McGillBIEN350 - Biosystems and Control (Fall 2017): Core undergraduate course (U2 students): Discrete- and continuous-time signals; basic system properties. Linear time-invariant systems; convolution. Frequency domain analysis; filtering; sampling. Laplace and Fourier transforms; transfer functions; poles and zeros; transient and steady state response. Z-transforms. Dynamic behaviour and PID control of first- and second-order processes. Stability. Applications to biological systems, such as central nervous, cognitive, and motor systems.BIEN462 - Engineering Principles in Physiological Systems (Fall 2015): Advanced undergraduate course. Review of Signals and
Systems, Time and Frequency Domain Analysis, Differential Equation Representations,
Parameter Estimation, Closed-loop Systems, Basic Control Principles,
Electrophysiology, The Circulatory System, The Respiratory System, The
Endocrine and Renal SystemsBMDE502 - Biomedical Modeling and Identification (Winter 2015, 2016, 2017 - with Prof. H. Galiana): Graduate course for M.S. and Ph.D. students: Random
Signals and Linear Systems, Black-box vs. Grey-box System Representations, Linear/Nonlinear
Models, Mapping from Continuous to Discrete-time Models, Identification Approaches
for Linear and Simple Nonlinear Models.University of CyprusECE220 - Signals and Systems I (Spring 2009, 2010, 2011, 2012, 2013): Core curriculum course
for 2^{nd} year ECE students: Continuous Time Signals and Systems,
Linear Time Invariant Systems, Convolution, Differential Equation Models,
Frequency Response and Filtering, Fourier and Laplace Transforms.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.ECE623 - Digital Signal Processing (Spring 2013, Fall 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.ECE636 - Systems Identification (Fall 2009, Fall 2011): Graduate course for M.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.ECE795 - Pattern Recognition (Fall 2010,
Fall 2012): Graduate course for M.S. and Ph.D. students:
Probability theory, Bayesian decision theory, Parameter estimation,
Nonparametric density estimation, Linear classifiers, Neural Networks, Kernel
methods, Support vector machines, Mixture models and expectation maximization,
Principal and independent component analysis, Unsupervised learning. |