Ingeniería en Sistemas, Electrónica e Industrial
Permanent URI for this communityhttp://repositorio.uta.edu.ec/handle/123456789/1
Browse
31 results
Search Results
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 Desarrollo de un sistema para la generación de contenido audiovisual utilizando recursos basados en inteligencia artificial y su incidencia en los tiempos de producción(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Software, 2025-02) Galarza Tamayo Daniel Gerardo; Jara Moya Santiago DavidThe production of educational videos poses significant challenges in terms of time and quality. This project developed an automated system to optimize audiovisual content creation using artificial intelligence tools. Key technologies include Microsoft Azure Text-to-Speech for voice synthesis, GPT-4 for scriptwriting, FFMPEG for programmatic video editing, and Electron JS for developing a scalable desktop application with a user-friendly interface. The system automates critical processes such as slide creation, narration generation, and scriptwriting, achieving a 67.43% reduction in production time. It addresses the needs of Ingedemy instructors, who faced bottlenecks in stages like editing and recording. Results validated significant improvements in efficiency and quality, emphasizing the system’s ability to personalize and standardize content. This project demonstrates that integrating these technological tools not only reduces manual workloads but also fosters the adoption of innovative solutions to meet the growing demand for high-quality educational content.Item Aplicación del algoritmo de aprendizaje por refuerzo Q-Learning para la generación de trayectorias óptimas en plataformas robóticas(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Tecnologías de la Información, 2024-09) Moya Quinatoa, Kevin Alejandro; Álvarez Mayorga, Edison HomeroTechnological innovation is fundamental for business development and efficiency in Industry 4.0, which demands staying updated with the latest technologies. The rapid growth in the use of artificial intelligence (AI) offers high benefits and low operational costs, creating more stable and automated work environments. However, one of the greatest challenges in applying AI is the complexity of trajectory planning for mobile robots, as their behavior varies according to the scenario and algorithms used, making it difficult to compare learning and performance between different methods. This research, as part of the project titled "Use of Deep Learning Techniques for Trajectory Planning of Mobile Robots within an Industrial Process," developed a trajectory planning algorithm using Q-learning and a multi-agent system that collaborate in decision-making. This algorithm employs odometry and laser sensor signals to manage states and rewards. Tests were conducted in a ROS simulation environment and replicated in a real-world scenario with the KUKA Youbot robot to implement the algorithm's actions. The simulated environment recreates a space with obstacles, where a master agent evaluates the decisions made by the odometry agent and the laser sensor agent to autonomously make a final decision. This contributes to the comparison of AI algorithms in terms of efficiency and effectiveness within the mentioned project, laying a foundation for future improvements in trajectory planning on mobile platforms.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 Análisis de la capacidad de las frecuencias 2.4ghz y 5ghz utilizando inteligencia artificial para la corrección de fallos en el servicio de internet doméstico proporcionado por la empresa Fiber Store(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Tecnologías de la Información, 2024-02) Silva Segura, Bryann Sebastian; Jara Moya, Santiago DavidThis project establishes the relationship between Wi-Fi networks and artificial intelligence, after analyzing the information collected from the review of previous research works and the instruments of data acquisition applied to customers and technicians, some problems in the home networks were detected such as intermittent Wi-Fi signal, low connection speed, high packages loss rate, saturated bandwidth, high jitter and latency for the 2.4 GHz and 5 GHz frequency. The prediction model “Decision Tree Algorithm for Regression and Classification” is used because it was the model that best fitted the software requirements. This model allows measurements, network analysis, optimal values predictions, and failure detection carried out through the comparison of data obtained in the measurement and the values predicted by the algorithm, contemplating the conditions and limits established based on the frequency used and the speed plan contracted by the customers together. The developed tool provides an option to gather reports once the scan is completed and it predicts the correct location of the router in the customer's home. A technical guide is included, which contains methods to mitigate the failures detected on Wi-Fi networks. Among the proposed corrections there are change of channel, device management and load balancing, which allows the network to be supervised in a technical way, reducing the time used by each technical support provided to customers.Item Estación de paletizado y clasificación de objetos mediante un brazo robótico Epson controlado por inteligencia artificial(Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Ingeniería Industrial, 2024-02) Cristian Rodrigo, Muñoz Vargas; Salazar Logroño, Franklin WilfridoFrom the Industrial Revolution, which introduced large-scale machines and equipment that reduced working times, the production capacity of industries improved. However, errors caused by human intervention led to thousands of problems each year. Due to these factors, the study aimed to control a palletizing and object sorting station through artificial intelligence to reduce the error percentage in such processes. This was based on three stages: first, it was identified that the S7-1200 PLC allows manipulation of the AI-controlled autonomous system to accurately determine the correct location of the entering object; second, through OPC communication, data was sent and received in real-time displayed on the control panel according to the processing time; and through operational tests, it was determined that there is an approximate error of 11%. Finally, it was concluded that the system is capable of properly placing 9 out of 10 objects entering the sorting system, using an average time of 36.35 s between inputs and outputs produced during the development of the study.