UE T9ISN-A - Technology

Level of knowledge :

N1: beginner
N2: intermediate
N3: confirmed
N4: expert


The knowledge expected at the end of the course
* To know and understand different estimation approaches exploiting adaptive and optimal filtering (LMS, Kalman, etc.) applied to different application contexts (tracking, enhancement, etc.).): (C3, N4)
* Have developed a general knowledge of different application fields, including a good understanding of the properties of dedicated signals and associated processing (radar, biomedical signals, GPS signals, inertial units): (C5, N3)
* Deepen knowledge of advanced methods in machine learning on networks for deep learning (RNN, LSTM, GAN, AE and VAE): (C8,N3)
* Know the principles of image segmentation in a processing chain : (C3, N2)
* Be familiar with the complete imaging chain, integrating the principles of 3D-2D projective geometry: (C3, N4)
* Be familiar with the advanced principles of digital communications, including 5G standards: (C7,N4)
Learning outcomes in terms of abilities, skills and attitudes expected at the end of EU courses
* Be able to exploit the various concepts of signal or image processing to meet the specifications required: (C5, N3)
* Develop, simulate, evaluate and validate signal processing approaches based on characterization, modeling, estimation and detection techniques in the context of a given application: (C3, N3)
* Evaluate the usefulness of each block in the radar chain, from waveform selection to the information block, via the RF stage and the signal processing receiver block: (C2, N2)
* Dimension each block of a digital communication chain: (C4, N3)
* Take into account the specifications of a communication standard: (C9, N3)
* Ability to implement processing chains for machine learning applications: (C8, N3)
* Use 2D/3D computer vision APIs in robotics, drone control, etc. (C3, N3)