Graduate course for M.S. and Ph.D. students: Discrete-time signals and systems, Random signals and linear systems, Sampling and reconstruction, Decimation and Interpolation, Discrete Time Fourier Transform and Fast Fourier Transform, Filter design, Power spectral density estimation, Autoregressive signal modeling, Hilbert transform, Spectrograms and Short time Fourier transform.

http://www.eng.ucy.ac.cy/gmitsis/ece623/

ECE636 -Systems Identification - Fall 2009, 2011
Graduate course for Master's and Ph.D. students. Random signals and linear systems, Models of linear and nonlinear systems, Nonparametric identification in the time and frequency domains, Model parametrizations, Parametric identification, Recursive identification, Identification of closed-loop systems, Model order selection and validation, Input design, Identification of nonlinear systems.
http://www.eng.ucy.ac.cy/gmitsis/ece636/

ECE795 - Pattern Recognition -Fall 2010, 2012

Graduate course for Master's and Ph.D. students. Probability and decision theory, Bayesian inference, Linear models for regression and classification, Nonlinear classifiers and neural networks, Kernel methods and support vector machines, Principal and independent component analysis, Mixture models and expectation maximization, Sampling methods.

http://www.eng.ucy.ac.cy/gmitsis/ece795/

 

 

 

 

   
  last updated : June 12, 2012