Technologies

19 pages, 6333 KB

Open AccessArticle

Performance of an Efficient Hybrid Dilated–Long Short-Term Memory with Residual Learning for High-Fidelity Electrocardiogram Denoising Signal

by Suchada Sitjongsataporn, Pipat Sakarin and Theerayod Wiangtong

Technologies 2026, 14(7), 453; https://doi.org/10.3390/technologies14070453 - 22 Jul 2026

Viewed by 416

Abstract

Addressing the critical challenge of signal degradation in biosensor cardiac monitoring, this paper introduces an efficient hybrid dilated–long short-term memory (LSTM) with residual learning (HDLR), which is a novel architecture engineered for a high-fidelity electrocardiogram (ECG) denoising signal. The proposed HDLR model synergistically [...] Read more.

Addressing the critical challenge of signal degradation in biosensor cardiac monitoring, this paper introduces an efficient hybrid dilated–long short-term memory (LSTM) with residual learning (HDLR), which is a novel architecture engineered for a high-fidelity electrocardiogram (ECG) denoising signal. The proposed HDLR model synergistically integrates with dilated convolutions to expand the receptive field for multi-scale feature extraction. This is an LSTM-based backbone used to resolve long term temporal dependencies with residual learning paths to stabilize gradient flow and accelerate convergence. The proposed HDLR architecture integrates three core functional components with dilated convolutional layers utilized for local temporal feature extraction, where varying dilation rates expand the receptive field to capture both local waveform patterns and broader morphological structures without increasing computational complexity. Experimental results demonstrate a significant leap in performance, with the HDLR model achieving a mean squared error (MSE) of 0.002176, a signal-to-noise ratio (SNR) of 14.4420 dB, and a Matthews correlation coefficient (MCC) of 0.9822. Beyond quantitative metrics, the proposed HDLR architecture exhibits exceptional robustness in preserving cardiac morphology, specifically the P-wave and QRS complex of the ECG signal under stochastic noise conditions. These findings underscore the HDLR model’s potential as a backbone for next generation, real time diagnostic systems in intelligent healthcare. Full article

(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques—2nd Edition)

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19 pages, 44664 KB

Open AccessArticle

Evaluation of YOLO, SAM2, and U-Net Methods for Facial Attribute Segmentation Across Classes and Viewing Angles

by Ines Frajtag, Bojan Šekoranja, Marko Švaco and Filip Šuligoj

Technologies 2026, 14(7), 452; https://doi.org/10.3390/technologies14070452 - 22 Jul 2026

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Abstract

Facial attribute segmentation is challenging because facial regions differ in size, shape, texture, and visibility, especially under non-frontal viewing conditions. This study evaluates the segmentation of four facial attribute classes: hair, eyebrows, mustache, and beard. A dataset of 5000 manually annotated FFHQ facial [...] Read more.

Facial attribute segmentation is challenging because facial regions differ in size, shape, texture, and visibility, especially under non-frontal viewing conditions. This study evaluates the segmentation of four facial attribute classes: hair, eyebrows, mustache, and beard. A dataset of 5000 manually annotated FFHQ facial images was used to train and compare approaches within the same evaluation framework: YOLO segmentation, YOLO detection combined with SAM2 segmentation, and U-Net. The methods were evaluated on annotated test set, per facial attribute, and on an additional controlled phantom-based dataset acquired at five viewing angles. The hybrid YOLO detection and SAM2 segmentation pipeline achieved the best overall performance, with micro IoU of 0.820 and a micro Dice score of 0.893 on the annotated test set. Hair and beard are segmented more reliably than eyebrows and mustache, while segmentation accuracy decreased as the viewing angle increased. These results show that facial attribute segmentation performance depends on the selected method, target class, and acquisition viewpoint. The findings provide a basis for selecting and further improving segmentation methods for future registration and medical robotic applications. Full article

(This article belongs to the Section Information and Communication Technologies)

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81 pages, 2927 KB

Open AccessSystematic Review

Behavioral Biometric Continuous Authentication for Mobile Devices with an Intelligent Personal Agent: A Systematic Review

by Madi Gali, Aray Kassenkhan, Yersain Chinibayev, Aigerim Abshukirova and Vassiliy Serbin

Technologies 2026, 14(7), 451; https://doi.org/10.3390/technologies14070451 - 22 Jul 2026

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Abstract

Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify [...] Read more.

Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments. Full article

(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)

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26 pages, 66554 KB

Open AccessArticle

Numerical Simulation of Buckling Behavior of Steel Frame-Reinforced Polyethylene Pipelines Under Reverse Faults with Different Inclination Angles

by Zhaoliang Zhu, Xin Huang and Shunzuo Qiu

Technologies 2026, 14(7), 450; https://doi.org/10.3390/technologies14070450 - 21 Jul 2026

Viewed by 281

Abstract

Buried steel frame-reinforced polyethylene (SRPE) pipelines are vulnerable to bending-compression buckling, sectional distortion, and tensile rupture when crossing reverse faults. Nevertheless, their multi-mode failure mechanisms and strain evaluation frameworks have not been systematically clarified. Given this challenge, a three-dimensional nonlinear finite element model [...] Read more.

Buried steel frame-reinforced polyethylene (SRPE) pipelines are vulnerable to bending-compression buckling, sectional distortion, and tensile rupture when crossing reverse faults. Nevertheless, their multi-mode failure mechanisms and strain evaluation frameworks have not been systematically clarified. Given this challenge, a three-dimensional nonlinear finite element model considering pipe–soil contact, material elasto-plasticity, and large deformation is established in this study to investigate the deformation characteristics, strain evolution, and buckling performance of SRPE pipelines under reverse fault inclination angles from 0° to 150°. The effects of internal pressure, steel frame diameter, and soil properties on pipeline mechanical behavior are analyzed, with the results being compared with the strain limits specified in the GB 50470-2017, CSA Z662-2023, and EN 13476-3 standards. The pipeline is subjected to a three-stage failure process involving local compressive buckling, overall bending, and tensile necking. Notably, coupled bending-compression failure is typical at low inclination angles, while high angles are dominated by shear-tension coupling. Excessive internal pressure increases the local buckling and concentration of compressive strains. An increase in the steel frame diameter contributes to an effective improvement in the buckling resistance. When the soil has higher cohesion and stiffness, the critical buckling displacement becomes larger. The standard constant strain limits currently adopted do not account for the transition in failure mode arising from inclination control and are therefore overly conservative for SRPE pipelines. This study provides a theoretical foundation and quantitative reference for the seismic design and safety assessment of SRPE pipelines crossing reverse fault zones. Full article

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19 pages, 457 KB

Open AccessArticle

A Regional-Demographic Assessment of Ultra-Low Flow Ablution Tap Technology for Water Conservation and Carbon Footprint Reduction in Saudi Arabia

by Hafiz Abdul Wajid and Muhammad Abid

Technologies 2026, 14(7), 449; https://doi.org/10.3390/technologies14070449 - 21 Jul 2026

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Abstract

Saudi Arabia is a water-stressed nation and meets much of its daily demand through desalination, an energy-intensive process with a significant carbon footprint. As a Muslim-majority country, residents perform ablution before five daily prayers, making this activity a substantial yet under-quantified component of [...] Read more.

Saudi Arabia is a water-stressed nation and meets much of its daily demand through desalination, an energy-intensive process with a significant carbon footprint. As a Muslim-majority country, residents perform ablution before five daily prayers, making this activity a substantial yet under-quantified component of residential water use. This study focuses on household-level ablution water savings across 13 regions for both Saudi and non-Saudi households by replacing standard taps with a flow rate of 5.7 L/min with a proposed Saudi Standards, Metrology and Quality Organization (SASO)-compliant ultra-low-flow tap (1.9 L/min). Moreover, this study evaluates this ultra-low-flow tap as an environmental technology capable of reducing ablution water consumption and found that per capita savings are identical for both demographic segments, but the total household savings differ because Saudi households are larger, supporting sustainable water management. Results show that under the stated assumptions, full national adoption of the proposed tap would reduce monthly ablution water use from 27 million m³ to 9 million m³, conserving 212.14 million m³ annually with 67% efficiency and offsetting 702,198 tonnes of desalination-related carbon emissions. This highlights the effectiveness of deploying a simple water-saving technology in a water-stressed environment. Conservation potential is concentrated in Riyadh, Makkah, and the Eastern Province due to their high household counts. A four-year phased implementation roadmap is proposed, beginning with 25% adoption in year one (53.01 million m³ annual savings), expanding to moderate-impact regions in year two, and reaching 75–100% adoption nationwide by years three and four. The findings demonstrate how simple and commercially available water-efficient technology can contribute to sustainable resource management by simultaneously reducing water demand, energy consumption associated with desalination, and related greenhouse gas emissions. This study supports Saudi Arabia’s Vision 2030 water strategy and can potentially support UN-SDGs 6, 7, and 13 by demonstrating the substantial water, carbon, and economic benefits of a simple, commercially available tap of 400 SAR. In addition, the study develops a regionally prioritized technology deployment framework that can support decision makers in planning large-scale implementation. The analysis assumes that household members perform ablution five times daily for approximately one minute, based on field measurements, and they require validation of projected gains through actual implementation. Full article

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16 pages, 907 KB

Open AccessArticle

Mobile Application for Online Control and Booking of Free Parking Spots

by Simona Filipova-Petrakieva

Technologies 2026, 14(7), 448; https://doi.org/10.3390/technologies14070448 - 20 Jul 2026

Viewed by 208

Abstract

Under conditions of increasing traffic and limited parking options, mobile apps for real-time monitoring and booking of available spots in public parking lots are becoming an indispensable tool for making our daily lives easier. These apps offer convenience, save time, and reduce the [...] Read more.

Under conditions of increasing traffic and limited parking options, mobile apps for real-time monitoring and booking of available spots in public parking lots are becoming an indispensable tool for making our daily lives easier. These apps offer convenience, save time, and reduce the hassles associated with finding a parking spot, while also contributing to a more efficient and organized urban space. Additionally, they help reduce traffic congestion and lower pollution levels by encouraging users to make more rational use of available parking resources, which benefits all city residents. This article describes the development of a mobile application for real-time booking of parking spots. The proposed application integrates useful features from existing mobile apps, adds its own new features, and is adapted to the conditions of life in Bulgaria. Its main advantage is that it is available completely free of charge. It offers the following functionalities: booking or recommending parking spots; checking parking spot availability; and providing feedback on current and past parking spot bookings, including ratings and comments. The application’s interface is intuitive, which makes it easy to use. The following technologies were used in its development: Java, Android Studio, XML, Gradle, Android SDK, and Firebase. The application was compared to similar ones, incorporating their best features and adding new ones that improve upon them. The current work aims to help Bulgarian users save time and money spent looking for parking spots. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

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36 pages, 5805 KB

Open AccessArticle

Probabilistic Assessment of Groundwater Potential Using Spatially Aware Machine Learning and Multimodal Geospatial Data

by Gulnara Kaziyeva, Shynar Turmaganbetova, Sandugash Bekenova, Gulzira Abdikerimova, Rysgul Baynazarova, Aliya Abdukarimova, Gulnaz Zhilkishbayeva, Zhanar Azhibekova and Bekezhan Zhumazhan

Technologies 2026, 14(7), 447; https://doi.org/10.3390/technologies14070447 - 20 Jul 2026

Viewed by 214

Abstract

This study presents a spatially aware machine learning model for probabilistic groundwater potential assessment using multimodal geospatial data derived from topography, hydrotopography, climate, soil, land use, Sentinel-1 SAR, and water balance variables. The model incorporates spatially consistent data partitioning, leakage-controlled model development, and [...] Read more.

This study presents a spatially aware machine learning model for probabilistic groundwater potential assessment using multimodal geospatial data derived from topography, hydrotopography, climate, soil, land use, Sentinel-1 SAR, and water balance variables. The model incorporates spatially consistent data partitioning, leakage-controlled model development, and probabilistic forecasting to improve the robustness and transferability of groundwater potential assessment. A total of 2402 spatial observations, including 601 groundwater-related locations and 1801 spatially filtered pseudo-absence samples, were used to evaluate eleven machine learning and deep learning models. The proposed hybrid framework achieved the best overall validation results with ROC-AUC of 0.9319, PR-AUC of 0.8363, F1-measure of 0.8226, balanced accuracy of 0.8847, and MCC of 0.7613. Independent spatial block testing further demonstrated strong generalization ability, showing ROC-AUC of 0.9535, PR-AUC of 0.9002, F1-measure of 0.7983, balanced accuracy of 0.8594, and MCC of 0.7336. Comparative experiments demonstrated that the proposed framework remains competitive with state-of-the-art machine learning and deep learning approaches while providing robust probabilistic estimates in spatially separated validation. The resulting groundwater potential maps identify areas with environmental conditions similar to known groundwater observations and provide a reliable basis for prioritizing hydrogeological studies, groundwater exploration, and regional water resources planning in data-poor settings. Full article

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44 pages, 7276 KB

Open AccessSystematic Review

Technological Evolution of Strategic Security Infrastructure: Transitioning from Reactive Models to Predictive Intelligence

by Omar Flor-Unda, David Puga, Hugo Alomoto, Gabriela Eguez, Xavier Chango, David Fabara, Freddy Villao and Carlos Toapanta

Technologies 2026, 14(7), 446; https://doi.org/10.3390/technologies14070446 - 20 Jul 2026

Viewed by 482

Abstract

Security infrastructure has evolved from the use of reactive models toward the adoption of predictive technologies, transforming and enhancing threat anticipation, improving monitoring capabilities, and strengthening strategic decision-making driven by the integration of emerging technologies and artificial intelligence. This review study synthesizes the [...] Read more.

Security infrastructure has evolved from the use of reactive models toward the adoption of predictive technologies, transforming and enhancing threat anticipation, improving monitoring capabilities, and strengthening strategic decision-making driven by the integration of emerging technologies and artificial intelligence. This review study synthesizes the changes occurring across spatial, maritime, aerial, border, and cybersecurity infrastructure, evidencing the impact of the transition toward predictive technologies; it addresses the challenges and limitations these technologies introduce as a consequence of their operational deployment, and concludes by examining future lines of development in this domain. The review was conducted following the PRISMA^® methodology, analyzing the scientific literature retrieved from seven indexed databases—SCOPUS, ScienceDirect, Web of Science, IEEE Xplore, Taylor & Francis, ProQuest, and PubMed—consistent with the full search scope. Articles were independently assessed by two reviewers, yielding an initial Cohen’s Kappa coefficient of 0.458; following structured discussion and consensus resolution, the final inter-rater reliability reached κ = 0.71, meeting the accepted threshold for scoping review methodology. The findings demonstrate a global transition toward intelligent security infrastructures, with measurable improvements in performance, accuracy, and response times in the generation of actionable intelligence to support strategic decision-making. Full article

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24 pages, 3653 KB

Open AccessArticle

PMCI: A Prototype-Based Diagnostic Index for Cross-Modal Affective Agreement

by Yernar Seksenbayev, Saule Kudubayeva, Abdykarim Baimankulov, Aigerim Yerimbetova, Elmira Daiyrbayeva, Ulmeken Berzhanova and Bakzhan Sakenov

Technologies 2026, 14(7), 445; https://doi.org/10.3390/technologies14070445 - 19 Jul 2026

Viewed by 304

Abstract

We introduce the Probabilistic Multimodal Consistency Index (PMCI), a prototype measure of probabilistic semantic agreement used to quantify how much two modalities share in terms of mutually compatible affective evidence. PMCI does not model feature fusion or emotion classification directly; instead, each modality [...] Read more.

We introduce the Probabilistic Multimodal Consistency Index (PMCI), a prototype measure of probabilistic semantic agreement used to quantify how much two modalities share in terms of mutually compatible affective evidence. PMCI does not model feature fusion or emotion classification directly; instead, each modality is mapped to a different probability distribution over learnable latent affective-agreement anchors, and the agreement of those distributions is quantified via the Jensen–Shannon divergence. Consequently, we propose PMCI not as a substitute for a discriminative model of pair matching, but rather as an auxiliary diagnostic index of cross-modal affective agreement. Experiments were conducted on pose-based facial and body keypoint sequences obtained from the RAVDESS and MELD datasets. In the updated RAVDESS setup, we extended the cache of preprocessed keypoint files to include all 24 actors, and we sampled four distinct pose-derived 12-frame crops per source video. This resulted in a total of 11,520 pose-derived windows, with the actor label determining the train/validation/test splits for the RAVDESS dataset. MELD contained 402 filtered pose-derived windows and served as an auxiliary in-the-wild validation setting with additional noise. When applying the updated RAVDESS standard pair-matching, DirectCosine_K0 achieved ROC–AUC = 0.943 and PR–AUC = 0.917, demonstrating that it is indeed the best exact pair-matching baseline and that PMCI-based configurations are not as accurate as DirectCosine_K0 when performing common exact face–body pair discrimination. Under this protocol, PMCI_K8, PMCI_K16, and PMCI_K32 produced ROC–AUC scores of 0.851, 0.805, and 0.887, respectively. Running a one-window-per-source-video experiment with 100 repetitions yielded similar results in the following order: DirectCosine_K0 = 0.942 plus-minus 0.009, PMCI_K16 = 0.807 plus-minus 0.016, and PMCI_K32 = 0.893 plus-minus 0.012, which shows that the results obtained were not the result of only repeated temporal crops. In the DirectCosine-mined hard negative setting, PMCI produced modest diagnostic separation above chance, where Direct+PMCI_K32 yielded ROC–AUC = 0.582. In a suite of shuffle control, permutation control, model initialization control, temperature control, anchor usage control, control latency, and pose perturbation control experiments, the diagnostic stability of PMCI was tested, confirming that PMCI is sensitive to both pose-estimation accuracy and domain shift. Finally, PMCI must be interpreted as a probabilistic diagnostic index of cross-modal affective agreement that is based on pose, not as a general-purpose emotion-recognition system. Full article

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17 pages, 1244 KB

Open AccessArticle

A Single-Point Adaptive Gaze-to-Cursor Correction Pipeline for Low-Burden Dwell-Based Eye-Tracking Interfaces: Design and Human Evaluation

by Paweł Krowicki, Fryderyk Gajdzik, Roman Olejniczak, Zofia Tomala and Grzegorz Żurek

Technologies 2026, 14(7), 444; https://doi.org/10.3390/technologies14070444 - 19 Jul 2026

Viewed by 273

Abstract

Background. Eye-controlled interfaces can support communication and selection tasks when physical interaction is difficult, but conventional multi-point calibration may be burdensome for users who fatigue quickly or have severe motor limitations. This study operationally evaluates a low-burden single-point adaptive gaze-to-cursor correction pipeline for [...] Read more.

Background. Eye-controlled interfaces can support communication and selection tasks when physical interaction is difficult, but conventional multi-point calibration may be burdensome for users who fatigue quickly or have severe motor limitations. This study operationally evaluates a low-burden single-point adaptive gaze-to-cursor correction pipeline for a dwell-based selection interface. Methods. The pipeline combines central single-point offset estimation, robust median/median absolute deviation (MAD) sample handling, recorded-stream jump rejection, exponential moving average filtering, dwell-based selection, and post-selection offset adaptation. It was evaluated using application-level gaze-to-cursor coordinates from 43 participants across 129 sessions. The primary endpoint was target-proximity Root Mean Square (RMS) during automatically logged dwell-active selection periods. Because these periods were defined by the pipeline’s online state, the endpoint was algorithm-conditioned. Whole-quiz RMS was analyzed as a secondary full-trajectory descriptor rather than as independent validation. Results. Averaged across three repeated measurements, target-proximity RMS during dwell-active periods decreased from 193.40 pixels (px) (approximately 3.15°) for the recorded uncorrected coordinates to 56.02 px (0.91°) for the complete pipeline output. The mean reduction was 137.38 px (95% confidence interval (CI), 116.59–158.17 px; Cohen’s d_z = 2.03), and all 43 participants showed a reduction. Conclusions. The results support the practical feasibility of the complete correction pipeline for target localization in the evaluated dwell-based interface. They do not independently validate native eye-tracker accuracy or establish superiority over conventional multi-point calibration. Full article

(This article belongs to the Section Assistive Technologies)

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16 pages, 7632 KB

Open AccessArticle

Technology for Producing Graphene-Coated Magnetic Iron Particles Decorated by Small Aurum Nanoparticles for Cancer Cell Therapy

by Ilya V. Baimler, Dmitriy A. Serov, Valeriy A. Kozlov, Eugeny M. Konchekov, Ismail R. Seriev, Sofia N. Bokova-Sirosh, Maxim E. Astashev, Ekaterina E. Karmanova, Egor A. Turovsky, Konstantin V. Sergienko, Mikhail A. Sevostyanov, Serazhutdin A. Abdullaev, Pavel A. Ivliev and Alexander V. Simakin

Technologies 2026, 14(7), 443; https://doi.org/10.3390/technologies14070443 - 19 Jul 2026

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Abstract

Nanotechnology currently offers two approaches to tumor therapy. The first involves coating the surface of nanoparticles with high-affinity molecules for targeted delivery. The second involves directing the nanoparticles to the desired area of the body using an external magnetic field. Such nanoparticles are [...] Read more.

Nanotechnology currently offers two approaches to tumor therapy. The first involves coating the surface of nanoparticles with high-affinity molecules for targeted delivery. The second involves directing the nanoparticles to the desired area of the body using an external magnetic field. Such nanoparticles are often made of magnetic metals (iron, nickel, cobalt, etc.), but in living systems, the main problem with such nanoparticles is their toxicity. To address the toxicity issue, various barriers and coatings are primarily used. In this work, a laser technology for producing multifunctional nanocomposites based on graphene-coated iron nanoparticles decorated with gold nanoparticles was developed. Graphene-coated iron nanoparticles (200 nm) were synthesized using laser ablation in isopropanol. The presence of a graphene coating on the surface of the iron nanoparticles was confirmed by TEM, Raman spectroscopy, and luminescence analysis. A technology for depositing gold nanoparticles approximately 10 nm in size onto the graphene shell of the resulting iron nanoparticles was invented. The essence of the technology lies in creating critical conditions in a nanoparticle colloid, leading to intense aggregation with each other. Multifunctional nanocomposites based on graphene-coated iron nanoparticles decorated with gold nanoparticles did not exhibit acute toxicity to cell cultures under normal conditions. Moreover, under the combined influence of an alternating magnetic field and laser radiation, nanocomposites damaged 96% of neuroblastoma cells in culture. Full article

(This article belongs to the Special Issue Advances in Magnetic Nanomaterials)

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32 pages, 20702 KB

Open AccessArticle

Evaluating Traffic Conflicts and Congestion Based on Right-Turning Driving Behaviour Using Evasive Actions Driven PET via UAV Video Analysis: A Case Study of Uncontrolled Heterogeneous T-Intersection in India

by Yagnik M. Bhavsar, Mazad S. Zaveri, Mehul S. Raval, Pancham Shukla and Shaheriar B. Zaveri

Technologies 2026, 14(7), 442; https://doi.org/10.3390/technologies14070442 - 18 Jul 2026

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Abstract

Adherence to right-of-way (RoW) rules at uncontrolled T-intersections helps avoid accidents and alleviate congestion. In non-uniform traffic, right-turning behaviour can be characterised by distinct driving traits, such as non-compliance (failure to yield), a nonchalant attitude, and competitive behaviour. This paper presents a cost-effective [...] Read more.

Adherence to right-of-way (RoW) rules at uncontrolled T-intersections helps avoid accidents and alleviate congestion. In non-uniform traffic, right-turning behaviour can be characterised by distinct driving traits, such as non-compliance (failure to yield), a nonchalant attitude, and competitive behaviour. This paper presents a cost-effective computer vision framework using UAV videos to analyse right-turning behaviour and assess safety and operational performance (congestion) at uncontrolled T-intersections. A conflict cone of a vehicle is defined to automatically detect a right-of-way violation (RoWV) and yield. The impact of driving-related parameters and external traffic on non-compliant behaviour is analysed using the Tweedie generalised linear model. This paper proposes an aggregated surrogate safety measure, condPET, and a novel parameter, congValue, to identify critical conflicts and congestion due to non-compliant behaviour. Lateral evasive action is used to detect a constrained path because of nonchalant and competitive behaviours. The results indicate that only 7.50% of vehicles yielded, 6.25% of conflicts were critical (compared to 38.94% using PET alone and 11.05% using CS), and localised congestion occurred for 44.00% of the total video time. Overall, 45.34% of vehicles created a constrained path, and 26.00% committed RoW violations, causing congestion and increasing the average travel time on major roads by 2.0 and 3.5 times, respectively. Our methodology enables computer vision-based automated assessments of both road traffic safety and operational performance at uncontrolled T-intersections under non-uniform traffic conditions, providing a valuable tool for road-traffic-monitoring systems. Full article

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20 pages, 4066 KB

Open AccessArticle

A Sleep Staging Method Based on Cardiopulmonary Signals Using a Unified Multimodal Model

by Lin Guo, Yuhang Yin, Chen Wang, Hongyu Chen, Qinghua Cui and Xiangkui Wan

Technologies 2026, 14(7), 441; https://doi.org/10.3390/technologies14070441 - 17 Jul 2026

Viewed by 239

Abstract

Sleep staging based on PSG is largely confined to clinical settings, while home-based sleep monitoring often faces the challenges of insufficient unimodal information and missing modalities. Aiming to overcome these challenges, this paper proposes a unified multimodal model for sleep staging based on [...] Read more.

Sleep staging based on PSG is largely confined to clinical settings, while home-based sleep monitoring often faces the challenges of insufficient unimodal information and missing modalities. Aiming to overcome these challenges, this paper proposes a unified multimodal model for sleep staging based on cardiopulmonary signals. First, a heterogeneous multi-scale feature encoder with long and short branches is adopted to adapt to the cross-modal heterogeneity of ECG and THX. It combines a Transformer encoder and a Dilated CNN to complete feature fusion and temporal modeling. Subsequently, the unified model adaptively handles flexible modality combinations by introducing global context via a modal feature alignment strategy, which is built upon a framework consisting of a bimodal global branch and unimodal dedicated branches. On the SHHS dataset, the proposed model achieved Cohen’s kappa coefficients of 0.7547, 0.7121, and 0.7305 for four-stage sleep classification under ECG+THX, ECG-only, and THX-only inputs, respectively, demonstrating consistent improvements over three separately trained individual models. Furthermore, the model exhibits robust generalization performance on the P2018 external dataset and across samples with different severity levels of SDB. This work establishes a reliable algorithmic baseline for unobtrusive, long-term home sleep monitoring with missing modalities. Full article

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30 pages, 8131 KB

Open AccessArticle

Modeling and Design of a Spherical Remote Center-of-Motion Surgical Robot

by Calin Vaida, Daniel Horvath, Ionut Zima, Marius Miclaus, Bogdan Gherman, Corina Radu, Paul Tucan, Stefan Vegh, Dragos Sebeni, Adrian Pisla, Damien Chablat, Nadim Al Hajjar and Doina Pisla

Technologies 2026, 14(7), 440; https://doi.org/10.3390/technologies14070440 - 17 Jul 2026

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Abstract

Remote center-of-motion mechanisms are essential in minimally invasive surgery because they allow surgical instruments or an endoscopic camera to pivot around a trocar entry point while eliminating lateral motion at the incision. This paper presents the design, kinematic modeling, prototype implementation and preliminary [...] Read more.

Remote center-of-motion mechanisms are essential in minimally invasive surgery because they allow surgical instruments or an endoscopic camera to pivot around a trocar entry point while eliminating lateral motion at the incision. This paper presents the design, kinematic modeling, prototype implementation and preliminary evaluation under laboratory conditions of a compact, spherical, remote center-of-motion robot for minimally invasive surgical orientation tasks. The proposed mechanism uses a spherical kinematic architecture actuated by a contra-rotating differential gearbox. This gearbox generates two coaxial output rotations of equal magnitude and opposite direction from a single input, mechanically synchronizing the opposed motion of the two base links and eliminating the need for cable-pulley transmission or dual electronically synchronized motors. A second actuator chain rotates the gearbox assembly around the base axis, thereby decoupling the extension–retraction motion from base-axis rotation. Forward and inverse kinematic formulations were derived for teleoperation of the robot using a 7 degrees of freedom haptic device and for remote center-of-motion orientation control using a 3-axis joystick. A proof-of-concept prototype was developed and integrated with a custom embedded controller, closed-loop motor control, a master-console interface and video feedback loop. The system was evaluated in a phantom-torso setup using a custom endoscopic camera, internal visual markers and an OptiTrack-based measurement of the remote center-of-motion accuracy. The qualitative experiment confirmed functional integration of the mechanical, electronic and software subsystems, while the optical-tracking measurement showed that the pivot constraint was maintained with a mean deviation of 1.69 mm and a root-mean-square deviation of 2.13 mm over the analyzed orientation sweep. The main limitations remain the 1:1 gearbox ratio, limited actuator torque, additively manufactured gearing and the absence of repeated-trial repeatability and full workspace characterization. Full article

(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies—2nd Edition)

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43 pages, 598 KB

Open AccessArticle

A Matheuristic Optimization Approach for Simultaneous Feeder Routing and Conductor Sizing in Unbalanced Distribution Networks

by Brandon Cortés-Caicedo, Oscar Danilo Montoya and Santiago Bustamante-Mesa

Technologies 2026, 14(7), 439; https://doi.org/10.3390/technologies14070439 - 17 Jul 2026

Viewed by 195

Abstract

The optimal expansion of unbalanced three-phase distribution networks in non-interconnected zones requires the simultaneous resolution of two highly complex planning decisions: the selection of feeder routes and the sizing of conductors. This problem, formulated as a non-convex mixed-integer nonlinear program (MINLP), poses significant [...] Read more.

The optimal expansion of unbalanced three-phase distribution networks in non-interconnected zones requires the simultaneous resolution of two highly complex planning decisions: the selection of feeder routes and the sizing of conductors. This problem, formulated as a non-convex mixed-integer nonlinear program (MINLP), poses significant computational challenges due to the combinatorial explosion of radial topologies, discrete conductor choices, and the nonlinearity of three-phase power-flow equations. While metaheuristics offer flexible exploration, they lack optimality guarantees and repeatability, whereas exact MINLP solvers provide rigorous solutions but become computationally intractable for systems of realistic size. To overcome these limitations, this paper introduces a novel hybrid exact–metaheuristic framework that synergistically combines the global exploration capabilities of the Equilibrium Optimizer (EO) with the rigorous evaluation power of an exact MINLP model. In this cascade architecture, EO efficiently navigates the discrete space of radial topologies, while the exact MINLP stage, solved using BONMIN with an interior-point branch-and-bound scheme, optimizes conductor selection and evaluates the full annualized cost, rigorously enforcing voltage, ampacity, and physical constraints. The proposed methodology was validated on 10-, 30-, 50-, and 110-node test systems derived from real Colombian non-interconnected zones (Nuquí, Leticia, San Andrés, and a large-scale urban case). Comparative analysis against pure metaheuristics (SSA, GWO, VSA) and standalone MINLP demonstrates that EO-MINLP consistently yields the lowest total annualized costs, achieving savings of up to 0.42%, 0.71%, and 1.36% over the best pure metaheuristic for the 10-, 30-, and 50-node systems, respectively. Crucially, the hybrid strategy dramatically enhances scalability, reducing the standalone MINLP computational time by 15.79%, 78.68%, and 88.95% for these cases, while preserving solution quality and improving repeatability (standard deviation reduced from over 1.2% to as low as 0.11%). For the challenging 110-node system, where the standalone MINLP proved computationally infeasible, the proposed method successfully delivered a feasible, high-quality solution with a standard deviation of just 0.43%, confirming its practical applicability to large-scale planning. These results demonstrate that the EO-MINLP framework provides a robust, scalable, and economically superior tool for the cost-effective design of unbalanced distribution networks, effectively bridging the gap between the flexibility of stochastic search and the rigor of mathematical programming. Full article

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37 pages, 3482 KB

Open AccessArticle

Comparative Analysis of Local Large Language Models for Ranking Higher Education Programmes Based on Applicant Digital Profiles

by Artem Sveshnikov, Yury Nikitnikov, Maxim Shiltsyn, Denis Dedov and Artem Obukhov

Technologies 2026, 14(7), 438; https://doi.org/10.3390/technologies14070438 - 17 Jul 2026

Viewed by 271

Abstract

Choosing among closely related higher-education programmes requires interpretation of heterogeneous applicant data while preserving data confidentiality. This study compares ten locally executable large language model (LLM) configurations as semantic rankers, contextualises their performance against a hybrid term frequency–inverse document frequency (TF–IDF) cosine baseline, [...] Read more.

Choosing among closely related higher-education programmes requires interpretation of heterogeneous applicant data while preserving data confidentiality. This study compares ten locally executable large language model (LLM) configurations as semantic rankers, contextualises their performance against a hybrid term frequency–inverse document frequency (TF–IDF) cosine baseline, and evaluates robustness when relevant competencies are expressed indirectly. The main benchmark comprised 50 anonymised applicant profiles and 10 degree programmes; an additional processed-profile robustness set comprising 10 profiles was used to reduce direct lexical overlap with the programme catalogue. Rankings were evaluated using Accuracy@1, Accuracy@3, normalised discounted cumulative gain at 5 (NDCG@5) and mean reciprocal rank at 5 (MRR@5), while structured-output validity and inference time were assessed for the LLMs. Among the local LLMs, gpt-oss-20b-MXFP4 achieved the highest Accuracy@1 (0.76), whereas gemma-4-E4B-it-Q8_0 achieved the highest Accuracy@3 (0.92), and Ministral-3-14B-Reasoning-2512-Q4_K_M provided a favourable quality–latency balance. On the main benchmark, TF–IDF achieved Accuracy@1 = 0.88 and NDCG@5 = 0.9557, exceeding all LLM configurations and demonstrating a strong lexical signal. On the processed-profile robustness set, the Accuracy@1 of the TF–IDF baseline decreased to 0.60, whereas gemma achieved 0.90. The results provide initial evidence that several local LLM configurations can reproduce observed programme-selection patterns in a limited pilot benchmark. However, the findings should be interpreted as task-specific model-comparison results rather than as full validation of an autonomous career guidance system. Full article

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36 pages, 1638 KB

Open AccessArticle

Metric-Reconciled Techno-Economic Reconstruction of PV–Battery–Hydrogen Microgrids for Tropical Off-Grid Residential Applications

by Abimael Rodríguez, Andree Aranda-Cen, Romeli Barbosa, Jaime Ortegón-Aguilar, Edith Osorio-de-la-Rosa and Carlos Couder-Castañeda

Technologies 2026, 14(7), 437; https://doi.org/10.3390/technologies14070437 - 16 Jul 2026

Viewed by 737

Abstract

Off-grid residential microgrids in tropical regions require storage architectures capable of maintaining renewable electricity supply under variable solar resources, evening demand peaks, and diverse household consumption levels. In PV–battery–hydrogen systems, however, economic indicators can be difficult to interpret when software-reported costs are compared [...] Read more.

Off-grid residential microgrids in tropical regions require storage architectures capable of maintaining renewable electricity supply under variable solar resources, evening demand peaks, and diverse household consumption levels. In PV–battery–hydrogen systems, however, economic indicators can be difficult to interpret when software-reported costs are compared directly with externally calculated LCOE values based on different accounting conventions. This study presents a metric-reconciled techno-economic reconstruction approach for retained PV–battery–hydrogen microgrid configurations serving off-grid residential demand in Chetumal, Mexico. The objective is not to introduce a new global optimization or to claim the universal superiority of a specific architecture, but to separate archived HOMER Pro benchmark outputs from an external techno-economic model (TEM). The TEM reconstructs net present cost, scheduled replacements, salvage treatment, discounted delivered electricity, HOMER-derived LCOE, TEM-derived LCOE, sensitivity indicators, and storage role metrics using declared accounting assumptions. The approach is applied to two representative residential demand scenarios of 16.67 and 53.42 kWh/day. Both retained configurations achieved a 100% renewable fraction with negligible unmet load. Battery discharge increased from 827.12 kWh/year in the low-demand case to 6125.52 kWh/year in the high-demand case, highlighting the increasing role of the battery in short-duration balancing. In contrast, the hydrogen pathway acted as a delayed-backup layer by converting surplus PV electricity into hydrogen and later recovering it through PEM fuel cell generation. The TEM closely matched the HOMER-derived LCOE benchmark, with deviations below 4%, yielding TEM-derived LCOE values of 0.3320 and 0.3571 USD/kWh for the low- and high-demand cases, respectively. Sensitivity analysis showed that delivered electricity, discount rate, PV cost, and battery cost were the main LCOE drivers, while deterministic multi-parameter scenarios confirmed the combined influence of financing, component costs, O&M, PV degradation, and electricity delivered. Overall, the proposed approach provides an auditable basis for metric reconciliation, early-stage technology assessment, and storage role interpretation in tropical off-grid microgrids. Future extensions should include architecture-level re-optimization, flexible loads, degradation-aware modeling, and part-load component behavior. Full article

(This article belongs to the Special Issue Next-Generation Power Electronics and Mechatronic Integration for Smart Energy Systems)

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23 pages, 6094 KB

Open AccessArticle

Impact of Evaporator Operating Mode Switching on the Performance of CO₂ Commercial Refrigeration Systems

by Ionuț Dumitriu, Costel Ungureanu and Ion V. Ion

Technologies 2026, 14(7), 436; https://doi.org/10.3390/technologies14070436 - 16 Jul 2026

Viewed by 316

Abstract

Commercial refrigeration systems represent some of the largest energy consumers in supermarkets, and therefore particular attention needs to be paid to increasing energy efficiency to reduce overall energy consumption and meet climate goals by 2030. This study investigates the performance of a CO [...] Read more.

Commercial refrigeration systems represent some of the largest energy consumers in supermarkets, and therefore particular attention needs to be paid to increasing energy efficiency to reduce overall energy consumption and meet climate goals by 2030. This study investigates the performance of a CO₂ (R744) commercial refrigeration system with evaporators operating alternately in dry and flooded modes. This operation is possible due to a particular adjustment using liquid sensors installed in the middle of both low-temperature (LT) and medium-temperature (MT) liquid separators, which transmit information to the controllers that regulate the compressor rack and evaporators, to switch from dry to flooded operation when the liquid level rises and vice versa when the level drops. The results show that the correct regulation of the system of 50% with 6K superheat operation and 50% with 3K superheat operation on MT evaporators, respectively, and 50% with 6K superheat operation and 50% with 4K superheat operation on LT evaporators leads to a reduction of energy consumption compared to 100% operation of all evaporators with 6K superheat by 6.9% per year for the compressor rack. Full article

(This article belongs to the Topic Advanced Hybrid Simulation for Future Power and Energy Systems)

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32 pages, 24187 KB

Open AccessArticle

Analyzing CNN-Based Glaucoma Decision Criteria Using Adversarial Examples

by Shinichiro Ishikawa, Hiyori Sakemi, Koki Hirose, Tahsina Nabiha Khan, Kenshin Mizoe, Ikki Osaka, Osamu Fukuda, Nobuhiko Yamaguchi, Masateru Kawakubo and Hiroshi Okumura

Technologies 2026, 14(7), 435; https://doi.org/10.3390/technologies14070435 - 16 Jul 2026

Viewed by 338

Abstract

Glaucoma is a leading cause of blindness, and early detection is critical. Convolutional neural networks (CNNs) have shown impressive performance in glaucoma diagnosis, but their black-box nature remains a barrier to clinical use. Existing explainable AI (XAI) methods such as Grad-CAM have limitations [...] Read more.

Glaucoma is a leading cause of blindness, and early detection is critical. Convolutional neural networks (CNNs) have shown impressive performance in glaucoma diagnosis, but their black-box nature remains a barrier to clinical use. Existing explainable AI (XAI) methods such as Grad-CAM have limitations in identifying and quantifying subtle regional features. In this study, we propose a method to clarify what CNNs focus on by analyzing how model performance changes under localized adversarial noise. Using VGG16 for glaucoma classification, we applied noise generated by the Fast Gradient Sign Method (FGSM) to the whole fundus image and to specific subregions, then compared the impact on classification performance. Results showed that perturbations to the optic disc, especially its outer margin, had the greatest effect on model performance. This suggests that the CNN captures fine anatomical features such as optic disc cupping and neuroretinal rim thinning, which aligns with what ophthalmologists typically look for. At the same time, perturbations in the macula and perivascular regions also affected performance, indicating gaps between current clinical diagnostic criteria and the CNN’s decision-making process. This approach can help establish the clinical reliability of CNNs and may also reveal features that have not been recognized in conventional clinical practice. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)

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16 pages, 463 KB

Open AccessEditorial

Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies: A Synthesis of the First Edition

by Liviu Marian Ungureanu and Iulian Sorin Munteanu

Technologies 2026, 14(7), 434; https://doi.org/10.3390/technologies14070434 - 16 Jul 2026

Viewed by 318

Abstract

The first edition of the Special Issue entitled “Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies”, published in Technologies within the Information and Communication Technologies section, was conceived around a practical observation that is now widely shared in engineering research: automation remains [...] Read more.

The first edition of the Special Issue entitled “Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies”, published in Technologies within the Information and Communication Technologies section, was conceived around a practical observation that is now widely shared in engineering research: automation remains useful, but contemporary technological systems increasingly require autonomy, real-time decision making, transparent reasoning, robust communication, and reliable interaction with the physical world [...] Full article

(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies)

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27 pages, 1820 KB

Open AccessArticle

Physics-Guided Multi-Modal Motion Prediction with Interaction-Aware GRU

by Umut Özkan, Ibraheem Shayea, Leila Rzayeva, Alisher Batkuldin and Nursultan Nyssanov

Technologies 2026, 14(7), 433; https://doi.org/10.3390/technologies14070433 - 15 Jul 2026

Viewed by 390

Abstract

In the Argoverse 2 experiments reported here, the simplest Constant Turn Rate and Acceleration (CTRA) decoder was stable but missed many interaction-driven turns and merges, while residual decoders without enough control improved early displacement but increased final-horizon error. This paper therefore studies a [...] Read more.

In the Argoverse 2 experiments reported here, the simplest Constant Turn Rate and Acceleration (CTRA) decoder was stable but missed many interaction-driven turns and merges, while residual decoders without enough control improved early displacement but increased final-horizon error. This paper therefore studies a compact decoder in which each of the six futures is represented as a CTRA anchor plus an autoregressive position residual. The residual gated recurrent unit (GRU) is initialized from fused target-history, top-k neighbor, and lane-polyline context, and its contribution is scaled by a mode-specific gate and learned exponential decay. On the 10k/2k sanity ablations, CTRA-only decoding reached

{minFDE}_{6} = 7.189

m, while autoregressive residuals with a larger correction GRU reduced it to

4.157

m; removing the gate increased it again to

4.946

m. On the full Argoverse 2 validation split, the final configuration achieves a minimum average displacement error of

{minADE}_{6} = 1.21

m and a minimum final displacement error of

{minFDE}_{6} = 2.78

m. The reported diagnostics show that the compact model generates a useful six-mode set, but still needs better probability ranking for top-1 selection. Full article

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37 pages, 1563 KB

Open AccessArticle

In Pursuit of the Emergence Point: Extracting Phase Transitions in Multi-Agent Communication

by Alexander Chernyavskiy, Ivan Tomilov, Natalia Gusarova and Aleksandra Vatian

Technologies 2026, 14(7), 432; https://doi.org/10.3390/technologies14070432 - 14 Jul 2026

Viewed by 332

Abstract

Modern multi-agent deep reinforcement learning algorithms have demonstrated empirical success in communication games, yet their black box nature precludes the analytical identification of the transition from random babbling to coordinated signalling. This study introduces an explicitly parameterised, interpretable surrogate model of belief evolution [...] Read more.

Modern multi-agent deep reinforcement learning algorithms have demonstrated empirical success in communication games, yet their black box nature precludes the analytical identification of the transition from random babbling to coordinated signalling. This study introduces an explicitly parameterised, interpretable surrogate model of belief evolution in Lewis signalling games. The proposed ordinary differential equation retains the strategic structure of cheap talk while permitting the closed-form computation of the Jacobian spectrum at the uniform babbling equilibrium. It was proven that the onset of communication corresponded to a supercritical pitchfork bifurcation with a critical threshold determined by the dissipation and sensitivity parameters. Consequently, the leading eigenvalue of the dynamics serves as a detector of the emergence point. The analytical predictions were validated through iterative simulations of Lewis signalling games, illustrating how the critical threshold dictates the consistent and stable transition from stochastic babbling to separating equilibrium. Moreover, a phenomenological experiment demonstrates a possible path toward extending spectral diagnostics to policies parameterised by neural networks in a low-dimensional setting, serving as a bridge towards potential method adaptation for general deep reinforcement learning policies, without fully validating the theoretical framework. Full article

(This article belongs to the Section Information and Communication Technologies)

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16 pages, 4220 KB

Open AccessCommunication

Static Verification of the FA125 Hydraulic Drilling Rig Mast Under a Code-Based Load Combination: A Beam–Shell Finite Element Study

by Andrei Dimitrescu, Claudiu Babiș, Iulian Sorin Munteanu and Sorin Alexandru Fica

Technologies 2026, 14(7), 431; https://doi.org/10.3390/technologies14070431 - 14 Jul 2026

Viewed by 504

Abstract

This paper presents a code-based static verification of the FA125 hydraulic drilling rig mast under its governing design load combination. Unlike the previously published dynamic investigation of the same platform, the present work establishes the baseline static load path, identifies the governing structural [...] Read more.

This paper presents a code-based static verification of the FA125 hydraulic drilling rig mast under its governing design load combination. Unlike the previously published dynamic investigation of the same platform, the present work establishes the baseline static load path, identifies the governing structural members, evaluates the local stress state in the mast-to-support connection plates, and computes the effective safety coefficients. The mixed finite element model integrates the lattice mast, the support frame, and the base assembly, utilizing beam elements for the slender load-bearing members and shell elements for the localized plate-type connection regions. The governing load combination encompasses structural self-weight, maximum hook load (14.90 kN), and the reactive torque transmitted by the drilling head (0.50 kNm). The maximum mast-top displacement was limited to 4.75 mm. The critical beam elements were located within the lateral base-support region, developing peak compressive and tensile stresses of 70.08 MPa and 69.21 MPa, respectively. The highest localized shell-level von Mises stress (23.62 MPa) was concentrated within the mast-to-support interface connection plates. The results mathematically confirm that the existing FA125 steel structure satisfies the active design criteria, providing a distinct static reference map required for subsequent structural optimization, lightweighting, and selective material substitution. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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41 pages, 1880 KB

Open AccessSystematic Review

Gesture-Based Navigation of Smart Wheelchairs: A Review of Current Trends and Future Directions

by Rakib Ahammed Diptho, Safiul Haque Chowdhury, Md Abdullah Al Mamun, Md. Shakhawat Hosen, Md. Shamsur Rahman, Sarnali Basak and Md Abul Kalam Azad

Technologies 2026, 14(7), 430; https://doi.org/10.3390/technologies14070430 - 14 Jul 2026

Viewed by 1092

Abstract

Gesture recognition systems powered by artificial intelligence provide a promising solution for mobility and independence for individuals with physical disabilities. However, the deployment of such systems remains limited due to some challenges related to robustness, different user requirements, affordability for lower income people, [...] Read more.

Gesture recognition systems powered by artificial intelligence provide a promising solution for mobility and independence for individuals with physical disabilities. However, the deployment of such systems remains limited due to some challenges related to robustness, different user requirements, affordability for lower income people, and adaptation to low-resource environments. This study presents a systematic review of gesture-controlled intelligent wheelchair systems published recently. After searching academic databases, 600 studies were found. After removing duplicate and irrelevant studies and applying the inclusion and exclusion criteria, 72 of the most relevant studies were selected for detailed analysis. The review identifies three major approaches: vision-based methods, sensor-based techniques, and signal-based techniques utilizing electromyography (EMG) and inertial measurement units (IMU), and hybrid multimodal frameworks. A comparative study is conducted to analyze performance metrics, computational requirements, datasets, and validation strategies among these approaches. The findings identify several critical research gaps, including limited real-world testing, insufficient handling of pathological tremors, weak environmental robustness, and the lack of culturally aligned gesture vocabularies. The findings identify important design considerations and research directions for developing robust, affordable, and accessible intelligent wheelchair systems suitable for underserved people in low-resource environments. Full article

(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives—Second Edition)

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27 pages, 37501 KB

Open AccessArticle

An Improved A* Path Planning Method for Unmanned Vehicles in Off-Road Environments Based on Geometric and Support Passability Analysis

by Pengfei Zhang, Jinshuai Liu, Rong Hou, Yawen Li, Yuhan Wang, Zhengxuan Li and Huiyan Han

Technologies 2026, 14(7), 429; https://doi.org/10.3390/technologies14070429 - 14 Jul 2026

Viewed by 210

Abstract

To address the insufficient representation of terrain constraints and surface resistance in traditional path planning for off-road environments, this study proposes an improved A* path planning method for unmanned ground vehicles. First, an off-road environment model is constructed using Digital Elevation Model (DEM) [...] Read more.

To address the insufficient representation of terrain constraints and surface resistance in traditional path planning for off-road environments, this study proposes an improved A* path planning method for unmanned ground vehicles. First, an off-road environment model is constructed using Digital Elevation Model (DEM) and land cover data, and environment–vehicle traversability is evaluated by integrating geometric and support-based traversability analyses. Geometric constraints are determined using slope thresholds, minimum ground clearance, and approach/departure angles, while support-based traversability is quantified through a surface velocity influence coefficient to reflect traversal-efficiency differences under various surface conditions. These terrain and surface constraints are incorporated into the actual cost function of the A* algorithm, and a direction-corrected heuristic function is designed to enhance goal-directed search. Experiments conducted in Jiancaoping District, Taiyuan, show that, compared with the traditional A* algorithm, the proposed method reduces cumulative travel time, maximum path slope, and expanded nodes by 15.3%, 22.9%, and 47.8%, respectively, with only a 2.4% increase in path length. The results demonstrate that the proposed method effectively avoids steep and high-resistance areas while achieving coordinated optimization of path length, traversal efficiency, and terrain safety. Full article

(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies—2nd Edition)

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33 pages, 6016 KB

Open AccessArticle

Planning and Design of a Photovoltaic Solar-Energy-Generation System in the Southeastern Amazon Region of Ecuador

by Carlos Brito-Brito, Luis Córdova-Cajamarca and Daniel Icaza-Alvarez

Technologies 2026, 14(7), 428; https://doi.org/10.3390/technologies14070428 - 14 Jul 2026

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Abstract

This research evaluates the feasibility of implementing photovoltaic solar systems in the Ecuadorian Amazon to harness solar energy and increase energy security in the region. It is based on the need to reduce direct dependence on fossil fuels and existing hydroelectric systems. The [...] Read more.

This research evaluates the feasibility of implementing photovoltaic solar systems in the Ecuadorian Amazon to harness solar energy and increase energy security in the region. It is based on the need to reduce direct dependence on fossil fuels and existing hydroelectric systems. The overall framework is to transform the energy matrix to utilize incident solar energy, integrating it with current hydroelectric and thermal generation. The fundamental goal is to evaluate the energy resource using specialized software such as Homer Pro and develop designs for the proper operation of photovoltaic solar technology, which will contribute its surplus energy to the National Interconnected System (SNI) and, therefore, reduce the country’s high dependence on the hydrological cycle. The results obtained demonstrate that solar power plants can be of great benefit to the country, especially when combined with wind and existing hydroelectric power. This will contribute to the diversification of energy sources and, consequently, to energy security through the increase in renewable energy. In the worst-case scenario, the cost of energy can be 7 cents per kWh, and in the best-case scenario, in a combined dispatch, 3 cents per kWh. Full article

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16 pages, 5157 KB

Open AccessArticle

A Robust Tunable Simulator of Atmospheric Turbulence for Performance Analysis of Wireless Optical Links

by Ilya Galaktionov

Technologies 2026, 14(7), 427; https://doi.org/10.3390/technologies14070427 - 14 Jul 2026

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Abstract

Atmospheric turbulence distorts the wavefront of propagating optical radiation, degrading image resolution in astronomical telescopes and reducing power density at the target in focusing applications. These effects can be studied under controlled laboratory conditions using turbulence-generating devices—such as fan heaters (rough control), phase [...] Read more.

Atmospheric turbulence distorts the wavefront of propagating optical radiation, degrading image resolution in astronomical telescopes and reducing power density at the target in focusing applications. These effects can be studied under controlled laboratory conditions using turbulence-generating devices—such as fan heaters (rough control), phase plates, or active mirrors (fine control)—in combination with a wavefront sensor for measurements. To support this research, we developed a software simulator for reconstructing atmospheric phase fluctuations. The integrated software–hardware system can generate phase screens following Kolmogorov turbulence statistics, incorporating parameters for wind velocity and the D/r₀ ratio. Phase screens were produced with an average approximation error of 0.01 µm (less than 5%). The average reconstruction error was 0.017 µm, corresponding to approximately 8%. The newly developed phase screen simulator outperforms the fastest existing version in several key aspects. Its aperture size is doubled, increasing from 400 mm to 800 mm, while the phase screen generation resolution expands by half, from 700 × 700 pixels to 1024 × 1024 pixels. The operating wavelength range also broadens significantly—from a maximum of 2.2 µm in the existing tool to 10 µm in the new one. Additionally, the wind velocity range becomes 1.5 times wider, extending from 30 m/s to 50 m/s. The developed tool might be useful for the performance analysis of wireless links, particularly in the estimation of bit error rate and quantum efficiency using the wavefront root mean square error. Full article

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16 pages, 5625 KB

Open AccessArticle

A Comprehensive Evaluation of 3D-Printed Breast Phantoms: Impacts of Printing Technology and STL Processing on Multimodal Fidelity

by Nikolay Dukov, Vencislav Nastev, Viktoria Petkova, Ivan Buliev, Zhivko Bliznakov, Valentina Dobreva and Kristina Bliznakova

Technologies 2026, 14(7), 426; https://doi.org/10.3390/technologies14070426 - 12 Jul 2026

Viewed by 306

Abstract

Anthropomorphic breast phantoms are increasingly used for the development and evaluation of breast imaging technologies. The aim of this study is to evaluate the impacts of the material combination and stereolithography or Standard Tessellation Language (STL) export methodology on the multimodal imaging performance [...] Read more.

Anthropomorphic breast phantoms are increasingly used for the development and evaluation of breast imaging technologies. The aim of this study is to evaluate the impacts of the material combination and stereolithography or Standard Tessellation Language (STL) export methodology on the multimodal imaging performance of patient-derived breast phantoms. A breast model derived from segmented magnetic resonance imaging (MRI) data was used to fabricate four multi-material phantom sections representing adipose- and glandular-equivalent regions. Two material combinations—acrylic styrene acrylonitrile and high-impact polystyrene (ASA-HIPS) and acrylic styrene acrylonitrile and acrylonitrile butadiene styrene (ASA-ABS)—and two STL export procedures were evaluated, including a conventional mesh-based workflow and an in-house voxel-preserving approach designed to eliminate interface gaps. The phantoms were imaged using clinical computed tomography (CT), mammography, and digital breast tomosynthesis systems. Quantitative evaluation included contrast-based image characteristics and region of interest-based intensity distribution analysis. The results showed that ASA-HIPS phantoms demonstrated higher inter-material contrast and greater attenuation separation than ASA-ABS across all imaging modalities. The voxel-preserving STL export procedure improved physical interface integrity and eliminated visible inter-material gaps, while producing only minor differences in global radiological metrics compared with the conventional workflow. CT imaging of the breast samples acquired at 70 kVp showed attenuation characteristics consistent with clinically reported Hounsfield unit ranges for low-density breast tissues observed in clinical CT examinations. Full article

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22 pages, 19724 KB

Open AccessArticle

KNA-SG: Keyframe–Node-Associated Open-Vocabulary 3D Scene Graphs from RGB Sequences

by Yangbin Xu, Wenhui Shi, Jing Xing, Jiangang Yang and Jian Liu

Technologies 2026, 14(7), 425; https://doi.org/10.3390/technologies14070425 - 12 Jul 2026

Viewed by 565

Abstract

3D scene graphs organize objects and their relationships in a scene into structured representations, providing an interpretable and queryable foundation for relational reasoning and object grounding. Existing open-vocabulary 3D scene graph construction methods primarily focus on object-level feature representation and open-ended edge reasoning. [...] Read more.

3D scene graphs organize objects and their relationships in a scene into structured representations, providing an interpretable and queryable foundation for relational reasoning and object grounding. Existing open-vocabulary 3D scene graph construction methods primarily focus on object-level feature representation and open-ended edge reasoning. However, they often lack explicit and retrievable associations between object nodes and keyframes, making it difficult to recall relevant visual evidence for target disambiguation and relationship verification in complex queries. Moreover, pre-constructed edges are inherently limited in their ability to cover the diverse linguistic expressions encountered in downstream tasks. To address these limitations, we propose KNA-SG, a framework for constructing open-vocabulary 3D scene graphs from RGB sequences with explicit keyframe–node associations. Built upon instance-grounded 3D reconstruction, KNA-SG represents each object instance as a graph node and uses a unique instance identifier to associate the node with the keyframes in which the instance is observed. The ID-annotated keyframes guide MLLM-based open-vocabulary semantic parsing, enabling semantic attributes to be assigned to these graph nodes. This design transforms keyframes into retrievable visual evidence for target disambiguation and relationship verification during query reasoning. Verified relationships are further written back into the scene graph as reusable relational memory to support subsequent queries. To ensure the effectiveness of selected keyframes, we design a two-stage keyframe selection strategy that combines visual quality assessment with semantic redundancy removal, preserving a set of clear keyframes that provide comprehensive scene coverage. Experimental results show that KNA-SG outperforms existing methods on open-vocabulary 3D semantic segmentation and 3D object grounding tasks. Full article

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16 pages, 884 KB

Open AccessArticle

An Improved Deep Learning Framework for In Situ Detection of Geometric Keypoints of Heliostats in Concentrated Solar Power Plants

by Fen Xu and Hongyu Miao

Technologies 2026, 14(7), 424; https://doi.org/10.3390/technologies14070424 - 11 Jul 2026

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Abstract

In situ detection of the tracking poses of heliostats can help improve the tracking accuracies of heliostats and reduce the task loads of heliostat calibration in a large-scale concentrated solar power (CSP) plant, as the traditional methods normally require the heliostats to be [...] Read more.

In situ detection of the tracking poses of heliostats can help improve the tracking accuracies of heliostats and reduce the task loads of heliostat calibration in a large-scale concentrated solar power (CSP) plant, as the traditional methods normally require the heliostats to be off from sun-tracking during the calibration process. This paper presents a deep learning-based framework for in situ detection of geometric keypoints of the heliostat surface. The proposed framework is built upon YOLOv8-Pose but integrates a high-resolution P2 feature branch to recover fine-grained spatial details that are otherwise lost in deep semantic layers. Further, a geometry-consistency loss is introduced to regularize the predicted quadrilateral, enforcing strict structural integrity under dynamically changing illumination. An experimental study on a real-world heliostat image dataset shows that the proposed framework achieves an end-to-end inference speed of 25.14 FPS. The mean end-point error (EPE) of detected keypoints is around 1.22 pixels, while the stringent mAP@0.5:0.95 metric reaches 0.9823. The keypoint detection framework could be integrated with an in-field heliostat control system for further improvement of the working efficiency of heliostats in a large-scale CSP plant in future. Full article

(This article belongs to the Special Issue Solar Thermal Power Generation Technology)

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