Tesis Telecomunicaciones
Permanent URI for this collectionhttp://repositorio.uta.edu.ec/handle/123456789/34848
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Item Sistema de gestión inteligente del parqueadero del edificio de Bienestar Estudiantil en la Universidad Técnica de Ambato mediante visión artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Gavilanez Jiménez Marlon Abel; Guamán Molina Jesús IsraelThis thesis project presents a smart management system for the parking lot of the student welfare building at the Universidad Técnica de Ambato using computer vision. To capture real-time images of the parking lot, two IP cameras are installed at the entrance and exit, with processing performed on the NVIDIA Jetson Nano development platform. The system employs the YOLOv8 computer vision algorithm for vehicle detection and counting. It monitors vehicle flow by recording entries and exits to maintain an updated count of the total occupancy of the parking lot, which has 166 spaces. The analysis results are displayed through a graphical interface developed in Python using Tkinter, running on an Ubuntu environment. This interface allows staff to monitor the number of vehicles in the parking lot and the remaining availability in real time.Finally, the collected data is stored in a local database, enabling historical tracking of vehicle flow and facilitating analysis for optimizing parking lot management.Item Sistema para la caracterización de enfermedades de cultivos de cebolla mediante el uso de procesamiento digital de imágenes(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Hidalgo Segovia Oscar Gustavo; Guamán Molina Jesús IsraelModern agriculture faces critical challenges related to disease detection and management in high-value crops such as onion, an essential commodity with an annual global production of approximately 300 million tons. This project proposes an innovative system for disease characterization in onion crops using digital image processing, IoT and artificial intelligence. The system integrates sensors to collect key environmental data, such as temperature, humidity and UV radiation, which are stored in a MongoDB database. This data is visualized with Chart.js, complementing digital image analysis to detect diseases such as Botrytis squamosa and powdery mildew. Through advanced deep learning algorithms, such as YOLOv8, the system identifies visual patterns associated with diseases, enabling early disease detection. The implementation of the system comprises four stages: data acquisition through sensors and cameras, preprocessing using OpenCV, training of models based on neural networks, and visualization of results through graphical interfaces. This integrated approach not only optimizes disease detection and management, but also improves efficiency in agricultural decision making, reducing losses and maximizing productivity. Thus, it contributes significantly to the sustainability of the agricultural sector, especially in regions with low technification such as Tungurahua, EcuadorItem Sistema de control de calidad de cultivo de fruta de temporada para etapa de precosecha empleando robótica aérea con planificación de trayectorias y visión artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-08) Ashqui Balseca, Michelle Ivette; Aucatoma Matias, Bryan Paul; Córdova Córdova, Edgar PatricioCurrently, agriculture plays a fundamental role in the global economy. To meet the growing food demand, advanced technological tools have been integrated to optimize agricultural practices, known as Precision Agriculture (PA). These tools offer solutions that will mitigate the difficulties faced by farmers in their daily tasks. In this context, a study was carried out with the aim of implementing a quality control system for seasonal fruit crops for the pre-harvest stage using aerial robotics with trajectory planning and artificial vision. The importance of fruit quality control in Ecuador lies in its high demand both in the national and international markets. Technical standard NTE INEN 1872 establishes criteria for classifying apples according to quality grades for export and consumption. This system is based on the use of YOLOv8, a deep learning tool that evaluates the quality grade and classifies the different types of apples. The system consists of four stages: acquisition, processing, training, and visualization. In the acquisition stage, the Dji Tello drone was used to capture images or videos in real-time. The acquired data undergo preprocessing using OpenCV. A neural network was employed to train a model capable of accurately recognizing the type and quality grade of apples. For visualization, an intuitive graphical interface was designed to allow visual representation of the data derived from the trained model. The system algorithm was developed in Python due to its multiple libraries.Item Sistema de evaluación inteligente de terceros molares basado en aprendizaje automático y procesamiento de imágenes.(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-08) Sánchez Villa, Juan Manuel; Sánchez Zumba, Andrea Patriciaproject explores the anatomical characteristics of third molars and applies advanced techniques in automated learning and image processing for their evaluation. The Pell and Gregory, and Winter classifications were used to structure a database of 2592 instances into 12 main categories, enabling precise model training using the YOLO algorithm in its YOLOv8x version. The selection of appropriate tools and software was pivotal for the successful development of the artificial intelligence model; Google Colab was chosen for training due to its high processing speed, achieving optimal training within 45 to 55 epochs for each category of third molars. Additionally, an interface for third molar evaluation was developed using CustomTkinter, leveraging YOLOv8's extensive compatibility with Python libraries compared to earlier versions and other available artificial vision algorithms. This facilitated the creation of an interactive and user-friendly platform for dental professionals. For clinical data management and storage, Firebase Admin was implemented, enabling the collection and organization of results from each clinical case. This led to the development of a comprehensive dental clinical template containing essential parameters necessary for subsequent reviews by dentists. Overall, this advanced system enhances accuracy in diagnosing and evaluating third molars, optimizing dental practices through effective integration of artificial intelligence technologies and development tools, providing an efficient and precise approach to managing clinical data.Item Sistema de reconocimiento facial utilizando visión artificial basado en una arquitectura iot, para el conteo de usuarios en las unidades de la Cooperativa de Transporte Público “Unión Ambateña”(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-08) Solís Santamaría, Alexis Adrián; Guamán Molina, Jesús IsraelOver the years, the transportation company Union Ambateña has seen it is necessary to use different control systems in each bus, in order to track the number of passengers who use the public transportation every single day and be able to know how much money they receive. However, it has been difficult to get accurate data due to limited technology. A system with IoT architecture is presented for detection, classification, and counting of people using the transportation service. An EZVIZ H6c 1080p Wi-Fi camera and an EXTREM 720p camera are used to capture real-time videos of people and faces inside each bus, which are processed on the NVIDIA Jetson Nano development board. The detection models count the total number of passengers boarding the bus and also detect and record the number of adults using the service. The system is developed with the YOLOv8 artificial vision algorithm, which handles adult detection, as well as detection of all types of people. The results of the user count obtained are displayed on the Node-RED platform, which is used with a local service to show the value of the final revenue report. The results are stored and managed on the Ubidots IoT platform, which allows for control of the transportation units and decision-making with real-time data.Item Sistema de teledetección aérea para control de agentes patógenos y enfermedades en cultivos de brócoli con el uso de visión artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2023-09) Laura Telenchana, Darwin Ronaldo; Urrutia Urrutia, Elsa PilarBroccoli is one of the agricultural products in Ecuador with the highest rate of imports worldwide, due to this the development of the crop must be routinely inspected to counteract the curses that harm its production such as pests or diseases and preserve its high rate of productive quality. For this reason, the present research project is oriented to carry out inspection of broccoli crops by air with pre programmed flight plans that help to analyze the health status of the crop to be treated on time. The design and implementation of the aerial remote sensing system for the control of pathogens and diseases in broccoli crops is developed through the use of the YOLO v5x artificial vision algorithm for deep learning of the system in various production circumstances. The aerial remote sensing system uses a drone that helps to move the active and passive sensors that interact in the system. This drone is autonomous thanks to the implementation of a GPS module on its flight control board, which helps planning grid-type pre-programmed flights. This system receives the analysis of the health status of the broccoli crop by capturing images through a high-definition camera incorporated into the drone. Each image is captured in a time frame of 10 seconds to carry out its respective processing in real time through a graphical interface programmed in Python. The image consists of positioning data such as altitude and latitude obtained through a geolocation system that was implemented in the drone. These data help to locate the specific point where the images were captured and interpret the results obtained in the analysis of the culture for each one of them. The results of the image processing are stored in a database, for the autonomous training of the artificial vision algorithm and improvement of the prototype in the detection of false positives and false negatives