Signal processing applications span an immense set of discipines that include communications, space explorations, and medicine just to name a few. In particular, digital signal processing deals with the representation, transformation, and manipulation of signals and the information therein.
This course aims to provide the basic knowledge on the basic methodologies for signal manipulation, digital signal processing. Particular attention will be given to biomedical applications of each presented techinque. This to provide to the student a view of direct usage of theoretical knowledge
Aims 1. Understanding the theoretical basis of the discrete signal processing. Learning outcomes for Aim 1. The students will be able to critically discuss the theoretical basis and fundamental elements of discrete signal processing (e.g. Discrete Fourier Transform, Filter design, power spectral analyses)
Aim 2. Apply signal processing techniques in clinical and scientific context of classical biomedical applications. Learning outcomes for Aim 2. The students will implement and apply the classical and more advanced methods for biomedical signal processing. They will also learn the physiological basis for classical biological signals and how these signals can be analysed to extract information relevant for diagnosis in specific pathologies.
Aim 3. Understand and appropriately apply an hypothesis test Learning outcomes for Aim 3. The students will be able to critically discuss the theoretical basis of hypothesis tests and they will be able to design and conduct an hypothesis test
Aim 4. Problem solving in real case examples of signal analysis. Learning outcomes for Aim 4. During the working groups, the students will acquire the capability to solve specific problems of data analysis by applying the techniques acquired during the course. The working groups will be organized such that different students will assume different roles as in real lab teams.
It is required for the student to have basic understanding of the fundamental elements of system engineering (e.g., Laplace Fourier transforms) and basic descriptive statistics - e.g., hisotgrams, mean and variance. It is strongly encouraged for the students to take a progamming course and specifically in Matlab. However, we will provide additional materials and autoevaluation tools for testing the above mentioned prerequistes
Frontal lessons with Matlab exercises that students will solve autonomously and deliver through aulaweb-platform for their evaluation during the semestrer.
Digital signal processing
Data processing
Ricevimento: On appointment
Ricevimento: With appointment: Tel. 0103532220 or marco.fato@unige.it
MARCO MASSIMO FATO (President)
VITTORIO SANGUINETI
GABRIELE ARNULFO (President Substitute)
https://corsi.unige.it/8713/p/studenti-orario
The final grade will be composed of the evaluations of each group of work, a written and an oral exam.Students will self-organise in small groups (max 3) and these groups will participate in several activities during the semester. Each assignment will be evaluated for its completeness and overall quality.
Aim 1 and 3 will mainly be evaluated during the written and oral exam. Aim 2 and 4, will be evaluated during the group assignments and during the oral examination.
