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Item type:Publication, Neutral pion reconstruction using machine learning in the MINERvA experiment at 〈Ev〉 ∼ 6 GeV(IOP Publishing Ltd, 2021-07-01)This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013–2017, with an average neutrino energy of 6 GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two γs from 70.7 ± 0.9% to 89.3 ± 0.7% and improves the efficiency of the reconstruction by approximately 40%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with 〈 E ν 〉 between 1–10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current ν e events arising from ν μ → ν e appearance.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A YOLO algorithm for pattern recognition in images of marine species in closed seasons(Springer Science and Business Media Deutschland GmbH, 2024-01-01)One of the main problems that arise in the process of extracting marine species during the closed seasons is the indiscriminate loading of marine species that are prohibited because they are in spawning season. These problems are often aggravated by the lack of transparency in inspection process, where inspectors receive bribes when they intervene with vessels on the high seas (Ministerio de la Producción in https://pescayconsumoresponsable.produce.gob.pe/presentacion.html, [1]). In addition to that, (Mar del Perú in https://mardelperu.pe/articulos_wikipesca/reglas-de-juego/, [2]) details how the process of identifying marine species does not have rigorous verification due to not having the appropriate tools and control mechanisms. For those reasons, this paper will show an implementation of a technological solution based on a Artificial Intelligence and mobile application integrated into identification and control processes of marine species in closed seasons by providing an adequate classification of the species detected by those devices. To achieve this, YOLO algorithm has been trained and used in an integrated app. YOLO is an algorithm whose architecture is based on convolutional neural networks. This algorithm, unlike other CNN-based architectures, seeks to perform detection in a single run, which allows it to be an extremely fast alternative by performing two fundamental tasks at the same time: identifying a region of interest and classifying it. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The LIBRA NeuroLimb: Hybrid Real-Time Control and Mechatronic Design for Affordable Prosthetics in Developing Regions(Multidisciplinary Digital Publishing Institute (MDPI), 2023-12-22)Globally, 2.5% of upper limb amputations are transhumeral, and both mechanical and electronic prosthetics are being developed for individuals with this condition. Mechanics often require compensatory movements that can lead to awkward gestures. Electronic types are mainly controlled by superficial electromyography (sEMG). However, in proximal amputations, the residual limb is utilized less frequently in daily activities. Muscle shortening increases with time and results in weakened sEMG readings. Therefore, sEMG-controlled models exhibit a low success rate in executing gestures. The LIBRA NeuroLimb prosthesis is introduced to address this problem. It features three active and four passive degrees of freedom (DOF), offers up to 8 h of operation, and employs a hybrid control system that combines sEMG and electroencephalography (EEG) signal classification. The sEMG and EEG classification models achieve up to 99% and 76% accuracy, respectively, enabling precise real-time control. The prosthesis can perform a grip within as little as 0.3 s, exerting up to 21.26 N of pinch force. Training and validation sessions were conducted with two volunteers. Assessed with the "AM-ULA" test, scores of 222 and 144 demonstrated the prosthesis's potential to improve the user's ability to perform daily activities. Future work will prioritize enhancing the mechanical strength, increasing active DOF, and refining real-world usability.