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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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24 pages, 2199 KB  
Review
Smart Walking Aids with Sensor Technology for Gait Support and Health Monitoring: A Scoping Review
by Stefan Resch, Aya Zirari, Thi Diem Quynh Tran, Luca Marco Bauer and Daniel Sanchez-Morillo
Technologies 2025, 13(8), 346; https://doi.org/10.3390/technologies13080346 - 7 Aug 2025
Cited by 3 | Viewed by 6159
Abstract
Smart walking aids represent a growing trend in assistive technologies designed to support individuals with mobility impairments in their daily lives and rehabilitation. Previous research has introduced sensor-integrated systems that provide user feedback to enhance safety and functional mobility. However, a comprehensive overview [...] Read more.
Smart walking aids represent a growing trend in assistive technologies designed to support individuals with mobility impairments in their daily lives and rehabilitation. Previous research has introduced sensor-integrated systems that provide user feedback to enhance safety and functional mobility. However, a comprehensive overview of their technological and functional characteristics is lacking. To address this gap, this scoping review systematically mapped the current state of research in sensor-based walking aids, focusing on device types, sensor technologies, application contexts, target populations, and reported outcomes. In addition, integrated artificial intelligence (AI)-based approaches for functional support and health monitoring were examined. Following PRISMA-ScR guidelines, 35 peer-reviewed articles were identified from three databases: ACM Digital Library, IEEE Xplore, and Web of Science. Extracted data were thematically analyzed and synthesized across device types (e.g., walking canes, crutches, walkers, rollators) and use cases, including gait training, fall prevention, and daily support. Findings show that, while many prototypes show promising features, few have been evaluated in clinical settings or over extended periods. A lack of standardized methods for sensor location assessment, often the superficial implementation of feedback modalities, and limited integration with other assistive technologies were identified. In addition, system validation and user testing lack consensus, with few long-term studies and often incomplete demographic data. Diversity in data communication approaches and the heterogeneous use of AI algorithms were also notable. The review highlights key challenges and research opportunities to guide the future development of intelligent, user-centered mobility systems. Full article
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23 pages, 2455 KB  
Review
Agent-Based Modeling of Epidemics: Approaches, Applications, and Future Directions
by Xiangyu Zhang, Jiaojiao Wang, Chunmiao Yu, Jiaqiang Fei, Tianyi Luo and Zhidong Cao
Technologies 2025, 13(7), 272; https://doi.org/10.3390/technologies13070272 - 26 Jun 2025
Cited by 4 | Viewed by 6519
Abstract
The spread of infectious diseases is inherently linked to human social behavior, characterized by complexity, diversity, and openness. Intelligent agents in computer science provide a powerful framework for capturing such dynamics, enabling complex epidemic patterns to emerge from simple local rules. These agents [...] Read more.
The spread of infectious diseases is inherently linked to human social behavior, characterized by complexity, diversity, and openness. Intelligent agents in computer science provide a powerful framework for capturing such dynamics, enabling complex epidemic patterns to emerge from simple local rules. These agents exhibit self-organization, adaptability, and self-optimization, making them well suited for individual-level modeling. Agent-based models (ABMs) have shown promising results in epidemic simulation and policy evaluation. However, current implementations often suffer from simplistic behavioral assumptions and rigid interaction mechanisms, limiting their realism and flexibility. This paper first reviews the current landscape of epidemic modeling approaches. It then analyzes the underlying mechanisms of advanced intelligent agents, highlighting their modeling capabilities. The study focuses on four key advantages of intelligent agent-based modeling and elaborates on three critical roles these agents play in evaluating and optimizing intervention strategies. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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27 pages, 5926 KB  
Article
Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing
by Waqar Shehbaz and Qingjin Peng
Technologies 2025, 13(6), 228; https://doi.org/10.3390/technologies13060228 - 3 Jun 2025
Cited by 4 | Viewed by 1765
Abstract
Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics, which is often a challenge by experimental methods [...] Read more.
Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics, which is often a challenge by experimental methods alone. Initially, experimental data are generated by systematically varying key AM parameters, layer height, infill density, infill pattern, build orientation, and number of shells. Subsequently, four ML models, Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, are trained and evaluated. Hyperparameter tuning is conducted using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Box constraints (L-BFGS-B) algorithm, which demonstrates the superior computational efficiency compared to traditional approaches such as grid and random search. Among the models, Random Forest yields the highest predictive accuracy and lowest mean squared error across all target sustainability indicators: energy consumption, part weight, scrap weight, and production time. The results confirm the efficacy of ML in predicting sustainability outcomes when supported by robust experimental data. This research offers a scalable and computationally efficient approach to enhancing sustainability in AM processes and contributes to data-driven decision-making in sustainable manufacturing. Full article
(This article belongs to the Special Issue Advanced Manufacturing Technologies: From Material Jetting to 3D Printing)
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20 pages, 1984 KB  
Article
The Use of Perlite and Rhyolite in Concrete Mix Design: Influence on Physical-Mechanical and Environmental Performance
by Giovanna Concu, Marco Zucca, Flavio Stochino, Monica Valdes and Francesca Maltinti
Technologies 2025, 13(6), 224; https://doi.org/10.3390/technologies13060224 - 29 May 2025
Cited by 2 | Viewed by 1845
Abstract
During the last decades, the ever-growing evolution of the construction industry has led to a significant increase in demand for increasingly high-performing construction materials both in terms of mechanical characteristics and sustainability. Focusing on concrete, several researchers have designed different mixes to improve [...] Read more.
During the last decades, the ever-growing evolution of the construction industry has led to a significant increase in demand for increasingly high-performing construction materials both in terms of mechanical characteristics and sustainability. Focusing on concrete, several researchers have designed different mixes to improve mechanical properties such as compressive strength, workability and durability, and in many of the proposed mixes, the use of industrial waste stands out both for their ability to improve the mechanical properties of concrete and for the importance of their reuse from a sustainability point of view. In this paper, the use of two waste materials, perlite and rhyolite, in concrete mix design was studied in detail, considering their influence on the compressive strength at 7 and 28 days of curing. The waste materials were introduced in the mix design as substitutes for cement in percentages of 15% and 30% in weight. In addition, perlite was micronized to two different particle sizes, 20 μm and 63 μm, respectively, according to what is already used in concrete within perlite in the mix design. The behavior of the structural concrete containing perlite and rhyolite was compared in terms of compressive strength, Young modulus and produced equivalent CO2 with that of a standard C25/30 reference concrete, and with that of a mix design created using other waste materials, namely fly ash, metakaolin and silica fume, considering cement replacements that are always at 15% and 30% by weight. Moreover, ultrasonic testing and rebound hammer tests were run to evaluate a possible relationship between the physical-mechanical properties of the design mixes and their volumetric and surface characteristics. Full article
(This article belongs to the Section Construction Technologies)
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25 pages, 2905 KB  
Article
Does the Choice of Topic Modeling Technique Impact the Interpretation of Aviation Incident Reports? A Methodological Assessment
by Aziida Nanyonga, Keith Joiner, Ugur Turhan and Graham Wild
Technologies 2025, 13(5), 209; https://doi.org/10.3390/technologies13050209 - 19 May 2025
Cited by 5 | Viewed by 2101
Abstract
This study presents a comparative analysis of four topic modeling techniques —Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), Probabilistic Latent Semantic Analysis (pLSA), and Non-negative Matrix Factorization (NMF)—applied to aviation safety reports from the ATSB dataset spanning 2013–2023. The evaluation [...] Read more.
This study presents a comparative analysis of four topic modeling techniques —Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), Probabilistic Latent Semantic Analysis (pLSA), and Non-negative Matrix Factorization (NMF)—applied to aviation safety reports from the ATSB dataset spanning 2013–2023. The evaluation focuses on coherence, interpretability, generalization, computational efficiency, and scalability. The results indicate that NMF achieves the highest coherence score (0.7987), demonstrating its effectiveness in extracting well-defined topics from structured narratives. pLSA performs competitively (coherence: 0.7634) but lacks the scalability of NMF. LDA and BERTopic, while effective in generalization (perplexity: −6.471 and −4.638, respectively), struggle with coherence due to their probabilistic nature and reliance on contextual embeddings. A preliminary expert review by two aviation safety specialists found that topics generated by the NMF model were interpretable and aligned well with domain knowledge, reinforcing its potential suitability for such aviation safety analysis. Future research should explore new hybrid modeling approaches and real-time applications to enhance aviation safety analysis further. The study contributes to advancing automated safety monitoring in the aviation industry by refining the most appropriate topic modeling techniques. Full article
(This article belongs to the Special Issue Aviation Science and Technology Applications)
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18 pages, 6692 KB  
Article
Ballistic Testing of an Aerogel/Starch Composite Designed for Use in Wearable Protective Equipment
by John LaRocco, Taeyoon Eom, Tanush Duggisani, Ian Zalcberg, Jinyi Xue, Ekansh Seth, Nicolas Zapata, Dheeraj Anksapuram, Nathaniel Muzumdar and Eric Zachariah
Technologies 2025, 13(5), 199; https://doi.org/10.3390/technologies13050199 - 14 May 2025
Cited by 1 | Viewed by 2786
Abstract
Concussion is a costly healthcare issue affecting sports, industry, and the defense sector. The financial impacts, however, extend beyond acute medical expenses, affecting an individual’s physical and cognitive abilities, as well as increasing the burden on coworkers, family members, and caregivers. More effective [...] Read more.
Concussion is a costly healthcare issue affecting sports, industry, and the defense sector. The financial impacts, however, extend beyond acute medical expenses, affecting an individual’s physical and cognitive abilities, as well as increasing the burden on coworkers, family members, and caregivers. More effective personal protective equipment may greatly reduce the risk of concussion and injury. Notably, aerogels are light, but traditionally fragile, non-Newtonian fluids, such as shear-thickening fluids, which generate more resistance when compressive force is applied. Herein, a composite material was developed by baking a shear-thickening fluid (i.e., starch) and combining it with a commercially available aerogel foam, thus maintaining a low cost. The samples were tested through the use of a ballistic pendulum system, using a spring-powered launcher and a gas-powered cannon, followed by ballistic penetration testing, using two electromagnetic accelerators and two different projectiles. During the cannon tests without a hardhat, the baked composite only registered 31 ± 2% of the deflection height observed for the pristine aerogel. The baked composite successfully protected the hygroelectric devices from coilgun projectiles, whereas the projectiles punctured the pristine aerogel. Leveraging the low-cost design of this new composite, personal protective equipment can be improved for various sporting, industrial, and defense applications. Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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27 pages, 658 KB  
Systematic Review
Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments
by Ricardo Abreu-Dias, Juan M. Santos-Gago, Fernando Martín-Rodríguez and Luis M. Álvarez-Sabucedo
Technologies 2025, 13(5), 187; https://doi.org/10.3390/technologies13050187 - 6 May 2025
Cited by 4 | Viewed by 4180
Abstract
The classification and identification of individual tree species in forest environments are critical for biodiversity conservation, sustainable forestry management, and ecological monitoring. Recent advances in drone technology and artificial intelligence have enabled new methodologies for detecting and classifying trees at an individual level. [...] Read more.
The classification and identification of individual tree species in forest environments are critical for biodiversity conservation, sustainable forestry management, and ecological monitoring. Recent advances in drone technology and artificial intelligence have enabled new methodologies for detecting and classifying trees at an individual level. However, significant challenges persist, particularly in heterogeneous forest environments with high species diversity and complex canopy structures. This systematic review explores the latest research on drone-based data collection and AI-driven classification techniques, focusing on studies that classify specific tree species rather than generic tree detection. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, peer review studies from the last decade were analyzed to identify trends in data acquisition instruments (e.g., RGB, multispectral, hyperspectral, LiDAR), preprocessing techniques, segmentation approaches, and machine learning (ML) algorithms used for classification. Findings of this study reveal that deep learning (DL) models, particularly convolutional neural networks (CNN), are increasingly replacing traditional ML methods such as random forest (RF) or support vector machines (SVMs) because there is no need for a feature extraction phase, as this is implicit in the DL models. The integration of LiDAR with hyperspectral imaging further enhances classification accuracy but remains limited due to cost constraints. Additionally, we discuss the challenges of model generalization across different forest ecosystems and propose future research directions, including the development of standardized datasets and improved model architectures for robust tree species classification. This review provides a comprehensive synthesis of existing methodologies, highlighting both advancements and persistent gaps in AI-driven forest monitoring. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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32 pages, 1004 KB  
Article
Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
by Mfonobong Uko, Sunday Ekpo, Sunday Enahoro, Fanuel Elias, Rahul Unnikrishnan and Yasir Al-Yasir
Technologies 2025, 13(4), 124; https://doi.org/10.3390/technologies13040124 - 22 Mar 2025
Cited by 4 | Viewed by 2395
Abstract
The deployment of fifth-generation (5G) and beyond-5G wireless communication systems necessitates advanced transceiver architectures to support high data rates, spectrum efficiency, and energy-efficient designs. This paper presents a highly adaptive reconfigurable receiver front-end (HARRF) designed for 5G and satellite applications, integrating a switchable [...] Read more.
The deployment of fifth-generation (5G) and beyond-5G wireless communication systems necessitates advanced transceiver architectures to support high data rates, spectrum efficiency, and energy-efficient designs. This paper presents a highly adaptive reconfigurable receiver front-end (HARRF) designed for 5G and satellite applications, integrating a switchable low noise amplifier (LNA) and a single pole double throw (SPDT) switch. The HARRF architecture supports both X-band (8–12 GHz) and K/Ka-band (23–28 GHz) operations, enabling seamless adaptation between radar, satellite communication, and millimeter-wave (mmWave) 5G applications. The proposed receiver front-end employs a 0.15 μm pseudomorphic high electron mobility transistor (pHEMT) process, optimised through a three-stage cascaded LNA topology. A switched-tuned matching network is utilised to achieve reconfigurability between X-band and K/Ka-band. Performance evaluations indicate that the X-band LNA achieves a gain of 23–27 dB with a noise figure below 7 dB, whereas the K/Ka-band LNA provides 23–27 dB gain with a noise figure ranging from 2.3–2.6 dB. The SPDT switch exhibits low insertion loss and high isolation, ensuring minimal signal degradation across operational bands. Network analysis and scattering parameter extractions were conducted using advanced design system (ADS) simulations, demonstrating superior return loss, power efficiency, and impedance matching. Comparative analysis with state-of-the-art designs shows that the proposed HARRF outperforms existing solutions in terms of reconfigurability, stability, and wideband operation. The results validate the feasibility of the proposed reconfigurable RF front-end in enabling efficient spectrum utilisation and energy-efficient transceiver systems for next-generation communication networks. Full article
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26 pages, 5752 KB  
Review
Towards a Holistic Approach for UAV-Based Large-Scale Photovoltaic Inspection: A Review on Deep Learning and Image Processing Techniques
by Zoubir Barraz, Imane Sebari, Kenza Ait El Kadi and Ibtihal Ait Abdelmoula
Technologies 2025, 13(3), 117; https://doi.org/10.3390/technologies13030117 - 14 Mar 2025
Cited by 7 | Viewed by 3311
Abstract
This paper provides an in-depth literature review on image processing techniques, focusing on deep learning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation and geolocation, anomaly classification, and optimizations [...] Read more.
This paper provides an in-depth literature review on image processing techniques, focusing on deep learning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation and geolocation, anomaly classification, and optimizations for model generalization. Furthermore, challenges related to domain adaptation, dataset limitations, and multimodal fusion of RGB and thermal data are also discussed. Finally, research gaps and opportunities are analyzed to create a holistic, scalable, and real-time inspection workflow for large-scale installation. This review serves as a reference for researchers and industry professionals to advance UAV-based PV inspection. Full article
(This article belongs to the Section Environmental Technology)
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29 pages, 4181 KB  
Review
Using Serious Games and Digital Games to Improve Students’ Computational Thinking and Programming Skills in K-12 Education: A Systematic Literature Review
by Sindre Wennevold Gundersen and Georgios Lampropoulos
Technologies 2025, 13(3), 113; https://doi.org/10.3390/technologies13030113 - 11 Mar 2025
Cited by 10 | Viewed by 10231
Abstract
Computational thinking and problem-solving skills have become vital for students to develop. Digital games and serious games are increasingly being used in educational settings and present great potential to aid students’ learning. This study aims to explore the role and impact of serious [...] Read more.
Computational thinking and problem-solving skills have become vital for students to develop. Digital games and serious games are increasingly being used in educational settings and present great potential to aid students’ learning. This study aims to explore the role and impact of serious games and digital games on students’ computational thinking and programming skills in primary, secondary, and K-12 education through a systematic review of the existing literature. Four research questions were set to be examined. Following the PRISMA framework, 78 studies deriving from IEEE, Scopus, and Web of Science over the period of 2011–2024 are examined. The studies are categorized into Theoretical and Review studies, Proposal and Showcase studies, and Experimental and Case studies. Based on the results, serious games and digital games arose as meaningful educational tools that are positively viewed by education stakeholders and that can effectively support and improve K-12 education students’ computational thinking and programming skills. Among the benefits identified, it was revealed that serious games offer enjoyable and interactive learning experiences that can improve students’ learning performance, engagement, and motivation, enhance students’ confidence and focus, and promote self-regulated learning and personalized learning. Additionally, serious games emerged as an educational means that can effectively support social learning and provide real-time feedback. The challenges identified were related to the selection of games and the game-related design elements, decisions, and approaches. Hence, the study highlights the significance of the design of serious games and the need to cultivate students’ computational thinking, problem-solving, and social skills from a young age. Finally, the study reveals key design principles and aspects to consider when developing serious games and digital games and highlights the need to involve education stakeholders throughout the design and development process. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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40 pages, 3792 KB  
Review
Recent Development of Corrosion Inhibitors: Types, Mechanisms, Electrochemical Behavior, Efficiency, and Environmental Impact
by Denisa-Ioana (Gheorghe) Răuță, Ecaterina Matei and Sorin-Marius Avramescu
Technologies 2025, 13(3), 103; https://doi.org/10.3390/technologies13030103 - 5 Mar 2025
Cited by 50 | Viewed by 20337
Abstract
This review examines recent advances in corrosion inhibitor technologies, with a focus on sustainable and environmentally friendly solutions that address both industrial efficiency and environmental safety. Corrosion is a ubiquitous problem, contributing to massive economic losses globally, with costs estimated between 1 and [...] Read more.
This review examines recent advances in corrosion inhibitor technologies, with a focus on sustainable and environmentally friendly solutions that address both industrial efficiency and environmental safety. Corrosion is a ubiquitous problem, contributing to massive economic losses globally, with costs estimated between 1 and 5% of GDP in different countries. Traditional inorganic corrosion inhibitors, while effective, are often based on toxic compounds, necessitating the development of more environmentally friendly and non-toxic alternatives. The present work highlights innovative eco-friendly corrosion inhibitors derived from natural sources, including plant extracts and oils, biopolymers, etc., being biodegradable substances that provide effective corrosion resistance with minimal environmental impact. In addition, this review explores organic–inorganic hybrid inhibitors and nanotechnology-enhanced coatings that demonstrate improved efficiency, durability, and adaptability across industries. Key considerations, such as application techniques, mechanisms of action, and the impact of environmental factors on inhibitor performance, are discussed. This comprehensive presentation aims to contribute to updating the data on the development of advanced corrosion inhibitors capable of meeting the requirements of modern industries while promoting sustainable and safe practices in corrosion management. Full article
(This article belongs to the Section Environmental Technology)
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26 pages, 3719 KB  
Article
Design of Multi-Sourced MIMO Multiband Hybrid Wireless RF-Perovskite Photovoltaic Energy Harvesting Subsystems for IoTs Applications in Smart Cities
by Fanuel Elias, Sunday Ekpo, Stephen Alabi, Mfonobong Uko, Sunday Enahoro, Muhammad Ijaz, Helen Ji, Rahul Unnikrishnan and Nurudeen Olasunkanmi
Technologies 2025, 13(3), 92; https://doi.org/10.3390/technologies13030092 - 1 Mar 2025
Cited by 6 | Viewed by 2978
Abstract
Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband [...] Read more.
Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband hybrid wireless RF-perovskite photovoltaic energy harvesting subsystems for IoT application. The research findings evaluate the efficiency and power output of different RF configurations (1 to 16 antennas) within MIMO RF subsystems. A Delon quadruple rectifier in the RF energy harvesting system demonstrates a system-level power conversion efficiency of 51%. The research also explores the I-V and P-V characteristics of the adopted perovskite tandem cell. This results in an impressive array capable of producing 6.4 V and generating a maximum power of 650 mW. For the first time, the combined mathematical modelling of the system architecture is presented. The achieved efficiency of the combined system is 90% (for 8 MIMO) and 98% (for 16 MIMO) at 0 dBm input RF power. This novel study holds great promise for next-generation 5G/6G smart IoT passive electronics. Additionally, it establishes the hybrid RF-perovskite energy harvester as a promising, compact, and eco-friendly solution for efficiently powering IoT devices in smart cities. This work contributes to the development of sustainable, scalable, and smart energy solutions for IoT integration into smart city infrastructures. Full article
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18 pages, 9690 KB  
Article
Reducing Energy Consumption in Embedded Systems Applications
by Ioannis Sofianidis, Vasileios Konstantakos and Spyridon Nikolaidis
Technologies 2025, 13(2), 82; https://doi.org/10.3390/technologies13020082 - 16 Feb 2025
Cited by 6 | Viewed by 3889
Abstract
One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically [...] Read more.
One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically changing the supply voltage and clock frequency. The proposed approach categorizes tasks according to their demands on timing requirements and analyzes speed–energy efficiency trade-offs. Results strongly indicate that energy performance is improved due to the proper adjustment of configurations towards required tasks. The findings are verified within a set of scenarios that highlight the potential balance between energy economy and operational demands for specialized IoT contexts. Full article
(This article belongs to the Collection Modern Circuits, System Technologies, and Selected Papers from MOCAST)
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28 pages, 12512 KB  
Article
The Design, Simulation, and Construction of an O2, C3H8, and CO2 Gas Detection System Based on the Electrical Response of MgSb2O6 Oxide
by José Trinidad Guillen Bonilla, Maricela Jiménez Rodríguez, Héctor Guillen Bonilla, Alex Guillen Bonilla, Emilio Huízar Padilla, María Eugenia Sánchez Morales, Ariadna Berenice Flores Jiménez and Juan Carlos Estrada Gutiérrez
Technologies 2025, 13(2), 79; https://doi.org/10.3390/technologies13020079 - 13 Feb 2025
Cited by 3 | Viewed by 1732
Abstract
In this paper, the prototype of a gas detector based on the electrical response of MgSb2O6 oxide at 400 °C and with a concentration of 560 ppm was designed, simulated, and fabricated. This design considers a PIC18F4550 microcontroller and a [...] Read more.
In this paper, the prototype of a gas detector based on the electrical response of MgSb2O6 oxide at 400 °C and with a concentration of 560 ppm was designed, simulated, and fabricated. This design considers a PIC18F4550 microcontroller and a response time of 3 s for the sensor. It is worth noting that the response system can be reduced in concordance with the mathematical model of the sensor’s electrical response. The proposed device is capable of detecting one to three gases: O2, C3H8, and CO2. The configuration is achieved through three switches. In programming the prototype, factors such as the gas sensor signals, device configuration, corrective gas signals, and indicator signals were carefully considered. The characteristic of the gas detector is an operational temperature of 400 °C, which is ideal for industrial processing. This can be configured to detect a single gas or all three of them O2,C3H8,and CO2. Each gas type has its corresponding corrective signal and an indicator-led diode. The operation concentration is 560 ppm, the device is scalable, and its programming can be extended to cover industrial networks. Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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22 pages, 14692 KB  
Review
A Systematic Review of Locomotion Assistance Exoskeletons: Prototype Development and Technical Challenges
by Weiqi Lin, Hui Dong, Yongzhuo Gao, Wenda Wang, Yi Long, Long He, Xiwang Mao, Dongmei Wu and Wei Dong
Technologies 2025, 13(2), 69; https://doi.org/10.3390/technologies13020069 - 5 Feb 2025
Cited by 13 | Viewed by 10144
Abstract
Exoskeletons can track the wearer’s movements in real time, thereby enhancing physical performance or restoring mobility for individuals with gait impairments. These wearable assistive devices have demonstrated significant potential in both rehabilitation and industrial applications. This review focuses on the major advancements in [...] Read more.
Exoskeletons can track the wearer’s movements in real time, thereby enhancing physical performance or restoring mobility for individuals with gait impairments. These wearable assistive devices have demonstrated significant potential in both rehabilitation and industrial applications. This review focuses on the major advancements in exoskeleton technology published since 2020, with particular emphasis on the development of structural designs for lower-limb exoskeletons employed in locomotion assistance. We employed a systematic literature review methodology, categorizing the included studies into three main types: rigid exoskeleton, soft exoskeleton, and tethered platform. The current development status of robotic exoskeletons is analyzed based on publication year, system weight, target assistive joints, and main effects. Furthermore, we examine the factors driving these advancements and their implications for the field. The key challenges and opportunities that may influence the future development of exoskeleton technologies are also highlighted in this review. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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45 pages, 7034 KB  
Review
A Review of Fused Filament Fabrication of Metal Parts (Metal FFF): Current Developments and Future Challenges
by Johnson Jacob, Dejana Pejak Simunec, Ahmad E. Z. Kandjani, Adrian Trinchi and Antonella Sola
Technologies 2024, 12(12), 267; https://doi.org/10.3390/technologies12120267 - 19 Dec 2024
Cited by 33 | Viewed by 11263
Abstract
Fused filament fabrication (FFF) is the most widespread and versatile material extrusion (MEX) technique. Although powder-based systems have dominated the metal 3D printing landscape in the past, FFF’s popularity for producing metal parts (“metal FFF”) is growing. Metal FFF starts from a polymer–metal [...] Read more.
Fused filament fabrication (FFF) is the most widespread and versatile material extrusion (MEX) technique. Although powder-based systems have dominated the metal 3D printing landscape in the past, FFF’s popularity for producing metal parts (“metal FFF”) is growing. Metal FFF starts from a polymer–metal composite feedstock and proceeds through three primary stages, namely shaping (i.e., printing), debinding, and sintering. As critically discussed in the present review, the final quality of metal FFF parts is influenced by the characteristics of the composite feedstock, such as the metal loading, polymer backbone, and presence of additives, as well as by the processing conditions. The literature shows that a diverse array of metals, including steel, copper, titanium, aluminium, nickel, and their alloys, can be successfully used in metal FFF. However, the formulation of appropriate polymer binders represents a hurdle to the adoption of new material systems. Meanwhile, intricate geometries are difficult to fabricate due to FFF-related surface roughness and sintering-induced shrinkage. Nonetheless, the comparison of metal FFF with other common metal AM techniques conducted herein suggests that metal FFF represents a convenient option, especially for prototyping and small-scale production. Whilst providing insights into the functioning mechanisms of metal FFF, the present review offers valuable recommendations, facilitating the broader uptake of metal FFF across various industries. Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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29 pages, 6302 KB  
Review
Impact of 3D Digitising Technologies and Their Implementation
by Paula Triviño-Tarradas, Diego Francisco García-Molina and José Ignacio Rojas-Sola
Technologies 2024, 12(12), 260; https://doi.org/10.3390/technologies12120260 - 14 Dec 2024
Cited by 4 | Viewed by 4074
Abstract
In recent years, 3D digitalisation has experienced significant growth, revolutionising the way we capture, process and use geometric data. Initially conceived for industrial applications, these technologies have expanded to multiple fields, offering unprecedented accuracy and versatility. Depending on the accuracy and efficiency to [...] Read more.
In recent years, 3D digitalisation has experienced significant growth, revolutionising the way we capture, process and use geometric data. Initially conceived for industrial applications, these technologies have expanded to multiple fields, offering unprecedented accuracy and versatility. Depending on the accuracy and efficiency to be achieved in a specific field of application, and on the analytical capacity, a specific 3D digitalisation technique or another will be used. This review aims to delve into the application of 3D scanning techniques, according to the implementation sector. The optimal geometry capturing and processing 3D data techniques for a specific case are studied as well as their limitations. Full article
(This article belongs to the Section Assistive Technologies)
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22 pages, 13321 KB  
Article
Particle Movement in DEM Models and Artificial Neural Network for Validation by Using Contrast Points
by Barbora Černilová, Jiří Kuře, Rostislav Chotěborský and Miloslav Linda
Technologies 2024, 12(12), 257; https://doi.org/10.3390/technologies12120257 - 12 Dec 2024
Cited by 4 | Viewed by 2752
Abstract
The calibration and validation of input parameters in the Discrete Element Method (DEM) are crucial for accurately simulating physical processes, typically achieved through experimental particle behavior analysis. Enhancing the accuracy of DEM models allows for more reliable predictions of material behavior, which is [...] Read more.
The calibration and validation of input parameters in the Discrete Element Method (DEM) are crucial for accurately simulating physical processes, typically achieved through experimental particle behavior analysis. Enhancing the accuracy of DEM models allows for more reliable predictions of material behavior, which is essential for optimizing engineering applications that involve particulate materials. In this study, we present a methodology for analyzing the movement properties of particulate materials, employing a combination of Caliscope software to obtain the real-world co-ordinates based on pixel values from both cameras and artificial neural networks for regression as straightforward and efficient tools. This approach enables the validation and calibration of digital twins of particulate matter systems with respect to motion characteristics. The method of contrast points was utilized to acquire spatial co-ordinates of particulate material movement from experimental measurements, facilitating precise trajectory determination and the subsequent verification of simulation predictions. The neural network analysis demonstrated high accuracy, achieving R2 values of 0.9988, 0.9972, and 0.9982 for the X–, Y–, and Z–axes, respectively. The standard deviation between the predicted and actual co-ordinates was found to be 1.8 mm. A comparative analysis of particle trajectories from both the model and experimental data indicated strong agreement, underscoring the soundness and reliability of this approach. Full article
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21 pages, 1503 KB  
Article
Deep Neural Networks for Estimating Regularization Parameter in Sparse Time–Frequency Reconstruction
by Vedran Jurdana
Technologies 2024, 12(12), 251; https://doi.org/10.3390/technologies12120251 - 1 Dec 2024
Cited by 2 | Viewed by 4055
Abstract
Time–frequency distributions (TFDs) are crucial for analyzing non-stationary signals. Compressive sensing (CS) in the ambiguity domain offers an approach for TFD reconstruction with high performance, but selecting the optimal regularization parameter for various signals remains challenging. Traditional methods for parameter selection, including manual [...] Read more.
Time–frequency distributions (TFDs) are crucial for analyzing non-stationary signals. Compressive sensing (CS) in the ambiguity domain offers an approach for TFD reconstruction with high performance, but selecting the optimal regularization parameter for various signals remains challenging. Traditional methods for parameter selection, including manual and experimental approaches, as well as existing optimization procedures, can be imprecise and time-consuming. This study introduces a novel approach using deep neural networks (DNNs) to predict regularization parameters based on Wigner–Ville distributions (WVDs). The proposed DNN is trained on a comprehensive dataset of synthetic signals featuring multiple linear and quadratic frequency-modulated components, with variations in component amplitudes and random positions, ensuring wide applicability and robustness. By utilizing DNNs, end-users need only provide the signal’s WVD, eliminating the need for manual parameter selection and lengthy optimization procedures. Comparisons between the reconstructed TFDs using the proposed DNN-based approach and existing optimization methods highlight significant improvements in both reconstruction performance and execution time. The effectiveness of this methodology is validated on noisy synthetic and real-world signals, emphasizing the potential of DNNs to automate regularization parameter determination for CS-based TFD reconstruction in diverse signal environments. Full article
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12 pages, 4513 KB  
Article
Malaria Cell Image Classification Using Compact Deep Learning Architectures on Jetson TX2
by Adán-Antonio Alonso-Ramírez, Alejandro-Israel Barranco-Gutiérrez, Iris-Iddaly Méndez-Gurrola, Marcos Gutiérrez-López, Juan Prado-Olivarez, Francisco-Javier Pérez-Pinal, J. Jesús Villegas-Saucillo, Jorge-Alberto García-Muñoz and Carlos-Hugo García-Capulín
Technologies 2024, 12(12), 247; https://doi.org/10.3390/technologies12120247 - 27 Nov 2024
Cited by 3 | Viewed by 4332
Abstract
Malaria is a significant global health issue, especially in tropical regions. Accurate and rapid diagnosis is critical for effective treatment and reducing mortality rates. Traditional diagnostic methods, like blood smear microscopy, are time-intensive and prone to error. This study introduces a deep learning [...] Read more.
Malaria is a significant global health issue, especially in tropical regions. Accurate and rapid diagnosis is critical for effective treatment and reducing mortality rates. Traditional diagnostic methods, like blood smear microscopy, are time-intensive and prone to error. This study introduces a deep learning approach for classifying malaria-infected cells in blood smear images using convolutional neural networks (CNNs); Six CNN models were designed and trained using a large labeled dataset of malaria cell images, both infected and uninfected, and were implemented on the Jetson TX2 board to evaluate them. The model was optimized for feature extraction and classification accuracy, achieving 97.72% accuracy, and evaluated using precision, recall, and F1-score metrics and execution time. Results indicate deep learning significantly improves diagnostic time efficiency on embedded systems. This scalable, automated solution is particularly useful in resource-limited areas without access to expert microscopic analysis. Future work will focus on clinical validation. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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18 pages, 5773 KB  
Article
Isolated High-Gain DC-DC Converter with Nanocrystalline-Core Transformer: Achieving 1:16 Voltage Boost for Renewable Energy Applications
by Tania Sandoval-Valencia, Dante Ruiz-Robles, Jorge Ortíz-Marín, Jesus Alejandro Franco, Quetzalcoatl Hernandez-Escobedo and Edgar Moreno-Goytia
Technologies 2024, 12(12), 246; https://doi.org/10.3390/technologies12120246 - 27 Nov 2024
Cited by 2 | Viewed by 2855
Abstract
This paper presents an isolated DC-DC converter with high voltage gain that features an advanced inter-built nanocrystalline-core medium-frequency transformer (NC-MFT). The isolated DC-DC converter with an NC-MFT is specifically designed for applications such as interconnect photovoltaic (PV) systems, DC microgrids, DC loads, and [...] Read more.
This paper presents an isolated DC-DC converter with high voltage gain that features an advanced inter-built nanocrystalline-core medium-frequency transformer (NC-MFT). The isolated DC-DC converter with an NC-MFT is specifically designed for applications such as interconnect photovoltaic (PV) systems, DC microgrids, DC loads, and DC buses, where voltage gain is one of the essential issues to consider. The NC-MFT inside the DC-DC converter is designed with a new approach that not only provides isolation but also contributes to achieving high efficiency and a higher step-up ratio. The high efficiency of the converters contributes to the integration of PV systems into DC microgrids. The converter yields a high voltage conversion ratio of 16.17. The experimental results obtained at 41.8 V/676 V and 275 W for the prototype revealed high efficiency (95.63% at full load). The experimental results validate the theoretical analysis and simulation, confirming that the converter achieves the main objective of high voltage conversion and high efficiency. These results will contribute to the interest in the use of this type of energy and its impact on the reduction in CO2 emissions. Full article
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29 pages, 2679 KB  
Article
Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques
by Claudio Urrea and Carlos Domínguez
Technologies 2024, 12(11), 225; https://doi.org/10.3390/technologies12110225 - 8 Nov 2024
Cited by 7 | Viewed by 3009
Abstract
This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, [...] Read more.
This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications. Full article
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20 pages, 2669 KB  
Review
Exploring Silica Nanoparticles: A Sustainable Solution for Pest Control in Sri Lankan Rice Farming
by Zeyu Wang, Nirusha Thavarajah and Xavier Fernando
Technologies 2024, 12(11), 210; https://doi.org/10.3390/technologies12110210 - 23 Oct 2024
Cited by 3 | Viewed by 5768
Abstract
Rice cultivation stands as a cornerstone of Sri Lanka’s economy, serving as a vital source of employment for rural communities. However, the constraints of limited land availability have prompted an escalating dependence on agrochemicals, notably for pest management, thereby posing significant threats to [...] Read more.
Rice cultivation stands as a cornerstone of Sri Lanka’s economy, serving as a vital source of employment for rural communities. However, the constraints of limited land availability have prompted an escalating dependence on agrochemicals, notably for pest management, thereby posing significant threats to human health and the environment. This review delves into the exploration of silica nanoparticles as a promising eco-friendly substitute for conventional pesticides in the context of Sri Lankan rice farming. It comprehensively examines various aspects, including the synthesis methods of silica nanoparticles, their encapsulation with synthetic pesticides, and an evaluation of their efficacy in pest control. Furthermore, it sheds light on the innovative utilization of agricultural waste such as rice husk and straw in the production of silica-based nanopesticides. This approach not only demonstrates a shift towards sustainable agricultural practices but also aligns with the principles of green chemistry and circular economy, offering a holistic solution to the challenges faced by the rice farming sector in Sri Lanka. Full article
(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)
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20 pages, 4236 KB  
Article
Enhancing Autonomous Visual Perception in Challenging Environments: Bilateral Models with Vision Transformer and Multilayer Perceptron for Traversable Area Detection
by Claudio Urrea and Maximiliano Vélez
Technologies 2024, 12(10), 201; https://doi.org/10.3390/technologies12100201 - 17 Oct 2024
Cited by 4 | Viewed by 3711
Abstract
The development of autonomous vehicles has grown significantly recently due to the promise of improving safety and productivity in cities and industries. The scene perception module has benefited from the latest advances in computer vision and deep learning techniques, allowing the creation of [...] Read more.
The development of autonomous vehicles has grown significantly recently due to the promise of improving safety and productivity in cities and industries. The scene perception module has benefited from the latest advances in computer vision and deep learning techniques, allowing the creation of more accurate and efficient models. This study develops and evaluates semantic segmentation models based on a bilateral architecture to enhance the detection of traversable areas for autonomous vehicles on unstructured routes, particularly in datasets where the distinction between the traversable area and the surrounding ground is minimal. The proposed hybrid models combine Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and Multilayer Perceptron (MLP) techniques, achieving a balance between precision and computational efficiency. The results demonstrate that these models outperform the base architectures in prediction accuracy, capturing distant details more effectively while maintaining real-time operational capabilities. Full article
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15 pages, 5547 KB  
Article
Improvement of Sound-Absorbing Wool Material by Laminating Permeable Nonwoven Fabric Sheet and Nonpermeable Membrane
by Shuichi Sakamoto, Kodai Sato and Gaku Muroi
Technologies 2024, 12(10), 195; https://doi.org/10.3390/technologies12100195 - 12 Oct 2024
Cited by 2 | Viewed by 3220
Abstract
Thin sound-absorbing materials are particularly desired in space-constrained applications, such as in the automotive industry. In this study, we theoretically analyzed the structure of relatively thin glass wool or polyester wool laminated with a nonpermeable polyethylene membrane and a permeable nonwoven fabric sheet. [...] Read more.
Thin sound-absorbing materials are particularly desired in space-constrained applications, such as in the automotive industry. In this study, we theoretically analyzed the structure of relatively thin glass wool or polyester wool laminated with a nonpermeable polyethylene membrane and a permeable nonwoven fabric sheet. We also measured and compared the sound-absorption coefficients of these samples between experimental and theoretical values. The sound-absorption coefficient was derived using the transfer matrix method. The Rayleigh model was applied to describe the acoustic behavior of glass wool and nonwoven sheet, while the Miki model was used for polyester wool. Mathematical formulas were employed to model an air layer without damping and a vibrating membrane. These acoustic components were integrated into a transfer matrix framework to calculate the sound-absorption coefficient. The sound-absorption coefficients of glass wool and polyester wool were progressively enhanced by sequentially adding suitable nonwoven fabric and PE membranes. A sample approximately 10 mm thick, featuring permeable and nonpermeable membranes as outer layers of porous sound-absorbing material, achieved a sound-absorption coefficient equivalent to that of a sample occupying 20 mm thickness (10 mm of porous sound-absorbing material with a 10 mm back air layer). Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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20 pages, 4837 KB  
Article
Optical Particle Tracking in the Pneumatic Conveying of Metal Powders through a Thin Capillary Pipe
by Lorenzo Pedrolli, Luigi Fraccarollo, Beatriz Achiaga and Alejandro Lopez
Technologies 2024, 12(10), 191; https://doi.org/10.3390/technologies12100191 - 3 Oct 2024
Viewed by 5332
Abstract
Directed Energy Deposition (DED) processes necessitate a consistent material flow to the melt pool, typically achieved through pneumatic conveying of metal powder via thin pipes. This study aims to record and analyze the multiphase fluid–solid flow. An experimental setup utilizing a high-speed camera [...] Read more.
Directed Energy Deposition (DED) processes necessitate a consistent material flow to the melt pool, typically achieved through pneumatic conveying of metal powder via thin pipes. This study aims to record and analyze the multiphase fluid–solid flow. An experimental setup utilizing a high-speed camera and specialized optics was constructed, and the flow through thin transparent pipes was recorded. The resulting information was analyzed and compared with coupled Computational Fluid Dynamics-Discrete Element Modeling (CFD-DEM) simulations, with special attention to the solids flow fluctuations. The proposed methodology shows a significant improvement in accuracy and reliability over existing approaches, particularly in capturing flow rate fluctuations and particle velocity distributions in small-scale systems. Moreover, it allows for accurately analyzing Particle Size Distribution (PSD) in the same setup. This paper details the experimental design, video analysis using particle tracking, and a novel method for deriving volumetric concentrations and flow rate from flat images. The findings confirm the accuracy of the CFD-DEM simulations and provide insights into the dynamics of pneumatic conveying and individual particle movement, with the potential to improve DED efficiency by reducing variability in material deposition rates. Full article
(This article belongs to the Section Manufacturing Technology)
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19 pages, 6834 KB  
Article
Advancing Nanopulsed Plasma Bubbles for the Degradation of Organic Pollutants in Water: From Lab to Pilot Scale
by Stauros Meropoulis and Christos A. Aggelopoulos
Technologies 2024, 12(10), 189; https://doi.org/10.3390/technologies12100189 - 3 Oct 2024
Cited by 12 | Viewed by 4494
Abstract
The transition from lab-scale studies to pilot-scale applications is a critical step in advancing water remediation technologies. While laboratory experiments provide valuable insights into the underlying mechanisms and method effectiveness, pilot-scale studies are essential for evaluating their practical feasibility and scalability. This progression [...] Read more.
The transition from lab-scale studies to pilot-scale applications is a critical step in advancing water remediation technologies. While laboratory experiments provide valuable insights into the underlying mechanisms and method effectiveness, pilot-scale studies are essential for evaluating their practical feasibility and scalability. This progression addresses challenges related to operational conditions, effectiveness and energy requirements in real-world scenarios. In this study, the potential of nanopulsed plasma bubbles, when scaled up from a lab environment, was explored by investigating critical experimental parameters, such as plasma gas, pulse voltage, and pulse repetition rate, while also analyzing plasma-treated water composition. To validate the broad effectiveness of this method, various classes of highly toxic organic pollutants were examined in terms of pollutant degradation efficiency and energy requirements. The pilot-scale plasma bubble reactor generated a high concentration of short-lived reactive species with minimal production of long-lived species. Additionally, successful degradation of all pollutants was achieved in both lab- and pilot-scale setups, with even lower electrical energy-per-order (EEO) values at the pilot scale, 2–3 orders of magnitude lower compared to other advanced oxidation processes. This study aimed to bridge the gap between lab-scale plasma bubbles and upscaled systems, supporting the rapid, effective, and energy-efficient destruction of organic pollutants in water. Full article
(This article belongs to the Section Environmental Technology)
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23 pages, 3516 KB  
Article
Proposed Modbus Extension Protocol and Real-Time Communication Timing Requirements for Distributed Embedded Systems
by Nicoleta Cristina Găitan, Ionel Zagan and Vasile Gheorghiță Găitan
Technologies 2024, 12(10), 187; https://doi.org/10.3390/technologies12100187 - 2 Oct 2024
Cited by 10 | Viewed by 6015
Abstract
The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and [...] Read more.
The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and remains the foundation for the development of numerous advanced communication protocols. In this paper, the conducted research resulted in a major contribution; specifically, it describes the mathematical model for the Modbus protocol and defines the acquisition cycle model that corresponds to incompletely defined protocols in order to provide a timestamp and achieve temporal consistency for proposed Modbus Extension. The derived technical contribution of the authors is to exemplify the functionality of a typical industrial protocol that can be decomposed to improve the performance of data acquisition systems. Research results in this area have significant implications for innovations in industrial automation networking because of increasing distributed installations and Industrial Internet of Things (IIoT) applications. Full article
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12 pages, 12365 KB  
Article
Comparing Elastocaloric Cooling and Desiccant Wheel Dehumidifiers for Atmospheric Water Harvesting
by John LaRocco, Qudsia Tahmina, John Simonis and Vidhaath Vedati
Technologies 2024, 12(10), 178; https://doi.org/10.3390/technologies12100178 - 30 Sep 2024
Cited by 1 | Viewed by 8079
Abstract
Approximately two billion people worldwide lack access to clean drinking water, negatively impacting national security, hygiene, and agriculture. Atmospheric water harvesting (AWH) is the conversion of ambient humidity into clean water; however, conventional dehumidification is energy-intensive. Improvement in AWH may be achieved with [...] Read more.
Approximately two billion people worldwide lack access to clean drinking water, negatively impacting national security, hygiene, and agriculture. Atmospheric water harvesting (AWH) is the conversion of ambient humidity into clean water; however, conventional dehumidification is energy-intensive. Improvement in AWH may be achieved with elastocaloric cooling, using temperature-sensitive materials in active thermoregulation. Potential benefits, compared to conventional desiccant wheel designs, include substantial reductions in energy use, size, and complexity. A nickel–titanium (NiTi) elastocaloric water harvester was designed and compared with a desiccant wheel design under controlled conditions of relative humidity, air volume, and power. In a 30 min interval, the NiTi device harvested more water on average at 0.18 ± 0.027 mL/WH, compared to the 0.1567 ± 0.023 mL/WH of the desiccant wheel harvester. Moreover, the NiTi harvester required half the power input and was thermoregulated more efficiently. Future work will focus on mechanical design parameter optimization. Elastocaloric cooling is a promising advancement in dehumidification, making AWH more economical and feasible. Full article
(This article belongs to the Section Environmental Technology)
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30 pages, 1427 KB  
Review
Wearable Fall Detectors Based on Low Power Transmission Systems: A Systematic Review
by Manny Villa and Eduardo Casilari
Technologies 2024, 12(9), 166; https://doi.org/10.3390/technologies12090166 - 13 Sep 2024
Cited by 8 | Viewed by 5943
Abstract
Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living [...] Read more.
Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living alone, the development of automatic fall alerting systems has garnered significant research attention over the past decade. A key element for deploying a fall detection system (FDS) based on wearables is the wireless transmission method employed to transmit the medical alarms. In this regard, the vast majority of prototypes in the related literature utilize short-range technologies, such as Bluetooth, which must be complemented by the existence of a gateway device (e.g., a smartphone). In other studies, standards like Wi-Fi or 3G communications are proposed, which offer greater range but come with high power consumption, which can be unsuitable for most wearables, and higher service fees. In addition, they require reliable radio coverage, which is not always guaranteed in all application scenarios. An interesting alternative to these standards is Low Power Wide Area Network (LPWAN) technologies, which minimize both energy consumption and hardware costs while maximizing transmission range. This article provides a comprehensive search and review of that works in the literature that have implemented and evaluated wearable FDSs utilizing LPWAN interfaces to transmit alarms. The review systematically examines these proposals, considering various operational aspects and identifying key areas that have not yet been adequately addressed for the viable implementation of such detectors. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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16 pages, 533 KB  
Article
Regularizing Lifetime Drift Prediction in Semiconductor Electrical Parameters with Quantile Random Forest Regression
by Lukas Sommeregger and Jürgen Pilz
Technologies 2024, 12(9), 165; https://doi.org/10.3390/technologies12090165 - 13 Sep 2024
Cited by 2 | Viewed by 3246
Abstract
Semiconductors play a crucial role in a wide range of applications and are integral to essential infrastructures. Manufacturers of these semiconductors must meet specific quality and lifetime targets. To estimate the lifetime of semiconductors, accelerated stress tests are conducted. This paper introduces a [...] Read more.
Semiconductors play a crucial role in a wide range of applications and are integral to essential infrastructures. Manufacturers of these semiconductors must meet specific quality and lifetime targets. To estimate the lifetime of semiconductors, accelerated stress tests are conducted. This paper introduces a novel approach to modeling drift in discrete electrical parameters within stress test devices. It incorporates a machine learning (ML) approach for arbitrary panel data sets of electrical parameters from accelerated stress tests. The proposed model involves an expert-in-the-loop MLOps decision process, allowing experts to choose between an interpretable model and a robust ML algorithm for regularization and fine-tuning. The model addresses the issue of outliers influencing statistical models by employing regularization techniques. This ensures that the model’s accuracy is not compromised by outliers. The model uses interpretable statistically calculated limits for lifetime drift and uncertainty as input data. It then predicts these limits for new lifetime stress test data of electrical parameters from the same technology. The effectiveness of the model is demonstrated using anonymized real data from Infineon technologies. The model’s output can help prioritize parameters by the level of significance for indication of degradation over time, providing valuable insights for the analysis and improvement of electrical devices. The combination of explainable statistical algorithms and ML approaches enables the regularization of quality control limit calculations and the detection of lifetime drift in stress test parameters. This information can be used to enhance production quality by identifying significant parameters that indicate degradation and detecting deviations in production processes. Full article
(This article belongs to the Topic Advances in Microelectronics and Semiconductor Engineering)
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38 pages, 17450 KB  
Article
Open-Source Hardware Design of Modular Solar DC Nanogrid
by Md Motakabbir Rahman, Sara Khan and Joshua M. Pearce
Technologies 2024, 12(9), 167; https://doi.org/10.3390/technologies12090167 - 13 Sep 2024
Cited by 2 | Viewed by 5053
Abstract
The technical feasibility of solar photovoltaic (PV) direct current (DC) nanogrids is well established, but the components of nanogrids are primarily commercially focused on alternating current (AC)-based systems. Thus, DC converter-based designs at the system level require personnel with high degree of technical [...] Read more.
The technical feasibility of solar photovoltaic (PV) direct current (DC) nanogrids is well established, but the components of nanogrids are primarily commercially focused on alternating current (AC)-based systems. Thus, DC converter-based designs at the system level require personnel with high degree of technical knowledge, which results in high costs. To enable a democratization of the technology by reducing the costs, this study provides a novel modular plug-and-play open-source DC nanogrid. The system can be customized according to consumer requirements, enabling the supply of various voltage levels to accommodate different device voltage needs. The step-by-step design process of the converter, controller, data logger, and assembly of the complete system is provided. A time-domain simulation and stability analysis of the designed system were conducted in MATLAB/Simulink (version 2024b) as well as experimental validation. The results show that transforming the nanogrid from a distribution network to a device makes it suitable for various user-specific applications, such as remotely supplying power to campsites, emergency vehicles like ambulances, and small houses lacking grid electricity. The modular DC nanogrid includes all the features available in a DC distribution network, as well as data logging, which enhances the user experience and promotes the use of solar-powered DC grid systems. Full article
(This article belongs to the Section Environmental Technology)
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31 pages, 73552 KB  
Article
Enhancing 3D Rock Localization in Mining Environments Using Bird’s-Eye View Images from the Time-of-Flight Blaze 101 Camera
by John Kern, Reinier Rodriguez-Guillen, Claudio Urrea and Yainet Garcia-Garcia
Technologies 2024, 12(9), 162; https://doi.org/10.3390/technologies12090162 - 12 Sep 2024
Cited by 3 | Viewed by 3383
Abstract
The mining industry faces significant challenges in production costs, environmental protection, and worker safety, necessitating the development of autonomous systems. This study presents the design and implementation of a robust rock centroid localization system for mining robotic applications, particularly rock-breaking hammers. The system [...] Read more.
The mining industry faces significant challenges in production costs, environmental protection, and worker safety, necessitating the development of autonomous systems. This study presents the design and implementation of a robust rock centroid localization system for mining robotic applications, particularly rock-breaking hammers. The system comprises three phases: assembly, data acquisition, and data processing. Environmental sensing was accomplished using a Basler Blaze 101 three-dimensional (3D) Time-of-Flight (ToF) camera. The data processing phase incorporated advanced algorithms, including Bird’s-Eye View (BEV) image conversion and You Only Look Once (YOLO) v8x-Seg instance segmentation. The system’s performance was evaluated using a comprehensive dataset of 627 point clouds, including samples from real mining environments. The system achieved efficient processing times of approximately 5 s. Segmentation accuracy was evaluated using the Intersection over Union (IoU), reaching 95.10%. Localization precision was measured by the Euclidean distance in the XY plane (EDXY), achieving 0.0128 m. The normalized error (enorm) on the X and Y axes did not exceed 2.3%. Additionally, the system demonstrated high reliability with R2 values close to 1 for the X and Y axes, and maintained performance under various lighting conditions and in the presence of suspended particles. The Mean Absolute Error (MAE) in the Z axis was 0.0333 m, addressing challenges in depth estimation. A sensitivity analysis was conducted to assess the model’s robustness, revealing consistent performance across brightness and contrast variations, with an IoU ranging from 92.88% to 96.10%, while showing greater sensitivity to rotations. Full article
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26 pages, 8417 KB  
Article
An Innovative Vision-Guided Feeding System for Robotic Picking of Different-Shaped Industrial Components Randomly Arranged
by Nicola Ivan Giannoccaro, Giuseppe Rausa, Roberta Rizzi, Paolo Visconti and Roberto De Fazio
Technologies 2024, 12(9), 153; https://doi.org/10.3390/technologies12090153 - 5 Sep 2024
Cited by 4 | Viewed by 5279
Abstract
Within an industrial plant, the handling of randomly arranged objects is becoming increasingly popular. The technology industry has introduced ever more powerful devices to the market, but they are often unable to meet the demands of the industry in terms of processing times. [...] Read more.
Within an industrial plant, the handling of randomly arranged objects is becoming increasingly popular. The technology industry has introduced ever more powerful devices to the market, but they are often unable to meet the demands of the industry in terms of processing times. Using a multi-component feeder, which facilitates the automatic picking of objects arranged in bulk, is the ideal element to speed up the identification of objects by the vision system. The innovative designed feeder eliminates the dead time of the vision system since the feeder has two working surfaces, thus making the viewing time hidden in relation to the total handling cycle time. In addition, the step feeder integrated into the feeder structure allows for control over the number of objects that fall onto the work surface, optimizing the material flow. The feeder was designed to palletize aluminum hinge fins but can also handle other products with different shapes and sizes. A two-dimensional (2D) vision system is integrated into the robotic cell to identify the components to be palletized, obtaining a reduced cycle time. The innovative feeder is fully adaptable to industrial applications and allows for easy integration into the robotic cell in which it is installed; by testing its operation with different aluminum fins, male and female, significant results were obtained in terms of cycle times ranging from 1.44 s to 1.68 s per piece, with an average productivity level (PL) of 1175 pcs every 30 min. Full article
(This article belongs to the Section Manufacturing Technology)
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21 pages, 5219 KB  
Article
Ensemble Learning for Nuclear Power Generation Forecasting Based on Deep Neural Networks and Support Vector Regression
by Jorge Gustavo Sandoval Simão and Leandro dos Santos Coelho
Technologies 2024, 12(9), 148; https://doi.org/10.3390/technologies12090148 - 2 Sep 2024
Cited by 1 | Viewed by 3376
Abstract
Forecasting nuclear energy production is essential for market operations such as security, economic efficiency, resource optimization, grid stability, and the integration of renewable energy sources. Forecasting approaches allow nuclear power plants to operate consistently, contributing to the overall reliability and long-term viability of [...] Read more.
Forecasting nuclear energy production is essential for market operations such as security, economic efficiency, resource optimization, grid stability, and the integration of renewable energy sources. Forecasting approaches allow nuclear power plants to operate consistently, contributing to the overall reliability and long-term viability of the energy system. It is noted that energy systems researchers are increasingly interested in machine learning models used to face the challenge of time series forecasting. This study evaluates a hybrid ensemble learning of three time series forecasting models including least-squares support vector regression, gated recurrent unit, and long short-term memory models applied to nuclear power time series forecasting on the dataset of French power plants from 2009 to 2020. Furthermore, this research evaluates forecasting results in which approaches are directed towards the optimized RreliefF (Robust relief Feature) selection algorithm using a hyperparameter optimization based on tree-structured Parzen estimator and following an ensemble learning approach, showing promising results in terms of performance metrics. The suggested ensemble learning model, which combines deep learning and the RreliefF algorithm using a hold-out, outperforms the other nine forecasting models in this study according to performance criteria such as 75% for the coefficient of determination, a root squared error average of 0.108, and an average absolute error of 0.080. Full article
(This article belongs to the Collection Electrical Technologies)
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14 pages, 3453 KB  
Article
MIRA: Multi-Joint Imitation with Recurrent Adaptation for Robot-Assisted Rehabilitation
by Ali Ashary, Ruchik Mishra, Madan M. Rayguru and Dan O. Popa
Technologies 2024, 12(8), 135; https://doi.org/10.3390/technologies12080135 - 16 Aug 2024
Cited by 2 | Viewed by 3027
Abstract
This work proposes a modular learning framework (MIRA) for rehabilitation robots based on a new deep recurrent neural network (RNN) that achieves adaptive multi-joint motion imitation. The RNN is fed with the fundamental frequencies as well as the ranges of the joint trajectories, [...] Read more.
This work proposes a modular learning framework (MIRA) for rehabilitation robots based on a new deep recurrent neural network (RNN) that achieves adaptive multi-joint motion imitation. The RNN is fed with the fundamental frequencies as well as the ranges of the joint trajectories, in order to predict the future joint trajectories of the robot. The proposed framework also uses a Segment Online Dynamic Time Warping (SODTW) algorithm to quantify the closeness between the robot and patient motion. The SODTW cost decides the amount of modification needed in the inputs to our deep RNN network, which in turn adapts the robot movements. By keeping the prediction mechanism (RNN) and adaptation mechanism (SODTW) separate, the framework achieves modularity, flexibility, and scalability. We tried both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) RNN architectures within our proposed framework. Experiments involved a group of 15 human subjects performing a range of motion tasks in conjunction with our social robot, Zeno. Comparative analysis of the results demonstrated the superior performance of the LSTM RNN across multiple task variations, highlighting its enhanced capability for adaptive motion imitation. Full article
(This article belongs to the Collection Selected Papers from the PETRA Conference Series)
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22 pages, 12633 KB  
Article
MediaPipe Frame and Convolutional Neural Networks-Based Fingerspelling Detection in Mexican Sign Language
by Tzeico J. Sánchez-Vicinaiz, Enrique Camacho-Pérez, Alejandro A. Castillo-Atoche, Mayra Cruz-Fernandez, José R. García-Martínez and Juvenal Rodríguez-Reséndiz
Technologies 2024, 12(8), 124; https://doi.org/10.3390/technologies12080124 - 1 Aug 2024
Cited by 11 | Viewed by 5170
Abstract
This research proposes implementing a system to recognize the static signs of the Mexican Sign Language (MSL) dactylological alphabet using the MediaPipe frame and Convolutional Neural Network (CNN) models to correctly interpret the letters that represent the manual signals coming from a camera. [...] Read more.
This research proposes implementing a system to recognize the static signs of the Mexican Sign Language (MSL) dactylological alphabet using the MediaPipe frame and Convolutional Neural Network (CNN) models to correctly interpret the letters that represent the manual signals coming from a camera. The development of these types of studies allows the implementation of technological advances in artificial intelligence and computer vision in teaching Mexican Sign Language (MSL). The best CNN model achieved an accuracy of 83.63% over the sets of 336 test images. In addition, considering samples of each letter, the following results are obtained: an accuracy of 84.57%, a sensitivity of 83.33%, and a specificity of 99.17%. The advantage of this system is that it could be implemented on low-consumption equipment, carrying out the classification in real-time, contributing to the accessibility of its use. Full article
(This article belongs to the Topic Theoretical and Applied Problems in Human-Computer Intelligent Systems)
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17 pages, 9779 KB  
Article
Optimizing Speech Emotion Recognition with Machine Learning Based Advanced Audio Cue Analysis
by Nuwan Pallewela, Damminda Alahakoon, Achini Adikari, John E. Pierce and Miranda L. Rose
Technologies 2024, 12(7), 111; https://doi.org/10.3390/technologies12070111 - 11 Jul 2024
Cited by 6 | Viewed by 5218
Abstract
In today’s fast-paced and interconnected world, where human–computer interaction is an integral component of daily life, the ability to recognize and understand human emotions has emerged as a crucial facet of technological advancement. However, human emotion, a complex interplay of physiological, psychological, and [...] Read more.
In today’s fast-paced and interconnected world, where human–computer interaction is an integral component of daily life, the ability to recognize and understand human emotions has emerged as a crucial facet of technological advancement. However, human emotion, a complex interplay of physiological, psychological, and social factors, poses a formidable challenge even for other humans to comprehend accurately. With the emergence of voice assistants and other speech-based applications, it has become essential to improve audio-based emotion expression. However, there is a lack of specificity and agreement in current emotion annotation practice, as evidenced by conflicting labels in many human-annotated emotional datasets for the same speech segments. Previous studies have had to filter out these conflicts and, therefore, a large portion of the collected data has been considered unusable. In this study, we aimed to improve the accuracy of computational prediction of uncertain emotion labels by utilizing high-confidence emotion labelled speech segments from the IEMOCAP emotion dataset. We implemented an audio-based emotion recognition model using bag of audio word encoding (BoAW) to obtain a representation of audio aspects of emotion in speech with state-of-the-art recurrent neural network models. Our approach improved the state-of-the-art audio-based emotion recognition with a 61.09% accuracy rate, an improvement of 1.02% over the BiDialogueRNN model and 1.72% over the EmoCaps multi-modal emotion recognition models. In comparison to human annotation, our approach achieved similar results in identifying positive and negative emotions. Furthermore, it has proven effective in accurately recognizing the sentiment of uncertain emotion segments that were previously considered unusable in other studies. Improvements in audio emotion recognition could have implications in voice-based assistants, healthcare, and other industrial applications that benefit from automated communication. Full article
(This article belongs to the Topic Theoretical and Applied Problems in Human-Computer Intelligent Systems)
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20 pages, 4441 KB  
Article
Adsorption of HFO-1234ze(E) onto Steam-Activated Carbon Derived from Sawmill Waste Wood
by Huiyuan Bao, Md. Amirul Islam and Bidyut Baran Saha
Technologies 2024, 12(7), 104; https://doi.org/10.3390/technologies12070104 - 5 Jul 2024
Cited by 6 | Viewed by 2383
Abstract
This study utilizes waste Albizia lebbeck wood from a sawmill to prepare activated carbon adsorbents and explores their potential application in adsorption cooling systems with a novel hydrofluoroolefin (HFO) refrigerant characterized by a low global warming potential. Activated carbon was synthesized through a [...] Read more.
This study utilizes waste Albizia lebbeck wood from a sawmill to prepare activated carbon adsorbents and explores their potential application in adsorption cooling systems with a novel hydrofluoroolefin (HFO) refrigerant characterized by a low global warming potential. Activated carbon was synthesized through a simple and green steam activation method, and the optimal carbon shows a specific surface area of 946.8 m2/g and a pore volume of 0.843 cm3/g. The adsorption isotherms of HFO-1234ze(E) (Trans-1,3,3,3-tetrafluoropropene) on the activated carbon were examined at 30, 40, and 50 °C up to 400 kPa using a customized constant-volume variable-pressure system, and significant adsorption of 1.041 kg kg−1 was achieved at 30 °C and 400 kPa. The experimental data were fitted using both the Dubinin–Astakhov and Tóth models, and both models provided excellent fit results. The D–A adsorption model simulated the net adsorption capacity at possible operating temperatures. The isosteric of adsorption was determined using the Clausius–Clapeyron and modified Dubinin–Astakhov equations. In addition, the specific cooling effect and coefficient of performance were also studied. Full article
(This article belongs to the Special Issue Recent Advances in Applied Activated Carbon Research)
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21 pages, 10290 KB  
Article
Smartphone-Based Citizen Science Tool for Plant Disease and Insect Pest Detection Using Artificial Intelligence
by Panagiotis Christakakis, Garyfallia Papadopoulou, Georgios Mikos, Nikolaos Kalogiannidis, Dimosthenis Ioannidis, Dimitrios Tzovaras and Eleftheria Maria Pechlivani
Technologies 2024, 12(7), 101; https://doi.org/10.3390/technologies12070101 - 3 Jul 2024
Cited by 25 | Viewed by 13364
Abstract
In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases [...] Read more.
In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices. Full article
(This article belongs to the Section Information and Communication Technologies)
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19 pages, 3968 KB  
Article
Transformer-Based Water Stress Estimation Using Leaf Wilting Computed from Leaf Images and Unsupervised Domain Adaptation for Tomato Crops
by Makoto Koike, Riku Onuma, Ryo Adachi and Hiroshi Mineno
Technologies 2024, 12(7), 94; https://doi.org/10.3390/technologies12070094 - 25 Jun 2024
Cited by 6 | Viewed by 3132
Abstract
Modern agriculture faces the dual challenge of ensuring sustainability while meeting the growing global demand for food. Smart agriculture, which uses data from the environment and plants to deliver water exactly when and how it is needed, has attracted significant attention. This approach [...] Read more.
Modern agriculture faces the dual challenge of ensuring sustainability while meeting the growing global demand for food. Smart agriculture, which uses data from the environment and plants to deliver water exactly when and how it is needed, has attracted significant attention. This approach requires precise water management and highly accurate real-time monitoring of crop water stress. Existing monitoring methods pose challenges such as the risk of plant damage, costly sensors, and the need for expert adjustments. Therefore, a low-cost, highly accurate water stress estimation model was developed that uses deep learning and commercially available sensors. The model uses the relative stem diameter as a water stress index and incorporates data from environmental sensors and an RGB camera, which are processed by the proposed daily normalization. In addition, domain adaptation in our Transformer model was implemented to enable robust learning in different areas. The accuracy of the model was evaluated using real cultivation data from tomato crops, achieving a coefficient of determination (R2) of 0.79 in water stress estimation. Furthermore, the model maintained a high level of accuracy when applied to different areas, with an R2 of 0.76, demonstrating its high adaptability under different conditions. Full article
(This article belongs to the Section Environmental Technology)
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16 pages, 12863 KB  
Article
Multi-Objective Optimisation of the Battery Box in a Racing Car
by Chao Ma, Caiqi Xu, Mohammad Souri, Elham Hosseinzadeh and Masoud Jabbari
Technologies 2024, 12(7), 93; https://doi.org/10.3390/technologies12070093 - 25 Jun 2024
Cited by 3 | Viewed by 3094
Abstract
The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for [...] Read more.
The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for the first time, attempts to use sensitivity analysis to screen the design variables and achieve an efficient optimisation design with a large number of original design variables. Specifically, the sensitivity analysis method was proposed to screen a certain number of optimisation variables, reducing the computational complexity while ensuring the efficiency of the optimisation process. A combination of the Generalised Regression Neural Network (GRNN) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to construct surrogate models and solve the optimisation problem. The optimisation model integrates these techniques to balance structural performance and weight reduction. The optimisation results demonstrate a significant reduction in battery box weight while maintaining structural integrity. Therefore, the proposed approach in this study provides important insights for achieving high-efficiency multi-objective optimisation of battery box structures. Full article
(This article belongs to the Collection Electrical Technologies)
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27 pages, 13538 KB  
Article
A New LCL Filter Design Method for Single-Phase Photovoltaic Systems Connected to the Grid via Micro-Inverters
by Heriberto Adamas-Pérez, Mario Ponce-Silva, Jesús Darío Mina-Antonio, Abraham Claudio-Sánchez, Omar Rodríguez-Benítez and Oscar Miguel Rodríguez-Benítez
Technologies 2024, 12(6), 89; https://doi.org/10.3390/technologies12060089 - 12 Jun 2024
Cited by 13 | Viewed by 5690
Abstract
This paper aims to propose a new sizing approach to reduce the footprint and optimize the performance of an LCL filter implemented in photovoltaic systems using grid-connected single-phase microinverters. In particular, the analysis is carried out on a single-phase full-bridge inverter, assuming the [...] Read more.
This paper aims to propose a new sizing approach to reduce the footprint and optimize the performance of an LCL filter implemented in photovoltaic systems using grid-connected single-phase microinverters. In particular, the analysis is carried out on a single-phase full-bridge inverter, assuming the following two conditions: (1) a unit power factor at the connection point between the AC grid and the LCL filter; (2) a control circuit based on unipolar sinusoidal pulse width modulation (SPWM). In particular, the ripple and harmonics of the LCL filter input current and the current injected into the grid are analyzed. The results of the Simulink simulation and the experimental tests carried out confirm that it is possible to considerably reduce filter volume by optimizing each passive component compared with what is already available in the literature while guaranteeing excellent filtering performance. Specifically, the inductance values were reduced by almost 40% and the capacitor value by almost 100%. The main applications of this new design methodology are for use in single-phase microinverters connected to the grid and for research purposes in power electronics and optimization. Full article
(This article belongs to the Topic Advances in Solar Technologies)
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24 pages, 8552 KB  
Article
Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms
by Jorge Galarza-Falfan, Enrique Efrén García-Guerrero, Oscar Adrian Aguirre-Castro, Oscar Roberto López-Bonilla, Ulises Jesús Tamayo-Pérez, José Ricardo Cárdenas-Valdez, Carlos Hernández-Mejía, Susana Borrego-Dominguez and Everardo Inzunza-Gonzalez
Technologies 2024, 12(6), 82; https://doi.org/10.3390/technologies12060082 - 3 Jun 2024
Cited by 13 | Viewed by 7292
Abstract
Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping [...] Read more.
Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs. Full article
(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)
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19 pages, 3842 KB  
Article
Intelligent Cane for Assisting the Visually Impaired
by Claudiu-Eugen Panazan and Eva-Henrietta Dulf
Technologies 2024, 12(6), 75; https://doi.org/10.3390/technologies12060075 - 27 May 2024
Cited by 15 | Viewed by 22129
Abstract
Those with visual impairments, including complete blindness or partial sight loss, constitute a significant global population. According to estimates by the World Health Organization (WHO), there are at least 2.2 billion people worldwide who have near or distance vision disorders. Addressing their needs [...] Read more.
Those with visual impairments, including complete blindness or partial sight loss, constitute a significant global population. According to estimates by the World Health Organization (WHO), there are at least 2.2 billion people worldwide who have near or distance vision disorders. Addressing their needs is crucial. Introducing a smart cane tailored for the blind can greatly improve their daily lives. This paper introduces a significant technical innovation, presenting a smart cane equipped with dual ultrasonic sensors for obstacle detection, catering to the visually impaired. The primary focus is on developing a versatile device capable of operating in diverse conditions, ensuring efficient obstacle alerts. The strategic placement of ultrasonic sensors facilitates the emission and measurement of high-frequency sound waves, calculating obstacle distances and assessing potential threats to the user. Addressing various obstacle types, two ultrasonic sensors handle overhead and ground-level barriers, ensuring precise warnings. With a detection range spanning 2 to 400 cm, the device provides timely information for user reaction. Dual alert methods, including vibrations and audio signals, offer flexibility to users, controlled through intuitive switches. Additionally, a Bluetooth-connected mobile app enhances functionality, activating audio alerts if the cane is misplaced or too distant. Cost-effective implementation enhances accessibility, supporting a broader user base. This innovative smart cane not only represents a technical achievement but also significantly improves the quality of life for visually impaired individuals, emphasizing the social impact of technology. The research underscores the importance of technological research in addressing societal challenges and highlights the need for solutions that positively impact vulnerable communities, shaping future directions in research and technological development. Full article
(This article belongs to the Section Assistive Technologies)
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10 pages, 1848 KB  
Article
Speckle Plethysmograph-Based Blood Pressure Assessment
by Floranne T. Ellington, Anh Nguyen, Mao-Hsiang Huang, Tai Le, Bernard Choi and Hung Cao
Technologies 2024, 12(5), 70; https://doi.org/10.3390/technologies12050070 - 18 May 2024
Viewed by 4156
Abstract
Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse [...] Read more.
Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse arrival time (PAT), the time difference between the proximal and distal signal peaks. The most widely employed pairing involves electrocardiography (ECG) and photoplethysmography (PPG). Possessing similar characteristics in terms of measuring blood flow changes, a recently investigated optical signal known as speckleplethysmography (SPG) showed its stability and high signal-to-noise ratio compared with PPG. Thus, SPG is a potential surrogate to pair with ECG for CNBP estimation. The present study aims to unlock the untapped potential of SPG as a signal for non-invasive blood pressure monitoring based on PAT. To ascertain SPG’s capabilities, eight subjects were enrolled in multiple recording sessions. A third-party device was employed for ECG and PPG measurements, while a commercial device served as the reference for arterial blood pressure (ABP). SPG measurements were obtained using a prototype smartphone-based system. Following the completion of three scenarios—sitting, walking, and running—the subjects’ signals and ABP were recorded to investigate the predictive capacity of systolic blood pressure. The collected data were processed and prepared for machine learning models, including support vector regression and decision tree regression. The models’ effectiveness was evaluated using root-mean-square error and mean absolute percentage error. In most instances, predictions utilizing PATSPG exhibited comparable or superior performance to PATPPG (i.e., SPG Rest ± 12.4 mmHg vs. PPG Rest ± 13.7 mmHg for RSME, and SPG 8% vs. PPG 9% for MAPE). Furthermore, incorporating an additional feature, namely the previous SBP value, resulted in reduced prediction errors for both signals in multiple model configurations (i.e., SPG Rest ± 12.4 mmHg to ±3.7 mmHg for RSME, and SPG Rest 8% to 3% for MAPE). These preliminary tests of SPG underscore the remarkable potential of this novel signal in PAT-based blood pressure predictions. Subsequent studies involving a larger cohort of test subjects and advancements in the SPG acquisition system hold promise for further improving the effectiveness of this newly explored signal in blood pressure monitoring. Full article
(This article belongs to the Topic Smart Healthcare: Technologies and Applications)
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20 pages, 1173 KB  
Article
Application and Challenges of the Technology Acceptance Model in Elderly Healthcare: Insights from ChatGPT
by Sang Dol Kim
Technologies 2024, 12(5), 68; https://doi.org/10.3390/technologies12050068 - 13 May 2024
Cited by 22 | Viewed by 15540
Abstract
The Technology Acceptance Model (TAM) plays a pivotal role in elderly healthcare, serving as a theoretical framework. This study aimed to identify TAM’s core components, practical applications, challenges arising from its applications, and propose countermeasures in elderly healthcare. This descriptive study was conducted [...] Read more.
The Technology Acceptance Model (TAM) plays a pivotal role in elderly healthcare, serving as a theoretical framework. This study aimed to identify TAM’s core components, practical applications, challenges arising from its applications, and propose countermeasures in elderly healthcare. This descriptive study was conducted by utilizing OpenAI’s ChatGPT, with an access date of 10 January 2024. The three open-ended questions administered to ChatGPT and its responses were collected and qualitatively evaluated for reliability through previous studies. The core components of TAMs were identified as perceived usefulness, perceived ease of use, attitude toward use, behavioral intention to use, subjective norms, image, and facilitating conditions. TAM’s application areas span various technologies in elderly healthcare, such as telehealth, wearable devices, mobile health apps, and more. Challenges arising from TAM applications include technological literacy barriers, digital divide concerns, privacy and security apprehensions, resistance to change, limited awareness and information, health conditions and cognitive impairment, trust and reliability concerns, a lack of tailored interventions, overcoming age stereotypes, and integration with traditional healthcare. In conclusion, customized interventions are crucial for successful tech acceptance among the elderly population. The findings of this study are expected to enhance understanding of elderly healthcare and technology adoption, with insights gained through natural language processing models like ChatGPT anticipated to provide a fresh perspective. Full article
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25 pages, 4707 KB  
Article
Digital Twin Models for Personalised and Predictive Medicine in Ophthalmology
by Miruna-Elena Iliuţă, Mihnea-Alexandru Moisescu, Simona-Iuliana Caramihai, Alexandra Cernian, Eugen Pop, Daniel-Ioan Chiş and Traian-Costin Mitulescu
Technologies 2024, 12(4), 55; https://doi.org/10.3390/technologies12040055 - 18 Apr 2024
Cited by 12 | Viewed by 6450
Abstract
This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured [...] Read more.
This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches. Full article
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22 pages, 3814 KB  
Article
Experimental and Numerical Analysis of a Novel Cycloid-Type Rotor versus S-Type Rotor for Vertical-Axis Wind Turbine
by José Eli Eduardo González-Durán, Juan Manuel Olivares-Ramírez, María Angélica Luján-Vega, Juan Emigdio Soto-Osornio, Juan Manuel García-Guendulain and Juvenal Rodriguez-Resendiz
Technologies 2024, 12(4), 54; https://doi.org/10.3390/technologies12040054 - 17 Apr 2024
Cited by 5 | Viewed by 3665
Abstract
The performance of a new vertical-axis wind turbine rotor based on the mathematical equation of the cycloid is analyzed and compared through simulation and experimental testing against a semicircular or S-type rotor, which is widely used. The study examines three cases: equalizing the [...] Read more.
The performance of a new vertical-axis wind turbine rotor based on the mathematical equation of the cycloid is analyzed and compared through simulation and experimental testing against a semicircular or S-type rotor, which is widely used. The study examines three cases: equalizing the diameter, chord length and the area under the curve. Computational Fluid Dynamics (CFD) was used to simulate these cases and evaluate moment, angular velocity and power. Experimental validation was carried out in a wind tunnel that was designed and optimized with the support of CFD. The rotors for all three cases were 3D printed in resin to analyze their experimental performance as a function of wind speed. The moment and Maximum Power Point (MPP) were determined in each case. The simulation results indicate that the cycloid-type rotor outperforms the semicircular or S-type rotor by 15%. Additionally, experimental evidence confirms that the cycloid-type rotor performs better in all three cases. In the MPP analysis, the cycloid-type rotor achieved an efficiency of 10.8% which was 38% better than the S-type rotor. Full article
(This article belongs to the Section Environmental Technology)
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14 pages, 6188 KB  
Article
Monitoring of Hip Joint Forces and Physical Activity after Total Hip Replacement by an Integrated Piezoelectric Element
by Franziska Geiger, Henning Bathel, Sascha Spors, Rainer Bader and Daniel Kluess
Technologies 2024, 12(4), 51; https://doi.org/10.3390/technologies12040051 - 9 Apr 2024
Cited by 5 | Viewed by 4602
Abstract
Resultant hip joint forces can currently only be recorded in situ in a laboratory setting using instrumented total hip replacements (THRs) equipped with strain gauges. However, permanent recording is important for monitoring the structural condition of the implant, for therapeutic purposes, for self-reflection, [...] Read more.
Resultant hip joint forces can currently only be recorded in situ in a laboratory setting using instrumented total hip replacements (THRs) equipped with strain gauges. However, permanent recording is important for monitoring the structural condition of the implant, for therapeutic purposes, for self-reflection, and for research into managing the predicted increasing number of THRs worldwide. Therefore, this study aims to investigate whether a recently proposed THR with an integrated piezoelectric element represents a new possibility for the permanent recording of hip joint forces and the physical activities of the patient. Hip joint forces from nine different daily activities were obtained from the OrthoLoad database and applied to a total hip stem equipped with a piezoelectric element using a uniaxial testing machine. The forces acting on the piezoelectric element were calculated from the generated voltages. The correlation between the calculated forces on the piezoelectric element and the applied forces was investigated, and the regression equations were determined. In addition, the voltage outputs were used to predict the activity with a random forest classifier. The coefficient of determination between the applied maximum forces on the implant and the calculated maximum forces on the piezoelectric element was R2 = 0.97 (p < 0.01). The maximum forces on the THR could be determined via activity-independent determinations with a deviation of 2.49 ± 13.16% and activity-dependent calculation with 0.87 ± 7.28% deviation. The activities could be correctly predicted using the classification model with 95% accuracy. Hence, piezoelectric elements integrated into a total hip stem represent a promising sensor option for the energy-autonomous detection of joint forces and physical activities. Full article
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