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Percorrer por autor "Saraiva, Luzia"

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  • Performance Comparison of Redis, Memcached, MySQL, and PostgreSQL: A Study on Key-Value and Relational Databases
    Publication . Almeida, Dany; Lopes, Maria; Saraiva, Luzia; Abbasi, Maryam; Martins, Pedro; Silva, José; ANTUNES VAZ, PAULO JOAQUIM
    This paper investigates and compares the performance of relational databases (MySQL and PostgreSQL) and key-value databases (Memcached and Redis) under various test loads. The study utilizes the Yahoo! Cloud Serving Benchmark to simulate diverse workloads and measure the behavior of these databases in different scenarios. The primary focus is on evaluating run time, throughput, and average latency metrics to understand how each database type handles varying thread levels and workload intensity. The outcomes of this research provide valuable insights into the scalability and efficiency aspects of relational databases. By conducting a comprehensive performance comparison, the study aims to assist database designers and developers in selecting the most suitable database option based on specific requirements. The findings contribute to informed decision-making regarding the choice between key-value and relational databases in various data storage scenarios.
    2023Texto Acesso restrito Ver mais
  • Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
    Publication . Silva, José; Coelho, Pedro; Saraiva, Luzia; Martins, Pedro; López-Rivero, Alfonso
    Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses’ video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system’s functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems.
    2024Texto Acesso aberto Ver mais