Technologies

27 pages, 7002 KiB

Open AccessArticle

Development of a Mobile Application for Musculoskeletal Rehabilitation Based on Computer Vision and Inertial Navigation Technologies

by Artem Obukhov, Andrey Volkov and Yuri Nikitnikov

Technologies 2024, 12(12), 243; https://doi.org/10.3390/technologies12120243 - 24 Nov 2024

Abstract

Monitoring the process of musculoskeletal rehabilitation is of great importance for ensuring a person’s health after suffering from illnesses, especially during the outpatient period when medical supervision is absent. The aim of this study is to create an accessible tool (a mobile application) [...] Read more.

Monitoring the process of musculoskeletal rehabilitation is of great importance for ensuring a person’s health after suffering from illnesses, especially during the outpatient period when medical supervision is absent. The aim of this study is to create an accessible tool (a mobile application) that allows for the monitoring of the execution of musculoskeletal rehabilitation exercises. To achieve this goal, the architecture of a mobile application has been developed, along with its functioning algorithm, and the methods for processing information from two tracking systems (inertial navigation and computer vision) have been examined to assess the quality of performed exercises. During the experimental research, procedures for processing data from mobile inertial navigation sensors were refined, a solution to the classification task of musculoskeletal rehabilitation exercises was explored (with an accuracy of 93–100%), and prototyping of the mobile application was carried out. The results obtained can be used for evaluating outpatient rehabilitation and as a basis for more complex and functional mobile systems for musculoskeletal rehabilitation. Full article

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

► Show Figures

Figure 1

21 pages, 804 KiB

Open AccessArticle

One-Dimensional Deep Residual Network with Aggregated Transformations for Internet of Things (IoT)-Enabled Human Activity Recognition in an Uncontrolled Environment

by Sakorn Mekruksavanich and Anuchit Jitpattanakul

Technologies 2024, 12(12), 242; https://doi.org/10.3390/technologies12120242 - 24 Nov 2024

Abstract

Human activity recognition (HAR) in real-world settings has gained significance due to the growth of Internet of Things (IoT) devices such as smartphones and smartwatches. Nonetheless, limitations such as fluctuating environmental conditions and intricate behavioral patterns have impacted the accuracy of the current [...] Read more.

Human activity recognition (HAR) in real-world settings has gained significance due to the growth of Internet of Things (IoT) devices such as smartphones and smartwatches. Nonetheless, limitations such as fluctuating environmental conditions and intricate behavioral patterns have impacted the accuracy of the current procedures. This research introduces an innovative methodology employing a modified deep residual network, called 1D-ResNeXt, for IoT-enabled HAR in uncontrolled environments. We developed a comprehensive network that utilizes feature fusion and a multi-kernel block approach. The residual connections and the split–transform–merge technique mitigate the accuracy degradation and reduce the parameter number. We assessed our suggested model on three available datasets, mHealth, MotionSense, and Wild-SHARD, utilizing accuracy metrics, cross-entropy loss, and F1 score. The findings indicated substantial enhancements in proficiency in recognition, attaining 99.97% on mHealth, 98.77% on MotionSense, and 97.59% on Wild-SHARD, surpassing contemporary methodologies. Significantly, our model attained these outcomes with considerably fewer parameters (24,130–26,118) than other models, several of which exceeded 700,000 parameters. The 1D-ResNeXt model demonstrated outstanding effectiveness under various ambient circumstances, tackling a significant obstacle in practical HAR applications. The findings indicate that our modified deep residual network presents a viable approach for improving the dependability and usability of IoT-based HAR systems in dynamic, uncontrolled situations while preserving the computational effectiveness essential for IoT devices. The results significantly impact multiple sectors, including healthcare surveillance, intelligent residences, and customized assistive devices. Full article

(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)

23 pages, 811 KiB

Open AccessReview

Exploring Post-Quantum Cryptography: Review and Directions for the Transition Process

by Kanza Cherkaoui Dekkaki, Igor Tasic and Maria-Dolores Cano

Technologies 2024, 12(12), 241; https://doi.org/10.3390/technologies12120241 - 23 Nov 2024

Abstract

As quantum computing advances, current cryptographic protocols are increasingly vulnerable to quantum attacks, particularly those based on Public Key Infrastructure (PKI) like RSA or Elliptic Curve Cryptography (ECC). This paper presents a comprehensive review of Post-Quantum Cryptography (PQC) as a solution to protect [...] Read more.

As quantum computing advances, current cryptographic protocols are increasingly vulnerable to quantum attacks, particularly those based on Public Key Infrastructure (PKI) like RSA or Elliptic Curve Cryptography (ECC). This paper presents a comprehensive review of Post-Quantum Cryptography (PQC) as a solution to protect digital systems in the quantum era. We provide an in-depth analysis of various quantum-resistant cryptographic algorithms, including lattice-based, code-based, hash-based, isogeny-based, and multivariate approaches. The review highlights the National Institute of Standards and Technology (NIST) PQC standardization process, highlighting key algorithms, such as CRYSTALS–Kyber, CRYSTALS–Dilithium, Falcon, and SPHINCS+, and discusses the strengths, vulnerabilities, and implementation challenges of the leading algorithms. In addition, we explore transition strategies for organizations, emphasizing hybrid cryptography to ensure backward compatibility during migration. This study offers key insights into the future of cryptographic standards and the critical steps necessary to prepare for the transition from classical to quantum-resistant systems. Full article

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

15 pages, 383 KiB

Open AccessArticle

AI-Powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning

by Loris Nanni, Daniela Cuza and Sheryl Brahnam

Technologies 2024, 12(12), 240; https://doi.org/10.3390/technologies12120240 - 22 Nov 2024

Abstract

Only 1.2 million out of an estimated 8.7 million species on Earth have been fully classified through taxonomy. As biodiversity loss accelerates, ecologists are urgently revising conservation strategies, but the “taxonomic impediment” remains a significant barrier, limiting effective access to and understanding of [...] Read more.

Only 1.2 million out of an estimated 8.7 million species on Earth have been fully classified through taxonomy. As biodiversity loss accelerates, ecologists are urgently revising conservation strategies, but the “taxonomic impediment” remains a significant barrier, limiting effective access to and understanding of taxonomic data for many researchers. As sequencing technologies advance, short DNA sequence fragments increasingly serve as DNA barcodes for species identification. Rapid acquisition of DNA sequences from diverse organisms is now possible, highlighting the increasing significance of DNA sequence analysis tools in species identification. This study introduces a new approach for species classification with DNA barcodes based on an ensemble of deep neural networks (DNNs). Several techniques are proposed and empirically evaluated for converting raw DNA sequence data into images fed into the DNNs. The best-performing approach is obtained by representing each pair of DNA bases with the value of a related physicochemical property. By utilizing different physicochemical properties, we can create an ensemble of networks. Our proposed ensemble obtains state-of-the-art performance on both simulated and real datasets. Full article

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

► Show Figures

Figure 1

14 pages, 1901 KiB

Open AccessArticle

Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker Fusion

by Vincenzo M. Scarrica and Antonino Staiano

Technologies 2024, 12(12), 239; https://doi.org/10.3390/technologies12120239 - 22 Nov 2024

Abstract

This paper presents an approach to improving visual object tracking performance by dynamically fusing the results of two trackers, where the scheduling of trackers is determined by a support vector machine (SVM). By classifying the outputs of other trackers, our method learns their [...] Read more.

This paper presents an approach to improving visual object tracking performance by dynamically fusing the results of two trackers, where the scheduling of trackers is determined by a support vector machine (SVM). By classifying the outputs of other trackers, our method learns their behaviors and exploits their complementarity to enhance tracking accuracy and robustness. Our approach consistently surpasses the performance of individual trackers within the ensemble. Despite being trained on only 4 sequences and tested on 144 sequences from the VOTS2023 benchmark, our approach achieves a Q metric of 0.65. Additionally, our fusion strategy demonstrates versatility across different datasets, achieving 73.7 MOTA on MOT17 public detections and 82.8 MOTA on MOT17 private detections. On the MOT20 dataset, it achieves 68.6 MOTA on public detections and 79.7 MOTA on private detections, setting new benchmarks in multi-object tracking. These results highlight the potential of using an ensemble of trackers with a learner-based scheduler to significantly improve tracking performance. Full article

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

► Show Figures

Figure 1

23 pages, 5837 KiB

Open AccessArticle

Mechanical Design, Analysis, and Dynamics Simulation of a Cable-Driven Wearable Flexible Exoskeleton System

by Xuetong Jin, Wenqian Ding, Mathias Baumert, Yan Wei, Qinglin Li, Wei Yang and Yuqiao Yan

Technologies 2024, 12(12), 238; https://doi.org/10.3390/technologies12120238 - 21 Nov 2024

Abstract

As a new development direction in exoskeleton research, wearable flexible exoskeleton systems are highly favored for their freedom of movement, flexibility, lightweight design, and comfortable wearability. These systems are gradually becoming the preferred choice for rehabilitation therapy, and enhancing physical performance. In this [...] Read more.

As a new development direction in exoskeleton research, wearable flexible exoskeleton systems are highly favored for their freedom of movement, flexibility, lightweight design, and comfortable wearability. These systems are gradually becoming the preferred choice for rehabilitation therapy, and enhancing physical performance. In this thesis, based on existing research in wearable flexible exoskeletons, we aim to design a lightweight wearable upper limb rehabilitation exoskeleton that meets the needs of stroke patients with a high likelihood of upper limb impairment. The system should provide sufficient flexibility for comfortable and convenient use while minimizing the weight to reduce the user’s burden during wear. Our proposed lightweight wearable flexible exoskeleton assists users in achieving rehabilitation exercises for both the shoulder (external/internal rotation) and forearm (flexion/extension) movements. The system consists of a flexible fabric section connecting the torso–shoulder–upper arm, a flexible fabric section for the forearm, and a back-mounted actuation device. The fabric sections primarily consist of elastic textile materials with a few rigid components. Emphasizing lightweight design, we strive to minimize the exoskeleton’s weight, ensuring optimal user comfort. The actuation device connects to the fabric sections via tensioned wires, driven by a motor to induce arm movement during rehabilitation exercises. To enhance safety and prevent secondary upper limb injuries due to exoskeleton malfunction, we incorporate a physical limiter retricting the exoskeleton’s range of motion. Additionally, we include tension-adjustment mechanisms and cushioning springs to improve the feasibility of this wearable flexible exoskeleton. After completing the structural design, this paper conducted a basic static and kinematic analysis of the exoskeleton system to provide theoretical support. Additionally, the feasibility and effectiveness of the exoskeleton system design were verified through dynamic simulations. Full article

(This article belongs to the Section Assistive Technologies)

► Show Figures

Figure 1

20 pages, 2074 KiB

Open AccessArticle

Assessment of Slow Feature Analysis and Its Variants for Fault Diagnosis in Process Industries

by Abid Aman, Yan Chen and Liu Yiqi

Technologies 2024, 12(12), 237; https://doi.org/10.3390/technologies12120237 - 21 Nov 2024

Abstract

Accurate monitoring of complex industrial plants is crucial for ensuring safe operations and reliable management of desired quality. Early detection of abnormal events is essential to preempt serious consequences, enhance system performance, and reduce manufacturing costs. In this work, we propose a novel [...] Read more.

Accurate monitoring of complex industrial plants is crucial for ensuring safe operations and reliable management of desired quality. Early detection of abnormal events is essential to preempt serious consequences, enhance system performance, and reduce manufacturing costs. In this work, we propose a novel methodology for fault detection based on Slow Feature Analysis (SFA) tailored for time series models and statistical process control. Fault detection is critical in process monitoring and can ensure that systems operate efficiently and safely. This study investigates the effectiveness of various multivariate statistical methods, including Slow Feature Analysis (SFA), Kernel Slow Feature Analysis (KSFA), Dynamic Slow Feature Analysis (DSFA), and Principal Component Analysis (PCA) in detecting faults within the Tennessee Eastman (TE), Benchmark Simulation Model No. 1 (BSM 1) datasets and Beijing wastewater treatment plant (real world). Our comprehensive analysis indicates that KSFA and DSFA significantly outperform traditional methods by providing enhanced sensitivity and fault detection capabilities, particularly in complex, nonlinear, and dynamic data environments. The comparative analysis underscores the superior performance of KSFA and DSFA in capturing comprehensive process behavior, making them robust, cutting-edge choices for advanced fault detection applications. Such methodologies promise substantial improvements in industrial plant monitoring, contributing to heightened system reliability, safety, and overall operational efficiency. Full article

► Show Figures

Figure 1

21 pages, 4894 KiB

Open AccessArticle

An Analytical Model for the Plastic Bending of Anisotropic Sheet Materials, Incorporating the Strain-Hardening Effect

by Yaroslav Erisov, Alexander Kuzin and Andry Sedelnikov

Technologies 2024, 12(12), 236; https://doi.org/10.3390/technologies12120236 - 21 Nov 2024

Abstract

This study develops an analytical model for the plastic bending of anisotropic sheet materials, incorporating strain-hardening effects. The model, experimentally validated with aluminum alloy samples and digital image correlation, accurately predicts stress–strain distributions, bending moments, and thinning behavior in the bending processes. The [...] Read more.

This study develops an analytical model for the plastic bending of anisotropic sheet materials, incorporating strain-hardening effects. The model, experimentally validated with aluminum alloy samples and digital image correlation, accurately predicts stress–strain distributions, bending moments, and thinning behavior in the bending processes. The results reveal that while plastic anisotropy significantly increases the strain intensity, enhancing it by up to 15% on the inner surface relative to the outer under identical bending radius, it does not affect the position of the neutral layer. Strain hardening, on the other hand, raises the bending moment by approximately 12% and contributes to material thinning, which can reach 3% at smaller bend radii. Furthermore, quantitative analysis shows that decreasing the bend radius intensifies the strain, impacting the final geometry of the workpiece. These findings provide valuable insights for optimizing die design and material selection in forming processes involving anisotropic materials, enabling engineers to more precisely control the force requirements and product dimensions in applications where accurate bending characteristics are critical. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)

► Show Figures

Figure 1

21 pages, 6945 KiB

Open AccessArticle

Automatic Segmentation and Statistical Analysis of the Foveal Avascular Zone

by Geanina Totolici, Mihaela Miron and Anisia-Luiza Culea-Florescu

Technologies 2024, 12(12), 235; https://doi.org/10.3390/technologies12120235 - 21 Nov 2024

Abstract

This study facilitates the extraction of foveal avascular zone (FAZ) metrics from optical coherence tomography angiography (OCTA) images, offering valuable clinical insights and enabling detailed statistical analysis of FAZ size and shape across three patient groups: healthy, type II diabetes mellitus and both [...] Read more.

This study facilitates the extraction of foveal avascular zone (FAZ) metrics from optical coherence tomography angiography (OCTA) images, offering valuable clinical insights and enabling detailed statistical analysis of FAZ size and shape across three patient groups: healthy, type II diabetes mellitus and both type II diabetes mellitus (DM) and high blood pressure (HBP). Additionally, it evaluates the performance of four deep learning (DL) models—U-Net, U-Net with DenseNet121, U-Net with MobileNetV2 and U-Net with VGG16—in automating the segmentation of the FAZ. Manual segmentation of the images by ophthalmological clinicians was performed initially, and data augmentation was used to enhance the dataset for robust model training and evaluation. Consequently, the original set of 103 full retina OCTA images was extended to 672 cases, including 42 images from normal patients, 357 images from DM patients, and 273 images from patients with both DM and HBP. Among the models, U-Net with DenseNet outperformed the others, achieving the highest accuracy, Intersection over Union (IoU), and Dice coefficient across all patient groups. This research is distinct in its focus on full retina OCTA images and its inclusion of patients with both hypertension and diabetes, an area that is less studied in the existing literature. Full article

(This article belongs to the Special Issue Medical Imaging & Image Processing III)

► Show Figures

Figure 1

15 pages, 912 KiB

Open AccessArticle

A Three-Level Service Quality Index System for Wind Turbine Groups Based on Fuzzy Comprehensive Evaluation

by Xueting Cheng, Jie Hao, Yuxiang Li, Juan Wei, Weiru Wang and Yaohui Lu

Technologies 2024, 12(11), 234; https://doi.org/10.3390/technologies12110234 - 20 Nov 2024

Abstract

The maintenance and upkeep costs of wind farms and their internal wind turbines have been increasing annually. Therefore, a systematic evaluation of their operating status is of great importance in guiding reductions in maintenance and upkeep costs. In this aspect, this article proposes [...] Read more.

The maintenance and upkeep costs of wind farms and their internal wind turbines have been increasing annually. Therefore, a systematic evaluation of their operating status is of great importance in guiding reductions in maintenance and upkeep costs. In this aspect, this article proposes a three-level service quality index system of “key component–wind turbine–wind farm” based on the fuzzy comprehensive evaluation method. Firstly, raw data on the wind farm are preprocessed to avoid the impact of abnormal data on the evaluation results. Then, the data types are classified and the degradation degree of each indicator is calculated. Based on the entropy weight method, the weight of each indicator is weighted and summed to obtain the overall membership degree. Finally, the overall health level is determined according to the “maximum membership degree”, which is the evaluation result. This article conducts an evaluation experiment based on the actual operating data of Gansu Huadian Nanqiu Wind Farm. The example shows that the proposed strategy can systematically evaluate the health level of wind farms and predict the future trends of health status changes. The research results can provide reference for the reasonable arrangement of unit scheduling, operation, and maintenance plans in wind farms. Full article

(This article belongs to the Special Issue Next-Generation Distribution System Planning, Operation, and Control)

► Show Figures

Figure 1

14 pages, 1707 KiB

Open AccessReview

Influence of Insulin Pen Needle Geometry on Pain Perception and Patient’s Acceptability: A Review

by Francesca De Tommasi and Sergio Silvestri

Technologies 2024, 12(11), 233; https://doi.org/10.3390/technologies12110233 - 19 Nov 2024

Abstract

Diabetes is one of the most common diseases worldwide, with an increasing number of people affected. Insulin therapy is still the major treatment for both Type 1 and Type 2 diabetes and has evolved from bulky syringes to modern insulin pens introduced in [...] Read more.

Diabetes is one of the most common diseases worldwide, with an increasing number of people affected. Insulin therapy is still the major treatment for both Type 1 and Type 2 diabetes and has evolved from bulky syringes to modern insulin pens introduced in 1985. An insulin pen consists of three major parts: a cartridge, a single-use pen needle (PN), and a precision dosing mechanism. Initially, PNs were long and thick, causing great discomfort and concern. Thanks to advances in design, shorter and thinner needles have appeared on the market, improving patient acceptability and pain perception. Studies highlight the influence of PN geometry and other characteristics on injection-related pain, including length, diameter, bevel design, and hub. Despite a lack of specific international regulations for PN geometry, scientific publications have focused on exploring different PNs’ characteristics to optimize patient comfort and reduce pain. To guide the selection of suitable PNs, this review provides a round-up of literature research findings on the impact of PN geometry on pain perception and patient acceptability. Specifically, it provides an overview of the PN manufacturing process, current international regulations, and the state-of-the-art research on PN geometry affecting pain perception. Full article

(This article belongs to the Collection Review Papers Collection for Advanced Technologies)

► Show Figures

Figure 1

25 pages, 3157 KiB

Open AccessArticle

Breaking Barriers: The Design and Development of an Assistive Technology Web App for Older Latinos with Disabilities in Daily Activities

by Elsa M. Orellano-Colón, Adriana I. Ramos-Marichal, Valeria R. González-Crespo, Bianca N. Zeballos-Hernández, Kenneth N. Ruiz-Márquez, Abiel Roche-Lima, Joan M. Adorno-Mercado, Norman A. Laborde-Torres, Joshua G. Berríos-Llopart, Angely M. Cruz-Ramos, Dana V. Montenegro, Carmen E. Lamoutte, Natasha D. Rosa-Casilla and David E. Meléndez-Berrios

Technologies 2024, 12(11), 232; https://doi.org/10.3390/technologies12110232 - 19 Nov 2024

Abstract

Latinos are among the populations who are the least likely to use assistive technology (AT) despite being a population with a high prevalence of functional disabilities (FDs). We aimed to create and test the usability of an AT web app for independent-living older [...] Read more.

Latinos are among the populations who are the least likely to use assistive technology (AT) despite being a population with a high prevalence of functional disabilities (FDs). We aimed to create and test the usability of an AT web app for independent-living older adults with FDs. In Phase I, we created the web app’s content guided by the Optimized Honeycomb Model and considered the AT needs and FDs of older Puerto Ricans found in our previous studies. In Phase II, we design the web application by adopting a Lean UX process and design heuristics for older adults. In Phase III, we conducted usability testing using focus groups and individual interviews with 14 older adults, interpreted through a directed content analysis. The Mi Guía de Asistencia Tecnológica (MGAT) was developed with ninety-four AT devices in eight areas of daily activities. The MGAT provides comprehensive information on AT, including photos and videos of older adults using AT. Participants reported that the MGAT was usable, accessible, credible, desirable, useful, and valuable in increasing their knowledge of AT. These findings are a foundation for developing efficient AT information strategies using such technology as a first step to improving AT adoption among older adults. Full article

► Show Figures

Figure 1

17 pages, 2482 KiB

Open AccessArticle

Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches

by Dipak Kumar Agrawal, Watcharin Jongpinit, Soodkhet Pojprapai, Wipawee Usaha, Pattra Wattanapan, Pornthep Tangkanjanavelukul and Timporn Vitoonpong

Technologies 2024, 12(11), 231; https://doi.org/10.3390/technologies12110231 - 19 Nov 2024

Abstract

Diabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% ending up with amputations, resulting in high morbidity, mortality, and costs. Plantar pressure screening shows potential for early detection of Diabetic Foot Ulcers (DFUs). Although [...] Read more.

Diabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% ending up with amputations, resulting in high morbidity, mortality, and costs. Plantar pressure screening shows potential for early detection of Diabetic Foot Ulcers (DFUs). Although foot ulcers often occur due to excessive pressure on the soles during dynamic activities, most studies focus on static pressure measurements. This study’s primary objective is to apply wireless plantar pressure sensor-embedded insoles to classify and detect diabetic feet from healthy ones based on dynamic plantar pressure. The secondary objective is to compare statistical-based and Machine Learning (ML) classification methods. Data from 150 subjects were collected from the insoles during walking, revealing that diabetic feet have higher plantar pressure than healthy feet, which is consistent with prior research. The Adaptive Boosting (AdaBoost) ML model achieved the highest accuracy of 0.85, outperforming the statistical method, which had an accuracy of 0.67. These findings suggest that ML models, combined with pressure sensor-embedded insoles, can effectively classify healthy and diabetic feet using plantar pressure features. Future research will focus on using these insoles with ML to classify various stages of diabetic neuropathy, aiming for early prediction of foot ulcers in home settings. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)

► Show Figures

Figure 1

25 pages, 20254 KiB

Open AccessArticle

IoT-Enhanced Decision Support System for Real-Time Greenhouse Microclimate Monitoring and Control

by Dragoș-Ioan Săcăleanu, Mihai-Gabriel Matache, Ștefan-George Roșu, Bogdan-Cristian Florea, Irina-Petra Manciu and Lucian-Andrei Perișoară

Technologies 2024, 12(11), 230; https://doi.org/10.3390/technologies12110230 - 14 Nov 2024

Abstract

Greenhouses have taken on a fundamental role in agriculture. The Internet of Things (IoT) is a key concept used in greenhouse-based precision agriculture (PA) to enhance vegetable quality and quantity while improving resource efficiency. Integrating wireless sensor networks (WSNs) into greenhouses to monitor [...] Read more.

Greenhouses have taken on a fundamental role in agriculture. The Internet of Things (IoT) is a key concept used in greenhouse-based precision agriculture (PA) to enhance vegetable quality and quantity while improving resource efficiency. Integrating wireless sensor networks (WSNs) into greenhouses to monitor environmental parameters represents a critical first step in developing a complete IoT solution. For further optimization of the results, including actuator nodes to control the microclimate is necessary. The greenhouse must also be remotely monitored and controlled via an internet-based platform. This paper proposes an IoT-based architecture as a decision support system for farmers. A web platform has been developed to acquire data from custom-developed wireless sensor nodes and send commands to custom-developed wireless actuator nodes in a greenhouse environment. The wireless sensor and actuator nodes (WSANs) utilize LoRaWAN, one of the most prominent Low-Power Wide-Area Network (LPWAN) technologies, known for its long data transmission range. A real-time end-to-end deployment of a remotely managed WSAN was conducted. The power consumption of the wireless sensor nodes and the recharge efficiency of installed solar panels were analyzed under worst-case scenarios with continuously active nodes and minimal intervals between data transmissions. Datasets were acquired from multiple sensor nodes over a month, demonstrating the system’s functionality and feasibility. Full article

(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)

► Show Figures

Figure 1

25 pages, 3232 KiB

Open AccessArticle

A Framework for Distributed Orchestration of Cyber-Physical Systems: An Energy Trading Case Study

by Kostas Siozios

Technologies 2024, 12(11), 229; https://doi.org/10.3390/technologies12110229 - 13 Nov 2024

Abstract

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine [...] Read more.

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers. Full article

(This article belongs to the Collection Selected Papers from the MOCAST Conference Series)

► Show Figures

Graphical abstract

23 pages, 5496 KiB

Open AccessArticle

Medical VR Simulator for Pediatric Strabismus Treatment

by Artem Obukhov, Elena Kutimova, Julia Matrosova, Daniil Teselkin and Maxim Shilcin

Technologies 2024, 12(11), 228; https://doi.org/10.3390/technologies12110228 - 11 Nov 2024

Abstract

In the process of treating pediatric strabismus, great difficulties arise with maintaining attention and a high level of motivation in patients. Existing computer programs and medical equipment should be supplemented with more modern tools and approaches based on virtual reality (VR) technologies, ensuring [...] Read more.

In the process of treating pediatric strabismus, great difficulties arise with maintaining attention and a high level of motivation in patients. Existing computer programs and medical equipment should be supplemented with more modern tools and approaches based on virtual reality (VR) technologies, ensuring the full immersion of children in the treatment process. The aim of this study is to develop and evaluate the effectiveness of a virtual reality medical simulator for the treatment of pediatric strabismus. The specifics of the realization of ophthalmic exercises for the virtual simulator and the methods for evaluation of the quality of their performance are considered. In the course of experimental research, a control group of 58 people using the VR simulator and a reference group of 59 people receiving the standard process of strabismus treatment were compared. The average value of visual acuity in the control group increased from 66.1% to 80.4% (p = 0.002); the average value of objective strabismus angle decreased from 5° to 0° (p < 0.001). The subjective strabismus angle was also found to improve from 3° to 0° (p < 0.001). In terms of exercise quality metrics, a selected subgroup of participants who have been training for a long time have shown positive dynamics in terms of improved accuracy and a reduction in their average number of errors. In summary, virtual reality technologies demonstrated a statistically significant improvement in the metrics used to evaluate orthoptic treatment in the control group and the superiority of this approach over standard treatment. Full article

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

► Show Figures

Figure 1

16 pages, 298 KiB

Open AccessEditorial

Advanced Processing Technologies for Innovative Materials

by Sergey N. Grigoriev, Marina A. Volosova and Anna A. Okunkova

Technologies 2024, 12(11), 227; https://doi.org/10.3390/technologies12110227 - 11 Nov 2024

Abstract

There is a need for further, in-depth research that explores the synthesis of newly developed materials created using advanced technologies [...] Full article

(This article belongs to the Special Issue Advanced Processing Technologies of Innovative Materials)

34 pages, 16736 KiB

Open AccessArticle

Optimized Energy Management Strategy for an Autonomous DC Microgrid Integrating PV/Wind/Battery/Diesel-Based Hybrid PSO-GA-LADRC Through SAPF

by AL-Wesabi Ibrahim, Jiazhu Xu, Abdullrahman A. Al-Shamma’a, Hassan M. Hussein Farh, Imad Aboudrar, Youssef Oubail, Fahad Alaql and Walied Alfraidi

Technologies 2024, 12(11), 226; https://doi.org/10.3390/technologies12110226 - 11 Nov 2024

Abstract

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, [...] Read more.

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios. Full article

► Show Figures

Figure 1

29 pages, 2679 KiB

Open AccessArticle

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

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

► Show Figures

Figure 1

Journal Description

Technologies

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI