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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Martinez, Fredy
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Computer Science & IT Education Electrical & Electronics Engineering Engineering

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A novel visual tracking scheme for unstructured indoor environments Martinez, Fredy; Montiel, Holman; Martinez, Fernando
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6216-6227

Abstract

In the ever-expanding sphere of assistive robotics, the pressing need for advanced methods capable of accurately tracking individuals within unstructured indoor settings has been magnified. This research endeavours to devise a realtime visual tracking mechanism that encapsulates high performance attributes while maintaining minimal computational requirements. Inspired by the neural processes of the human brain’s visual information handling, our innovative algorithm employs a pattern image, serving as an ephemeral memory, which facilitates the identification of motion within images. This tracking paradigm was subjected to rigorous testing on a Nao humanoid robot, demonstrating noteworthy outcomes in controlled laboratory conditions. The algorithm exhibited a remarkably low false detection rate, less than 4%, and target losses were recorded in merely 12% of instances, thus attesting to its successful operation. Moreover, the algorithm’s capacity to accurately estimate the direct distance to the target further substantiated its high efficacy. These compelling findings serve as a substantial contribution to assistive robotics. The proficient visual tracking methodology proposed herein holds the potential to markedly amplify the competencies of robots operating in dynamic, unstructured indoor settings, and set the foundation for a higher degree of complex interactive tasks.
Optimizing EV charging stations: a simulation-based approach to performance and grid integration Sanchez Diaz, William Fabián; Vargas, Jonatan Tolosa; Martinez, Fredy
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.8027

Abstract

This study addresses the optimization of electric vehicle (EV) charging stations, focusing on enhancing performance and grid integration through a comprehensive simulation approach. By employing advanced simulation tools in Simulink® and MATLAB®, alongside electrical installation planning with SIMARIS®, we meticulously analyze the charging process, infrastructure requirements, and their implications on the power grid. Our results demonstrate significant improvements in charging station efficiency and reliability, highlighting the effectiveness of our proposed control strategies and harmonic mitigation techniques. Notably, the integration of renewable energy sources emerges as a pivotal factor in reducing operational costs and carbon emissions, furthering the sustainability of EV charging solutions. The research delineates the environmental benefits, emphasizing the reduction of greenhouse gas emissions and enhancement of urban air quality, pivotal in the global shift towards cleaner transportation modes. This work contributes valuable insights into the design and grid integration of EV charging stations, offering a scalable model for future infrastructure development. It serves as a critical resource for engineers, policymakers, and stakeholders in the realm of electric mobility, advocating for a strategic transition to EVs supported by robust and efficient charging infrastructure.
From concept to application: building and testing a low-cost light detection and ranging system for small mobile robots using time-of-flight sensors García, Andrés; Díaz, Mauricio; Martínez, Fredy
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp292-302

Abstract

Advancements in light detection and ranging (LiDAR) technology have significantly improved robotics and automated navigation. However, the high cost of traditional LiDAR sensors restricts their use in small-scale robotic projects. This paper details the development of a low-cost LiDAR prototype for small mobile robots, using time-of-flight (ToF) sensors as a cost-effective alternative. Integrated with an ESP32 microcontroller for real-time data processing and Wi-Fi connectivity, the prototype facilitates accurate distance measurement and environmental mapping, crucial for autonomous navigation. Our approach included hardware design and assembly, followed by programming the ToF sensors and ESP32 for data collection and actuation. Experiments validated the accuracy of the ToF sensors under static, dynamic, and varied lighting conditions. Results show that our low-cost system achieves accuracy and reliability comparable to more expensive options, with an average mapping error within acceptable limits for practical use. This work offers a blueprint for affordable LiDAR systems, expanding access to technology for research and education, and demonstrating the viability of ToF sensors in economical robotic navigation and mapping solutions.
Flame analysis and combustion estimation using large language and vision assistant and reinforcement learning Martınez, Fredy; Rendón, Angélica; Penagos, Cristian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1853-1862

Abstract

In this study, we present an advanced approach for flame analysis and combustion quality estimation in carbonization furnaces utilizing large language and vision assistant (LLaVA) and reinforcement learning from human feedback (RLHF). The traditional methods of estimating combustion quality in carbonization processes rely heavily on visual inspection and manual control, which can be subjective and imprecise. Our proposed methodology leverages multimodal AI techniques to enhance the accuracy and reliability of flame similarity measures. By integrating LLaVA’s high-resolution image processing capabilities with RLHF, we create a robust system that iteratively improves its predictive accuracy through human feedback. The system analyzes real-time video frames of the flame, employing sophisticated similarity metrics and reinforcement learning algorithms to optimize combustion parameters dynamically. Experimental results demonstrate significant improvements in estimating oxygen levels and overall combustion quality compared to conventional methods. This approach not only automates and refines the combustion monitoring process but also provides a scalable solution for various industrial applications. The findings underscore the potential of AI-driven techniques in advancing the precision and efficiency of combustion systems.
Integrating low-cost vision for autonomous tracking in assistive robots Martínez, Fredy; Martínez, Fernando; Penagos, Cristian
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9242

Abstract

This study presents the implementation of a real-time tracking system for the ARMOS TurtleBot, a robot designed for assistive applications in domestic environments. The system integrates two OmniVision 7670 (OV7670) camera modules positioned 7 cm apart to emulate human-like stereoscopic vision, enabling depth perception and three-dimensional object tracking. An embedded system platform 32-bit (ESP32) microcontroller captures and processes images from both cameras, calculates disparities, and transmits data to a Raspberry Pi via WebSockets. The Raspberry Pi, equipped with robot operating system (ROS), performs further analysis using open computer vision (OpenCV) and visualizes results in real-time with ROS visualization (RViz), allowing the robot to autonomously track moving objects such as humans or pets. Key optimizations, including image resolution reduction and data filtering, were implemented to enhance processing efficiency within the hardware constraints. The proposed approach demonstrates the feasibility of low-cost, real-time object tracking in assistive robotics, highlighting its potential for applications that require humanrobot interaction in dynamic indoor settings. This work contributes to the field by providing a practical solution for integrating stereoscopic vision and real-time decision-making capabilities into small-scale robots, promoting further research and development in affordable robotic assistance systems.
Benchmarking spectral handoff rate performance in cognitive wireless networks with real multi-user access Hernández, Cesar; Giral, Diego; Martínez, Fredy
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp190-201

Abstract

Cognitive radio (CR) has proven to be an excellent alternative to the problem of inefficient spectrum use in wireless networks. However, the vast majority of proposals found in the current literature are restricted to the access of a single secondary user (SU) to the network, and the few proposals with multiple access do not take into account the access of other primary users (PUs) during the opportunistic transmission of the SU. The objective of this work is to perform a comparative evaluation of the spectral handoff (SH) rate in cognitive wireless networks with multi-user access in an environment with other PUs interacting. To carry out this evaluation, four SH models with better performance were selected: deep learning (DL), feedback fuzzy analytic hierarchy process (FFAHP), simple additive weighting (SAW), and Naïve Bayes (NB), which were validated according to the metric of the number of total handoffs, under four scenarios given by the combination of the following parameters: low spectral availability, high spectral availability, active presence of others SUs, and passive presence of others SUs. The results show that each model performs well according to the scenario in which it is executed, suggesting an adaptive multi-model as a proposal.
Enhancing urban cyclist safety through integrated smart backpack system Gómez, Sergio; Mejía, Daniel; Martínez, Fredy
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp118-130

Abstract

The increasing adoption of bicycles as a sustainable mode of urban transportation has underscored the urgent need for enhanced safety measures for cyclists. This paper presents the development and implementation of an integrated smart backpack system designed to improve the safety and visibility of urban cyclists. The system leverages advanced technologies, including the ESP32 microcontroller, GPS modules, proximity sensors, and LED lighting, to create a semiautomatic solution that adapts to environmental conditions and cyclist behavior in real-time. Extensive testing under various conditions, including low visibility and adverse weather, demonstrated the system’s reliability in enhancing cyclist visibility and reducing accident risks. The smart backpack also features a userfriendly mobile application, providing real-time data on speed, distance, and location, which further contributes to rider safety. The results indicate significant potential for this technology to be widely adopted, offering a practical and effective solution to the growing safety concerns of urban cyclists. This work not only advances the field of wearable safety technologies but also sets the foundation for future innovations in smart transportation systems, contributing to safer and more sustainable urban mobility
Enhancing learning outcomes through course redesign using self-assessment and inquiry models Martinez, Fredy; Hernández, César; Giral, Diego
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i5.32215

Abstract

This study addresses the challenge of enhancing learning outcomes in propaedeutic education by redesigning an undergraduate deep learning course. To achieve this, the self-assessment and quality model (SQM) was combined with the community of inquiry (CoI) framework, which emphasizes cognitive, social, and teaching presence in online education. The redesigned course aligns with the guidelines of the Colombian Ministry of National Education and incorporates continuous feedback from students. Initial implementation led to improved student performance but revealed gaps in perceived learning experiences. Iterative adjustments were made to the course design based on CoI survey results, particularly focusing on increasing teacher involvement. The findings demonstrate that integrating SQM with a responsive, design-based approach can significantly improve learning outcomes and student satisfaction. This study highlights the importance of dynamic course design in higher education and offers a replicable model for other institutions.
Co-Authors Díaz, Mauricio García, Andrés Giral, Diego Gómez, Sergio Hernandez, Cesar Martinez, Fernando Mejía, Daniel Montiel, Holman Penagos, Cristian Rendón, Angélica Sanchez Diaz, William Fabián Vargas, Jonatan Tolosa