OVERVIEW

The class aims at giving the students an overview of the theory and applications of digital signal processing, digital systems and applications.

AIMS AND CONTENT

LEARNING OUTCOMES

The class aims at giving the students the knowledge about digital signal processing and digital systems both in the time and frequency domains, digital filters and applications of the theory applied to processing, compression and transmission of audio signals, still images and video sequences.

AIMS AND LEARNING OUTCOMES

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

Knowledge and understanding

• Explain the theoretical foundations of discrete-time signal analysis and processing, describing their analytical differences compared to continuous-time signals.
• Describe the theoretical principles and corresponding compression standards for encoding key multimedia signals (audio and video).

Applying knowledge and understanding

• Apply the Discrete-Time Fourier Transform (DTFT), the Discrete Fourier Transform (DFT/FFT), and the Z-transform for spectral analysis and the study of discrete-time systems.
• Design, implement, and evaluate digital filters (FIR and IIR) and discrete processing systems based on real-world application specifications.
• Develop and implement digital signal processing algorithms, with particular emphasis on DFT/FFT-based spectral estimation techniques and various types of linear filtering.

Making judgements

• Evaluate and compare the effectiveness of different processing approaches and multimedia compression formats, independently selecting the optimal solution in terms of signal fidelity and computational resource consumption.

Communication skills

• Present and justify, both in written and oral form, design choices as well as filtering or processing results, using appropriate and rigorous technical terminology.

Learning skills

• Integrate theoretical mathematical knowledge and continuous-time signal theory to independently model complex digital processing problems and adapt to evolving multimedia technology standards.

PREREQUISITES

Basic elements of mathematical analysis and geometry, fundamentals of programming in C/C++, Java, and Python.

TEACHING METHODS

The course consists of different types of activities to foster both theoretical and practical learning:

• Lectures (in-class): dedicated to explaining the theoretical, methodological, and mathematical foundations of signal processing, image processing, and multimedia encoding.
• Guided practical exercises: carried out in class (also with the aid of simulation software tools), aimed at the concrete application of theoretical concepts through the resolution of analytical problems and exercises.

Class attendance is strongly recommended to achieve optimal learning outcomes and to facilitate the understanding of the practical aspects of the syllabus

SYLLABUS/CONTENT

Part One (1st Semester, 6 CFU) – Prof. A. Sciarrone

• Module 1: Foundations of Discrete-Time Signals and Systems
  • Definition, properties, and spectral representation of discrete-time signals.
  • Fundamental operations: linear convolution and correlation.
  • Discrete-time Linear Time-Invariant (LTI) systems: properties and representation via impulse response and difference equations.
• Module 2: Sampling, Conversion, and Linear Transforms
  • Sampling theorem, aliasing, and signal reconstruction.
  • Frequency analysis tools: Z-transform (region of convergence, stability, and causality), Discrete-Time Fourier Transform (DTFT), and Discrete Fourier Transform (DFT/FFT).
  • Circular convolution, block filtering (Overlap-Add and Overlap-Save), and spectral estimation using FFT.
• Module 3: Design and Analysis of Digital Filters
  • Structure and properties of non-recursive Finite Impulse Response (FIR) filters and recursive Infinite Impulse Response (IIR) filters.
  • Design techniques for FIR filters (window method) and IIR filters (bilinear transformation).
• Module 4: Image Processing and Multimedia Signals
  • Two-dimensional signal representation: sampling and quantization of spatial images.
  • The Discrete Cosine Transform (DCT) and its energy compaction properties (Introduction/Overview).

RECOMMENDED READING/BIBLIOGRAPHY

Course Notes provided by the Teachers.

• A. V. Oppenheim, R. W. Schafer, Elaborazione Numerica dei Segnali, Franco Angeli, 1996.
• A.V. Oppenheim, R.W. Shafer, J.R. Buck, discrete-Time Signal Processing, secon edition, Prentice Hall, 1999
• J. G. Proakis, C.M.Rader, F. Ling, C. L. Nikias, et alii, Algorithms for Statistical Signal Processing, Prentice Hall, Upper Saddle River, NJ, 2002.
• M. Bellanger, Digital Processing of Signals, Third Ed. John Wiley & Sons, 2000.
• R. C. Gonzalez, R. E. Woods, Digital Image Processing, Second edition, Prentice Hall, NJ, 2002.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/9273/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam consists of three separate assessments corresponding to the three parts that make up the course:

Part One (Prof. A. Sciarrone)

The exam for the first part of the course (Prof. A. Sciarrone) aims to verify the level of achievement of the expected learning outcomes and is structured into two sequential phases:

• Written Test (or Partial Exams): the assessment includes intermediate written exams (partial exams during the semester) or, alternatively, a full written exam during the official exam sessions. The test focuses on solving numerical exercises and analytical problems, aimed at verifying the student's ability to apply mathematical tools (transforms, filters, sampling) to discrete-time signals and systems. Passing the written test with a positive grade is mandatory to access the optional oral exam.
• Oral Exam (Optional): consists of an interview aimed at verifying a thorough understanding of the theoretical principles of the subject, the ability to make connections between the different modules of the program, and the student's command of technical and expository language. The final grade for the first part will take into account the results achieved in both tests.

The second part exam includes a multiple choice and answer test aimed at verifying the student's understanding of theoretical topics carried out during the second semester with Prof. Lavagetto. In addition it student will also have to carry out a practical test on Matlab in which he will be asked to solve practical problems on real signals (eg voice signal) through signal processing algorithms (Prof. Sciarrone).

Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

ASSESSMENT METHODS

Part One (Prof. A. Sciarrone)

The assessment of learning outcomes and passing the exam are carried out through an integrated verification of the theoretical knowledge, application skills, and soft skills acquired by the student. Specifically, the various tests will assess the learning outcomes according to the following criteria:

• Through the Written Test (or Partial Exams): applying knowledge and understanding is assessed. In particular, the evaluation will focus on the student's ability to apply mathematical tools (Z-transform, DFT/FFT) for spectral analysis, and their capability to design, implement, and evaluate the performance of digital filters (FIR and IIR) and discrete-time systems based on application specifications.
• Through the Oral Exam: knowledge and understanding are assessed, requiring the student to explain the theoretical foundations of signal processing, image processing, and multimedia compression standards. Learning skills will also be evaluated, defined as the ability to link and integrate continuous-time signal theory with discrete-time models.
• Cross-cutting evaluation criteria (Making judgements and Communication skills): during the interview and the discussion of the tests, key evaluation elements will include the use of appropriate and rigorous technical terminology (communication skills), as well as the ability to critically evaluate and independently compare the effectiveness of different algorithmic approaches or compression formats in terms of computational costs and signal fidelity (making judgements).