Factorization of multidimensional observation

Observations of a physical system depending on D variables (also called diversities) naturally provide a D-way hypercube of data. A simple data model is based on the decomposition of the observations into a sum of R products between simpler terms, each simple term being related to a unique diversity. In most cases, the factorization is not unique and the search for a solution must be regularized by resorting to constraints. In fact, the goal is to explain observations by R latent variables in a unique way, with a physical meaning. In this context, we present factorization methods, either on matrices (D = 2 diversities) or on tensors (D > 2), exploiting complementary features that are known beforehand, such as: source statistical independence, source nonnegativity, source sparsity, etc… In addition, theoretical principles and algorithms are illustrated by actual unmixing applications in brain and hyperspectral imaging, chemical engineering, communications, internet recommendation systems, etc.

http://phelma.grenoble-inp.fr/en/studies/factorization-of-multidimensional-observation-wpmtfmo7 Introduction of methods for the analysis and representation of multivariate, multidimensional data. Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique Observations of a physical system depending on D variables (also called diversities) naturally provide a D-way hypercube of data. A simple data model is based on the decomposition of the observations into a sum of R products between simpler terms, each simple term being related to a unique diversity. In most cases, the factorization is not unique and the search for a solution must be regularized by resorting to constraints. In fact, the goal is to explain observations by R latent variables in a unique way, with a physical meaning. In this context, we present factorization methods, either on matrices (D = 2 diversities) or on tensors (D > 2), exploiting complementary features that are known beforehand, such as: source statistical independence, source nonnegativity, source sparsity, etc… In addition, theoretical principles and algorithms are illustrated by actual unmixing applications in brain and hyperspectral imaging, chemical engineering, communications, internet recommendation systems, etc. Elementary linear algebra. Basic probability. Christian Jutten 2 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.

– 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). Continuous assessment international.cic_tsukuba@grenoble-inp.fr

Inverse problem and optimisation

This course focuses on formulating signal and image processing problems as convex optimization problems, analyzing their properties (e.g. existence and uniqueness of solutions) and designing efficient algorithms to solve them numerically. We aim at giving students the background and skills to formulate problems and use appropriate algorithms on their own applications.
Syllabus: * Convex optimization: existence and uniqueness of solutions, subdifferential and gradient, constraints and indicator functions, monotone inclusions, nonexpansive operators and fixed point algorithms, duality, proximal operator, splitting algorithms. * From estimation to optimization: formulating priors and constraints, regularity and parsimony, Bayesian interpretation.
* Inverse problems: well- and ill-posed problems, data fidelity and regularization, study of signal and image recovery problems.

The objective is to introduce the concepts of convex optimization and applications to inverse problems. Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique This course focuses on formulating signal and image processing problems as convex optimization problems, analyzing their properties (e.g. existence and uniqueness of solutions) and designing efficient algorithms to solve them numerically. We aim at giving students the background and skills to formulate problems and use appropriate algorithms on their own applications.
Syllabus:
* Convex optimization: existence and uniqueness of solutions, subdifferential and gradient, constraints and indicator functions, monotone inclusions, nonexpansive operators and fixed point algorithms, duality, proximal operator, splitting algorithms.
* From estimation to optimization: formulating priors and constraints, regularity and parsimony, Bayesian interpretation.
* Inverse problems: well- and ill-posed problems, data fidelity and regularization, study of signal and image recovery problems. basic analysis and linear algebra Laurent Condat 2 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.

– 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). Lab work report and written exam international.cic_tsukuba@grenoble-inp.fr

Image and signal processing apllications and processing (astrophysical and geophysical sciences

Part I : in-orbit remote sensing of planets
Introduction : optical sensors for planetary exploration,
correcting artifacts on images and spectra using linear transforms : quality improvement,
statistical inference (SIR) and bayesian methods for inverting physical models applied to analysing images,
optimisation problems for generating digital elevation models.

Part II : telescopic imagery of the Universe
Image formation in astronomy,
High contrast imaging,
Aperture synthesis imaging,
image reconstruction.

http://phelma.grenoble-inp.fr/en/studies/image-and-signal-processing-apllications-and-processing-astrophysical-and-geophysical-sciences-wpmtisp7 This course presents advanced numerical methods and algorithms for the restoration/analysis of planetary and astrophysical data. It is not intended as a comprehensive courses on the discipline but as the presentation of some key techniques. It is intended to convey a multi-disciplinary view of the image exploitation problem in astrophysics. Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique Part I : in-orbit remote sensing of planets
Introduction : optical sensors for planetary exploration,
correcting artifacts on images and spectra using linear transforms : quality improvement,
statistical inference (SIR) and bayesian methods for inverting physical models applied to analysing images,
optimisation problems for generating digital elevation models.

Part II : telescopic imagery of the Universe
Image formation in astronomy,
High contrast imaging,
Aperture synthesis imaging,
image reconstruction. Basics of linear transforms (PCA), optimization/estimation methods, and Bayesian inference. Sylvain DOUTE 2 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.

– 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

Multi and hyperspectral methods in image processing

The core of the course will focus on the following aspects:
– color theory, interaction between light an matter
– spectral unmixing with linear and non linear mixing models
– dimension reduction and sparse representations
– classification of hyperspectral data
– anomaly detection
– high performance computing
The course will also include basic notions of physics and vision, which are important to understand and take into account when designing an algorithm (for instance, spectral unmixing is a source separation problem, but not all source separation techniques are suited to this problem).

http://phelma.grenoble-inp.fr/en/studies/multi-and-hyperspectral-methods-in-image-processing-wpmtmhm7 Multispectral imaging consists in acquiring several images of the same scene using tens of narrow (e.g. 10 nm) and contiguous spectral bands (e.g. higher than 100 nm) in the visible range (380-780 nm). This enables to characterize objects by their color appearance or by their spectral reflectance function and to study the influence of lighting conditions, viewing geometry, sensor sensibility and material properties on color appearance. This course will cover the main issues related to the acquisition, the processing and the visualization of multispectral images. A variety of study cases (e.g. food industry, medical imaging, automotive industry, cultural heritage) will illustrate this course.
Refining the concept, hyperspectral imaging, also called imaging spectroscopy, consists in acquiring simultaneously hundreds of images of the same scene using hundreds of narrow and contiguous spectral bands. This enables a fine description of the materials that are observed. This field is blooming in a number of applications, from planetary exploration to material sciences in industry, from quality control to astrophysics, from biomedical imaging to airborne and satellite remote sensing. This course will cover the main issues related to the signal and image processing challenges raised by these data and cover the whole chain, from basic notions in instrumentations to a variety of applications (the applications being addressed during the lab sessions). Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique The core of the course will focus on the following aspects:

color theory, interaction between light an matter
spectral unmixing with linear and non linear mixing models
dimension reduction and sparse representations
classification of hyperspectral data
anomaly detection
high performance computing
The course will also include basic notions of physics and vision, which are important to understand and take into account when designing an algorithm (for instance, spectral unmixing is a source separation problem, but not all source separation techniques are suited to this problem). Basics in digital signal and image processing. Jocelyn Chanussot 2 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.

– 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

5PMSAST6 : Machine statistical Learning

Introduction to the statistical learning theory and prediction (regression/classification)
* Review of Models/Algorithms for supervised/unsupervised learning
* Illustration de ces algorithmes sur différents jeux de données on different dataset
(intelligence artificielle, Bioinformatics, vision, etc …)

http://phelma.grenoble-inp.fr/en/studies/5pmsast6-machine-statistical-learning-wpmbdas9 Introduction to the statistical learning theory and prediction (regression/classification)

Review of Models/Algorithms for supervised/unsupervised learning
Illustration of these algorithms on different dataset
(Artificial Intelligence, Bioinformatics, vision, etc …) Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique * General introduction to the statistical learning theory and prediction (regression/classification)
* Generative approaches: Gaussian discriminant analysis, naïve Bayes hypothesis
* Discriminative approaches: logistic regression
* Prototype approaches: support vector machines (SVM)
* Unsupervised classification (kmeans and mixture model)
* Dictionnary learning / Sparse reconstruction
* Source separation Basic elements of probability/statistics, filtering Florent Chatelain 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.

– 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). Written exam international.cic_tsukuba@grenoble-inp.fr

5PMBIPB8 : Image Processing first Level

* Digital images and representations
* Image quality improvment
* Image restauration and filtering
* Contours extraction
* Image segmentation
* Visual perception
* Colour images

3 lab sessions: 2x3h to illustrate some basic image processing methods + 1x3h to study an iris recognition algorithm

http://phelma.grenoble-inp.fr/en/studies/image-processing-first-level-5pmbipb8 Introduction to the basis techniques of image processing Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique Numerical images and representations
Image improvement
Image restoration and filtering
Contour extraction
Threshold, segmentation and classification
Visual perception
Digital color images Basic notions of signal processing
Basic notions of Matlab programming Alice Caplier 4 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.

– 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). Continuous assessment international.cic_tsukuba@grenoble-inp.fr

English (Masters II)

Aspects covered in class will include:
– pronunciation, intonation, voice projection, body language
– language work including: general and specialised vocabulary in context; use of appropriate tenses, connectors and register in oral & written discourse
– adequate use of professional presentation tools for slide and poster design
– insights into the history of research and the development of technologies (and their presentation based on scientific sources)
– listening-comprehension, note-taking and abstract writing practice
– ethical considerations in research

http://phelma.grenoble-inp.fr/en/studies/english-masters-ii-wpmcang2 This course description applies to several M2R programmes. Our aim will be:
– to improve oral/written comprehension and speaking skills through regular practice
– to master professional presentation tools (using adequately designed slides and posters) and discuss research findings using clear, accurate language
– to improve note-taking and reporting skills, to be able to structure and write-up an abstract
– to work as a team on joint projects
– to learn to convince others of the value of one’s research interests and to answer questions constructively, taking into account the specificity of once’s audience
– to understand the value of sharing a joint technical and scientific culture in a multicultural context Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique Aspects covered in class will include:
– pronunciation, intonation, voice projection, body language
– language work including: general and specialised vocabulary in context; use of appropriate tenses, connectors and register in oral & written discourse
– adequate use of professional presentation tools for slide and poster design
– insights into the history of research and the development of technologies (and their presentation based on scientific sources)
– listening-comprehension, note-taking and abstract writing practice
– ethical considerations in research Minimum entrance level: B1
B2 level required to obtain the grade of Master
All students will take the Bulats test at the end of the semester to certify their B2 level Laurence Pierret, Veronique Beguin 2 2nd year of master Tutoring 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.

– 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). Continuous assessment international.cic_tsukuba@grenoble-inp.fr

Bayesian methods for data image analysis

* Introduction
* Bayesian estimators
* Priori choice
* Approximate Bayesian inference
** Deterministic approximation methods
** Stochastic approximation methods
* Case study: Bayesian inference for speech recognition

http://phelma.grenoble-inp.fr/en/studies/bayesian-methods-for-data-image-analysis-wpmtbmd7 The aim is to introduce fundamentals on Bayesian inference, and to develop applications in the framework of image and signal processing. Grenoble INP Institute of Engineering Univ. Grenoble Alpes Grenoble – Polygone scientifique * Introduction
* Bayesian estimators
* Priori choice
* Approximate Bayesian inference
** Deterministic approximation methods
** Stochastic approximation methods
* Case study: Bayesian inference for speech recognition Basic notion in both estimation and detection theory Olivier Michel, Hacheme Ayasso, Florent Chatelain 2 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.

– 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). Written exam international.cic_tsukuba@grenoble-inp.fr

Verification and test of secure circuits

1 Verification and test of critical and secure digital systems: Introduction (Context and issues; Verification vs Test; DO-254 Standard); Hardware systems verification (Simulation; Emulation & Prototyping); Hardware Testing (Defects and faults modeling; Automatic Test Pattern Generation (ATPG); Design for Test and Bult-in-Self-Test (DfT, BIST); Digital board testing (boundary scan).
2 HW/SW Co-Verification & Co-Development: Microelectronic context and trends (SoC, MPSOC); SoC design flow (Hadware/Software Co-design approach; Plateform based design); Introduction to SystemC (Starting with SystemC; Communication channels; New abstraction level: Transaction Level Modeling); Co-verification of Harware and Software systems (Context and definitions; Co-verification approaches based on ISS, BFM, TLM and emulation, criteria to choose a verification approach)
3 Hardware Security: Introduction & cryptography basis; Hardware Vulnerabilities (Fault Attacks; Side Chanel Attacks; Integrated Circuit Trustworthiness (Countermeasures, Security Certification and Case studies) Smartcard; FPGA)
Laboratories:
– VHDL & PSL Simulation with QuestaSim (Mentor GraphiCs)
– Simulation vs “prototyping and integrated logical analyzer” ChipScopePro (Xilinx)
– SRAM embedded memory test on FPGA Spartan 3 card (Xilinx)
– On the use of communication channels (Fifo, Mutex, Semaphore) to model a communication architecture
– SoCLib – “Emulation of a Hardware/Software architecture used for image processing”

http://esisar.grenoble-inp.fr/en/academics/verification-and-test-of-secure-circuits-5amse515 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Valence – Autres At the end of the lecture, the students will be able to verify, to test digital architectures and to analyse the vulnerabilities of embedded systemes. Then, they will be able to perform attacks and to design appropriate countermeasures. Neccessary: Hardware Description Language (HDL, verilog or VHDL) for simulation (testbench) and design, logical synthesis, FPGA, processor architecture (processor models, instruction set architecture), C programming
Ideally: bases of object oriented programming David HELY; Vincent BEROULLE 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.

– 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). Terminal Exam, First session, written, 3h, only document allowed “syntaxe VHDL”, no calculator
Labs: average of laboratory exams international.cic_tsukuba@grenoble-inp.fr

Innovation Project

Groups consist of at least 4 students following different specialties. Subjects (open and multidisciplinary) are offered by responsible of 5th year module. The job is done by each group independently; groups have access to the SACCO platform and TP classrooms of the school.

http://esisar.grenoble-inp.fr/en/academics/innovation-project-5ampx504 Grenoble INP Institute of Engineering Univ. Grenoble Alpes Valence – Autres Assess and enhance: Skills for the development of multidisciplinary systems; Work in a multidisciplinary team; The ability to innovate; Autonomy David HELY, Etienne PERRET, Vincent BEROULLE, Damien KOENIG 4 2nd year of master Seminar 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.

– 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). P1 = Mean of report evaluation and oral presentation international.cic_tsukuba@grenoble-inp.fr

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.

– 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). international.cic_tsukuba@grenoble-inp.fr

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.