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SL-DRT-24-0106 - Artful guidance of test generation tools

Wed, 11 Oct 2023 02:15:11 Z

Category : Engineering science
Contract : Thèse
Thesis topic details :
Fuzzing is an automatic test generation technique. It consists in repeatedly executing a program with automatically generated inputs, in order to trigger crashes, symptoms of underlying bugs in the code, which can then be fixed. A major challenge in this area is moving from indiscriminate exploration of how programs work to artful guidance towards the discovery of important bugs. Taking advantage of the expertise of our team and our previous work, the objective of the thesis is to propose, implement and evaluate means to meet this challenge, taking advantage of finer-grained guidance mechanisms.

Artful guidance of test generation tools

SL-DRT-24-0005 - Learning Fine-Grained Dexterous Manipulation through Vision and Kinesthetic Observations

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
Fine-grained dexterous manipulation presents significant challenges for robots due to the need for precise object handling, coordination of contact forces, and utilization of visual observations. This research aims to address these challenges by investigating the integration of vision and kinesthetic sensors, sim2real techniques, and generalization through embodiment. The objective is to develop end-to-end algorithms and models that enable robots to manipulate objects with exceptional precision and adaptability. The research will focus on learning from large-scale data, transferring knowledge from simulations to real-world scenarios, and efficiently generalizing through low-shot fine-tuning.

Learning Fine-Grained Dexterous Manipulation through Vision and Kinesthetic Observations

SL-DRT-24-0030 - Scenario-Based Testing for Automated Systems: Enhancing Safety and Reliability in Compliance with Regula

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
This research aims to investigate the effectiveness of scenario-based testing as a comprehensive and robust approach for evaluating ASs' performance while enhancing their safety and reliability with respect to regulations and standards. The primary objective of this thesis will be to investigate the benefits of scenario-based testing for automated systems and its compliance with regulations and standards. The following sub-objectives will be pursued: - Conduct an in-depth review of relevant literature and industry practices on ASs testing methodologies, with a focus on the unique challenges posed by ASs' complex decision-making algorithms and interaction with the dynamic environment. - Develop a scenario-based testing framework for systematic identification, generation, selection, and execution of realistic and diverse scenarios, for automated systems - Analyze gaps or areas of non-compliance about the key concepts and requirements outlined in regulations and standards and examine their applicability, implications and improvments for scenario-based testing. - Conduct extensive validation of the proposed scenario-based testing framework through different real-world and near-realistic transportation applications (various types, varying levels of automation, and diverse real-world scenarios) to evaluate the practical applicability and benefits of the methodology. The findings and recommendations from this research will ultimately guide AV manufacturers, regulators, and stakeholders in developing and validating ASs that comply with the regulatory framework, fostering the safe and responsible deployment of automated systems in the future.

Scenario-Based Testing for Automated Systems: Enhancing Safety and Reliability in Compliance with Regulations and Standards

SL-DRT-24-0026 - Deep Neural Network Uncertainty Estimation on Embedded Targets

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
Over the last decade, Deep NeuralNetworks (DNNs) have become a popular choice to implement Learning-Enabled Components LECs in automated systems thanks to their effectiveness in processing complex sensory inputs, and their powerful representation learning that surpasses the performance of traditional methods. Despite the remarkable progress in representation learning, DNNs should also represent the confidence in their predictions to deploy them in safety-critical systems. Bayesian Neural Networks (BNNs) offer a principled framework to model and capture uncertainty in LECs. However, exact inference in BNNs is difficult to compute. Thus, we rely on sampling techniques to approximate the true posterior of the weights for computing the posterior predictive distribution (inference). In this regard, relatively simple though computationally and memory expensive sample-based methods have been pro posed for approximate Bayesian inference to quantify uncertainty in DNNs, e.g., Monte-Carlo dropout or Deep Ensembles. Efficient DNN uncertainty estimation in resource-constrained hardware platforms remains an open problem, limiting the adoption within applications from highly automated systems that possess strict computation and memory budgets, tight time constraints, and safety requirements. This thesis aims to develop novel methods and hardware optimizations for efficient and reliable uncertainty estimation in modern DNN architectures deployed in hardware platforms with limited computation resources.

Deep Neural Network Uncertainty Estimation on Embedded Targets

SL-DRF-24-0102 - Imaging with Micromegas detectors with Optical readout

Wed, 11 Oct 2023 02:15:11 Z

Category : Corpuscular physics and outer space
Contract : Thèse
Thesis topic details :
Recent developments have shown that coupling a Micromegas gaseous detector on a glass substrate with a transparent anode and a CCD camera enable the optical readout of Micromegas detectors with an impressive spatial resolution showing that the glass Micromegas detector is well-suited for imaging. This feasibility test has been effectuated with low-X-ray photons permitting energy resolved imaging. This test opens the way to different applications. Here we will focus, on one hand, on neutron imaging for non-destructive examination of highly gamma-ray emitting objects, such as fresh irradiated nuclear fuel or radioactive waste and on the other hand, we would like to develop a beta imager at the cell level in the field of anticancerous drug studies. Both applications require gas simulations to optimize light yields, optimization of the camera operation mode and design of the detectors in view of the specific constraints of reactor dismantling and medical applications: spatial resolution and strong gamma suppression for neutron imaging and precise rate and energy spectrum measurements for the beta. The image acquisition will be optimized for each case and dedicated processing algorithms will be developed.

Imaging with Micromegas detectors with Optical readout

SL-DRF-24-0046 - Alignment of the muon spectrometer and measurement of the electroweak mixing angle at the TeV scale

Wed, 11 Oct 2023 02:15:11 Z

Category : Corpuscular physics and outer space
Contract : Thèse
Thesis topic details :
This thesis project concerns the precise measurement of the electroweak mixing angle with the ATLAS experiment, at the LHC. The evolution with energy of this fundamental parameter will also be tested. The measurement will be based on the di-muon data set from runs 2 and 3 of the LHC, and will use the muon spectrometer as the main instrument. The determination of the mixing angle is based on the measurement of the forward-backward asymmetry of the Z boson decay muons. For precise control of systematic uncertainties, the internal alignment of the spectrometer must be optimized. This alignment constitutes an important part of the project. The performance of the New Small Wheel, the new muon detector installed for run 3, will also need to be understood in detail. The actual measurement will be carried out at the end of these preliminary studies, and an interpretation of the result will complete the thesis.

Alignment of the muon spectrometer and measurement of the electroweak mixing angle at the TeV scale

SL-DRT-24-0004 - Study of inversion methods based on simulation and machine learning for defect characterisation in ultra

Wed, 11 Oct 2023 02:15:11 Z

Category : Technological challenges
Contract : Thèse
Thesis topic details :
The thesis work is part of the activities of CEA-List department dedicated to Non-Destructive Testing (NDT), and aims to study simulation-based inversion methods to characterise defects from ultrasonic images, such as TFM (Total Focusing Method) or PWI (Plane Wave Imaging) images. The inversion methodology will rely on machine learning algorithms and numerical training databases generated with the CIVA software platform. A first part will study the ability of such an inversion method to characterise a defect (location, size, orientation...) without any a priori information, by exploiting the noise and reconstruction artefacts due to the use of unsuitable propagation modes. In a second part, the simulation-based inversion will be evaluated in more realistic situations where images are of poor quality due to uncertainties on the properties of the component and/or on the experimental setup. In order to reduce the generation time of the training database, and to gain in robustness and accuracy, the feasibility of inverting fast imaging (e.g.: combining PWI and fast reconstruction algorithms in the Fourier domain) will be studied, as well as the feasibility of directly inverting signals or spectra without the need to compute images. The inversion method will be experimentally evaluated with different mock-ups representative of industrial components and, at the end of the thesis, a real-time proof of concept will be demonstrated by implementing the imaging and inversion algorithms in a laboratory prototype system.

Study of inversion methods based on simulation and machine learning for defect characterisation in ultrasonic array imaging

SL-DRF-24-0013 - Synthesis and properties of water-soluble graphene quantum dots

Wed, 11 Oct 2023 02:15:11 Z

Category : Condensed Matter Physics, chemistry, nanosciences
Contract : Thèse
Thesis topic details :
Graphene as a constituent of graphite was close to us for almost 500 years. However, it is only in 2005 that A. Geim and K. Novoselov (Nobel Prize in 2010) reported for the first time the obtaining of a nanostructure composed by a single layer of carbon atom. The exceptional electronic properties of graphene make it a very promising material for applications in electronic and renewable energies. For many applications, one should be able to modify and control precisely the electronic properties of graphene. In this context, we propose to synthesize chemically graphene nanoparticles and study their absorption and photoluminescence properties. We will focus on their solubilty in water in order to study potential applications in biology. This project will be developed in collaboration with Physicists so the candidate will work in a multidisciplinary environment.

Synthesis and properties of water-soluble graphene quantum dots

SL-DRF-24-0034 - Design and manufacturing of a 4D-emittancemeter for characterization of very high current ion sources

Wed, 11 Oct 2023 02:15:11 Z

Category : Corpuscular physics and outer space
Contract : Thèse
Thesis topic details :
Ion accelerators, including protons, at very high intensity (> 50 mA), find numerous applications in various fields of nuclear physics or material characterization for medical, nuclear, and other applications. The Department of Accelerators, Cryogenics, and Magnetism (DACM) at CEA-Saclay specializes in the design and realization of very high-intensity ion sources. With the increase in beam current, these sources become increasingly complex. Therefore, mastering the quality of the beam becomes critical to limit power deposition and the activation of accelerator elements. To better understand and describe this beam, it is necessary to determine its 4D emittance, which includes both the geometric shape of the beam and its trajectory. The device used for this measurement is called a 4D emittance meter. Such a device based on a scintillator has already been designed and tested. This scintillator converts a portion of the beam into an image, which is then captured by a camera. Unfortunately, while this technology is functional at high energy, it is not suitable at the source outlet, at low energy, as the scintillation layers are quickly damaged by the ion flux. The charge reading method proposed in this thesis is novel and benefits from the synergy between particle detector research for high-energy physics and proton source research. Instead of using a camera for reading, the idea is to measure, from a PCB placed directly in the beam, the current carried by the ions. This method allows reading this current at several thousand positions to obtain the 4D emittance. The fast acquisition system will also allow observing the temporal variation of the emittance during the start-up and shut-down phases of the source. This device will be used for analyzing the beam generated by the ALISES sources developped by the laboratory.

Design and manufacturing of a 4D-emittancemeter for characterization of very high current ion sources