OVERVIEW

The Digital Signal and Image Processing course introduces the fundamental principles of digital signal analysis and processing, providing the theoretical and practical tools required for the representation, analysis, and processing of one-dimensional signals in the time, spatial, and frequency domains.

An initial part of the course is devoted to reviewing and strengthening key concepts from mathematical analysis, linear algebra, and probability, which are essential for understanding the models and methodologies used in signal processing.

Lectures are complemented by guided laboratory sessions aimed at the practical application of the concepts presented in class and at developing familiarity with software tools for signal analysis and processing.

AIMS AND CONTENT

LEARNING OUTCOMES

The purpose of the teaching unit is to explore learning the basic tools for the analysis of 1D signals in both the time/space and frequency domains.

AIMS AND LEARNING OUTCOMES

The course provides the theoretical and methodological foundations of digital signal processing, introducing the mathematical tools required for the representation, analysis, and processing of signals in the time, spatial, and frequency domains. Particular emphasis is placed on understanding the fundamental concepts of Digital Signal Processing and on their application through computational tools.

Upon successful completion of the course, students will be able to:

• understand and correctly use the terminology and fundamental concepts of digital signal processing;

• describe the main mathematical models and tools used for signal analysis;

• apply fundamental processing techniques in the time, spatial, and frequency domains;

• interpret and compare the results obtained through different analysis and processing methodologies;

• implement and use computational tools for the analysis and processing of digital signals.

PREREQUISITES

Basic knowledge of mathematical analysis, linear algebra, probability and statistics, complex numbers, and programming is required. Students should be able to develop simple programs and have a basic familiarity with numerical computing tools

TEACHING METHODS

The course consists of lectures, laboratory activities, and individual assignments. Practical activities and assigned exercises allow students to deepen their understanding of and apply the topics covered during the course.

SYLLABUS/CONTENT

The course covers the main theoretical and computational tools for the analysis and processing of one-dimensional signals in the time, spatial, and frequency domains.

Topics include:

• review of mathematical analysis, linear algebra, and probability concepts relevant to signal processing;

• representation and analysis of signals and systems;

• Fourier series and Fourier transform;

• linear systems and convolution;

• filtering in the time and frequency domains;

• Kalman filtering and estimation methods for dynamic signals;

• multiresolution analysis and wavelet transforms;

• frames and redundant signal representations;

• introduction to sparse signal representations and dictionary learning;

• examples and applications of digital signal processing.

Laboratory activities involve the implementation and practical experimentation of the methodologies presented during the course using dedicated Python software packages.

RECOMMENDED READING/BIBLIOGRAPHY

Lecture notes, readings, and slides provided by the instructors through the AulaWeb platform.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

According to the calendar approved by the Degree Program Board: https://corsi.unige.it/en/corsi/11964/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The examination consists of the development of a project on a topic selected from a list proposed by the instructor and an oral examination. During the oral examination, students will discuss both the theoretical topics covered in the course and the project and assignments completed throughout the semester.

Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/11964/studenti-disabilita-dsa

ASSESSMENT METHODS

Students are expected to demonstrate a solid understanding of the fundamental concepts of digital signal processing and the ability to apply the mathematical and computational tools presented during the course.

Assessment is based on the project and the oral examination. Evaluation will take into account the understanding of the theoretical concepts, the ability to apply the methodologies studied, the quality of the work produced, and the ability to critically discuss the obtained results.

FURTHER INFORMATION

For further information, please refer to the course’s AulaWeb module or contact the instructor.