Search results for "SIGNAL"

showing 10 items of 6924 documents

Contributions à l’imagerie polarimétrique et à ses applications en vision pour la robotique

2019

Ce mémoire présente le bilan de l'ensemble de mes travaux de recherche effectués de septembre 2006 à juin 2019 au sein de l'équipe Creusotine du laboratoire Le2i devenue équipe Vibot ERL CNRS 6000 depuis janvier 2018. Les principales activités de recherche que j'ai menées au cours de ces 10 dernières années autour de l'imagerie polarimétrique, de la vision omnidirectionnelle et de leurs applications en vision pour la robotique seront particulièrement détaillées dans ce document. Elles seront présentées selon deux grandes parties : la première concernera plutôt l'aspect mise en œuvre et calibrage des caméras et la seconde se concentrera sur les applications potentielles en vision pour la rob…

robotics[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Polarimetric imaging systemcomputer visionImagerie polarimétriquevision pour la robotique
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Bcl-xL as a Modulator of Senescence and Aging

2021

Many features of aging result from the incapacity of cells to adapt to stress conditions. When cells are overwhelmed by stress, they can undergo senescence to avoid unrestricted growth of damaged cells. Recent findings have proven that cellular senescence is more than that. A specific grade of senescence promotes embryo development, tissue remodeling and wound healing. However, constant stresses and a weakening immune system can lead to senescence chronicity with aging. The accumulation of senescent cells is directly related to tissue dysfunction and age-related pathologies. Centenarians, the most aged individuals, should accumulate senescent cells and suffer from their deleterious effects,…

senescenceReviewmedicine.disease_causelcsh:Chemistry0302 clinical medicineImmunologic Surveillancelcsh:QH301-705.5SpectroscopyCellular Senescenceimmunosenescence0303 health sciencesapoptosisGeneral MedicineImmunosenescenceComputer Science ApplicationsCell biologyOrgan Specificity030220 oncology & carcinogenesisDisease SusceptibilitycentenariansProtein BindingSignal TransductionSenescencebcl-X ProteinBcl-xLBiologyCatalysisInorganic Chemistry03 medical and health sciencesImmune systemStress PhysiologicalmedicineAnimalsHumansPhysical and Theoretical ChemistrySenolyticMolecular Biology030304 developmental biologyBcl-xLOrganic ChemistryIntrinsic apoptosisagingGene Expression Regulationlcsh:Biology (General)lcsh:QD1-999senolyticsbiology.proteinWound healingOxidative stressBiomarkersDNA DamageInternational Journal of Molecular Sciences
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Early development of hedonic and motivational aspects of eating behaviour

2015

Eating is essential for survival. However, the newborn is not an autonomous eater, and has to learn ‘how', ‘what', ‘when', and ‘how much' to eat quickly enough to ensure harmonious growth and development. In other nutritional areas, it has been shown during the past 20 years that early experiences are likely to impact long-term health outcomes. Thus, it appears fundamental to understand the early development of hedonic and motivational aspects of eating behaviour. This presentation will describe several studies conducted in our group during the past 10 years, in order to gain more knowledge about the development of what and how much children eat, in relation with food sensory and nutritiona…

sensory signaleating behaviourNutrition and Dieteticsbreastfeedingnutrient[ SDV.AEN ] Life Sciences [q-bio]/Food and Nutritiondigestive oral and skin physiologycomplementary feedingDevelopmental psychology[SDV.AEN] Life Sciences [q-bio]/Food and Nutritionsugarfatsaltfood preferencePsychologyEating behaviour[SDV.AEN]Life Sciences [q-bio]/Food and NutritiondevelopmentGeneral Psychology
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Deep-learning based reconstruction of the shower maximum X max using the water-Cherenkov detectors of the Pierre Auger Observatory

2021

The atmospheric depth of the air shower maximum $X_{\mathrm{max}}$ is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of $X_{\mathrm{max}}$ are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of $X_{\mathrm{max}}$ from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of $X_{\mathrm{max}}$. The reconstruction relies on the signals induced by shower particles in the groun…

showers: energylongitudinal [showers]interaction: modelPhysics::Instrumentation and DetectorsAstronomyCalibration and fitting methods; Cluster finding; Data analysis; Large detector systems for particle and astroparticle physics; Particle identification methods; Pattern recognition01 natural sciencesHigh Energy Physics - ExperimentAugerHigh Energy Physics - Experiment (hep-ex)Particle identification methodscluster findingsurface [detector]ObservatoryLarge detector systemsInstrumentationMathematical PhysicsHigh Energy Astrophysical Phenomena (astro-ph.HE)astro-ph.HEPhysicsPattern recognition cluster finding calibration and fitting methodsPhysicsSettore FIS/01 - Fisica Sperimentalemodel [interaction]DetectorAstrophysics::Instrumentation and Methods for AstrophysicsData analysicalibration and fitting methodsenergy [showers]AugerobservatoryPattern recognition cluster finding calibration and fitting methodastroparticle physicsAstrophysics - Instrumentation and Methods for AstrophysicsAstrophysics - High Energy Astrophysical Phenomenaatmosphere [showers]airneural networkAstrophysics::High Energy Astrophysical PhenomenaUHE [cosmic radiation]Data analysisFOS: Physical sciences610Cosmic raydetector: fluorescencePattern recognition0103 physical sciencesddc:530High Energy Physicsddc:610[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]cosmic radiation: UHEstructureparticle physicsnetwork: performance010306 general physicsInstrumentation and Methods for Astrophysics (astro-ph.IM)Ciencias ExactasCherenkov radiationfluorescence [detector]Pierre Auger ObservatoryCalibration and fitting methodsmass spectrum [nucleus]showers: atmospheredetector: surfacehep-ex010308 nuclear & particles physicsLarge detector systems for particle and astroparticle physicsCluster findingFísicaresolutioncalibrationComputational physicsperformance [network]Cherenkov counterAir showerLarge detector systems for particle and astroparticle physicExperimental High Energy PhysicsHigh Energy Physics::Experimentnucleus: mass spectrumshowers: longitudinalRAIOS CÓSMICOSEnergy (signal processing)astro-ph.IM
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SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG

2022

In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Various existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, it is a big challenge to handle the task with raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-conv…

signaalinkäsittelyBiomedical EngineeringsignaalianalyysiHealth InformaticsSleep stage classificationConvolutional neural networkRaw single-channel EEGneuroverkotuni (lepotila)koneoppiminenSignal ProcessingContextual inputEEGunihäiriöt
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Image inpainting using directional wavelet packets originating from polynomial splines

2020

The paper presents a new algorithm for the image inpainting problem. The algorithm is using a recently designed versatile library of quasi-analytic complex-valued wavelet packets (qWPs) which originate from polynomial splines of arbitrary orders. Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions. For example, a set of the fourth-level qWPs comprises 62 different directions. The properties of the presented qWPs such as refined frequency resolution, directionality of waveforms with unlimited number of orientations, (anti-)symmetry of waveforms and windowed oscillating structure of waveforms with a variety of frequencies, make them efficient in image pro…

signaalinkäsittelyComputer scienceImage and Video Processing (eess.IV)Inpainting020206 networking & telecommunicationsImage processing02 engineering and technologykuvankäsittelyElectrical Engineering and Systems Science - Image and Video ProcessingWavelet packet decompositionImage (mathematics)Set (abstract data type)Tensor productalgoritmitSignal Processing0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineeringWaveform020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringAlgorithmSoftwareVariable (mathematics)
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Extraction of event-related potentials from electroencephalography data

2009

signaalinkäsittelydenoisingelektrofysiologiaElectroencephalographyEEGEvoked potentialsevent-related potentialssignal processingERPherätepotentiaalit
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French PhDs employed in private sector. The signal effect of chaotic pathways

2017

International audience; This research deals with the question of french PhDs´ career trajectories and especially those that lead to private sector employment. Using longitudinal survey "Generation" from Cereq, which allows to observe professional paths over the first five years of working life, we show that for PhDs graduated in 2010, public-sector research remains the main opening.There are few career paths leading to private sector and PhDs working in firms found their job at a very early stage in their working life. Using data analysis and econometrics methods we find that thesis conditions, professional expectations and cahotic pathways are obstacles to employment in firms.

signal effect[SHS.EDU]Humanities and Social Sciences/Education[SHS.EDU] Humanities and Social Sciences/Educationemploymentprivate sectorPhD[ SHS.EDU ] Humanities and Social Sciences/EducationFrancechaotic pathway
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"Postfit yields Y(4S)" of "Search for $B^{+}\to K^{+}\nu\bar{\nu}$ decays using an inclusive tagging method at Belle II"

2022

Yields in on-resonance data and as predicted by the simultaneous fit to the on- and off-resonance data, corresponding to an integrated luminosity of 63 and 9 fb$^{−1}$, respectively. The predicted yields are shown individually for charged and neutral B-meson decays and the five continuum background categories. The leftmost three bins belong to the first control region (CR1) with BDT$_{2} \in [0.93; 0.95]$ and the other nine bins correspond to the signal region (SR), three for each range of BDT$_{2} \in [0.95; 0.97; 0.99; 1.0]$. Each set of three bins is defined by $p_{T}(K^{+}) \in [0.5; 2.0; 2.4; 3.5] \rm{GeV}/c^{2}$.

signal strenghth $\mu$electroweak penguin decay$B^+ \rightarrow K^+\nu\bar\nu$FCNCmissing energyb --> s l l transition
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"Postfit yields off-resonance" of "Search for $B^{+}\to K^{+}\nu\bar{\nu}$ decays using an inclusive tagging method at Belle II"

2022

Yields in off-resonance data and as predicted by the simultaneous fit to the on- and off-resonance data, corresponding to an integrated luminosity of 63 and 9 fb$^{−1}$, respectively. The predicted yields are shown individually for the five continuum background categories. The leftmost three bins belong to the third control region (CR3) with BDT$_{2} \in [0.93; 0.95]$ and the other nine bins correspond to the second control region (CR2), three for each range of BDT$_{2} \in [0.95; 0.97; 0.99; 1.0]$. Each set of three bins is defined by $p_{T}(K^{+}) \in [0.5; 2.0; 2.4; 3.5] \rm{GeV}/c^{2}$.

signal strenghth $\mu$electroweak penguin decay$B^+ \rightarrow K^+\nu\bar\nu$FCNCmissing energyb --> s l l transition
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