Signaux et Systèmes 2

Signal Processing and Spectral Analysis

Description: The digital world produces large volumes of data of all kinds (audio, images, video, physical measurements) associated with human activities in fields as diverse as healthcare, telecommunications, industry, and the environment. Extracting information from these signals is increasingly necessary for decision-making (e.g., medical diagnosis), information encoding (e.g., data compression), the analysis of physical phenomena (e.g., detection of mechanical faults), and signal restoration (e.g., removal of unwanted noise from an audio signal).

Signal processing lies at the interface of mathematics, physics, and computer science. Mathematical concepts provide tools for signal representation and the operators required for their manipulation. Physical models make it possible to link measured data to the information sought. Finally, computer science is essential for the implementation of any digital processing. 

Bibliography:

• Ref. [1] : A.V. Oppenheim and R.W. Schafer, Discrete Time Signal Processing, Prentice Hall
• Ref. [2] : G. Fleury, Analyse Spectrale. Méthodes non-paramétriques et paramétriques, Ellipses (2001)

Learning outcomes: At the end of the second-year course, students will be able to understand and use deterministic (AA1) and statistical (AA2) signal processing methods to solve various problems in information sciences, such as filtering (AA3), advanced spectral analysis (AA4), and others. These problems arise in applications as diverse as automatic recognition of audio signals (speakers, etc.), radar source localization, climate data analysis, medical image reconstruction in MRI, gravitational wave detection in astrophysics, and the development of future-generation cellular networks (5G, IoT).

Evaluated skills:

• Physical Modeling
• Data Processing

Course supervisor: Stéphane Rossignol

Geode ID: SPM-SIC-002