Caractérisation de guides d'onde fabriqués par échange K-Na sur verre

Profils d'indice -- Échange ionique : aperçu générale -- Échange K+ -Na+ -- Profils d'indice -- Caractérisation du profil d'indice -- Technique WKB inverse -- Fabrication de guides plans -- Mesure d'indices effectifs -- Résultats de la caractérisation -- Calcul des modes de propagation -- Formulations variationnelles Ex mn et Ey mn -- Matrices élémentaires -- Programmation -- Exemples d'utilisation -- Profils d'intensité -- Fabrication de guides canal -- Méthode d'observation des profils d'intensité -- Résultats de mesures et de calculs

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

Common Path swept-source OCT interferometer with artifact removal

Peer reviewed: YesNRC publication: Ye

Artifacts removal with a piezoelectric fiber stretcher in Fourier domain OCT

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

Durable coronary artery phantoms for optical coherence tomography

Peer reviewed: YesNRC publication: Ye

Deformable and durable phantoms with controlled density of scatterers

We have developed deformable and durable optical tissue phantoms with a simple and well-defined microstructure including a novel combination of scatterers and a matrix material. These were developed for speckle and elastography investigations in optical coherence tomography, but should prove useful in many other fields. We present in detail the fabrication process which involves embedding silica microspheres in a silicone matrix. We also characterize the resulting phantoms with scanning electron microscopy and optical measurements. To our knowledge, no such phantoms were proposed in the literature before. Our technique has a wide range of applicability and could also be adapted to fabricate phantoms with various optical and mechanical properties.Peer reviewed: YesNRC publication: Ye

On the speckle size in optical coherence tomography

Peer reviewed: YesNRC publication: Ye

Deformable and durable optical phantoms with controlled density of scatterers

Peer reviewed: YesNRC publication: Ye