Engineering & technology | Subject

Dependability and security of computing systems

I. Dependability: Functional and structural redundancy; Structural redundancy techniques (hardware, temporal, information and software); Dependability evaluation techniques: combinatorial and Markov models; The FMEA analysis.
II. Software Testing: Goals and limitations of testing; Testing techniques based on the program structures or on specifications; Regression testing, conformance testing.
III. Industrial Case Study: Software vulnerability: pragmatic dependability of software (IR); Application to aeronautics (EIS)

http://esisar.grenoble-inp.fr/en/academics/dependability-and-security-of-computing-systems-5amse504 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Valence – Autres Students should be able to :
determine safety properties for computing systems;
implement appropriate fault tolerance approaches depending on the nature of studied systems;
evaluate dependability attributes using analytical approaches;
improve system robustness by using fault detection and elimination techniques; – Computer architecture
– Programming skills
– Graph theory basics Ioannis PARISSIS, Oum-El-Kheir AKTOUF, Stéphanie CHOLLET 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Real Time kernels

1. Introduction to time constraints and basic definitions.
2. Architecture and functioning of a real time kernel (tasks, interrupts,…)
3. Mutual exclusion: mutex, semaphores, priority inversion (priority inheritance protocols, ceiling priority protocol)
4. Task synchronisation and communication in a real time kernel.
5. Introduction to real time scheduling.
6. Memory management within a real time executive.
7. UML for designing real-time applications

http://esisar.grenoble-inp.fr/en/academics/real-time-kernels-5amos517 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Valence – Autres This course is an introduction to Real Time kernels. At the end of this course, the students will be able to:
understand the main tools of a RT kernel and use them efficiently,
design a real time application using to the best the capabilities of a RT kernel. – Operating System basics
– Linux system programming (processes, signals, pipes, IPC)
– C programming language
– Computer architecture basics (interrupt handling, timer, …) Oum-El-Kheir AKTOUF 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

E2 : second session exam mark international.cic_tsukuba@grenoble-inp.fr

Modeling, analysis and simulation of dynamical systems

Multiphysics modelling – Systems of conservation laws – Irreversible systems and thermodynamics – Compartment and reactional systems – Discrete dynamical systems

http://esisar.grenoble-inp.fr/en/academics/modeling-analysis-and-simulation-of-dynamical-systems-5amac524 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Valence – Autres Building dynamical models for complex multiphysics systems with the appropriate resolution for predefined control objectives – Analysing internal and input-uotput dynamical properties of these models – Performing numerical time integration (dynamical simulations) for these systems and making connections with discrete time systems and digital control problems. Any introductive course on scientific computing (including time integration of ordinary differential equations)
Any introductive course the state space approach for control systems Laurent LEFEVRE 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

PAPER SCIENCES, BIOREFINERY & BIOMATERIALS – Materials : cellulosic media

Paper physics
Paper physics – Labwork
Dimensional stability of paper

http://pagora.grenoble-inp.fr/en/international/fall-semester-paper-sciences-biorefinery-biomaterials-30-ects-1#page-programme Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Isabelle Desloges 5 1st year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
– April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
– September 1st, 2020 for Spring Semester intake (February – June). Final exam international.cic_tsukuba@grenoble-inp.fr

PAPER SCIENCES, BIOREFINERY & BIOMATERIALS – Materials : biopolymers

Biopolymers, biocomposites
Polymer structures and properties
Tissue and specialty papers

Digital chain for Industry 4.0 including virtual and augmented reality

The deployment of industry 4.0 is based on various innovative technologies but also requires the construction a complete digital chain
from the design office to enable these technologies. The digital chain in industry 4.0 depends on a relevant information system.The
lesson teaches how to deploy a complete digital chain from design office to production system applications based on virtual reality and
augmented reality applications.

http://genie-industriel.grenoble-inp.fr/en/studies/digital-chain-for-industry-4-0-including-virtual-and-augmented-reality-5guc3319 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Autres The lesson is presenting 3 complementary aspects:data sources available in industry at design or production levelcreating an information system to connect and share various source of informationvirtual reality and augmented reality technologies and applicationsThe
role of the lesson is to learn how to connect these 3 aspects to build a
complete digital chain that can be deployed for industrial practise.Labworks learn non sql information systemsThey remind basics of scripting languages (python, C#)Students experience a wide set of VR/AR devices within prepared labworksStudents apply this knowledge to specify and create a digital chain to solve a specific industrial process. CAD/3D modeling: almost basic 3D modeling is expected. Students who have
few experiences about this pint will be oriented on basic tutorials
but it will not be part of the lesson activityComputer science
development basics : basics will be reminded but students will need to
develop few codes mainly in python or C# Frédéric NOEL 6 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Natural Hazards and Soil Improvements

This course provides an overview of the issues natural hazards and climate change on the stability of structures (especially related to the transition between unsaturated and saturated soil). It covers in particular the seismic risk and the slope stability. The physical and mechanical phenomena induced in the soil and structures are addressed. The consequences in terms of stability of structures and natural slopes are analyzed. Finally the techniques usually used for risk mitigation (including soil improvement or installation of protective structures) are identified and discussed in more depth in the context of a design project.

http://ense3.grenoble-inp.fr/en/academics/natural-hazards-and-soil-improvements-5eus5rnc-1 To understand the tools used for defining the regional and local seismic hazard
• Advanced knowledge of soil dynamics and the associated mechanical phenomena (including liquefaction)
• To know how to design a structure considering the seismic hazard (including dams, natural slopes, retaining structures and foundations)
• Knowledge of physical and mechanical phenomena at the origin of gravity risk (landslide, rock fall, avalanche)
• To understand the behavior of unsaturated soils and the impact on the stability of structures or natural slopes
• Knowledge of the main techniques used for improving soil and the design of protective structures Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique • To understand the tools used for defining the regional and local seismic hazard
• Advanced knowledge of soil dynamics and the associated mechanical phenomena (including liquefaction)
• To know how to design a structure considering the seismic hazard (including dams, natural slopes, retaining structures and foundations)
• Knowledge of physical and mechanical phenomena at the origin of gravity risk (landslide, rock fall, avalanche)
• To understand the behavior of unsaturated soils and the impact on the stability of structures or natural slopes
• Knowledge of the main techniques used for improving soil and the design of protective structures Soil and rock mechanics, groundwater flow, rheology, structural dynamics. Fabrice Emeriault 5 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Data Management in large-scale distributed systems

In this course, we will study the fundamentals and research trends of distributed data management, including distributed query evaluation, consistency models and data integration. We will give an overview of large-scale data management systems, peer-to-peer approches, MapReduce frameworks and NoSQL systems. Ubiquitous data management and crowdsourcing will also be discussed.

Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Data management and knowledge extraction have become the core activities of most organizations. The increasing speed at which systems and users generate data has led to many interesting challenges, both in the industry and in the research community. Fundamentals of DBMS, parallel programming (threads) Thomas Ropars 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Advanced Learning models

Statistical learning is about the construction and study of systems that can automatically learn from data. With the emergence of massive datasets commonly encountered today, the need for powerful machine learning is of acute importance. Examples of successful applications include effective web search, anti-spam software, computer vision, robotics, practical speech recognition, and a deeper understanding of the human genome. This course gives an introduction to this exciting field, with a strong focus on kernels methods and neural network models as a versatile tools to represent data

http://ensimag.grenoble-inp.fr/fr/formation/advanced-learning-models-wmms536i-1 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Introduction to statistical learning theory and kernel-based methods. Fundamental notions in linear algebra and statistics. Basic programming skills to implement a machine method of choice encountered in the course from scratch Julien Mairal – Xavier Alameda 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

High Performance computing for mathematical models

In this course, we will introduce parallel programming paradigms to the students in the context of applied mathematics. The students will learn to identify the parallel pattern in numerical algorithm. Introduction to parallelism, Models of parallel programming, Programming tools: OpenMP, OpenMPI

http://ensimag.grenoble-inp.fr/fr/formation/high-performance-computing-for-mathematical-models-5mm253c3 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Applications in bioinformatics, computer vision, text mining, audio processing, etc. C or C++, Compiling, Data structures, Architecture, Concurrency Christophe Picard 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
– April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
– September 1st, 2020 for Spring Semester intake (February – June). Practical work international.cic_tsukuba@grenoble-inp.fr

Distributed Systems

Review of core principles of distributed systems. Characteristics and design issues of distributed systems, briefly revisiting the basic notions on network support, naming and binding.Main concepts and terminology of fault tolerance, including replicated servers for high-availability. Peer-to-peer Distributed Systems. Both structured and unstructured P2P architectures and designs.

http://ensimag.grenoble-inp.fr/fr/formation/distributed-systems-wmm535e-1 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères The objectives of this course are:

– to study the basic concepts used to design distributed algorithms: processes, failures, etc.
– to study a set of distributed algorithms that are used in modern distributed systems.

At the end of the course, the student will be familiar with a set of widely used algorithms. In particular, the following families of algorithms will be introduced: consensus algorithms, broadcast algorithms, synchronisation algorithms, etc. Basic notions of operating systems.
Basic notions of networks. Vivien Quema 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Machine Learning Fundamentals

Machine Learning is one of the key areas of Artificial Intelligence and it concerns the study and the development of quantitative models that enables a computer to perform tasks without being explicitly programmed to do them. Learning in this context is hence to recognize complex forms and to make intelligent decisions. Given all existing entries, the difficulty of this task lies in the fact that all possible decisions is usually very complex to enumerate. To get around that, machine learning algorithms are designed in order to gain knowledge on the problem to be addressed based on a limited set of observed data extracted from this problem.

http://ensimag.grenoble-inp.fr/fr/formation/machine-learning-fundamentals-wmm9mo21 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères The intent of this course is to propose a broad introduction to the field of Machine Learning, including discussions of each of the major frameworks, supervised, unsupervised and semi-supervised learning. Massih-Reza Amini 3 2nd year of master Lecture Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.