Persona: García Magariño, A.
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García Magariño
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Publicación Acceso Abierto Interferometric laser imaging for droplet sizing method for long range measurements(Elsevier, 2021-01-15) García Magariño, A.; Sor, Suthyvann; Muñoz Campillejo, Javier; Bardera, Rafael; García Magariño, A.; Sor, Suthyvann; Muñoz-Campillejo, Javier; Instituto Nacional de Técnica Aeroespacial (INTA)A recent appendix in the aircraft regulations comprises testing supercooled large droplets impinging on its surfaces. For those tests, the size and distributions of droplets need to be characterized in icing wind tunnels. In this paper, the applicability of implementation of the “Interferometric Laser Imaging for Droplet Sizing” technique inside a wind tunnel with a 3 m × 2 m open elliptical test section has been discussed. Experiments have been conducted in the laboratory for the discussion at object distance of 1.6 m and 2.29 m and droplets diameters between 360 µm and 850 µm. All the streams were previously characterized by means of the shadowgraph imaging technique. A novel approach of the Interferometric Laser Imaging for Droplet Sizing technique where droplets are not fully defocused to avoid excessive overlapping is presented. Two new image processing approaches provide in general good results as compared to previous methods.Publicación Acceso Abierto From pomegranate byproducts waste to worth: A review of extraction techniques and potential applications for their revalorization(EDP Sciences, 2024-04-17) García Rodríguez, J. A.; Sor, Suthyvann; García Magariño, A.The food industry is quite interested in the use of (techno)-functional bioactive compounds from byproducts to develop ‘clean label’ foods in a circular economy. The aim of this review is to evaluate the state of the knowledge and scientific evidence on the use of green extraction technologies (ultrasound-, microwave-, and enzymatic-assisted) of bioactive compounds from pomegranate peel byproducts, and their potential application via the supplementation/fortification of vegetal matrixes to improve their quality, functional properties, and safety. Most studies are mainly focused on ultrasound extraction, which has been widely developed compared to microwave or enzymatic extractions, which should be studied in depth, including their combinations. After extraction, pomegranate peel byproducts (in the form of powders, liquid extracts, and/or encapsulated, among others) have been incorporated into several food matrixes, as a good tool to preserve ‘clean label’ foods without altering their composition and improving their functional properties. Future studies must clearly evaluate the energy efficiency/consumption, the cost, and the environmental impact leading to the sustainable extraction of the key bio-compounds. Moreover, predictive models are needed to optimize the phytochemical extraction and to help in decision-making along the supply chain.Publicación Restringido Application of YOLOv8 and a model based on vision transformers and UNet for LVNC diagnosis: advantages and limitations(Springer, 2025-05-25) Poyatos Martínez, D.; Díaz García, Pedro; García Magariño, A.Hypertrabeculation or left ventricular non-compaction (LVNC) is a cardiac condition that has recently been recognized. While several methods exist for accurately measuring the trabeculae in the ventricle, there is still no consensus within the medical community regarding the optimal approach. In previous work, we introduced DL-LVTQ, a tool based on a UNet convolutional neural network designed to quantify the trabeculae in the left ventricle. In this paper, we present an expanded dataset that includes new patients affected by a cardiomyopathy known as Titin, necessitating the retraining of the models involved in our study on this updated dataset to accurately infer future patients with this condition. We also introduce ViTUNet, a hybrid architecture that aims to merge the benefits of UNet and Vision Transformers for precise segmentation of the left ventricle. Furthermore, we train a YOLOv8 model to detect the left ventricle and integrate it with the hybrid model to focus segmentation on a region of interest around the ventricle. Regarding the precision quality achieved by ViTUNet using YOLOv8, results are quite similar to those obtained by the DL-LVTQ tool, suggesting that the dataset is a limiting factor in our improvement. To substantiate this, we conduct a detailed analysis of the MRI slices in the current dataset. By identifying and removing problematic slices, results significantly improve. The introduction of a YOLOv8 model alongside a deep learning model presents a promising approach.
