|3 YEAR||2 semester||6 CREDITS* – 9 CREDITS (22-23)|
|Prof. Marina Ruggieri||2019-20|
*the number of credits depends from your study plan
LEARNING OUTCOMES: The course aims at providing to the students the theoretical and practical tools for the development of design capabilities and implementation awareness of Digital Signal Processing (DSP) systems and applications.
KNOWLEDGE AND UNDERSTANDING: Students are envisaged to understand the DSP theoretical, design and algorithm elements and to be able to apply them in design exercises.
APPLYING KNOWLEDGE AND UNDERSTANDING: Students are envisaged to apply broadly and, if applicable, to personalize the design techniques and algorithm approaches taught during the lessons.
MAKING JUDGEMENTS: Students are envisaged to provide a reasoned description of the design and algorithm techniques and tools, with proper integrations and links.
COMMUNICATION SKILLS: Students are envisaged to describe analytically the theoretical elements and to provide a description of the design techniques and the algorithm steps, also providing eventual examples.
LEARNING SKILLS: Students are envisaged to deal with design tools and manuals. The correlation of topics is important, particularly when design trade-offs are concerned.
PART 1- Discrete-time signals and systems; representation in the time domain; sampling process; Discrete-time Fourier transform (DTFT); Z-transform; Discrete Time Fourier Series (DTFS).
PART 2 – Processing algorthms: introduction to processing; Discrete Fourier Transform (DFT); finite and long processing; DFT-based Processing; Fast Fourier Transform (FFT); processing with FFT.
PART 3 – Filter Design: introduction to digital filters: FIR and IIR classification; structures, design and implementation of IIR and FIR filters; analysis of finite word length effects; DSP system design and applications; VLAB and applications (Dr. Tommaso Rossi) with design examples and applications of IIR and FIR filters, Matlab-based lab and exercises (optional).