Tesis Telecomunicaciones
Permanent URI for this collectionhttp://repositorio.uta.edu.ec/handle/123456789/34848
Browse
10 results
Search Results
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 Identificación temprana de presencia de plagas en cultivos de ambiente controlado empleando visión artificial y deep learning(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Lascano Villafuerte Erick Fernando; Córdova Córdova Edgar PatricioThe cultivation of crops in controlled environments offers optimal conditions for their growth. However, the presence of pests can adversely impact both the quantity and the quality of the produce. This phenomenon exerts a deleterious effect on the economic viability of farmers. To address these challenges, farmers have adopted technological solutions, such as Agriculture 5.0, to enhance their productivity and quality of produce. A study was conducted with the objective of implementing a system for pest detection, utilizing Computer Vision and Deep Learning technologies. It is imperative to detect pests in crops at an early stage to avert production losses. Consequently, the system is predicated on a neural network capable of accurately detecting various pests. The system is comprised of four distinct stages: acquisition, training, processing, and visualization. In the initial acquisition stage, four cameras were utilized to capture images and video. The training stage entailed the utilization of collected data in conjunction with a model adept at functioning with constrained resources while maintaining optimal detection accuracy. The image processing stage entailed the utilization of a microcomputer that had been optimized to operate in conjunction with artificial intelligence. The visualization and information management stage involved the development of a graphic interface capable of displaying the data obtained. The trained model demonstrated an accuracy of 95.7% in the detection of pests, and subsequent system tests yielded a reliability of 93.7%, thus confirming the system's reliability.Item Sistema para detección de automóviles robados empleando visión artificial en vehículo aéreo no tripulado (UAV)(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Chanahuano Azogue Jose Luis; Santo Taipicaña Danny Gabriel ; Salazar Logroño Franklin WilfridoThe present research focuses on the design and implementation of an advanced technological system for the detection of stolen cars, integrating various technologies such as computer vision, YOLOv8 convolutional neural networks, optical character recognition (OCR) and unmanned aerial vehicles (UAV). This innovative system captures, processes and analyzes images of vehicles in parking lots, allowing license plates to be identified and compared with cloud databases managed in Azure. The results obtained are notified through a web platform and applications such as Telegram and email, thus facilitating an agile and efficient response. The system is composed of a UAV equipped with an FPV camera that transmits real-time video to a receiver connected to a computer. The processed information is stored in a database and automatically verified. The UAV configuration allows stable and safe flights, while the artificial intelligence models used guarantee high accuracy in license plate detection, even under variable lighting conditions. In addition, field tests were carried out in different locations to evaluate the system's performance in terms of flight time, energy consumption and data processing. Among the most outstanding results is an 82.78% reliability rate in the detection of vehicle license plates with average information sending times of 20.26 seconds per detected event and sending of respective notifications. This project represents a significant contribution to strengthening public security by providing scalable and technologically advanced.Item Sistema de detección temprana de eventos delictivos en entidades comerciales con visión artificial y deep learning(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Oyaque Moncayo Christian Manuel; Córdova Córdova Edgar PatricioPresently, video surveillance systems in commercial entities depend considerably on human monitoring, which results in increased costs for these entities. Consequently, a system for early detection of criminal events using computer vision and deep learning has been proposed. This system employs two complementary processing methods: the first utilizes YOLOv8 for suspicious object detection, and the second employs an SVM algorithm that classifies key points extracted with MediaPipe for threat posture detection. The system operates on an Nvidia Jetson Nano module, which processes videos in real time and displays them in an information management system developed with Flask. It also stores detections in a SQLite database, which continuously feeds into a newly automatically tagged dataset for future model updates, and is capable of displaying historical records of detections. The system's efficacy was evaluated across three key aspects: detection of suspicious objects, with an f1-score of 87.5%; detection of threatening postures, with 74%; and early detection, with 78%.The study's findings underscore the significance of processing complementary parts, facilitating the establishment of a more comprehensive contextual understanding of the scene. The quality and breadth of the training dataset are foundational for the success or failure of computer vision and machine learning models, particularly in the context of object detection, where a diverse array of images of varying sizes, shapes, and perspectives of the object to be detected is essential for the model to generalize correctly. In the case of body postures, support vector machine (SVM) models demonstrate efficacy, though they are constrained in their ability to establish spatio-temporal contexts.Item Sistema remoto de detección de caídas en adultos mayores utilizando visión artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-08) Cuyo Gutiérrez, Jonathan Andrés; Castro Martin, Ana PamelaThis research project develops a monitoring system to detect falls in older adults using computer vision. According to the WHO, falls among older adults are a leading cause of injuries and can be fatal if not promptly addressed. The objective is to implement a system that identifies falls and notifies caregivers via Telegram. For fall detection, the YOLO v8s AI model trained on the COCO dataset, which includes over 220,000 images for key point and bounding box detection, has been selected. The system utilizes the Kinect V2, equipped with an RGB camera for live video, an infrared camera for nighttime operation, and a proximity sensor. The AI detects key points and bounding boxes in the images, while fall parameters are defined programmatically by analyzing aspect ratios and key point positions. In tests across three different scenarios, the system achieved a 92.92% accuracy in fall detection. Caregivers can access live monitoring through a web page, viewing real-time video. This system proves to be a valuable tool for elderly care, enabling a prompt and effective response to incidents, and significantly contributing to their well-being and quality of life.Item Sistema de detección temprana de plagas en cultivos de mora mediante visión artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-08) Arequipa Tipantuña, Jonathan Marcelo; Castro Martin, Ana PamelaThe blackberry production in Ecuador has experienced significant growth in recent years, becoming one of the most in-demand fruits. However, several factors, such as pests and diseases, affect the production and quality of this fruit, directly impacting the farmers' economy. Based on this context, the present research project focuses on the implementation of a pest and disease detection system using an artificial vision model, which serves as a tool to help farmers identify diseases in their crops quickly and efficiently. The system consists of a mobile application that integrates the artificial vision model. This model is designed to detect four types of pests with the highest incidence in the area. The application offers two detection modes: the first one through real-time video via the device's camera and the second one through image analysis, thus allowing the integration of multimedia resources for transmitting information to the model for analysis. The system has demonstrated an accuracy of 90.34% in pest detection, according to tests conducted in various environments where crops are located. The implementation of this system has significantly reduced pest identification errors by avoiding human errors and reducing economic losses by identifying pests and diseases at early stages of infection.Item Sistema electrónico de control y monitorización de vehículos en caso de robo para la empresa confecciones Galar mediante visión artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-02) Lema Galarza, Fernando Paul; Cuji Rodríguez, Julio EnriqueThe present research project, an electronic vehicle control and monitoring system against theft is implemented for the company "Confecciones GALAR" using artificial vision. The system addresses the issue of vehicle security with an innovative solution that detects and monitors potential thefts on the company's premises, providing quick and efficient responses to risky situations. The implementation relies on security cameras equipped with artificial vision algorithms that automatically detect suspicious activities, especially those related to attempts to steal vehicles. The system analyzes people's behavior in the parking area and recognizes patterns associated with criminal activities. The electronic architecture of the system includes high-resolution cameras and a real-time image processing system. Deep learning techniques are used to identify and classify specific events, such as unauthorized presence near vehicles. The user interface provides real-time visualization and automatic alerts in suspicious situations. Technical aspects, such as the selection of artificial vision algorithms, hardware configuration, and effective integration into the operational environment of "Confecciones GALAR," are addressed. The system evaluation demonstrates a 95% effectiveness in simulated and real situations, highlighting its potential to reduce risks associated with vehicle thefts with a low margin of error.Item Sistema inteligente de selección de cosecha en cultivo de pitahaya mediante visión artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-02) Gutiérrez Sánchez, Lenin Andrés; Castro Martin, Ana PamelaQuality control of export fruits in Ecuador, such as yellow pitahaya, is essential because it is widely accepted in both domestic and international markets. The technical standard NTE INEN 025 establishes criteria for the classification of fruit by degrees of ripeness for export and domestic consumption. The objective of this research work is the development of an artificial vision system for crop selection in pitahaya cultivation that allows the farmer to automatically select this fruit for export, avoiding human error. This system involves the use of the YOLOv5 model for deep learning in the agricultural context, which evaluates the ripening of pitahayas in three stages. The prototype is based on a cabin composed of primary elements for artificial vision, which is a high-resolution webcam and artificial white light. The system captures the image of six fruits and classifies them according to the degree of ripeness of the pitahaya in an estimated time of 4.48 seconds. A graphical interface programmed in Python is used for data processing. Tests were carried out to evaluate the functionality and verification of the prototype, obtaining a reliability level of 91.82%. These tests involved various pitahayas at different degrees of ripeness, which leads to the conclusion that the system is useful for farmers or persons in charge of grading yellow pitahayas destined for both export and domestic consumption.Item Sistema de riego inteligente de corto alcance para jardines a partir de visión artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-02) Jiménez Albán, Gissela Abigail; Castro Martin, Ana PamelaEl presente proyecto de investigación desarrolla un sistema de riego inteligente de corto alcance para jardines a partir de visión artificial. El objetivo principal es implementar un sistema de riego inteligente como una herramienta de riego eficiente para superficies de césped, que brinde reconocimiento del área y genere un riego acorde utilizando visión artificial. A diferencia de proyectos similares, no controla una red de aspersores o carros robot. El algoritmo de detección, se compone de un entrenamiento en yolov5 para reconocer el césped y utiliza Python para detectar contornos de césped y objetos. El sistema incorpora componentes electrónicos para lograr un riego eficiente y automatizado. En la adquisición de datos, se optó por el sensor de humedad de suelo FC-28 y la ESP32-CAM como cámara IP. En actuadores, se seleccionó una válvula solenoide normalmente cerrada para controlar el flujo de agua y servomotores TD-8120MG para direccionar el aspersor. La ESP32 controla el sistema, la comunicación, se establece mediante tecnología Wi-Fi con una base de datos local. El monitoreo, se realiza con una aplicación móvil desarrollada en Android Studio por compatibilidad de usuarios y culminar en un sistema integral que demuestra la aplicación práctica de la visión artificial en el riego inteligente para jardines. Se realizaron pruebas de funcionamiento del sistema, se logró una precisión en la detección del césped del 95,6%. Las pruebas realizadas en un jardín indicaron que el sistema inteligente puede ser beneficioso para el riego eficiente al identificar con precisión contornos de objetos y áreas verdes.Item Sistema de rehabilitación física para pacientes con lesiones de tobillo empleando visión artificial para la Unidad de Rehabilitación y Fisioterapia Gabo’s(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2023-08) Flores Achachi, Gilson German; Córdova Córdova, Edgar PatricioThis project is oriented to the implementation of a physical rehabilitation system for patients with ankle injuries using artificial vision in the Gabo's Rehabilitation and Physiotherapy Unit. For the virtual environment a Kinect V2.0 camera is used which captures the patient's movements and replicates them in the avatar that was developed in Unity, this avatar is presented on a monitor for the patient to observe their movements. The implemented system consists of a Jetson Nano minicomputer, which runs the Python program to analyze and process the patient's joint data. The angles formed by the three reference points are calculated and sent using the MQTT protocol to Node-Red, which is responsible for collecting the data and subsequently storing them in Influxdb. The database with the information of the ankle angles is generated, the respective configuration of Influxdb is made in Grafana, to observe the information stored in a graphical interface where the ankle angles generated by the ankles can be appreciated. The system implemented in the physiotherapy unit in comparison with the measurements obtained manually using a goniometer presents a reliability of 95.28% in total average, in obtaining the angles of the joint.