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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- Design and Implementation of a UAV-based Platform for Air Pollution Monitoring and Source Identification(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2018-05-15) Yungaicela Naula, Noé Marcelo; Garza Castañón, Luis Eduardo; Ponce Cuspinera, Luis; Vargas Martínez, AdrianaThis document presents the thesis proposal for obtaining the Master of Science in Intelligent Systems. Technology, industry and government forecasts coincide that the planet will withstand a maximum of 50 years at the rate of current air pollution. Air pollution has reached critical levels causing major impacts on health and economy across the globe. Environmental monitoring and control agencies, as well as industries, require a reliable and cost-effective tool that is easy to deploy where required to assess contamination levels, and on that basis, take the necessary actions. Current measurement methods using pressurized balloons, satellite imagery, or earth stations result in considerable investment, as well as providing low spatial and temporal resolution. There are also systems for measuring air pollution using Unmanned Air Vehicles (UAV), which are financed by large government institutions or international organizations whose budget and resources allow costly implementations. Other related works are limited to the capture of atmospheric data using the UAVs and offline analysis. This work presents the design and implementation of an open-source UAV-based platform for measuring atmospheric pollutants and an algorithm for the localization of the air pollutant sources with the use of a UAV and in-line processing of the pollutants data. The development of the UAV-based platform includes: the UAV mounting and characterization and the control system to guide the navigation of the vehicle, the appropriate sensors selection and integration to the UAV, the data transmission from the sensors onboard the UAV to the ground station, and the implementation of the user interface which is based on a web design. The algorithm for the air pollutant source localization is based on a metaheuristic component, to follow the increasing gradient of the pollutant concentration, and complemented with a probabilistic component to concentrate the searching to the most promising areas in the targeted environment. The results of this work are: Outdoors experiments of the UAV-based platform for the air pollutant monitoring and indoor experiments to validate the algorithm for the source localization. The results show effectiveness and robustness of the UAV-based platform and of the algorithm for the source identification.
- A decision tree learning hyper-heuristic for decision-making in simulated self-driving cars.(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2018-05) García Escalante, Marcelo Roger; Terashima Marín, Hugo; Özcan, Ender; Gutiérrez Rodríguez, Andres Eduardo; Conant Pablos, Santiago EnriqueThis document describes a feasible way of implementing hyper-heuristics into self-driving cars for decision-making. Hyper-heuristics techniques are used as an automated procedure for selecting or generating among a set of low-level heuristics when solving a particular type of problem. This project aims to contribute and bridging the gap between the fields of self-driving cars and hyper-heuristics since there is not any known approach linking them together to date. The decision-making process for self-driving cars has been a trend in recent years. Thus, there exist a variety of techniques applied to path planning at the moment, such as A*, Dijkstra, Artificial Potential Field, Probabilistic Roadmap, Ant Colony, Particle Swarm Optimization, etc. However, since there is no information of the complete environment at the beginning of the trip and also fast dynamic measurements of the surroundings are obtained while a decision plan is raised, selection or combination among various low-level heuristics such as the path planning techniques mentioned above could be helpful, or perhaps to create new heuristics and this way build another branch for decision-making of autonomous vehicles as a path planning method. Hyper-Heuristic approach with the help of Machine learning techniques harnesses the past driving experience of a self-driving car, which results in an improvement of the decision-making of the vehicle to different kind of scenarios. This thesis proposes a hyper-heuristic approach for decision-making of a self-driving car on a highway with different types of traffic and real-life constraints. The hyper-heuristics model introduced is of a generative type; thus, it creates a most suitable heuristic to drive the car on the road based on previously existing heuristic methods. Information is obtained by the vehicle through different onboard sensors such as Radar, Camera, LIDAR, Stereo-vision, GPS and IMU that combined establish a sensor fusion approach. Experimental study of the algorithms is performed in a simulation environment for self-driving cars built on a Unity platform. The generation hyper-heuristic proposed has a Decision Tree classifier as a high-level heuristic, which will be in charge of generating a new heuristic from the low-level heuristics presented. The Decision Tree classifier is defined with the optimal hyper-parameters obtained by a Grid-search method. In this work, there is also an explanation of the simulator's setup environment since it has evolved from a robotics' building-from-scratch level to a self-driving car platform modified from an open source resource. Thus, creating a framework suitable for extraction of instances and implementation of hyper-heuristic results to a self-driving car. Finally, the result of the hyper-heuristic performance is compared against a Finite state machine defined with greedy instructions based on the current state of the car, three heuristics built for the project: left heuristic, center heuristic, right heuristic, and a human driver.
- Automatic placement of electronic components to maximize heat dissipation on PCB's using particle swarm optimization(2017-12) Ramirez Velazco, Omar Alexander; Conant Pablos, Santiago Enrique; Ortiz Bayliss, José Carlos; Amaya Contreras, Iván MauricioThis thesis documents my personal research as candidate for the academic degree of Master of Science in Intelligent Systems. The purpose of this work is to optimize the electronic components layout in a printed circuit board based on metrics related to its thermal dissipation. The continuous evolution of smaller, more complex and compact integrated circuits pushes the design and fabrication techniques of printed circuit board, also known as PCB, to new limits. Moore’s Law states: “The number of transistors incorporated in a chip will approximately double every 24 months.” Nowadays, it is possible to see several examples of this in the new microprocessors used in computers, smart phones and tablets that have more than 600 million transistors (e.g. Apple A10, Intel Core i9, Qualcomm Snapdragon). A direct result of this is reflected in a significant and dramatic complexity of packages and interconnection traces contained in a single integrated circuit. Integrated circuits are increasingly dense and perform a large number of operations, requiring more current consumption and generating a rise in the circuit temperature that needs to be dissipated through the environment. The optimal placement of electronic components over a PCB can ameliorate the problem, but requires meeting multiple design objectives mostly due to the dierent power dissipation of the components, their operating temperatures, kind of materials, terminals and dimensions. It is important to notice that to approach the complexity of this type of problems, heuristics methods such as the proposed in this work, are required. Although many global companies focus in the design of electronics devices and invest in computer aided design software, specifically in Electronic Design Automation platforms known as EDA, to expedite prototype development, just a few of them have the necessary computational tools that can automatically meet all the components placement constraints. Particle Swarm Optimization (PSO) is used to address the problem through a weighted sum method for multi-objective optimization (MOO). Regardless of the deficiencies with respect to depicting the Pareto optimal set, the weighted sum method continues to be used extensively not only to provide multiple solution points by varying the weights consistently, but also to provide a single solution point that reflects preferences presumably incorporated in the selection of a single set of weights. Experiments performed were based on three main categories. Broadly speaking, the first one consisted on identifying the most important properties of PSO algorithm, its response and behavior under a dierent set of scenarios. The second kind of experiments aimed to observe and analyze which variables had more impact, and how these dominate over the rest. Lastly, the third kind cared to analyze the response of the algorithm under more complex instances of a problem. Results produced by the PSO algorithm were compared against a finite element analysis software, and finally, a general discussion was elaborated, where validity was given to the hypothesis proposed in this paper when analyzing the performance shown