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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- Attention semi-siamese U-Net as a novel quantification protocol for the biomarkers of tauopathies(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022) Campero García, Luis Angel; Cantoral Ceballos, José Antonio; puelquio, emipsanchez; Ochoa Ruiz, Gilberto; Nolazco Flores, Juan Arturo; Castañeda Miranda, Alejandro; School of Engineering and Sciences; Campus Estado de México; Gutiérrez Rodríguez, Andrés EduardoEfforts have been made to diagnose and predict the course of different neurodegenerative diseases through various imaging techniques. Particularly tauopathies, a group of diseases where the tau polypeptide is a key participant in molecular pathogenesis, have significantly increased their morbidity and mortality in the human population over the years. However, the standard approach to exploring the phenomenon of neurodegeneration in tauopathies has not been directed at understanding the molecular mechanism that causes the aberrant polymeric and fibrillar behavior of the tau protein, which forms neurofibrillary tangles that replace neuronal populations in the hippocampal and cortical regions. The main objective of this thesis is to implement a novel quantification protocol for different biomarkers based on pathological post-translational modifications undergone by tau in the brains of patients with tauopathies. The quantification protocol consists of a novel neural network architecture based on the U-Net with attention modules. We used the resulting segmentation masks for the quantification of combined fluorescent signals of the different molecular changes tau underwent in neurofibrillary tangles. The quantification considers the neurofibrillary tangles as an individual study structure separated from the rest of the quadrant present in the images. This allows us to detect unconventional interaction signals between the different biomarkers. Our algorithm provides information that will be fundamental to understanding the pathogenesis of dementias with another computational analysis approach in subsequent studies.
- Design of a proprietary self driving car platform and development of autonomous driving algorithms based on computational vision and deep neural networks(Instituto Tecnológico y de Estudios Superiores de Monterrey) Aguilar Aldecoa, Aldo Iván; ACEVES LOPEZ, ALEJANDRO; 120834; Aceves López, Alejandro; puelquio, emipsanchez; González Mendoza, Miguel; González Hernández, Hugo Gustavo; Escuela de Ingeniería y Ciencias; Campus Ciudad de MéxicoThis research project presents a detailed description of the design and development of the first small-sized self driving development platform at the ITESM Campus Estado de México. The implemented hardware and software is presented based on the state of the art research platforms. Additionally, the required sensor and instrumentation implementation is described as well as the platform's mechanic and electric design. Moreover, the dynamic identification of the vehicle actuators is presented for the linear velocity control of the platform however no clear evidence of a linear dynamic behavior could be identified, leading to the implementation of a herustically tuned PI velocity control system. Specific Computer Vision (grayscale color thresholding) and Deep Neural Network (U-Net semantic segmentation) based road lane segmentation mechanisms were developed, tested and validated. These segmentation mechanisms served as the main input of the final autonomous driving system proposal based on a road lane following strategy. Finally, a well-defined autonomous driving performance evaluation methodology is described and implemented to compare the proposed systems response, identifying comparable performances between CV and DNN segmentation systems.