Technologies

16 pages, 5184 KiB

Open AccessArticle

Boa Fumigator: An Intelligent Robotic Approach for Mosquito Control

by Sriniketh Konduri, Prithvi Krishna Chittoor, Bhanu Priya Dandumahanti, Zhenyuan Yang, Mohan Rajesh Elara and Grace Hephzibah Jaichandar

Technologies 2024, 12(12), 255; https://doi.org/10.3390/technologies12120255 - 10 Dec 2024

Abstract

The mosquitoe population is reaching critical levels globally, posing significant threats to public health and ecosystems due to their role as vectors for diseases. This paper presents the development of a mobile robotic platform named Boa Fumigator with autonomous fumigation and prioritized path [...] Read more.

The mosquitoe population is reaching critical levels globally, posing significant threats to public health and ecosystems due to their role as vectors for diseases. This paper presents the development of a mobile robotic platform named Boa Fumigator with autonomous fumigation and prioritized path planning capabilities in urban landscapes. The robot’s locomotion is based on a differential drive, facilitating easier maneuverability on semi-automated planar surfaces in landscaping infrastructure. The robot’s fumigator payload consists of a spray gun and a chemical tank, which can pan and fumigate up to 4.5 m from the ground. The system incorporates a wireless charging mechanism to allow for the autonomous charging of the mosquito catchers. A genetic algorithm fused with an A*-based prioritized path planning algorithm is developed for efficient navigation and charging of mosquito catchers. The algorithm, designed for maximizing charging efficiency, considers the initial charge percentage of mosquito catchers and the time required for fumigation to determine the optimal path for charging and fumigation. The experiment results show that the path planning algorithm can generate an optimized path for charging and fumigating multiple mosquito catchers based on their initial charge percentage. This paper concludes by summarizing the key findings and highlighting the significance of the fumigation robot in landscaping applications. Full article

(This article belongs to the Section Assistive Technologies)

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17 pages, 3073 KiB

Open AccessArticle

The Gradient of Spontaneous Oscillations Across Cortical Hierarchies Measured by Wearable Magnetoencephalography

by Xiaoyu Liang, Yuyu Ma, Huanqi Wu, Ruilin Wang, Ruonan Wang, Changzeng Liu, Yang Gao and Xiaolin Ning

Technologies 2024, 12(12), 254; https://doi.org/10.3390/technologies12120254 - 9 Dec 2024

Abstract

The spontaneous oscillations within the brain are intimately linked to the hierarchical structures of the cortex, as evidenced by the cross-cortical gradient between parametrized spontaneous oscillations and cortical locations. Despite the significance of both peak frequency and peak time in characterizing these oscillations, [...] Read more.

The spontaneous oscillations within the brain are intimately linked to the hierarchical structures of the cortex, as evidenced by the cross-cortical gradient between parametrized spontaneous oscillations and cortical locations. Despite the significance of both peak frequency and peak time in characterizing these oscillations, limited research has explored the relationship between peak time and cortical locations. And no studies have demonstrated that the cross-cortical gradient can be measured by optically pumped magnetometer-based magnetoencephalography (OPM-MEG). Therefore, the cross-cortical gradient of parameterized spontaneous oscillation was analyzed for oscillations recorded by OPM-MEG using restricted maximum likelihood estimation with a linear mixed-effects model. It was validated that OPM-MEG can measure the cross-cortical gradient of spontaneous oscillations. Furthermore, results demonstrated the difference in the cross-cortical gradient between spontaneous oscillations during eye-opening and eye-closing conditions. The methods and conclusions offer potential to integrate electrophysiological and structural information of the brain, which contributes to the analysis of oscillatory fluctuations across the cortex recorded by OPM-MEG. Full article

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

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16 pages, 3359 KiB

Open AccessArticle

Integrated System of Reverse Osmosis and Forward Pressure-Assisted Osmosis from ZrO₂ Base Polymer Membranes for Desalination Technology

by Saleh O. Alaswad, Heba Abdallah and Eman S. Mansor

Technologies 2024, 12(12), 253; https://doi.org/10.3390/technologies12120253 - 6 Dec 2024

Abstract

In this work, reverse osmosis and forward osmosis membranes were prepared using base cellulosic polymers with ZrO₂. The prepared membranes were rolled on the spiral-wound configuration module. The modules were tested on a pilot unit to investigate the efficiency of the [...] Read more.

In this work, reverse osmosis and forward osmosis membranes were prepared using base cellulosic polymers with ZrO₂. The prepared membranes were rolled on the spiral-wound configuration module. The modules were tested on a pilot unit to investigate the efficiency of the RO membrane and the hydraulic pressure effect on both sides of the FO membranes. The RO membrane provided a rejection of 99% for the seawater desalination, and the brine was used as a draw solution for the FO system. First, seawater was used as a draw solution to indicate the best hydraulic pressure, where the best one was 3 bar for the draw solution side, and 2 bar for the feed side, where the water flux reached 48.89 L/m²·h (LMH) with a dilution percentage of 80% and a low salt reverse flux of 0.128 g/m²·h (gMH) after 5 h of operation time. The integrated system of RO and forward-assisted osmosis (PAO) was investigated using river water as a feed and RO brine as a draw solute, where the results of PAO indicate a high-water flux of 68.6 LMH with a dilution of 93.2% and a salt reverse flux of 0.18 gMH. Therefore, using PAO improves the performance of the system. Full article

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

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18 pages, 6729 KiB

Open AccessArticle

Experimental Study on Ignition and Pressure-Gain Achievement in Low-Vacuum Conditions for a Pulsed Detonation Combustor

by Andrei Vlad Cojocea, Mihnea Gall, George Ionuț Vrabie, Tudor Cuciuc, Ionuț Porumbel, Gabriel Ursescu and Daniel Eugeniu Crunţeanu

Technologies 2024, 12(12), 252; https://doi.org/10.3390/technologies12120252 - 2 Dec 2024

Abstract

Pressure-gain combustion (PGC) represents a promising alternative to conventional propulsion systems for interplanetary travel due to its key advantages, including higher thermodynamic efficiency, increased specific impulse, and more compact engine designs. However, to elevate this technology to a sufficient technology readiness level (TRL) [...] Read more.

Pressure-gain combustion (PGC) represents a promising alternative to conventional propulsion systems for interplanetary travel due to its key advantages, including higher thermodynamic efficiency, increased specific impulse, and more compact engine designs. However, to elevate this technology to a sufficient technology readiness level (TRL) for practical application, extensive experimental validation, particularly under vacuum conditions, is essential. This study focuses on the performance of a pulsed-detonation combustor (PDC) under near-vacuum conditions, with two primary objectives: to assess the combustor’s ignition capabilities and to characterize the shock wave behavior at the exit plane. To achieve these objectives, high-frequency pressure sensors are strategically positioned within both the vacuum chamber and the combustor prototype to capture the pressure cycles during operation, providing insights into pressure augmentation over a period of approximately 0.5 s. Additionally, the Schlieren visualization technique is employed to analyze and interpret the flow structures of the exhaust jet. The combination of these experimental methods enables a comprehensive understanding of the ignition dynamics and the development of shock waves, contributing valuable data to advance PGC technology for space-exploration applications. Full article

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

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22 pages, 1503 KiB

Open AccessArticle

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

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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21 pages, 42074 KiB

Open AccessArticle

The Low-Cost Mechanism of a Defined Path Guide Slot-Based Passive Solar Tracker Intended for Developing Countries

by José Luis Pérez-Gudiño, Marco Antonio Gómez-Guzmán, Chayanne García-Valdez, Roberto Valentín Carrillo-Serrano, Gerardo Israel Pérez-Soto and Juvenal Rodríguez-Reséndiz

Technologies 2024, 12(12), 250; https://doi.org/10.3390/technologies12120250 - 30 Nov 2024

Abstract

Solar trackers represent a significant advancement in enhancing the efficiency of solar energy collection. This study describes the development and implementation of a passive solar tracker featuring a single horizontal axis of rotation and an innovative guide slot mechanism. The tracker is designed [...] Read more.

Solar trackers represent a significant advancement in enhancing the efficiency of solar energy collection. This study describes the development and implementation of a passive solar tracker featuring a single horizontal axis of rotation and an innovative guide slot mechanism. The tracker is designed to be used with solar radiation-capturing devices. The guide slot mechanism is specifically engineered for a designated date, location, and period to follow the solar trajectory accurately. A contact follower moves along the guide slot, which drives a tracker disk to rotate by the solar trajectory. The mechanism is activated by the movement of a liquid container attached to a spring, thereby storing potential energy. The container releases the liquid through a mechanical valve that regulates the container’s movement, while the guide slot mechanism converts this movement into controlled rotational motion, which is transferred to a mobile structure mounting the solar panel. Notably, the majority of materials utilized in this construction are recycled. Furthermore, the solar tracker proposed in this work is designed to be operable by individuals with limited prior knowledge on the topic, emphasizing the primary contribution of this study: its potential to revolutionize energy collection in developing countries and marginalized urban areas. No similar systems are found upon comparison with existing models in the literature. Experiments conducted with a static solar panel and the same panel integrated into the passive solar tracker revealed a 30.87% improvement in energy collection efficiency over static solar panels. Full article

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

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22 pages, 17763 KiB

Open AccessArticle

Computer Vision Technology for Short Fiber Segmentation and Measurement in Scanning Electron Microscopy Images

by Evgenii Kurkin, Evgenii Minaev, Andrey Sedelnikov, Jose Gabriel Quijada Pioquinto, Vladislava Chertykovtseva and Andrey Gavrilov

Technologies 2024, 12(12), 249; https://doi.org/10.3390/technologies12120249 - 29 Nov 2024

Abstract

Computer vision technology for the automatic recognition and geometric characterization of carbon and glass fibers in scanning electron microscopy images is proposed. The proposed pipeline, combining the SAM model and DeepLabV3+, provides the generalizability and accuracy of the foundational SAM model and the [...] Read more.

Computer vision technology for the automatic recognition and geometric characterization of carbon and glass fibers in scanning electron microscopy images is proposed. The proposed pipeline, combining the SAM model and DeepLabV3+, provides the generalizability and accuracy of the foundational SAM model and the ability to quickly train on a small amount of data via the DeepLabV3+ model. The pipeline was trained several times more rapidly with lower requirements for computing resources than fine-tuning the SAM model, with comparable inference time. On the basis of the pipeline, an end-to-end technology for processing images of electron microscopic fibers was developed, the input of which is images with metadata and the output of which is statistics on the distribution of the geometric characteristics of the fibers. This innovation is of great practical importance for modeling the physical characteristics of materials. This paper proposes a few-shot training procedure for the DeepLabV3+/SAM pipeline, combining the training of the DeepLabV3+ model weights and the SAM model parameters. It allows effective training of the pipeline using only 37 real labeled images. The pipeline was then adapted to a new type of fiber and background using 15 additional real labeled images. This article also proposes a method for generating synthetic data for training neural network models, which improves the quality of segmentation by the IoU and PixAcc metrics from 0.943 and 0.949 to 0.953 and 0.959, i.e., by 1% on average. The developed pipeline significantly reduces the time required to evaluate fiber length in scanning electron microscope images. Full article

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23 pages, 5895 KiB

Open AccessArticle

Energy-Efficient Data Fusion in WSNs Using Mobility-Aware Compression and Adaptive Clustering

by Emad S. Hassan, Marwa Madkour, Salah E. Soliman, Ahmed S. Oshaba, Atef El-Emary, Ehab S. Ali and Fathi E. Abd El-Samie

Technologies 2024, 12(12), 248; https://doi.org/10.3390/technologies12120248 - 28 Nov 2024

Abstract

To facilitate energy-efficient information dissemination from multiple sensors to the sink within Wireless Sensor Networks (WSNs), in-network data fusion is imperative. This paper presents a new WSN topology that incorporates the Mobility-Efficient Data Fusion (MEDF) algorithm, which integrates a data-compression protocol with an [...] Read more.

To facilitate energy-efficient information dissemination from multiple sensors to the sink within Wireless Sensor Networks (WSNs), in-network data fusion is imperative. This paper presents a new WSN topology that incorporates the Mobility-Efficient Data Fusion (MEDF) algorithm, which integrates a data-compression protocol with an adaptive-clustering mechanism. The primary goals of this topology are, first, to determine a dynamic sequence of cluster heads (CHs) for each data transmission round, aiming to prolong network lifetime by implementing an adaptive-clustering mechanism resilient to network dynamics, where CH selection relies on residual energy and minimal communication distance; second, to enhance packet delivery ratio (PDR) through the application of a data-compression technique; and third, to mitigate the hot-spot issue, wherein sensor nodes nearest to the base station endure higher relay burdens, consequently influencing network longevity. To address this issue, mobility models provide a straightforward solution; specifically, a Random Positioning of Grid Mobility (RPGM) model is employed to alleviate the hot-spot problem. The simulation results show that the network topology incorporating the proposed MEDF algorithm effectively enhances network longevity, optimizes average energy consumption, and improves PDR. Compared to the Energy-Efficient Multiple Data Fusion (EEMDF) algorithm, the proposed algorithm demonstrates enhancements in PDR and energy efficiency, with gains of 5.2% and 7.7%, respectively. Additionally, it has the potential to extend network lifetime by 13.9%. However, the MEDF algorithm increases delay by 0.01% compared to EEMDF. The proposed algorithm is also evaluated against other algorithms, such as the tracking-anchor-based clustering method (TACM) and Energy-Efficient Dynamic Clustering (EEDC), the obtained results emphasize the MEDF algorithm’s ability to conserve energy more effectively than the other algorithms. Full article

(This article belongs to the Special Issue Perpetual Sensor Nodes for Sustainable Wireless Network Applications)

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12 pages, 4513 KiB

Open AccessArticle

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

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 KiB

Open AccessArticle

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

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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16 pages, 2379 KiB

Open AccessArticle

Resource Sizing for Virtual Environments of Networked Interconnected System Services

by Alexandr Albychev, Dmitry Ilin and Evgeny Nikulchev

Technologies 2024, 12(12), 245; https://doi.org/10.3390/technologies12120245 - 27 Nov 2024

Abstract

Networked interconnected systems are often deployed in infrastructures with resource allocation using isolated virtual environments. The technological implementation of such systems varies significantly, making it difficult to accurately estimate the required volume of resources to allocate for each virtual environment. This leads to [...] Read more.

Networked interconnected systems are often deployed in infrastructures with resource allocation using isolated virtual environments. The technological implementation of such systems varies significantly, making it difficult to accurately estimate the required volume of resources to allocate for each virtual environment. This leads to overprovisioning of some services and underprovisioning of others. The problem of distributing the available computational resources between the system services arises. To efficiently use resources and reduce resource waste, the problem of minimizing free resources under conditions of unknown ratios of resource distribution between services is formalized; an approach to determining regression dependencies of computing resource consumption by services on the number of requests and a procedure for efficient resource distribution between services are proposed. The proposed solution is experimentally evaluated using the networked interconnected system model. The results show an increase in throughput by 20.75% compared to arbitrary resource distribution and a reduction in wasted resources by 55.59%. The dependences of the use of resources by networked interconnected system services on the number of incoming requests, identified using the proposed solution, can also be used for scaling in the event of an increase in the total volume of allocated resources. Full article

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

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28 pages, 771 KiB

Open AccessReview

Formal Verification of Code Conversion: A Comprehensive Survey

by Amira T. Mahmoud, Ahmad A. Mohammed, Mahitap Ayman, Walaa Medhat , Sahar Selim , Hala Zayed, Ahmed H. Yousef and Nahla Elaraby

Technologies 2024, 12(12), 244; https://doi.org/10.3390/technologies12120244 - 26 Nov 2024

Abstract

Code conversion, encompassing translation, optimization, and generation, is becoming increasingly critical in information systems and the software industry. Traditional validation methods, such as test cases and code coverage metrics, often fail to ensure the correctness, completeness, and equivalence of converted code to its [...] Read more.

Code conversion, encompassing translation, optimization, and generation, is becoming increasingly critical in information systems and the software industry. Traditional validation methods, such as test cases and code coverage metrics, often fail to ensure the correctness, completeness, and equivalence of converted code to its original form. Formal verification emerges as a crucial methodology to address these limitations. Although numerous surveys have explored formal verification in various contexts, a significant research gap exists in pinpointing appropriate formal verification approaches to code conversion tasks. This paper provides a detailed survey of formal verification techniques applicable to code conversion. This survey identifies the strengths and limitations of contemporary adopted approaches while outlining a trajectory for future research, emphasizing the need for automated and scalable verification tools. The novel categorization of formal verification methods provided in this paper serves as a foundational guide for researchers seeking to enhance the reliability of code conversion processes. Full article

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

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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)

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21 pages, 803 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)

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23 pages, 1580 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)

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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)

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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)

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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)

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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

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