Master MOC - parcours Smart internet of things

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (7h)

Tutorials (6h)

Practices (12h)

Pre-requisites:

Scientific degree level programming and algorithms.
Mathematics and analog and digital signal processing at scientific degree level.

Objectives:

This module deals on the one hand with all the elements of an acquisition chain from the sensor to the digital representation of the measurand, and on the other hand with the communicating aspect of these sensors particularly in the context of connected objects. The first part therefore deals with the metrological and classification aspects of sensors as well as the strategies commonly used in this field for the digitization of measurements. The second part deals with the issues linked to wireless communications problems through a first simple link budget model and an initial awareness of the compromise between energy autonomy and communications needs.

Learning outcomes:

• Implement a signal/image acquisition device. Specify, select materials, assemble and acquire.
• Master basic digital tools for analyzing and processing signals/measurements.
• Master the acquisition chain, from the sensor to the digital exploitation of the signal.
• Master the calculation of a simple link budget
• Master the strategy for calculating the autonomy of an on-board energy reserve.

Indicative contents:

Master the signal/image acquisition chain, from the sensor to the digital exploitation of the signal. Intelligent sensors, signal acquisition (physical measurements), image or video.
Know how to return to physical measurements (length, distance, force, color, etc.) from a digital acquisition. The emphasis will be placed on the choice of sensors according to the quantity to be measured, then on the problems of calibration and calibration of the sensor and the acquisition chain. Know how to calculate a link budget between a transmitter and a receiver in the case of a direct path and estimate the autonomy of an on-board sensor taking into account the energy budgets of each on-board function.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (14h)

Tutorials (8h)

Practices (4h)

Project (4h)

Pre-requisites:

• Scientific degree level programming and algorithms.
• Mathematics at scientific degree level.

Objectives:

The objective of the data analysis module is to master the main data mining techniques in the context of measurement.

As we have seen, given the context of sharing as well as the possible varied origins of the students, the first objective is the acquisition of elementary concepts associated with probabilities as well as the usual characteristics allowing measurements to be described.

After this preamble, the second objective is to study in detail the main methods of exploring tables of measurements, namely principal component analysis as well as discriminant factor analysis (in order to make the student aware of the classification problem automatic). The objective is to extract useful information, structures, patterns or indicators from a set of data. The educational choice will be to study both the concept of the method but also its practical implementation.

The last objective concerns the study of the problem of automatic grouping of data according to similarity criteria at the level of measurements through the study of two methods:

• K-Means
• Dendograms

Learning outcomes:

• Computerically implement a data analysis algorithm on real data.
• Master the measurement analysis tools and the associated justification
• Analyze the information associated with a series of measurements using measurement analysis tools.
• Computerically implement an algorithm for estimating a quantity from real data, considering the notion of confidence interval.
• Master basic statistical tests and the associated justification
• Computerically implement a statistical test on real data in order to answer a question.

Indicative contents:

As part of this module we will study different data mining methods in the context of measurement. Given the context of sharing as well as the possible varied origin of the students, the educational content will begin with a presentation of the elementary concepts associated with probabilities as well as the usual characteristics allowing to describe measurements such as the notion of mean, variance and correlation and finally the description of the most used laws. Through these principles from descriptive statistics, it is a question of providing the student with sufficient background to understand the rest of the training concerning data. The notions of probabilities will be illustrated through the methods of estimating quantities from a series of measurements.

We will then study the main methods of analyzing measurement tables such as PCA, through both the analysis of the methods but also their computer implementations. Finally, we conclude with the discovery of so-called clustering algorithms allowing us to extract groups of data from their measurements.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 6
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (8h)

Tutorials (14h)

Practices (28h)

Pre-requisites:

• Basics of structured programming (C language). Basic knowledge of IT development tools (GCC-based compilation chain).
• Basics of electricity and digital electronics (numeration, logic, coding).

Objectives:

This unit deals with embedded programming on microcontrollers which do not integrate an operating system and is a preamble to the “Communicating embedded system” module of the second semester. It addresses the main technological solutions of current embedded systems based on an ARM Cortex M core, as well as the elements of choice for these systems in terms of power, energy, etc. We then approach the development environment (EDI and compilation chain) and finally the design methodologies from specification to validation by integrating function libraries at different levels of abstraction. Input/output devices and their access methods as well as I2C and SPI communications buses are studied and implemented. This module also introduces concepts related to interruptible or non-interruptible processes as well as techniques for minimizing energy consumption.

The goal of this course is to provide the elements necessary for programming and implementing a microcontroller system. It also aims to understand development environments and issues related to energy consumption.

Learning outcomes:

Design and implement an embedded system as part of a sensor network application.

Indicative contents:

• Introduction to embedded systems. Architecture of a microcontroller. The compilation chain and development environments. The STM32 ecosystem (STM32CubeMX and HAL library).
• Digital-to-analog and analog-to-digital conversion. Implementation of converters on the STM32.
• Interruptions. State machine analysis.
• Timers with advanced features (capture of input events and generation of PWM signals at output).
• I2C and SPI buses and communication with sensors.
• Interfacing with graphic touch screen (HMI).
• Low energy operating modes.
• Synthesis around a complete project (e.g.: meteorological station, TP).

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Tutorials (20h)

Pre-requisites:

• Scientific degree level programming and algorithms.
• Mathematics at scientific degree level.

Objectives:

The objective of the data analysis module is to master the main data mining techniques in the context of measurement.

As we have seen, given the context of sharing as well as the possible varied origins of the students, the first objective is the acquisition of elementary concepts associated with probabilities as well as the usual characteristics allowing measurements to be described.

After this preamble, the second objective is to study in detail the main methods of exploring tables of measurements, namely principal component analysis as well as discriminant factor analysis (in order to make the student aware of the classification problem automatic). The objective is to extract useful information, structures, patterns or indicators from a set of data. The educational choice will be to study both the concept of the method but also its practical implementation.

The last objective concerns the study of the problem of automatic grouping of data according to similarity criteria at the level of measurements through the study of two methods:

• K-Means
• Dendograms

Learning outcomes:

• Computerically implement a data analysis algorithm on real data.
• Master the measurement analysis tools and the associated justification
• Analyze the information associated with a series of measurements using measurement analysis tools.
• Computerically implement an algorithm for estimating a quantity from real data, considering the notion of confidence interval.
• Master basic statistical tests and the associated justification
• Computerically implement a statistical test on real data in order to answer a question.

Indicative contents:

As part of this module we will study different data mining methods in the context of measurement. Given the context of sharing as well as the possible varied origin of the students, the educational content will begin with a presentation of the elementary concepts associated with probabilities as well as the usual characteristics allowing to describe measurements such as the notion of mean, variance and correlation and finally the description of the most used laws. Through these principles from descriptive statistics, it is a question of providing the student with sufficient background to understand the rest of the training concerning data. The notions of probabilities will be illustrated through the methods of estimating quantities from a series of measurements.

We will then study the main methods of analyzing measurement tables such as PCA, through both the analysis of the methods but also their computer implementations. Finally, we conclude with the discovery of so-called clustering algorithms allowing us to extract groups of data from their measurements.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 6
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (12h)

Tutorials (6h)

Practices (20h)

Project (12h)

Pre-requisites:

Scientific degree level programming and algorithms.

Objectives:

The implementation and deployment of software solutions linked to problems of data analysis, communication of said data or even acquisition relies more and more often on the use of SDKs (Software Development Kits).

This module satisfies a technical (and organizational) need linked to the modules S1 Data analysis, S1 Embedded systems, S1 Sensor and image acquisition, S2 Wireless technology, S2 Network and security, S2 Supervised learning, S2 Communicating embedded systems, S3 Machine Learning and S3 Computer Vision.

Beyond the mastery of a language or an environment (C++, Python, Matlab, etc.), it raises students’ awareness more generally about the use of existing bricks, good development practices as well as to the agile management of an IT-type project.

Learning outcomes:

• Create digital calculation software by assembling existing bricks.
• Design a protocol and test data to validate a software solution.
• Master the fundamental concepts and tools in software engineering.
• Master object-oriented programming

Indicative contents:

The implementation of software solutions linked to problems of data analysis, communication of said data or even acquisition relies more and more often on the use and deployment of development kits (SDK: Software Development Kits) or libraries. A library is a set of functions, grouped and made available so that they can be used without having to rewrite them. In this module, we explore, on the one hand, the technical development practices associated with their use by addressing more generally a whole set of software life cycle management solutions, and the associated agile project management practices.

Methods of assessment:

Written test

Suggested bibliography:

in progress
 

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (10h)

Practices (15h)

Pre-requisites:

None

Objectives:

Since 2015, image sensors have shown the strongest growth in the sensor market. They are present everywhere, even in a hidden way (case of distance sensors for example). This module addresses the essential questions necessary to transform an image capture into a measurement. It will make it possible to choose the components of a vision chain (sensor, lens, lighting) to help in decision-making based on the measurements taken. Beyond the fundamental elements, this module relies heavily on the acquisition of know-how using sensors and advanced means made available by the XLIM laboratory.

The module addresses questions relating to spatial, colorimetric measurements and optical properties characteristic of the surfaces observed. These measurements are the entry points for image-based deep learning and artificial intelligence tools. The module also addresses new sensors and acquisition methods in and outside the visible domain. It also addresses aspects of international standards and recommendations. The management of calibration and calibration is dealt with in a concrete manner during the various practical works.

Learning outcomes:

• Implement an image acquisition device:
• Specify and select the materials (sensor, lens, lighting),
• Set up the acquisition geometry and configure the acquisition.
• Implement calibration and calibration elements for spatial, colorimetric and photometric measurements
• Develop software tools for extracting spatial, colorimetric, photometric (spectral) measurements from acquired images.

Indicative contents:

• Perform spatial measurements from an image (size, distance, depth, speed) based on scene geometry, acquisition parameters (shutter speed, sensitivity), lens-related parameters (focal length, depth of field, aperture diameter).
• Carry out colorimetric measurements independent of lighting by controlling illuminants, white balance and references.
• Estimate the optical properties of surfaces (reflectance) from the mastery of spectral aspects and the use of color, multi and hyperspectral imagers. Make the connection between physical properties and colorimetric properties.
• Measure color and spectral differences in standardized spaces
• Extension of the subject beyond the visible domain: UV, Infra-red, radio domain and Giga-Hertz…

Methods of assessment:

Written test

Suggested bibliography:

in progress
 

Credits:3
Language:

French

Course mode:

On-site

Methods of delivery:

Tutorials (39h)

Pre-requisites:

None

Objectives:

Part 1 Communication : This module is designed to help students apply for internships. It equips them with methodological tools and enables them to understand the challenges and stages of recruitment. In addition, students reinforce their oral fluency through a number of exercises: a 180-second Elevator pitch; a critical analysis of a socio-technical controversy in its polemical and media dimensions, combined with a presentation of the players involved and the arguments associated with the different positions. The aim is to develop convincing and adaptable skills. Team-building exercises are designed to get students to work together and put them in a collective interview situation. – Pay attention to posture and body language – Express yourself with ease – Synthesize – Analyze documents and identify arguments – Present a project, justifying the choices made

Part 2 Management : This module aims to make students think about the issues facing a company, how the right strategy is determined, using methodological tools, and to identify interested parties and their performance management.

Learning outcomes:

• Develop your human and relational qualities
• Communicate in writing, orally, in several languages
• Work as a team, self-assess (strengths and weaknesses)
• Develop your abilities to enter professional life
• Demonstrate cultural openness, be curious, have a critical mind
• Work on your dynamism, be capable of commitment, leadership
• Know how to integrate business and societal issues in an international context
• Know and understand the business world
• Manage projects

Indicative contents:

Part 1 Communication : Job interview simulations (individual and collective) are offered as well as the creation of the key elements of a file, namely the CV, cover letter, LinkedIn profile, online applications, etc. A current review (scientific and technical news) is produced at each tutorial as well as a final presentation on a subject related to the professional world. This requires documentary research and preparation of the speech as well as the visual support used for the defense. Work on argumentation and the rhetorical aspects of speech is presented. Students approach a socio-technical controversy by identifying the various positions and issues at stake in the debate, particularly in its media dimension. They report on their documentary research and the choices they have made to address the controversy in an oral presentation.

Part 2 Management :

Chap 1. The company and its environment

• The company
• Analysis of its environment, its market
• The choice of a strategy thanks to a good diagnosis
• React to changes in the environment

Chap 2. The company and its strategic choices

• Notions – strategy, organizational policy, competitive advantage, the different levels of strategy
• The 3 strategies resulting from Porter methods
• Growth strategies * Innovation * Entrepreneurial and managerial logic
• The purpose of a company

Chap 3. Company performance.

• Company management and performance
• Identify stakeholders and their objectives
• Concept- governance, management, performance, decision-makers

Methods of assessment:

Written test, oral, presentation

Suggested bibliography:

• Perez D., CV, lettre de motivation, entretien d’embauche, L’Étudiant, Ed. Paris, 2014, 416 pages.
• Engrand S., Projet professionnel gagnant ! Une méthode innovante pour cibler stages et premier emploi, Dunod, Ed. Paris, 2014, 180 pages.
• Davidenkoff E., Le guide des entreprises qui recrutent : hors-série 2015 : faire la différence en entretien, négocier son premier salaire, débuter à l’étranger, L’Étudiant, Ed. Paris, 2015
• Charline Licette, Savoir parler en public, Studyrama Pro, 2018
• Fabrice Carlier, Réussir ma première prise de parole en public, StudyramaPro, 2018
• Cyril Gely, Savoir improviser : l’art de s’exprimer sans préparation, Groupe Studyrama-Vocatis, 2010
• Lelli A., 2003, Les écrits professionnels : la méthode des 7C – Soyez correct, clair, concis, courtois, convivial, convaincant, compétent, Dunod, Ed. Paris, 2003, 168 pages.

Credits: 6
Language:

French/English

Course mode:

On-site

Pre-requisites:

None

Objectives:

Consolidation of the experience acquired during training within a research laboratory.

Indicative contents:

Depending on the topic of the laboratory work.

Learning outcomes:

• Integrate into and within a work team
• Show initiative
• Test your curiosity
• Structure your ideas and the stages of their implementation
• Demonstrate scientific rigor
• Learn to meet deadlines
• Know the safety rules in force within the structure

Methods of assessment:

Report, evaluation sheet (lab behavior), oral presentation

Suggested bibliography:

Depending on the topic of the laboratory work.

Credits: 4
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (8h)

Tutorials (14h)

Practices (28h)

Pre-requisites:

Embedded systems and wireless technologies course.

Objectives:

The objective of this module is to size and implement a wireless communicating embedded system for IoT integrating efficiency, robustness and energy saving constraints using ad hoc design methods.

Learning outcomes:

Design and implement a communicating embedded system for IoT.

Indicative contents:

• Link assessment and radio standards for IoT
• SoC: Handling of the multi-standard ESP32 SoC (BLE + WiFi) and Espressif’s ESP-IDF open source development environment
• RTOS: Implementation of FreeRTOS and concepts associated with real-time OS (task parallelization, mechanisms for synchronization and sharing of data/resources/stacks, files, lists, etc.)
• ·Data recovery tools and methods for IoT, Use of ad-hoc protocols (MQTT)
• Security standards for IoT

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (14h)

Tutorials (16h)

Practices (20h)

Pre-requisites:

• Using a wired IPv4 network
• Wireless transmission methods and standards

Objectives:

The Networks and Security course covers the principles of network administration, particularly as they relate to the wireless sensor network environment. These networks are generally controlled and administered by a gateway which constitutes the interface between wireless sensors and wired networks connected to storage services, for example. The protocols allowing such an implementation must be studied, from the simple routing of information up to a higher level, the sending of data to be stored by the server. Cybersecurity is at the center of concerns for connected objects: in this course, this aspect is considered from the perspective of the main encryption algorithms and good security practices as an administrator.

Learning outcomes:

• Design a network map of sensors connected to a gateway based on contextual constraints;
• Set up and configure a wireless sensor network, configure the network gateway, communicate the network with an external storage unit;
• Implement mechanisms to ensure the security of the sensor network; secure communications to the storage unit
• Test the level of communications security

Indicative contents:

The first goal of this course is to design, implement and configure a network so that wireless sensors can communicate with an information storage unit.

The second goal is to understand the challenges of cybersecurity, particularly in the world of connected objects. The student is confronted with security issues specific to networks (availability, traceability, confidentiality, authentication, integrity). Through practical case studies, the aim will be to study different attack modes, implement cryptography algorithms using dedicated tools and understand the vocabulary and issues.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (26h)

Tutorials (34h)

Pre-requisites:

• Methodology part of electronic design of IoT
• Physics of components and semiconductors in IoT

Objectives:

in progress

Learning outcomes:

in progress

Indicative contents:

The part 1 addresses the problem of optimizing the quality of service (QoS) required of a digital communication system when energy consumption is constrained. These are strategies related to adaptive modulations or the use of optical rather than electromagnetic communications … Quality of service can be defined by transmission distance, bit error rate, bit rate and requires knowledge of the link budget.

In the part 2, we define the constituent elements of an IOT …, all the elements « composing » their behaviour and in particular their consumption characteristics. We will also look at the physics of some components. The following chapters will be discussed: Sensors for IoT (temperature, gaz, light…), antennas for IoT, transmitters / receivers for IoT Materials and technologies for sensors, antennas, transceivers for IoT, harvesting and storage module. Materials and processes for each device will be described.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 4
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (16h)

Tutorials (8h)

Practices (20h)

Project (6h)

Pre-requisites:

Data analysis module (S1)

Objectives:

This module complements the data analysis module. We discuss prediction and automatic decision-making techniques using self-learning algorithms from labeled data. By “labeled data” we mean the introduction of semantic information. Compared to more traditional so-called “unsupervised” learning algorithms, which are based on the recognition of geometric patterns or statistical coherence in the data, semantic information requires the emulation of a cognitive process, hence its regular association with the principle of artificial intelligence.

Learning outcomes:

• Mastery of the different issues of supervised learning
• Automatically predict the evolution and/or membership of a set of raw data from a learning database
• Master the main techniques allowing automatic completion of prediction, decision support or effective data representation tasks

Indicative contents:

In a first part, a complete approach will be proposed: collection of requirements, performance metrics, choice of algorithm, development, tests and validation. Several algorithms will be studied, including KPPV, Boosting, and SVM techniques.

In a second part, the objective will be the construction of measurement vectors allowing decision-making in the context of complex data. This consists of understanding the methods for calculating descriptors and modeling and extracting characteristics from signals and images, this concerns texture descriptors, representation dictionaries as well as the main geometric descriptors.

For these two components, the educational choice will be to study both the concept of the method but also its practical implementation.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 4
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (10h)

Tutorials (20h)

Practices (12h)

Project (8h)

Pre-requisites:

Bachelor level mathematics and signal processing

Objectives:

The objective of this course is to study the parameters which determine the behavior of wireless technologies, to understand them in the form of fundamental blocks and to set their operating limits in order to best manage the numerous compromises inherent in the chains of digital communication in compliance with application specifications.

Learning outcomes:

• Master the principles of digital communication systems
• Evaluate the performance of a wireless communication system
• Choose a transmission standard based on specifications
• Know how to compare standards with each other

Indicative contents:

• General introduction to wireless technologies
• Shannon transmission chain
• From binary message to baseband electrical signal
• M-ary baseband message
• Ideal channel transmission and reception
• Transmission channels, noise and interference
• Single carrier modulations
• Demodulation and performance of a radio link
• Connection assessment
• Application: from needs analysis to the implementation of wireless networks within a structure

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 3
Language:

English

Course mode:

On-site

Methods of delivery:

Tutorials (30h)

Pre-requisites:

B1 level required.

Objectives:

To bring students towards the European B2/C1 level. The operational and evaluable objectives of this training are:

• Understand most situations that might be encountered at work or while traveling in a region where English is spoken for example
• Develop oral and written language skills
• International English communication

Learning outcomes:

Acquisition of English language skills (objective B2/C1). International, specialty and professional English (CV, cover letters, etc.)

Indicative contents:

• Written and oral comprehension/production work on authentic specialist or general English documents
• Interactive debates on general themes
• Language lab work (pronunciation, listening, repetition, etc.)
• Professional English (writing cover letters, CV, professional interview) academic (summary of documents, emails, sum-ups, etc.)
• Work on specialization and general English vocabulary.
• Presentation of a specialty presentation

Methods of assessment:

Written test, oral

Credits:3
Language:

French

Course mode:

On-site

Methods of delivery:

Tutorials (39h)

Pre-requisites:

None

Objectives:

Part 1 Communication : This module is designed to help students apply for internships. It equips them with methodological tools and enables them to understand the challenges and stages of recruitment. In addition, students reinforce their oral fluency through a number of exercises: a 180-second Elevator pitch; a critical analysis of a socio-technical controversy in its polemical and media dimensions, combined with a presentation of the players involved and the arguments associated with the different positions. The aim is to develop convincing and adaptable skills. Team-building exercises are designed to get students to work together and put them in a collective interview situation. – Pay attention to posture and body language – Express yourself with ease – Synthesize – Analyze documents and identify arguments – Present a project, justifying the choices made

Part 2 Management : This module aims to make students think about the issues facing a company, how the right strategy is determined, using methodological tools, and to identify interested parties and their performance management.

Learning outcomes:

• Develop your human and relational qualities
• Communicate in writing, orally, in several languages
• Work as a team, self-assess (strengths and weaknesses)
• Develop your abilities to enter professional life
• Demonstrate cultural openness, be curious, have a critical mind
• Work on your dynamism, be capable of commitment, leadership
• Know how to integrate business and societal issues in an international context
• Know and understand the business world
• Manage projects

Indicative contents:

Part 1 Communication : Job interview simulations (individual and collective) are offered as well as the creation of the key elements of a file, namely the CV, cover letter, LinkedIn profile, online applications, etc. A current review (scientific and technical news) is produced at each tutorial as well as a final presentation on a subject related to the professional world. This requires documentary research and preparation of the speech as well as the visual support used for the defense. Work on argumentation and the rhetorical aspects of speech is presented. Students approach a socio-technical controversy by identifying the various positions and issues at stake in the debate, particularly in its media dimension. They report on their documentary research and the choices they have made to address the controversy in an oral presentation.

Part 2 Management :

Chap 1. The company and its environment

• The company
• Analysis of its environment, its market
• The choice of a strategy thanks to a good diagnosis
• React to changes in the environment

Chap 2. The company and its strategic choices

• Notions – strategy, organizational policy, competitive advantage, the different levels of strategy
• The 3 strategies resulting from Porter methods
• Growth strategies * Innovation * Entrepreneurial and managerial logic
• The purpose of a company

Chap 3. Company performance.

• Company management and performance
• Identify stakeholders and their objectives
• Concept- governance, management, performance, decision-makers

Methods of assessment:

Written test, oral, presentation

Suggested bibliography:

• Perez D., CV, lettre de motivation, entretien d’embauche, L’Étudiant, Ed. Paris, 2014, 416 pages.
• Engrand S., Projet professionnel gagnant ! Une méthode innovante pour cibler stages et premier emploi, Dunod, Ed. Paris, 2014, 180 pages.
• Davidenkoff E., Le guide des entreprises qui recrutent : hors-série 2015 : faire la différence en entretien, négocier son premier salaire, débuter à l’étranger, L’Étudiant, Ed. Paris, 2015
• Charline Licette, Savoir parler en public, Studyrama Pro, 2018
• Fabrice Carlier, Réussir ma première prise de parole en public, StudyramaPro, 2018
• Cyril Gely, Savoir improviser : l’art de s’exprimer sans préparation, Groupe Studyrama-Vocatis, 2010
• Lelli A., 2003, Les écrits professionnels : la méthode des 7C – Soyez correct, clair, concis, courtois, convivial, convaincant, compétent, Dunod, Ed. Paris, 2003, 168 pages.

Credits: 6
Language:

French/English

Course mode:

On-site

Pre-requisites:

None

Objectives:

Consolidation of the experience acquired during training within a research laboratory.

Indicative contents:

Depending on the topic of the laboratory work.

Learning outcomes:

• Integrate into and within a work team
• Show initiative
• Test your curiosity
• Structure your ideas and the stages of their implementation
• Demonstrate scientific rigor
• Learn to meet deadlines
• Know the safety rules in force within the structure

Methods of assessment:

Report, evaluation sheet (lab behavior), oral presentation

Suggested bibliography:

Depending on the topic of the laboratory work.

Credits: 3
Language:

French/English

Course mode:

On-site (internship)

Pre-requisites:

None

Objectives:

Discover the world of business or international research work.

Learning outcomes:

Compare the skills acquired during training with the demands of the socio-professional world.

Indicative contents:

At least two months spent within the company (or in an international research laboratory) as an intern.

Methods of assessment:

Report, evaluation sheet, oral presentation

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Tutorials (30h)

Pre-requisites:

Python, data analysis

Objectives:

The objectives are to master imaging system technologies, the medical data acquisition, their analysis but also the design and deployment of technical and algorithmic solutions. Artificial methods based advanced methods will notably be developed in given biomedical contexts.

Learning outcomes:

in progress

Indicative contents:

Part 1 : understanding the main medical and biological imagers (magnetic resonance imaging, microscopy, histology, scanner,) as well as the specific algorithms: cellular image analysis, histopathological data analysis,
polarimetric biological tissue study, biomedical data mining, registration and reconstruction of heterogeneous biomedical data …

Part 2 : Control of processings and analyses of data collected by connected objects and biomedical sensors tools: semiotic analysis, multisensor context, Design, analyses and deployement of data analysis and machine learning solutions that allow a set of quantitative and/or qualitative characteristics
to provide solutions for diagnostic assistance or understanding phenomena.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 6
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (20h)

Practices (30h)

Pre-requisites:

Programming practices and tools, sensor acquisition, vision and data analysis.

Objectives:

This “Computer Vision” course aims to study, analyze and design image and video analysis methods.

The course allows students to implement computer programs in innovative application and theoretical contexts linked to image representations and processing.

Learning outcomes:

Master advanced image-based control and decision support processes for intelligent objects (‘smart systems’) in services, industrial, medical, heritage applications, etc.

Indicative contents:

The objective of this module is to analyze and design an image processing chain (color, spectral, multi-variate) or videos (image towards decision).

Different fundamental elements of the chain are studied such as image registration and fusion, denoising, restoration, reconstruction, segmentation. The module will also address video processing, particularly in the context of scene analysis.

The different elements of the chain studied are based, among other things, on bio-inspired approaches and variational approaches.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Course unit: Machine learning
Methods of delivery:

Lectures (12h)

Practices (12h)

Project (8h)

Pre-requisites:

The data analysis course (M1, 1st semester), and basic algorithmic skills.

Objectives:

This “Machine Learning” course aims to study, analyze and design automatic decision-making methods adapted to data, particularly mass data. More precisely, it involves automatically predicting the evolution and/or membership of a set of raw data from a learning database. It should be noted that this module complements the “Supervised learning” module (M1, 2nd semester) but without this being a prerequisite.

This course is particularly relevant in the context of neural algorithms, deep learning and large masses of data.

Learning outcomes:

• Computationally implement a KNN or naive Gaussian type automatic decision algorithm on real data.
• Master the tools for machine learning analysis, interpretation of results and in particular evaluation of the quality of the method
• Computationally implement an automatic decision-making algorithm based on a neural structure.
• Master the different neural techniques and know how to make the right choice based on the data to be processed
• Computerically implement a statistical test on real data in order to answer a question.
• Deploy a data analysis solution adapted to big data challenges

Indicative contents:

The objective of this “Machine Learning” course is the complete scientific and technical understanding of automatic decision-making methods adapted to data, particularly mass data.

To precisely define the context, the vocabulary as well as the different steps necessary for decision-making by a computer, the Bayes decision framework will be introduced with its practical translations through the naive Gaussian classifier and the K-plus method. close neighbors. Through these methods, the student will master the basic notion of learning and associated constraints (such as size), parameters and estimation of these and finally evaluation of results. These algorithms will be applied to different types of measurements. For students who have followed the “Supervised Learning” module, this will constitute a complement, for others it will allow the integration of artificial intelligence concepts.

Then the course will aim to understand, analyze and develop different neural methods. This will involve analyzing multilayer structures (such as the Perceptron) as well as associated learning strategies, and also new structures allowing, from a large set of data, to provide descriptors such as convolutional neural networks. . The study of these structures will obviously be positioned within the framework of Deep-Learning and large masses of data. It should be noted that the choice is made to have the student carry out each method in order to avoid the black box phenomenon.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Course unit: Machine learning
Methods of delivery:

Lectures (6h)

Tutorials (12h)

Pre-requisites:

The data analysis course (M1, 1st semester), and basic algorithmic skills.

Objectives:

This “Machine Learning” course aims to study, analyze and design automatic decision-making methods adapted to data, particularly mass data. More precisely, it involves automatically predicting the evolution and/or membership of a set of raw data from a learning database. It should be noted that this module complements the “Supervised learning” module (M1, 2nd semester) but without this being a prerequisite.

This course is particularly relevant in the context of neural algorithms, deep learning and large masses of data.

Learning outcomes:

• Computationally implement a KNN or naive Gaussian type automatic decision algorithm on real data.
• Master the tools for machine learning analysis, interpretation of results and in particular evaluation of the quality of the method
• Computationally implement an automatic decision-making algorithm based on a neural structure.
• Master the different neural techniques and know how to make the right choice based on the data to be processed
• Computerically implement a statistical test on real data in order to answer a question.
• Deploy a data analysis solution adapted to big data challenges

Indicative contents:

The objective of this “Machine Learning” course is the complete scientific and technical understanding of automatic decision-making methods adapted to data, particularly mass data.

To precisely define the context, the vocabulary as well as the different steps necessary for decision-making by a computer, the Bayes decision framework will be introduced with its practical translations through the naive Gaussian classifier and the K-plus method. close neighbors. Through these methods, the student will master the basic notion of learning and associated constraints (such as size), parameters and estimation of these and finally evaluation of results. These algorithms will be applied to different types of measurements. For students who have followed the “Supervised Learning” module, this will constitute a complement, for others it will allow the integration of artificial intelligence concepts.

Then the course will aim to understand, analyze and develop different neural methods. This will involve analyzing multilayer structures (such as the Perceptron) as well as associated learning strategies, and also new structures allowing, from a large set of data, to provide descriptors such as convolutional neural networks. . The study of these structures will obviously be positioned within the framework of Deep-Learning and large masses of data. It should be noted that the choice is made to have the student carry out each method in order to avoid the black box phenomenon.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 6
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (10h)

Tutorials (20h)

Practices (20h)

Pre-requisites:

Programming practices and tools, Wireless technologies, Communicating embedded systems, Networks and security.

Objectives:

This course focuses on latest generation intelligent systems and networks. The first application aspect is dedicated to the implementation of a digital communication chain with a fixed quality of service (priority to throughput, quality, robustness, etc.). In this complex communication chain, certain particular elements studied in our research laboratories will be analyzed through their effects and the issues they represent. The second aspect is more prospective and takes the form of scientific conferences given by researchers and industrialists in the sector.

Learning outcomes:

Identify, choose, design and implement the functions necessary to create a communication chain meeting a specified quality of service.

Indicative contents:

• Define the concept of a system approach to digital communications in the context of the Internet of Things.
• Simulate the behavior of an advanced digital communication chain.

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 3
Language:

English

Course mode:

On-site

Methods of delivery:

Tutorials (30h)

Pre-requisites:

B1 level required.

Objectives:

To bring students towards the European B2/C1 level. The operational and evaluable objectives of this training are:

• Understand most situations that might be encountered at work or while traveling in a region where English is spoken for example
• Develop oral and written language skills
• International English communication

Learning outcomes:

Acquisition of English language skills (objective B2/C1). International, specialty and professional English (CV, cover letters, etc.)

Indicative contents:

• Written and oral comprehension/production work on authentic specialist or general English documents
• Interactive debates on general themes
• Language lab work (pronunciation, listening, repetition, etc.)
• Professional English (writing cover letters, CV, professional interview) academic (summary of documents, emails, sum-ups, etc.)
• Work on specialization and general English vocabulary.
• Presentation of a specialty presentation

Methods of assessment:

Written test, oral

Credits: 6
Language:

French/English

Course mode:

On-site/Hybrid

Methods of delivery:

Scientific project (one day/week)

Pre-requisites:

None

Objectives:

Carry a scientific or entrepreunarial project. 3 options:

• continue their « research » project carried out in M1 within the framework of the Cordées de la recherche
• carry out their project within the framework of the « Ateliers de l’innovation » offered by the IAE Limoges
• carry out their project in conjunction with a company, a CRT, a LabCom, etc.

Methods of assessment:

Project

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (12h)

Tutorials (18h)

Pre-requisites:

• Methodology part of electronic design of IoT
• Physics of components and semiconductors in IoT

Objectives:

in progress

Learning outcomes:

in progress

Indicative contents:

The part 1 concerns the management of energy integrated into a smart communicating object: the different parts of the sensors associated with their energy costs: radiofrequency, sensors, embedded μc, optimized smart power embedded software, as well as the transition between energy recovery and its storage performed by specialized integrated components.

The part 2 will explore with a device physics approach the most used devices for energy harvesting (RF, piezo, thermo, photo conversion into electricity) and storage (batteries and supercapacitors) for IoT. RF energy transport will be also addressed. External quantum efficiencies and device sizing
calculations will be addressed according to the external condition (indoor/outdoor)

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Lectures (30h)

Pre-requisites:

in progress

Objectives:

in progress

Learning outcomes:

in progress

Indicative contents:

in progress

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 3
Language:

French

Course mode:

On-site

Methods of delivery:

Tutorials (30h)

Pre-requisites:

Digital communication

Objectives:

The aim of this module is to study 5G technologies and Network architecture in order to optimize the following items to guarantee the Quality of Services/Experience:

• Peak data rate (Gbit/s);
• User experienced data rate (Mbit/s);
• Spectrum efficiency (bit/Hz);
• Device mobility (km/h);
• Latency (ms);
• Connection density (number of connected/accessible objects per km²);
• Network’s energy efficiency;
• Area traffic capacity (Mbit/s/m²).

Learning outcomes:

in progress

Indicative contents:

• 5G technologies: (Wireless channel concept) “millimetre” wave frequencies, Massive MIMO, Full Duplex transmission, NOMA Multiplexing (Non Orthogonal Multiple Access), New high spectral efficiency modulation concepts
• 5G Network architecture skills: Software-defined networking (SDN) and network functions virtualisation (NFV)

Methods of assessment:

Written test

Suggested bibliography:

in progress

Credits: 6
Language:

French/English

Course mode:

On-site/Hybrid

Methods of delivery:

Scientific project (one day/week)

Pre-requisites:

None

Objectives:

Carry a scientific or entrepreunarial project. 3 options:

• continue their « research » project carried out in M1 within the framework of the Cordées de la recherche
• carry out their project within the framework of the « Ateliers de l’innovation » offered by the IAE Limoges
• carry out their project in conjunction with a company, a CRT, a LabCom, etc.

Methods of assessment:

Project

Credits: 24
Language:

French/English

Course mode:

On-site

Methods of delivery:

6 months internship

Pre-requisites:

None

Objectives:

6 months training period in a company or in a research laboratory

Methods of assessment:

Report, oral, evaluation sheet