Lecture notes and reading material
Preliminary lecture notes will be available in PDF format before the class (usually Wednesday evening) while definitive lecture notes will be available only after the class has been held, in a timely fashion (usually, at the latest, a couple of days after the lecture).
Lecture notes will be complemented by possible reading material listed on the syllabus and further pointers available in the student area. Due to copyright issues, electronic copies of the material are only available to EPFL students officially enrolled in this course. Students interested in downloading this material can do so from the student area by logging in using their GASPAR account.
This page will include the information relevant to each week’s lecture and corresponding material.
Organization of the course (team, workload, credits); overview of the course content; introduction to signal processing – signals, time continuity and time discretization, analog and digital signals, baseline concepts.
Introduction to signal processing – Fourier series and transform, convolution.
Introduction to signal processing – sampling, reconstruction, and aliasing.
Introduction to signal processing – the Laplace transform for continuous-time signals and systems; frequency response, impulse response and transfer function; analog filter analysis and synthesis.
Introduction to signal processing – Bode plots; the Z-Transform for discrete-time signals and systems; filter order and type; digital filter analysis and synthesis.
Introduction to embedded systems – terminology, main modules (perception, communication, computation, and action); sensor types and performance, power consumption, management, generation, and storage.
Introduction to embedded systems – communication and embedded computation.
From embedded systems to mobile robotics – real-time computing and high-fidelity simulation.
Introduction to mobile robotics – control architectures and positioning systems.
Introduction to mobile robotics – localization through odometry.
Introduction to mobile robotics – Localization with odometry augmented with exteroceptive sensing; Kalman filtering.
Stationary and mobile sensor systems for environmental monitoring.
Robotic sensor systems for environmental monitoring. Course take home messages. Discussion about the course and student feedback.