Ingeniería en Sistemas, Electrónica e Industrial

URI permanente para esta comunidadhttp://repositorio.uta.edu.ec/handle/123456789/1

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2022 - 20264

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Tema: search.filters.subject.CONTROL DE ACCESO

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Mostrando 1 - 4 de 4
  • Miniatura
    Item type:Ítem,
    Sistema de reconocimiento facial para control de acceso con Deep Learning en la Cooperativa de Ahorro y Crédito Sol en el área de cajas/bóveda.
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas Electrónica e Industrial. Carrera de Software., 2026-07-08)
    Yambay Miño, Santiago Estiven
    ;
    Balarezo López, Julio Enrique
    ;
    Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas Electrónica e Industrial. Carrera de Software.
    Access control to the cash register and vault areas of Cooperativa de Ahorro y Crédito SOL presented security vulnerabilities due to the reliance on manual logs, which generated risks of identity impersonation, recording errors, and lack of traceability. Therefore, the main objective of this research was to develop an automated access validation application based on facial recognition using convolutional neural networks. The study applied a field and applied methodology with a mixed approach, using surveys, interviews, and observation sheets addressed to a sample of 12 employees in order to diagnose the initial situation. As a result, the proposed application mitigated four high-severity risks and fulfilled seven operational requirements. Furthermore, when comparing two technological models, the transfer learning program implemented with the DeepFace library showed better comparative performance than the local supervised learning classifier, since it used 100 reference photographs per authorized employee, achieving an accuracy of 98 percent, a precision of 96.15 percent, and a sensitivity of 100 percent. In contrast, the local model required 300 photographs and obtained 55 percent accuracy with 89 false negatives. Thus, it was concluded that the implementation of neural networks reduced dependence on physical records and automated biometric verification in real time, providing a more secure, traceable, and auditable access management process for the financial institution.
  • Miniatura
    Item type:Ítem,
    Aplicación móvil utilizando Flutter para el control del acceso a la comunidad de Llimpe Grande
    (2025-07)
    Paredes Garcés Edison Ariel
    ;
    Jerez Mayorga Daniel Sebastián
    ;
    Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Tecnologías de la Información
    In the community of Llimpe Grande, located in Quero canton, Tungurahua province, there were significant limitations in access control management, as they relied on manual methods without electronic assistance or monitoring. This situation put residents at risk and made it difficult to respond to unexpected events or access to restricted areas. Faced with this problem, the objective of this project was to develop and implement a multiplatform mobile application to improve entry and exit procedures in the community. A mixed approach study was conducted using questionnaires and interviews with residents and local authorities to identify key operational needs and assess readiness for mobile technology adoption. The development of the application was carried out using the Mobile-D agile methodology, following a user-centered process. Tools such as Flutter were used for user interface design and programming logic, and Firebase served as the real-time data management system. The result is a mobile application that facilitates community access control by managing online records, verifying identities, assigning roles and sending location-based alerts via an emergency button. Its flexible design allows it to be adapted to other communities that require a technological solution focused on community management.
  • Miniatura
    Item type:Ítem,
    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 Danilo
    The 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.
  • Miniatura
    Item type:Ítem,
    Algoritmos de procesamiento de señales para el reconocimiento facial y de voz empleando redes neuronales
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Ingeniería en Electrónica y Comunicaciones, 2022-09)
    Orozco Analuiza, Carlos Alexander
    ;
    Pallo Noroña, Juan Pablo
    The present titling work deals with the development of an access control system through facial and voice recognition, for the authentication of people in a home. Currently, the levels of insecurity have increased and this has led to an increase in theft and damage to real estate due to the low level of security in a home. The device developed in this project is based on a bimodal access biometric, through machine learning and neural networks, a subfield of Artificial Intelligence. The system has two authentication methods: facial and voice, for which neural network models designed by the researcher were used with the stages of: database formation, image and audio processing, and neural network design. He implements two methods of facial and voice authentication to avoid identity theft, a camera is responsible for capturing the person's face and performing recognition through the neural network, if the user is registered, the microphone is activated to capture the key of access and process it through the neural network, to record the data a LAMP server is used where the system information and user notifications are stored through the Telegram application. This project is aimed at controlling the access of people to a home, avoiding the use of traditional authentication methods.