Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

3D Objects discovery in 3D scene

DPLOIRE (CTReg)

Autre

01-01-2019

SL-DRT-19-0269

anthony.mouraud@cea.fr

Object detection and localization in images is a problem studied since many years. The latest technological developments now allow the real-time acquisition of depth data coupled to color data (RGBD). At the same time, modern machine computing capabilities and intelligent image processing methods have led to significant advances in the detection / localization of 2D objects with many different approaches (bounding boxes, contours, from CAD models ...). An important step is being taken in recent years with the research conducted to directly extract the volume of detected objects and their position in 3D. These works are still in their infancy, but the first results are encouraging, both from 2D images (eg DeepManta) and from 3D images (eg Deep Sliding Shapes). However, there remain several identifiable scientific / technological barriers before allowing the democratization of this type of approach for the automatic extraction of objects in potentially unknown scenes. The objective of this work is to identify the current approaches of detection / localization of 3D objects, to target their weaknesses and work on new processing technologies to mitigate them. Moreover, the object discovery in unknown environments and the inference of the operator's intention by observation / location of his attention are two areas of interest that this work aims at addressing. Beyond their applications for demonstration learning, the software bricks resulting from this project can also be reused for other applications such as augmented reality ("smart" scanning, etc.), surveillance or mobile mobility for example.

Person re-identification and cross-domain adaptability

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Vision & Ingénierie des Contenus (SAC)

01-02-2019

SL-DRT-19-0283

romaric.audigier@cea.fr

Automatically re-identifying people viewed by cameras is a key functionality for videoprotection applications. It consists in retrieving occurrences of a person from a set of images. Despite the many studies on this topic in the past few years, modeling human appearance remains a challenge. Indeed, re-identification models have to discriminate distinct people (in spite of their possible similarity) while being robust against the high variability of their visual appearance (caused by their posture, lighting conditions, camera viewpoint, sensitivity and resolution, ?). Besides, partial occlusion and alignment errors on the detected people have to be coped with. Even if deep supervised learning methods have been greatly improving re-identification performances on some academic datasets, difficulties remain for real implementations in operational environments. Indeed, a model trained on a specific dataset usually does not perform well if applied on other datasets as it is. Furthermore, manual data annotation in the target domain is a tedious thus costly task. In this thesis, we will study the appearance model adaptability to target domains in which only data without annotation is available. Unsupervised transfer learning methods can be used. The proposed approaches will cope with scalability issues in order to address large datasets.

modeling biomass torrefaction at pilot scale with data measured in laboratory at small scale

Département Thermique Biomasse et Hydrogène (LITEN)

Laboratoire de Préparation de la Bioressource

01-10-2019

SL-DRT-19-0288

thierry.melkior@cea.fr

Torrefaction is a thermal pretreatment applied to biomass, carried out under neutral atmosphere for several tens of minutes, at temperatures between 200 and 300°C. Once treated, the solid exhibits properties closer to those of coal (fossil), making it suitable to the same industrial facilities as this latter. The biomass platform of CEA Grenoble has been equipped with a pilot-scale torrefaction oven (capacity: 150kg/h of wood). The results obtained in this pilot oven are always out of sync with the torrefaction data measured in the laboratory. Therefore, the validity of the change of scale for this process is questionable. The aim of this thesis is to improve the extrapolation at pilot scale of data measured with small analytical equipment. Three successive phd prepared in the laboratory, have led to a model representing the different chemical transformations of biomass during torrefaction. This model will be used in the proposed phd. This work will require to perform a lot of experimental investigations, in the laboratory (small scale) as well as to participate to torrefaction campaigns with the pilot.

Study and design of an integrated system for the automatic calibration of dispersions within a transducers array and application to a PMUT array

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Intégration Gestion d'Energie Capteurs et Actionneurs

01-09-2019

SL-DRT-19-0293

gwenael.bechet@cea.fr

The purpose of this thesis is to study and design an integrated electronic system dedicated to the automatic and continuous compensation of dispersions within a MEMS (Microelectromechanical Systems) array. With the dissemination and the continual expansion of Internet of Things (IoT) and Cyber-Physical Systems (CPS), man-machine and machine-machine interfaces require increasingly efficient and sophisticated sensors. In addition to advantages in cost, reliability, size and power consumption, MEMS based transducers enable sensors to integrate more and more intelligence in their front-end electronics. They also allow innovative topological configurations giving access to measurement ranges that are not addressable by their discrete counterparts. Arrays of MEMS based transducers enable the spatial discretization of the transduction surfaces and improve the measurements yields and accuracies (gas detector, mass spectrometry, pressure distribution, etc.). They also enable the resolution improvement of electromagnetic and acoustic beams (location, navigation, communication, etc.). Despite the considerable technological advancements that MEMS are continually enjoying, some application requirements are beyond the transducers intrinsic performances. It is then necessary to implement calibration systems to correct the transducers biases introduced during manufacture or evolving with the operating conditions. The evaluation and compensation of these errors requires costly calibration process in a dedicated test laboratory, that are not compatible with massive production. The aim of this thesis is to achieve an integrated electronic diagnostic alternative, an electromechanical BIST (Built-In Self-Test) specific to transducers arrays, combined with an automatic correction system, which will operate in coexistence with the main functions of the sensor interface. The proposed use-case is that of PMUT (Piezoelectric Micromachined Ultrasonic Transducer) arrays. These devices offer alternatives and complementary solutions to electromagnetic sensors for detection and localization [1], gesture recognition [2] or wake-up signals detection [3]. For most applications, these resonant transducers operate in transmit / receive modes (TX / RX) and need to be actuate at their resonance frequency to optimize the transmission power. The emitted and received beam is focused and steered by phase control. Errors and dispersion in the PMUT characteristics generates biases in their resonant frequency, gain and quality factor, leading to losses and distortions in the emitted and received beams. For example, a few percent of dispersions on the mechanical stiffness of the transducers can lead to several tens of percent loss on the acoustic power transmitted to a target. As a first step, the doctoral student will get familiar with the quantities and physical phenomena characterizing PMUT arrays. Based on an analytical model developed within the host laboratory, he will be able to understand the sensitivities to dispersions and their impact on the beam power and directivity. He will then define the electronic methods and architectures that will allow the system to converge towards the optimal operating conditions, for example by identifying the average resonance frequency of the array the required phase and gain correction coefficients to allocate to each transducer. The architecture and implementation choices must allow the system to adapt itself according to dispersions and drifts in a continuous and autonomous way, without disrupting the main measurement functions. The chosen solution will be implemented and validated in a mixed design environment in order to result in a functional demonstrator. [1] Przybyla, R. J., Tang, H. -., Guedes, A., Shelton, S. E., Horsley, D. A., & Boser, B. E. (2015). 3D ultrasonic rangefinder on a chip. IEEE Journal of Solid-State Circuits, 50(1), 320-334. [2] Ling, K., Dai, H., Liu, Y., & Liu, A. X. (2018). Ultragesture: Fine-grained gesture sensing and recognition. Paper presented at the 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2018, 1-9. [3] Yadav, K., Kymissis, I., & Kinget, P. R. (2013). A 4.4-µ W wake-up receiver using ultrasound data. IEEE Journal of Solid-State Circuits, 48(3), 649-660.

SPAD Imager for HDR ToF using multimodal data fusion

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Circuits Intégrés, Intelligents pour l'Image

01-10-2019

SL-DRT-19-0301

william.guicquero@cea.fr

Depth sensors are currently a very high trending topic. Indeed, in the fields of autonomous vehicles, portable electronic devices and the Internet of Things, new technology enablers now tend to provide handy 3D image data for future innovative end-user applications. There is a great diversity of 3D sensor types, either using passive imaging (depth from defocus, stereovision, phase pixels...) or using active imaging (ultrasounds, structured light, Time-of-Flight...). Each of these systems addresses specifications in terms of depth dynamic range (accuracy of the measurement versus maximum distance). In this thesis, we will study the specific case of Single Photon Avalanche Diodes (SPAD). Recent scientific results regarding this electro-photonic component demonstrate its relevance in the context of Time-of-Flight (ToF) imaging, especially in the case of integration in a 3D-stacked design flow exhibiting a pixel pitch of the order of ten micrometers. However, the nature of the data gathered by this type of component requires significant signal processing within the sensor to extract relevant information. This thesis will aim to revise traditional approaches related to histogram processing by directly extracting statistical features from raw data. Depending on the background and skills of the PhD candidate, two research axes would be investigated. First, on the hardware side, possible modifications of SPAD based sensor architecture in order to provide ?augmented? multi-modal information. Second, on the theoretical and algorithmic side, data fusion methods to improve the final reconstruction rendering of depth maps from sensed data.

DC-DC Power Converter at micro-Watt and millimeter scales

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Intégration Gestion d'Energie Capteurs et Actionneurs

01-09-2019

SL-DRT-19-0314

antony.quelen@cea.fr

The aim of the PhD is to develop compact (mm3) power supplies with high efficiency at low power delivery (nW to µW).

108 Results found (Page 2 of 18)
first   previous  1 - 2 - 3 - 4 - 5  next   last

Voir toutes nos offres