The objective of this course is to provide a basic introduction to the theory of digital signal processing (DSP)

01 Introduction to digital signal processing
01.01 Characterization and classification of signals
01.02 Digital signal processing: pros and cons

02 Discrete-time signals
02.01 Time domain representation
02.02 Operations on sequences
02.03 Classification of sequences 
02.04 Energy and average power
02.05 Basic sequences
02.06 Complex sequences and sinusoids

03 Discrete-time systems
03.01 Properties of discrete-time systems
03.02 Example of basic systems
03.03 LTI systems
03.04 Impulse response and discrete convolution
03.05 Difference equations

04 Discrete-time signals in the frequency domain
04.01 The Discrete-Time Fourier Transform (DTFT)
04.02 DTFT examples
04.03 Properties of DTFT
04.04 The convolution theorem for DTFT
04.05 Parseval's relation
04.06 Frequency response
04.07 Phase and group delay

05 Sampling and quantization
05.01 Ideal sampling
05.02 The sampling theorem
05.03 Aliasing
05.04 Signal reconstruction
05.05 Quantization ad quantization error

06 The Discrete Fourier Transform
06.01 The Discrete Fourier Transform (DFT)
06.02 The relation between DFT and DTFT
06.03 DTFT sampling with the DFT
06.04 Properties of the DFT
06.05 Linear convolution and circular convolution
06.06 Frequency analysis with DFT
06.07 The Fast Fourier Transform (FFT)

07 The Z-Transform
07.01 The Z-Transform
07.02 The relation between DTFT and Z-transform
07.03 Regions of convergence
07.04 Pole-zero diagrams
07.05 Properties of the Z-Transform
07.06 The inverse Z-Transform
07.07 The Transfer Function, stability and causality

08 Discrete-time LTI systems in the frequency domain
08.01 Ideal filters
08.02 Practical filter specifications
08.03 FIR filters
08.04 Example of simple FIR filters
08.05 Linear phase FIR filters
08.06 Zero-phase filters
08.07 IIR filters
08.08 IIR filter design with pole-zero placement method
08.09 Comb filters
08.10 All-pass filters
08.11 Minimum/maximum phase systems
08.12 Inverse system
08.13 The window method for FIR filter design

09 Laboratory 
09.01 Introduction to Python language
09.02 DSP algorithms using Python

Although there are no mandatory prerequisites for this exam, students are strongly encouraged to take it after the exams of: Calculus, Discrete Structures and Linear Algebra.

Knowledge and understanding:

At the end of the course the student will learn the fundaments of signal analysis, will know how signals can be processed using digital filters, will know the main digital filter structures and how these can be designed and she/he will acquire the ability to understand the principles and methods at the basis of any DSP system.

Applying knowledge and understanding:

The student will learn the methodologies of digital signal processing (DSP) and will be able to apply them for processing audio signals. In particular, she/he will be able to analyze signals, to process them using digital filters and to design different kinds of DSP filters.The ability to apply these techniques will be developed and sharpened in the laboratory exercitations, where audio signals will be analyzed.

Making judgements:

The student will be able to apply the methodologies of DSP for understanding and solving novel problems involving signal processing.

Communication skills:

The student will acquire the ability to communicate the fundamental concepts of DSP with an appropriate and rigorous terminology. She/he will learn to describe the problems related to signal processing and the methodologies adopted for their solution.

Learning skills:

The student will acquire the ability to study and learn novel techniques for signal analysis and signal processing, and she/he will be able to develop autonomously solutions for novel problems related to DSP.

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

Teaching

Frontal lessons and laboratory activities

Attendance

Although recommended, attendance of this course is not mandatory.

Course books

S. Mitra, "Digital signal processing", McGraw-Hill, 2001, 2011.
A. V. Hoppenheim e R. W. Schafer, "Discrete-time signal processing", Prentice Hall, 2010.

Assessment

Written exam and oral exam.

The written exam is evaluated in thirtieths and it is passed if the mark, which holds for the whole academic year, is at least 15/30. The oral exam, which is composed by open answer questions, can be performed only after passing the written exam and predominantly contributes to the final mark. The evalution of the oral exam considers the acquired knowledge, the comprehension of the subject and the ability to properly present the topic.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Teaching

As for attendees.

Attendance

As for attendees.

Course books

As for attendees.

Assessment

As for attendees.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.