OVERVIEW

The course is aimed at:

Students who intend to enroll in the MSc in Computer Science, with the aim of providing a "warm up" for the courses of the Artificial Intelligence and Data Analytics curriculum.
Students who do not intend to continue their studies, with the aim of introducing the basic information for the processing of signals and images through the application of the Discrete Fourier Transformation.

AIMS AND CONTENT

LEARNING OUTCOMES

Learning the basic tools for signal and image processing. Delving into some classic processing problems. Learning how to structure, implement, and validate methods that address a signal processing problem on real data.

AIMS AND LEARNING OUTCOMES

By the end of the course, the student will have acquired the tools to process signals and images in their original domains (time or space) as well as in the frequency domain.

Specifically, the student will be able to:

• Enhance signals and images by increasing the signal-to-noise ratio or reducing noise
• Highlight interesting features in signals or images.

PREREQUISITES

The course assumes prior knowledge of calculus, linear algebra, and a solid foundation in programming.

TEACHING METHODS

The course includes:

• theoretical lessons supplemented with in-depth study material for independent study
• An individual project activity conducted in class, under the supervision of the instructor

Students who hold valid certificates relating to Specific Learning Difficulties (SLD), disabilities or other educational needs are invited to contact the lecturer and the school’s disability liaison officer at the start of the course to agree on any teaching arrangements which, whilst respecting the course objectives, take into account individual learning styles. 

The contact details for the university’s disability liaison officer are available at the following link: https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita.

SYLLABUS/CONTENT

• Signals 1D and 2D and Sampling
• Introduction to Discrete Fourier Transform
• Discrete Convolution
• Linear Filters on 1D and 2D Signals (Noise Reduction and Feature Enhancement)
• 1D Applications: Sound and Time Series
• 2D Applications: Color Image Processing, Pixel Operations, Geometric Operations

RECOMMENDED READING/BIBLIOGRAPHY

Handouts, links, and slides will be provided by the instructor.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

According to the calendar approved by the Degree Program Board: https://easyacademy.unige.it/portalestudenti/index.php?view=easycourse&_lang=it&include=corso

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The assessment consists of two parts:

• Evaluation of the project activity, which includes a brief presentation of the work done  
• Written exam on the covered program

Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at  https://corsi.unige.it/en/corsi/8759/studenti-disabilita-dsa

ASSESSMENT METHODS

The student will need to demonstrate, through the development of software and the analysis of results conducted independently, an understanding of the theoretical and practical aspects of signal or image analysis.

Additionally, the writing of the project report and the presentation will aim to evaluate the student's expression and summarization skills.

FURTHER INFORMATION

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

Students with valid certifications for Specific Learning Disorders (SLD) may request accommodations for exams at least 7 days prior to the exam date by filling out the “accommodation request form” (available via online services at https://modulionline.unige.it/richiesta-adattamenti# no-back), which will be automatically forwarded by the system to the instructor in charge of the course and to the faculty liaison for students with disabilities and SLDs in their School/Department. 

The student will receive a copy of their request.