Tesis Telecomunicaciones

Permanent URI for this collectionhttp://repositorio.uta.edu.ec/handle/123456789/34848

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Subject: search.filters.subject.REDES NEURONALES

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    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 Patricio
    Emotions 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.
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    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 Patricio
    The 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.
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    Sistema automático para el control de la calidad del calzado mediante visión artificial
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Telecomunicaciones, 2023-03) Laura Nata, Ana Gabriela; Jurado Lozada, Marco Antonio
    The objective of the current work was to implement an automatic system for the quality control of footwear. It pretends to provide the industrial sector with a tool to improve the process to evaluate the quality, identifying the existence of defective shoes through the detection of failures using Deep Learning and Computer Vision algorithms. To continue, the implementation of the automatic quality control system for footwear starts from the selection of the electronic components to be part of the project, considering the hardware and software requirements that help the compatibility between them. Subsequent, the prototype has a conveyor belt that is responsible for moving the footwear to the artificial vision booth. This compartment has an ultrasonic sensor that detects if there is a product inside it and sends the signal to the Arduino to stop the band for an estimated time of 20 seconds. Then, the four cameras capture photos and detect any problem, later, save the results to the database. In fact, the development of the system presents a detection about of footwear defect types such as threads, paint, and glue using the YOLOv5 model, which is trained through a process that is responsible for learning the neural network. Finally, the results are presented through evaluation parameters of the failure detection system through confusion matrices and validation of the results of the network training, obtaining an accuracy of 94.7%. In addition, regarding the quality of the footwear, there is an average detection of 83.80% of coincidences in the recognition.