Obiettivi Formativi

Fornire conoscenza sui concetti di modellizzazione ed elaborazione dei segnali ed immagini e sugli approcci statistici all'analisi dei dati. Tali approcci verranno applicati e ottimizzati per l'analisi dei segnali biomedici. In particolare, sono fondamentali i seguenti argomenti: processi aleatori e la loro caratterizzazione nel tempo e in frequenza. Stimatori. acquisizione, campionamento e quantizzazione dei segnali. algoritmi di ricostruzione metodi per la riduzione del rumore

Learning Goals

Provide knowledge on the concepts of modeling and processing of signals and images and on statistical approaches to data analysis. These approaches will be applied and optimized for the analysis of biomedical signals. In particular, the following topics are of fundamental importance: random processes and their characterization over time and frequency. Estimators. acquisition, sampling and quantization of the signals. reconstruction algorithms methods for noise reduction.

Metodi didattici

- Lezioni frontali - Esercitazioni

Teaching Methods

- Classroom lectures - Practice

Prerequisiti

Familiarità con strumenti di programmazione e con software di analisi dati e visualizzazione grafica. Analisi di Fourier.

Prerequisites

Familiarity with programming tools and with data analysis and graphic visualization software. Fourier analysis.

Verifiche dell'apprendimento

- Valutazione delle attività svolte durante le esercitazioni (propedeutica per l’accesso alla prova orale) - Esame orale

Assessment

- Exercitation activity evaluation (mandatory for access to oral test) - Oral test

Programma del Corso

Descriptive Statistics : Univariate Statistics, Bivariate and Multivariate Statistics, ROC Analysis • Inferential Statistics: Basic Concepts, Parametric Hypothesis Testing, Nonparametric Hypothesis Testing, Multiple and Multivariate Hypothesis Testing • Experimental Design and Sample Size Calculations • Statistical Shape Models • Introduction to Computer Programming and MATLAB: Control Structures, Functions, Program Development and Testing, Program Development and Testing,Data Types:,File Input/Output,Program Design,Visualization,Code Efficiency,Signal and Image Processing,Graphical User Interfaces • MATLAB Case Study on Descriptive and Inferential Statistics.

Course Syllabus

Descriptive Statistics : Univariate Statistics, Bivariate and Multivariate Statistics, ROC Analysis • Inferential Statistics: Basic Concepts, Parametric Hypothesis Testing, Nonparametric Hypothesis Testing, Multiple and Multivariate Hypothesis Testing • Experimental Design and Sample Size Calculations • Statistical Shape Models • Introduction to Computer Programming and MATLAB: Control Structures, Functions, Program Development and Testing, Program Development and Testing,Data Types:,File Input/Output,Program Design,Visualization,Code Efficiency,Signal and Image Processing,Graphical User Interfaces • MATLAB Case Study on Descriptive and Inferential Statistics.