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This program - taught in English in Hanoi, Vietnam, in collaboration with the University of Engineering and Technology of the Vietnam National University (VNU-UET) - is a high-quality research master program in sciences and technologies for information and communication, aimed at educating future PhD students, consistent with international standards, open to Vietnamese students or other incoming students. The fee will be a few thousands euro (EUR 4000 at the maximum) to be paid to VNU-UET.
Course Prerequisites
Stochastic Processes, Digital Signal Processing, and Advanced Programming
Skills
To create knowledge with a scientific approach.
To learn about a scientific issue by identifying relevant sources of information.
To have a deep understanding of communication and data engineering.
To be able to analyze an article or a scientific presentation and understand its issues and limitations.
Post-graduate profile
Skills in :
* digital communication, mobile networks, data mining, internet of thing, cloud computing,
* research methodology,
* depending on electives, channel coding, network security or machine learning.
Career prospects
The study path aims to prepare students for a thesis, ideally jointly with Université Paris-Saclay.
Collaboration(s)
Academic partner
University of Engineering and Technology - Vietnam National University (VNU-UET)
Laboratories
Laboratoire des Signaux et Systèmes
Advanced Institute of Engineering and Technology (VNU-UET)
Programme
1 tronc commun à 26 ECTS
1 UE optionnelle à 4 ECTS à choisir sur une liste de 2.
Overview: This course presents the so-called 4th and 5th generations of mobile networks (4G, 5G). New radio transmission techniques and network architectures will be highlighted. The course will provide an overview of the flagship technologies on which the LTE network is based, its evolution LTE Advanced, and 5G._x000D_
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Objectives: This course explains the structure of the 4th generation of mobile networks, and the evolution towards 5G._x000D_
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Course outline:_x000D_
• Introduction_x000D_
• Architecture_x000D_
• Radio interface_x000D_
• A core network_x000D_
• Evolution towards 5G.
Prerequisites :
1. Course "Digital Communications" or equivalent"_x000D_
2. Course “Mobile Network" or equivalent.
Bibliographie :
1. Stefania Sesia, Issam Toufik, Matthew Baker, LTE-the UMTS long term evolution, John Wiley & Sons, 2011_x000D_
2. Chris Johnson, 5G new radio in bullets, Independently Published, 2019_x000D_
3. Harri Holma, Antti Toskala, Takehiro Nakamura. 5G Technology, John Wiley & Sons, 2020.
Silviu Maniu, Emmanuel Vazquez, Tran Trong Hieu, Nguyen Viet Anh.
Objectifs pédagogiques visés :
Contenu :
Overview: The course presents a set of algorithms for transforming, modeling, and interpreting data that can be directly applied to Data Science tasks, or can be necessary as a pre-processing step, before the data can be presented to an, e.g., machine learning task._x000D_
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Objectives: The course focuses on the algorithms involved in data-related tasks, collectively grouped under the concept of “data mining”._x000D_
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Course outline_x000D_
• Introduction (basic ML and data science)_x000D_
• Data mining algorithms _x000D_
• Applications of data mining_x000D_
• Data stream mining_x000D_
• Reinforcement Learning, Multi-Armed Bandits_x000D_
• Graphical Models/Bayesian Networks_x000D_
• Gaussian Processes.
Prerequisites :
Algorithms, Programming (Python/C/Java).
Bibliographie :
1. J. Leskovec, A. Rajaraman, J. Ullman. Mining of Massive Datasets, Cambridge University Press, 2014_x000D_
2. S. Abiteboul, I. Manolescu, P. Rigaux, M.-C. Rousset, P. Senellart. Web Data Management, Cambridge University Press, 2012 _x000D_
3. R. Sutton, A. Barto. Reinforcement Learning, MIT Press, 2017_x000D_
4. T. Lattimore, C. Szepesvári. Bandit Algorithms, Cambridge University Press, 2020_x000D_
5. C.A. Rasmussen, C. Williams. Gaussian Processes for Machine Learning., MIT press, 2005_x000D_
6. D. Koller, N. Friedman. Probabilistic Graphical Models. MIT press, 2009.
Jocelyn Fiorina, Pierre Duhamel, Nguyen Linh Trung.
Objectifs pédagogiques visés :
Contenu :
Overview: this course gives the basic principles of digital communications including baseband transmission, modulation techniques, spread spectrum signaling, orthogonal frequency-division multiplexing (OFDM), optimum reception, adaptive equalization, synchronization, fading channels, and diversity techniques._x000D_
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Objectives: To know how to design a digital communication chain, estimating the performance in function of the technique and the various parameters in order to reach an objective under various constraints._x000D_
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Course outline:_x000D_
• Baseband Transmission_x000D_
• Single-Carrier Transmission_x000D_
• Multicarrier Transmission_x000D_
• Spread Spectrum Signaling_x000D_
• Fading Channels and Diversity.
Prerequisites :
1. Course "Mathematical Basis for Communications" or equivalent_x000D_
2. Course "Information theory" or equivalent.
Bibliographie :
1. S. Benedetto, E. Biglieri, Principles of Digital Transmission with Wireless Applications, Kluwer Academic Plenum Publishers, 1999 _x000D_
2. J. Proakis, Digital Communications, McGraw-Hill, 2000_x000D_
3. S. Haykin, Communication Systems, Wiley, 2002 _x000D_
4. D. Tse, P. Viswanath, Fundamentals of Wireless Communications, Cambridge University Press, 2005.
Overview: Introduction to the main principles of networks by studying the architecture and protocols of the Internet._x000D_
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Objectives: Acquire an understanding of the basic mechanisms and protocols implemented in telecommunications networks._x000D_
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Course outline:_x000D_
• Introduction to the Internet _x000D_
• IPv4, IPv6, NAT, subnetting_x000D_
• Advanced TCP_x000D_
• TCP and congestion control. Other transport protocols: CUBIC, SCTP, MPTCP, TCP Compound_x000D_
• HTTP2.0, HTTP3.0, Multicast_x000D_
• Quality of service_x000D_
• Standard applications_x000D_
• Multimedia Applications and Over the Top.
Bibliographie :
1. J. F. Kurose and K. W. Ross, “Computer Networking, A Top-Down Approach Featuring the Internet”, Addison Welsey, 2013_x000D_
2. Tanenbaum, “Computer Networks”, Prentice Hall, 2010_x000D_
3. William Stallings, “Data & Computer Communications”, 2006_x000D_
4. Peter L Dordal, “An Introduction to Computer Networks”, Loyola University Chicago, edition 2.0.1. online. _x000D_
5. Olivier Bonaventure, « Computer Networking : Principles, Protocols and Practice », Ed. 2019. Online._x000D_
6. W. R. Stevens, TCP/IP Illustrated, protocols, Addison Wesley, 2011.
Overview: _x000D_
Part 1 addresses the major features of current cloud systems, namely virtualization, storage, security, elasticity, programming models, optimal allocation of resources. Part 2 presents an overview of the technologies used in the IoTs, recalls the different wireless technologies currently available to achieve them and then detailed Lora technology._x000D_
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Objectives: The objectives of the cloud-computing part are to enable the students to gain a deep understanding of the software implementation concepts of a cloud so that they can be users - expert developers but also administrators or contributors for such infrastructures. Second part about IoTs, offers students a complete and detailed landscape on the different protocols and technologies that will be used to realize the new system in which we are going to evolve.
Prerequisites :
1. Basic knowledge on "Operating systems","Computer Network" and " "Relational database management system"_x000D_
2. Programming language (Java/C ++).
Bibliographie :
1. Perry Lea, Internet of Things for Architects, Packt Publishing Ltd, 2018_x000D_
2. David Hanes, Gonzalo Salgueiro, Patrick Grossetete, Robert Barton, Jerome Henry, IoT fundamentals, Cisco Press, 2017_x000D_
3. Nayan B. Ruparelia, Cloud computing, MIT Press, 2016_x000D_
4. Michael J. Kavis, Architecting the cloud, John Wiley & Sons, 2014_x000D_
5. Kevin Jackson, Cody Bunch, Egle Sigler, James Denton, OpenStack Cloud Computing Cookbook, Packt Publishing Ltd, 2018.
Overview: This course starts from a "complete system" vision (mobile and networks), to go to the different communication protocols. It addresses the issues related to a wireless technology (mobility, radio link, data transfer performance), all of which brings with it strong protocol constraints._x000D_
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Objectives: The objective of this course is to present all wireless technologies (GSM/ GPRS/ EDGE/ 3G/ Wifi). _x000D_
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Course outline:_x000D_
• GSM_x000D_
• GPRS_x000D_
• EDGE_x000D_
• UMTS_x000D_
• Wifi (802.11).
Prerequisites :
1. Course "Digital communications" or equivalent_x000D_
2. Course “Electronics systems for telecoms" or equivalent.
Bibliographie :
Ajay R. Mishra, Fundamentals of Network Planning and Optimisation 2G/3G/4G: Evolution to 5G, Wiley, 2018.
Pierre Duhamel, Le Sy Vinh, Dinh Trieu Duong, Nguyen Linh Trung, Luu Manh Ha.
Objectifs pédagogiques visés :
Contenu :
Overview: Each year new seminars will be proposed to the students. The topics covered by the seminars are up-to-date subjects currently under studies and development by the presenters._x000D_
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Objective: The objective is to help students understand current research activities in the domains of wireless communications and networking, 5G and 6G systems, applications of machine learning, as well as other research topics... The second objective is to train student how to search for relevant information, how to read and summarize scientific papers.
Période(s) et lieu(x) d’enseignement :
Location :
Hanoi, Vietnam
2 UE optionnelles à choisir sur une liste de 3, chacune à 5 ECTS
1 stage à 20 ECTS obligatoire.
Overview: The first part of the course is carried out from practical work intended to learn how to apply machine learning algorithms and statistical pattern recognition on real data. The practical know-how necessary to train and evaluate model performances are teach through examples of implementation on real data._x000D_
The second part is on the principles of machine learning in general and deep learning in particular. We will explore both the fundamentals advances in the area of deep learning and the recent applications to the field of IoT and in general communications. Our focus will be on recent applications of deep learning to perform data analytics on the Internet of Things (IoT) communications, including neural networks, auto-encoders, convolutional neural networks and recurrent networks. We will also consider well-known probabilistic graphical models, including undirected models and directed models that have recently shown promise (e.g. Boltzmann machines, Deep Belief Nets)._x000D_
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Objectives: This course provides knowledge of advanced machine learning, deep learning and using such techniques in real application using IoT data._x000D_
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Course outline:_x000D_
Supervised learning (regression, classification) _x000D_
Unsupervised learning (clustering, dimensionality reduction)_x000D_
Introduction to Neural Networks_x000D_
Advanced Neural Networks_x000D_
Variations on auto-encoders and probabilistic Graphical Models_x000D_
Modern architectural variations for communications and IoT data analytics.
Prerequisites :
1. Course "Information theory" or equivalent_x000D_
2. Course "Probability " or equivalent.
Bibliographie :
1. Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016_x000D_
2. Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012._x000D_
3. Tom M. Mitchell, Machine Learning, McGraw-Hill Education, 1997 _x000D_
4. Li Deng and Dong Yu, Deep Learning - Methods and Applications, Now publishers, 2014_x000D_
5. Christopher M. Bishop, Pattern recognition and machine learning, Springer, 2006_x000D_
6. Simon Haykin, Neural Networks and Leaning Machines, Pearson, 2009.
Overview: This course provides an overview of various theoretical and practical aspects of network security. After completing this course, the students should be able to identify and assess current and anticipated security risks and vulnerabilities in an information system, develop a network security plan and policies. The acquired competencies include establishing a firewall with ACL, deploying a virtual private network, working with SSH tunnels and management of X.509 certificate life cycle._x000D_
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Objectives: The course is intended to provide a solid understanding of modern cryptography, and its application in network protocols. _x000D_
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Course outline:_x000D_
• Methods and algorithms of modern cryptography._x000D_
• Network encryption and authentication protocols on various OSI layers._x000D_
• Practical and infrastructural aspects of network security.
Prerequisites :
1. Mastering of communication protocols (TCP-IP, ICMP, ARP)_x000D_
2. Basic probability theory and discrete mathematics (knowledge of basic abstract algebra and number theory is recommended, but not formally required)_x000D_
3. Beginner level Python+ SQL.
Bibliographie :
1. Keith M. Martin, Everyday cryptography, Oxford University Press, 2012_x000D_
2. Al Sweigart, Cracking Codes with Python, No Starch Press, 2018._x000D_
3. Bryan Sullivan and Vincent Liu, Web Application Security, McGraw-Hill Osborne Media, 2011_x000D_
4. John R. Vacca (ed), Computer and Information Security Handbook, Morgan Kaufmann, 2017_x000D_
5. Michael Schwartz and Maciej Machulak, Securing the Perimeter, Apress, 2018_x000D_
6. Evan Gilman and Doug Barth, Zero Trust Networks, O'Reilly, 2017.
Ntoine Berthet, Pierre Desesquelles (source), Pierre Duhamel (channel), Tran Thi Thuy Quynh (source), Le Vu Ha (source), Nguyen Linh Trung (channel).
Objectifs pédagogiques visés :
Contenu :
Overview: In the first part of the course, we remind students of the basics of the algebraic coding theory for conventional binary-input output-symmetric memoryless channels. The second part of the course is devoted to sparse graph codes. We review in detail code construction aspects, iterative decoding, and mathematical tools for design optimization. We then expound the principles of non-binary coding for the Gaussian channel and show how and why coded modulations can also benefit from sparse-graph codes optimized for binary-input channels and iterative decoding._x000D_
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Objectives: The first objective is to understand the basics of source coding (without memory, with memory, …). The second is to understand the basics of algebraic coding (linear codes, polynomial, convolutional, cyclic, BCH, Reed-Solomon, ...) on channels with binary inputs without memory._x000D_
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Course outline:_x000D_
• Introduction to error-correcting codes; TD1 on error-correcting codes_x000D_
• Linear cyclic codes; TD2 on Algebraic decoding_x000D_
• Linear convolutional codes_x000D_
• Performance of linear codes under MLD_x000D_
• Factor graphs and the sum-product algorithm_x000D_
• Sparse-graph codes : LDPC codes ; TD3 on LDPC codes_x000D_
• Sparse-graph codes: Density evolution, Code design optimization under iterative decoding; TD4 on turbo codes_x000D_
• Sparse-graph codes: Ensemble enumerators, Code design optimization under MLD _x000D_
• Source coding; TD5 on source coding.
Prerequisites :
Sound knowledge in Probability, Random processes, and Linear algebra.
Bibliographie :
1. R.G. Gallager, Information Theory and Reliable Communications, John Wiley, 1968._x000D_
2. T. Cover, Elements of Information Theory, John Wiley, 1991._x000D_
3. F.J. MacWilliams, N.J.A. Sloane, Theory of Error-Correcting Codes, North Holland Publishing, 1977._x000D_
4. R.J. McEliece, Finite Fields for Computer Scientists and Engineers, Kluwer Academic Publishers, 1987. _x000D_
5. W.E. Ryan, S. Lin, Channel Codes, Cambridge University press, 2009_x000D_
6. A.J. Viterbi, J.K. Omura, Principles of Digital Communications and Coding, McGraw Hill, 1979_x000D_
7. K. Sayood, Introduction to data
