Technologies

20 pages, 4150 KiB

Open AccessArticle

Characterization of Commercial and Custom-Made Printing Filament Materials for Computed Tomography Imaging of Radiological Phantoms

by Filippos Okkalidis, Chrysoula Chatzigeorgiou, Nikiforos Okkalidis, Nikolay Dukov, Minko Milev, Zhivko Bliznakov, Giovanni Mettivier, Paolo Russo and Kristina Bliznakova

Technologies 2024, 12(8), 139; https://doi.org/10.3390/technologies12080139 - 20 Aug 2024

Cited by 1 | Viewed by 1841

Abstract

In recent years, material extrusion-based additive manufacturing, particularly fused filament fabrication (FFF), has gained significant attention due to its versatility and cost-effectiveness in producing complex geometries. This paper presents the characterization of seven novel materials for FFF and twenty-two commercially available filaments in [...] Read more.

In recent years, material extrusion-based additive manufacturing, particularly fused filament fabrication (FFF), has gained significant attention due to its versatility and cost-effectiveness in producing complex geometries. This paper presents the characterization of seven novel materials for FFF and twenty-two commercially available filaments in terms of X-ray computed tomography (CT) numbers, as tissue mimicking materials for the realization of 3D printed radiological phantoms. Two technical approaches, by 3D printing of cube samples and by producing cylinders of melted materials, are used for achieving this goal. Results showed that the CT numbers, given in Hounsfield unit (HU), of all the samples depended on the beam kilovoltage (kV). The CT numbers ranged from +411 HU to +3071 HU (at 80 kV), from −422 HU to +3071 HU (at 100 kV), and from −442 HU to +3070 HU (at 120 kV). Several commercial and custom-made filaments demonstrated suitability for substituting soft and hard human tissues, for realization of 3D printed phantoms with FFF in CT imaging. For breast imaging, an anthropomorphic phantom with two filaments could be fabricated using ABS-C (conductive acrylonitrile butadiene styrene) as a substitute for breast adipose tissue, and ASA-A (acrylic styrene acrylonitrile) for glandular breast tissue. Full article

(This article belongs to the Special Issue 3D Printing Technologies II)

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15 pages, 4196 KiB

Open AccessArticle

Sequestration of Dyes from Water into Poly(α-Olefins) Using Polyisobutylene Sequestering Agents

by Neil Rosenfeld, Mara P. Alonso, Courtney Humphries and David E. Bergbreiter

Technologies 2024, 12(8), 138; https://doi.org/10.3390/technologies12080138 - 20 Aug 2024

Viewed by 2090

Abstract

Trace concentrations of dyes are often present in textile wastewater streams and present a serious environmental problem. Thus, these dyes must be removed from wastewater either by degradation or sequestration prior to discharge of the wastewater into the environment. Existing processes to remove [...] Read more.

Trace concentrations of dyes are often present in textile wastewater streams and present a serious environmental problem. Thus, these dyes must be removed from wastewater either by degradation or sequestration prior to discharge of the wastewater into the environment. Existing processes to remove these wastewater contaminants include the use of solid sorbents to sequester dyes or the use of biochemical or chemical methods of dye degradation. However, these processes typically generate their own waste products, are not necessarily rapid because of the low dye concentration, and often use expensive or non-recyclable sequestrants or reagents. This paper describes a simple, recyclable, liquid–liquid extraction scheme where ionic dyes can be sequestered into poly(α-olefin) (PAO) solvent systems. The partitioning of anionic and cationic dyes from water into PAOs is facilitated by ionic PAO-phase anchored sequestering agents that are readily prepared from commercially available vinyl-terminated polyisobutylene (PIB). This is accomplished by a sequence of reactions involving hydroboration/oxidation, conversion of an alcohol into an iodide, and conversion of the resulting primary alkyl iodide into a cationic nitrogen derivative. The products of this synthetic sequence are cationic nitrogen iodide salts which serve as anionic sequestrants that are soluble in PAO. These studies showed that the resulting series of cationic PIB-bound cationic sequestering agents facilitated efficient extraction of anionic, azo, phthalein, and sulfonephthalein dyes from water into a hydrocarbon PAO phase. Since the hydrocarbon PAO phase is completely immiscible with water and the PIB derivatives are also insoluble in water, neither the sequestration solvent nor the sequestrants contaminate wastewater. The effectiveness and efficiency of these sequestrations were assayed by UV–visible spectroscopy. These spectroscopic studies showed that extraction efficiencies were in most cases >99%. These studies also involved procedures that allowed for the regeneration and recycling of these PAO sequestration systems. This allowed us to recycle the PAO solvent system for at least 10 sequential batch extractions where we sequestered sodium salts of methyl red and 4′,5′-dichlorofluorescein dyes from water with extraction efficiencies of >99%. These studies also showed that a PIB-bound derivative of the sodium salt of 1,1,1-trifluoromethylpentane-2,4-dione could be prepared from a PIB-bound carboxylic acid ester by a Claisen-like reaction and that the sodium salt of this β-diketone could be used to sequester cationic dyes from water. This PIB-bound anion rapidly and efficiently extracted >99% of methylene blue, malachite green, and safranine O from water based on UV–visible and ¹H NMR spectroscopic assays. Full article

(This article belongs to the Section Environmental Technology)

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21 pages, 2915 KiB

Open AccessArticle

A Smart Approach to Electric Vehicle Optimization via IoT-Enabled Recommender Systems

by Padmanabhan Amudhavalli, Rahiman Zahira, Subramaniam Umashankar and Xavier N. Fernando

Technologies 2024, 12(8), 137; https://doi.org/10.3390/technologies12080137 - 20 Aug 2024

Viewed by 2558

Abstract

Electric vehicles (EVs) are becoming of significant interest owing to their environmental benefits; however, energy efficiency concerns remain unsolved and require more investigation. A major issue is a lack of EV charging infrastructure, which can lead to operational difficulties. Effective infrastructure development, including [...] Read more.

Electric vehicles (EVs) are becoming of significant interest owing to their environmental benefits; however, energy efficiency concerns remain unsolved and require more investigation. A major issue is a lack of EV charging infrastructure, which can lead to operational difficulties. Effective infrastructure development, including well-placed charging stations (CS), is critical to enhancing connectivity. To overcome this, consumers want real-time data on charging station availability, neighboring station locations, and access times. This work leverages the Distance Vector Multicast Routing Protocol (DVMRP) to enhance the information collection process for charging stations through the Internet of Things (IoT). The evolving IoT paradigm enables the use of sensors and data transfer to give real-time information. Strategic sensor placement helps forecast server access to neighboring stations, optimize vehicle scheduling, and estimate wait times. A recommender system is designed to identify stations with more rapidly charging rates, along with uniform pricing. In addition, the routing protocol has a privacy protection strategy to prevent unauthorized access and safeguard EV data during exchanges between charging stations and user locations. The system is simulated with MATLAB 2020a, and the data are controlled and secured in the cloud. The predicted algorithm’s performance is evaluated using several kinds of standards, including power costs, vehicle counts, charging costs, energy consumption, and optimization values. Full article

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

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21 pages, 10838 KiB

Open AccessArticle

A Comparative Evaluation of Conveyor Belt Disc Brakes and Drum Brakes: Integrating Structural Topology Optimization and Weight Reduction

by Daniel Chelopo and Kapil Gupta

Technologies 2024, 12(8), 136; https://doi.org/10.3390/technologies12080136 - 19 Aug 2024

Viewed by 1847

Abstract

Topology optimization is a well known and sophisticated method for designing structures. Through a finite element analysis, this method optimizes the design and material distribution to obtain an ideal strength-to-weight ratio and improved strain-to-weight ratio. This study involves the development of a comprehensive [...] Read more.

Topology optimization is a well known and sophisticated method for designing structures. Through a finite element analysis, this method optimizes the design and material distribution to obtain an ideal strength-to-weight ratio and improved strain-to-weight ratio. This study involves the development of a comprehensive model for a brake using the ANSYS Parametric Design Language. The purpose of the model is to accurately characterize the geometry of the disc or drum. The technique of a complex eigenvalue analysis is used to identify the presence of unstable modes occurring at distinct frequencies, indicating instability. A braking force of 17,492 kN was exerted at a rotational velocity of 55 rad/s for 10 s. The optimization process resulted in significant mass reduction while maintaining structural integrity. In the drum brake, the mass was reduced from 114.01 kg to 104.07 kg, while the disc brake’s mass decreased from 68.81 kg to 56.68 kg. Full article

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14 pages, 3453 KiB

Open AccessArticle

MIRA: Multi-Joint Imitation with Recurrent Adaptation for Robot-Assisted Rehabilitation

by Ali Ashary, Ruchik Mishra, Madan M. Rayguru and Dan O. Popa

Technologies 2024, 12(8), 135; https://doi.org/10.3390/technologies12080135 - 16 Aug 2024

Viewed by 1750

Abstract

This work proposes a modular learning framework (MIRA) for rehabilitation robots based on a new deep recurrent neural network (RNN) that achieves adaptive multi-joint motion imitation. The RNN is fed with the fundamental frequencies as well as the ranges of the joint trajectories, [...] Read more.

This work proposes a modular learning framework (MIRA) for rehabilitation robots based on a new deep recurrent neural network (RNN) that achieves adaptive multi-joint motion imitation. The RNN is fed with the fundamental frequencies as well as the ranges of the joint trajectories, in order to predict the future joint trajectories of the robot. The proposed framework also uses a Segment Online Dynamic Time Warping (SODTW) algorithm to quantify the closeness between the robot and patient motion. The SODTW cost decides the amount of modification needed in the inputs to our deep RNN network, which in turn adapts the robot movements. By keeping the prediction mechanism (RNN) and adaptation mechanism (SODTW) separate, the framework achieves modularity, flexibility, and scalability. We tried both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) RNN architectures within our proposed framework. Experiments involved a group of 15 human subjects performing a range of motion tasks in conjunction with our social robot, Zeno. Comparative analysis of the results demonstrated the superior performance of the LSTM RNN across multiple task variations, highlighting its enhanced capability for adaptive motion imitation. Full article

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

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15 pages, 7044 KiB

Open AccessArticle

Fast Detection of the Stick–Slip Phenomenon Associated with Wheel-to-Rail Sliding Using Acceleration Sensors: An Experimental Study

by Gabriel Popa, Mihail Andrei, Emil Tudor, Ionuț Vasile and George Ilie

Technologies 2024, 12(8), 134; https://doi.org/10.3390/technologies12080134 - 13 Aug 2024

Viewed by 4871

Abstract

The stick–slip phenomenon, the initial stage when the traction wheel starts sliding on the rail, is a critical operation that needs to be detected quickly to control the traction drive. In this study, we have developed an experimental model that uses acceleration sensors [...] Read more.

The stick–slip phenomenon, the initial stage when the traction wheel starts sliding on the rail, is a critical operation that needs to be detected quickly to control the traction drive. In this study, we have developed an experimental model that uses acceleration sensors mounted on the wheel to evaluate the amplitude of the stick–slip phenomena. These sensors can alert the driver or assist the traction control unit when a stick–slip occurs. We propose a method to reduce the amplitude of the stick–slip phenomenon using special hydraulic dampers and viscous dampers mounted on the tractive axles of the locomotive to prevent slipping during acceleration. This practical solution, validated through numerical simulation, can be readily implemented in railway systems. The paper’s findings can be used to select the necessary sensors and corresponding vibration dampers. By implementing these sliding reducers, a locomotive can significantly improve traction, apply more torque to the wheel, and increase the load of a carrier train, instilling confidence in the efficiency of the proposed solution. Full article

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

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15 pages, 549 KiB

Open AccessArticle

Math for Everybody: A Sonification Module for Computer Algebra Systems Aimed at Visually Impaired People

by Ana M. Zambrano, Mateo N. Salvador, Felipe Grijalva, Henry Carvajal Mora and Nathaly Orozco Garzón

Technologies 2024, 12(8), 133; https://doi.org/10.3390/technologies12080133 - 12 Aug 2024

Viewed by 2109

Abstract

Computer Algebra Systems (CAS) currently lack an effective auditory representation, with most existing solutions relying on screen readers that provide limited functionality. This limitation prevents blind users from fully understanding and interpreting mathematical expressions, leading to confusion and self-doubt. This paper addresses the [...] Read more.

Computer Algebra Systems (CAS) currently lack an effective auditory representation, with most existing solutions relying on screen readers that provide limited functionality. This limitation prevents blind users from fully understanding and interpreting mathematical expressions, leading to confusion and self-doubt. This paper addresses the challenges blind individuals face when comprehending mathematical expressions within a CAS environment. We propose “Math for Everybody” (Math4e, version 1.0), a software module to reduce barriers for blind users in education. Math4e is a Sonification Module for CAS that generates a series of auditory tones, prosodic cues, and variations in audio parameters such as volume and speed. These resources are designed to eliminate ambiguity and facilitate the interpretation and understanding of mathematical expressions for blind users. To assess the effectiveness of Math4e, we conducted standardized tests employing the methodologies outlined in the Software Engineering Body of Knowledge (SWEBOK), International Software Testing Qualifications Board (ISTBQ), and ISO/IEC/IEEE 29119. The evaluation encompassed two scenarios: one involving simulated blind users and another with real blind users associated with the “Asociación de Invidentes Milton Vedado” foundation in Ecuador. Through the SAM methodology and verbal surveys (given the condition of the evaluated user), results are obtained, such as 90.56% for pleasure, 90.78% for arousal, and 91.56% for dominance, which demonstrates significant acceptance of the systems by the users. The outcomes underscored the users’ commendable ability to identify mathematical expressions accurately. Full article

(This article belongs to the Section Assistive Technologies)

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27 pages, 1797 KiB

Open AccessArticle

A Formal Verification Approach for Linux Kernel Designing

by Zi Wang, Yuqing Lan, Xinlei He and Jianghua Lv

Technologies 2024, 12(8), 132; https://doi.org/10.3390/technologies12080132 - 12 Aug 2024

Viewed by 1845

Abstract

Although the Linux kernel is widely used, its complexity makes errors common and potentially serious. Traditional formal verification methods often have high overhead and rely heavily on manual coding. They typically verify only specific functionalities of the kernel or target microkernels and do [...] Read more.

Although the Linux kernel is widely used, its complexity makes errors common and potentially serious. Traditional formal verification methods often have high overhead and rely heavily on manual coding. They typically verify only specific functionalities of the kernel or target microkernels and do not support continuous verification of the entire kernel. To address these limitations, we introduce LMVM (Linux Kernel Modeling and Verification Method), a formal method based on type theory that ensures the correct design of the Linux architecture. In the model, the kernel is treated as a top-level type, subdivided into the following sublevels: subsystem, dentry, file, struct, function, and base. These types are defined in the structure and relationships. The verification process includes checking the design specifications for both type relationships and the presence of each type. Our contribution lies primarily in the following two points: 1. This is a lightweight verification. As long as the modeling is complete, architectural errors in the design phase can be identified promptly. 2. The designed “model refactor” module supports kernel updating, and the kernel can be continuously verified by extending the kernel model. To test its usefulness, we develop a set of security communication mechanisms in the kernel, which are verified using our method. Full article

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

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9 pages, 3837 KiB

Open AccessArticle

Using Principal Component Analysis for Temperature Readings from YF₃:Pr³⁺ Luminescence

by Anđela Rajčić, Zoran Ristić, Jovana Periša, Bojana Milićević, Saad Aldawood, Abdullah N. Alodhayb, Željka Antić and Miroslav D. Dramićanin

Technologies 2024, 12(8), 131; https://doi.org/10.3390/technologies12080131 - 12 Aug 2024

Cited by 2 | Viewed by 1947

Abstract

The method of measuring temperature using luminescence by analyzing the emission spectra of Pr³⁺-doped YF₃ using principal component analysis is presented. The Pr³⁺-doped YF₃ is synthesized using a solid-state technique, and its single-phase orthorhombic crystal structure is [...] Read more.

The method of measuring temperature using luminescence by analyzing the emission spectra of Pr³⁺-doped YF₃ using principal component analysis is presented. The Pr³⁺-doped YF₃ is synthesized using a solid-state technique, and its single-phase orthorhombic crystal structure is confirmed using X-ray diffraction. The emission spectra measured within the 93–473 K temperature range displays characteristic Pr³⁺ f-f electronic transitions. The red emission from the ³P_0,1→³H₆,³F₂ electronic transition mostly dominates the spectra. However, at low temperatures, the intensity of the green emissions from the ³P_0,1→³H₅, deep-red ³P_0,1→³F₄, and the deep-red emissions from the ³P_0,1→³F₄ transitions are considerably lower compared to the intensity of the red emissions. Temperature variations directly impact the photoluminescent spectra, causing a notable increase in the green and deep-red emissions from the ³P₁ excited state. We utilized the entire spectrum as an input for principal component analysis, considering each temperature as an independent group of data. The first principal component explained 99.3% of the variance in emission spectra caused by temperature and we further used it as a reliable temperature indicator for luminescence thermometry. The approach has a maximum absolute sensitivity of around 0.012 K⁻¹. The average accuracy and precision values are 0.7 K and 0.5 K, respectively. Full article

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18 pages, 7633 KiB

Open AccessArticle

Roles of Micropillar Topography and Surface Energy on Cancer Cell Dynamics

by Hoang Huy Vu, Nam-Trung Nguyen, Sharda Yadav, Thi Thanh Ha Nguyen and Navid Kashaninejad

Technologies 2024, 12(8), 130; https://doi.org/10.3390/technologies12080130 - 10 Aug 2024

Viewed by 2019

Abstract

Microstructured surfaces are renowned for their unique properties, such as waterproofing and low adhesion, making them highly applicable in the biomedical field. These surfaces play a crucial role in influencing cell response by mimicking the native microenvironment of biological tissues. In this study, [...] Read more.

Microstructured surfaces are renowned for their unique properties, such as waterproofing and low adhesion, making them highly applicable in the biomedical field. These surfaces play a crucial role in influencing cell response by mimicking the native microenvironment of biological tissues. In this study, we engineered a series of biomimetic micropatterned surfaces using polydimethylsiloxane (PDMS) to explore their effects on primary breast cancer cell lines, contrasting these effects with those observed on conventional flat surfaces. The surface topography was varied to direct cells’ attachment, growth, and morphology. Our findings elucidate that surface-free energy is not merely a background factor but plays a decisive role in cell dynamics, strongly correlating with the spreading behaviour of breast cancer cells. Notably, on micropillar surfaces with high surface-free energy, an increase in the population of cancer cells was observed. Conversely, surfaces characterised by lower surface-free energies noted a reduction in cell viability. Moreover, the structural parameters, such as the gaps and diameters of the pillars, were found to critically influence cellular dispersion and adherence, underscoring the importance of the microstructures’ topography in biomedical applications. These insights pave the way for designing advanced microstructured surfaces tailored to specific cellular responses, opening new avenues for targeted cancer therapies and tissue engineering. Full article

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12 pages, 3831 KiB

Open AccessArticle

Image Reconstruction in Ultrasonic Speed-of-Sound Computed Tomography Using Time of Flight Estimated by a 2D Convolutional Neural Networks

by Yuki Mimura, Yudai Suzuki, Toshiyuki Sugimoto, Tadashi Saitoh, Tatsuhisa Takahashi and Hirotaka Yanagida

Technologies 2024, 12(8), 129; https://doi.org/10.3390/technologies12080129 - 7 Aug 2024

Viewed by 1920

Abstract

In ultrasonic nondestructive testing (NDT), accurately estimating the time of flight (TOF) of ultrasonic waves is crucial. Traditionally, TOF estimation involves the signal processing of a single measured waveform. In recent years, deep learning has also been applied to estimate the TOF; however, [...] Read more.

In ultrasonic nondestructive testing (NDT), accurately estimating the time of flight (TOF) of ultrasonic waves is crucial. Traditionally, TOF estimation involves the signal processing of a single measured waveform. In recent years, deep learning has also been applied to estimate the TOF; however, these methods typically process only single waveforms. In contrast, this study acquired fan-beam ultrasonic waveform profile data from 64 paths using an ultrasonic-speed computed tomography (CT) simulation of a circular column and developed a TOF estimation model using two-dimensional convolutional neural networks (CNNs) based on these data. We compared the accuracy of the TOF estimation between the proposed method and two traditional signal processing methods. Additionally, we reconstructed ultrasonic-speed CT images using the estimated TOF and evaluated the generated CT images. The results showed that the proposed method could estimate the longitudinal TOF more accurately than traditional methods, and the evaluation scores for the reconstructed images were high. Full article

(This article belongs to the Special Issue Smart Systems (SmaSys2023))

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33 pages, 2814 KiB

Open AccessArticle

Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices

by Areti Karamanou, Petros Brimos, Evangelos Kalampokis and Konstantinos Tarabanis

Technologies 2024, 12(8), 128; https://doi.org/10.3390/technologies12080128 - 6 Aug 2024

Viewed by 2016

Abstract

In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of [...] Read more.

In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of this study is to leverage Graph Neural Networks (GNNs) on open statistics knowledge graphs to model these spatial dependencies and predict house prices across Scotland’s 2011 data zones. The methodology involves retrieving integrated statistical indicators from the official Scottish Open Government Data portal and applying three representative GNN algorithms: ChebNet, GCN, and GraphSAGE. These GNNs are compared against traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP), demonstrating superior prediction accuracy. Innovative contributions of this study include the use of GNNs to model spatial dependencies in real estate economics and the application of local and global explainability techniques to enhance transparency and trust in the predictions. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainability results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both the global surrogate model and the SHAP approach underscored the comparative illness factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones. Full article

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

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17 pages, 475 KiB

Open AccessArticle

Feedback Collection and Nearest-Neighbor Profiling for Recommendation Systems in Healthcare Scenarios

by João António, Ricardo Malheiro and Sandra Jardim

Technologies 2024, 12(8), 127; https://doi.org/10.3390/technologies12080127 - 6 Aug 2024

Viewed by 1797

Abstract

The rise in the dimension and complexity of information generated in the clinical field has motivated research on the automation of tasks in personalized healthcare. Recommendation systems are a filtering method that utilizes patterns and data relationships to generate items of interest for [...] Read more.

The rise in the dimension and complexity of information generated in the clinical field has motivated research on the automation of tasks in personalized healthcare. Recommendation systems are a filtering method that utilizes patterns and data relationships to generate items of interest for a particular user. In healthcare, these systems can be used to potentiate physical therapy by providing the user with specific exercises for rehabilitation, albeit facing issues pertaining to low accuracy in earlier iterations (cold-start) and a lack of gradual optimization. In this study, we propose a physical activity recommendation system that utilizes a K-nearest neighbor (KNN) sampling strategy and feedback collection modules to improve the adequacy of recommendations at different stages of a rehabilitation period when compared to traditional collaborative filtering (CF) or human-constrained methods. The results from a trial show significant improvements in the quality of initial recommendations, achieving 81.2% accuracy before optimization. Moreover, the introduction of short-term adjustments based on frequent player feedback can be an efficient manner of improving recommendation accuracy over time, achieving overall better convergence periods than those of human-based systems, topping at a measured 98.1% accuracy at K = 7 cycles. Full article

(This article belongs to the Section Assistive Technologies)

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11 pages, 1539 KiB

Open AccessArticle

GAT-Based Bi-CARU with Adaptive Feature-Based Transformation for Video Summarisation

by Ka-Hou Chan and Sio-Kei Im

Technologies 2024, 12(8), 126; https://doi.org/10.3390/technologies12080126 - 5 Aug 2024

Viewed by 1809

Abstract

Nowadays, video is a common social media in our lives. Video summarisation has become an interesting task for information extraction, where the challenge of high redundancy of key scenes leads to difficulties in retrieving important messages. To address this challenge, this work presents [...] Read more.

Nowadays, video is a common social media in our lives. Video summarisation has become an interesting task for information extraction, where the challenge of high redundancy of key scenes leads to difficulties in retrieving important messages. To address this challenge, this work presents a novel approach called the Graph Attention (GAT)-based bi-directional content-adaptive recurrent unit model for video summarisation. The model makes use of the graph attention approach to transform the visual features of interesting scene(s) from a video. This transformation is achieved by a mechanism called Adaptive Feature-based Transformation (AFT), which extracts the visual features and elevates them to a higher-level representation. We also introduce a new GAT-based attention model that extracts major features from weight features for information extraction, taking into account the tendency of humans to pay attention to transformations and moving objects. Additionally, we integrate the higher-level visual features obtained from the attention layer with the semantic features processed by Bi-CARU. By combining both visual and semantic information, the proposed work enhances the accuracy of key-scene determination. By addressing the issue of high redundancy among major information and using advanced techniques, our method provides a competitive and efficient way to summarise videos. Experimental results show that our approach outperforms existing state-of-the-art methods in video summarisation. Full article

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

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18 pages, 1584 KiB

Open AccessArticle

Computer Simulation-Based Multi-Objective Optimisation of Additively Manufactured Cranial Implants

by Brian J. Moya, Marcelino Rivas, Ramón Quiza and J. Paulo Davim

Technologies 2024, 12(8), 125; https://doi.org/10.3390/technologies12080125 - 2 Aug 2024

Viewed by 1916

Abstract

Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance [...] Read more.

Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality, but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach. Full article

(This article belongs to the Section Manufacturing Technology)

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22 pages, 12633 KiB

Open AccessArticle

MediaPipe Frame and Convolutional Neural Networks-Based Fingerspelling Detection in Mexican Sign Language

by Tzeico J. Sánchez-Vicinaiz, Enrique Camacho-Pérez, Alejandro A. Castillo-Atoche, Mayra Cruz-Fernandez, José R. García-Martínez and Juvenal Rodríguez-Reséndiz

Technologies 2024, 12(8), 124; https://doi.org/10.3390/technologies12080124 - 1 Aug 2024

Cited by 3 | Viewed by 2221

Abstract

This research proposes implementing a system to recognize the static signs of the Mexican Sign Language (MSL) dactylological alphabet using the MediaPipe frame and Convolutional Neural Network (CNN) models to correctly interpret the letters that represent the manual signals coming from a camera. [...] Read more.

This research proposes implementing a system to recognize the static signs of the Mexican Sign Language (MSL) dactylological alphabet using the MediaPipe frame and Convolutional Neural Network (CNN) models to correctly interpret the letters that represent the manual signals coming from a camera. The development of these types of studies allows the implementation of technological advances in artificial intelligence and computer vision in teaching Mexican Sign Language (MSL). The best CNN model achieved an accuracy of 83.63% over the sets of 336 test images. In addition, considering samples of each letter, the following results are obtained: an accuracy of 84.57%, a sensitivity of 83.33%, and a specificity of 99.17%. The advantage of this system is that it could be implemented on low-consumption equipment, carrying out the classification in real-time, contributing to the accessibility of its use. Full article

(This article belongs to the Topic Theoretical and Applied Problems in Human-Computer Intelligent Systems)

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20 pages, 4305 KiB

Open AccessArticle

Experimental Benchmarking of Existing Offline Parameter Estimation Methods for Induction Motor Vector Control

by Butukuri Koti Reddy, Krishna Sandeep Ayyagari, Yemula Pradeep Kumar, Nimay Chandra Giri, Panganamamula Venkata Rajgopal, Georgios Fotis and Valeri Mladenov

Technologies 2024, 12(8), 123; https://doi.org/10.3390/technologies12080123 - 1 Aug 2024

Viewed by 1776

Abstract

Induction motors dominate industrial applications due to their unwavering reliability. However, optimal vector control, critical for maximizing dynamic performance, hinges on accurate parameter estimation. This control strategy necessitates precise knowledge of the motor’s parameters, obtainable through experimentation or calculation based on its design [...] Read more.

Induction motors dominate industrial applications due to their unwavering reliability. However, optimal vector control, critical for maximizing dynamic performance, hinges on accurate parameter estimation. This control strategy necessitates precise knowledge of the motor’s parameters, obtainable through experimentation or calculation based on its design specifications. Numerous methods, ranging from traditional to computational, have been proposed by various researchers, often relying on specific assumptions that might compromise the performance of modern motor control techniques. This paper meticulously reviews the most frequently utilized methods and presents experimental results from a single motor. We rigorously compare these results against established benchmark methods, including IEEE Standard 112-2017, and subsequently identify the superior approach, boasting a maximum error of only 6.5% compared to 19.65% for competing methods. Our study investigates the parameter estimation of induction motor. The methodology primarily utilizes RMS values for measurement tasks. Moreover, the impact of harmonics, particularly when an induction motor is supplied by an inverter is briefly addressed. The pioneering contribution of this work lies in pinpointing a more accurate parameter estimation method for enhanced vector control performance. These findings pave the way for exceptional vector control, particularly at lower speeds, ultimately elevating both vector control and drive performance. Full article

(This article belongs to the Collection Electrical Technologies)

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23 pages, 3955 KiB

Open AccessArticle

iKern: Advanced Intrusion Detection and Prevention at the Kernel Level Using eBPF

by Hassan Jalil Hadi, Mubashir Adnan, Yue Cao, Faisal Bashir Hussain, Naveed Ahmad, Mohammed Ali Alshara and Yasir Javed

Technologies 2024, 12(8), 122; https://doi.org/10.3390/technologies12080122 - 30 Jul 2024

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Abstract

The development of new technologies has significantly enhanced the monitoring and analysis of network traffic. Modern solutions like the Extended Berkeley Packet Filter (eBPF) demonstrate a clear advancement over traditional techniques, allowing for more customized and efficient filtering. These technologies are crucial for [...] Read more.

The development of new technologies has significantly enhanced the monitoring and analysis of network traffic. Modern solutions like the Extended Berkeley Packet Filter (eBPF) demonstrate a clear advancement over traditional techniques, allowing for more customized and efficient filtering. These technologies are crucial for influencing system performance as they operate at the lowest layer of the operating system, such as the kernel. Network-based Intrusion Detection/Prevention Systems (IDPS), including Snort, Suricata, and Bro, passively monitor network traffic from terminal access points. However, most IDPS are signature-based and face challenges on large networks, where the drop rate increases due to limitations in capturing and processing packets. High throughput leads to overheads, causing IDPS buffers to drop packets, which can pose serious threats to network security. Typically, IDPS are targeted by volumetric and multi-vector attacks that overload the network beyond the reception and processing capacity of IDPS, resulting in packet loss due to buffer overflows. To address this issue, the proposed solution, iKern, utilizes eBPF and Virtual Network Functions (VNF) to examine and filter packets at the kernel level before forwarding them to user space. Packet stream inspection is performed within the iKern Engine at the kernel level to detect and mitigate volumetric floods and multi-vector attacks. The iKern detection engine, operating within the Linux kernel, is powered by eBPF bytecode injected from user space. This system effectively handles volumetric Distributed Denial of Service (DDoS) attacks. Real-time implementation of this scheme has been tested on a 1Gbps network and shows significant detection and reduction capabilities against volumetric and multi-vector floods. Full article

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25 pages, 10247 KiB

Open AccessArticle

Development of Power-Delay Product Optimized ASIC-Based Computational Unit for Medical Image Compression

by Tanya Mendez, Tejasvi Parupudi, Vishnumurthy Kedlaya K and Subramanya G. Nayak

Technologies 2024, 12(8), 121; https://doi.org/10.3390/technologies12080121 - 29 Jul 2024

Cited by 1 | Viewed by 2054

Abstract

The proliferation of battery-operated end-user electronic devices due to technological advancements, especially in medical image processing applications, demands low power consumption, high-speed operation, and efficient coding. The design of these devices is centered on the Application-Specific Integrated Circuits (ASIC), General Purpose Processors (GPP), [...] Read more.

The proliferation of battery-operated end-user electronic devices due to technological advancements, especially in medical image processing applications, demands low power consumption, high-speed operation, and efficient coding. The design of these devices is centered on the Application-Specific Integrated Circuits (ASIC), General Purpose Processors (GPP), and Field Programmable Gate Array (FPGA) frameworks. The need for low-power functional blocks arises from the growing demand for high-performance computational units that are part of high-speed processors operating at high clock frequencies. The operational speed of the processor is determined by the computational unit, which is the workhorse of high-speed processors. A novel approach to integrating Very Large-Scale Integration (VLSI) ASIC design and the concepts of low-power VLSI compatible with medical image compression was embraced in this research. The focus of this study was the design, development, and implementation of a Power Delay Product (PDP) optimized computational unit targeted for medical image compression using ASIC design flow. This stimulates the research community’s quest to develop an ideal architecture, emphasizing on minimizing power consumption and enhancing device performance for medical image processing applications. The study uses area, delay, power, PDP, and Peak Signal-to-Noise Ratio (PSNR) as performance metrics. The research work takes inspiration from this and aims to enhance the efficiency of the computational unit through minor design modifications that significantly impact performance. This research proposes to explore the trade-off of high-performance adder and multiplier designs to design an ASIC-based computational unit using low-power techniques to enhance the efficiency in power and delay. The computational unit utilized for the digital image compression process was synthesized and implemented using gpdk 45 nm standard libraries with the Genus tool of Cadence. A reduced PDP of 46.87% was observed when the image compression was performed on a medical image, along with an improved PSNR of 5.89% for the reconstructed image. Full article

(This article belongs to the Topic Advances in Microelectronics and Semiconductor Engineering)

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25 pages, 3396 KiB

Open AccessReview

Technology in Forensic Sciences: Innovation and Precision

by Xavier Chango, Omar Flor-Unda, Pedro Gil-Jiménez and Hilario Gómez-Moreno

Technologies 2024, 12(8), 120; https://doi.org/10.3390/technologies12080120 - 26 Jul 2024

Cited by 1 | Viewed by 10216

Abstract

The advancement of technology and its developments have provided the forensic sciences with many cutting-edge tools, devices, and applications, allowing forensics a better and more accurate understanding of the crime scene, a better and optimal acquisition of data and information, and faster processing, [...] Read more.

The advancement of technology and its developments have provided the forensic sciences with many cutting-edge tools, devices, and applications, allowing forensics a better and more accurate understanding of the crime scene, a better and optimal acquisition of data and information, and faster processing, allowing more reliable conclusions to be obtained and substantially improving the scientific investigation of crime. This article describes the technological advances, their impacts, and the challenges faced by forensic specialists in using and implementing these technologies as tools to strengthen their field and laboratory investigations. The systematic review of the scientific literature used the PRISMA^® methodology, analyzing documents from databases such as SCOPUS, Web of Science, Taylor & Francis, PubMed, and ProQuest. Studies were selected using a Cohen Kappa coefficient of 0.463. In total, 63 reference articles were selected. The impact of technology on investigations by forensic science experts presents great benefits, such as a greater possibility of digitizing the crime scene, allowing remote analysis through extended reality technologies, improvements in the accuracy and identification of biometric characteristics, portable equipment for on-site analysis, and Internet of things devices that use artificial intelligence and machine learning techniques. These alternatives improve forensic investigations without diminishing the investigator’s prominence and responsibility in the resolution of cases. Full article

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

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20 pages, 2308 KiB

Open AccessArticle

Enhanced Energy Transfer Efficiency for IoT-Enabled Cyber-Physical Systems in 6G Edge Networks with WPT-MIMO-NOMA

by Agbon Ehime Ezekiel, Kennedy Chinedu Okafor, Sena Timothy Tersoo, Christopher Akinyemi Alabi, Jamiu Abdulsalam, Agbotiname Lucky Imoize, Olamide Jogunola and Kelvin Anoh

Technologies 2024, 12(8), 119; https://doi.org/10.3390/technologies12080119 - 24 Jul 2024

Cited by 1 | Viewed by 2030

Abstract

The integration of wireless power transfer (WPT) with massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks can provide operational capabilities to energy-constrained Internet of Things (IoT) devices in cyber-physical systems such as smart autonomous vehicles. However, during downlink WPT, co-channel interference (CCI) [...] Read more.

The integration of wireless power transfer (WPT) with massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks can provide operational capabilities to energy-constrained Internet of Things (IoT) devices in cyber-physical systems such as smart autonomous vehicles. However, during downlink WPT, co-channel interference (CCI) can limit the energy efficiency (EE) gains in such systems. This paper proposes a user equipment (UE)–base station (BS) connection model to assign each UE to a single BS for WPT to mitigate CCI. An energy-efficient resource allocation scheme is developed that integrates the UE–BS connection approach with joint optimization of power control, time allocation, antenna selection, and subcarrier assignment. The proposed scheme improves EE by 24.72% and 33.76% under perfect and imperfect CSI conditions, respectively, compared to a benchmark scheme without UE–BS connections. The scheme requires fewer BS antennas to maximize EE and the distributed algorithm exhibits fast convergence. Furthermore, UE–BS connections’ impact on EE provided significant gains. Dedicated links improve EE by 24.72% (perfect CSI) and 33.76% (imperfect CSI) over standard connections. Imperfect CSI reduces EE, with the proposed scheme outperforming by 6.97% to 12.75% across error rates. More antennas enhance EE, with improvements of up to 123.12% (conventional MIMO) and 38.14% (massive MIMO) over standard setups. Larger convergence parameters improve convergence, achieving EE gains of 7.09% to 11.31% over the baseline with different convergence rates. The findings validate the effectiveness of the proposed techniques in improving WPT efficiency and EE in wireless-powered MIMO–NOMA networks. Full article

(This article belongs to the Topic Cyber-Physical Security for IoT Systems)

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19 pages, 8777 KiB

Open AccessArticle

Development of a Body Weight Support System Employing Model-Based System Engineering Methodology

by Alberto E. Loaiza, Jose I. Garcia and Jose T. Buitrago

Technologies 2024, 12(8), 118; https://doi.org/10.3390/technologies12080118 - 23 Jul 2024

Viewed by 1817

Abstract

Partial body weight support systems have proven to be a vital tool in performing physical therapy for patients with lower limb disabilities to improve gait. Developing this type of equipment requires rigorous design process that obtains a robust system, allowing physiotherapy exercises to [...] Read more.

Partial body weight support systems have proven to be a vital tool in performing physical therapy for patients with lower limb disabilities to improve gait. Developing this type of equipment requires rigorous design process that obtains a robust system, allowing physiotherapy exercises to be performed safely and efficiently. With this in mind, a “Model-Based Systems Engineering” design process using SysML improves communication between different areas, thereby increasing the synergy of interdisciplinary workgroups and positively impacting the development process of cyber-physical systems. The proposed development process presents a work sequence that defines a clear path in the design process, allowing traceability in the development phase. This also ensures the observability of elements related to a part that has suffered a failure. This methodology reduces the integration complexity between subsystems that compose the partial body weight support system because is possible to have a hierarchical and functional system vision at each design stage. The standard allowed requirements to be established graphically, making it possible to observe their system dependencies and who satisfied them. Consequently, the Partial Weight Support System was implemented through with a clear design route obtained by the MBSE methodology. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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Technologies, Volume 12, Issue 8 (August 2024) – 22 articles

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