Ciencias Exactas y Ciencias de la Salud

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Smart camera FPGA hardware implementation for semantic segmentation of wildfire imagery
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-13) Garduño Martínez, Eduardo; Rodriguez Hernández, Gerardo; mtyahinojosa, emipsanchez; Gonzalez Mendoza, Miguel; Hinojosa Cervantes, Salvador Miguel; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, Gilberto
In the past few years, the more frequent occurrence of wildfires, which are a result of climate change, has devastated society and the environment. Researchers have explored various technologies to address this issue, including deep learning and computer vision solutions. These techniques have yielded promising results in semantic segmentation for detecting fire using visible and infrared images. However, implementing deep learning neural network models can be challenging, as it often requires energy-intensive hardware such as a GPU or a CPU with large cooling systems to achieve high image processing speeds, making it difficult to use in mobile applications such as drone surveillance. Therefore, to solve the portability problem, an FPGA hardware implementation is proposed to satisfy low power consumption requirements, achieve high accuracy, and enable fast image segmentation using convolutional neural network models for fire detection. This thesis employs a modified UNET model as the base model for fire segmentation. Subsequently, compression techniques reduce the number of operations performed by the model by removing filters from the convolutional layers and reducing the arithmetic precision of the CNN, decreasing inference time and storage requirements and allowing the Vitis AI framework to map the model architecture and parameters onto the FPGA. Finally, the model was evaluated using metrics utilized in prior studies to assess the performance of fire detection segmentation models. Additionally, two fire datasets are used to compare different data types for fire segmentation models, including visible images, a fusion of visible and infrared images generated by a GAN model, fine-tuning of the fusion GAN weights, and the use of visible and infrared images independently to evaluate the impact of visible-infrared information on segmentation performance.
Neutrino classification through deep learning amid the Hyper-Kamiokande project development
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-10) Romo Fuentes, María Fernanda; Falcón Morales, Luis Eduardo; emipsanchez; Cuen Rochin, Saul; De la Fuente Acosta, Eduardo; School of Engineering and Sciences; Campus Estado de México
Neutrinos are a type of elemental particle that are characterized by the fact that their mass is really small, that they have no electric charge and present a special behavior called oscillation in which they can be measured to be of a kind different to the one they actually are. All these characteristics make neutrinos one of the most studied particles by different researchers and in different facilities nowadays, since the information we can obtain from its study allows us to solve some of the Universe’s greatest mysteries. One of these projects where neutrinos are studied is the Hyper-Kamiokande which refers to both, the international collaboration of researchers, to which Mexico belongs to, and the neutrino grand-scale detector based on Cherenkov radiation currently being built in Japan. In this detector the data of a neutrino event is collected by a special kind of sensors located in its walls called Photo Multiplier Tubes or PMTs, to then be analyzed, and this analysis usually starts by the identification of the particles involved in an event, which is where this project comes forth, since an appropriate method to classify neutrinos based on the radiation pattern they leave as they pass through the detector is needed. Hence, in the following project to obtain the Master in Computer Science degree, we implement and test 4 deep learning architectures: VGG19, ResNet50, PointNet and Vision Transformer, for the classification of neutrinos since they are state of the art methods, this is, they are architectures used as the starting point for any classification task and, moreover, we can tune them and/or apply different techniques such as regularization to get the best possible performance while reducing overfitting. Using the mentioned architectures we process a dataset composed of neutrino events simulated by a software called WCSim in 2021. These events are of single ring type, correspond to the IWCD tank, a smaller tank being built to aid in the tasks of the Hyper-Kamiokande and range from 9 thousand to 8 million per each of the 3 particles considered in the project: muon and electron neutrinos and gamma particles. The results show that ResNet50 was the architecture that gave the best results while also minimizing the computational resources needed, though its performance is similar to the one given by VGG19 and PointNet, they require a larger time to process any dataset, whereas Vision Transformer provided the poorest results, however, all results improved by processing the largest datasets. Then, in comparison with a state of the art custom CNN we found that our highest average accuracy is within the same range as the one they obtained, whereas, in comparison with the ResNet50 model currently being used in the HK collaboration we found that the obtained AUC for the TPR signal (electron) vs FPR background (gamma) curve for our best model is 0.71, whereas this AUC value for the collaboration is 0.77, nonetheless, we have to consider that to obtain this value the whole results are not analyzed by the collaboration but cuts are applied and therefore, our results can be considered close.

Ciencias Exactas y Ciencias de la Salud

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