At the end of the course, the student: - will know the basic signal processing techniques like sampling, digital filtering, correlation and spectral estimation. - will understand the relationship between signal processing theory and algorithms for different engineering areas. - will be able to design and perform a Matlab or Python-based development project on signal processing and its report. - will know in detail how some state-of-the-art applications based on digital signal processing work.

Students must use their own laptops with Matlab or Python.

The course will cover the following topics: - Introduction to Signal Processing (6 hours) - Signal correlation (6 hours) - Spectral analysis (6 hours) - Sampling (6 hours) - Digital Filtering (6 hours) - Digital Signal Processing applications: sampling, spectral analysis, correlation (6 hours) - Digital Signal Processing applications: alarm signal recognition, tags for findind objects (6 hours) - Digital Signal Processing Applications: bio signals and pulse oximeters (6 hours) - Digital Signal Processing Applications: introduction to audio processing (6 hours) - Digital Signal Processing Applications: communication signals for aerospace and satellites (6 hours)

The course is organized in two parts. The first part introduce the basis of signal processing: sampling, Fourier transform, correlation and digital filtering. The second part presents some important DSP applications: localization techniques like UWB tags and GPS, alarm signal recognition, bio-signals and pulse-oximetry, basic audio processing and image processing. During the course, 4 assignments are proposed on DSP applications, consisting in the implementation of a Matlab or Python program and the preparation of a presentation. Each assignment topic has at least 6 hours of tutoring where the teacher is available to help students with Matlab/Python implementation.

Dutoit, Thierry, Marques, Ferran, "Applied Signal Processing: A MATLAB-Based Proof of Concept", Springer. Samuel D. Stearns, Donald R. Hush, "Digital Signal Processing with Examples in MATLAB, CRC Press. Course notes provided by the teacher.

Exam: Optional oral exam; Group project;

The scope of the exam is to verify that the student has acquired the basis of signal processing and is able to develop Matlab-based projects on engineering problems requiring signal processing. There are two examination modes: exam A and exam B. The exam A is organized as follows. During the course four assignments are proposed. They must be solved individually or in groups of up to three students. The students must deliver • a presentation containing all the results, the figures and the required answers • all the Matlab or Python programs. Each assignment is evaluated and receives a grade (max = 30). The students who deliver their assignment within 2 weeks from when it is assigned will receive 2 bonus points (max = 32), to encourage the students to stay aligned to the course. All the assignments must be submitted by the end of the September session; submissions will not be accepted after this deadline. At the end of the course the mean value of the assignment grades is computed and proposed as the final grade. Students who reach at least 30.5 get a grade of 30 cum laude. The students who want to further improve their average assignment mark (at most +4/30) can deliver one or two of the extra assignments which are proposed at the end of the course. For each of them the student can get up to +2/30 points for the final mark. As an alternative, the exam B is organized as follows. Students who opt not to submit the assignments must take an oral exam consisting of 4 questions taken from a list of about 40 questions. (maximum mark = 24) The students who want to further improve this mark can deliver some of the extra assignments which are proposed during the course. For each of them the student can get up to +2/30 points for the final mark (with the optional delivery, the maximum mark becomes 30).

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.