Introduction : System vs Embedded Systems; SOC design challenges, SOC modelling, Hardware Software partitionning.
System on programmable chip Architectures
SOPC design flow
Applications

http://esisar.grenoble-inp.fr/en/academics/advanced-processor-architecture-and-soc-design-5amce514 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Valence – Autres To be able to choose and to exploit the more appropriate processor architecture for a given application.
To be familliar with SOC design techniques and challenges – Digital design (VHDL or Verilog; FPGA design)
– Embedded software Programming (C; Assembly Language)
– Processor Architecture (RISC Architecture, ARM processor) David HELY 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). Exam 1h30 + Labs international.cic_tsukuba@grenoble-inp.fr

Models for security analysis;
The need for cryptographic primitives and protocols;
Symmetric cryptosystems: design, make-up, analysis;
Other symmetric protocols and algorithms;
Arithmetic for asymmetric cryptography;
Examples of asymmetric cryptosystems;
Implementation of cryptographic primitives

Grenoble INP Institute of Engineering Univ. Grenoble Alpes Valence – Autres After the course, the student should be able to:
analyze the security needs of a communication and/or computation system at an algorithmic/informational level;
grasp the design principles of cryptographic primitives;
implement a cryptographic primitive in hardware knowing its specification. Hardware design courses: digital design, FPGA, VHDL or Verilog Yann KIEFFER 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). E1: result of end-term written exam (90 min);
E2: individual oral examination (30 min);
CC: semester-long assessment international.cic_tsukuba@grenoble-inp.fr

1. An optimization-based approach for control of complex systems (Optimization-based control; Generic prediction models; Generation of a reference trajectory/profile; Set-theoretic elements; Mixed-integer representations in control design)
2. Cooperative control of multi-agent dynamical systems (System description; collision avoidance formulation; Area coverage for multi-agent systems in multi-obstacle environment; A tight configuration of multi-agent formation; centralized MPC, Distributed MPC; decentralized MPC)
3. Stability analysis
4. Examples, simulations, benchmarks and applications (Flight control experiments of Unmanned Aerial Vehicles; Microgrid energy management; Decentralized supervision and control of water networks)

http://esisar.grenoble-inp.fr/en/academics/decentralized-control-of-complex-systems-5amac554 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Valence – Autres The goal of this course is the optimal constrained control of complex dynamical systems. Elements from control theory and optimization will be merged together in order to provide useful tools which will be further applied to various problems involving multi-agent dynamical systems and interconnected systems in general. Beside classic control challenges related to the centralized vs distributed vs decentralized approaches, the stabilization and the tracking performances of each agent, there are a series of constraints imposed by the interaction with the environment and between themselves (anti-collision, avoidance constraints) as well as solving a collaborative task (e.g., maintain a fixed formation). This is generally the case with vehicles evolving in the same physical space, collaborative robots or drones covering a certain area. Some application benchmarks like control and coordination of multiple drones, energy management in complex energy systems and water distribution networks are discussed. Algorithms and programming, Linear and non-linear control, Optimal and predictive control Ionela PRODAN 2.5 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.

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). E1 : Oral exam (in English) of 20 minutes for a team of students. E2 : Oral exam (in English) of 20 minutes and a report (in English). international.cic_tsukuba@grenoble-inp.fr

There ar only tree main approaches. The observer-based approach, the parity-space approach, and parameter identification-based methods. In order to optimize FDI indications, the following two step are developped :
The first step is to design a filter based on a model of the plant to generate a vector known as the residual. The residual should ideally be zero (or zero mean) under no-fault conditions.
The second step is to make decisions on whether a fault has occurred. This step is usually done using statistical tools to test if the residual has significantly deviated from zero.

http://esisar.grenoble-inp.fr/en/academics/model-based-fault-diagnosis-for-linear-systems-5amac514 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Valence – Autres Fault detection and isolation (FDI) is a subfield of control engineering which involves monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location. Model-based techniques of fault detection and isolation use a model to investigate/analyze the occurrence of faults. The system model may be mathematical or knowledge-based. We focus our attention on mathematical models. State space representation
Observer design
Identification H2
Algebre of matrice : Rank, ker, eigenvalue … Damien KOENIG 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). Final exam session 1, calculators authorized + 1 handwritten sheet A4 R/V, duration 1h30. international.cic_tsukuba@grenoble-inp.fr

The web has been constantly evolving from a distributed hypertext system to a very large information processing machine. As fast as it is, this evolution is grounded on theoretical principles borrowing to several fields of computer science such as programming languages, data bases, structured documentation, logic and artificial intelligence. The smooth operation of the past and future web at a large scale is relying on these foundations. The goal of this course is to present them, the problem that they solve as those that they uncover. It considers three milestones of this evolution: XML, the social web and the semantic web.

Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères The first part aims at introducing programming language foundations, algorithms and tools for processing tree-structured information, and for the analysis of queries and programs that manipulate trees. This part consists in an introduction to relevant theoretical tools with an application to NoSQL and XML technologies in particular. The theoretical part introduces tree grammars, finite tree automata, classical tree logics and a recent mu-calculus of finite trees, in connection to practical problems and technologies such as XPath/XQuery, DTD, schemas, etc. Applications are illustrated through scalable validation of document streams, efficient query evaluation, static analysis of expressive queries in the presence of constraints, and static type-checking of programs manipulating labeled trees. The course also aims at presenting challenges, important results, and open theoretical issues in the area of NoSQL programming.

The second part summarizes data models and algorithms required to extract, manage and access massive amounts of social content. The course examples are drawn from real-world applications such as URL search and recommendation on Delicious, group recommendation in MovieLens and extracting travel itineraries from Flickr photos. The course goals are: acquire knowledge on scalable algorithms for processing large volumes of social data and extracting value from that data and learn how to run and interpret large-scale user studies.

The third part introduces the semantics of knowledge representation on the web. The semantic web extends the web with richer and more precise information because it is expressed in a formal language using a vocabulary defined in an ontology (a structured vocabulary of concepts and properties defined in a logic). Ontologies are used for describing web resource content and reasoning about these resources formally. We introduce the semantic web languages (RDF, RDFS, OWL) and show their relations with knowledge representation formalisms (conceptual graphs, description logics) and XML. This provides tools for reasoning with ontologies and, in particular, to evaluate queries. However, the distributed nature of the web leads to heterogeneous ontologies which must be matched before using them. We discuss ontology matching and explain how to semantically interpret the relations between ontologies. Finally, this is applied to network of peers using knowledge together. Sihem Amer Yahia 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.

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

A prior algorithms (Frequent item sets) & Page Rank, Monte-carlo, MCMC methods: Metropolis-Hastings and Gibbs Sampling, Matrix Factorization (Stochastic Gradient Descent, SVD), Generalized kmeans and its variants (Bach, Online, large scale), Kernel clustering (Support Vector Clustering), Spectral clustering, Classification and Regression Trees, Support Vector regression,Alignment and matching algorithms (local/global, pairwise/multiple), dynamic programming, Hungarian algorithm,…Alignment and matching algorithms (local/global, pairwise/multiple), dynamic programming, Hungarian algorithm,…

Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Domaine universitaire – Saint-Martin-d’Hères Fundamentals of probability/statistics, linear algebra and computer science (data structures and algorithms) Eric Gaussier 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). Final exam international.cic_tsukuba@grenoble-inp.fr

Designing modern products and systems in an innovative, sustainable and
competitive way demands the implementation of new paradigms in
development organisations. Design is no longer concentrated on a
specific phase. It goes beyond aesthetics to cover all functional
aspects of a product or a system, thus driving the entire development
process. Consequently, more and more actors of the complete product
life-cycle have to be integrated in the design process. This creates new
challenges for design engineers, as well as for development project
managers. This course adresses all these challenges looking at examples
from the design of automotive mechatronic systems.

http://genie-industriel.grenoble-inp.fr/en/studies/idesigner-tackling-complexity-by-integration-5guc0904 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Autres The definition and motivation of integration in design:

• the targets of integration, including the product life-cycle,
• essential methods of integration, including concurrent engineering and product modelling,
• mastering complexity and innovation,
• knowledge management for integration,
• collaborative integrated design
•
selected aspects of integration in design, including sustainable
design, risk assessment, safety design, virtual development tools and
techniques, etc… Basic knowledge on the product design process. Andreas RIEL 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.

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

ROBUST CONTROL COURSE

Introduction
Industrial examples (automotive and electromechanical applications).
1 Tools
Hinf norm: how to dene the gain of a MIMO system ?
Singular values of a transfer matrix, introduction to H2 and H1 norms.
Example of a mass/srping/damper system.
Internal stability: Notion of well-posedness, Small Gain theorem
2 Performance analysis
 Denition of the sensitivity functions
 frequency-domain performance indices (sensitivity functions, stability and robustness margins, bandwidth, SISO and MIMO cases)
3 Hinf control design
Performance Specications: selection of weighting functions.
Loop-shaping Mixed sensitivity problem.
Solving the Hinf control problem:  Obtaining the General control conguration
 Hinf controller structure (state feedback, dynamic output feedback)
 Problem solution using Riccati equations or LMIs -Bounded Real Lemma)
 Illustrative examples
4 Uncertainty and robustness
Representing uncertainties: unmodelled dynamics, frequency forms, unstructured uncertainties
Parametric uncertainties, LFT forms, structured uncertainties
Robust stability analysis: M
structure, small gain theorem
Robust stability for unstructured uncertainties.
Robust performance analysis: A simplied Hinf criterion,
Introduction to mu-analysis – structured uncertainties

5 Introduction to LMIs
What is an Linear Matrix Inequality ? Brief optimisa-
tion background, Denition
Stability issue: From Lyapunov equation to LMIs
Control design: problem formulation: Example on
State feedback
6 Short introduction to LPV systems
Denition of Linear Parameter Varying systems, stability issue, control design
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

http://ense3.grenoble-inp.fr/en/academics/non-linear-and-robust-controls-5eus5cnl Ability for design and analysis of Hinfinity controlllers, robustness analysis, and the limits of the linearization, the analytical tools for nonlinear stability, and the basic principles of feedback control nonlinear state. Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique Ability for design and analysis of Hinfinity controlllers, robustness analysis, and the limits of the linearization, the analytical tools for nonlinear stability, and the basic principles of feedback control nonlinear state. Linear Systems, Transfer and state space approach, frequency and time-domain analysis Olivier Sename 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.

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). Exam
Homework
Project reports
Individual evaluation in Matlab tutorials international.cic_tsukuba@grenoble-inp.fr

Issue: Understanding Opportunities and Challenges of the Industry of the Future … for Competitive Firms Design
In
2050, Manufacturing will look very different from today. Key words are
faster, responsive, clean and green, close to customers, interconnected,
smart. Firms will adapt a massive flow of new technologies: both
digital and physical.
As in the past, this 4th Industrial Revolution
will shift the role and place of the industry in society, it will
generate new business models, new forms of firm organizations and value
chains, new sources of value. For successful firms, strategic choices
will be crucial.
This course is designed to help students
forecasting the future, and acquiring the skills to develop strategic
choices in the coming environment. A good strategy traces the paths to
innovation: products, processes, organizations, value chains.

http://genie-industriel.grenoble-inp.fr/fr/formation/ue-foresight-and-strategy-wgu2str7 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Autres List of 11 themes (Part II)
I. Final Products 1. Individualization
II. Final Product 2. Interconnection, Ecosystem
III. Final Product 3. Matching (Including C2C)
IV. Value Chain 1. Vertical Relations
V. Value Chain 2. Externalities, Ecosystem
VI. Value Chain 3. Capturing Value and Leadership
VII. Value Chain 4. Standards, Norms, and Interoperability
VIII. New Production Process 1. The Future of Work
IX. New Production Process 2. Explore and Exploit, Complexity in Dynamics
X. New Production Process 3. Disruption and Continuity, Innovation and Growth
XI. Macro Issues and Politics Microeconomics, organisation and basic management are welcomed but not compulsory Bernard RUFFIEUX 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.

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

1) Predictive control : Illustrative example ; Prediction equations for linear time invariant systems ; Definition of the cost function ; Link with the unconstrained optimal regulator ; Constraints definition ; Constrained predictive control ; Control parametrization ; Application examples ; Nonlinear Predictive control
2) Model-based Diagnosis : Introduction, basic concepts, motivation and preliminaries: fault detection and isolation and its use for fault-tolerance and complex systems monitoring and safety. Process models and fault modelling. Presentation of the different approaches and focus on the model-based approach. ; Data validation and reconciliation: measurement errors, balance equations, state estimation for constrained and unconstrained systems, linear and bilinear systems ; Fault detection with parity equations – Static and dynamic cases: Analytical redundancy, parity equations and generation of residuals. Enhanced and structured residuals. Properties and analysis of residual signals ; Fault detection and isolation with state observers and state estimation. Unknown inputs observers. Observers banks.

3) Embedded system code design & implementation : Real-time and Embedded Systems design : Real-Time scheduling algorithms on uni and multiprocessor systems, programming techniques

http://ense3.grenoble-inp.fr/en/academics/advanced-control-methods-and-practical-implementation-tools-5eus5aua The aim of this course is to present advanced control systems methods for optimal and predictive control and fault detection and isolation & fault tolerance. Tools and methods for real-time implementation of control algorithms on embedded systems are also presented Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique The aim of this course is to present advanced control systems methods for optimal and predictive control and fault detection and isolation & fault tolerance. Tools and methods for real-time implementation of control algorithms on embedded systems are also presented Basic course in control systems, scientific programming and real-time computer systems Christophe Berenguer 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.

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). Session 1 : 60%CT + 40% CC
Session 2 : R Remplace CT international.cic_tsukuba@grenoble-inp.fr

This course presents advanced techniques for modeling, simulation and
optimization in technical product development. This 3 points are viewed
as cornerstones for the DMU development and usage.Lessons are describing the concepts while lab works are centered on operating basic tools.

http://genie-industriel.grenoble-inp.fr/fr/formation/ue-modelling-and-optimization-in-product-development-wgumode9 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Autres System modeling and simulationProduct architectureSimulation preparation Data and process models (principles, UML), system simulationIdealization, simplification for simulationCAD simulation link
Digital mock upGeometrical aspects, creation, maintenance, Parameterization, Direct modeling Product familyHeterogeneous Data Integration (Space Claim, PTC, catia)CAD modeling API : external control
Robust and optimal designDesign structure matrix: optimization problemContinuous optimization with or without constraints: modeling, solving, constraintsConstraint satisfaction problem meta heuristic methods: annealing, genetic algorithms, PSO CAD modeling : basics about 3D modeling
Applied mathematics : derivative, integration, etc.
Computer science: scripting language: python, scilab, visual basic 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.

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

Modelisation, simulation and conception of mechatronic systems. Industrial and mobile robotics notions. Specification and evaluation of dynamical performances. Embedded control systems and reference generation. Disturbance estimation and adaptive rejection of disturbances. Fault-tolerant control notions. Code implementation in microprocessors.

http://ense3.grenoble-inp.fr/en/academics/mecatronics-5eus5met study a mechatronics approach for conceiving and for dimensioning intelligent systems. Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique The main objective is to study a mechatronics approach for conceiving and for dimensioning intelligent systems.
UE Automatique 1 (Automatic control 1)
UE Actionneurs (Actuators)
UE Systèmes Temps-réel (Real-time systems) John-Jairo Martinez-Molina 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.

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). DS 2h + CC en BE.
(Individual exam 2h and computer assisted exercises). international.cic_tsukuba@grenoble-inp.fr