Artery phantoms for intravascular optical coherence tomography: healthy arteries

We present a method to make phantoms of coronary arteries for intravascular optical coherence tomography (IV-OCT). The phantoms provide a calibrated OCT response similar to the layered structure of arteries. The optical properties of each layer are achieved with specific concentrations of alumina and carbon black in a silicone matrix. This composition insures high durability and also approximates the elastic properties of arteries. The phantoms are fabricated in a tubular shape by the successive deposition and curing of liquid silicone mixtures on a lathe setup

Implications fonctionnelles du PACAP dans la sécrétion des catécholamines par la médullosurrénale

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Local Field effects on the radiative lifetime of emitters in surrounding media: virtual- or real-cavity model?

For emitters embedded in media of various refractive indices, different macroscopic or microscopic theoretical models predict different dependencies of the spontaneous emission lifetime on refractive index. Among those models are the two most promising models: the virtual-cavity model and the real-cavity model. It is a priori not clear which model is more relevant for a given situation. By close analysis of the available experimental results and examining the assumptions underlying the two models, we reach a consistent interpretation of the experimental results and give the criteria which model should apply for a given situation.Comment: 12 pages with 4 figure

Calculation of the energy spectrum of a two-electron spherical quantum dot

We study the energy spectrum of the two-electron spherical parabolic quantum dot using the exact Schroedinger, the Hartree-Fock, and the Kohn-Sham equations. The results obtained by applying the shifted-1/N method are compared with those obtained by using an accurate numerical technique, showing that the relative error is reasonably small, although the first method consistently underestimates the correct values. The approximate ground-state Hartree-Fock and local-density Kohn-Sham energies, estimated using the shifted-1/N method, are compared with accurate numerical self-consistent solutions. We make some perturbative analyses of the exact energy in terms of the confinement strength, and we propose some interpolation formulae. Similar analysis is made for both mean-field approximations and interpolation formulae are also proposed for these exchange-only ground-state cases.Comment: 18 pages, LaTeX, 2 figures-ep

Artifact removal in FD-OCT with a B-M mode scanning technique : Condition on the transverse step

One of the main drawbacks of Fournier-domain optical coherence tomography is its inability to differentiate between positive and negative locations relative to the zero-delay position. In the recently proposed B-M mode scanning technique, these artifacts are removed by introducing a transverse modulation between successive A-scans followed by a Fourier filtering in the transverse direction. This paper deals with the relation between the transverse step size and the efficency of artifact removal. This relation is illustrated with measurements performed on an onion with different transverse step sizes and different focusing optics. These experimental results are used to support a proposed criterion for the transverse step. The criterion aims at insuring efficient artifact removal while limiting the amount of oversampling in the transverse scan.Un des principaux probl\ue8mes pos\ue9s par la tomographie par coh\ue9rence optique \ue0 domaine de Fourier (TCO-DF) est son incapacit\ue9 \ue0 faire la diff\ue9rence entre les endroits positifs ou n\ue9gatifs par rapport \ue0 la position de d\ue9lai z\ue9ro. Dans la technique de balayage en mode B-M r\ue9cemment propos\ue9, ces art\ue9facts sont \ue9limin\ue9s en introduisant une modulation transversale entre des A-scans successifs, suivie d\u2019un filtrage Fourier dans la direction transversale. Dans le pr\ue9sent article, on traite de la relation entre la taille du pas transversal et l\u2019efficacit\ue9 de l\u2019\ue9limination des art\ue9facts. Cette relation est illustr\ue9e gr\ue2ce \ue0 des mesures r\ue9alis\ue9es sur un oignon avec diff\ue9rentes tailles de pas transversal et diff\ue9rentes optiques de focalisation. Les r\ue9sultats exp\ue9rimentaux sont utilis\ue9s pour appuyer un crit\ue8re propos\ue9 pour le pas transversal. Le crit\ue8re vise \ue0 assurer une \ue9limination efficace des art\ue9facts tout en limitant la quantit\ue9 de recouvrement du balayage transversal.Peer reviewed: YesNRC publication: Ye

Artifact removal in Fourier-domain optical coherence tomography with a piezoelectric fiber stretcher

Peer reviewed: YesNRC publication: Ye

Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams

Recent applications of machine learning in metal additive manufacturing (MAM) have demonstrated significant potential in addressing critical barriers to the widespread adoption of MAM technology. Recent research in this field emphasizes the importance of utilizing melt pool signatures for real-time defect prediction. While high-quality melt pool image data holds the promise of enabling precise predictions, there has been limited exploration into the utilization of cutting-edge spatiotemporal models that can harness the inherent transient and sequential characteristics of the additive manufacturing process. This research introduces and puts into practice some of the leading deep spatiotemporal learning models that can be adapted for the classification of melt pool image streams originating from various materials, systems, and applications. Specifically, it investigates two-stream networks comprising spatial and temporal streams, a recurrent spatial network, and a factorized 3D convolutional neural network. The capacity of these models to generalize when exposed to perturbations in melt pool image data is examined using data perturbation techniques grounded in real-world process scenarios. The implemented architectures demonstrate the ability to capture the spatiotemporal features of melt pool image sequences. However, among these models, only the Kinetics400 pre-trained SlowFast network, categorized as a two-stream network, exhibits robust generalization capabilities in the presence of data perturbations.Comment: This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-N

Common Path swept-source OCT interferometer with artifact removal

Peer reviewed: YesNRC publication: Ye