AIMS AND CONTENT

LEARNING OUTCOMES

This course develops the practical aspect of the models and methodologies studied in the "Fundamentals of Telcommunications and Signal Processing" course, demonstrated through applications.

AIMS AND LEARNING OUTCOMES

Aims:

The teaching aims to provide students with a solid foundation in digital signal processing (DSP) principles and techniques. It introduces the theoretical underpinnings and practical tools needed to analyze, design, and implement DSP systems. The course prepares students to apply DSP methods across a wide range of real-world applications.

Learning Outcomes:

By the end of the course, students will be able to:

1. Understand the mathematical foundations of discrete-time signals and systems.

2. Analyze signals in the time and frequency domains using tools such as the Discrete-Time Fourier Transform (DTFT), Discrete Fourier Transform (DFT).

3. Implement DSP algorithms using software tools (e.g., MATLAB or Python) and understand their computational aspects.

TEACHING METHODS

The course combines theoretical and practical approaches to facilitate a comprehensive understanding of digital signal processing concepts. Lectures are delivered using a mix of slides for structured content presentation and the blackboard for detailed derivations and interactive problem-solving.

To reinforce learning, hands-on sessions with software tools such as MATLAB or Python are integrated into the course. These sessions allow students to apply theoretical knowledge to real-world signal processing problems, visualize results, and develop practical skills in algorithm implementation and data analysis.

Active participation, guided exercises, and examples from applied domains ensure a balanced and engaging learning experience.

SYLLABUS/CONTENT

• Introduction to DSP: Overview of signal types, basic operations, sampling theorem, and quantization

• Discrete-Time Signals and Systems: Linear time-invariant (LTI) systems, convolution, difference equations

• Frequency Domain Analysis: Discrete-Time Fourier Transform (DTFT), Discrete Fourier Transform (DFT), FFT algorithms

• Applications of DSP: Audio and speech processing, biomedical signals, communications, and IoT

• Software Tools: Hands-on exercises using MATLAB, Python (NumPy/Scipy), or equivalent DSP libraries

RECOMMENDED READING/BIBLIOGRAPHY

1. A.V. Oppenheim, R.W. Schafer, "Discrete-Time Signal Processing", Pearson / Prentice Hall
    
2. J.G. Proakis, D.G. Manolakis, "Digital Signal Processing: Principles, Algorithms, and Applications", Pearson

Students might also consider referencing online resources like MATLAB documentation, Python libraries (NumPy, SciPy, Matplotlib), or DSP-specific resources (e.g., Coursera, edX) for practical, up-to-date material.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

See the official polytechnical school calendar.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Written test and/or oral exam.

ASSESSMENT METHODS

The assessment for this teaching is designed to evaluate both theoretical understanding and practical application skills through a combination of the following components:

• Written Exam: Evaluates comprehension of core concepts, analytical ability, and problem-solving skills related to digital signal analysis, transforms, and filter design.

• Oral Examination (optional): Assesses the ability to explain key principles, demonstrate reasoning, and apply concepts to unseen problems or system scenarios.

FURTHER INFORMATION

For any further information please contact andrea.sciarrone@unige.it