M1 - Electronics, Electrical Engineering, Automation - Robotics Track

The module will cover the following topics:

• The Relationship Between Transfer Functions and Differential Equations
• Representation and Continuous Status Feedback (Eigenvalues, Stability)
• Sampled State Representation and Feedback
• Feedback Control Without and With Full-Loop Control, LQR Control
• State observers
• Nonlinear Control with Examples

Practical exercises: applying what you've learned to real-world examples (e.g., electric motors), programming in Python (using the NumPy and Control libraries).

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This course complements a basic education in signal processing with in-depth knowledge of deterministic and random digital signals. This knowledge is essential in all engineering disciplines, as digital signal processing is currently used in the majority of applications.

In the first part (10:30 a.m. lectures, 6 hours of lab work), the course covers the sampling and quantization of continuous signals and the relationship between digital signals and the original continuous signal. It defines the discrete Fourier transform of digital signals, its estimation, and its application to real deterministic signals.

The second part of the course (9 hours of lectures, 4 hours and 30 minutes of tutorials, 3 hours of lab) is devoted to random signals and how the properties of certain random signals can be used either to reduce the random component of a signal when the deterministic component is of primary interest (filtering, increasing the signal-to-noise ratio, etc.), to improve information transmission, or to identify linearized complex systems.

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• This course complements the foundational training in analog electronics with in-depth knowledge of signal filtering, amplification, and modulation. This knowledge is essential for understanding and designing analog electronic systems in all fields of engineering.
• Instruction is organized in the form of lectures, tutorials, and lab sessions, which provide opportunities for mini-projects.

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This course, which focuses on the fundamentals of digital electronics, is structured in a unique way around a technical project—carried out individually or in pairs—whose progress will follow the pace of the associated lectures.

Each project topic will be assigned at the beginning of the course.

The key concepts of digital electronics will be explored in depth through lectures, and lab sessions will supplement the theoretical aspects to guide the project’s progress.

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This course consists of several sections. The first section covers the power electronics structures required to power an electronic system. The second section will focus on current- or voltage-based control of these structures. A third section will cover the conversion functions required to control MCC and brushless DC actuators.

The final section presents actuator topologies for robotics and their implementation. The control of a DC motor and the self-driving control of a synchronous motor will illustrate this final section.

Hands-on exercises will allow students to observe the principles and implementation of controlled systems for electronics and actuators. This course unit may serve as the basis for M1 project topics.

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Computer engineering is the discipline that deals with the design, development, and manufacture of computer systems, from both a hardware and software perspective.

This field has become fundamental to engineering sciences—whether in electronics, robotics, signal processing, or measurement—due to the significant role that computers have come to play in all of these areas.

This module aims to help students develop computer code on a scale commensurate with that of a complete software application. The volume of code involved naturally creates a need to structure the code so that it remains manageable, and the concepts related to code structuring will therefore be introduced or reinforced.
Instruction is thus organized primarily around practical exercises and projects. The context largely involves core topics of the EEA program: signal processing (data acquisition chain), instrument interfacing, and data transmission over the Internet via an embedded Linux platform. The topic of event-driven programming through the development of graphical user interfaces will also be covered. The programming languages used will be LabVIEW and Python. Students may choose to incorporate elements of C/C++ into their projects.

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- Controller overview.

- Robust synthesis and risk management.

- Representation and synthesis of synchronous machines.

- Description/synthesis language.

- The basics of the VHDL language (entities, architecture, etc.).

- Behavioral and structural descriptions.

- Simulation (Testbench).

- Reprogrammable circuits (CPLDs, FPGAs).

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A tutorial course in specialized English and communicative English designed to foster professional autonomy in the English language.

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A project in partnership with a research laboratory and/or a company, highlighting the student’s scientific skills, independence, and adaptability.

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The internship or final project should highlight the student’s scientific skills, independence, and adaptability:

• A 2- to 3-month internship (maximum 5 months) to be completed in a research laboratory or at a company;
• or a 3-month final project in a research laboratory or an educational project room.

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Description*:

1 - The goal is to help students understand the importance of a well-prepared application that matches an internship or job posting, or aligns with the activities of a professional organization in the case of an unsolicited application; to write resumes and cover letters; to gain a better understanding of their own personalities; to use new technologies (social media and job boards); and to tailor their job search to their career goals. Finally, to know how to prepare for and conduct themselves during job interviews.

2 - The goal is to enable students to write a scientific article following the completion of a project. To do so, they must understand the article’s objectives and characteristics, the outline to follow, the various stages of the writing process, and the formatting guidelines. Next, to present their project orally, students must understand and be able to apply the general structure of a presentation; select appropriate and relevant visual aids; follow the rules of oral communication to express themselves correctly and professionally (vocabulary, syntax, etc.); and adopt behaviors that energize their speech and help engage their audience.

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Today, image processing is ubiquitous in information technology: medicine, biology, agriculture, entertainment, culture, measurement, mechanical engineering …

Image processing involves applying mathematical transformations to images in order to modify their appearance or extract information from them. More generally, image processing aims to manipulate the underlying information contained in an image. While it was long performed using electronic circuits, image processing is now carried out almost exclusively digitally—that is, via algorithms typically programmed in an imperative language (C, C++, Java, Python, etc.).

This course aims to provide a solid foundation in image processing. Among other topics, it covers image formation and acquisition, color transformations, morphological operations, geometric transformations, compression, frequency transformations, recognition and matching techniques, … and an introduction to deep learning methods. The lectures are supplemented by supporting videos.

The course consists primarily of 11 lectures covering the fundamentals of the main areas of image processing and 3 lab sessions, for which students must choose a topic from among 6 options. Students may choose to complete the lab assignments using images they provide that are relevant to their field of study.

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The module will cover the following topics:

• Introduction to the Git Version Control System
• Introduction to ROS Middleware for Developing Robotics Applications
• Modularization of a Robotics Application

Hands-on exercises: Setting up a ROS application, testing on a simulator, and verifying on a real robot

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The module will cover the following topics:

• Introduction to Robotics: History, Types of Robots, Serial and Parallel Mechanisms, Applications
• Components (sensors and actuators)
• Trajectory Generation (in the Articular and Operational Spaces)
• Direct/inverse geometric models, direct/inverse kinematic models
• Kinematics and Singularities
• Issues and Applications in Mobile Robotics
• Non-holonomic models: unicycle, bicycle, car
• Sensors and Odometry
• Localization using a rangefinder and data fusion (Kalman filter)
• Mapping (Homogeneous Transformations and ICP)
• Navigation (course control, path tracking)

Hands-on exercises: applying what you've learned to a real robot (either a robotic arm or a wheeled robot), ROS programming with Git and Python.

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