Technologies

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)

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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)

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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)

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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)

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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

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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

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13 pages, 6335 KiB

Open AccessArticle

Double Gold/Nitrogen Nanosecond-Laser-Doping of Gold-Coated Silicon Wafer Surfaces in Liquid Nitrogen

by Sergey Kudryashov, Alena Nastulyavichus, Victoria Pryakhina, Evgenia Ulturgasheva, Michael Kovalev, Ivan Podlesnykh, Nikita Stsepuro and Vadim Shakhnov

Technologies 2024, 12(11), 224; https://doi.org/10.3390/technologies12110224 - 7 Nov 2024

Abstract

A novel double-impurity doping process for silicon (Si) surfaces was developed, utilizing nanosecond-laser melting of an 11 nm thick gold (Au) top film and a Si wafer substrate in a laser plasma-activated liquid nitrogen (LN) environment. Scanning electron microscopy revealed a fluence- and [...] Read more.

A novel double-impurity doping process for silicon (Si) surfaces was developed, utilizing nanosecond-laser melting of an 11 nm thick gold (Au) top film and a Si wafer substrate in a laser plasma-activated liquid nitrogen (LN) environment. Scanning electron microscopy revealed a fluence- and exposure-independent surface micro-spike topography, while energy-dispersive X-ray spectroscopy identified minor Au (~0.05 at. %) and major N (~1–2 at. %) dopants localized within a 0.5 μm thick surface layer and the slight surface post-oxidation of the micro-relief (oxygen (O), ~1.5–2.5 at. %). X-ray photoelectron spectroscopy was used to identify the bound surface (SiN_x) and bulk doping chemical states of the introduced nitrogen (~10 at. %) and the metallic (<0.01 at. %) and cluster (<0.1 at. %) forms of the gold dopant, and it was used to evaluate their depth distributions, which were strongly affected by the competition between gold dopants due to their marginal local concentrations and the other more abundant dopants (N, O). In this study, 532 nm Raman microspectroscopy indicated a slight reduction in the crystalline order revealed in the second-order Si phonon band; the tensile stresses or nanoscale dimensions of the resolidified Si nano-crystallites envisioned by the main Si optical–phonon peak; a negligible a-Si abundance; and a low-wavenumber peak of the Si₃N₄ structure. In contrast, Fourier transform infrared (FT-IR) reflectance and transmittance studies exhibited only broad structureless absorption bands in the range of 600–5500 cm⁻¹ related to dopant absorption and light trapping in the surface micro-relief. The room-temperature electrical characteristics of the laser double-doped Si layer—a high carrier mobility of 1050 cm²/Vs and background carrier sheet concentration of ~2 × 10¹⁰ cm⁻² (bulk concentration ~10¹⁴–10¹⁵ cm⁻³)—are superior to previously reported parameters of similar nitrogen-implanted/annealed Si samples. This novel facile double-element laser-doping procedure paves the way to local maskless on-demand introductions of multiple intra-gap intermediate donor and acceptor bands in Si, providing related multi-wavelength IR photoconductivity for optoelectronic applications. Full article

(This article belongs to the Section Innovations in Materials Processing)

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4 pages, 157 KiB

Open AccessEditorial

Summary Editorial on Wearable Technology in Exercise and Sport Applications

by James W. Navalta

Technologies 2024, 12(11), 223; https://doi.org/10.3390/technologies12110223 - 7 Nov 2024

Abstract

We recently closed the second of two Special Issues centered around wearable technology use in exercise and sport applications [...] Full article

(This article belongs to the Special Issue Wearable Technologies III)

59 pages, 11596 KiB

Open AccessReview

Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas

by Sheetal Harris, Hassan Jalil Hadi, Naveed Ahmad and Mohammed Ali Alshara

Technologies 2024, 12(11), 222; https://doi.org/10.3390/technologies12110222 - 6 Nov 2024

Abstract

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national [...] Read more.

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation. Full article

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14 pages, 3480 KiB

Open AccessReview

Towards the Future of Ubiquitous Hyperspectral Imaging: Innovations in Sensor Configurations and Cost Reduction for Widespread Applicability

by Ivan Podlesnykh, Michael Kovalev and Pavel Platonov

Technologies 2024, 12(11), 221; https://doi.org/10.3390/technologies12110221 - 6 Nov 2024

Abstract

Hyperspectral imaging is currently under active development as a method for remote sensing, environmental monitoring and biomedical diagnostics. The development of hyperspectral sensors is aimed at their miniaturization and reducing the cost of components for the purpose of the widespread use of such [...] Read more.

Hyperspectral imaging is currently under active development as a method for remote sensing, environmental monitoring and biomedical diagnostics. The development of hyperspectral sensors is aimed at their miniaturization and reducing the cost of components for the purpose of the widespread use of such devices on unmanned aerial vehicles and satellites. In this review, we present a broad overview of recent work on the development of hyperspectral devices’ configurations, studies aimed at modifying sensors and the possibility of reducing the cost of components of such devices. In addition, we will present the main trends in the development of hyperspectral device configurations for ubiquitous applications. Full article

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23 pages, 9327 KiB

Open AccessArticle

Increasing the Robustness of Image Quality Assessment Models Through Adversarial Training

by Anna Chistyakova, Anastasia Antsiferova, Maksim Khrebtov, Sergey Lavrushkin, Konstantin Arkhipenko, Dmitriy Vatolin and Denis Turdakov

Technologies 2024, 12(11), 220; https://doi.org/10.3390/technologies12110220 - 5 Nov 2024

Abstract

The adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth research has examined adversarial training as [...] Read more.

The adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth research has examined adversarial training as a way to improve IQA model robustness. This study introduces an enhanced adversarial training approach tailored to IQA models; it adjusts the perceptual quality scores of adversarial images during training to enhance the correlation between an IQA model’s quality and the subjective quality scores. We also propose a new method for comparing IQA model robustness by measuring the Integral Robustness Score; this method evaluates the IQA model resistance to a set of adversarial perturbations with different magnitudes. We used our adversarial training approach to increase the robustness of five IQA models. Additionally, we tested the robustness of adversarially trained IQA models to 16 adversarial attacks and conducted an empirical probabilistic estimation of this feature. Full article

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21 pages, 655 KiB

Open AccessArticle

Generative Models for Source Code: Fine-Tuning Techniques for Structured Pattern Learning

by Valentina Franzoni, Silvia Tagliente and Alfredo Milani

Technologies 2024, 12(11), 219; https://doi.org/10.3390/technologies12110219 - 4 Nov 2024

Abstract

This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding [...] Read more.

This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding standards, essential for integration into existing application development projects and long-term software maintenance. By training the model on specific code structures, including a dataset with Italian annotations, the proposed methodology ensures that the generated code is compliant with both the functional requirements and the pre-defined coding standards. The methodology proposed in this study applies transfer learning techniques on the DeepSeek Coder model, to refine pre-trained models to generate code that integrates additional structuring constraints. By training the model on specific code structures, including a dataset with Italian comments, the proposed methodology ensures that the generated code meets both functional requirements and coding structure. Experimental results, evaluated using the perplexity metric, demonstrate the effectiveness of the proposed approach, which impacts the goals of reducing errors, and ultimately improves software development quality. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Information Systems: Trends and Innovations)

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28 pages, 32137 KiB

Open AccessArticle

Path Tracking Control for Four-Wheel Independent Steering and Driving Vehicles Based on Improved Deep Reinforcement Learning

by Xia Hua, Tengteng Zhang, Xiangle Cheng and Xiaobin Ning

Technologies 2024, 12(11), 218; https://doi.org/10.3390/technologies12110218 - 4 Nov 2024

Abstract

We propose a compound control framework to improve the path tracking accuracy of a four-wheel independent steering and driving (4WISD) vehicle in complex environments. The framework consists of a deep reinforcement learning (DRL)-based auxiliary controller and a dual-layer controller. Samples in the 4WISD [...] Read more.

We propose a compound control framework to improve the path tracking accuracy of a four-wheel independent steering and driving (4WISD) vehicle in complex environments. The framework consists of a deep reinforcement learning (DRL)-based auxiliary controller and a dual-layer controller. Samples in the 4WISD vehicle control framework have the issues of skewness and sparsity, which makes it difficult for the DRL to converge. We propose a group intelligent experience replay (GER) mechanism that non-dominantly sorts the samples in the experience buffer, which facilitates within-group and between-group collaboration to achieve a balance between exploration and exploitation. To address the generalization problem in the complex nonlinear dynamics of 4WISD vehicles, we propose an actor-critic architecture based on the method of two-stream information bottleneck (TIB). The TIB method is used to remove redundant information and extract high-dimensional features from the samples, thereby reducing generalization errors. To alleviate the overfitting of DRL to known data caused by IB, the reverse information bottleneck (RIB) alters the optimization objective of IB, preserving the discriminative features that are highly correlated with actions and improving the generalization ability of DRL. The proposed method significantly improves the convergence and generalization capabilities of DRL, while effectively enhancing the path tracking accuracy of 4WISD vehicles in high-speed, large-curvature, and complex environments. Full article

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30 pages, 618 KiB

Open AccessArticle

Benchmarking Big Data Systems: Performance and Decision-Making Implications in Emerging Technologies

by Leonidas Theodorakopoulos, Aristeidis Karras, Alexandra Theodoropoulou and Georgios Kampiotis

Technologies 2024, 12(11), 217; https://doi.org/10.3390/technologies12110217 - 3 Nov 2024

Abstract

Systems for graph processing are a key enabler for insights from large-scale graphs that are critical to many new advanced technologies such as Artificial Intelligence, Internet of Things, and blockchain. In this study, we benchmark another two widely utilized graph processing systems, Apache [...] Read more.

Systems for graph processing are a key enabler for insights from large-scale graphs that are critical to many new advanced technologies such as Artificial Intelligence, Internet of Things, and blockchain. In this study, we benchmark another two widely utilized graph processing systems, Apache Spark GraphX and Apache Fink, concerning the key performance criterion by means of response time, scalability, and computational complexity. We demonstrate our results which show the capability of each system for real-world graph applications, and hence, providing a quantitative understanding to select the system for our purpose. GraphX’s strength was in processing batch in-memory workloads typical of blockchain and machine learning model optimization, while Flink excelled in processing stream data, which is timely and important to the IoT world. These performance characteristics emphasize how the capabilities of graph processing systems can match the requirements for the performance of different emerging technology applications. Our findings ultimately inform practitioners about system efficiencies and limitations, but also the recent advances in hardware accelerators and algorithmic improvements aimed at shaping the new graph processing frontier in diverse technology domains. Full article

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14 pages, 5779 KiB

Open AccessArticle

Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting Conditions

by Maryam Abbasi, Paulo Váz, José Silva and Pedro Martins

Technologies 2024, 12(11), 216; https://doi.org/10.3390/technologies12110216 - 3 Nov 2024

Abstract

The visual fidelity of virtual reality (VR) and augmented reality (AR) environments is essential for user immersion and comfort. Dynamic lighting often leads to chromatic distortions and reduced clarity, causing discomfort and disrupting user experience. This paper introduces an AI-driven chromatic adjustment system [...] Read more.

The visual fidelity of virtual reality (VR) and augmented reality (AR) environments is essential for user immersion and comfort. Dynamic lighting often leads to chromatic distortions and reduced clarity, causing discomfort and disrupting user experience. This paper introduces an AI-driven chromatic adjustment system based on a modified U-Net architecture, optimized for real-time applications in VR/AR. This system adapts to dynamic lighting conditions, addressing the shortcomings of traditional methods like histogram equalization and gamma correction, which struggle with rapid lighting changes and real-time user interactions. We compared our approach with state-of-the-art color constancy algorithms, including Barron’s Convolutional Color Constancy and STAR, demonstrating superior performance. Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. The study also included eye-tracking data, which confirmed increased user engagement with AI-enhanced images. Our system provides a practical solution for developers aiming to improve image quality, reduce visual discomfort, and enhance overall user satisfaction in immersive environments. Future work will focus on extending the model’s capability to handle more complex lighting scenarios. Full article

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

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16 pages, 4393 KiB

Open AccessArticle

A Field-Programmable Gate Array-Based Quasi-Cyclic Low-Density Parity-Check Decoder with High Throughput and Excellent Decoding Performance for 5G New-Radio Standards

by Bilal Mejmaa, Ismail Akharraz and Abdelaziz Ahaitouf

Technologies 2024, 12(11), 215; https://doi.org/10.3390/technologies12110215 - 31 Oct 2024

Abstract

This work presents a novel fully parallel decoder architecture designed for high-throughput decoding of Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) codes within the context of 5G New-Radio (NR) communication. The design uses the layered Min-Sum (MS) algorithm and focuses on increasing throughput to meet the [...] Read more.

This work presents a novel fully parallel decoder architecture designed for high-throughput decoding of Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) codes within the context of 5G New-Radio (NR) communication. The design uses the layered Min-Sum (MS) algorithm and focuses on increasing throughput to meet the strict needs of enhanced Mobile BroadBand (eMBB) applications. We incorporated a Sub-Optimal Low-Latency (SOLL) technique to enhance the critical check node processing stage inherent to the MS algorithm. This technique efficiently computes the two minimum values, rendering the architecture well-suited for specific Ultra-Reliable Low-Latency Communication (URLLC) scenarios. We design the decoder to be reconfigurable, enabling efficient operation across all expansion factors. We rigorously validate the decoder’s effectiveness through meticulous bit-error-rate (BER) performance evaluations using Hardware Description Language (HDL) co-simulation. This co-simulation utilizes a well-established suite of tools encompassing MATLAB/Simulink for system modeling and Vivado, a prominent FPGA design suite, for hardware representation. With 380,737 Look-Up Tables (LUTs) and 32,898 registers, the decoder’s implementation on a Virtex-7 XC7VX980T FPGA platform by AMD/Xilinx shows good hardware utilization. The architecture attains a robust operating frequency of 304.5 MHz and a normalized throughput of 49.5 Gbps, marking a 36% enhancement compared to the state-of-the-art. This advancement propels decoding capabilities to meet the demands of high-speed data processing. Full article

(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering II)

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15 pages, 18517 KiB

Open AccessArticle

Rice Leaf Disease Classification—A Comparative Approach Using Convolutional Neural Network (CNN), Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), and MobileNet-V2 Architectures

by Monoronjon Dutta, Md Rashedul Islam Sujan, Mayen Uddin Mojumdar, Narayan Ranjan Chakraborty, Ahmed Al Marouf, Jon G. Rokne and Reda Alhajj

Technologies 2024, 12(11), 214; https://doi.org/10.3390/technologies12110214 - 29 Oct 2024

Abstract

Classifying rice leaf diseases in agricultural technology helps to maintain crop health and to ensure a good yield. In this work, deep learning algorithms were, therefore, employed for the identification and classification of rice leaf diseases from images of crops in the field. [...] Read more.

Classifying rice leaf diseases in agricultural technology helps to maintain crop health and to ensure a good yield. In this work, deep learning algorithms were, therefore, employed for the identification and classification of rice leaf diseases from images of crops in the field. The initial algorithmic phase involved image pre-processing of the crop images, using a bilateral filter to improve image quality. The effectiveness of this step was measured by using metrics like the Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). Following this, this work employed advanced neural network architectures for classification, including Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), MobileNetV2, and Convolutional Neural Network (CNN). The proposed CNN model stood out, since it demonstrated exceptional performance in identifying rice leaf diseases, with test Accuracy of 98% and high Precision, Recall, and F1 scores. This result highlights that the proposed model is particularly well suited for rice leaf disease classification. The robustness of the proposed model was validated through k-fold cross-validation, confirming its generalizability and minimizing the risk of overfitting. This study not only focused on classifying rice leaf diseases but also has the potential to benefit farmers and the agricultural community greatly. This work highlights the advantages of custom CNN models for efficient and accurate rice leaf disease classification, paving the way for technology-driven advancements in farming practices. Full article

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

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1 pages, 173 KiB

Open AccessRetraction

RETRACTED: Galeoto et al. Assessment Capacity of the Armeo^® Power: Cross-Sectional Study. Technologies 2023, 11, 125

by Giovanni Galeoto, Anna Berardi, Massimiliano Mangone, Leonardo Tufo, Martina Silvani, Jerónimo González-Bernal and Jesús Seco-Calvo

Technologies 2024, 12(11), 213; https://doi.org/10.3390/technologies12110213 - 28 Oct 2024

Abstract

The Technologies Editoral Office retracts the article titled “Assessment Capacity of the Armeo^® Power: Cross-Sectional Study” [...] Full article

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