Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Development of mobile crowd sensing based models for fire risk assessments in constrained devices(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Low Castro, Jesús Antonio; Rodríguez Hernandez, Gerardo; emimmayorquin; Gonzalez Mendoza, Miguel; Sanchez Ante, Gildardo; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, GilbertoWildfires have become one of the most critical challenges to address due to their increasing frequency as a result of climate change, causing significant damage to ecosystems, lives, and property. Although various strategies have been explored for wildfire management, a promising approach focuses on wildfire risk assessment through fuel identification, where fuels are sources of stored potential energy that combust under specific environmental and physical conditions. Since fuels are key determinants of fire behavior, identifying fire-prone areas inadvance can help reduce the severity, spread, and intensity of wildfires. Traditional fuel mapping techniques are commonly used for this purpose and rely primarily on satellite and aerial imagery, but face limitations in resolution, cost, and real-time accessibility, highlighting the need for complementary ground-based systems. This thesis explores a wildfire risk assessment approach based on ground-level fuel identification using computer vision models deployed on resource-constrained devices, specifically smartphones. To enable distributed data collection and inference, a mobile crowdsensing scheme is proposed. The methodology includes training, quantization, and deployment of object detection and semantic segmentation models for fuel identification on mobile devices. The research includes case studies on optimized object detection using the Edmonton Wildland-Urban Interface dataset, the deployment of lightweight semantic segmentation models using a custom dataset from the Arteaga Mountain Range in Mexico, and a semisupervised labeling strategy that uses a robust semantic segmentation model to augment training data. The results demonstrate that the proposed models achieve high accuracy while meeting the computational and storage constraints of mobile devices, supporting the feasibility of using mobile crowd-sensing and optimized vision models for a low-cost real-time assessment of wildfire risk.
- Component Detection based on Mask R CNN(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023) Charles Garza, Daniel; Morales, Rubén; emimmayorquin; Vallejo Guevara, Antonio; Guedea Elizalde, Federico; Escuela de Ingeniería y Ciencias; Campus MonterreyThis thesis delves into the evolution and utilization of deep learning methodologies in the specific context of object detection and segmentation within the manufacturing industry. It thoroughly examines several state-of-the-art object detection techniques, including YOLO, RCNN, Fast R-CNN, etc. These methods are explored in detail, assessing their effectiveness and applicability in complex object identification and classification tasks. The study then focuses on Mask R-CNN, a method chosen for its outstanding performance in object segmentation and identification; especially, in cluttered and unstructured environments common in manufacturing settings.