Pub Date : 2026-01-06DOI: 10.1109/TLA.2026.11334045
{"title":"Table of Contents January 2026","authors":"","doi":"10.1109/TLA.2026.11334045","DOIUrl":"https://doi.org/10.1109/TLA.2026.11334045","url":null,"abstract":"","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"24 1","pages":"1-1"},"PeriodicalIF":1.3,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1109/TLA.2025.11231192
Francisco Antonio Mejía Domínguez;Manuel A. Quintana;Ram´on R. Palacio;Gilberto Borrego;Samuel Gonz´alez-L´opez
In agile software development, user stories are a common tool for capturing functional requirements, valued for their simplicity and user-centric approach. Nevertheless, their inherent informality can introduce ambiguity, especially when acceptance criteria are lacking or poorly defined. Such ambiguity may hinder early design activities, including the development of UML robustness diagrams. This study investigates the impact of acceptance criteria and developer experience on the accuracy and efficiency of robustness diagram construction.A controlled experiment was conducted with thirty participants, divided into newbie and advanced developers, who were tasked with creating robustness diagrams from user stories both with and without acceptance criteria. Performance was assessed based on task duration and diagram completeness. Statistical analysis (Mann-Whitney U test) revealed that acceptance criteria significantly reduce errors and improve completion times, particularly among newbie developers. Experienced developers consistently produced higher-quality diagrams more efficiently, underscoring the role of expertise as a moderating factor.The findings suggest that well-specified acceptance criteria mitigate ambiguity, facilitating more accurate requirements interpretation and improving early design outcomes. Additionally, the results highlight the value of structured requirement practices in agile methodologies, especially in teams with varying levels of experience. This research advances the understanding of how requirement clarity and developer expertise collectively influence software modeling, providing actionable recommendations for enhancing agile design processes.
{"title":"Role of Acceptance Criteria and Developer Expertise in Enhancing the Quality of Robustness Diagrams in Agile Software Development","authors":"Francisco Antonio Mejía Domínguez;Manuel A. Quintana;Ram´on R. Palacio;Gilberto Borrego;Samuel Gonz´alez-L´opez","doi":"10.1109/TLA.2025.11231192","DOIUrl":"https://doi.org/10.1109/TLA.2025.11231192","url":null,"abstract":"In agile software development, user stories are a common tool for capturing functional requirements, valued for their simplicity and user-centric approach. Nevertheless, their inherent informality can introduce ambiguity, especially when acceptance criteria are lacking or poorly defined. Such ambiguity may hinder early design activities, including the development of UML robustness diagrams. This study investigates the impact of acceptance criteria and developer experience on the accuracy and efficiency of robustness diagram construction.A controlled experiment was conducted with thirty participants, divided into newbie and advanced developers, who were tasked with creating robustness diagrams from user stories both with and without acceptance criteria. Performance was assessed based on task duration and diagram completeness. Statistical analysis (Mann-Whitney U test) revealed that acceptance criteria significantly reduce errors and improve completion times, particularly among newbie developers. Experienced developers consistently produced higher-quality diagrams more efficiently, underscoring the role of expertise as a moderating factor.The findings suggest that well-specified acceptance criteria mitigate ambiguity, facilitating more accurate requirements interpretation and improving early design outcomes. Additionally, the results highlight the value of structured requirement practices in agile methodologies, especially in teams with varying levels of experience. This research advances the understanding of how requirement clarity and developer expertise collectively influence software modeling, providing actionable recommendations for enhancing agile design processes.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 12","pages":"1211-1218"},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a novel structural test strategy for a single MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) source designed for Fast Field-Cycling Nuclear Magnetic Resonance (FFC-NMR) systems. The proposed methodology enables in-field fault detection during idle intervals or before experiment initiation, a critical step to ensure the reliability and validity of the experimental outcomes. The circuit under test is divided into two sections: low-power and high-power. Each one is evaluated using tailored analog testing techniques: OBT (Oscillation-Based Test) and direct current testing are applied to the low-power section, while transient analysis with DTW (Dynamic Time Warping) is used for fault detection in the high-power section. This approach achieves high fault coverage 93.7% for the low-power section and 100% for the high-power section without requiring complex signal processing. The effectiveness of the method is validated through simulation studies complement-ed by experimental fault injection on a scaled-down prototype. The results demonstrate that this test strategy significantly enhances system reliability, offering a valuable contribution to the development of more robust and maintainable FFC-NMR instrumentation for scientific and industrial applications.
{"title":"A Test Strategy for a Current Source Designed for Fast Field-Cycling Nuclear Magnetic Resonance","authors":"Delfina Vélez Ibarra;Gonzalo Vodanovic;Agustín Laprovitta;Gabriela Peretti;Eduardo Romero;Esteban Anoardo","doi":"10.1109/TLA.2025.11231230","DOIUrl":"https://doi.org/10.1109/TLA.2025.11231230","url":null,"abstract":"This article presents a novel structural test strategy for a single MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) source designed for Fast Field-Cycling Nuclear Magnetic Resonance (FFC-NMR) systems. The proposed methodology enables in-field fault detection during idle intervals or before experiment initiation, a critical step to ensure the reliability and validity of the experimental outcomes. The circuit under test is divided into two sections: low-power and high-power. Each one is evaluated using tailored analog testing techniques: OBT (Oscillation-Based Test) and direct current testing are applied to the low-power section, while transient analysis with DTW (Dynamic Time Warping) is used for fault detection in the high-power section. This approach achieves high fault coverage 93.7% for the low-power section and 100% for the high-power section without requiring complex signal processing. The effectiveness of the method is validated through simulation studies complement-ed by experimental fault injection on a scaled-down prototype. The results demonstrate that this test strategy significantly enhances system reliability, offering a valuable contribution to the development of more robust and maintainable FFC-NMR instrumentation for scientific and industrial applications.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 12","pages":"1335-1345"},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231230","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1109/TLA.2025.11231213
Carlos Reis;Carlos Tojeiro;Thiago Lucas;Kelton Costa
The increase in the incidence of cyberattacks, especially through the use of complex mechanisms for exploiting vulnerabilities, such as malware obfuscation, has driven the adoption of Machine Learning (ML) techniques in cybersecurity. This study investigates the application of Federated Learning (FL), a decentralized approach that preserves data privacy and overcomes challenges in transferring large volumes of information. Two labeled datasets were used, CIC-MalMem-2022 and Malware Detection Dataset, along with two FL frameworks, Flower Framework and TensorFlow Federated. A decentralized model based on a Linear Neural Network (LNN) with federated averaging (FedAvg) was compared to a centralized model using a Recurrent Neural Network (RNN) in supervised binary classifications of malware. The results demonstrate high accuracy across all analyzed scenarios, highlighting the outcomes obtained in centralized training for the CIC-Malware dataset, achieving an accuracy of 0.99, precision of 1.0, and recall of 0.99, emphasizing the potential of FL in cybersecurity.
{"title":"Detection of Obfuscation Malware: A Federated Transfer Learning-based Approach with Hybrid Neural Networks","authors":"Carlos Reis;Carlos Tojeiro;Thiago Lucas;Kelton Costa","doi":"10.1109/TLA.2025.11231213","DOIUrl":"https://doi.org/10.1109/TLA.2025.11231213","url":null,"abstract":"The increase in the incidence of cyberattacks, especially through the use of complex mechanisms for exploiting vulnerabilities, such as malware obfuscation, has driven the adoption of Machine Learning (ML) techniques in cybersecurity. This study investigates the application of Federated Learning (FL), a decentralized approach that preserves data privacy and overcomes challenges in transferring large volumes of information. Two labeled datasets were used, CIC-MalMem-2022 and Malware Detection Dataset, along with two FL frameworks, Flower Framework and TensorFlow Federated. A decentralized model based on a Linear Neural Network (LNN) with federated averaging (FedAvg) was compared to a centralized model using a Recurrent Neural Network (RNN) in supervised binary classifications of malware. The results demonstrate high accuracy across all analyzed scenarios, highlighting the outcomes obtained in centralized training for the CIC-Malware dataset, achieving an accuracy of 0.99, precision of 1.0, and recall of 0.99, emphasizing the potential of FL in cybersecurity.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 12","pages":"1249-1260"},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1109/TLA.2025.11231212
Fernando Lucio-Reyna;Ricardo Tapia-Herrera;Tonatiuh Hernández-Cortés;Israel Lizardo-Parra;Jesús A. Meda-Campaña
The concept of modularity in mobile robots has been aimed to enhance their capabilities, including functionality, adaptability, or robustness. However, modularity often involves the complex design of robotic modules. In this context, this paper introduces the development of a reconfigurable modular mobile robot in differential drive configuration, which main advantage is the generation of an omnidirectional mobile robot when at least two modules are coupled, in consequence, mobility and load capacity are increased. Each robotic module comprises a frame, two Mecanum wheels, a controller, and an active connection mechanism designed to simultaneously perform two functions: 1) locking a module-to-module connection, and 2) automatic reconfiguration of the robot's architecture by lifting a freely rotating spherical wheel. Owing to the integration of Mecanum wheels, the kinematic analysis of differential drive configuration considers the influence of a rollers' angles relative to the robot frame, allowing the model to be extended to a system with n coupled modules. To validate the kinematic models, a multibody simulation was conducted using Simscape Multibody LinktextsuperscriptTM. Finally, a prototype is presented to showcase the modularity capability, including the docking and undocking process, as well as the omnidirectional mobility, even in the presence of backlash.
{"title":"Development of an Omnidirectional Modular Robot","authors":"Fernando Lucio-Reyna;Ricardo Tapia-Herrera;Tonatiuh Hernández-Cortés;Israel Lizardo-Parra;Jesús A. Meda-Campaña","doi":"10.1109/TLA.2025.11231212","DOIUrl":"https://doi.org/10.1109/TLA.2025.11231212","url":null,"abstract":"The concept of modularity in mobile robots has been aimed to enhance their capabilities, including functionality, adaptability, or robustness. However, modularity often involves the complex design of robotic modules. In this context, this paper introduces the development of a reconfigurable modular mobile robot in differential drive configuration, which main advantage is the generation of an omnidirectional mobile robot when at least two modules are coupled, in consequence, mobility and load capacity are increased. Each robotic module comprises a frame, two Mecanum wheels, a controller, and an active connection mechanism designed to simultaneously perform two functions: 1) locking a module-to-module connection, and 2) automatic reconfiguration of the robot's architecture by lifting a freely rotating spherical wheel. Owing to the integration of Mecanum wheels, the kinematic analysis of differential drive configuration considers the influence of a rollers' angles relative to the robot frame, allowing the model to be extended to a system with n coupled modules. To validate the kinematic models, a multibody simulation was conducted using Simscape Multibody LinktextsuperscriptTM. Finally, a prototype is presented to showcase the modularity capability, including the docking and undocking process, as well as the omnidirectional mobility, even in the presence of backlash.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 12","pages":"1163-1171"},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1109/TLA.2025.11231228
Camilo Santos;Maria Bravo;Carlos Fajardo
The Sequential Organ Failure Assessment (SOFA) score is a widely employed scoring system in clinical practice for predicting mortality in patients with sepsis. The integration of machine learning techniques into clinical scoring systems has enhanced predictive performance; however, many of these models function as "black boxes," offering limited interpretability regarding the contribution of individual clinical variables to the final prediction.This study aims to develop an interpretable machine learning model based on the SOFA score, leveraging its most relevant variables, to predict mortality in Intensive Care Unit (ICU) patients with sepsis using a multicenter validation.The model was trained on data from 15,100 ICU patients in the MIMIC-IV v3.0 dataset and externally validated on 8,201 patients from the eICU v2.0 dataset. The application of an Odds Ratio analysis enabled the identification of the SOFA components demonstrating the strongest association with mortality. This approach facilitated the reduction of variables while enhancing the performance of the model.The interpretability of the model was further addressed by employing SHapley Additive exPlanations (SHAP) values to elucidate the contribution of each variable to the model's predictions. The resulting model demonstrated superior predictive accuracy in comparison to the conventional SOFA score, while exhibiting enhanced efficiency and transparency. This interpretable machine learning model, which is based on a SOFA variant, has the potential to support the earlier and more precise intervention required in the clinical management of sepsis ICU patients.
{"title":"Interpretable Machine Learning Model Based on SOFA Score for ICU Sepsis Mortality Prediction with Multicenter Validation","authors":"Camilo Santos;Maria Bravo;Carlos Fajardo","doi":"10.1109/TLA.2025.11231228","DOIUrl":"https://doi.org/10.1109/TLA.2025.11231228","url":null,"abstract":"The Sequential Organ Failure Assessment (SOFA) score is a widely employed scoring system in clinical practice for predicting mortality in patients with sepsis. The integration of machine learning techniques into clinical scoring systems has enhanced predictive performance; however, many of these models function as \"black boxes,\" offering limited interpretability regarding the contribution of individual clinical variables to the final prediction.This study aims to develop an interpretable machine learning model based on the SOFA score, leveraging its most relevant variables, to predict mortality in Intensive Care Unit (ICU) patients with sepsis using a multicenter validation.The model was trained on data from 15,100 ICU patients in the MIMIC-IV v3.0 dataset and externally validated on 8,201 patients from the eICU v2.0 dataset. The application of an Odds Ratio analysis enabled the identification of the SOFA components demonstrating the strongest association with mortality. This approach facilitated the reduction of variables while enhancing the performance of the model.The interpretability of the model was further addressed by employing SHapley Additive exPlanations (SHAP) values to elucidate the contribution of each variable to the model's predictions. The resulting model demonstrated superior predictive accuracy in comparison to the conventional SOFA score, while exhibiting enhanced efficiency and transparency. This interpretable machine learning model, which is based on a SOFA variant, has the potential to support the earlier and more precise intervention required in the clinical management of sepsis ICU patients.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 12","pages":"1240-1248"},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145456007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1109/TLA.2025.11231221
Sergio López;Miguel A. Llama
Thanks to recent advances in artificial intelligence, interest in autonomous mobile systems has increased, and consequently, the development and validation of advanced control schemes for them has also seen a rise. This work introduces a two-layer neuro-adaptive compensation control scheme designed to address the trajectory tracking problem for an omnidirectional wheeled mobile robot equipped with four independent Mecanum wheels. The two-layer artificial neural network is used to compensate for the unknown dynamics of the mobile robot; the filtered error technique is used to obtain the weights of the artificial neural network. This approach does not require offline training. A key contribution of this approach is the integration of a novel auxiliary signal to provide robustness, particularly in non-ideal scenarios. This robust term effectively bounds the disturbance commonly encountered in such control approaches. A significant advantage of this approach is its independence from precise knowledge of plant parameters or the overall plant dynamics. Experimental results demonstrate the effectiveness of the proposed controller in achieving desired performance for the 4-wheeled omnidirectional mobile robot.
{"title":"Two-Layer Neuro-Adaptive Compensation Control Applied to a 4-Wheeled Omnidirectional Mobile Robot","authors":"Sergio López;Miguel A. Llama","doi":"10.1109/TLA.2025.11231221","DOIUrl":"https://doi.org/10.1109/TLA.2025.11231221","url":null,"abstract":"Thanks to recent advances in artificial intelligence, interest in autonomous mobile systems has increased, and consequently, the development and validation of advanced control schemes for them has also seen a rise. This work introduces a two-layer neuro-adaptive compensation control scheme designed to address the trajectory tracking problem for an omnidirectional wheeled mobile robot equipped with four independent Mecanum wheels. The two-layer artificial neural network is used to compensate for the unknown dynamics of the mobile robot; the filtered error technique is used to obtain the weights of the artificial neural network. This approach does not require offline training. A key contribution of this approach is the integration of a novel auxiliary signal to provide robustness, particularly in non-ideal scenarios. This robust term effectively bounds the disturbance commonly encountered in such control approaches. A significant advantage of this approach is its independence from precise knowledge of plant parameters or the overall plant dynamics. Experimental results demonstrate the effectiveness of the proposed controller in achieving desired performance for the 4-wheeled omnidirectional mobile robot.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 12","pages":"1318-1324"},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1109/TLA.2025.11231219
Carolina Del-Valle-Soto;Demián Velasco Gómez Llanos;Santiago Arreola Munguía;Marco Antonio Manjarrez Fernandez;Juan Pablo Villaseñor Navares;Violeta Corona;José Varela-Aldás;Jesus GomezRomero-Borquez
Understanding how different virtual reality (VR) game genres modulate physiological arousal is crucial for designing emotionally adaptive immersive systems. This study introduces a novel experimental framework combining high-resolution Skin Conductance Response (SCR) data and neural predictive modeling to compare emotional activation across horror, skill-based, and exercise VR games. Using Galvanic Skin Response (GSR) sensors, we recorded phasic peaks in SCR from 25 university-aged participants during gameplay sessions with controlled exposure times and standardized transitions. However, given the minimal difference relative to the large variability, this observation should be considered preliminary and specific to the tested games and cohort. A feed-forward neural network was developed to forecast individual arousal levels based solely on genre-induced features, achieving strong predictive performance. This dual contribution empirical genre comparison and lightweight predictive modeling offers a scalable tool for integrating emotional responsiveness into VR systems without continuous biosignal monitoring. The findings not only advance the state of the art in affective computing but also open new avenues for therapeutic, educational, and entertainment applications grounded in physiological adaptation.
{"title":"Genre-Sensitive Prediction of Emotional Arousal in Virtual Reality: A Neural Modeling Approach Using Skin Conductance Peaks","authors":"Carolina Del-Valle-Soto;Demián Velasco Gómez Llanos;Santiago Arreola Munguía;Marco Antonio Manjarrez Fernandez;Juan Pablo Villaseñor Navares;Violeta Corona;José Varela-Aldás;Jesus GomezRomero-Borquez","doi":"10.1109/TLA.2025.11231219","DOIUrl":"https://doi.org/10.1109/TLA.2025.11231219","url":null,"abstract":"Understanding how different virtual reality (VR) game genres modulate physiological arousal is crucial for designing emotionally adaptive immersive systems. This study introduces a novel experimental framework combining high-resolution Skin Conductance Response (SCR) data and neural predictive modeling to compare emotional activation across horror, skill-based, and exercise VR games. Using Galvanic Skin Response (GSR) sensors, we recorded phasic peaks in SCR from 25 university-aged participants during gameplay sessions with controlled exposure times and standardized transitions. However, given the minimal difference relative to the large variability, this observation should be considered preliminary and specific to the tested games and cohort. A feed-forward neural network was developed to forecast individual arousal levels based solely on genre-induced features, achieving strong predictive performance. This dual contribution empirical genre comparison and lightweight predictive modeling offers a scalable tool for integrating emotional responsiveness into VR systems without continuous biosignal monitoring. The findings not only advance the state of the art in affective computing but also open new avenues for therapeutic, educational, and entertainment applications grounded in physiological adaptation.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 12","pages":"1356-1364"},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1109/TLA.2025.11231216
Andres Isaza;Roger Alexander Martínez Ciro;Francisco Eugenio Lopez Giraldo
This paper describes the development of a bidirectional visible light communication system using a 5 mm red, green, and blue (RGB) light emitting diode (LED), which only serve as data transmitters and receivers but also function as power generators. A distinctive feature of the system is the implementation of a power divider using an RGB LED, which mitigates the complexity of the implementation of the optical transceiver and the collection of energy generated by the LED. The primary objective is to model a visible light communication identification system (VLC-ID) that is capable of operating efficiently in access applications by leveraging the ability of RGB LEDs to perform multiple functions simultaneously. To achieve this, the system employs OOK modulation and capacitor voltage accumulation. The research adopts an experimental approach, evaluating the bit error rate and the voltage accumulated by the system to demonstrate the viability and efficiency of the proposed model for access systems based on visible light communication technology.
{"title":"R3 Bidirectional LED-to-LED communication and energy generator for a VLC-ID access system","authors":"Andres Isaza;Roger Alexander Martínez Ciro;Francisco Eugenio Lopez Giraldo","doi":"10.1109/TLA.2025.11231216","DOIUrl":"https://doi.org/10.1109/TLA.2025.11231216","url":null,"abstract":"This paper describes the development of a bidirectional visible light communication system using a 5 mm red, green, and blue (RGB) light emitting diode (LED), which only serve as data transmitters and receivers but also function as power generators. A distinctive feature of the system is the implementation of a power divider using an RGB LED, which mitigates the complexity of the implementation of the optical transceiver and the collection of energy generated by the LED. The primary objective is to model a visible light communication identification system (VLC-ID) that is capable of operating efficiently in access applications by leveraging the ability of RGB LEDs to perform multiple functions simultaneously. To achieve this, the system employs OOK modulation and capacitor voltage accumulation. The research adopts an experimental approach, evaluating the bit error rate and the voltage accumulated by the system to demonstrate the viability and efficiency of the proposed model for access systems based on visible light communication technology.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 12","pages":"1346-1355"},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1109/TLA.2025.11231224
Guillermo Vera-Amaro;José Rafael Rojano-Cáceres
Web accessibility remains a persistent challenge, particularly for visually impaired users who rely on screen readers. This study investigates the potential of large language models (LLMs) to remediate accessibility issues only through structured prompt engineering without accessibility specialization. We evaluate GPT-4o and Gemini 2.0 Flash across 20 variants from two websites using different input formats (HTML, Markdown) and template-guided strategies. Outputs were assessed with automated tools, token efficiency metrics, and manual evaluations with experts and blind users. Results show an average Lighthouse score of 93.25, with WAVE errors reduced by 92.85%. Usability evaluation yielded an average success rate of 95.83% on completed tasks, with accuracy values reaching up to 0.86. GPT-4o demonstrated greater token efficiency, while Gemini produced more visually dynamic outputs. Certain violations persisted, confirming the need for human-in-the-loop validation. Overall, findings suggest that effectively guided LLMs can streamline remediation and foster more inclusive web experiences.
{"title":"Accessible Web Content Generation Using LLMs: An Empirical Study on Prompting Strategies and Template-Guided Remediation","authors":"Guillermo Vera-Amaro;José Rafael Rojano-Cáceres","doi":"10.1109/TLA.2025.11231224","DOIUrl":"https://doi.org/10.1109/TLA.2025.11231224","url":null,"abstract":"Web accessibility remains a persistent challenge, particularly for visually impaired users who rely on screen readers. This study investigates the potential of large language models (LLMs) to remediate accessibility issues only through structured prompt engineering without accessibility specialization. We evaluate GPT-4o and Gemini 2.0 Flash across 20 variants from two websites using different input formats (HTML, Markdown) and template-guided strategies. Outputs were assessed with automated tools, token efficiency metrics, and manual evaluations with experts and blind users. Results show an average Lighthouse score of 93.25, with WAVE errors reduced by 92.85%. Usability evaluation yielded an average success rate of 95.83% on completed tasks, with accuracy values reaching up to 0.86. GPT-4o demonstrated greater token efficiency, while Gemini produced more visually dynamic outputs. Certain violations persisted, confirming the need for human-in-the-loop validation. Overall, findings suggest that effectively guided LLMs can streamline remediation and foster more inclusive web experiences.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 12","pages":"1230-1239"},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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