Pub Date : 2026-01-26DOI: 10.1109/ACCESS.2026.3658020
Ghassan Aladool;Olivier Togni
The emerging advances in mobile technology, along with the anticipated global increase in the blind and low-vision population, emphasize the importance of introducing this community to such technology as an effective and low-cost communication tool. Aiming to enable blind and deaf-blind individuals to use mobile devices, many tactile-based methods and systems have been presented in literature over the last decade. Although these methods are well-regarded, their limitations include the need for object localization on mobile devices’ embedded touchscreens and the requirement to apply multiple gesture types, often involving several fingers from both hands. Addressing these limitations, this work presents a framework offering a new tactile-based communication method for blind individuals on mobile devices, with a touchscreen cover as a core component. The invented cover splits the touchscreen into eight equally-sized cells—six for data entry and two for control input—and provides an effective solution to localization and navigation challenges faced by blind individuals on touchscreens. In particular, the proposed framework enables Braille-based character entry. An Android app has also been developed to identify entered characters and save them for further processing. The proposed framework was tested at a foundation providing care for blind and low-vision individuals. Two-phases experiments, preliminary and final, were designed and conducted with ten participants. Experimental analysis based on measuring data entry time and input error indicates that the proposed framework performs well. Furthermore, two participant surveys were conducted prior and post the experiments to justify the calculated performance measures, asses cognitive load, and verify the usability and learnability of the proposed framework.
{"title":"A New Touchscreen Cover for Braille-Based Data Entry on Mobile Devices","authors":"Ghassan Aladool;Olivier Togni","doi":"10.1109/ACCESS.2026.3658020","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3658020","url":null,"abstract":"The emerging advances in mobile technology, along with the anticipated global increase in the blind and low-vision population, emphasize the importance of introducing this community to such technology as an effective and low-cost communication tool. Aiming to enable blind and deaf-blind individuals to use mobile devices, many tactile-based methods and systems have been presented in literature over the last decade. Although these methods are well-regarded, their limitations include the need for object localization on mobile devices’ embedded touchscreens and the requirement to apply multiple gesture types, often involving several fingers from both hands. Addressing these limitations, this work presents a framework offering a new tactile-based communication method for blind individuals on mobile devices, with a touchscreen cover as a core component. The invented cover splits the touchscreen into eight equally-sized cells—six for data entry and two for control input—and provides an effective solution to localization and navigation challenges faced by blind individuals on touchscreens. In particular, the proposed framework enables Braille-based character entry. An Android app has also been developed to identify entered characters and save them for further processing. The proposed framework was tested at a foundation providing care for blind and low-vision individuals. Two-phases experiments, preliminary and final, were designed and conducted with ten participants. Experimental analysis based on measuring data entry time and input error indicates that the proposed framework performs well. Furthermore, two participant surveys were conducted prior and post the experiments to justify the calculated performance measures, asses cognitive load, and verify the usability and learnability of the proposed framework.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"14931-14941"},"PeriodicalIF":3.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1109/ACCESS.2026.3657414
Jun-Seon Kim;Uk Jin Jung;Su Hong Park;Donghyun Kim;Moonhong Kim;Dongwoo Sohn;Dong-Wook Seo
This paper presents a novel surrogate modeling approach for estimating the dynamic radar cross-section (RCS) of chaff clouds under diverse launch and environmental conditions. A high-fidelity computational fluid dynamic–discrete element method (CFD-DEM) framework is first used to simulate the multiphysics behavior of chaff clouds generated by both naval and aircraft dispensers. These simulations generate detailed aerodynamic datasets, which are used to train a Gaussian process regression (GPR)–based surrogate model. The surrogate model enables efficient prediction of the spatiotemporal distribution of chaff clouds, incorporating variables such as wind speed, wind direction, and launch parameters. To estimate dynamic RCS, the spatiotemporal distributions are combined with approximation techniques, specifically the generalized equivalent conductor (GEC) and vector radiative transfer (VRT) methods. A real-time chaff cloud simulator with a graphical user interface is also developed, integrating aerodynamic modeling, RCS calculations, and signal fluctuation modeling. Simulation results demonstrate that the proposed surrogate model achieves high prediction accuracy, with normalized mean absolute errors (NMAE) of 0.0085 for naval chaff and 0.0176 for aircraft chaff. The dynamic RCS obtained via the surrogate model closely matches the CFD-DEM results while substantially reducing computational cost, thus offering practical utility for real-time system applications.
{"title":"Dynamic Radar Cross-Section Estimation of Chaff Clouds Based on a Surrogate Model for Spatiotemporal Distribution","authors":"Jun-Seon Kim;Uk Jin Jung;Su Hong Park;Donghyun Kim;Moonhong Kim;Dongwoo Sohn;Dong-Wook Seo","doi":"10.1109/ACCESS.2026.3657414","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3657414","url":null,"abstract":"This paper presents a novel surrogate modeling approach for estimating the dynamic radar cross-section (RCS) of chaff clouds under diverse launch and environmental conditions. A high-fidelity computational fluid dynamic–discrete element method (CFD-DEM) framework is first used to simulate the multiphysics behavior of chaff clouds generated by both naval and aircraft dispensers. These simulations generate detailed aerodynamic datasets, which are used to train a Gaussian process regression (GPR)–based surrogate model. The surrogate model enables efficient prediction of the spatiotemporal distribution of chaff clouds, incorporating variables such as wind speed, wind direction, and launch parameters. To estimate dynamic RCS, the spatiotemporal distributions are combined with approximation techniques, specifically the generalized equivalent conductor (GEC) and vector radiative transfer (VRT) methods. A real-time chaff cloud simulator with a graphical user interface is also developed, integrating aerodynamic modeling, RCS calculations, and signal fluctuation modeling. Simulation results demonstrate that the proposed surrogate model achieves high prediction accuracy, with normalized mean absolute errors (NMAE) of 0.0085 for naval chaff and 0.0176 for aircraft chaff. The dynamic RCS obtained via the surrogate model closely matches the CFD-DEM results while substantially reducing computational cost, thus offering practical utility for real-time system applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"14857-14869"},"PeriodicalIF":3.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11363218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1109/ACCESS.2026.3655024
Brahim Brahmi
Human–robot interaction (HRI) remains a critical research area, particularly in assistive robotics, where intuitive, safe, and adaptive collaboration with human users is essential for real-world deployment. Despite significant advances in impedance-based and adaptive control strategies, many existing approaches rely on full-state measurements, exhibit limited capability in handling user intent, and lack adaptability to unstructured environments. This paper addresses these challenges by proposing a novel sensorless control architecture that integrates an optimal Linear Quadratic Regulator (LQR), a nonlinear force observer, and a robust sliding mode controller with nonlinear model-based switching function. The primary objective is to achieve accurate trajectory tracking while minimizing user-applied interaction forces without requiring force or velocity sensors. The proposed controller dynamically generates reference trajectories through a human-cooperative LQR paradigm that penalizes both robot effort and human torque, enabling adaptive behavior based on inferred user intent. A nonlinear observer estimates interaction torques using only joint position measurements, facilitating intent inference and real-time impedance adaptation. These estimates are subsequently incorporated into the construction of model-based switching manifolds within the sliding mode controller, enhancing chattering mitigation and control decoupling under uncertainty and partial state observability. Theoretical analysis establishes global asymptotic stability, and comprehensive simulations conducted on a 2-DOF rehabilitation robot validate the proposed approach under multiple disturbance scenarios. Compared to conventional impedance control strategies, the proposed method demonstrates improved accuracy, robustness, and energy efficiency, highlighting its potential for deployment in sensor-limited, human-in-the-loop applications such as prosthetics, exoskeletons, and adaptive rehabilitation robotics.
{"title":"Sensorless Collaborative Impedance Control of Rehabilitation Robots via LQR and Model-Based Sliding Manifold","authors":"Brahim Brahmi","doi":"10.1109/ACCESS.2026.3655024","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3655024","url":null,"abstract":"Human–robot interaction (HRI) remains a critical research area, particularly in assistive robotics, where intuitive, safe, and adaptive collaboration with human users is essential for real-world deployment. Despite significant advances in impedance-based and adaptive control strategies, many existing approaches rely on full-state measurements, exhibit limited capability in handling user intent, and lack adaptability to unstructured environments. This paper addresses these challenges by proposing a novel sensorless control architecture that integrates an optimal Linear Quadratic Regulator (LQR), a nonlinear force observer, and a robust sliding mode controller with nonlinear model-based switching function. The primary objective is to achieve accurate trajectory tracking while minimizing user-applied interaction forces without requiring force or velocity sensors. The proposed controller dynamically generates reference trajectories through a human-cooperative LQR paradigm that penalizes both robot effort and human torque, enabling adaptive behavior based on inferred user intent. A nonlinear observer estimates interaction torques using only joint position measurements, facilitating intent inference and real-time impedance adaptation. These estimates are subsequently incorporated into the construction of model-based switching manifolds within the sliding mode controller, enhancing chattering mitigation and control decoupling under uncertainty and partial state observability. Theoretical analysis establishes global asymptotic stability, and comprehensive simulations conducted on a 2-DOF rehabilitation robot validate the proposed approach under multiple disturbance scenarios. Compared to conventional impedance control strategies, the proposed method demonstrates improved accuracy, robustness, and energy efficiency, highlighting its potential for deployment in sensor-limited, human-in-the-loop applications such as prosthetics, exoskeletons, and adaptive rehabilitation robotics.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"10760-10781"},"PeriodicalIF":3.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357932","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1109/ACCESS.2026.3655507
El Mastapha Sammou
Delay tolerant networks (DTN/s) represent an evolution of traditional ad hoc networks, specifically designed for extreme environments characterized by intermittent connectivity, unpredictable node mobility, high transmission delays, and frequent disruptions. Conventional routing protocols prove ineffective in these contexts, necessitating adaptive and robust solutions. In this paper, we propose PF-DTN (Predictive Forwarding for DTN), a hybrid adaptive routing algorithm that combines the prediction of future trajectories of DTN nodes using an LSTM (Long Short-Term Memory) model with relay selection leveraging deterministic, probabilistic, and uncertain strategies. PF-DTN also integrates uncertainty estimation via the Monte Carlo Dropout technique, enabling the dynamic adaptation of the routing strategy based on prediction reliability. The proposed PF-DTN architecture is structured into three phases: contextual mobility data collection, trajectory prediction with uncertainty estimation, and adaptive relay selection. Experimental evaluations conducted on the ONE simulator demonstrate that PF-DTN outperforms the benchmark protocol Prophet and remains competitive with recent state-of-the-art approaches. Our approach achieves a high delivery rate with relative gains over Prophet reaching up to + 46.15% in low- to medium-density networks, along with latency reduction of up to + 22.89% in high-density environments. The elevated overhead ratio, attributable to the computational demands of predictive and adaptive modules, represents a justified trade-off given the improvements in delivery reliability and latency control. These results demonstrate PF-DTN’s effectiveness and robustness across diverse DTN environments, ranging from predictable to highly dynamic and uncertain networks, establishing an optimal balance between reliability, speed, and communication cost.
{"title":"PF-DTN: Predictive Routing for Intelligent Delay Tolerant Networks Using RNN-LSTM Deep Learning With Monte Carlo Dropout Uncertainty Estimation and Hybrid Deterministic, Probabilistic, and Uncertain Routing Strategies","authors":"El Mastapha Sammou","doi":"10.1109/ACCESS.2026.3655507","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3655507","url":null,"abstract":"Delay tolerant networks (DTN/s) represent an evolution of traditional ad hoc networks, specifically designed for extreme environments characterized by intermittent connectivity, unpredictable node mobility, high transmission delays, and frequent disruptions. Conventional routing protocols prove ineffective in these contexts, necessitating adaptive and robust solutions. In this paper, we propose PF-DTN (Predictive Forwarding for DTN), a hybrid adaptive routing algorithm that combines the prediction of future trajectories of DTN nodes using an LSTM (Long Short-Term Memory) model with relay selection leveraging deterministic, probabilistic, and uncertain strategies. PF-DTN also integrates uncertainty estimation via the Monte Carlo Dropout technique, enabling the dynamic adaptation of the routing strategy based on prediction reliability. The proposed PF-DTN architecture is structured into three phases: contextual mobility data collection, trajectory prediction with uncertainty estimation, and adaptive relay selection. Experimental evaluations conducted on the ONE simulator demonstrate that PF-DTN outperforms the benchmark protocol Prophet and remains competitive with recent state-of-the-art approaches. Our approach achieves a high delivery rate with relative gains over Prophet reaching up to + 46.15% in low- to medium-density networks, along with latency reduction of up to + 22.89% in high-density environments. The elevated overhead ratio, attributable to the computational demands of predictive and adaptive modules, represents a justified trade-off given the improvements in delivery reliability and latency control. These results demonstrate PF-DTN’s effectiveness and robustness across diverse DTN environments, ranging from predictable to highly dynamic and uncertain networks, establishing an optimal balance between reliability, speed, and communication cost.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"10841-10859"},"PeriodicalIF":3.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357868","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1109/ACCESS.2026.3655482
Koy Motita;Sang Hoon Han;Younho Lee
Recent template inversion methods achieve strong performance on Western datasets, but poor results on Korean facial images. This gap poses security risks for the authentication system using facial recognition in Korea, as the stakeholders may misunderstand that protecting facial templates on their databases is not necessary. We propose an enhanced template inversion method with three improvements: refined preprocessing using per-eye alignment and GFPGAN upscaling, MSE-enhanced loss function, and dynamic weight adjustment. Our method outperforms existing approaches, achieving 0.882 cosine similarity, $9.7~L_{2}$ norm, and 0.312 LPIPS. Beyond reconstruction quality, we evaluate the security implications of template inversion attacks by analyzing verification robustness using Successful Attack Rate (SAR) at fixed False Match Rate (FMR) operating points, Receiver Operating Characteristic (ROC) curves, and Equal Error Rate (EER), providing a comprehensive assessment under realistic authentication thresholds. These results emphasize the critical need for robust facial template protection in facial recognition authentication systems.
{"title":"An Improved Template Inversion Attack Against Korean Face Images","authors":"Koy Motita;Sang Hoon Han;Younho Lee","doi":"10.1109/ACCESS.2026.3655482","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3655482","url":null,"abstract":"Recent template inversion methods achieve strong performance on Western datasets, but poor results on Korean facial images. This gap poses security risks for the authentication system using facial recognition in Korea, as the stakeholders may misunderstand that protecting facial templates on their databases is not necessary. We propose an enhanced template inversion method with three improvements: refined preprocessing using per-eye alignment and GFPGAN upscaling, MSE-enhanced loss function, and dynamic weight adjustment. Our method outperforms existing approaches, achieving 0.882 cosine similarity, <inline-formula> <tex-math>$9.7~L_{2}$ </tex-math></inline-formula> norm, and 0.312 LPIPS. Beyond reconstruction quality, we evaluate the security implications of template inversion attacks by analyzing verification robustness using Successful Attack Rate (SAR) at fixed False Match Rate (FMR) operating points, Receiver Operating Characteristic (ROC) curves, and Equal Error Rate (EER), providing a comprehensive assessment under realistic authentication thresholds. These results emphasize the critical need for robust facial template protection in facial recognition authentication systems.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"10871-10882"},"PeriodicalIF":3.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357908","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1109/ACCESS.2026.3655081
Jimin Gu;Jeonghyeon Choi;Youbean Kim
As the operating frequency of automated test equipment (ATE) increases, the thermal degradation of the components that constitute the channel accelerates. Degraded components cause signal integrity (SI) issues in the channel, which is a major factor in reducing the test quality and, thus, degrading the reliability of the ATE. Traditionally, test engineers have detected degraded components through direct probing; however, this process is time-consuming and necessitates an automated faulty component diagnosis framework. Accordingly, in this study, we propose a deep learning-based faulty component diagnosis framework to identify components that cause signal quality degradation due to heat in the ATE transmission channel. To analyze the effect of the thermal degradation of individual components on signal quality, a component modeling approach utilizing electromagnetic (EM) simulation was employed to construct a database of S-parameter data based on the temperature of the component. The simulation model demonstrated a high correlation with the measurement waveform data, with an average consistency of 97.1%, thereby ensuring its reliability. Furthermore, to address the issue of data scarcity in industrial environments, a conditional generative adversarial network (CGAN) was developed to generate S-parameter image data. The generated data showed a high similarity to the original S-parameter image data, with an average structural similarity index measure (SSIM) of 0.9845 and a peak signal-to-noise ratio (PSNR) of 35.21 dB. The convolutional neural network (CNN)-based faulty component diagnosis model trained with augmented data exhibited excellent performance, classifying faulty component types with an accuracy of 99.78%.
{"title":"Deep Learning-Based Faulty Component Diagnosis of Transmission Channels in ATE Affected by Thermal Degradation","authors":"Jimin Gu;Jeonghyeon Choi;Youbean Kim","doi":"10.1109/ACCESS.2026.3655081","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3655081","url":null,"abstract":"As the operating frequency of automated test equipment (ATE) increases, the thermal degradation of the components that constitute the channel accelerates. Degraded components cause signal integrity (SI) issues in the channel, which is a major factor in reducing the test quality and, thus, degrading the reliability of the ATE. Traditionally, test engineers have detected degraded components through direct probing; however, this process is time-consuming and necessitates an automated faulty component diagnosis framework. Accordingly, in this study, we propose a deep learning-based faulty component diagnosis framework to identify components that cause signal quality degradation due to heat in the ATE transmission channel. To analyze the effect of the thermal degradation of individual components on signal quality, a component modeling approach utilizing electromagnetic (EM) simulation was employed to construct a database of S-parameter data based on the temperature of the component. The simulation model demonstrated a high correlation with the measurement waveform data, with an average consistency of 97.1%, thereby ensuring its reliability. Furthermore, to address the issue of data scarcity in industrial environments, a conditional generative adversarial network (CGAN) was developed to generate S-parameter image data. The generated data showed a high similarity to the original S-parameter image data, with an average structural similarity index measure (SSIM) of 0.9845 and a peak signal-to-noise ratio (PSNR) of 35.21 dB. The convolutional neural network (CNN)-based faulty component diagnosis model trained with augmented data exhibited excellent performance, classifying faulty component types with an accuracy of 99.78%.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"11019-11034"},"PeriodicalIF":3.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357867","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1109/ACCESS.2026.3655411
Christian Woesle;Leopold Fischer-Brandies;Ricardo Buettner
Uncrewed aerial vehicles equipped with long-wave infrared cameras are a promising tool for locating missing persons, yet their performance collapses when more than 70% of a human body is obscured by vegetation. We address this limitation by reformulating the task as a thermal classification problem rather than a detection problem, allowing the system to focus on thermal appearance cues that remain visible under heavy occlusion. Recognition robustness is further affected by high-frequency infrared sensor noise, which increases with flight altitude. Using five-fold cross-validation at flight altitudes of 30 m, 50 m, and 70 m, we show that the proposed classification pipeline achieves an accuracy of 99.07% and maintains strong recall under heavy occlusion while operating at a low computational cost. Analysis of altitude-specific models demonstrates that the Gaussian-enhanced pipeline provides a statistically significant improvement in robustness, with the largest gains observed at higher flight altitudes due to more effective suppression of altitude-dependent sensor noise. These findings establish an operational robustness benchmark for occlusion-aware thermal person classification and provide a reproducible foundation for improving the reliability of uncrewed aerial vehicle search-and-rescue systems.
{"title":"A Robust Occlusion-Aware Deep Learning Architecture for Thermal Aerial Person Classification in Search-and-Rescue Missions","authors":"Christian Woesle;Leopold Fischer-Brandies;Ricardo Buettner","doi":"10.1109/ACCESS.2026.3655411","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3655411","url":null,"abstract":"Uncrewed aerial vehicles equipped with long-wave infrared cameras are a promising tool for locating missing persons, yet their performance collapses when more than 70% of a human body is obscured by vegetation. We address this limitation by reformulating the task as a thermal classification problem rather than a detection problem, allowing the system to focus on thermal appearance cues that remain visible under heavy occlusion. Recognition robustness is further affected by high-frequency infrared sensor noise, which increases with flight altitude. Using five-fold cross-validation at flight altitudes of 30 m, 50 m, and 70 m, we show that the proposed classification pipeline achieves an accuracy of 99.07% and maintains strong recall under heavy occlusion while operating at a low computational cost. Analysis of altitude-specific models demonstrates that the Gaussian-enhanced pipeline provides a statistically significant improvement in robustness, with the largest gains observed at higher flight altitudes due to more effective suppression of altitude-dependent sensor noise. These findings establish an operational robustness benchmark for occlusion-aware thermal person classification and provide a reproducible foundation for improving the reliability of uncrewed aerial vehicle search-and-rescue systems.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"10923-10938"},"PeriodicalIF":3.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1109/ACCESS.2026.3655591
Mayumi Nakano;Yuya Seki;Shuta Kikuchi;Shu Tanaka
Derivative-free (DF) optimization problems aim to identify an input that maximizes or minimizes the output of an objective function whose input-output relationship is unknown. Factorization machine with quadratic-optimization annealing (FMQA) is a promising approach to this task, employing a factorization machine (FM) as a surrogate model to iteratively guide the solution search via an Ising machine. Although FMQA has demonstrated strong optimization performance across various applications, its performance often stagnates as the number of optimization iterations increases. One contributing factor to this stagnation is the growing number of data points in the dataset used to train FM. As more data are accumulated, the contribution of newly added data points tends to become diluted within the entire dataset. Based on this observation, we hypothesize that such dilution reduces the impact of new data on improving the prediction accuracy of FM. To address this issue, we propose a novel method named sliding window for iterative factorization training combined with FMQA (SWIFT-FMQA). This method improves upon FMQA by utilizing a sliding-window strategy to sequentially construct a dataset that retains at most a specified number of the most recently added data points. SWIFT-FMQA is designed to enhance the influence of newly added data points on the surrogate model. Numerical experiments demonstrate that SWIFT-FMQA obtains lower-cost solutions with fewer objective function evaluations compared to FMQA.
{"title":"SWIFT-FMQA: Enhancing Factorization Machine With Quadratic-Optimization Annealing via Sliding Window","authors":"Mayumi Nakano;Yuya Seki;Shuta Kikuchi;Shu Tanaka","doi":"10.1109/ACCESS.2026.3655591","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3655591","url":null,"abstract":"Derivative-free (DF) optimization problems aim to identify an input that maximizes or minimizes the output of an objective function whose input-output relationship is unknown. Factorization machine with quadratic-optimization annealing (FMQA) is a promising approach to this task, employing a factorization machine (FM) as a surrogate model to iteratively guide the solution search via an Ising machine. Although FMQA has demonstrated strong optimization performance across various applications, its performance often stagnates as the number of optimization iterations increases. One contributing factor to this stagnation is the growing number of data points in the dataset used to train FM. As more data are accumulated, the contribution of newly added data points tends to become diluted within the entire dataset. Based on this observation, we hypothesize that such dilution reduces the impact of new data on improving the prediction accuracy of FM. To address this issue, we propose a novel method named sliding window for iterative factorization training combined with FMQA (SWIFT-FMQA). This method improves upon FMQA by utilizing a sliding-window strategy to sequentially construct a dataset that retains at most a specified number of the most recently added data points. SWIFT-FMQA is designed to enhance the influence of newly added data points on the surrogate model. Numerical experiments demonstrate that SWIFT-FMQA obtains lower-cost solutions with fewer objective function evaluations compared to FMQA.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"10977-10990"},"PeriodicalIF":3.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1109/ACCESS.2026.3655701
Savitha G. Kini;J. Lokesh;Anjan N. Padmasali
In the current era, LED lighting technology is the most widely used illumination source in all applications worldwide. Accurately predicting lumen degradation and lifetime performance has become critical for ensuring long-term reliability and cost-effectiveness. Traditional models often fail to capture the complex, non-linear nature of real-world degradation behavior. The work systematically models the lumen degradation behavior of LED luminaires using a four-parameter double exponential Gompertz function. The proposed model effectively captures the asymmetric, mirrored S-curve behavior observed in long-term degradation profiles of LED luminaires, which traditional exponential models fail to represent accurately. Experimental data from accelerated degradation tests conducted on three different commercial 16W LED luminaires were used to develop the model. The SEM-EDS analysis identified silver mirror tarnishing as a dominant physical degradation mechanism, providing material-level insight into the observed steep lumen drop during mid-life operation. A key contribution of this work is the development of a predictive framework that correlates proposed model coefficients with temperature using only three accelerated degradation tests. This enables accurate estimation of lumen maintenance performance at untested operating conditions, significantly reducing the need for exhaustive physical testing. The proposed methodology provides a practical, scalable, and cost-effective solution for predicting LED lifetime, making it highly applicable to both research and industry. It supports sustainable lighting development by improving lifetime prediction accuracy while reducing experimental burden, thereby contributing to energy-efficient operation and responsible resource utilization.
{"title":"Development of a Realistic Model to Accurately Predict the “Mirrored S-Curve” Nature of LED Luminaire Lumen Maintenance for Any Operating Conditions","authors":"Savitha G. Kini;J. Lokesh;Anjan N. Padmasali","doi":"10.1109/ACCESS.2026.3655701","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3655701","url":null,"abstract":"In the current era, LED lighting technology is the most widely used illumination source in all applications worldwide. Accurately predicting lumen degradation and lifetime performance has become critical for ensuring long-term reliability and cost-effectiveness. Traditional models often fail to capture the complex, non-linear nature of real-world degradation behavior. The work systematically models the lumen degradation behavior of LED luminaires using a four-parameter double exponential Gompertz function. The proposed model effectively captures the asymmetric, mirrored S-curve behavior observed in long-term degradation profiles of LED luminaires, which traditional exponential models fail to represent accurately. Experimental data from accelerated degradation tests conducted on three different commercial 16W LED luminaires were used to develop the model. The SEM-EDS analysis identified silver mirror tarnishing as a dominant physical degradation mechanism, providing material-level insight into the observed steep lumen drop during mid-life operation. A key contribution of this work is the development of a predictive framework that correlates proposed model coefficients with temperature using only three accelerated degradation tests. This enables accurate estimation of lumen maintenance performance at untested operating conditions, significantly reducing the need for exhaustive physical testing. The proposed methodology provides a practical, scalable, and cost-effective solution for predicting LED lifetime, making it highly applicable to both research and industry. It supports sustainable lighting development by improving lifetime prediction accuracy while reducing experimental burden, thereby contributing to energy-efficient operation and responsible resource utilization.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"10860-10870"},"PeriodicalIF":3.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358875","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1109/ACCESS.2026.3655826
Tingting Guo;Sainan Yang;Yao Fu;Daitao Wang
Multi-task joint learning for complex scene image understanding faces multiple challenges, including diverse visual elements, task-specific demands, and constrained computational resources. These challenges are particularly prominent in specialized domains such as Intangible Cultural Heritage (ICH), where current research lacks effective joint modeling approaches for image classification, semantic segmentation, and object localization tasks. To address this gap, we introduce a novel multi-task visual understanding problem tailored for ICH scenarios, and construct a high-quality dataset—ICH-Scene3800—comprising 3,800 annotated images across 12 representative ICH categories. To tackle this task, we propose the first lightweight multi-task learning framework capable of performing image-level classification, instance-level localization, and instance-level detection simultaneously. The framework employs a shared backbone to learn general-purpose features and integrates an attention-guided dynamic fusion mechanism that facilitates cross-task semantic interaction. Furthermore, a group-convolution-based lightweight architecture is introduced to enable efficient feature extraction and resource-aware deployment. These designs significantly enhance the model’s generalization ability across tasks and scenes. Extensive experiments on ICH-Scene3800 and the Cityscapes dataset demonstrate that our model achieves 92.19% mIoU and 82.36% mIoU, respectively, with only 0.024M parameters and 0.085 GFLOPs. It reaches a real-time processing speed of 98.5 FPS on an NVIDIA GeForce GTX 1060 (6GB) and significantly outperforms existing methods on the LSES metric, achieving state-of-the-art performance. This research provides a practical and efficient solution for intelligent visual understanding in cultural heritage preservation and other resource-constrained application scenarios. The code and related materials are available at https://github.com/Upno111/ICH
{"title":"A Unified Lightweight Network for Complex Scene Image Understanding via Multi-Task Joint Learning","authors":"Tingting Guo;Sainan Yang;Yao Fu;Daitao Wang","doi":"10.1109/ACCESS.2026.3655826","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3655826","url":null,"abstract":"Multi-task joint learning for complex scene image understanding faces multiple challenges, including diverse visual elements, task-specific demands, and constrained computational resources. These challenges are particularly prominent in specialized domains such as Intangible Cultural Heritage (ICH), where current research lacks effective joint modeling approaches for image classification, semantic segmentation, and object localization tasks. To address this gap, we introduce a novel multi-task visual understanding problem tailored for ICH scenarios, and construct a high-quality dataset—ICH-Scene3800—comprising 3,800 annotated images across 12 representative ICH categories. To tackle this task, we propose the first lightweight multi-task learning framework capable of performing image-level classification, instance-level localization, and instance-level detection simultaneously. The framework employs a shared backbone to learn general-purpose features and integrates an attention-guided dynamic fusion mechanism that facilitates cross-task semantic interaction. Furthermore, a group-convolution-based lightweight architecture is introduced to enable efficient feature extraction and resource-aware deployment. These designs significantly enhance the model’s generalization ability across tasks and scenes. Extensive experiments on ICH-Scene3800 and the Cityscapes dataset demonstrate that our model achieves 92.19% mIoU and 82.36% mIoU, respectively, with only 0.024M parameters and 0.085 GFLOPs. It reaches a real-time processing speed of 98.5 FPS on an NVIDIA GeForce GTX 1060 (6GB) and significantly outperforms existing methods on the LSES metric, achieving state-of-the-art performance. This research provides a practical and efficient solution for intelligent visual understanding in cultural heritage preservation and other resource-constrained application scenarios. The code and related materials are available at <uri>https://github.com/Upno111/ICH</uri>","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"14916-14930"},"PeriodicalIF":3.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358991","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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