Digital Data Processing

Period: Second semester

Course unit contents: 

THEORETICAL CONCEPTS ABOUT DIGITAL DATA PROCESSING:
Sampling of a continuous signal: sampling frequency and resolution. Nyquist-Shannon sampling theorem.
Signal statistics: outlier detection and removal, detrend, offset removal. The Least-Square method. Signal-to-noise ratio.
Discrete-time Fourier transform: amplitude and phase spectra computation, f-k spectra computation.
Convolution and digital filters, tapers.
Cross-correlation and autocorrelation: search for similarity between different signals, search for periodicity within a signal.

MATLAB PROGRAMMING:
Matrices, vectors and scalars. Numerical formats. Scripts versus functions.
Operations between scalars, vectors and matrices. Mathematical and trigonometric functions. 1D plots.
Statistical operators.
Complex numbers, periodic functions, spectra computation and representation.
For versus while loops. Writing and reading files. 2D and 3D plots.
Convolution and cross-correlation.
Design of digital filters.

Planned learning activities and teaching methods: Class lectures. Practical exercises with MATLAB.

In addition to contacting the course instructor, students with disabilities, Specific Learning Disorders (SLD), Special Educational Needs (SEN), and other health conditions can reach out to the Student Services Office - Inclusion Unit to receive more information about opportunities to access teaching with specific support and tools