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This M1 master provides a wide range of optional courses in electrical engineering:
* computer (science, vision and graphics)
* signal processing
* robotics
* artificial intelligence
* communication
* microelectronics and photonics.
The first semester includes basic common blocks. The second semester involves more in-depth classes. The 1st year master (M1) allows students to build their programs according to their interests. Students who want to further develop their knowledge of specific field can integrate one of many M2 majors:
* Machine intelligence, computer vision, and robotics
* Advanced wireless communication systems
* Multimedia networking
* Optical networks and photonic systems
* Integration circuits and systems
* Nanosciences.
Location
ORSAY
GIF SUR YVETTE
EVRY
Course Prerequisites
A minimum of a "licence, Bachelor" degree in either Computer Science, Electronic Engineering, Mathematics, or Physics, or a recognized equivalent international qualification. We'll also consider relevant degree subjects or significant relevant work experience if you don't meet these requirements.
Skills
Understand computer science, electrical engineering and signal processing problems.
Solve problems related to specific fields of electrical engineering.
Implement solution to real-world related problems.
Conduct experimental analysis.
Design AI future invention.
Career prospects
Graduates with the M1 have several opportunities to choose from including M2 specialization. Students can integrate Machine intelligence, computer vision, and robotics major which leads to:
* Robotic Engineer
* Computer science Engineer
* Machine Learning Engineer
* AI Developer
* Data Scientist
* PhD studies
We further offer careers information, advice and guidance to our students.
Collaboration(s)
Laboratories
Informatique, Biologie Intégrative & Systèmes Complexes
Centre de Nanosciences et de Nanotechnologies
Laboratoire des Signaux et Systèmes.
Programme
5 UE de base à choisir dans une liste (encore en chantier)
1 UE français/soft skills obligatoire.
-Linear programming model formulation and graphical solution
-Analysis of special linear programming solutions
-Linear programming applications and their computer solution
-The Simplex method,
-Duality and sensitivity analysis in linear programming,
-Int.
Bibliographie :
- B Render, RM Stair Jr. and N Balakrishnan (2012). Managerial Decision Modelling with Spreadsheets.
- DR Anderson, DJ Sweeney, TA Williams AND M Wisniewski (2010). Introduction to Management Science: Quantitative Approach to Decision Making (Int'l Ed).
Période(s) et lieu(x) d’enseignement :
Period(s) :
Septembre - Octobre - Novembre - Décembre - Janvier.
- Introduction to Automation and Control
- Architecture of Automation Systems
- Measurement Systems Specification
- Process Control
- Predictive Control, Control of Inverse Response Systems
- Sequence/Logic Control and Programmable Logic Controllers
- Hyd.
Prerequisites :
Advanced Mathematical Tools for engineering: (Optimization and decision making).
Période(s) et lieu(x) d’enseignement :
Period(s) :
Septembre - Octobre - Novembre - Décembre - Janvier.
- Introduction to digital communication systems
- Information theory: entropy and information, channel capacity, shannon theorm,
- Multiplxing: TDMA, FDMA, Circuit commutation, Message and Packet commutation, Digital multiplex hierarchy (T-carrier and E-c.
Prerequisites :
- Analog signal processing
- Modeling of continuous systems
- Electronic circuits and oscillators.
Bibliographie :
- J. Penttinen, The telecommunications handbook, John Wiley & Sons, 2015
-B. Lathi, Modern digital and analog communication systems, Oxford university press, 2018 (Edition 3 available 1998).
Période(s) et lieu(x) d’enseignement :
Period(s) :
Septembre - Octobre - Novembre - Décembre - Janvier.
- Design methodologies of numerical systems: processors, microcontrollers, programmable logic, HDL design
- Overview on VHDL system description and advanced VHDL
- RT methodology: datapath, FSM, ASM, ASMD, algorithm implementation on FPGA
- ARM processor.
Prerequisites :
- Digital hardware: Combinational and sequential logic
- Computer architecture for Bachelor Level
- C or C++ language programming
- Basic knowledge about algorithmic.
Bibliographie :
- S. Brown, Fundamental of digital logic with VHDL design, McGraw Hill, 2009
- P. Ashenden, The designer's guide to VHDL, Elsevier
- P. Chu, RTL Hardware design using VHDL, Wiley-Interscience, 2006
- D. Black, SystemC: from the ground up, Springer 2010
-.
Période(s) et lieu(x) d’enseignement :
Period(s) :
Septembre - Octobre - Novembre - Décembre - Janvier.
-Cox, B. J. (1986). Object-oriented programming: an evolutionary approach.
-Bruegge, B., & Dutoit, A. H. (2009). Object-?Oriented Software Engineering. Using UML, Patterns, and Java. Learning, 5(6), 7.
Objectifs pédagogiques visés :
Contenu :
Object-oriented programming basics
Object modelling
Extensibility and reusability
Dynamic Modelling
Design Methodologies
Advanced algorithmics and Computational Complexity
P and NP
Space-Bounded Complexity Classes
Probabilistic Complexity Classes.
Prerequisites :
Algorithmics.
Période(s) et lieu(x) d’enseignement :
Period(s) :
Septembre - Octobre - Novembre - Décembre - Janvier.
S. Mammar, H. Arioui, N. Ait-oufroukh, S. Bouchafa.
Objectifs pédagogiques visés :
Contenu :
1. Introduction to Digital Signal Processing applications
2. Signal Sampling and Quantization
3. Digital Signals and Systems
4. Discrete Fourier Transform and Signal Spectra
5. The Z-Transform
6. Digital Signal Processing Systems, Basic Filtering Types, and Digital Filter Realizations
7. Finite Impulse Response Filter Design
8. Infinite Impulse Response Filter Design
9. Adaptive Filters and Applications
10. Waveform Quantization and Compression
11. Multirate Digital Signal Processing, Oversampling Analog-To-Digital Conversion, and Undersampling of Bandpass Signals
12. Subband and Wavelet-Based Coding
13. Hardware and Software for Digital Signal Processors.
Bibliographie :
Signal representation/modelization and continuous systems.
Période(s) et lieu(x) d’enseignement :
Period(s) :
Septembre - Octobre - Novembre - Décembre - Janvier.
1 projet obligatoire
5 UE à choisir dans une liste (encore en chantier).
Some Fundamentals of Lyapunov Stability
Linear Systems : Motivation & Tools
Controller Design : LMI synthesis
Observer Design : LMI synthesis
Extension to Multiple Model approach
Application to Vehicles.
Prerequisites :
Linear systems, Linear algebra, ordinary differential equations.
Bibliographie :
J-J. E. Slotine and W. Li. Applied Nonlinear Control, Prentice Hall, 1991.
H. K. Khalil. Nonlinear Systems, Prentice Hall, 1996.
M. Chadli and P. Borne. Multiple Models Approach in Automation: Takagi-Sugeno Fuzzy Systems. Wiley 2013.
Introduction
Image analysis (filters, Frequency domain, etc)
Camera Models and Views
Image features extraction
Shape from shading
Image Motion
Tracking
Classification and Recognition.
Prerequisites :
Advanced Mathematical Tools for engineering: (Optimization and decision making)
Algorithmics, structured programming.
Materials and devices for electronics and optoelectronics
Language(s) of instruction :
AN
ECTS :
5
Détail du volume horaire :
Lecture :22
Directed study :20
Practical class :8
Modalités d'organisation et de suivi :
Coordinator :
Pedagogical team :
Delphine Morini
Adel Bousseksou.
Objectifs pédagogiques visés :
Contenu :
Semiconductor optoelectronic devices and their application in optical telecommunications will be studied. The lecture will begin by refresh and supplements in electromagnetism and semi-conductor physics. Then the lecture will focus on understanding the operation and behaviour of semiconductor optoelectronic devices: laser, modulator and photodetector.
Outline:
* Refresh and supplements on electromagnetism: from Maxwell equations to optical waveguides
- Photonic integrated circuits
- Waveguides
- Integrated beam splitters, Fabry Perot cavity…
* Refresh and supplements on semiconductor physics: from band structure theory to optical transitions (emission, absorption)
- Time-dependent perturbations
- Light/matter interaction : absorption, stimulated and spontaneous emission,
* Optoelectronic devices
- Lasers : two level system
- Semiconductor laser : non equilibrium statistics, population inversion, Bernard and Durrafoug condition, spectral properties, comparison of homojunction, heterojunction, multiple quantum well lasers ; comparison of Fabry Perot and Distributed Feedback lasers.
- Optical modulator : electroabsorption, electrorefraction, resonators, Mach Zehnder
- Photodetector: PIN photodiode
* Fabrication technique
* Lab work
- Experimental study of a fiber optic link.
S. Alfayad, H. Arioui, L. Nehaoua, L. Bencheikh, N. Seguy, G.Simon, A.Melhaoui.
Objectifs pédagogiques visés :
Contenu :
- Introduction to Mechatronics Design
- Why machine Elements in Mechatronics Design
- Strength under static & dynamic circumstances
- Threaded & unthreaded fasteners
- Bolted and riveted joints
- Bearings & Lubrication
- Couplings
- Belt
- Chain Drives
- Gears
- Shafts
- Thesaurus of mechanisms & linkages
- Robots and Smart machines.
Prerequisites :
CAD software; fundamental physics.
Bibliographie :
- STANDARD HANDBOOK OF MACHINE DESIGN; Editors in chief, Joseph E. Shigley, Charles R. Mischke; ISBN 0-07-056958-4
- Mémotech plus - Ingénierie et mécanique; Claude Barlier, René Bourgeois; ISBN-13: 978-2-7135-3310-5
- Ingénierie mécanique - Transmission.
S. Alfayad, L. Bencheikh, L. Beji, S. Alfayed, F.B.Ouezdou, G.Simon, O.Bruneau.
Objectifs pédagogiques visés :
Contenu :
- Terminology and general definitions
- Transformation matrix between vectors, frames and screws
- Direct geometric model (DGM) of serial robots
- Inverse geometric model (IGM) of serial robots
- Direct kinematic model (DKM) of serial robots
- Inverse kin.
Prerequisites :
Linear algebra, Mechanics of Rigid Body, Matlab,.
Bibliographie :
- Modeling, Identification and Control of Robots; W Khalil, E Dombre; ISBN: 978-1-903996-66-9
- Modeling, performance analysis and control of robot manipulators; Etienne Dombre, Wisama Khalil; ISBN-13: 978-1-905209-10-1
- Robot Modeling and Control; Mark.
L. NOUVELIERE, D. Sidibé, H. Tabia, M. Chadli, V. Vigneron, H. Arioui.
Objectifs pédagogiques visés :
Contenu :
1. Introduction to Machine Learning
2. Fundamentals of probability and statistics
- Overview of the basic notions of random variables and probability distributions
- Examples of discrete and continuous probability distributions
- Concepts used in multidimensional data analysis
- Examples of multidimensional probability distributions
- Discussion on the asymptotic behavior of the sum of independent random variables
- Various inequalities related to random variables
3. Fundamentals of statistical estimation and hypothesis testing
4. Different tasks in machine learning
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Regression
- Classification
- Clustering
- Outlier detection
5. Application to car vehicle in practical works (trajectory forecasting).
Bibliographie :
- Clarence W. de Silva « Sensors and Actuators: Engineering System Instrumentation », Second Edition, 2015, CRC press edition
- Bhattacharya, S., Agarwal, A.K., Prakash, O., Singh, S. « Sensors for Automotive and Aerospace Applications », 2019, Springer.
