Tesis Telecomunicaciones
Permanent URI for this collectionhttp://repositorio.uta.edu.ec/handle/123456789/34848
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Item Arquitectura de sensores IoT para la redistribución de la carga de procesamiento mediante inteligencia artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Zurita Villalba Francisco Javier; Pallo Noroña Juan PabloThis project aims to implement an IoT sensor architecture for the redistribution of processing load using Artificial Intelligence (AI). Specific objectives include the analysis of available IoT architectures, the evaluation of AI algorithms for process redistribution, and the design of an optimized architecture. The analysis of IoT architectures revealed that technologies such as ESP-32 and communication protocols such as Heartbeat are crucial for scalability, energy efficiency, and handling large volumes of data. The integration of machine learning models, such as neural networks, improves decision making and real-time resource management. The choice of architecture must be aligned with the specific requirements of the application to ensure optimal and sustainable performance. Regarding AI algorithms, efficient solutions for resource management were identified, highlighting neural networks for their ability to balance load, reduce latency and minimize energy consumption. These algorithms enable dynamic adaptation to changing network conditions, improving the scalability and sustainability of IoT networks. The IoT sensor architecture design proved to be effective, achieving a balanced workload distribution and improving scalability. The proposal includes automatic recovery mechanisms and extensive testing to measure efficiency and monitor performance. In conclusion, the integration of AI in IoT networks provides a robust foundation for applications that require high efficiency and adaptability in dynamic environments.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 acceso automatizado con inteligencia artificial para el monitoreo de estudiantes y docentes en los talleres tecnológicos de la FISEI(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2025-02) Barba Proaño Silvia Guadalupe; Brito Moncayo Geovanni DaniloThe present research work focuses on the development of an automated access control system utilizing artificial intelligence for monitoring students and teachers of the faculty in the technological workshops of the FISEI at the Technical University of Ambato. The project encompasses everything from analyzing technical and operational requirements to implementing an intelligent system that integrates specialized hardware and software. Integration schemes for the system were designed, which include the use of biometric capture devices and facial recognition cameras connected to an artificial intelligence platform. This system enables automatic identification and registration of user entries, ensuring efficient control. Additionally, parameters were established to manage realtime alerts and generate detailed reports on user attendance and duration in the workshops. The implementation of the system included the development of machine learning algorithms to optimize facial recognition and user authentication, as well as the integration of a user-friendly interface that facilitates its use by administrative personnel. Functional tests were conducted in both simulated and real environments, verifying the accuracy of recognition and the robustness of the system under various operational conditions. Finally, the system was validated through pilot tests in the technological workshops, demonstrating its effectiveness in access management and continuous monitoring, contributing to security, and optimizing the use of available resources at FISEI.Item Prototipo de un sistema de semaforización inteligente para la optimización del tráfico vehicular empleando inteligencia artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-08) Coello Ibañez, Antony Josue; Cuji Rodriguez, Julio EnriqueThis research project develops a prototype of an intelligent traffic light system to optimize vehicular traffic using an artificial intelligence model. The methodology is divided into four stages. In the first stage, vehicle flow data was collected using four cameras located at the intersection of Rodrigo Pachano Avenue and Montalvo Street in the city of Ambato. The second stage consisted of vehicle detection and counting using the YOLOv5 model and the SORT tracking algorithm, which allowed for an accurate analysis of vehicle flow. In the third stage, a data storage system with MySQL was implemented to record the number of detected vehicles. In addition, an adaptive control algorithm was developed to autonomously manage traffic light states according to the amount of traffic. Finally, in the fourth stage, a graphical interface was designed with Tkinter to supervise and control the system, and traffic was simulated with the Pygame library. A prototype using 10 mm LEDs and an ESP32 microcontroller was also integrated, which communicates with the system via the WebSocket protocol to manage the operation of the traffic lights. The results show that the system significantly improves vehicle flow, increasing traffic management capacity by 182.06%. This translates into a significant improvement in the quality of life of citizens by reducing the time needed to travel between different parts of the city.Item 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 detección de situaciones delictivas en establecimientos comerciales usando inteligencia artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-02) Sandoval Robayo, Erick Fabian; Castro Martin, Ana PamelaThe constant increase in insecurity in Ecuador is a phenomenon that manifests annually through a variety of crimes, with armed robberies in commercial establishments being the main focus of the present research. In this context, a study was carried out with the purpose of developing an artificial intelligence-based system for the detection of criminal situations in commercial premises. The primary objective of this system is to generate alerts in the face of potential assaults, with the aim of expediting the response from the relevant authorities. This system was built in three stages: data acquisition, processing, and visualization. In the acquisition phase, a DS-2CD2147G2 IP camera with 5 megapixels is used to capture real-time images. The captured data is transmitted to the NVIDIA JETSON NANO microcomputer for processing. The system was developed in Python, and Yolov8 was chosen as the artificial vision algorithm, responsible for processing the data and recognizing indicators associated with armed robbery. The visualization of the results and generated alerts is sent through Telegram, providing images of the crime, a detailed description of the situation, and the location. It is relevant to highlight that the information is processed and transmitted in real-time, ensuring an efficient response to potential criminal incidents. The system achieved an effectiveness of 84% after conducting the corresponding tests.Item Sistema de detección de emociones mediante el análisis de indicadores faciales empleando inteligencia artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2024-02) Fiallos Valladares, Daniel Rodrigo; Córdova Córdova, Edgar PatricioEmotions are essential in several areas of life, however, their detection and understanding can become complicated, this will lead to misunderstandings and hinder communication, negatively impacting people's social relationships. In this context, the research was carried out with the aim of implementing an emotion detection system by analyzing facial indicators using artificial intelligence and visualizing the emotions that a person may have for a period of time. The system is divided into three stages, starting with the acquisition and processing of data through the activation and use of a webcam, supported by the OpenCV library for image processing techniques. The training phase involves the development of a deep learning model using Convolutional Neural Networks from facial recognition using FaceNet, perfected its design through data fitting, the architecture of the neural network focused on the extraction and learning of relevant features. Finally, the storage and visualization stage, the data is processed by the Jetson Nano and sent to a web hosting environment that receives the results and transmits them to the administrative interface for the management and visualization of the user's emotion report. The test results indicated that the system captured frames every 4 seconds, and boasts a classification accuracy of 92%, considering that the model has an outstanding ability to classify emotions in real time.Item Sistema de reconocimiento de indicadores de somnolencia mediante inteligencia artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2023-09) Altamirano Guerra, Mayra Dennise; Córdova Córdova, Edgar PatricioThe lack of sleep not only affects safety but also increases the risk of other health problems. Sleepiness, mainly caused by sleep deprivation, negatively impacts daily human functions, including reaction time, performance, and attention, leading to a decrease in alertness and concentration. In this context, a study was conducted with the aim of implementing an artificial intelligence-based system to recognize signs of sleepiness and issue alerts to individuals in that state, in order to restore their attention and allow them to continue with their activities. The system consists of four stages: acquisition, processing, training, and visualization. In the acquisition stage, the Pi Noir V2 camera was used to capture real-time images or videos. The acquired data was sent to the NVIDIA Jetson Nano for processing. Neural networks were used to train a model capable of accurately recognizing indicators of sleepiness. For practical use and deployment, the system was implemented in a cloud hosting environment. The system's algorithm was developed in Python due to the variety of available libraries, and the OpenCV library was used for image processing due to its wide range of commands. Test results showed that the system processes and sends information at an average time of 2.38 milliseconds for real-time video.Item Renderizado de coloración capilar utilizando inteligencia artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2023-09) Balladares Armendariz, Johanna Elizabeth; Manzano Villafuerte, Víctor SantiagoThe importance in the world of hair coloring has become a trend and people who decide to change their hair color go to an esthetics where they usually ask for recommendations without knowing the final result. The purpose of creating a hair color rendering application is to provide a digital tool that shows color recommendations through a process of hair colorimetry by previewing a hair tone virtually. The application collects three physical characteristics: eye, skin and hair color that are stored in a MySQL database. The Support Vector Machine algorithm uses the TensorFlow and Keras libraries for the hair colorimetry process to predict hair color. The DeepLabv3 model segments the hair of a loaded image resulting in a mask. Finally, a process that couples the results of the previous algorithms is applied to display the customized hair color using the RGB model. The evaluation of the hair colorimetry algorithm uses cross-validation and obtains a value of 0.78 in training and testing, being able to predict the color adequately. The quality metrics for the DeepLabv3 model present average results: recall 98%. Dice Coefficient 98% and IoU 97%, considering that the model has good ability to segment hair in images. The application offers benefits of personalized and realistic counseling, providing a favorable change in the user's appearance by combining theoretical and technological creativity.Item Optimización de trayectorias en plataformas robóticas móviles usando técnicas de inteligencia artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2023-08) Soto Rodríguez, Andrés David; Manzano Villafuerte, Víctor SantiagoAs time progresses, the growth of the robotics industry is exponential and companies that seek to automate their processes do so to optimize resources, such as the time spent. The use of artificial intelligence is one of the current solutions for the optimization of various processes, by allowing learning in supervised environments, where robotic instrumentation tends to minimize the margin of error in the future thanks to implementations of AI algorithms. In the present project, a solution for the optimization of trajectories is exposed using as support an AI algorithm of reinforcement learning with neural networks implemented in the omnidirectional robotic platform of the KUKA youBot robot to move from one point to another avoiding obstacles presented in its path. The AI algorithm used for learning is Deep Q Network (DQN), this algorithm consists of deep neural networks to maximize some notion of rewards in a cumulative way. whereby means of a Hokuyo lidar motion sensor, placed in the front part of the robotic platform, they are acquired. You sample data from an environment, which is processed in the algorithm to be recognized as collisions or rewards. As the rewards learned by the algorithm are greater. the possibility of collision with an obstacle decreases, moving the robotic platform towards an obstacle-free zone. The programming language of this DON algorithm is based on Python 2, this language works together with ROS (robotic operating system) and allows to know, in an understandable way, how the execution of the movement is carried out through the publication and subscription to the topics corresponding to the robotic platform, thus facilitating the calibration of the parameters used in it.