 Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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- Technologies, Vol. 12, Pages 140: IoT Energy Management System Based on a
Wireless Sensor/Actuator Network
Authors: Omar Arzate-Rivas, Víctor Sámano-Ortega, Juan Martínez-Nolasco, Mauro Santoyo-Mora, Coral Martínez-Nolasco, Roxana De León-Lomelí
First page: 140
Abstract: The use of DC microgrids (DC-µGs) offers a variety of environmental benefits; albeit, a successful implementation depends on the implementation of an Energy Management System (EMS). An EMS is broadly implemented with a hierarchical and centralized structure, where the communications layer presents as a key element of the system to achieve a successful operation. Additionally, the relatively low cost of wireless communication technologies and the advantages offered by remote monitoring have promoted the inclusion of the Internet of Things (IoT) and Wireless Sensor and Actuator Network (WSAN) technologies in the energy sector. In this article is presented the development of an IoT EMS based on a WSAN (IoT-EMS-WSAN) for the management of a DC-µG. The proposed EMS is composed of a WiFi-based WSAN that is interconnected to a DC-µG, a cloud server, and a User Web App. The proposed system was compared to a conventional EMS with a high latency wired communication layer. In comparison to the conventional EMS, the IoT-EMS-WSAN increased the updating time from 100 ms to 1200 ms; also, the bus of the DC-µG maintained its stability even though its variations increased; finally, the DC bus responded to an energy-outage scenario with a recovery time of 1 s instead of 150 ms, as seen with the conventional EMS. Despite the reduced latency, the developed IoT-EMS-WSAN was demonstrated to be a reliable tool for the management, monitoring, and remote controlling of a DC-µG.
Citation: Technologies
PubDate: 2024-08-24
DOI: 10.3390/technologies12090140
Issue No: Vol. 12, No. 9 (2024)
- Technologies, Vol. 12, Pages 141: Wireless Ranging by Evaluating Received
Signal Strength of UWB Chaotic Radio Pulses: Effects of Signal Propagation
Conditions
Authors: Elena V. Efremova, Lev V. Kuzmin
First page: 141
Abstract: Ultra-wideband radio signals have been the subject of study for several decades. They are used to solve problems of communications and ranging. Measuring the strength (power) of a radio signal is a technically simple way to estimate the distance between the emitter and the receiver of the signal. However, the conditions of signal propagation have a significant impact on the power of the received signal. This work is relevant because chaotic radio pulses are a relatively new type of carrier in wireless technologies, and actual knowledge about the change in signal power in different types of premises is relatively small, so such a study is necessary. In this paper, we study the variation in signal power with distance for chaotic ultra-wideband radio pulses under various propagation conditions. Using experimental measurements in several outdoor (field, roadside) and indoor (corridors, conference room, office) environments, we investigate the effect of propagation conditions on ultra-wideband chaotic radio signals and determine the limits within which the dependence of the calculated power on distance can be approximated by a power law. For this purpose, the results of experimental measurements of the received signal power (a total of about 17.5 M values) were accumulated and analyzed. The accuracy of distance measurement that can be achieved in different conditions is compared and analyzed. It was found that for a 9.5 dBm signal, the range of distances at which the average accuracy is only 15–50 cm when using a power law is 5–7 m indoors and 10–15 m outdoors.
Citation: Technologies
PubDate: 2024-08-25
DOI: 10.3390/technologies12090141
Issue No: Vol. 12, No. 9 (2024)
- Technologies, Vol. 12, Pages 142: Enhancing Diagnostic Accuracy for Skin
Cancer and COVID-19 Detection: A Comparative Study Using a Stacked
Ensemble Method
Authors: Hafza Qayyum, Syed Tahir Hussain Rizvi, Muddasar Naeem, Umamah bint Khalid, Musarat Abbas, Antonio Coronato
First page: 142
Abstract: In recent years, COVID-19 and skin cancer have become two prevalent illnesses with severe consequences if untreated. This research represents a significant step toward leveraging machine learning (ML) and ensemble techniques to improve the accuracy and efficiency of medical image diagnosis for critical diseases such as COVID-19 (grayscale images) and skin cancer (RGB images). In this paper, a stacked ensemble learning approach is proposed to enhance the precision and effectiveness of diagnosis of both COVID-19 and skin cancer. The proposed method combines pretrained models of convolutional neural networks (CNNs) including ResNet101, DenseNet121, and VGG16 for feature extraction of grayscale (COVID-19) and RGB (skin cancer) images. The performance of the model is evaluated using both individual CNNs and a combination of feature vectors generated from ResNet101, DenseNet121, and VGG16 architectures. The feature vectors obtained through transfer learning are then fed into base-learner models consisting of five different ML algorithms. In the final step, the predictions from the base-learner models, the ensemble validation dataset, and the feature vectors extracted from neural networks are assembled and applied as input for the meta-learner model to obtain final predictions. The performance metrics of the stacked ensemble model show high accuracy for COVID-19 diagnosis and intermediate accuracy for skin cancer.
Citation: Technologies
PubDate: 2024-08-27
DOI: 10.3390/technologies12090142
Issue No: Vol. 12, No. 9 (2024)
- Technologies, Vol. 12, Pages 143: Revolutionary Integration of Artificial
Intelligence with Meta-Optics-Focus on Metalenses for Imaging
Authors: Nikolay L. Kazanskiy, Svetlana N. Khonina, Ivan V. Oseledets, Artem V. Nikonorov, Muhammad A. Butt
First page: 143
Abstract: Artificial intelligence (AI) significantly enhances the development of Meta-Optics (MOs), which encompasses advanced optical components like metalenses and metasurfaces designed to manipulate light at the nanoscale. The intricate design of these components requires sophisticated modeling and optimization to achieve precise control over light behavior, tasks for which AI is exceptionally well-suited. Machine learning (ML) algorithms can analyze extensive datasets and simulate numerous design variations to identify the most effective configurations, drastically speeding up the development process. AI also enables adaptive MOs that can dynamically adjust to changing imaging conditions, improving performance in real-time. This results in superior image quality, higher resolution, and new functionalities across various applications, including microscopy, medical diagnostics, and consumer electronics. The combination of AI with MOs thus epitomizes a transformative advancement, pushing the boundaries of what is possible in imaging technology. In this review, we explored the latest advancements in AI-powered metalenses for imaging applications.
Citation: Technologies
PubDate: 2024-08-28
DOI: 10.3390/technologies12090143
Issue No: Vol. 12, No. 9 (2024)
- Technologies, Vol. 12, Pages 144: Fault Detection of Wheelset Bearings
through Vibration-Sound Fusion Data Based on Grey Wolf Optimizer and
Support Vector Machine
Authors: Tianhao Wang, Hongying Meng, Fan Zhang, Rui Qin
First page: 144
Abstract: This study aims to detect faults in wheelset bearings by analyzing vibration-sound fusion data, proposing a novel method based on Grey Wolf Optimizer (GWO) and Support Vector Machine (SVM). Wheelset bearings play a vital role in transportation. However, malfunctions in the bearing might result in extensive periods of inactivity and maintenance, disrupting supply chains, increasing operational costs, and causing delays that affect both businesses and consumers. Fast fault identification is crucial for minimizing maintenance expenses. In this paper, we proposed a new integration of GWO for optimizing SVM hyperparameters, specifically tailored for handling sound-vibration signals in fault detection. We have developed a new fault detection method that efficiently processes fusion data and performs rapid analysis and prediction within 0.0027 milliseconds per data segment, achieving a test accuracy of 98.3%. Compared to the SVM and neural network models built in MATLAB, the proposed method demonstrates superior detection performance. Overall, the GWO-SVM-based method proposed in this study shows significant advantages in fault detection of wheelset bearing vibrations, providing an efficient and reliable solution that is expected to reduce maintenance costs and improve the operational efficiency and reliability of equipment.
Citation: Technologies
PubDate: 2024-08-28
DOI: 10.3390/technologies12090144
Issue No: Vol. 12, No. 9 (2024)
- Technologies, Vol. 12, Pages 118: Development of a Body Weight Support
System Employing Model-Based System Engineering Methodology
Authors: Alberto E. Loaiza, Jose I. Garcia, Jose T. Buitrago
First page: 118
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 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.
Citation: Technologies
PubDate: 2024-07-23
DOI: 10.3390/technologies12080118
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 119: Enhanced Energy Transfer Efficiency for
Authors: Agbon Ehime Ezekiel, Kennedy Chinedu Okafor, Sena Timothy Tersoo, Christopher Akinyemi Alabi, Jamiu Abdulsalam, Agbotiname Lucky Imoize, Olamide Jogunola, Kelvin Anoh
First page: 119
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) 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.
Citation: Technologies
PubDate: 2024-07-24
DOI: 10.3390/technologies12080119
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 120: Technology in Forensic Sciences:
Innovation and Precision
Authors: Xavier Chango, Omar Flor-Unda, Pedro Gil-Jiménez, Hilario Gómez-Moreno
First page: 120
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, 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.
Citation: Technologies
PubDate: 2024-07-26
DOI: 10.3390/technologies12080120
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 121: Development of Power-Delay Product
Optimized ASIC-Based Computational Unit for Medical Image Compression
Authors: Tanya Mendez, Tejasvi Parupudi, Vishnumurthy Kedlaya K, Subramanya G. Nayak
First page: 121
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), 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.
Citation: Technologies
PubDate: 2024-07-29
DOI: 10.3390/technologies12080121
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 122: iKern: Advanced Intrusion Detection and
Prevention at the Kernel Level Using eBPF
Authors: Hassan Jalil Hadi, Mubashir Adnan, Yue Cao, Faisal Bashir Hussain, Naveed Ahmad, Mohammed Ali Alshara, Yasir Javed
First page: 122
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 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.
Citation: Technologies
PubDate: 2024-07-30
DOI: 10.3390/technologies12080122
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 123: Experimental Benchmarking of Existing
Offline Parameter Estimation Methods for Induction Motor Vector Control
Authors: Butukuri Koti Reddy, Krishna Sandeep Ayyagari, Yemula Pradeep Kumar, Nimay Chandra Giri, Panganamamula Venkata Rajgopal, Georgios Fotis, Valeri Mladenov
First page: 123
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 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.
Citation: Technologies
PubDate: 2024-08-01
DOI: 10.3390/technologies12080123
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 124: MediaPipe Frame and Convolutional Neural
Networks-Based Fingerspelling Detection in Mexican Sign Language
Authors: Tzeico J. Sánchez-Vicinaiz, Enrique Camacho-Pérez, Alejandro A. Castillo-Atoche, Mayra Cruz-Fernandez, José R. García-Martínez, Juvenal Rodríguez-Reséndiz
First page: 124
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. 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.
Citation: Technologies
PubDate: 2024-08-01
DOI: 10.3390/technologies12080124
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 125: Computer Simulation-Based
Multi-Objective Optimisation of Additively Manufactured Cranial Implants
Authors: Brian J. Moya, Marcelino Rivas, Ramón Quiza, J. Paulo Davim
First page: 125
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 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.
Citation: Technologies
PubDate: 2024-08-02
DOI: 10.3390/technologies12080125
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 126: GAT-Based Bi-CARU with Adaptive
Feature-Based Transformation for Video Summarisation
Authors: Ka-Hou Chan, Sio-Kei Im
First page: 126
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 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.
Citation: Technologies
PubDate: 2024-08-05
DOI: 10.3390/technologies12080126
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 127: Feedback Collection and Nearest-Neighbor
Profiling for Recommendation Systems in Healthcare Scenarios
Authors: João António, Ricardo Malheiro, Sandra Jardim
First page: 127
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 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.
Citation: Technologies
PubDate: 2024-08-06
DOI: 10.3390/technologies12080127
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 128: Explainable Graph Neural Networks: An
Application to Open Statistics Knowledge Graphs for Estimating House
Prices
Authors: Areti Karamanou, Petros Brimos, Evangelos Kalampokis, Konstantinos Tarabanis
First page: 128
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 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.
Citation: Technologies
PubDate: 2024-08-06
DOI: 10.3390/technologies12080128
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 129: Image Reconstruction in Ultrasonic
Speed-of-Sound Computed Tomography Using Time of Flight Estimated by a 2D
Convolutional Neural Networks
Authors: Yuki Mimura, Yudai Suzuki, Toshiyuki Sugimoto, Tadashi Saitoh, Tatsuhisa Takahashi, Hirotaka Yanagida
First page: 129
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, 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.
Citation: Technologies
PubDate: 2024-08-07
DOI: 10.3390/technologies12080129
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 130: Roles of Micropillar Topography and
Surface Energy on Cancer Cell Dynamics
Authors: Hoang Huy Vu, Nam-Trung Nguyen, Sharda Yadav, Thi Thanh Ha Nguyen, Navid Kashaninejad
First page: 130
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, 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.
Citation: Technologies
PubDate: 2024-08-10
DOI: 10.3390/technologies12080130
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 131: Using Principal Component Analysis for
Temperature Readings from YF3:Pr3+ Luminescence
Authors: Anđela Rajčić, Zoran Ristić, Jovana Periša, Bojana Milićević, Saad Aldawood, Abdullah N. Alodhayb, Željka Antić, Miroslav D. Dramićanin
First page: 131
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 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 3P0,1→3H6,3F2 electronic transition mostly dominates the spectra. However, at low temperatures, the intensity of the green emissions from the 3P0,1→3H5, deep-red 3P0,1→3F4, and the deep-red emissions from the 3P0,1→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.
Citation: Technologies
PubDate: 2024-08-12
DOI: 10.3390/technologies12080131
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 132: A Formal Verification Approach for Linux
Kernel Designing
Authors: Zi Wang, Yuqing Lan, Xinlei He, Jianghua Lv
First page: 132
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 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.
Citation: Technologies
PubDate: 2024-08-12
DOI: 10.3390/technologies12080132
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 133: Math for Everybody: A Sonification
Module for Computer Algebra Systems Aimed at Visually Impaired People
Authors: Ana M. Zambrano, Mateo N. Salvador, Felipe Grijalva, Henry Carvajal Mora, Nathaly Orozco Garzón
First page: 133
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 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.
Citation: Technologies
PubDate: 2024-08-12
DOI: 10.3390/technologies12080133
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 134: Fast Detection of the Stick–Slip
Phenomenon Associated with Wheel-to-Rail Sliding Using Acceleration
Sensors: An Experimental Study
Authors: Gabriel Popa, Mihail Andrei, Emil Tudor, Ionuț Vasile, George Ilie
First page: 134
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 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.
Citation: Technologies
PubDate: 2024-08-13
DOI: 10.3390/technologies12080134
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 135: MIRA: Multi-Joint Imitation with
Recurrent Adaptation for Robot-Assisted Rehabilitation
Authors: Ali Ashary, Ruchik Mishra, Madan M. Rayguru, Dan O. Popa
First page: 135
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, 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.
Citation: Technologies
PubDate: 2024-08-16
DOI: 10.3390/technologies12080135
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 136: A Comparative Evaluation of Conveyor
Belt Disc Brakes and Drum Brakes: Integrating Structural Topology
Optimization and Weight Reduction
Authors: Daniel Chelopo, Kapil Gupta
First page: 136
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 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.
Citation: Technologies
PubDate: 2024-08-19
DOI: 10.3390/technologies12080136
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 137: A Smart Approach to Electric Vehicle
Optimization via IoT-Enabled Recommender Systems
Authors: Padmanabhan Amudhavalli, Rahiman Zahira, Subramaniam Umashankar, Xavier N. Fernando
First page: 137
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 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.
Citation: Technologies
PubDate: 2024-08-20
DOI: 10.3390/technologies12080137
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 138: Sequestration of Dyes from Water into
Poly(α-Olefins) Using Polyisobutylene Sequestering Agents
Authors: Neil Rosenfeld, Mara P. Alonso, Courtney Humphries, David E. Bergbreiter
First page: 138
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 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.
Citation: Technologies
PubDate: 2024-08-20
DOI: 10.3390/technologies12080138
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 139: Characterization of Commercial and
Custom-Made Printing Filament Materials for Computed Tomography Imaging of
Radiological Phantoms
Authors: Filippos Okkalidis, Chrysoula Chatzigeorgiou, Nikiforos Okkalidis, Nikolay Dukov, Minko Milev, Zhivko Bliznakov, Giovanni Mettivier, Paolo Russo, Kristina Bliznakova
First page: 139
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 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.
Citation: Technologies
PubDate: 2024-08-20
DOI: 10.3390/technologies12080139
Issue No: Vol. 12, No. 8 (2024)
- Technologies, Vol. 12, Pages 93: Multi-Objective Optimisation of the
Battery Box in a Racing Car
Authors: Chao Ma, Caiqi Xu, Mohammad Souri, Elham Hosseinzadeh, Masoud Jabbari
First page: 93
Abstract: The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for the first time, attempts to use sensitivity analysis to screen the design variables and achieve an efficient optimisation design with a large number of original design variables. Specifically, the sensitivity analysis method was proposed to screen a certain number of optimisation variables, reducing the computational complexity while ensuring the efficiency of the optimisation process. A combination of the Generalised Regression Neural Network (GRNN) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to construct surrogate models and solve the optimisation problem. The optimisation model integrates these techniques to balance structural performance and weight reduction. The optimisation results demonstrate a significant reduction in battery box weight while maintaining structural integrity. Therefore, the proposed approach in this study provides important insights for achieving high-efficiency multi-objective optimisation of battery box structures.
Citation: Technologies
PubDate: 2024-06-25
DOI: 10.3390/technologies12070093
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 94: Transformer-Based Water Stress Estimation
Using Leaf Wilting Computed from Leaf Images and Unsupervised Domain
Adaptation for Tomato Crops
Authors: Makoto Koike, Riku Onuma, Ryo Adachi, Hiroshi Mineno
First page: 94
Abstract: Modern agriculture faces the dual challenge of ensuring sustainability while meeting the growing global demand for food. Smart agriculture, which uses data from the environment and plants to deliver water exactly when and how it is needed, has attracted significant attention. This approach requires precise water management and highly accurate real-time monitoring of crop water stress. Existing monitoring methods pose challenges such as the risk of plant damage, costly sensors, and the need for expert adjustments. Therefore, a low-cost, highly accurate water stress estimation model was developed that uses deep learning and commercially available sensors. The model uses the relative stem diameter as a water stress index and incorporates data from environmental sensors and an RGB camera, which are processed by the proposed daily normalization. In addition, domain adaptation in our Transformer model was implemented to enable robust learning in different areas. The accuracy of the model was evaluated using real cultivation data from tomato crops, achieving a coefficient of determination (R2) of 0.79 in water stress estimation. Furthermore, the model maintained a high level of accuracy when applied to different areas, with an R2 of 0.76, demonstrating its high adaptability under different conditions.
Citation: Technologies
PubDate: 2024-06-25
DOI: 10.3390/technologies12070094
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 95: Integrating Artificial Intelligence to
Biomedical Science: New Applications for Innovative Stem Cell Research and
Drug Development
Authors: Minjae Kim, Sunghoi Hong
First page: 95
Abstract: Artificial intelligence (AI) is rapidly advancing, aiming to mimic human cognitive abilities, and is addressing complex medical challenges in the field of biological science. Over the past decade, AI has experienced exponential growth and proven its effectiveness in processing massive datasets and optimizing decision-making. The main content of this review paper emphasizes the active utilization of AI in the field of stem cells. Stem cell therapies use diverse stem cells for drug development, disease modeling, and medical treatment research. However, cultivating and differentiating stem cells, along with demonstrating cell efficacy, require significant time and labor. In this review paper, convolutional neural networks (CNNs) are widely used to overcome these limitations by analyzing stem cell images, predicting cell types and differentiation efficiency, and enhancing therapeutic outcomes. In the biomedical sciences field, AI algorithms are used to automatically screen large compound databases, identify potential molecular structures and characteristics, and evaluate the efficacy and safety of candidate drugs for specific diseases. Also, AI aids in predicting disease occurrence by analyzing patients’ genetic data, medical images, and physiological signals, facilitating early diagnosis. The stem cell field also actively utilizes AI. Artificial intelligence has the potential to make significant advances in disease risk prediction, diagnosis, prognosis, and treatment and to reshape the future of healthcare. This review summarizes the applications and advancements of AI technology in fields such as drug development, regenerative medicine, and stem cell research.
Citation: Technologies
PubDate: 2024-06-26
DOI: 10.3390/technologies12070095
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 96: Deep Learning for Skeleton-Based Human
Activity Segmentation: An Autoencoder Approach
Authors: Md Amran Hossen, Abdul Ghani Naim, Pg Emeroylariffion Abas
First page: 96
Abstract: Automatic segmentation is essential for enhancing human activity recognition, especially given the limitations of publicly available datasets that often lack diversity in daily activities. This study introduces a novel segmentation method that utilizes skeleton data for a more accurate and efficient analysis of human actions. By employing an autoencoder, this method extracts representative features and reconstructs the dataset, using the discrepancies between the original and reconstructed data to establish a segmentation threshold. This innovative approach allows for the automatic segmentation of activity datasets into distinct segments. Rigorous evaluations against ground truth across three publicly available datasets demonstrate the method’s effectiveness, achieving impressive average annotation error, precision, recall, and F1-score values of 3.6, 90%, 87%, and 88%, respectively. This illustrates the robustness of the proposed method in accurately identifying change points and segmenting continuous skeleton-based activities as compared to two other state-of-the-art techniques: one based on deep learning and another using the classical time-series segmentation algorithm. Additionally, the dynamic thresholding mechanism enhances the adaptability of the segmentation process to different activity dynamics improving overall segmentation accuracy. This performance highlights the potential of the proposed method to significantly advance the field of human activity recognition by improving the accuracy and efficiency of identifying and categorizing human movements.
Citation: Technologies
PubDate: 2024-06-27
DOI: 10.3390/technologies12070096
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 97: Tongue Disease Prediction Based on
Machine Learning Algorithms
Authors: Hassoon, Al-Naji, Khalid, Chahl
First page: 97
Abstract: The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.
Citation: Technologies
PubDate: 2024-06-28
DOI: 10.3390/technologies12070097
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 98: Evaluating Factors Shaping Real-Time
Internet-of-Things-Based License Plate Recognition Using Single-Board
Computer Technology
Authors: Paniti Netinant, Siwakron Phonsawang, Meennapa Rukhiran
First page: 98
Abstract: Reliable and cost-efficient license plate recognition (LPR) systems enhance security, traffic management, and automated toll collection in real-world applications. This study addresses optimal unique configurations for enhancing LPR system accuracy and reliability by evaluating the impact of camera angle, object velocity, and distance on the efficacy of real-time LPR systems. The Internet of Things (IoT) LPR framework is proposed and utilized on single-board computer (SBC) technology, such as the Raspberry Pi 4 platform, with a high-resolution webcam using advanced OpenCV and OCR–Tesseract algorithms applied. The research endeavors to simulate common deployment scenarios of the real-time LPR system and perform thorough testing by leveraging SBC computational capabilities and the webcam’s imaging capabilities. The testing process is not just comprehensive, but also meticulous, ensuring the system’s reliability in various operational settings. We performed extensive experiments with a hundred repetitions at diverse angles, velocities, and distances. An assessment of the data’s precision, recall, and F1 score indicates the accuracy with which Thai license plates are identified. The results show that camera angles close to 180° significantly reduce perspective distortion, thus enhancing precision. Lower vehicle speeds (<10 km/h) and shorter distances (<10 m) also improve recognition accuracy by reducing motion blur and improving image clarity. Images captured from shorter distances (approximately less than 10 m) are more accurate for high-resolution character recognition. This study substantially contributes to SBC technology utilizing IoT-based real-time LPR systems for practical, accurate, and cost-effective implementations.
Citation: Technologies
PubDate: 2024-07-01
DOI: 10.3390/technologies12070098
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 99: Analysis of the Use of Artificial
Intelligence in Software-Defined Intelligent Networks: A Survey
Authors: Bayron Jesit Ospina Cifuentes, Álvaro Suárez, Vanessa García Pineda, Ricardo Alvarado Jaimes, Alber Oswaldo Montoya Benitez, Juan David Grajales Bustamante
First page: 99
Abstract: The distributed structure of traditional networks often fails to promptly and accurately provide the computational power required for artificial intelligence (AI), hindering its practical application and implementation. Consequently, this research aims to analyze the use of AI in software-defined networks (SDNs). To achieve this goal, a systematic literature review (SLR) is conducted based on the PRISMA 2020 statement. Through this review, it is found that, bottom-up, from the perspective of the data plane, control plane, and application plane of SDNs, the integration of various network planes with AI is feasible, giving rise to Intelligent Software Defined Networking (ISDN). As a primary conclusion, it was found that the application of AI-related algorithms in SDNs is extensive and faces numerous challenges. Nonetheless, these challenges are propelling the development of SDNs in a more promising direction through the adoption of novel methods and tools such as route optimization, software-defined routing, intelligent methods for network security, and AI-based traffic engineering, among others.
Citation: Technologies
PubDate: 2024-07-02
DOI: 10.3390/technologies12070099
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 100: Effect of Physical Parameters on Fatigue
Life of Materials and Alloys: A Critical Review
Authors: Amit Kaimkuriya, Balaguru Sethuraman, Manoj Gupta
First page: 100
Abstract: Fatigue refers to the progressive and localized structural damage that occurs when a material is subjected to repeated loading and unloading, typically at levels below its ultimate strength. Several failure mechanisms have been observed in practical scenarios, encompassing high-cycle, low-cycle, thermal, surface, corrosion, and fretting fatigue. Fatigue, connected to the failure of numerous engineered products, stands out as a prevalent cause of structural failure in service. Conducting research on the advancement and application of fatigue analysis technologies is crucial because fatigue analysis plays a critical role in determining the service life of components and mitigating the risk of failure. This study compiles data from a wide range of sources and offers a thorough summary of the state of fatigue analysis. It focuses on the effects of different parameters, including hardness, temperature, residual stresses, and hardfacing, on the fatigue life of different materials and their alloys. The fatigue life of alloys is typically high at low temperatures, but it is significantly reduced at high temperatures or under high-stress conditions. One of the main causes of lower fatigue life is residual stress. High-temperature conditions and hardfacing processes cause the development of tensile residual stresses, which in turn decreases fatigue life. But, if the hardness of the material significantly increases due to hardfacing, then the fatigue life also increases. This manuscript focuses on reviewing the research on fatigue-life prediction methods, shortcomings, and recommendations.
Citation: Technologies
PubDate: 2024-07-03
DOI: 10.3390/technologies12070100
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 101: Smartphone-Based Citizen Science Tool
Authors: Panagiotis Christakakis, Garyfallia Papadopoulou, Georgios Mikos, Nikolaos Kalogiannidis, Dimosthenis Ioannidis, Dimitrios Tzovaras, Eleftheria Maria Pechlivani
First page: 101
Abstract: In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices.
Citation: Technologies
PubDate: 2024-07-03
DOI: 10.3390/technologies12070101
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 102: Technique of High-Field Electron
Injection for Wafer-Level Testing of Gate Dielectrics of MIS Devices
Authors: Dmitrii V. Andreev, Vladimir V. Andreev, Marina Konuhova, Anatoli I. Popov
First page: 102
Abstract: We propose a technique for the wafer-level testing of the gate dielectrics of metal–insulator–semiconductor (MIS) devices by the high-field injection of electrons into the dielectric using a mode of increasing injection current density up to a set level. This method provides the capability to control a change in the charge state of the gate dielectric during all the testing. The proposed technique makes it possible to assess the integrity of the thin dielectric and at the same time to control the charge effects of its degradation. The method in particular can be used for manufacturing processes to control integrated circuits (ICs) based on MIS structures. In the paper, we propose an advanced algorithm of the Bounded J-Ramp testing of the gate dielectric and receive its approval when monitoring the quality of the gate dielectrics of production-manufactured MIS devices. We found that the maximum value of positive charge obtained when tested by the proposed method was a value close to that obtained when the charge was injected into the dielectric under a constant current with a Bounded J value despite large differences in the rate of degradation of the dielectric.
Citation: Technologies
PubDate: 2024-07-04
DOI: 10.3390/technologies12070102
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 103: Defining a Metric-Driven Approach for
Learning Hazardous Situations
Authors: Mario Fiorino, Muddasar Naeem, Mario Ciampi, Antonio Coronato
First page: 103
Abstract: Artificial intelligence has brought many innovations to our lives. At the same time, it is worth designing robust safety machine learning (ML) algorithms to obtain more benefits from technology. Reinforcement learning (RL) being an important ML method is largely applied in safety-centric scenarios. In such a situation, learning safety constraints are necessary to avoid undesired outcomes. Within the traditional RL paradigm, agents typically focus on identifying states associated with high rewards to maximize its long-term returns. This prioritization can lead to a neglect of potentially hazardous situations. Particularly, the exploration phase can pose significant risks, as it necessitates actions that may have unpredictable consequences. For instance, in autonomous driving applications, an RL agent might discover routes that yield high efficiency but fail to account for sudden hazardous conditions such as sharp turns or pedestrian crossings, potentially leading to catastrophic failures. Ensuring the safety of agents operating in unpredictable environments with potentially catastrophic failure states remains a critical challenge. This paper introduces a novel metric-driven approach aimed at containing risk in RL applications. Central to this approach are two developed indicators: the Hazard Indicator and the Risk Indicator. These metrics are designed to evaluate the safety of an environment by quantifying the likelihood of transitioning from safe states to failure states and assessing the associated risks. The fact that these indicators are characterized by a straightforward implementation, a highly generalizable probabilistic mathematical foundation, and a domain-independent nature makes them particularly interesting. To demonstrate their efficacy, we conducted experiments across various use cases, showcasing the feasibility of our proposed metrics. By enabling RL agents to effectively manage hazardous states, this approach paves the way for a more reliable and readily implementable RL in practical applications.
Citation: Technologies
PubDate: 2024-07-04
DOI: 10.3390/technologies12070103
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 104: Adsorption of HFO-1234ze(E) onto
Steam-Activated Carbon Derived from Sawmill Waste Wood
Authors: Huiyuan Bao, Md. Amirul Islam, Bidyut Baran Saha
First page: 104
Abstract: This study utilizes waste Albizia lebbeck wood from a sawmill to prepare activated carbon adsorbents and explores their potential application in adsorption cooling systems with a novel hydrofluoroolefin (HFO) refrigerant characterized by a low global warming potential. Activated carbon was synthesized through a simple and green steam activation method, and the optimal carbon shows a specific surface area of 946.8 m2/g and a pore volume of 0.843 cm3/g. The adsorption isotherms of HFO-1234ze(E) (Trans-1,3,3,3-tetrafluoropropene) on the activated carbon were examined at 30, 40, and 50 °C up to 400 kPa using a customized constant-volume variable-pressure system, and significant adsorption of 1.041 kg kg−1 was achieved at 30 °C and 400 kPa. The experimental data were fitted using both the Dubinin–Astakhov and Tóth models, and both models provided excellent fit results. The D−A adsorption model simulated the net adsorption capacity at possible operating temperatures. The isosteric of adsorption was determined using the Clausius–Clapeyron and modified Dubinin–Astakhov equations. In addition, the specific cooling effect and coefficient of performance were also studied.
Citation: Technologies
PubDate: 2024-07-05
DOI: 10.3390/technologies12070104
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 105: Discovering Data Domains and Products in
Data Meshes Using Semantic Blueprints
Authors: Michalis Pingos, Andreas S. Andreou
First page: 105
Abstract: Nowadays, one of the greatest challenges in data meshes revolves around detecting and creating data domains and data products for providing the ability to adapt easily and quickly to changing business needs. This requires a disciplined approach to identify, differentiate and prioritize distinct data sources according to their content and diversity. The current paper tackles this highly complicated issue and suggests a standardized approach that integrates the concept of data blueprints with data meshes. In essence, a novel standardization framework is proposed that creates data products using a metadata semantic enrichment mechanism, the latter also offering data domain readiness and alignment. The approach is demonstrated using real-world data produced by multiple sources in a poultry meat production factory. A set of functional attributes is used to qualitatively compare the proposed approach to existing data structures utilized in storage architectures, with quite promising results. Finally, experimentation with different scenarios varying in data product complexity and granularity suggests a successful performance.
Citation: Technologies
PubDate: 2024-07-07
DOI: 10.3390/technologies12070105
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 106: Analysis and Development of an IoT
System for an Agrivoltaics Plant
Authors: Francesco Zito, Nicola Ivan Giannoccaro, Roberto Serio, Sergio Strazzella
First page: 106
Abstract: This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system is made up of two main components: the central unit, which enables the precise deployment and distribution of water and fertilizers in different areas of the agricultural field, and the sensor node, which oversees collecting environmental and soil data. This article delves into the evolution of the system, focusing on structural and architectural changes to develop an infrastructure suitable for implementing a predictive model based on artificial intelligence and big data. Aspects concerning both the sensor node, such as energy management, accuracy of solar radiation readings, and qualitative soil moisture measurements, as well as implementations to the hydraulic system and the control and monitoring system of the central unit, are explored. This article provides an overview of the results obtained from solar radiation and soil moisture measurements. In addition, the results of an experimental campaign, in which 300 salad plants were grown using the SolarFertigation system in a photovoltaic field, are presented. This study demonstrated the effectiveness and applicability of the system under real-world conditions and highlighted its potential in optimizing resources and increasing agricultural productivity, especially in agrivoltaic settings.
Citation: Technologies
PubDate: 2024-07-07
DOI: 10.3390/technologies12070106
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 107: Numerical Simulation and Design of a
Mechanical Structure of an Ankle Exoskeleton for Elderly People
Authors: Ammir Rojas, Julio Ronceros, Carlos Raymundo, Gianpierre Zapata, Leonardo Vinces, Gustavo Ronceros
First page: 107
Abstract: This article presents the numerical simulation and design of an ankle exoskeleton oriented to elderly users. For the design, anatomical measurements were taken from a user of this age group to obtain an ergonomic, resistant, and exceptionally reliable mechanical structure. In addition, the design was validated to support a “weight range” of users between 50 and 80 kg in order to evaluate the reaction of the mechanism within the range of loads generated in relation to the first principal stress, the safety coefficient, the Von Mises stress, and principal deformations, for which the 3D CAD software Autodesk Inventor and theoretical correlations were used to calculate the displacement and rotation angles of the ankle in the structure. Likewise, two types of materials were evaluated: ABS (acrylonitrile butadiene styrene) and a polymer reinforced with carbon fiber. Finally, the designed pieces were assembled with the guarantee that the mobility of the system had been validated through the numerical simulation environment, highlighting that by being generated through 3D printing, manufacturing costs are reduced, allowing them to be accessible and ensuring that more people can benefit from this ankle exoskeleton.
Citation: Technologies
PubDate: 2024-07-09
DOI: 10.3390/technologies12070107
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 108: The Measurement of Contrast Sensitivity
in Near Vision: The Use of a Digital System vs. a Conventional Printed
Test
Authors: Kevin J. Mena-Guevara, David P. Piñero, María José Luque, Dolores de Fez
First page: 108
Abstract: In recent years, there has been intense development of digital diagnostic tests for vision. All of these tests must be validated for clinical use. The current study enrolled 51 healthy individuals (age 19–72 years) in which achromatic contrast sensitivity function (CSF) in near vision was measured with the printed Vistech VCTS test (Stereo Optical Co., Inc., Chicago, IL, USA) and the Optopad-CSF (developed by our research group to be used on an iPad). Likewise, chromatic CSF was evaluated with a digital test. Statistically significant differences between tests were only found for the two higher spatial frequencies evaluated (p = 0.012 and <0.001, respectively). The mean achromatic index of contrast sensitivity (ICS) was 0.02 ± 1.07 and −0.76 ± 1.63 for the Vistech VCTS and Optopad tests, respectively (p < 0.001). The ranges of agreement between tests were 0.55, 0.76, 0.78, and 0.69 log units for the spatial frequencies of 1.5, 3, 6, and 12 cpd, respectively. The mean chromatic ICS values were −20.56 ± 0.96 and −0.16 ± 0.99 for the CSF-T and CSF-D plates, respectively (p < 0.001). Furthermore, better achromatic, red–green, and blue–yellow CSF values were found in the youngest groups. The digital test allows the fast measurement of near-achromatic and chromatic CSF using a colorimetrically calibrated iPad, but the achromatic measures cannot be used interchangeably with those obtained with a conventional printed test.
Citation: Technologies
PubDate: 2024-07-09
DOI: 10.3390/technologies12070108
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 109: HUB3D: Intelligent Manufacturing HUB
System
Authors: Antonio Trejo-Morales, Edgar Adrián Franco-Urquiza, Hansell David Devilet-Castellanos, Dario Bringas-Posadas
First page: 109
Abstract: HUB3D represents a cutting-edge solution for managing and operating a 3D printer farm through the integration of advanced hardware and software. It features intuitive, responsive interfaces that support seamless interaction across various devices. Leveraging cloud services ensures the system’s stability, security, and scalability, enabling users from diverse locations to effortlessly upload and manage their 3D printing projects. The hardware component includes a purpose-built rack capable of housing up to four 3D printers, each synchronized and managed by a manipulator arm controlled via Raspberry Pi technology. This setup facilitates continuous operation and high automation, optimizing production efficiency and reducing downtime significantly. This integrated approach positions HUB3D at the forefront of additive manufacturing management. By combining robust hardware capabilities with sophisticated software functionalities and cloud integration, the system offers unparalleled advantages. It supports continuous manufacturing processes, enhances workflow efficiency, and enables remote monitoring and management of printing operations. Overall, HUB3D’s innovative design and comprehensive features cater to both individual users and businesses seeking to streamline 3D printing workflows. With scalability, automation, and remote accessibility at its core, HUB3D represents a pivotal advancement in modern manufacturing technology, promising increased productivity and operational flexibility in the realm of additive manufacturing.
Citation: Technologies
PubDate: 2024-07-09
DOI: 10.3390/technologies12070109
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 110: Securing Blockchain-Based Supply Chain
Management: Textual Data Encryption and Access Control
Authors: Imran Khan, Qazi Ejaz Ali, Hassan Jalil Hadi, Naveed Ahmad, Gauhar Ali, Yue Cao, Mohammed Ali Alshara
First page: 110
Abstract: A supply chain (SC) encompasses a network of businesses, individuals, events, data, and resources orchestrating the movement of goods or services from suppliers to customers. Leveraging a blockchain-based platform, smart contracts play a pivotal role in aligning business logic and tracking progress within supply chain activities. Employing two distinct ledgers, namely Hyperledger and Ethereum, introduces challenges in handling the escalating volume of data and addressing the technical expertise gap related to supply chain management (SCM) tools in blockchain technology. Within the domain of blockchain-based SCM, the growing volume of data activities introduces challenges in the efficient regulation of data flow and the assurance of privacy. To tackle these challenges, a straightforward approach is recommended to manage data growth and thwart unauthorized entries or spam attempts within blockchain ledgers. The proposed technique focuses on validating hashes to ensure blockchain integrity. Emphasizing the authentication of sensitive data on the blockchain to bolster SCM, this approach compels applications to shoulder increased accountability. The suggested technique involves converting all data into textual format, implementing code encryption, and establishing permission-based access control. This strategy aims to address inherent weaknesses in blockchain within SCM. The results demonstrate the efficacy of the proposed technique in providing security and privacy for various types of data within SCM. Overall, the approach enhances the robustness of blockchain-based SCM, offering a comprehensive solution to navigate evolving challenges in data management and privacy assurance.
Citation: Technologies
PubDate: 2024-07-09
DOI: 10.3390/technologies12070110
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 111: Optimizing Speech Emotion Recognition
with Machine Learning Based Advanced Audio Cue Analysis
Authors: Nuwan Pallewela, Damminda Alahakoon, Achini Adikari, John E. Pierce, Miranda L. Rose
First page: 111
Abstract: In today’s fast-paced and interconnected world, where human–computer interaction is an integral component of daily life, the ability to recognize and understand human emotions has emerged as a crucial facet of technological advancement. However, human emotion, a complex interplay of physiological, psychological, and social factors, poses a formidable challenge even for other humans to comprehend accurately. With the emergence of voice assistants and other speech-based applications, it has become essential to improve audio-based emotion expression. However, there is a lack of specificity and agreement in current emotion annotation practice, as evidenced by conflicting labels in many human-annotated emotional datasets for the same speech segments. Previous studies have had to filter out these conflicts and, therefore, a large portion of the collected data has been considered unusable. In this study, we aimed to improve the accuracy of computational prediction of uncertain emotion labels by utilizing high-confidence emotion labelled speech segments from the IEMOCAP emotion dataset. We implemented an audio-based emotion recognition model using bag of audio word encoding (BoAW) to obtain a representation of audio aspects of emotion in speech with state-of-the-art recurrent neural network models. Our approach improved the state-of-the-art audio-based emotion recognition with a 61.09% accuracy rate, an improvement of 1.02% over the BiDialogueRNN model and 1.72% over the EmoCaps multi-modal emotion recognition models. In comparison to human annotation, our approach achieved similar results in identifying positive and negative emotions. Furthermore, it has proven effective in accurately recognizing the sentiment of uncertain emotion segments that were previously considered unusable in other studies. Improvements in audio emotion recognition could have implications in voice-based assistants, healthcare, and other industrial applications that benefit from automated communication.
Citation: Technologies
PubDate: 2024-07-11
DOI: 10.3390/technologies12070111
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 112: Improvement of the ANN-Based Prediction
Technology for Extremely Small Biomedical Data Analysis
Authors: Ivan Izonin, Roman Tkachenko, Oleh Berezsky, Iurii Krak, Michal Kováč, Maksym Fedorchuk
First page: 112
Abstract: Today, the field of biomedical engineering spans numerous areas of scientific research that grapple with the challenges of intelligent analysis of small datasets. Analyzing such datasets with existing artificial intelligence tools is a complex task, often complicated by issues like overfitting and other challenges inherent to machine learning methods and artificial neural networks. These challenges impose significant constraints on the practical application of these tools to the problem at hand. While data augmentation can offer some mitigation, existing methods often introduce their own set of limitations, reducing their overall effectiveness in solving the problem. In this paper, the authors present an improved neural network-based technology for predicting outcomes when analyzing small and extremely small datasets. This approach builds on the input doubling method, leveraging response surface linearization principles to improve performance. Detailed flowcharts of the improved technology’s operations are provided, alongside descriptions of new preparation and application algorithms for the proposed solution. The modeling, conducted using two biomedical datasets with optimal parameters selected via differential evolution, demonstrated high prediction accuracy. A comparison with several existing methods revealed a significant reduction in various errors, underscoring the advantages of the improved neural network technology, which does not require training, for the analysis of extremely small biomedical datasets.
Citation: Technologies
PubDate: 2024-07-12
DOI: 10.3390/technologies12070112
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 113: Probabilistic Confusion Matrix: A Novel
Method for Machine Learning Algorithm Generalized Performance Analysis
Authors: Ioannis Markoulidakis, Georgios Markoulidakis
First page: 113
Abstract: The paper addresses the issue of classification machine learning algorithm performance based on a novel probabilistic confusion matrix concept. The paper develops a theoretical framework which associates the proposed confusion matrix and the resulting performance metrics with the regular confusion matrix. The theoretical results are verified based on a wide variety of real-world classification problems and state-of-the-art machine learning algorithms. Based on the properties of the probabilistic confusion matrix, the paper then highlights the benefits of using the proposed concept both during the training phase and the application phase of a classification machine learning algorithm.
Citation: Technologies
PubDate: 2024-07-13
DOI: 10.3390/technologies12070113
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 114: Development and Evaluation of an mHealth
App That Promotes Access to 3D Printable Assistive Devices
Authors: Jeffrey Bush, Sara Benham, Monica Kaniamattam
First page: 114
Abstract: Three-dimensional printing is an emerging service delivery method for on-demand access to customized assistive technology devices. However, barriers exist in locating and designing appropriate models and having the devices printed. The purpose of this work is to outline the development of an app, 3DAdapt, which allows users to overcome these issues by searching within a curated list of 3D printable assistive devices, customizing models that support it, and ordering the device to be printed by manufacturers linked within the app or shared with local 3D printing operators. The app integrates searching and filters based on the International Classification of Functioning, Disability, and Health, with the available devices including those developed from fieldwork collaborations with multiple professionals and students within clinical, community, and educational settings. It provides users the ability to customize select models to meet their needs. The model can then be shared, downloaded, or ordered from a third-party 3D printing service. This development and expert testing phase to assess feasibility and modify the app based on identified themes then prepared the team for the next phases of beta testing to reach the overall aim of 3DAdapt to connect individuals to affordable and customizable devices to increase independence and quality of life.
Citation: Technologies
PubDate: 2024-07-13
DOI: 10.3390/technologies12070114
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 115: Oxygen Measurement in Cuprate
Superconductors Using the Dissolved Oxygen/Chlorine Method
Authors: Yuliang Wei, Chengcheng Yan, Shiro Kambe
First page: 115
Abstract: We have developed a dissolved oxygen (DO) method with differential equation (DE) correction. We measured the oxygen content in La-based and Y-based superconductors, and succeeded in measuring the oxygen content simply in one-third of the time required by the iodometric titration method. However, there was a problem with Bi-based superconductors where the measured oxygen content was smaller compared to the iodometric titration method. We hypothesized that not only O2 but also Cl2 gas is generated when dissolving Bi-based superconductors and developed a dissolved oxygen/chlorine (DO/Cl) method with DE correction. This method uses only a dissolved oxygen sensor and a dissolved chlorine sensor to measure the dissolved oxygen and dissolved chlorine content in Bi2Sr2−xLaxCuOy, allowing for the calculation of copper valence and oxygen content. The results from the DO/Cl method with DE correction show that the measured copper valence and oxygen content differ very little from those obtained using the iodometric titration method, with discrepancies within 0.016 and 0.008, respectively. Additionally, this method reduces the measurement time by one-third compared to the iodometric titration method. The results demonstrate that the DO/Cl method with DE correction can effectively measure the copper valence and oxygen content in cuprate superconductors, and using hydrochloric acid as the experimental solution is superior to sulfuric acid and nitric acid.
Citation: Technologies
PubDate: 2024-07-16
DOI: 10.3390/technologies12070115
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 116: Development of a New Prototype
Paediatric Central Sleep Apnoea Monitor
Authors: Saatchi, Elphick, Rowson, Wesseler, Marris, Shortland, Thomas
First page: 116
Abstract: A new prototype device to monitor breathing in children diagnosed with central sleep apnoea (CSA) was developed. CSA is caused by the failure of central nervous system signals to the respiratory muscles and results in intermittent breathing pauses during sleep. Children diagnosed with CSA require home respiration monitoring during sleep. Apnoea monitors initiate an audio alarm when the breath-to-breath respiration interval exceeds a preset time. This allows the child’s parents to attend to the child to ensure safety. The article describes the development of the monitor’s hardware, software, and evaluation. Features of the device include the detection of abnormal respiratory pauses and the generation of an associated alarm, the ability to record the respiratory signal and its storage using an on-board disk, miniaturised hardware, child-friendliness, cost-effectiveness, and ease of use. The device was evaluated on 10 healthy adult volunteers with a mean age of 46.6 years (and a standard deviation of 14.4 years). The participants randomly intentionally paused their breathing during the recording. The device detected and provided an alarm when the respiratory pauses exceeded the preset time. The respiration rates determined from the device closely matched the values from a commercial respiration monitor. The study indicated the peak-detection method of the respiration rate measurement is more robust than the zero-crossing method.
Citation: Technologies
PubDate: 2024-07-17
DOI: 10.3390/technologies12070116
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 117: Nano-Level Additive Manufacturing:
Condensed Review of Processes, Materials, and Industrial Applications
Authors: Ismail Fidan, Mohammad Alshaikh Ali, Vivekanand Naikwadi, Shamil Gudavasov, Mushfig Mahmudov, Mahdi Mohammadizadeh, Zhicheng Zhang, Ankit Sharma
First page: 117
Abstract: Additive manufacturing, commonly known as 3D printing, represents the forefront of modern manufacturing technology. Its growing popularity spans across research and development, material science, design, processes, and everyday applications. This review paper presents a crucial review of nano-level 3D printing, examining it from the perspectives of processes, materials, industrial applications, and future trends. The authors have synthesized the latest insights from a wide range of archival articles and source books, highlighting the key findings. The primary contribution of this study is a condensed review report that consolidates the newest research on nano-level 3D printing, offering a broad overview of this innovative technology for researchers, inventors, educators, and technologists. It is anticipated that this review study will significantly advance research in nanotechnology, additive manufacturing, and related technological fields.
Citation: Technologies
PubDate: 2024-07-18
DOI: 10.3390/technologies12070117
Issue No: Vol. 12, No. 7 (2024)
- Technologies, Vol. 12, Pages 73: Gamified VR Storytelling for Cultural
Tourism Using 3D Reconstructions, Virtual Humans, and 360° Videos
Authors: Kontogiorgakis, Zidianakis, Kontaki, Partarakis, Manoli, Ntoa, Stephanidis
First page: 73
Abstract: This work addresses the lack of methodologies for the seamless integration of 360° videos, 3D digitized artifacts, and virtual human agents within a virtual reality environment. The proposed methodology is showcased in the context of a tour guide application and centers around the innovative use of a central hub, metaphorically linking users to various historical locations. Leveraging a treasure hunt metaphor and a storytelling approach, this combination of digital structures is capable of building an exploratory learning experience. Virtual human agents contribute to the scenario by offering personalized narratives and educational content, contributing to an enriched cultural heritage journey. Key contributions of this research include the exploration of the symbolic use of the central hub, the application of a gamified approach through the treasure hunt metaphor, and the seamless integration of various technologies to enhance user engagement. This work contributes to the understanding of context-specific cultural heritage applications and their potential impact on cultural tourism. The output of this research work is the reusable methodology and its demonstration in the implemented showcase application that was assessed by a heuristic evaluation.
Citation: Technologies
PubDate: 2024-05-22
DOI: 10.3390/technologies12060073
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 74: Effect of Oscillating Area on Generating
Microbubbles from Hollow Ultrasonic Horn
Authors: Kodai Hasegawa, Nobuhiro Yabuki, Toshinori Makuta
First page: 74
Abstract: Microbubbles, which are tiny bubbles with a diameter of less than 100 µm, have been attracting attention in recent years. Conventional methods of microbubble generation using porous material and swirling flows have problems such as large equipment size and non-uniform bubble generation. Therefore, we have been developing a hollow ultrasonic horn with an internal flow path as a microbubble-generating device. By supplying gas and ultrasonic waves simultaneously, the gas–liquid interface is violently disturbed to generate microbubbles. Although this device can generate microbubbles even in highly viscous fluids and high-temperature fluids such as molten metals, it has the problem of generating many relatively large bubbles of 1 mm or more. Since the generation of a large amount of microbubbles in a short period of time is required to realize actual applications in agriculture, aquaculture, and medicine, conventional research has tried to solve this problem by increasing the amplitude of the ultrasonic oscillation. However, it is difficult to further increase the amplitude due to the structural reasons of the horn and the behavior of bubbles at the horn tip; therefore, the oscillating area of the tip of the horn, which had not received attention before, was enlarged by a factor of 2.94 times to facilitate the ultrasonic wave transmission to the bubbles, and the effect of this was investigated. As a result, a large number of gases were miniaturized, especially at high gas flow rates, leading to an increase in the amount of microbubbles generated.
Citation: Technologies
PubDate: 2024-05-25
DOI: 10.3390/technologies12060074
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 75: Intelligent Cane for Assisting the
Visually Impaired
Authors: Claudiu-Eugen Panazan, Eva-Henrietta Dulf
First page: 75
Abstract: Those with visual impairments, including complete blindness or partial sight loss, constitute a significant global population. According to estimates by the World Health Organization (WHO), there are at least 2.2 billion people worldwide who have near or distance vision disorders. Addressing their needs is crucial. Introducing a smart cane tailored for the blind can greatly improve their daily lives. This paper introduces a significant technical innovation, presenting a smart cane equipped with dual ultrasonic sensors for obstacle detection, catering to the visually impaired. The primary focus is on developing a versatile device capable of operating in diverse conditions, ensuring efficient obstacle alerts. The strategic placement of ultrasonic sensors facilitates the emission and measurement of high-frequency sound waves, calculating obstacle distances and assessing potential threats to the user. Addressing various obstacle types, two ultrasonic sensors handle overhead and ground-level barriers, ensuring precise warnings. With a detection range spanning 2 to 400 cm, the device provides timely information for user reaction. Dual alert methods, including vibrations and audio signals, offer flexibility to users, controlled through intuitive switches. Additionally, a Bluetooth-connected mobile app enhances functionality, activating audio alerts if the cane is misplaced or too distant. Cost-effective implementation enhances accessibility, supporting a broader user base. This innovative smart cane not only represents a technical achievement but also significantly improves the quality of life for visually impaired individuals, emphasizing the social impact of technology. The research underscores the importance of technological research in addressing societal challenges and highlights the need for solutions that positively impact vulnerable communities, shaping future directions in research and technological development.
Citation: Technologies
PubDate: 2024-05-27
DOI: 10.3390/technologies12060075
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 76: Vertical Balance of an Autonomous
Two-Wheeled Single-Track Electric Vehicle
Authors: David Rodríguez-Rosa, Andrea Martín-Parra, Andrés García-Vanegas, Francisco Moya-Fernández, Ismael Payo-Gutiérrez, Fernando J. Castillo-García
First page: 76
Abstract: In the dynamic landscape of autonomous transport, the integration of intelligent transport systems and embedded control technology is pivotal. While strides have been made in the development of autonomous agents and multi-agent systems, the unique challenges posed by two-wheeled vehicles remain largely unaddressed. Dedicated control strategies for these vehicles have yet to be developed. The vertical balance of an autonomous two-wheeled single-track vehicle is a challenge for engineering. This type of vehicle is unstable and its dynamic behaviour changes with the forward velocity. We designed a scheduled-gain proportional–integral controller that adapts its gains to the forward velocity, maintaining the vertical balance of the vehicle by means of the steering front-wheel angle. The control law was tested with a prototype designed by the authors under different scenarios, smooth and uneven floors, maintaining the vertical balance in all cases.
Citation: Technologies
PubDate: 2024-05-28
DOI: 10.3390/technologies12060076
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 77: Deep Learning Approaches for Water Stress
Forecasting in Arboriculture Using Time Series of Remote Sensing Images:
Comparative Study between ConvLSTM and CNN-LSTM Models
Authors: Ismail Bounoua, Youssef Saidi, Reda Yaagoubi, Mourad Bouziani
First page: 77
Abstract: Irrigation is crucial for crop cultivation and productivity. However, traditional methods often waste water and energy due to neglecting soil and crop variations, leading to inefficient water distribution and potential crop water stress. The crop water stress index (CWSI) has become a widely accepted index for assessing plant water status. However, it is necessary to forecast the plant water stress to estimate the quantity of water to irrigate. Deep learning (DL) models for water stress forecasting have gained prominence in irrigation management to address these needs. In this paper, we present a comparative study between two deep learning models, ConvLSTM and CNN-LSTM, for water stress forecasting using remote sensing data. While these DL architectures have been previously proposed and studied in various applications, our novelty lies in studying their effectiveness in the field of water stress forecasting using time series of remote sensing images. The proposed methodology involves meticulous preparation of time series data, where we calculate the crop water stress index (CWSI) using Landsat 8 satellite imagery through Google Earth Engine. Subsequently, we implemented and fine-tuned the hyperparameters of the ConvLSTM and CNN-LSTM models. The same processes of model compilation, optimization of hyperparameters, and model training were applied for the two architectures. A citrus farm in Morocco was chosen as a case study. The analysis of the results reveals that the CNN-LSTM model excels over the ConvLSTM model for long sequences (nine images) with an RMSE of 0.119 and 0.123, respectively, while ConvLSTM provides better results for short sequences (three images) than CNN-LSTM with an RMSE of 0.153 and 0.187, respectively.
Citation: Technologies
PubDate: 2024-06-01
DOI: 10.3390/technologies12060077
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 78: Smart Energy Systems Based on
Next-Generation Power Electronic Devices
Authors: Nikolay Hinov
First page: 78
Abstract: Power electronics plays a key role in the management and conversion of electrical energy in a variety of applications, including the use of renewable energy sources such as solar, wind and hydrogen energy, as well as in electric vehicles, industrial technologies, homes and smart grids. These technologies are essential for the successful implementation of the green transition, as they help reduce carbon emissions and promote the production and consumption of cleaner and more sustainable energy. The present work presents a new generation of power electronic devices and systems, which includes the following main aspects: advances in semiconductor technologies, such as the use of silicon carbide (SiC) and gallium nitride (GaN); nanomaterials for the realization of magnetic components; using a modular principle to construct power electronic devices; applying artificial intelligence techniques to device lifecycle design; and the environmental aspects of design. The new materials allow the devices to operate at higher voltages, temperatures and frequencies, making them ideal for high-power applications and high-frequency operation. In addition, the development of integrated and modular power electronic systems that combine energy management, diagnostics and communication capabilities contributes to the more intelligent and efficient management of energy resources. This includes integration with the Internet of Things (IoT) and artificial intelligence (AI) for automated task solving and work optimization.
Citation: Technologies
PubDate: 2024-06-01
DOI: 10.3390/technologies12060078
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 79: Comparison of a Custom-Made Inexpensive
Air Permeability Tester with a Standardized Measurement Instrument
Authors: Dietrich Spädt, Niclas Richter, Cornelia Golle, Andrea Ehrmann, Lilia Sabantina
First page: 79
Abstract: The air permeability of a textile fabric belongs to the parameters which characterize its potential applications as garments, filters, airbags, etc. Calculating the air permeability is complicated due to its dependence on many other fabric parameters, such as porosity, thickness, weaving parameters and others, which is why the air permeability is usually measured. Standardized measurement instruments according to EN ISO 9237, however, are expensive and complex, prohibiting small companies or many universities from using them. This is why a simpler and inexpensive test instrument was suggested in a previous paper. Here, we show correlations between the results of the standardized and the custom-made instrument and verify this correlation using fluid dynamics calculations.
Citation: Technologies
PubDate: 2024-06-02
DOI: 10.3390/technologies12060079
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 80: Applications of Brain Wave Classification
for Controlling an Intelligent Wheelchair
Authors: Maria Carolina Avelar, Patricia Almeida, Brigida Monica Faria, Luis Paulo Reis
First page: 80
Abstract: The independence and autonomy of both elderly and disabled people have been a growing concern in today’s society. Therefore, wheelchairs have proven to be fundamental for the movement of these people with physical disabilities in the lower limbs, paralysis, or other type of restrictive diseases. Various adapted sensors can be employed in order to facilitate the wheelchair’s driving experience. This work develops the proof concept of a brain–computer interface (BCI), whose ultimate final goal will be to control an intelligent wheelchair. An event-related (de)synchronization neuro-mechanism will be used, since it corresponds to a synchronization, or desynchronization, in the mu and beta brain rhythms, during the execution, preparation, or imagination of motor actions. Two datasets were used for algorithm development: one from the IV competition of BCIs (A), acquired through twenty-two Ag/AgCl electrodes and encompassing motor imagery of the right and left hands, and feet; and the other (B) was obtained in the laboratory using an Emotiv EPOC headset, also with the same motor imaginary. Regarding feature extraction, several approaches were tested: namely, two versions of the signal’s power spectral density, followed by a filter bank version; the use of respective frequency coefficients; and, finally, two versions of the known method filter bank common spatial pattern (FBCSP). Concerning the results from the second version of FBCSP, dataset A presented an F1-score of 0.797 and a rather low false positive rate of 0.150. Moreover, the correspondent average kappa score reached the value of 0.693, which is in the same order of magnitude as 0.57, obtained by the competition. Regarding dataset B, the average value of the F1-score was 0.651, followed by a kappa score of 0.447, and a false positive rate of 0.471. However, it should be noted that some subjects from this dataset presented F1-scores of 0.747 and 0.911, suggesting that the movement imagery (MI) aptness of different users may influence their performance. In conclusion, it is possible to obtain promising results, using an architecture for a real-time application.
Citation: Technologies
PubDate: 2024-06-03
DOI: 10.3390/technologies12060080
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 81: A Survey of Machine Learning in Edge
Computing: Techniques, Frameworks, Applications, Issues, and Research
Directions
Authors: Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi, Mohammad N. Alanazi
First page: 81
Abstract: Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.
Citation: Technologies
PubDate: 2024-06-03
DOI: 10.3390/technologies12060081
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 82: Path Planning for Autonomous Mobile Robot
Using Intelligent Algorithms
Authors: Jorge Galarza-Falfan, Enrique Efrén García-Guerrero, Oscar Adrian Aguirre-Castro, Oscar Roberto López-Bonilla, Ulises Jesús Tamayo-Pérez, José Ricardo Cárdenas-Valdez, Carlos Hernández-Mejía, Susana Borrego-Dominguez, Everardo Inzunza-Gonzalez
First page: 82
Abstract: Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs.
Citation: Technologies
PubDate: 2024-06-03
DOI: 10.3390/technologies12060082
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 83: Data Readout Techniques on FPGA for the
ATLAS RPC-BIS78 Detectors
Authors: Andreas Vgenopoulos, Kostas Kordas, Federico Lasagni, Sabrina Perrella, Alessandro Polini, Riccardo Vari
First page: 83
Abstract: The firmware developed for the readout and trigger processing of the information emerging from the BIS78-RPC Muon Spectrometer chambers in the ATLAS experiment at CERN is presented here, together with data processing techniques, data acquisition software, and tests of the readout chain system, which represent efforts to make these chambers operational in the ATLAS experiment. This work is performed in the context of the BIS78-RPC project, which deals with the pilot deployment of a new generation of sMDT+RPCs in the experiment. Such chambers are planned to be fully deployed in the whole barrel inner layer of the Muon Spectrometer during the Phase II upgrade of the ATLAS experiment. On-chamber front-ends include an amplifier, a discriminator ASIC, and an LVDS transmitter. The signal is digitized by CERN HPTDC chips and then processed by an FPGA, which is the heart of the readout and trigger processing, using various techniques.
Citation: Technologies
PubDate: 2024-06-04
DOI: 10.3390/technologies12060083
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 84: Dual-Band Antenna at 28 and 38 GHz Using
Internal Stubs and Slot Perturbations
Authors: Parveez Shariff Bhadravathi Ghouse, Pradeep Kumar, Pallavi R. Mane, Sameena Pathan, Tanweer Ali, Alexandros-Apostolos A. Boulogeorgos, Jaume Anguera
First page: 84
Abstract: A double-stub matching technique is used to design a dual-band monopole antenna at 28 and 38 GHz. The transmission line stubs represent the matching elements. The first matching network comprises series capacitive and inductive stubs, causing impedance matching at the 28 GHz band with a wide bandwidth. On the other hand, the second matching network has two shunt inductive stubs, generating resonance at 38 GHz. A Smith chart is utilized to predict the stub lengths. While incorporating their dimensions physically, some of the stub lengths are fine-tuned. The proposed antenna is compact with a profile of 0.75λ1×0.66λ1 (where λ1 is the free-space wavelength at 28 GHz). The measured bandwidths are 27–28.75 GHz and 36.20–42.43 GHz. Although the physical series capacitance of the first matching network is a slot in the ground plane, the antenna is able to achieve a good gain of 7 dBi in both bands. The proposed antenna has a compact design, good bandwidth and gain, making it a candidate for 5G wireless applications.
Citation: Technologies
PubDate: 2024-06-06
DOI: 10.3390/technologies12060084
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 85: Behind the Door: Practical
Parameterization of Propagation Parameters for IEEE 802.11ad Use Cases
Authors: Luciano Ahumada, Erick Carreño, Albert Anglès, Diego Dujovne, Pablo Palacios Játiva
First page: 85
Abstract: The integration of the 60 GHz band into the IEEE 802.11 standard has revolutionized indoor wireless services. However, this band presents unique challenges to indoor wireless communication infrastructure, originally designed to handle data traffic in residential and office environments. Estimating 60 GHz signal propagation in indoor settings is particularly complicated due to dynamic contextual factors, making it essential to ensure adequate coverage for all connected devices. Consequently, empirical channel modeling plays a pivotal role in understanding real-world behavior, which is characterized by a complex interplay of stationary and mobile elements. Given the highly directional nature of 60 GHz propagation, this study addresses a seemingly simple but important question: what is the impact of employing highly directive antennas when deviating from the line of sight' To address this question, we conducted an empirical measurement campaign of wireless channels within an office environment. Our assessment focused on power losses and distribution within an angular range while an indoor base station served indoor users, simulating the operation of an IEEE 802.11ad high-speed WLAN at 60 GHz. Additionally, we explored scenarios with and without pedestrian movement in the vicinity of wireless terminals. Our observations reveal the presence of significant antenna lobes even in obstructed links, indicating potential opportunities to use angular combiners or beamformers to enhance link availability and the data rate. This empirical study provides valuable information and channel parameters to simulate 60 GHz millimeter wave (mm-wave) links in indoor environments, paving the way for more efficient and robust wireless communication systems.
Citation: Technologies
PubDate: 2024-06-07
DOI: 10.3390/technologies12060085
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 86: Advancements in 3D Printing: Directed
Energy Deposition Techniques, Defect Analysis, and Quality Monitoring
Authors: Muhammad Mu’az Imran, Azam Che Idris, Liyanage Chandratilak De Silva, Yun-Bae Kim, Pg Emeroylariffion Abas
First page: 86
Abstract: This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems.
Citation: Technologies
PubDate: 2024-06-07
DOI: 10.3390/technologies12060086
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 87: Electron Energy-Loss Spectroscopy Method
for Thin-Film Thickness Calculations with a Low Incident Energy Electron
Beam
Authors: Ahmad M. D. (Assa’d) Jaber, Ammar Alsoud, Saleh R. Al-Bashaish, Hmoud Al Dmour, Marwan S. Mousa, Tomáš Trčka, Vladimír Holcman, Dinara Sobola
First page: 87
Abstract: In this study, the thickness of a thin film (tc) at a low primary electron energy of less than or equal to 10 keV was calculated using electron energy-loss spectroscopy. This method uses the ratio of the intensity of the transmitted background spectrum to the intensity of the transmission electrons with zero-loss energy (elastic) in the presence of an accurate average inelastic free path length (λ). The Monte Carlo model was used to simulate the interaction between the electron beam and the tested thin films. The total background of the transmitted electrons is considered to be the electron transmitting the film with an energy above 50 eV to eliminate the effect of the secondary electrons. The method was used at low primary electron energy to measure the thickness (t) of C, Si, Cr, Cu, Ag, and Au films below 12 nm. For the C and Si films, the accuracy of the thickness calculation increased as the energy of the primary electrons and thickness of the film increased. However, for heavy elements, the accuracy of the film thickness calculations increased as the primary electron energy increased and the film thickness decreased. High accuracy (with 2% uncertainty) in the measurement of C and Si thin films was observed at large thicknesses and 10 keV, where . However, in the case of heavy-element films, the highest accuracy (with an uncertainty below 8%) was found for thin thicknesses and 10 keV, where . The present results show that an accurate film thickness measurement can be obtained at primary electron energy equal to or less than 10 keV and a ratio of . This method demonstrates the potential of low-loss electron energy-loss spectroscopy in transmission electron microscopy as a fast and straightforward method for determining the thin-film thickness of the material under investigation at low primary electron energies.
Citation: Technologies
PubDate: 2024-06-07
DOI: 10.3390/technologies12060087
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 88: Accurate Surge Arrester Modeling for
Optimal Risk-Aware Lightning Protection Utilizing a Hybrid Monte
Carlo–Particle Swarm Optimization Algorithm
Authors: Amir Hossein Kimiai Asadi, Mohsen Eskandari, Hadi Delavari
First page: 88
Abstract: The application of arresters is critical for the safe operation of electric grids against lightning. Arresters limit the consequences of lightning-induced over-voltages. However, surge arrester protection in electric grids is challenging due to the intrinsic complexities of distribution grids, including overhead lines and power components such as transformers. In this paper, an optimal arrester placement technique is developed by proposing a multi-objective function that includes technical, safety and risk, and economic indices. However, an effective placement model demands a comprehensive and accurate modeling of an electric grid’s components. In this light, appropriate models of a grid’s components including an arrester, the earth, an oil-immersed transformer, overhead lines, and lightning-induced voltage are developed. To achieve accurate models, high-frequency transient mathematical models are developed for the grid’s components. Notably, to have an accurate model of the arrester, which critically impacts the performance of the arrester placement technique, a new arrester model is developed and evaluated based on real technical data from manufacturers such as Pars, Tridelta, and Siemens. Then, the proposed model is compared with the IEEE, Fernandez, and Pinceti models. The arrester model is incorporated in an optimization problem considering the performance of the over-voltage protection and the risk, technical, and economic indices, and it is solved using the particle swarm optimization (PSO) and Monte Carlo (MC) techniques. To validate the proposed arrester model and the placement technique, real data from the Chopoghloo feeder in Bahar, Hamedan, Iran, are simulated. The feeder is expanded over three different geographical areas, including rural, agricultural plain, and mountainous areas.
Citation: Technologies
PubDate: 2024-06-08
DOI: 10.3390/technologies12060088
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 89: A New LCL Filter Design Method for
Single-Phase Photovoltaic Systems Connected to the Grid via
Micro-Inverters
Authors: Heriberto Adamas-Pérez, Mario Ponce-Silva, Jesús Darío Mina-Antonio, Abraham Claudio-Sánchez, Omar Rodríguez-Benítez, Oscar Miguel Rodríguez-Benítez
First page: 89
Abstract: This paper aims to propose a new sizing approach to reduce the footprint and optimize the performance of an LCL filter implemented in photovoltaic systems using grid-connected single-phase microinverters. In particular, the analysis is carried out on a single-phase full-bridge inverter, assuming the following two conditions: (1) a unit power factor at the connection point between the AC grid and the LCL filter; (2) a control circuit based on unipolar sinusoidal pulse width modulation (SPWM). In particular, the ripple and harmonics of the LCL filter input current and the current injected into the grid are analyzed. The results of the Simulink simulation and the experimental tests carried out confirm that it is possible to considerably reduce filter volume by optimizing each passive component compared with what is already available in the literature while guaranteeing excellent filtering performance. Specifically, the inductance values were reduced by almost 40% and the capacitor value by almost 100%. The main applications of this new design methodology are for use in single-phase microinverters connected to the grid and for research purposes in power electronics and optimization.
Citation: Technologies
PubDate: 2024-06-12
DOI: 10.3390/technologies12060089
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 90: A Modified Criss-Cross-Based T-Type MLI
with Reduced Power Components
Authors: Kailash Kumar Mahto, Bidyut Mahato, Bikramaditya Chandan, Durbanjali Das, Priyanath Das, Swati Kumari, Vasiliki Vita, Christos Pavlatos, Georgios Fotis
First page: 90
Abstract: Significant advancements in the field of power electronics have created an ideal opportunity to introduce various topologies of multilevel inverters. These multilevel inverter topologies comprise different notable characteristics, such as staircase sinusoidal output voltage with high quality, a lowered number of power switches, no filter requirement, etc. In this literature, a new asymmetrical MLI topology is proposed to reduce the number of components of the inverter with admirable voltage-step creation. The proposed topology provides a 17-level, staircase-type, nearly sinusoidal output voltage waveform. The number of switches required for the proposed multilevel inverter topology is fewer compared to the existing topology for the same level. A carrier-based sinusoidal pulse-width modulation technique is used for the proposed topology at a switching frequency of 3 kHz. The functioning of the proposed inverter topology is thoroughly examined. A 17-level asymmetrical inverter is executed; both the MATLAB/SIMULINK as well as the experimental results using dSPACE-1103 controller. The simulation results are verified using the experimental results for the proposed 17-level multilevel inverter for modulation indexes of 1 and 0.6.
Citation: Technologies
PubDate: 2024-06-18
DOI: 10.3390/technologies12060090
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 91: A Computational Framework for Enhancing
Industrial Operations and Electric Network Management: A Case Study
Authors: Pedroso, Silva, Campilho, Sales-Contini, Pinto, Moreira
First page: 91
Abstract: Automotive industries require constant technological development and the capacity to adapt to market needs. Hence, component suppliers must be able to adapt to persistent trend changes and technical improvements, acting in response to customers’ expectations and developing their manufacturing methods to be as flexible as possible. Concepts such as layout flexibility, management of industrial facilities, and building information modeling (BIM) are becoming ever more addressed within the automotive industry in order to envision and select the necessary information exchanges. Given this question and based on the gap in the literature regarding this subject, this work proposes a solution, developing a novel tool that allows the monitoring and assignment of newer/relocated equipment to the switchboards within a given industrial plant. The solution intends to increase the flexibility of production lines through the assessment, analysis, improvement, and reorganization of the electrical load distribution to develop projects accurately implying layout changes. The tool is validated with an automotive manufacturer. With the implementation of this open-source tool, a detailed electrical flow management system is accomplished, and it has proven successful and essential in raising levels of organizational flexibility. This has guaranteed the company’s competitiveness with effective integrated administration methods and tools, such as a much easier study upon inserting new/relocated equipment without production line breaks.
Citation: Technologies
PubDate: 2024-06-19
DOI: 10.3390/technologies12060091
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 92: A Review of Automatic Pain Assessment
from Facial Information Using Machine Learning
Authors: Najib Ben Aoun
First page: 92
Abstract: Pain assessment has become an important component in modern healthcare systems. It aids medical professionals in patient diagnosis and providing the appropriate care and therapy. Conventionally, patients are asked to provide their pain level verbally. However, this subjective method is generally inaccurate, not possible for non-communicative people, can be affected by physiological and environmental factors and is time-consuming, which renders it inefficient in healthcare settings. So, there has been a growing need to build objective, reliable and automatic pain assessment alternatives. In fact, due to the efficiency of facial expressions as pain biomarkers that accurately expand the pain intensity and the power of machine learning methods to effectively learn the subtle nuances of pain expressions and accurately predict pain intensity, automatic pain assessment methods have evolved rapidly. This paper reviews recent spatial facial expressions and machine learning-based pain assessment methods. Moreover, we highlight the pain intensity scales, datasets and method performance evaluation criteria. In addition, these methods’ contributions, strengths and limitations will be reported and discussed. Additionally, the review lays the groundwork for further study and improvement for more accurate automatic pain assessment.
Citation: Technologies
PubDate: 2024-06-20
DOI: 10.3390/technologies12060092
Issue No: Vol. 12, No. 6 (2024)
- Technologies, Vol. 12, Pages 57: Miniaturized Microstrip Dual-Channel
Diplexer Based on Modified Meander Line Resonators for Wireless and
Computer Communication Technologies
Authors: Yaqeen Sabah Mezaal, Shahad K. Khaleel, Ban M. Alameri, Kadhum Al-Majdi, Aqeel A. Al-Hilali
First page: 57
Abstract: There has been a lot of interest in microstrip diplexers lately due to their potential use in numerous wireless and computer communication technologies, including radio broadcasts, mobile phones, broadband wireless, and satellite-based communication systems. It can do this because it has a communication channel that can combine two distinct filters into one. This article presents a narrow-band microstrip diplexer that uses a stepped impedance resonator, a uniform impedance resonator, tiny square patches, and a meander line resonator. The projected diplexer might be made smaller than its initial dimensions by utilizing the winding construction. To model the microstrip diplexer topology for WiMAX and WIFI/WLAN at 1.66 GHz and 2.52 GHz, the Advanced Wave Research (AWR) solver was employed. It exhibited an insertion loss of 3.2 dB and a return loss of 16 dB for the first channel, while the insertion loss and return loss were 2.88 dB and 21 dB, respectively, for the second channel. When both filters were simulated, the band isolation was 31 dB. The projected microstrip diplexer has been fabricated using an FR4 epoxy laminate with dimensions of 32 × 26 mm2. The simulated S-parameters phase and group delay closely matched the measurements.
Citation: Technologies
PubDate: 2024-04-24
DOI: 10.3390/technologies12050057
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 58: RFID Tags for On-Metal Applications: A
Brief Survey
Authors: Emanuel Pereira, Sandoval Júnior, Luís Felipe Vieira Silva, Mateus Batista, Eliel Santos, Ícaro Araújo, Jobson Araújo, Erick Barboza, Francisco Gomes, Ismael Trindade Fraga, Daniel Oliveira Dos Dos Santos, Roger Davanso
First page: 58
Abstract: Radio-frequency identification technology finds extensive use in various industrial applications, including those involving metallic surfaces. The integration of radio-frequency identification systems with metal surfaces, such as those found in the automotive sector, presents distinct challenges that can notably affect system efficacy due to metal’s tendency to reflect electromagnetic waves, thus degrading the functionality of conventional radio-frequency identification tags. This highlights the importance of conducting research into academic publications and patents to grasp the current advancements and challenges in this field, aiming to improve the applications of radio-frequency identification tags technology on metal. Consequently, this research undertakes a concise review of both the literature and patents exploring radio-frequency identification technology’s use for on-metal tags, utilizing resources like Google Scholar and Google Patents. The research categorized crucial aspects such as tag flexibility, operating frequency, and geographic origins of the research. Findings highlight China’s prominent role in contributing to metal-focused radio-frequency identification tag research, with a considerable volume of articles and patents. In particular, flexible tags and the Ultra-High Frequency range are dominant in both scholarly and patent documents, reflecting their significance in radio-frequency identification technology applications. The research underscores a vibrant area of development within radio-frequency identification technology, with continued innovation driven by specific industrial needs. Despite the noted advances, the presence of a significant percentage of no longer valid patents suggests substantial opportunities for further research and innovation in radio-frequency identification technology for on-metal applications, especially considering the demand for flexible tags and for solutions in systems that offer specialized characteristics or are tailored for specific uses.
Citation: Technologies
PubDate: 2024-04-27
DOI: 10.3390/technologies12050058
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 59: Neural Network-Based Body Weight
Prediction in Pelibuey Sheep through Biometric Measurements
Authors: Alfonso J. Chay-Canul, Enrique Camacho-Pérez, Fernando Casanova-Lugo, Omar Rodríguez-Abreo, Mayra Cruz-Fernández, Juvenal Rodríguez-Reséndiz
First page: 59
Abstract: This paper presents an intelligent system for the dynamic estimation of sheep body weight (BW). The methodology used to estimate body weight is based on measuring seven biometric parameters: height at withers, rump height, body length, body diagonal length, total body length, semicircumference of the abdomen, and semicircumference of the girth. A biometric parameter acquisition system was developed using a Kinect as a sensor. The results were contrasted with measurements obtained manually with a flexometer. The comparison gives an average root mean square error (RMSE) of 9.91 and a mean R2 of 0.81. Subsequently, the parameters were used as input in a back-propagation artificial neural network. Performance tests were performed with different combinations to make the best choice of architecture. In this way, an intelligent body weight estimation system was obtained from biometric parameters, with a 5.8% RMSE in the weight estimations for the best architecture. This approach represents an innovative, feasible, and economical alternative to contribute to decision-making in livestock production systems.
Citation: Technologies
PubDate: 2024-04-30
DOI: 10.3390/technologies12050059
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 60: A Cyber–Physical System Based on
Digital Twin and 3D SCADA for Real-Time Monitoring of Olive Oil Mills
Authors: Cristina Martinez-Ruedas, Jose-Maria Flores-Arias, Isabel M. Moreno-Garcia, Matias Linan-Reyes, Francisco Jose Bellido-Outeiriño
First page: 60
Abstract: Cyber–physical systems involve the creation, continuous updating, and monitoring of virtual replicas that closely mirror their physical counterparts. These virtual representations are fed by real-time data from sensors, Internet of Things (IoT) devices, and other sources, enabling a dynamic and accurate reflection of the state of the physical system. This emphasizes the importance of data synchronization, visualization, and interaction within virtual environments as a means to improve decision-making, training, maintenance, and overall operational efficiency. This paper presents a novel approach to a cyber–physical system that integrates virtual reality (VR)-based digital twins and 3D SCADA in the context of Industry 4.0 for the monitoring and optimization of an olive mill. The methodology leverages virtual reality to create a digital twin that enables immersive data-driven simulations for olive mill monitoring. The proposed CPS takes data from the physical environment through the existing sensors and measurement elements in the olive mill, concentrates them, and exposes them to the virtual environment through the Open Platform Communication United Architecture (OPC-UA) protocol, thus establishing bidirectional and real-time communication. Furthermore, in the proposed virtual environment, the digital twin is interfaced with the 3D SCADA system, allowing it to create virtual models of the process. This innovative approach has the potential to revolutionize the olive oil industry by improving operational efficiency, product quality, and sustainability while optimizing maintenance practices.
Citation: Technologies
PubDate: 2024-04-30
DOI: 10.3390/technologies12050060
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 61: New Upgrade to Improve Operation of
Conventional Grid-Connected Photovoltaic Systems
Authors: Manuel Cáceres, Alexis Raúl González González Mayans, Andrés Firman, Luis Vera, Juan de la Casa de la Casa Higueras
First page: 61
Abstract: The incorporation of distributed generation with photovoltaic systems entails a drawback associated with intermittency in the generation capacity due to variations in the solar resource. In general, this aspect limits the level of penetration that this resource can have without producing an appreciable impact on the quality of the electrical supply. With the intention of reducing its intermittency, this paper presents the characterization of a methodology for maximizing grid-connected PV system operation under low-solar-radiation conditions. A new concept of a hybrid system based on a constant current source and capable of integrating different sources into a conventional grid-connected PV system is presented. Results of an experimental characterization of a low-voltage grid–PV system connection with a DC/DC converter for constant-current source application are shown in zero and non-zero radiation conditions. The results obtained demonstrate that the proposed integration method works efficiently without causing appreciable effects on the parameters that define the quality of the electrical supply. In this way, it is possible to efficiently incorporate another source of energy, taking advantage of the characteristics of the GCPVS without further interventions in the system. It is expected that this topology could help to integrate other generation and/or storage technologies into already existing PV systems, opening a wide field of research in the PV systems area.
Citation: Technologies
PubDate: 2024-05-02
DOI: 10.3390/technologies12050061
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 62: Inference Analysis of Video Quality of
Experience in Relation with Face Emotion, Video Advertisement, and ITU-T
P.1203
Authors: Tisa Selma, Mohammad Mehedy Masud, Abdelhak Bentaleb, Saad Harous
First page: 62
Abstract: This study introduces an FER-based machine learning framework for real-time QoE assessment in video streaming. This study’s aim is to address the challenges posed by end-to-end encryption and video advertisement while enhancing user QoE. Our proposed framework significantly outperforms the base reference, ITU-T P.1203, by up to 37.1% in terms of accuracy and 21.74% after attribute selection. Our study contributes to the field in two ways. First, we offer a promising solution to enhance user satisfaction in video streaming services via real-time user emotion and user feedback integration, providing a more holistic understanding of user experience. Second, high-quality data collection and insights are offered by collecting real data from diverse regions to minimize any potential biases and provide advertisement placement suggestions.
Citation: Technologies
PubDate: 2024-05-03
DOI: 10.3390/technologies12050062
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 63: Hunting Search Algorithm-Based Adaptive
Fuzzy Tracking Controller for an Aero-Pendulum
Authors: Ricardo Rojas-Galván, José R. García-Martínez, Edson E. Cruz-Miguel, Omar A. Barra-Vázquez, Luis F. Olmedo-García, Juvenal Rodríguez-Reséndiz
First page: 63
Abstract: The aero-pendulum is a non-linear system used broadly to develop and test new controller strategies. This paper presents a new methodology for an adaptive PID fuzzy-based tracking controller using a Hunting Search (HuS) algorithm. The HuS algorithm computes the parameters of the membership functions of the fuzzification stage. As a novelty, the algorithm guarantees the overlap of the membership functions to ensure that all the functions are interconnected, generating new hunters to search for better solutions in the overlapping area. For the defuzzification stage, the HuS algorithm sets the singletons in optimal positions to evaluate the controller response using the centroid method. To probe the robustness of the methodology, the PID fuzzy controller algorithm is implemented in an embedded system to track the angular position of an aero-pendulum test bench. The results show that the adaptive PID fuzzy controller proposed presents root mean square error values of 0.42, 0.40, and 0.49 for 80, 90, and 100 degrees, respectively.
Citation: Technologies
PubDate: 2024-05-04
DOI: 10.3390/technologies12050063
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 64: Atomic Quantum Technologies for Quantum
Matter and Fundamental Physics Applications
Authors: Jorge Yago Malo, Luca Lepori, Laura Gentini, Maria Luisa Chiofalo
First page: 64
Abstract: Physics is living an era of unprecedented cross-fertilization among the different areas of science. In this perspective review, we discuss the manifold impact that state-of-the-art cold and ultracold-atomic platforms can have in fundamental and applied science through the development of platforms for quantum simulation, computation, metrology and sensing. We illustrate how the engineering of table-top experiments with atom technologies is engendering applications to understand problems in condensed matter and fundamental physics, cosmology and astrophysics, unveil foundational aspects of quantum mechanics, and advance quantum chemistry and the emerging field of quantum biology. In this journey, we take the perspective of two main approaches, i.e., creating quantum analogues and building quantum simulators, highlighting that independently of the ultimate goal of a universal quantum computer to be met, the remarkable transformative effects of these achievements remain unchanged. We wish to convey three main messages. First, this atom-based quantum technology enterprise is signing a new era in the way quantum technologies are used for fundamental science, even beyond the advancement of knowledge, which is characterised by truly cross-disciplinary research, extended interplay between theoretical and experimental thinking, and intersectoral approach. Second, quantum many-body physics is unavoidably taking center stage in frontier’s science. Third, quantum science and technology progress will have capillary impact on society, meaning this effect is not confined to isolated or highly specialized areas of knowledge, but is expected to reach and have a pervasive influence on a broad range of society aspects: while this happens, the adoption of a responsible research and innovation approach to quantum technologies is mandatory, to accompany citizens in building awareness and future scaffolding. Following on all the above reflections, this perspective review is thus aimed at scientists active or interested in interdisciplinary research, providing the reader with an overview of the current status of these wide fields of research where cold and ultracold-atomic platforms play a vital role in their description and simulation.
Citation: Technologies
PubDate: 2024-05-07
DOI: 10.3390/technologies12050064
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 65: Fluorine-Free Single-Component
Polyelectrolyte of Poly(ethylene glycol) Bearing Lithium
Methanesulfonylsulfonimide Terminal Groups: Effect of Structural Variance
on Ionic Conductivity
Authors: Bungo Ochiai, Koki Hirabayashi, Yudai Fujii, Yoshimasa Matsumura
First page: 65
Abstract: Fluorine-free single-component polyelectrolytes were developed via the hybridization of lithium methanesulfonylsulfonimide (LiMSSI) moieties to poly(ethylene glycol) (PEG) derivatives with different morphologies, and the relationship between the structure and its ionic conductivity was investigated. The PEG-LiMSSI derivatives with one, two, and three LiMSSI end groups were prepared via the concomitant Michael-type addition and lithiation of PEGs and N-methanesulfonylvinylsulfonimide. The ionic conductivity at 60 °C ranged from 1.8 × 10−7 to 2.0 × 10−4 S/cm. PEG-LiMSSI derivatives with one LiMSSI terminus and with two LiMSSI termini at both ends show higher ionic conductivity, that is as good as fluorine-free single-component polyelectrolytes, than that with two LiMSSI termini at one end and that with three LiMSSI termini.
Citation: Technologies
PubDate: 2024-05-09
DOI: 10.3390/technologies12050065
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 66: Converging Artificial Intelligence and
Quantum Technologies: Accelerated Growth Effects in Technological
Evolution
Authors: Coccia
First page: 66
Abstract: One of the fundamental problems in the field of technological studies is to clarify the drivers and dynamics of technological evolution for sustaining industrial and economic change. This study confronts the problem by analyzing the converging technologies to explain effects on the evolutionary dynamics over time. This paper focuses on technological interaction between artificial intelligence and quantum technologies using a technometric model of technological evolution based on scientific and technological information (publications and patents). Findings show that quantum technology has a growth rate of 1.07, artificial intelligence technology has a rate of growth of 1.37, whereas the technological interaction of converging quantum and artificial intelligence technologies has an accelerated rate of growth of 1.58, higher than trends of these technologies taken individually. These findings suggest that technological interaction is one of the fundamental determinants in the rapid evolution of path-breaking technologies and disruptive innovations. The deductive implications of results about the effects of converging technologies are: (a) accelerated evolutionary growth; (b) a disproportionate (allometric) growth of patents driven by publications supporting a fast technological evolution. Our results support policy and managerial implications for the decision making of policymakers, technology analysts, and R&D managers that can direct R&D investments towards fruitful inter-relationships between radical technologies to foster scientific and technological change with positive societal and economic impacts.
Citation: Technologies
PubDate: 2024-05-10
DOI: 10.3390/technologies12050066
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 67: Study of an LLC Converter for
Thermoelectric Waste Heat Recovery Integration in Shipboard Microgrids
Authors: Nick Rigogiannis, Ioannis Roussos, Christos Pechlivanis, Ioannis Bogatsis, Anastasios Kyritsis, Nick Papanikolaou, Michael Loupis
First page: 67
Abstract: Static waste heat recovery, by means of thermoelectric generator (TEG) modules, constitutes a fast-growing energy harvesting technology on the way towards greener transportation. Many commercial solutions are already available for small internal combustion engine (ICE) vehicles, whereas further development and cost reductions of TEG devices expand their applicability at higher-power transportation means (i.e., ships and aircrafts). In this light, the integration of waste heat recovery based on TEG modules in a shipboard distribution network is studied in this work. Several voltage step-up techniques are considered, whereas the most suitable ones are assessed via the LTspice simulation platform. The design procedure of the selected LLC resonant converter is presented and analyzed in detail. Furthermore, a flexible control strategy is proposed, capable of either output voltage regulation (constant voltage) or maximum power point tracking (MPPT), according to the application demands. Finally, both simulations and experiments (on a suitable laboratory testbench) are performed. The obtained measurements indicate the high efficiency that can be achieved with the LLC converter for a wide operating area as well as the functionality and adequate performance of the control scheme in both operating conditions.
Citation: Technologies
PubDate: 2024-05-11
DOI: 10.3390/technologies12050067
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 68: Application and Challenges of the
Technology Acceptance Model in Elderly Healthcare: Insights from ChatGPT
Authors: Sang Dol Kim
First page: 68
Abstract: The Technology Acceptance Model (TAM) plays a pivotal role in elderly healthcare, serving as a theoretical framework. This study aimed to identify TAM’s core components, practical applications, challenges arising from its applications, and propose countermeasures in elderly healthcare. This descriptive study was conducted by utilizing OpenAI’s ChatGPT, with an access date of 10 January 2024. The three open-ended questions administered to ChatGPT and its responses were collected and qualitatively evaluated for reliability through previous studies. The core components of TAMs were identified as perceived usefulness, perceived ease of use, attitude toward use, behavioral intention to use, subjective norms, image, and facilitating conditions. TAM’s application areas span various technologies in elderly healthcare, such as telehealth, wearable devices, mobile health apps, and more. Challenges arising from TAM applications include technological literacy barriers, digital divide concerns, privacy and security apprehensions, resistance to change, limited awareness and information, health conditions and cognitive impairment, trust and reliability concerns, a lack of tailored interventions, overcoming age stereotypes, and integration with traditional healthcare. In conclusion, customized interventions are crucial for successful tech acceptance among the elderly population. The findings of this study are expected to enhance understanding of elderly healthcare and technology adoption, with insights gained through natural language processing models like ChatGPT anticipated to provide a fresh perspective.
Citation: Technologies
PubDate: 2024-05-13
DOI: 10.3390/technologies12050068
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 69: Evaluating a Controlled Electromagnetic
Launcher for Safe Remote Drug Delivery
Authors: John LaRocco, Qudsia Tahmina, John Simonis
First page: 69
Abstract: Biologists and veterinarians rely on dart projectors to inject animals with drugs, take biopsies from specimens, or inject tracking chips. Firearms, air guns, and other launchers are limited in their ability to precisely control the kinetic energy of a projectile, which can injure the animal if too high. In order to improve the safety of remote drug delivery, a lidar-modulated electromagnetic launcher and a soft drug delivery dart were prototyped. A single-stage revolver coilgun and soft dart were designed and tested at distances up to 8 m. With a coil efficiency of 2.25%, the launcher could consistently deliver a projectile at a controlled kinetic energy of 1.00 ± 0.006 J and an uncontrolled kinetic energy of 2.66 ± 0.076 J. Although modifications to charging time, sensors, and electronics could improve performance, our launcher performed at the required level at the necessary distances. The precision achieved with commercial components enables many other applications, from law enforcement to manufacturing.
Citation: Technologies
PubDate: 2024-05-17
DOI: 10.3390/technologies12050069
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 70: Speckle Plethysmograph-Based Blood
Pressure Assessment
Authors: Floranne T. Ellington, Anh Nguyen, Mao-Hsiang Huang, Tai Le, Bernard Choi, Hung Cao
First page: 70
Abstract: Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse arrival time (PAT), the time difference between the proximal and distal signal peaks. The most widely employed pairing involves electrocardiography (ECG) and photoplethysmography (PPG). Possessing similar characteristics in terms of measuring blood flow changes, a recently investigated optical signal known as speckleplethysmography (SPG) showed its stability and high signal-to-noise ratio compared with PPG. Thus, SPG is a potential surrogate to pair with ECG for CNBP estimation. The present study aims to unlock the untapped potential of SPG as a signal for non-invasive blood pressure monitoring based on PAT. To ascertain SPG’s capabilities, eight subjects were enrolled in multiple recording sessions. A third-party device was employed for ECG and PPG measurements, while a commercial device served as the reference for arterial blood pressure (ABP). SPG measurements were obtained using a prototype smartphone-based system. Following the completion of three scenarios—sitting, walking, and running—the subjects’ signals and ABP were recorded to investigate the predictive capacity of systolic blood pressure. The collected data were processed and prepared for machine learning models, including support vector regression and decision tree regression. The models’ effectiveness was evaluated using root-mean-square error and mean absolute percentage error. In most instances, predictions utilizing PATSPG exhibited comparable or superior performance to PATPPG (i.e., SPG Rest ± 12.4 mmHg vs. PPG Rest ± 13.7 mmHg for RSME, and SPG 8% vs. PPG 9% for MAPE). Furthermore, incorporating an additional feature, namely the previous SBP value, resulted in reduced prediction errors for both signals in multiple model configurations (i.e., SPG Rest ± 12.4 mmHg to ±3.7 mmHg for RSME, and SPG Rest 8% to 3% for MAPE). These preliminary tests of SPG underscore the remarkable potential of this novel signal in PAT-based blood pressure predictions. Subsequent studies involving a larger cohort of test subjects and advancements in the SPG acquisition system hold promise for further improving the effectiveness of this newly explored signal in blood pressure monitoring.
Citation: Technologies
PubDate: 2024-05-18
DOI: 10.3390/technologies12050070
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 71: Analysis, Evaluation, and Future
Directions on Multimodal Deception Detection
Authors: Arianna D’Ulizia, Alessia D’Andrea, Patrizia Grifoni, Fernando Ferri
First page: 71
Abstract: Multimodal deception detection has received increasing attention from the scientific community in recent years, mainly due to growing ethical and security issues, as well as the growing use of digital media. A great number of deception detection methods have been proposed in several domains, such as political elections, security contexts, and job interviews. However, a systematic analysis of the current situation and the evaluation and future directions of deception detection based on cues coming from multiple modalities seems to be lacking. This paper, starting from a description of methods and metrics used for the analysis and evaluation of multimodal deception detection on video, provides a vision of future directions in this field. For the analysis, the PRISMA recommendations are followed, which allow the collection and synthesis of all the available research on the topic and the extraction of information on the multimodal features, the fusion methods, the classification approaches, the evaluation datasets, and metrics. The results of this analysis contribute to the assessment of the state of the art and the evaluation of evidence on important research questions in multimodal deceptive deception. Moreover, they provide guidance on future research in the field.
Citation: Technologies
PubDate: 2024-05-18
DOI: 10.3390/technologies12050071
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 72: A Comprehensive Survey on the
Investigation of Machine-Learning-Powered Augmented Reality Applications
in Education
Authors: Haseeb Ali Khan, Sonain Jamil, Md. Jalil Piran, Oh-Jin Kwon, Jong-Weon Lee
First page: 72
Abstract: Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and gestures from kindergarten through university. The purpose of this survey is to provide an overview of various ways in which ML techniques can be applied within the field of AR within education. The first step is to describe the background of AR. In the next step, we discuss the ML models that are used in AR education applications. Additionally, we discuss how ML is used in AR. Each subgroup’s challenges and solutions can be identified by analyzing these frameworks. In addition, we outline several research gaps and future research directions in ML-based AR frameworks for education.
Citation: Technologies
PubDate: 2024-05-19
DOI: 10.3390/technologies12050072
Issue No: Vol. 12, No. 5 (2024)
- Technologies, Vol. 12, Pages 45: An Artificial Bee Colony Algorithm for
Coordinated Scheduling of Production Jobs and Flexible Maintenance in
Permutation Flowshops
Authors: Asma Ladj, Fatima Benbouzid-Si Tayeb, Alaeddine Dahamni, Mohamed Benbouzid
First page: 45
Abstract: This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard’s benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times.
Citation: Technologies
PubDate: 2024-03-25
DOI: 10.3390/technologies12040045
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 46: Enhancing Patient Care in Radiotherapy:
Proof-of-Concept of a Monitoring Tool
Authors: Guillaume Beldjoudi, Rémi Eugène, Vincent Grégoire, Ronan Tanguy
First page: 46
Abstract: Introduction: A monitoring tool, named Oncology Data Management (ODM), was developed in radiotherapy to generate structured information based on data contained in an Oncology Information System (OIS). This study presents the proof-of-concept of the ODM tool and highlights its applications to enhance patient care in radiotherapy. Material & Methods: ODM is a sophisticated SQL query which extracts specific features from the Mosaiq OIS (Elekta, UK) database into an independent structured database. Data from 2016 to 2022 was extracted to enable monitoring of treatment units and evaluation of the quality of patient care. Results: A total of 25,259 treatments were extracted. Treatment machine monitoring revealed a daily 11-treatement difference between two units. ODM showed that the unit with fewer daily treatments performed more complex treatments on diverse locations. In 2019, the implementation of ODM led to the definition of quality indicators and in organizational changes that improved the quality of care. As consequences, for palliative treatments, there was an improvement in the proportion of treatments prepared within 7 calendar days between the scanner and the first treatment session (29.1% before 2020, 40.4% in 2020 and 46.4% after 2020). The study of fractionation in breast treatments exhibited decreased prescription variability after 2019, with distinct patient age categories. Bi-fractionation once a week for larynx prescriptions of 35 x 2.0Gy achieved an overall treatment duration of 47.0 ± 3.0 calendar days in 2022. Conclusions: ODM enables data extraction from the OIS and provides quantitative tools for improving organization of a department and the quality of patient care in radiotherapy.
Citation: Technologies
PubDate: 2024-03-29
DOI: 10.3390/technologies12040046
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 47: Impact Localization for Haptic Input
Devices Using Hybrid Laminates with Sensoric Function
Authors: René Schmidt, Alexander Graf, Ricardo Decker, Stephan Lede, Verena Kräusel, Lothar Kroll, Wolfram Hardt
First page: 47
Abstract: The required energy savings can be achieved in all automotive domains through weight savings and the merging of manufacturing processes in production. This fact is taken into account through functional integration in lightweight materials and manufacturing in a process close to large-scale production. In previous work, separate steps of a process chain for manufacturing a center console cover utilizing a sensoric hybrid laminate have been developed and evaluated. This includes the process steps of joining, forming and inline polarization as well as connecting to an embedded system. This work continues the research process by evaluating impact localization methods to use the center console as a haptic input device. For this purpose, different deep learning methods are derived from the state of the art and analyzed for their applicability in two consecutive studies. The results show that MLPs, LSTMs, GRUs and CNNs are suitable to localize impacts on the novel laminate with high localization rates of up to 99 %, and thus the usability of the developed laminate as a haptic input device has been proven.
Citation: Technologies
PubDate: 2024-04-01
DOI: 10.3390/technologies12040047
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 48: Carbon Fiber Polymer Reinforced 3D
Printed Composites for Centrifugal Pump Impeller Manufacturing
Authors: Gabriel Mansour, Vasileios Papageorgiou, Dimitrios Tzetzis
First page: 48
Abstract: Centrifugal pumps are used extensively in various everyday applications. The occurrence of corrosion phenomena during operation often leads to the failure of a pump’s operating components, such as the impeller. The present research study examines the utilization of composite materials for fabricating centrifugal pump components using additive manufacturing as an effort to fabricate corrosion resistant parts. To achieve the latter two nanocomposite materials, carbon fiber reinforced polyamide and carbon fiber reinforced polyphenylene sulfide were compared with two metal alloys, cast iron and brass, which are currently used in pump impeller manufacturing. The mechanical properties of the materials are extracted by performing a series of experiments, such as uniaxial tensile tests, nanoindentation and scanning electron microscope (SEM) examination of the specimen’s fracture area. Then, computational fluid dynamics (CFD) analysis is performed using various impeller designs to determine the fluid pressure exerted on the impeller’s geometry during its operation. Finally, the maximum power rating of an impeller that can be made from such composites will be determined using a static finite element model (FEM). The FEM static model is developed by integrating the data collected from the experiments with the results obtained from the CFD analysis. The current research work shows that nanocomposites can potentially be used for developing impellers with rated power of up to 9.41 kW.
Citation: Technologies
PubDate: 2024-04-03
DOI: 10.3390/technologies12040048
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 49: Numerical Study of the Influence of the
Structural Parameters on the Stress Dissipation of 3D Orthogonal Woven
Composites under Low-Velocity Impact
Authors: Wang Xu, Mohammed Zikry, Abdel-Fattah M. Seyam
First page: 49
Abstract: This study investigates the effects of the number of layers, x-yarn (weft) density, and z-yarn (binder) path on the mechanical behavior of E-glass 3D orthogonal woven (3DOW) composites during low-velocity impacts. Meso-level finite element (FE) models were developed and validated for 3DOW composites with different yarn densities and z-yarn paths, providing analyses of stress distribution within reinforcement fibers and matrix, energy absorption, and failure time. Our findings revealed that lower x-yarn densities led to accumulations of stress concentrations. Furthermore, changing the z-yarn path, such as transitioning from plain weaves to twill or basket weaves had a noticeable impact on stress distributions. The research highlights the significance of designing more resilient 3DOW composites for impact applications by choosing appropriate parameters in weaving composite designs.
Citation: Technologies
PubDate: 2024-04-05
DOI: 10.3390/technologies12040049
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 50: Past, Present, and Future of New
Applications in Utilization of Eddy Currents
Authors: Nestor O. Romero-Arismendi, Juan C. Olivares-Galvan, Jose L. Hernandez-Avila, Rafael Escarela-Perez, Victor M. Jimenez-Mondragon, Felipe Gonzalez-Montañez
First page: 50
Abstract: Eddy currents are an electromagnetic phenomenon that represent an inexhaustible source of inspiration for technological innovations in the 21st century. Throughout history, these currents have been a subject of research and technological development in multiple fields. This article delves into the fascinating world of eddy currents, revealing their physical foundations and highlighting their impact on a wide range of applications, ranging from non-destructive evaluation of materials to levitation phenomena, as well as their influence on fields as diverse as medicine, the automotive industry, and aerospace. The nature of eddy currents has stimulated the imaginations of scientists and engineers, driving the creation of revolutionary technologies that are transforming our society. As we progress through this article, we will cover the main aspects of eddy currents, their practical applications, and challenges for future works.
Citation: Technologies
PubDate: 2024-04-09
DOI: 10.3390/technologies12040050
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 51: Monitoring of Hip Joint Forces and
Physical Activity after Total Hip Replacement by an Integrated
Piezoelectric Element
Authors: Franziska Geiger, Henning Bathel, Sascha Spors, Rainer Bader, Daniel Kluess
First page: 51
Abstract: Resultant hip joint forces can currently only be recorded in situ in a laboratory setting using instrumented total hip replacements (THRs) equipped with strain gauges. However, permanent recording is important for monitoring the structural condition of the implant, for therapeutic purposes, for self-reflection, and for research into managing the predicted increasing number of THRs worldwide. Therefore, this study aims to investigate whether a recently proposed THR with an integrated piezoelectric element represents a new possibility for the permanent recording of hip joint forces and the physical activities of the patient. Hip joint forces from nine different daily activities were obtained from the OrthoLoad database and applied to a total hip stem equipped with a piezoelectric element using a uniaxial testing machine. The forces acting on the piezoelectric element were calculated from the generated voltages. The correlation between the calculated forces on the piezoelectric element and the applied forces was investigated, and the regression equations were determined. In addition, the voltage outputs were used to predict the activity with a random forest classifier. The coefficient of determination between the applied maximum forces on the implant and the calculated maximum forces on the piezoelectric element was R2 = 0.97 (p < 0.01). The maximum forces on the THR could be determined via activity-independent determinations with a deviation of 2.49 ± 13.16% and activity-dependent calculation with 0.87 ± 7.28% deviation. The activities could be correctly predicted using the classification model with 95% accuracy. Hence, piezoelectric elements integrated into a total hip stem represent a promising sensor option for the energy-autonomous detection of joint forces and physical activities.
Citation: Technologies
PubDate: 2024-04-09
DOI: 10.3390/technologies12040051
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 52: A Comparison of Machine Learning-Based
and Conventional Technologies for Video Compression
Authors: Lesia Mochurad
First page: 52
Abstract: The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video compression methods without loss of quality. Particularly, H.255, VP9, AV1, convolutional neural network (CNN), recurrent neural network (RNN), and deep autoencoder (DAE). The proposed decision is to use a dataset of high-quality videos to implement and compare the performance of classical compression algorithms and algorithms based on machine learning. Evaluations of the compression efficiency and the quality of the received images were made on the basis of two metrics: PSNR and SSIM. This comparison revealed the strengths and weaknesses of each approach and provided insights into how machine learning algorithms can be optimized in future research. In general, it contributed to the development of more efficient and effective video compression algorithms that can be useful for a wide range of applications.
Citation: Technologies
PubDate: 2024-04-15
DOI: 10.3390/technologies12040052
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 53: Developing a Performance Evaluation
Framework Structural Model for Educational Metaverse
Authors: Elena Tsappi, Ioannis Deliyannis, George Nathaniel Papageorgiou
First page: 53
Abstract: In response to the transformative impact of digital technology on education, this study introduces a novel performance management framework for virtual learning environments suitable for the metaverse era. Based on the Structural Equation Modeling (SEM) approach, this paper proposes a comprehensive evaluative model, anchored on the integration of the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Community of Inquiry Framework (CoI). The model synthesizes five Key Performance Indicators (KPIs)—content delivery, student engagement, metaverse tool utilization, student performance, and adaptability—to intricately assess academic avatar performances in virtual educational settings. This theoretical approach marks a significant stride in understanding and enhancing avatar efficacy in the metaverse environment. It enriches the discourse on performance management in digital education and sets a foundation for future empirical studies. As virtual online environments gain prominence in education and training, this research study establishes the basic principles and highlights the key points for further empirical research in the new era of the metaverse educational environment.
Citation: Technologies
PubDate: 2024-04-16
DOI: 10.3390/technologies12040053
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 54: Experimental and Numerical Analysis of a
Novel Cycloid-Type Rotor versus S-Type Rotor for Vertical-Axis Wind
Turbine
Authors: José Eli Eduardo González-Durán, Juan Manuel Olivares-Ramírez, María Angélica Luján-Vega, Juan Emigdio Soto-Osornio, Juan Manuel García-Guendulain, Juvenal Rodriguez-Resendiz
First page: 54
Abstract: The performance of a new vertical-axis wind turbine rotor based on the mathematical equation of the cycloid is analyzed and compared through simulation and experimental testing against a semicircular or S-type rotor, which is widely used. The study examines three cases: equalizing the diameter, chord length and the area under the curve. Computational Fluid Dynamics (CFD) was used to simulate these cases and evaluate moment, angular velocity and power. Experimental validation was carried out in a wind tunnel that was designed and optimized with the support of CFD. The rotors for all three cases were 3D printed in resin to analyze their experimental performance as a function of wind speed. The moment and Maximum Power Point (MPP) were determined in each case. The simulation results indicate that the cycloid-type rotor outperforms the semicircular or S-type rotor by 15%. Additionally, experimental evidence confirms that the cycloid-type rotor performs better in all three cases. In the MPP analysis, the cycloid-type rotor achieved an efficiency of 10.8% which was 38% better than the S-type rotor.
Citation: Technologies
PubDate: 2024-04-17
DOI: 10.3390/technologies12040054
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 55: Digital Twin Models for Personalised and
Predictive Medicine in Ophthalmology
Authors: Miruna-Elena Iliuţă, Mihnea-Alexandru Moisescu, Simona-Iuliana Caramihai, Alexandra Cernian, Eugen Pop, Daniel-Ioan Chiş, Traian-Costin Mitulescu
First page: 55
Abstract: This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches.
Citation: Technologies
PubDate: 2024-04-18
DOI: 10.3390/technologies12040055
Issue No: Vol. 12, No. 4 (2024)
- Technologies, Vol. 12, Pages 56: An End-to-End Lightweight Multi-Scale CNN
for the Classification of Lung and Colon Cancer with XAI Integration
Authors: Mohammad Asif Hasan, Fariha Haque, Saifur Rahman Sabuj, Hasan Sarker, Md. Omaer Faruq Goni, Fahmida Rahman, Md Mamunur Rashid
First page: 56
Abstract: To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes of data quickly. Different machine learning approaches to the classification and detection of lung and colon cancer have been proposed by multiple research studies. However, when it comes to self-learning classification and detection tasks, deep learning (DL) excels. This paper suggests a novel DL convolutional neural network (CNN) model for detecting lung and colon cancer. The proposed model is lightweight and multi-scale since it uses only 1.1 million parameters, making it appropriate for real-time applications as it provides an end-to-end solution. By incorporating features extracted at multiple scales, the model can effectively capture both local and global patterns within the input data. The explainability tools such as gradient-weighted class activation mapping and Shapley additive explanation can identify potential problems by highlighting the specific input data areas that have an impact on the model’s choice. The experimental findings demonstrate that for lung and colon cancer detection, the proposed model was outperformed by the competition and accuracy rates of 99.20% have been achieved for multi-class (containing five classes) predictions.
Citation: Technologies
PubDate: 2024-04-21
DOI: 10.3390/technologies12040056
Issue No: Vol. 12, No. 4 (2024)
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