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

PhD : selection by topics

Engineering science >> Instrumentation
3 proposition(s).

Design, development and evaluation of sensors based on electrical methods for detecting and quantifying airborne ultrafine particles

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire de Nanocaractérisation et Nanosécurité



Research field: Air quality monitoring is a real societal challenge that leads to strong expectations from the public. Currently, there is no reliable low-cost particulate matter sensors that covers a wide range of particle size. Many optical sensors are reported but respond to particles larger than 300 nm by providing their mass concentration (PM10 and PM2.5). Only few ergonomic and accurate personal monitors allow the assessment of individual exposure to manufactured nanomaterials and ultrafine particles. This is indicative of a high potential for exploitation. Description of the research topic: We propose to develop particle microsensors offering granulometric sizing over the 5-300 nm range and the chemical composition of the collected material. The purpose of this PhD thesis is to develop, assess, theoretically and experimentally, the performances of an integrated device for the detection and the quantification of particles based on ion diffusion charging. The device is aiming to sort the particles according to their electrical mobility and to collect them selectively on a substrate according to size-resolved concentric rings. Quantitative analysis of particle charging and losses will be carried out. The electrical detection using electrometers will allow quantification in real time thanks to an appropriate signal processing algorithm. Several metrics of interest will be explored such as number-based concentration, LDSA (lung-deposited surface area) concentration and mass concentration. We propose the development of a simplified system allowing the monitoring of several channels (5-20 nm, 20-100 nm, 100-300 nm) in order to propose a solution able to determine and locate sources of ultrafine particles in real time (application to urban pollution).

Compressed Sensing for elastic guided wave tomography applied to Structural Health Monitoring

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Méthodes CND



Structural Health Monitoring (SHM) relies on the permanent integration of sensors to continuously inspect the integrity of industrial structures. In SHM, Guided Waves (GW) allow large area monitoring due to low attenuation coefficients and high defect sensitivity. Applications of SHM include the detection of corrosion in metals in the oil & gas and nuclear industries, as well as the detection of delamination in composite materials in aeronautics. Guided Wave tomography is one approach to conduct monitoring over time of a region of interest and lead to an accurate 3D visualization of the inspected region providing detection, localization and quantification of defects. The performances of GW tomography algorithms highly depends on the number of sensors used for the inspection. Currently the number of sensors required may be prohibitive in some industries because it leads to additional wiring, complexity and added mass. One approach to reduce significantly the number of sensors is to use Compressed Sensing (CS). CS is a group of signal processing techniques to sample signals below the traditional Nyquist-Shannon sampling theorem. To do this, CS relies on two fundamental components: the measurements must be incoherent and the signal to reconstruct must have a sparse description. In GW tomography, this translates to mathematically reformulate the reconstruction algorithms to reveal these properties during the resolution process. As in SHM, sensors are permanently bounded and in CS the measurement points must be incoherent, a major task will be to optimize sensor positioning. The use of CS in GW tomography is expected to reduce the number of sensors required by at least a factor of 2. The objectives of this thesis are the following: 1) development of the GW tomography algorithm using CS in the reconstruction step with a specific sensor positioning methodology; 2) optimization and automation of the reconstruction process; 3) Implementation of the methodology on numerical and experimental data. This thesis is the result of a collaboration between the NDT department of the CEA and the Neurospin institute, also at CEA. While the first has made significant development in GW tomography the second has made extensive contributions in the field of CS.

Numerical methods for a personalized autonomous transcutaneous gas monitoring device

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Electronique et Systèmes pour la Santé



Respiratory diseases do affect the gas exchange between blood and exhaled air, and thus the blood concentration of biomarkers. Measuring gas skin emanations of volatile blood components such as carbon dioxide allows a continuous monitoring of their concentration. The laboratory LS2P dedicated to wearable devices for healthcare is developing an innovative wristband device based on optical infrared measurement to quantify the partial pressure of transcutaneous carbon dioxide (PtCO2). The subject of this Ph. D. thesis is to study digital signal processing methods to improve the autonomy of these devices and to allow a personalized follow-up at home of the patient. This requires in particular to study a new generation of autonomous devices based on self-awareness techniques combining optical and fluidic models. Research works will address the building of a numerical model of the device and of its interaction with the human body, the development of the associated simulation software, the study of statistical signal processing methods and compress sensing algorithms. The Ph. D. candidate should be skilled in signal processing, applied mathematics or biomedical engineering.

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