A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
The end of the list has been reached or no journals were found for your choice.
Similar Journals
Journal Cover
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2227-7080
Published by MDPI Homepage  [258 journals]
  • Technologies, Vol. 11, Pages 113: An Exposimetric Electromagnetic
           Comparison of Mobile Phone Emissions: 5G versus 4G Signals Analyses by
           Means of Statistics and Convolutional Neural Networks Classification

    • Authors: Simona Miclaus, Delia B. Deaconescu, David Vatamanu, Andreea M. Buda
      First page: 113
      Abstract: To gain a deeper understanding of the hotly contested topic of the non-thermal biological effects of microwaves, new metrics and methodologies need to be adopted. The direction proposed in the current work, which includes peak exposure analysis and not just time-averaged analysis, aligns well with this objective. The proposed methodology is not intended to facilitate a comparison of the general characteristics between 4G and 5G mobile communication signals. Instead, its purpose is to provide a means for analyzing specific real-life exposure conditions that may vary based on multiple parameters. A differentiation based on amplitude-time features of the 4G versus 5G signals is followed, with the aim of describing the peculiarities of a user’s exposure when he runs four types of mobile applications on his mobile phone on either of the two mobile networks. To achieve the goals, we used signal and spectrum analyzers with adequate real-time analysis bandwidths and statistical descriptions provided by the amplitude probability density (APD) function, the complementary cumulative distribution function (CCDF), channel power measurements, and recorded spectrogram databases. We compared the exposimetric descriptors of emissions specific to file download, file upload, Internet video streaming, and video call usage in both 4G and 5G networks based on the specific modulation and coding schemes. The highest and lowest electric field strengths measured in the air at a 10 cm distance from the phone during emissions are indicated. The power distribution functions with the highest prevalence are highlighted and commented on. Afterwards, the capability of a convolutional neural network that belongs to the family of single-shot detectors is proven to recognize and classify the emissions with a very high degree of accuracy, enabling traceability of the dynamics of human exposure.
      Citation: Technologies
      PubDate: 2023-08-24
      DOI: 10.3390/technologies11050113
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 114: A Foreign Object Detection Method for
           Belt Conveyors Based on an Improved YOLOX Model

    • Authors: Rongbin Yao, Peng Qi, Dezheng Hua, Xu Zhang, He Lu, Xinhua Liu
      First page: 114
      Abstract: As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine’s intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and mAP50 of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms.
      Citation: Technologies
      PubDate: 2023-08-26
      DOI: 10.3390/technologies11050114
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 115: Efficient Deep Learning-Based
           Data-Centric Approach for Autism Spectrum Disorder Diagnosis from Facial
           Images Using Explainable AI

    • Authors: Mohammad Shafiul Alam, Muhammad Mahbubur Rashid, Ahmed Rimaz Faizabadi, Hasan Firdaus Mohd Zaki, Tasfiq E. Alam, Md Shahin Ali, Kishor Datta Gupta, Md Manjurul Ahsan
      First page: 115
      Abstract: The research describes an effective deep learning-based, data-centric approach for diagnosing autism spectrum disorder from facial images. To classify ASD and non-ASD subjects, this method requires training a convolutional neural network using the facial image dataset. As a part of the data-centric approach, this research applies pre-processing and synthesizing of the training dataset. The trained model is subsequently evaluated on an independent test set in order to assess the performance matrices of various data-centric approaches. The results reveal that the proposed method that simultaneously applies the pre-processing and augmentation approach on the training dataset outperforms the recent works, achieving excellent 98.9% prediction accuracy, sensitivity, and specificity while having 99.9% AUC. This work enhances the clarity and comprehensibility of the algorithm by integrating explainable AI techniques, providing clinicians with valuable and interpretable insights into the decision-making process of the ASD diagnosis model.
      Citation: Technologies
      PubDate: 2023-08-29
      DOI: 10.3390/technologies11050115
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 116: An Intelligent System-Based Coffee Plant
           Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica

    • Authors: Eric Hitimana, Omar Janvier Sinayobye, J. Chrisostome Ufitinema, Jane Mukamugema, Peter Rwibasira, Theoneste Murangira, Emmanuel Masabo, Lucy Cherono Chepkwony, Marie Cynthia Abijuru Kamikazi, Jeanne Aline Ukundiwabo Uwera, Simon Martin Mvuyekure, Gaurav Bajpai, Jackson Ngabonziza
      First page: 116
      Abstract: Rwandan coffee holds significant importance and immense value within the realm of agriculture, serving as a vital and valuable commodity. Additionally, coffee plays a pivotal role in generating foreign exchange for numerous developing nations. However, the coffee plant is vulnerable to pests and diseases weakening production. Farmers in cooperation with experts use manual methods to detect diseases resulting in human errors. With the rapid improvements in deep learning methods, it is possible to detect and recognize plan diseases to support crop yield improvement. Therefore, it is an essential task to develop an efficient method for intelligently detecting, identifying, and predicting coffee leaf diseases. This study aims to build the Rwandan coffee plant dataset, with the occurrence of coffee rust, miner, and red spider mites identified to be the most popular due to their geographical situations. From the collected coffee leaves dataset of 37,939 images, the preprocessing, along with modeling used five deep learning models such as InceptionV3, ResNet50, Xception, VGG16, and DenseNet. The training, validation, and testing ratio is 80%, 10%, and 10%, respectively, with a maximum of 10 epochs. The comparative analysis of the models’ performances was investigated to select the best for future portable use. The experiment proved the DenseNet model to be the best with an accuracy of 99.57%. The efficiency of the suggested method is validated through an unbiased evaluation when compared to existing approaches with different metrics.
      Citation: Technologies
      PubDate: 2023-09-01
      DOI: 10.3390/technologies11050116
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 117: Connected and Automated Vehicles:
           Infrastructure, Applications, Security, Critical Challenges, and Future

    • Authors: Memoona Sadaf, Zafar Iqbal, Abdul Rehman Javed, Irum Saba, Moez Krichen, Sajid Majeed, Arooj Raza
      First page: 117
      Abstract: Autonomous vehicles (AV) are game-changing innovations that promise a safer, more convenient, and environmentally friendly mode of transportation than traditional vehicles. Therefore, understanding AV technologies and their impact on society is critical as we continue this revolutionary journey. Generally, there needs to be a detailed study available to assist a researcher in understanding AV and its challenges. This research presents a comprehensive survey encompassing various aspects of AVs, such as public adoption, driverless city planning, traffic management, environmental impact, public health, social implications, international standards, safety, and security. Furthermore, it presents emerging technologies such as artificial intelligence (AI), integration of cloud computing, and solar power usage in automated vehicles. It also presents forensics approaches, tools used, standards involved, and challenges associated with conducting digital forensics in the context of autonomous vehicles. Moreover, this research provides an overview of cyber attacks affecting autonomous vehicles, attack management, traditional security devices, threat modeling, authentication schemes, over-the-air updates, zero-trust architectures, data privacy, and the corresponding defensive strategies to mitigate such risks. It also presents international standards, guidelines, and best practices for AVs. Finally, it outlines the future directions of AVs and the challenges that must be addressed to achieve widespread adoption.
      Citation: Technologies
      PubDate: 2023-09-04
      DOI: 10.3390/technologies11050117
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 118: The Influence of Technological Factors
           and Polar Molecules on the Structure of Fibrillar Matrices Based on
           Ultrafine Poly-3-hydroxybutyrate Fibers Obtained via Electrospinning

    • Authors: Anatoly A. Olkhov, Polina M. Tyubaeva, Yulia N. Zernova, Valery S. Markin, Regina Kosenko, Anna G. Filatova, Kristina G. Gasparyan, Alexey L. Iordanskii
      First page: 118
      Abstract: The article examines the regularities of structure formation of ultrafine fibers based on poly-3-hydroxybutyrat under the influence of technological (electrical conductivity, viscosity), molecular (molecular weight), and external factors (low-molecular and nanodispersed substances of different chemical natures). Systems with polar substances are characterized by the presence of intermolecular interactions and the formation of a more perfect crystalline fiber structure. Changes in technological and molecular characteristics affect the fiber formation process, resulting in alterations in the morphology of the nonwoven fabric, fiber geometry, and supramolecular fiber structure. Polymer molecular weight, electrical conductivity, and solution viscosity influence fiber formation and fiber diameter. The fiber structure is heterogeneous, consisting of both crystalline and non-equilibrium amorphous phases. This article shows that with an increase in the molecular weight and concentration of the polymer, the diameter of the fiber increases. At the same time, the increase in the productivity of the electrospinning process does not affect the fiber geometry. The chemical structure of the solvent and the concentration of polar substances play a decisive role in the formation of fibers of even geometry. As the polarity of the solvent increases, the intermolecular interaction with the polar groups of poly-3-hydroxybutyrate increases. As a result of this interaction, the crystallites are improved, and the amorphous phase of the polymer is compacted. The action of polar molecules on the polymer is similar to the action of polar nanoparticles. They increase crystallinity via a nucleation mechanism. This is significant in the development of matrix-fibrillar systems for drug delivery, bioactive substances, antiseptics, tissue engineering constructs, tissue engineering scaffolds, artificial biodegradable implants, sorbents, and other applications.
      Citation: Technologies
      PubDate: 2023-09-06
      DOI: 10.3390/technologies11050118
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 119: Speaker Profiling Based on the
           Short-Term Acoustic Features of Vowels

    • Authors: Mohammad Ali Humayun, Junaid Shuja, Pg Emeroylariffion Abas
      First page: 119
      Abstract: Speech samples can provide valuable information regarding speaker characteristics, including their social backgrounds. Accent variations with speaker backgrounds reflect corresponding acoustic features of speech, and these acoustic variations can be analyzed to assist in tracking down criminals from speech samples available as forensic evidence. Speech accent identification has recently received significant consideration in the speech forensics research community. However, most works have utilized long-term temporal modelling of acoustic features for accent classification and disregarded the stationary acoustic characteristics of particular phoneme articulations. This paper analyzes short-term acoustic features extracted from a central time window of English vowel speech segments for accent discrimination. Various feature computation techniques have been compared for the accent classification task. It has been found that using spectral features as an input gives better performance than using cepstral features, with the lower filters contributing more significantly to the classification task. Moreover, detailed analysis has been presented for time window durations and frequency bin resolution to compute short-term spectral features concerning accent discrimination. Using longer time durations generally requires higher frequency resolution to optimize classification performance. These results are significant, as they show the benefits of using spectral features for speaker profiling despite the popularity of cepstral features for other speech-related tasks.
      Citation: Technologies
      PubDate: 2023-09-07
      DOI: 10.3390/technologies11050119
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 120: Using Simple Interactive Technology to
           Help People with Intellectual and Visual Disabilities Exercise Functional
           Physical Responses: A Case Series Study

    • Authors: Giulio E. Lancioni, Gloria Alberti, Chiara Filippini, Valeria Chiariello, Nirbhay N. Singh, Mark F. O’Reilly, Jeff Sigafoos
      First page: 120
      Abstract: The study assessed a new interactive technology system for helping six people with intellectual and visual disabilities exercise relevant physical responses embedded within a fairly straightforward activity (i.e., placing objects in containers). Activity responses consisted of the participants taking objects from the floor or a low shelf and placing those objects in a container high up in front of them (thus bending their body and legs and stretching their arms and hands). The technology involved a portable computer, a webcam, and three mini speakers whose basic functions included monitoring the participants’ responses, delivering preferred stimulation contingent on the responses and verbal encouragements/prompts for lack of responses, and assisting in data recording. The study was conducted following a non-concurrent multiple baseline design across participants. During baseline (i.e., when the system was used only for data recording), the participants’ mean frequency of responses per session varied between zero and nearly 12. During intervention (i.e., when the system was fully working), the participants’ mean frequency of responses per session increased to between about 34 and 59. Mean session duration varied between nearly 10 and over 14 min. The new system may be a valuable tool for supporting relevant physical activity engagement in people with intellectual and multiple disabilities.
      Citation: Technologies
      PubDate: 2023-09-07
      DOI: 10.3390/technologies11050120
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 121: A Hypertuned Lightweight and Scalable
           LSTM Model for Hybrid Network Intrusion Detection

    • Authors: Aysha Bibi, Gabriel Avelino Sampedro, Ahmad Almadhor, Abdul Rehman Javed, Tai-hoon Kim
      First page: 121
      Abstract: Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial Intelligence (AI)-based solutions into historical methods. However, AI-driven approaches often encounter challenges, including lower detection rates and the complexity of feature engineering requirements. Finding solutions to overcome these hurdles is critical for enhancing the effectiveness of intrusion detection systems. This research paper introduces a deep learning-based approach for network intrusion detection to overcome these challenges. The proposed approach utilizes various classification algorithms, including the AutoEncoder (AE), Long-short-term-memory (LSTM), Multi-Layer Perceptron (MLP), Linear Support Vector Machine (L-SVM), Quantum Support Vector Machine (Q-SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). To validate the effectiveness of the proposed approach, three datasets, namely IOT23, CICIDS2017, and NSL KDD, are used for experimentation. The results demonstrate impressive accuracy, particularly with the LSTM algorithm, achieving a 97.7% accuracy rate on the NSL KDD dataset, 99% accuracy rate on the CICIDS2017 dataset, and 98.7% accuracy on the IOT23 dataset. These findings highlight the potential of deep learning algorithms in enhancing network intrusion detection. By providing network administrators with robust security measures for accurate and timely intrusion detection, the proposed approach contributes to network safety and helps mitigate the impact of network attacks.
      Citation: Technologies
      PubDate: 2023-09-07
      DOI: 10.3390/technologies11050121
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 122: Ant Colony Algorithm for Energy Saving
           to Optimize Three-Dimensional Bonding Chips’ Thermal Layout

    • Authors: Bihao Sun, Peizhi Yang, Zhiyuan Zhu
      First page: 122
      Abstract: The thermal effect and heat dissipation have a significant impact on three-dimensional stacked chips, and the positional layout of the chip’s three-dimensional layout directly affects the internal temperature field. One effective way is to plan the overall layout of three-dimensional integrated circuits by considering the thermal effect and layout utilization. In this paper, an ant colony algorithm is used to search for the most planned paths and achieve the overall layout optimization by considering the effects of power, temperature, and location on the thermal layout and using feedback optimization of pheromone concentration. The simulation results show that the optimization of the thermal layout of 3D integrated circuits can be well realized by adjusting the algorithm parameters. The maximum temperature, temperature gradient, and layout scheme verify reliability and practicability. It improves the utilization rate of chips, optimizes the layout, realizes energy conservation, and reduces resource waste.
      Citation: Technologies
      PubDate: 2023-09-10
      DOI: 10.3390/technologies11050122
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 123: Knowledge Graph Construction for Social
           Customer Advocacy in Online Customer Engagement

    • Authors: Bilal Abu-Salih, Salihah Alotaibi
      First page: 123
      Abstract: The rise of online social networks has revolutionized the way businesses and consumers interact, creating new opportunities for customer word-of-mouth (WoM) and brand advocacy. Understanding and managing customer advocacy in the online realm has become crucial for businesses aiming to cultivate a positive brand image and engage with their target audience effectively. In this study, we propose a framework that leverages the pre-trained XLNet- (bi-directional long-short term memory) BiLSTM- conditional random field (CRF) architecture to construct a Knowledge Graph (KG) for social customer advocacy in online customer engagement (CE). The XLNet-BiLSTM-CRF model combines the strengths of XLNet, a powerful language representation model, with BiLSTM-CRF, a sequence labeling model commonly used in natural language processing tasks. This architecture effectively captures contextual information and sequential dependencies in CE data. The XLNet-BiLSTM-CRF model is evaluated against several baseline architectures, including variations of BERT integrated with other models, to compare their performance in identifying brand advocates and capturing CE dynamics. Additionally, an ablation study is conducted to analyze the contributions of different components in the model. The evaluation metrics, including accuracy, precision, recall, and F1 score, demonstrate that the XLNet-BiLSTM-CRF model outperforms the baseline architectures, indicating its superior ability to accurately identify brand advocates and label customer advocacy entities. The findings highlight the significance of leveraging pre-trained contextual embeddings, sequential modeling, and sequence labeling techniques in constructing effective models for constructing a KG for customer advocacy in online engagement. The proposed framework contributes to the understanding and management of customer advocacy by facilitating meaningful customer-brand interactions and fostering brand loyalty.
      Citation: Technologies
      PubDate: 2023-09-11
      DOI: 10.3390/technologies11050123
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 124: RETRACTED: Mladenov et al. Policy
           Framework Enabling Flexibility Markets—Bulgarian Case. Technologies
           2022, 10, 126

    • Authors: Valeri Mladenov, Vesselin Chobanov, Verzhinia Ivanova
      First page: 124
      Abstract: The journal retracts the article “Policy Framework Enabling Flexibility Markets—Bulgarian Case” by Mladenov et al [...]
      Citation: Technologies
      PubDate: 2023-09-12
      DOI: 10.3390/technologies11050124
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 125: Assessment Capacity of the Armeo®
           Power: Cross-Sectional Study

    • Authors: Giovanni Galeoto, Anna Berardi, Massimiliano Mangone, Leonardo Tufo, Martina Silvani, Jerónimo González-Bernal, Jesús Seco-Calvo
      First page: 125
      Abstract: The use of robotics in rehabilitating motor functions has increased exponentially in recent decades. One of the most used robotic tools is undoubtedly the Armeo® Power, which has proved to have excellent qualities as a rehabilitation tool. However, none of these studies has investigated the ability of Armeo® Power to assess the upper limb by correlating the data resulting from the software with patient-reported outcome measures (PROMs). The present study aims to evaluate the variability between the standardized PROMs, Stroke Upper Limb Capacity Scale (SULCS), Fugl–Meyer upper limb assessment (FMA-UL), and the Armeo® Power measurements. To evaluate the correlation between SULCS and FMA-UL and the strength and joint assessments obtained with the Armeo® Power, Pearson’s correlation coefficient was used. A total of 102 stroke survivors were included in this cross-sectional study, and all participants finished the study. The results showed many statistically significant correlations between PROM items and Armeo® Power data. In conclusion, from this study, it can be stated that Armeo® Power, based on the analysis of the data collected, can be an objective evaluation tool, which can be combined with the operator-employee traditional evaluation techniques, especially when compared to a patient-reported outcome measures (PROMs).
      Citation: Technologies
      PubDate: 2023-09-13
      DOI: 10.3390/technologies11050125
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 126: A Monotonic Early Output Asynchronous
           Full Adder

    • Authors: Padmanabhan Balasubramanian, Douglas L. Maskell
      First page: 126
      Abstract: This article introduces a novel asynchronous full adder that operates in an input–output mode (IOM), displaying both monotonicity and an early output characteristic. In a monotonic asynchronous circuit, the intermediate and primary outputs exhibit similar signal transitions as the primary inputs during data and spacer application. The proposed asynchronous full adder ensures monotonicity for processing data and spacer, utilizing dual-rail encoding for inputs and outputs, and corresponds to return-to-zero (RtZ) and return-to-one (RtO) handshaking. The early output feature of the proposed full adder allows the production of sum and carry outputs based on the adder inputs regardless of the carry input when the spacer is supplied. When utilized in a ripple carry adder (RCA) architecture, the proposed full adder achieves significant reductions in design metrics, such as cycle time, area, and power, compared to existing IOM asynchronous full adders. For a 32-bit RCA implementation using a 28 nm CMOS technology, the proposed full adder outperforms an existing state-of-the-art high-speed asynchronous full adder by reducing the cycle time by 10.4% and the area by 15.8% for RtZ handshaking and reduces the cycle time by 9.8% and the area by 15.8% for RtO handshaking without incurring any power penalty. Further, in terms of the power-cycle time product, which serves as a representative measure of energy, the proposed full adder yields an 11.8% reduction for RtZ handshaking and an 11.2% reduction for RtO handshaking.
      Citation: Technologies
      PubDate: 2023-09-14
      DOI: 10.3390/technologies11050126
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 127: Comparing Performance and Preference of
           Visually Impaired Individuals in Object Localization: Tactile, Verbal, and
           Sonification Cueing Modalities

    • Authors: Shatha Abu Rass, Omer Cohen, Eliav Bareli, Sigal Portnoy
      First page: 127
      Abstract: Audio guidance is a common means of helping visually impaired individuals to navigate, thereby increasing their independence. However, the differences between different guidance modalities for locating objects in 3D space have yet to be investigated. The aim of this study was to compare the time, the hand’s path length, and the satisfaction levels of visually impaired individuals using three automatic cueing modalities: pitch sonification, verbal, and vibration. We recruited 30 visually impaired individuals (11 women, average age 39.6 ± 15.0), who were asked to locate a small cube, guided by one of three cueing modalities: sonification (a continuous beep that increases in frequency as the hand approaches the cube), verbal prompting (“right”, “forward”, etc.), and vibration (via five motors, attached to different locations on the hand). The three cueing modalities were automatically activated by computerized motion capture systems. The subjects separately answered satisfaction questions for each cueing modality. The main finding was that the time to find the cube was longer using the sonification cueing (p = 0.016). There were no significant differences in the hand path length or the subjects’ satisfaction. It can be concluded that verbal guidance may be the most effective for guiding people with visual impairment to locate an object in a 3D space.
      Citation: Technologies
      PubDate: 2023-09-16
      DOI: 10.3390/technologies11050127
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 128: Multi-Classification of Lung Infections
           Using Improved Stacking Convolution Neural Network

    • Authors: Usharani Bhimavarapu, Nalini Chintalapudi, Gopi Battineni
      First page: 128
      Abstract: Lung disease is a respiratory disease that poses a high risk to people worldwide and includes pneumonia and COVID-19. As such, quick and precise identification of lung disease is vital in medical treatment. Early detection and diagnosis can significantly reduce the life-threatening nature of lung diseases and improve the quality of life of human beings. Chest X-ray and computed tomography (CT) scan images are currently the best techniques to detect and diagnose lung infection. The increase in the chest X-ray or CT scan images at the time of training addresses the overfitting dilemma, and multi-class classification of lung diseases will deal with meaningful information and overfitting. Overfitting deteriorates the performance of the model and gives inaccurate results. This study reduces the overfitting issue and computational complexity by proposing a new enhanced kernel convolution function. Alongside an enhanced kernel convolution function, this study used convolution neural network (CNN) models to determine pneumonia and COVID-19. Each CNN model was applied to the collected dataset to extract the features and later applied these features as input to the classification models. This study shows that extracting deep features from the common layers of the CNN models increased the performance of the classification procedure. The multi-class classification improves the diagnostic performance, and the evaluation metrics improved significantly with the improved support vector machine (SVM). The best results were obtained using the improved SVM classifier fed with the features provided by CNN, and the success rate of the improved SVM was 99.8%.
      Citation: Technologies
      PubDate: 2023-09-17
      DOI: 10.3390/technologies11050128
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 129: Level of Technological Maturity of
           Telemonitoring Systems Focused on Patients with Chronic Kidney Disease
           Undergoing Peritoneal Dialysis Treatment: A Systematic Literature Review

    • Authors: Alejandro Villanueva Cerón, Eduardo López Domínguez, Saúl Domínguez Isidro, María Auxilio Medina Nieto, Jorge De La Calleja, Saúl Eduardo Pomares Hernández
      First page: 129
      Abstract: In the field of eHealth, several works have proposed telemonitoring systems focused on patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD) treatment. Nevertheless, no secondary study presents a comparative analysis of these works regarding the technology readiness level (TRL) framework. The TRL scale goes from 1 to 9, with 1 being the lowest level of readiness and 9 being the highest. This paper analyzes works that propose telemonitoring systems focused on patients with CKD undergoing PD treatment to determine their TRL. We also analyzed the requirements and parameters that the systems of the selected works provide to the users to perform telemonitoring of the patient’s treatment undergoing PD. Fourteen works were relevant to the present study. Of these works, eight were classified within TRL 9, two were categorized within TRL 7, three were identified within TRL 6, and one within TRL 4. The works reported with the highest TRL partially cover the requirements for appropriate telemonitoring of patients based on the specialized literature; in addition, those works are focused on the treatment of patients in the automated peritoneal dialysis (APD) modality, which limits the care of patients undergoing the continuous ambulatory peritoneal dialysis (CAPD) modality.
      Citation: Technologies
      PubDate: 2023-09-18
      DOI: 10.3390/technologies11050129
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 130: PDSCM: Packet Delivery Assured Secure
           Channel Selection for Multicast Routing in Wireless Mesh Networks

    • Authors: Seetha S, Esther Daniel, S Durga, Jennifer Eunice R, Andrew J
      First page: 130
      Abstract: The academic and research communities are showing significant interest in the modern and highly promising technology of wireless mesh networks (WMNs) due to their low-cost deployment, self-configuration, self-organization, robustness, scalability, and reliable service coverage. Multicasting is a broadcast technique in which the communication is started by an individual user and is shared by one or multiple groups of destinations concurrently as one-to-many allotments. The multicasting protocols are focused on building accurate paths with proper channel optimization techniques. The forwarder nodes of the multicast protocol may behave with certain malicious characteristics, such as dropping packets, and delayed transmissions that cause heavy packet loss in the network. This leads to a reduced packet delivery ratio and throughput of the network. Hence, the forwarder node validation is critical for building a secure network. This research paper presents a secure forwarder selection between a sender and the batch of receivers by utilizing the node’s communication behavior. The parameters of the malicious nodes are analyzed using orthogonal projection and statistical methods to distinguish malicious node behaviors from normal node behaviors based on node actions. The protocol then validates the malicious behaviors and subsequently eliminates them from the forwarder selection process using secure path finding strategies, which lead to dynamic and scalable multicast mesh networks for communication.
      Citation: Technologies
      PubDate: 2023-09-18
      DOI: 10.3390/technologies11050130
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 131: Optimal Integration of Machine Learning
           for Distinct Classification and Activity State Determination in Multiple
           Sclerosis and Neuromyelitis Optica

    • Authors: Maha Gharaibeh, Wlla Abedalaziz, Noor Aldeen Alawad, Hasan Gharaibeh, Ahmad Nasayreh, Mwaffaq El-Heis, Maryam Altalhi, Agostino Forestiero, Laith Abualigah
      First page: 131
      Abstract: The intricate neuroinflammatory diseases multiple sclerosis (MS) and neuromyelitis optica (NMO) often present similar clinical symptoms, creating challenges in their precise detection via magnetic resonance imaging (MRI). This challenge is further compounded when detecting the active and inactive states of MS. To address this diagnostic problem, we introduce an innovative framework that incorporates state-of-the-art machine learning algorithms applied to features culled from MRI scans by pre-trained deep learning models, VGG-NET and InceptionV3. To develop and test this methodology, we utilized a robust dataset obtained from the King Abdullah University Hospital in Jordan, encompassing cases diagnosed with both MS and NMO. We benchmarked thirteen distinct machine learning algorithms and discovered that support vector machine (SVM) and K-nearest neighbor (KNN) algorithms performed superiorly in our context. Our results demonstrated KNN’s exceptional performance in differentiating between MS and NMO, with precision, recall, F1-score, and accuracy values of 0.98, 0.99, 0.99, and 0.99, respectively, using leveraging features extracted from VGG16. In contrast, SVM excelled in classifying active versus inactive states of MS, achieving precision, recall, F1-score, and accuracy values of 0.99, 0.97, 0.98, and 0.98, respectively, using leveraging features extracted from VGG16 and VGG19. Our advanced methodology outshines previous studies, providing clinicians with a highly accurate, efficient tool for diagnosing these diseases. The immediate implication of our research is the potential to streamline treatment processes, thereby delivering timely, appropriate care to patients suffering from these complex diseases.
      Citation: Technologies
      PubDate: 2023-09-20
      DOI: 10.3390/technologies11050131
      Issue No: Vol. 11, No. 5 (2023)
  • Technologies, Vol. 11, Pages 82: A Novel Methodology for Classifying
           Electrical Disturbances Using Deep Neural Networks

    • Authors: Alma E. Guerrero-Sánchez, Edgar A. Rivas-Araiza, Mariano Garduño-Aparicio, Saul Tovar-Arriaga, Juvenal Rodriguez-Resendiz, Manuel Toledano-Ayala
      First page: 82
      Abstract: Electrical power quality is one of the main elements in power generation systems. At the same time, it is one of the most significant challenges regarding stability and reliability. Due to different switching devices in this type of architecture, different kinds of power generators as well as non-linear loads are used for different industrial processes. A result of this is the need to classify and analyze Power Quality Disturbance (PQD) to prevent and analyze the degradation of the system reliability affected by the non-linear and non-stationary oscillatory nature. This paper presents a novel Multitasking Deep Neural Network (MDL) for the classification and analysis of multiple electrical disturbances. The characteristics are extracted using a specialized and adaptive methodology for non-stationary signals, namely, Empirical Mode Decomposition (EMD). The methodology’s design, development, and various performance tests are carried out with 28 different difficulties levels, such as severity, disturbance duration time, and noise in the 20 dB to 60 dB signal range. MDL was developed with a diverse data set in difficulty and noise, with a quantity of 4500 records of different samples of multiple electrical disturbances. The analysis and classification methodology has an average accuracy percentage of 95% with multiple disturbances. In addition, it has an average accuracy percentage of 90% in analyzing important signal aspects for studying electrical power quality such as the crest factor, per unit voltage analysis, Short-term Flicker Perceptibility (Pst), and Total Harmonic Distortion (THD), among others.
      Citation: Technologies
      PubDate: 2023-06-21
      DOI: 10.3390/technologies11040082
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 83: A Survey of Advancements in Real-Time
           Sign Language Translators: Integration with IoT Technology

    • Authors: Maria Papatsimouli, Panos Sarigiannidis, George F. Fragulis
      First page: 83
      Abstract: Real-time sign language translation systems are of paramount importance in enabling communication for deaf and hard-of-hearing individuals. This population relies on various communication methods, including sign languages and visual techniques, to interact with others. While assistive technologies, such as hearing aids and captioning, have improved their communication capabilities, a significant communication gap still exists between sign language users and non-users. In order to bridge this gap, numerous sign language translation systems have been developed, encompassing sign language recognition and gesture-based controls. Our research aimed to analyze the advancements in real-time sign language translators developed over the past five years and their integration with IoT technology. By closely examining these technologies, we aimed to attain a deeper comprehension of their practical applications and evolution in the domain of sign language translation. We analyzed the current literature, technical reports, and conference papers on real-time sign language translation systems. Our results offer insights into the current state of the art in real-time sign language translation systems and their integration with IoT technology. We also provide a deep understanding of the recent developments in sign language translation technology and the potential for their fusion with Internet of Things technology to improve communication and promote inclusivity for the deaf and hard-of-hearing population.
      Citation: Technologies
      PubDate: 2023-06-22
      DOI: 10.3390/technologies11040083
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 84: Modernizing the Legacy Healthcare System
           to Decentralize Platform Using Blockchain Technology

    • Authors: Abdulaziz Aljaloud, Abdul Razzaq
      First page: 84
      Abstract: The use of blockchain technology is expanding in various industries, including finance, supply chain management, food, energy, IoT, and healthcare. The article aims to address the challenges of complex medical procedures, large-scale medical data management, and cost optimization in the healthcare industry. By employing blockchain technology, the article aims to enhance data security and privacy while ensuring the integrity and efficiency of the healthcare system. This article focuses on the application of blockchain technology in the healthcare system by reviewing the existing literature and proposing multiple workflows for better data management. These workflows were implemented using the Ethereum blockchain platform and involve complex medical procedures such as surgery and clinical trials, as well as managing a large amount of medical data. The feasibility of the proposed system is analyzed in terms of associated costs, and a model-driven engineering approach is used to recover the architecture of traditional healthcare systems. The aim is to provide stakeholders in the healthcare system with better healthcare services and cost optimization. The solution being proposed automates interactions between different parties involved. Smart contracts were created using Solidity language, and their functions were tested using the Remix IDE. This paper illustrates that our smart contract code was designed to avoid common security vulnerabilities and attacks. To test the framework, a prototype of the smart contract was deployed on an Ethereum TESTNET blockchain in a Windows environment. This study found that the proposed approach is both practical and efficient.
      Citation: Technologies
      PubDate: 2023-06-29
      DOI: 10.3390/technologies11040084
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 85: Self-Directed and Self-Designed Learning:
           Integrating Imperative Topics in the Case of COVID-19

    • Authors: Alireza Ebrahimi
      First page: 85
      Abstract: Self-directed learning and self-design became unexpectedly popular and common during the COVID-19 era. Learners are encouraged to take charge of their learning and, often the opportunity to independently design their learning experience. This research illustrates the use of technology in teaching and learning technology with a central theme of promoting self-directed learning with engaging self-design for both educators and learners. The technology used includes existing tools such as web page design, Learning Management Systems (LMS), project management tools, and basic programming foundations and concepts of big data and databases. In addition, end-users and developers can create their own tools with simple coding. Planning techniques, such as Visual Plan Construct Language with its embedded AI, are used to integrate course material and rubrics with time management. Educators may use project management tools instead. The research proposes a self-directed paradigm with self-designed resources using the existing technology with LMS modules, discussions, and self-tests. The research establishes its criteria for ensuring the quality of content and design, known as 7x2C. Additionally, other criteria for analysis, such as Design Thinking, are included. The approach is examined for a technology-based business course in creating an experiential learning system for COVID-19 awareness. Likewise, among other projects, an environment for educating learners about diabetes and obesity has been designed. The project is known as Sunchoke, which has a theme of Grow, Eat, and Heal. Educators can use their own content and rubrics to adapt this approach to their own customized teaching methods.
      Citation: Technologies
      PubDate: 2023-06-29
      DOI: 10.3390/technologies11040085
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 86: Enhancement of Handshake Attraction
           through Tactile, Visual, and Auditory Multimodal Stimulation

    • Authors: Taishu Kumagai, Yoshimune Nonomura
      First page: 86
      Abstract: “Handshaking parties,” where pop idols shake hands with fans, can be exciting. The multimodal stimulation of tactile, visual, and auditory sensations can be captivating. In this study, we presented subjects with stimuli eliciting three sensory responses: tactile, visual, and auditory sensations. We found that the attraction scores of subjects increased because they felt the smoothness and obtained a human-like sensory experience grasping a grip handle covered with artificial skin, faux fur, and abrasive cloth with their dominant hand as they looked at a picture of a pop idol or listened to a song. When no pictures or songs were presented, a simple feeling of slight warmth was correlated with the attraction score. Results suggest that multimodal stimuli alter tactile sensations and the feelings evoked. This finding may be useful for designing materials that activate the human mind through tactile sensation and for developing humanoid robots and virtual reality systems.
      Citation: Technologies
      PubDate: 2023-07-01
      DOI: 10.3390/technologies11040086
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 87: Optimizing EMG Classification through
           Metaheuristic Algorithms

    • Authors: Marcos Aviles, Juvenal Rodríguez-Reséndiz, Danjela Ibrahimi
      First page: 87
      Abstract: This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average classification rate of 93% for the validation phase. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications.
      Citation: Technologies
      PubDate: 2023-07-02
      DOI: 10.3390/technologies11040087
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 88: Digital Technologies to Provide
           Humanization in the Education of the Healthcare Workforce: A Systematic

    • Authors: María Gonzalez-Moreno, Carlos Monfort-Vinuesa, Antonio Piñas-Mesa, Esther Rincon
      First page: 88
      Abstract: Objectives: The need to incentivize the humanization of healthcare providers coincides with the development of a more technological approach to medicine, which gives rise to depersonalization when treating patients. Currently, there is a culture of humanization that reflects the awareness of health professionals, patients, and policy makers, although it is unknown if there are university curricula incorporating specific skills in humanization, or what these may include. Therefore, the objectives of this study are as follows: (1) to identify what type of education in humanization is provided to university students of Health Sciences using digital technologies; and (2) determine the strengths and weaknesses of this education. The authors propose a curriculum focusing on undergraduate students to strengthen the humanization skills of future health professionals, including digital health strategies. Methods: A systematic review, based on the scientific literature published in EBSCO, Ovid, PubMed, Scopus, and Web of Science, over the last decade (2012–2022), was carried out in November 2022. The keywords used were “humanization of care” and “humanization of healthcare” combined both with and without “students”. Results: A total of 475 articles were retrieved, of which 6 met the inclusion criteria and were subsequently analyzed, involving a total of 295 students. Three of them (50%) were qualitative studies, while the other three (50%) involved mixed methods. Only one of the studies (16.7%) included digital health strategies to train humanization. Meanwhile, another study (16.7%) measured the level of humanization after training. Conclusions: There is a clear lack of empirically tested university curricula that combine education in humanization and digital technology for future health professionals. Greater focus on the training of future health professionals is needed, in order to guarantee that they begin their professional careers with the precept of medical humanities as a basis.
      Citation: Technologies
      PubDate: 2023-07-05
      DOI: 10.3390/technologies11040088
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 89: Features of Metalorganic Chemical Vapor
           Deposition Selective Area Epitaxy of AlzGa1−zAs (0 ≤ z ≤ 0.3)
           Layers in Arrays of Ultrawide Windows

    • Authors: Viktor Shamakhov, Sergey Slipchenko, Dmitriy Nikolaev, Ilya Soshnikov, Alexander Smirnov, Ilya Eliseyev, Artyom Grishin, Matvei Kondratov, Artem Rizaev, Nikita Pikhtin, Peter Kop’ev
      First page: 89
      Abstract: AlzGa1−zAs layers of various compositions were grown using metalorganic chemical vapor deposition on a GaAs substrate with a pattern of alternating SiO2 mask/window stripes, each 100 µm wide. Microphotoluminescence maps and thickness profiles of AlzGa1−zAs layers that demonstrated the distribution of the growth rate and z in the window were experimentally studied. It was shown that the layer growth rate and the AlAs mole fraction increased continuously from the center to the edge of the window. It was experimentally shown that for a fixed growth time of 10 min, as z increased from 0 to 0.3, the layer thickness difference between the center of the window and the edge increased from 700 Å to 1100 Å, and the maximum change in z between the center of the window and the edge reached Δz 0.016, respectively. Within the framework of the vapor -phase diffusion model, simulations of the spatial distribution of the layer thickness and z across the window were carried out. It was shown that the simulation results were in good agreement with the experimental results for the effective diffusion length D/k: Ga—85 µm, Al—50 µm.
      Citation: Technologies
      PubDate: 2023-07-07
      DOI: 10.3390/technologies11040089
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 90: Characterization of Triplet State of
           Cyanine Dyes with Two Chromophores Effect of Molecule Structure

    • Authors: Iouri E. Borissevitch, Pablo J. Gonçalves, Lucimara P. Ferreira, Alexey A. Kostyukov, Vladimir A. Kuzmin
      First page: 90
      Abstract: Quantum yields (φT) and energies (ET) of the first triplet state T1 for four molecules of cyanine dyes with two chromophores (BCDs), promising photoactive compounds for various applications, for example, as photosensitizers in photodynamic therapy (PDT) and fluorescence diagnostics (FD), were studied in 1-propanol solutions by steady-state and time-resolved optical absorption techniques. BCDs differ by the structure of the central heterocycle, connecting the chromophores. The heterocycle structure is responsible for electron tunneling between chromophores, for which efficiency can be characterized by splitting of the BCD triplet energy levels. It was shown that the increase in the tunneling efficiency reduces ET values and increases φT values. This aspect is very promising for the synthesis of new effective photosensitizers based on cyanine dyes with two interacting chromophores for various applications, including photodynamic therapy.
      Citation: Technologies
      PubDate: 2023-07-08
      DOI: 10.3390/technologies11040090
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 91: Implementation of Deep Learning Models on
           an SoC-FPGA Device for Real-Time Music Genre Classification

    • Authors: Muhammad Faizan, Ioannis Intzes, Ioana Cretu, Hongying Meng
      First page: 91
      Abstract: Deep neutral networks (DNNs) are complex machine learning models designed for decision-making tasks with high accuracy. However, DNNs require high computational power and memory, which limits such models to fitting on edge devices, resulting in unnecessary processing delays and high energy consumption. Graphical processing units (GPUs) offer reliable hardware acceleration, but their bulky sizes prevent their utilization in portable equipment. System-on-chip field programmable gated arrays (SoC-FPGAs) provide considerable computational power with low energy consumption, making them ideal for edge computing applications, owing to their innovative, flexible, and small design. In this paper, we implement a deep-learning-based music genre classification system on a SoC-FPGA board, evaluate the model’s performance, and provide a comparative analysis across different platforms. Specifically, we compare the performance of long short-term memory (LSTM), convolutional neural networks (CNNs), and a hybrid model (CNN-LSTM) on an Intel Core i7-8550U by Intel Cooperation. The models are fed an acoustic feature called the Mel-frequency cepstral coefficient (MFCC) for training and testing (inference). Then, by using the advanced Vitis AI tool, a deployable version of the model is generated. The experimental results show that the execution speed is increased by 80%, and the throughput rises four times when the CNN-based music genre classification system is implemented on SoC-FPGA.
      Citation: Technologies
      PubDate: 2023-07-10
      DOI: 10.3390/technologies11040091
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 92: FPGA-Based Implementation of a New 3-D
           Multistable Chaotic Jerk System with Two Unstable Balance Points

    • Authors: Sundarapandian Vaidyanathan, Esteban Tlelo-Cuautle, Khaled Benkouider, Aceng Sambas, Brisbane Ovilla-Martínez
      First page: 92
      Abstract: Mechanical jerk systems have applications in several areas, such as oscillators, microcontrollers, circuits, memristors, encryption, etc. This research manuscript reports a new 3-D chaotic jerk system with two unstable balance points. It is shown that the proposed mechanical jerk system exhibits multistability with coexisting chaotic attractors for the same set of system constants but for different initial states. A bifurcation analysis of the proposed mechanical jerk system is presented to highlight the special properties of the system with respect to the variation of system constants. A field-programmable gate array (FPGA) implementation of the proposed mechanical jerk system is given by synthesizing the discrete equations that are obtained by applying one-step numerical methods. The hardware resources are reduced by performing pipeline operations, and, finally, the paper concludes that the experimental results of the proposed mechanical jerk system using FPGA-based design show good agreement with the MATLAB simulations of the same system.
      Citation: Technologies
      PubDate: 2023-07-11
      DOI: 10.3390/technologies11040092
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 93: Optical Properties of AgInS2 Quantum Dots
           Synthesized in a 3D-Printed Microfluidic Chip

    • Authors: Konstantin Baranov, Ivan Reznik, Sofia Karamysheva, Jacobus W. Swart, Stanislav Moshkalev, Anna Orlova
      First page: 93
      Abstract: Colloidal nanoparticles, and quantum dots in particular, are a new class of materials that can significantly improve the functionality of photonics, electronics, sensor devices, etc. The main challenge addressed in the article is modification of the syntheses of colloidal NP to launch them into mass production. It is proposed to use an additive printing method of chips for microfluidic synthesis, and it is shown that our approach allows to offer a cheap, easily scalable and automated synthesis method which allows to increase the product yield up to 60% with improved optical properties of AgInS2 quantum dots.
      Citation: Technologies
      PubDate: 2023-07-12
      DOI: 10.3390/technologies11040093
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 94: Regenerating Iron-Based Adsorptive Media
           Used for Removing Arsenic from Water

    • Authors: Ilaria Ceccarelli, Luca Filoni, Massimiliano Poli, Ciro Apollonio, Andrea Petroselli
      First page: 94
      Abstract: Of all the substances that can be present in water intended for human consumption, arsenic (As) is one of the most toxic. Many treatment technologies can be used for removing As from water, for instance, adsorption onto iron media, where commercially available adsorbents are removed and replaced with new media when they are exhausted. Since this is an expensive operation, in this work, a novel and portable plant for regenerating iron media has been developed and tested in four real case studies in Central Italy. The obtained results highlight the good efficiency of the system, which was able, from 2019 to 2023, to regenerate the iron media and to restore its capability to adsorb the As from water almost entirely. Indeed, when the legal threshold value of 10 μg/L is exceeded, the regeneration process is performed and, after that, the As concentration in the water effluent is at the minimum level in all the investigated case studies.
      Citation: Technologies
      PubDate: 2023-07-12
      DOI: 10.3390/technologies11040094
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 95: Field Performance Monitoring of
           Energy-Generating High-Transparency Agrivoltaic Glass Windows

    • Authors: Mikhail Vasiliev, Victor Rosenberg, Jamie Lyford, David Goodfield
      First page: 95
      Abstract: Currently, there are strong and sustained growth trends observed in multi-disciplinary industrial technologies such as building-integrated photovoltaics and agrivoltaics, where renewable energy production is featured in building envelopes of varying degrees of transparency. Novel glass products can provide a combination of thermal energy savings and solar energy harvesting, enabled by either patterned-semiconductor thin-film energy converters on glass substrates, or by using luminescent concentrator-type approaches to achieve high transparency. Significant progress has been demonstrated recently in building integrated solar windows featuring visible light transmission of up to 70%, with electric power outputs of up to Pmax ~ 30–33 Wp/m2. Several slightly different designs were tested during 2021–2023 in a greenhouse installation at Murdoch University in Perth, Western Australia; their long-term energy harvesting performance differences were found to be on the scale of ~10% in wall-mounted locations. Solar greenhouse generated electricity at rates of up to 19 kWh/day, offsetting nearly 40% of energy costs. The objective of this paper is to report on the field performance of these PV windows in the context of agrivoltaics and to provide some detail of the performance differences measured in several solar window designs related to their glazing structure materials. Methods for the identification and quantification of long-term field performance differences and energy generation trends in solar windows of marginally different design types are reported. The paper also aims to outline the practical application potential of these transparent construction materials in built environments, focusing on the measured renewable energy figures and seasonal trends observed during the long-term study.
      Citation: Technologies
      PubDate: 2023-07-12
      DOI: 10.3390/technologies11040095
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 96: A Deep Reinforcement Learning Method for
           Economic Power Dispatch of Microgrid in OPAL-RT Environment

    • Authors: Faa-Jeng Lin, Chao-Fu Chang, Yu-Cheng Huang, Tzu-Ming Su
      First page: 96
      Abstract: This paper focuses on the economic power dispatch (EPD) operation of a microgrid in an OPAL-RT environment. First, a long short-term memory (LSTM) network is proposed to forecast the load information of a microgrid to determine the output of a power generator and the charging/discharging control strategy of a battery energy storage system (BESS). Then, a deep reinforcement learning method, the deep deterministic policy gradient (DDPG), is utilized to develop the power dispatch of a microgrid to minimize the total energy expense while considering power constraints, load uncertainties and electricity price. Moreover, a microgrid built in Cimei Island of Penghu Archipelago, Taiwan, is investigated to examine the compliance with the requirements of equality and inequality constraints and the performance of the deep reinforcement learning method. Furthermore, a comparison of the proposed method with the experience-based energy management system (EMS), Newton particle swarm optimization (Newton-PSO) and the deep Q-learning network (DQN) is provided to evaluate the obtained solutions. In this study, the average deviation of the LSTM forecast accuracy is less than 5%. In addition, the daily operating cost of the proposed method obtains a 3.8% to 7.4% lower electricity cost compared to that of the other methods. Finally, a detailed emulation in the OPAL-RT environment is carried out to validate the effectiveness of the proposed method.
      Citation: Technologies
      PubDate: 2023-07-12
      DOI: 10.3390/technologies11040096
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 97: Segmentation of Retinal Blood Vessels
           Using Focal Attention Convolution Blocks in a UNET

    • Authors: Rafael Ortiz-Feregrino, Saul Tovar-Arriaga, Jesus Carlos Pedraza-Ortega, Juvenal Rodriguez-Resendiz
      First page: 97
      Abstract: Retinal vein segmentation is a crucial task that helps in the early detection of health problems, making it an essential area of research. With recent advancements in artificial intelligence, we can now develop highly reliable and efficient models for this task. CNN has been the traditional choice for image analysis tasks. However, the emergence of visual transformers with their unique attention mechanism has proved to be a game-changer. However, visual transformers require a large amount of data and computational power, making them unsuitable for tasks with limited data and resources. To deal with this constraint, we adapted the attention module of visual transformers and integrated it into a CNN-based UNET network, achieving superior performance compared to other models. The model achieved a 0.89 recall, 0.98 AUC, 0.97 accuracy, and 0.97 sensitivity on various datasets, including HRF, Drive, LES-AV, CHASE-DB1, Aria-A, Aria-D, Aria-C, IOSTAR, STARE and DRGAHIS. Moreover, the model can recognize blood vessels accurately, regardless of camera type or the original image resolution, ensuring that it generalizes well. This breakthrough in retinal vein segmentation could improve the early diagnosis of several health conditions.
      Citation: Technologies
      PubDate: 2023-07-13
      DOI: 10.3390/technologies11040097
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 98: Quantum Effects in General Relativity:
           Investigating Repulsive Gravity of Black Holes at Large Distances

    • Authors: Chiarelli
      First page: 98
      Abstract: This paper proposes a theoretical study that investigates quantum effects on the gravity of black holes. This study utilizes a gravitational model that incorporates quantum mechanics derived from the classical-like quantum hydrodynamic representation. This research calculates the mass density distribution of quantum black holes, specifically in the case of central symmetry. The gravity of a quantum black hole shows contributions coming from quantum potential energy, which is also sensitive to the presence of a background of gravitational noise. The additional energy, stored in quantum potential fluctuations and constituting a form of dark energy, leads to a repulsive gravity in the weak gravity limit. This repulsive gravity overcomes the attractive classical Newtonian force at large distances of order of the intergalactic length.
      Citation: Technologies
      PubDate: 2023-07-14
      DOI: 10.3390/technologies11040098
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 99: Comparative Analysis of Image
           Classification Models for Norwegian Sign Language Recognition

    • Authors: Benjamin Svendsen, Seifedine Kadry
      First page: 99
      Abstract: Communication is integral to every human’s life, allowing individuals to express themselves and understand each other. This process can be challenging for the hearing-impaired population, who rely on sign language for communication due to the limited number of individuals proficient in sign language. Image classification models can be used to create assistive systems to address this communication barrier. This paper conducts a comprehensive literature review and experiments to find the state of the art in sign language recognition. It identifies a lack of research in Norwegian Sign Language (NSL). To address this gap, we created a dataset from scratch containing 24,300 images of 27 NSL alphabet signs and performed a comparative analysis of various machine learning models, including the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Convolutional Neural Network (CNN) on the dataset. The evaluation of these models was based on accuracy and computational efficiency. Based on these metrics, our findings indicate that SVM and CNN were the most effective models, achieving accuracies of 99.9% with high computational efficiency. Consequently, the research conducted in this report aims to contribute to the field of NSL recognition and serve as a foundation for future studies in this area.
      Citation: Technologies
      PubDate: 2023-07-15
      DOI: 10.3390/technologies11040099
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 100: Impact of Post-Process Heat Treatments
           Performed on Ti6Al4V Titanium Alloy Specimens Obtained Using LPBF

    • Authors: Silvia Gaiani, Elisa Ferrari, Marica Gozzi, Maria Teresa Di Giovanni, Magdalena Lassinantti Gualtieri, Elena Colombini, Paolo Veronesi
      First page: 100
      Abstract: Additive manufacturing technology has emerged over the past decade as one of the best solutions for building prototypes and components with complex geometries and reduced thicknesses. Its application has rapidly spread to various industries, such as motorsport, automotive, aerospace, and biomedical. In particular, titanium alloy Ti-6Al-4V, due to its exceptional mechanical properties, low density, and excellent corrosion resistance, turns out to be one of the most popular for the production of parts with additive manufacturing technology across all the market segments listed above. However, when producing components using Laser Powder Bed Fusion (LPBF) technology, it is always necessary to perform appropriate heat treatments whose main purpose is to reduce the residual stresses typically generated during the manufacturing process. Post-process heat treatments on Ti6Al4V components obtained by way of additive technology have been extensively studied in the literature, with the aim of identifying optimal thermal cycles, which may allow for the effective reduction of residual stresses combined with proper microstructural conditions. However, despite the usual target of maximizing relevant mechanical properties, it is mandatory for industrial production to achieve a robust process, i.e., minimizing the sensitivity to noise-induced variation. Therefore, the aim of the present work is to compare several post-process heat treatment strategies by performing different thermal cycles in the temperature range of 750–955 °C and investigating how these affect the average mechanical properties and their variance. The treated samples are then analyzed running a complete mechanical and microstructural characterization, and the latter particularly focused on the determination of the typical microstructure present in the treated samples by using the XRD technique.
      Citation: Technologies
      PubDate: 2023-07-15
      DOI: 10.3390/technologies11040100
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 101: Cleaning Big Data Streams: A Systematic
           Literature Review

    • Authors: Obaid Alotaibi, Eric Pardede, Sarath Tomy
      First page: 101
      Abstract: In today’s big data era, cleaning big data streams has become a challenging task because of the different formats of big data and the massive amount of big data which is being generated. Many studies have proposed different techniques to overcome these challenges, such as cleaning big data in real time. This systematic literature review presents recently developed techniques that have been used for the cleaning process and for each data cleaning issue. Following the PRISMA framework, four databases are searched, namely IEEE Xplore, ACM Library, Scopus, and Science Direct, to select relevant studies. After selecting the relevant studies, we identify the techniques that have been utilized to clean big data streams and the evaluation methods that have been used to examine their efficiency. Also, we define the cleaning issues that may appear during the cleaning process, namely missing values, duplicated data, outliers, and irrelevant data. Based on our study, the future directions of cleaning big data streams are identified.
      Citation: Technologies
      PubDate: 2023-07-26
      DOI: 10.3390/technologies11040101
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 102: Modern DC–DC Power Converter
           Topologies and Hybrid Control Strategies for Maximum Power Output in
           Sustainable Nanogrids and Picogrids—A Comprehensive Survey

    • Authors: Anupama Ganguly, Pabitra Kumar Biswas, Chiranjit Sain, Taha Selim Ustun
      First page: 102
      Abstract: Sustainable energy exhibited immense growth in the last few years. As compared to other sustainable sources, solar power is proved to be the most feasible source due to some unanticipated characteristics, such as being clean, noiseless, ecofriendly, etc. The output from the solar power is entirely unpredictable since solar power generation is dependent on the intensity of solar irradiation and solar panel temperature. Further, these parameters are weather dependent and thus intermittent in nature. To conquer intermittency, power converters play an important role in solar power generation. Generally, photovoltaic systems will eventually suffer from a decrease in energy conversion efficiency along with improper stability and intermittent properties. As a result, the maximum power point tracking (MPPT) algorithm must be incorporated to cultivate maximum power from solar power. To make solar power generation reliable, a proper control technique must be added to the DC–DC power converter topologies. Furthermore, this study reviewed the progress of the maximum power point tracking algorithm and included an in-depth discussion on modern and both unidirectional and bidirectional DC–DC power converter topologies for harvesting electric power. Lastly, for the reliability and continuity of the power demand and to allow for distributed generation, this article also established the possibility of integrating solar PV systems into nanogrids and picogrids in a sustainable environment. The outcome of this comprehensive survey would be of strong interest to the researchers, technologists, and the industry in the relevant field to carry out future research.
      Citation: Technologies
      PubDate: 2023-08-01
      DOI: 10.3390/technologies11040102
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 103: Adapting the H.264 Standard to the
           Internet of Vehicles

    • Authors: Yair Wiseman
      First page: 103
      Abstract: We suggest two steps of reducing the amount of data transmitted on Internet of Vehicle networks. The first step shifts the image from a full-color resolution to only an 8-color resolution. The reduction of the color numbers is noticeable; however, the 8-color images are enough for the requirements of common vehicles’ applications. The second step suggests modifying the quantization tables employed by H.264 to different tables that will be more suitable to an image with only 8 colors. The first step usually reduces the size of the image by more than 30%, and when continuing and performing the second step, the size of the image decreases by more than 40%. That is to say, the combination of the two steps can provide a significant reduction in the amount of data required to be transferred on vehicular networks.
      Citation: Technologies
      PubDate: 2023-08-03
      DOI: 10.3390/technologies11040103
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 104: The U-Net Family for Epicardial Adipose
           Tissue Segmentation and Quantification in Low-Dose CT

    • Authors: Lu Liu, Runlei Ma, Peter M. A. van Ooijen, Matthijs Oudkerk, Rozemarijn Vliegenthart, Raymond N. J. Veldhuis, Christoph Brune
      First page: 104
      Abstract: Epicardial adipose tissue (EAT) is located between the visceral pericardium and myocardium, and EAT volume is correlated with cardiovascular risk. Nowadays, many deep learning-based automated EAT segmentation and quantification methods in the U-net family have been developed to reduce the workload for radiologists. The automatic assessment of EAT on non-contrast low-dose CT calcium score images poses a greater challenge compared to the automatic assessment on coronary CT angiography, which requires a higher radiation dose to capture the intricate details of the coronary arteries. This study comprehensively examined and evaluated state-of-the-art segmentation methods while outlining future research directions. Our dataset consisted of 154 non-contrast low-dose CT scans from the ROBINSCA study, with two types of labels: (a) region inside the pericardium and (b) pixel-wise EAT labels. We selected four advanced methods from the U-net family: 3D U-net, 3D attention U-net, an extended 3D attention U-net, and U-net++. For evaluation, we performed both four-fold cross-validation and hold-out tests. Agreement between the automatic segmentation/quantification and the manual quantification was evaluated with the Pearson correlation and the Bland–Altman analysis. Generally, the models trained with label type (a) showed better performance compared to models trained with label type (b). The U-net++ model trained with label type (a) showed the best performance for segmentation and quantification. The U-net++ model trained with label type (a) efficiently provided better EAT segmentation results (hold-out test: DCS = 80.18±0.20%, mIoU = 67.13±0.39%, sensitivity = 81.47±0.43%, specificity = 99.64±0.00%, Pearson correlation = 0.9405) and EAT volume compared to the other U-net-based networks and the recent EAT segmentation method. Interestingly, our findings indicate that 3D convolutional neural networks do not consistently outperform 2D networks in EAT segmentation and quantification. Moreover, utilizing labels representing the region inside the pericardium proved advantageous in training more accurate EAT segmentation models. These insights highlight the potential of deep learning-based methods for achieving robust EAT segmentation and quantification outcomes.
      Citation: Technologies
      PubDate: 2023-08-05
      DOI: 10.3390/technologies11040104
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 105: Electrochemical Detection of Furaltadone
           Antibiotic Drug by the Rare Earth Metal Tungstate Decorated Screen Printed
           Carbon Electrode

    • Authors: Sivaramakrishnan Vinothini, Te-Wei Chiu, Subramanian Sakthinathan
      First page: 105
      Abstract: Furaltadone (FLD) is an antibiotic drug that is widely treated for coccidiosis, intestinal infection, and turkey blackhead. Moreover, excessive use of FLD may have some negative consequences for humans and domestic animals. Therefore, practical, sensitive, selective, and facile detection of FLD is still needed. In this exploration, a Eu2(WO4)3-nanoparticles-modified screen-printed carbon electrode was developed for the low-level detection of FLD. Hydrothermal techniques were used effectively to prepare the Eu2(WO4)3 complex. Scanning electron microscopy and X-ray diffraction investigations were used to confirm the Eu2(WO4)3. The results revealed that the Eu2(WO4)3 was well formed, crystalline, and uniformly distributed. Furthermore, the electrochemical behavior of the SPCE/Eu2(WO4) electrode was examined by differential pulse voltammetry and cyclic voltammetry studies. The SPCE/Eu2(WO4) electrode demonstrated improved electrocatalytic activity in the detection of FLD with a detection limit of 97 µM (S/N = 3), linear range of 10 nM to 300 µM, and sensitivity of 2.1335 µA µM−1 cm−2. The SPCE/Eu2(WO4) electrode detected FLD in the presence of 500-fold excess concentrations of other interfering pollutant ions. The practical feasibility of the SPCE/Eu2(WO4) electrode was tested on different antibiotic medicines and showed adequate recovery. Moreover, the SPCE/Eu2(WO4) electrode shows appreciable repeatability, high stability, and reproducibility.
      Citation: Technologies
      PubDate: 2023-08-06
      DOI: 10.3390/technologies11040105
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 106: Biodegradable Polyhydroxyalkanoates
           Formed by 3- and 4-Hydroxybutyrate Monomers to Produce Nanomembranes
           Suitable for Drug Delivery and Cell Culture

    • Authors: Tatiana G. Volova, Aleksey V. Demidenko, Anastasiya V. Murueva, Alexey E. Dudaev, Ivan Nemtsev, Ekaterina I. Shishatskaya
      First page: 106
      Abstract: Biodegradable polyhydroxyalkanoates, biopolymers of microbiological origin, formed by 3- and 4-hydroxybutyrate monomers P(3HB-co-4HB), were used to obtain nanomembranes loaded with drugs as cell carriers by electrospinning. Resorbable non-woven membranes from P(3HB-co-4HB) loaded with ceftazidime, doripinem, and actovegin have been obtained. The loading of membranes with drugs differently affected the size of fibers and the structure of membranes, and in all cases increased the hydrophilicity of the surface. The release of drugs in vitro was gradual, which corresponded to the Higuchi and Korsmeyer-Peppas models. Antibiotic-loaded membranes showed antibacterial activity against S. aureus and E. coli, in which growth inhibition zones were 41.7 ± 1.1 and 38.6 ± 1.7 mm for ceftazidime and doripinem, respectively. The study of the biological activity of membranes in the NIH 3T3 mouse fibroblast culture based on the results of DAPI and FITC staining of cells, as well as the MTT test, did not reveal a negative effect despite the presence of antibiotics in them. Samples containing actovegin exhibit a stimulating effect on fibroblasts. Biodegradable polyhydroxyalkanoates formed by 3-hydroxybutyrate and 4-hydroxybutyrate monomers provide electrospinning non-woven membranes suitable for long-term delivery of drugs and cultivation of eukaryotic cells, and are promising for the treatment of wound defects complicated by infection.
      Citation: Technologies
      PubDate: 2023-08-07
      DOI: 10.3390/technologies11040106
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 107: Deep Learning Techniques for Web-Based
           Attack Detection in Industry 5.0: A Novel Approach

    • Authors: Abdu Salam, Faizan Ullah, Farhan Amin, Mohammad Abrar
      First page: 107
      Abstract: As the manufacturing industry advances towards Industry 5.0, which heavily integrates advanced technologies such as cyber-physical systems, artificial intelligence, and the Internet of Things (IoT), the potential for web-based attacks increases. Cybersecurity concerns remain a crucial challenge for Industry 5.0 environments, where cyber-attacks can cause devastating consequences, including production downtime, data breaches, and even physical harm. To address this challenge, this research proposes an innovative deep-learning methodology for detecting web-based attacks in Industry 5.0. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models are examples of deep learning techniques that are investigated in this study for their potential to effectively classify attacks and identify anomalous behavior. The proposed transformer-based system outperforms traditional machine learning methods and existing deep learning approaches in terms of accuracy, precision, and recall, demonstrating the effectiveness of deep learning for intrusion detection in Industry 5.0. The study’s findings showcased the superiority of the proposed transformer-based system, outperforming previous approaches in accuracy, precision, and recall. This highlights the significant contribution of deep learning in addressing cybersecurity challenges in Industry 5.0 environments. This study contributes to advancing cybersecurity in Industry 5.0, ensuring the protection of critical infrastructure and sensitive data.
      Citation: Technologies
      PubDate: 2023-08-08
      DOI: 10.3390/technologies11040107
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 108: A Novel Approach to Quantitative
           Characterization and Visualization of Color Fading

    • Authors: Woo Sik Yoo, Kitaek Kang, Jung Gon Kim, Yeongsik Yoo
      First page: 108
      Abstract: Color fading naturally occurs with time under light illumination. It is triggered by the high photon energy of light. The rate of color fading and darkening depends on the substance, lighting condition, and storage conditions. Color fading is only observed after some time has passed. The current color of objects of interest can only be compared with old photographs or the observer’s perception at the time of reference. Color fading and color darkening rates between two or more points in time in the past can only be determined using photographic images from the past. For objective characterization of color difference between two or more different times, quantification of color in either digital or printed photographs is required. A newly developed image analysis and comparison software (PicMan) has been used for color quantification and pixel-by-pixel color difference mapping in this study. Images of two copies of Japanese wood-block prints with and without color fading have been selected for the exemplary study of quantitative characterization of color fading and color darkening. The fading occurred during a long period of exposure to light. Pixel-by-pixel, line-by-line, and area-by-area comparisons of color fading and darkening between two images were very effective in quantifying color change and visualization of the phenomena. RGB, HSV, CIE L*a*b* values between images and their differences of a single pixel to areas of interest in any shape can be quantified. Color fading and darkening analysis results were presented in numerical, graphical, and image formats for completeness. All formats have their own advantages and disadvantages over the other formats in terms of data size, complexity, readability, and communication among parties of interest. This paper demonstrates various display options for color analysis, a summary of color fading, or color difference among images of interest for practical artistic, cultural heritage conservation, and museum applications. Color simulation for various moments in time was proposed and demonstrated by interpolation or extrapolation of color change between images, with and without color fading, using PicMan. The degree of color fading and color darkening over the various moments in time (past and future) can be simulated and visualized for decision-making in public display, storage, and restoration planning.
      Citation: Technologies
      PubDate: 2023-08-08
      DOI: 10.3390/technologies11040108
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 109: Fuzzy Logic System for Classifying
           Multiple Sclerosis Patients as High, Medium, or Low Responders to

    • Authors: Edgar Rafael Ponce de Leon-Sanchez, Jorge Domingo Mendiola-Santibañez, Omar Arturo Dominguez-Ramirez, Ana Marcela Herrera-Navarro, Alberto Vazquez-Cervantes, Hugo Jimenez-Hernandez, Horacio Senties-Madrid
      First page: 109
      Abstract: Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system, based on the opinion of a neurology expert, to classify relapsing–remitting multiple sclerosis patients as high, medium, or low responders to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated with interferon-beta responses is proposed, for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results showed that the fuzzy system presented 100% efficiency, compared to an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model was evaluated, and 0.8 testing accuracy was achieved. Hence, a pipeline model, including data standardization, data compression, and a learning algorithm, could be a useful tool for getting reliable predictions about responses to interferon-beta.
      Citation: Technologies
      PubDate: 2023-08-09
      DOI: 10.3390/technologies11040109
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 110: Challenges of Using the L-Band and
           S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems

    • Authors: Alexander Pastukh, Valery Tikhvinskiy, Svetlana Dymkova, Oleg Varlamov
      First page: 110
      Abstract: This article presents a comprehensive study of the potential utilization of the L-band and S-band frequency ranges for satellite non-terrestrial network (NTN) technologies. This study encompasses an interference analysis in the S-band, investigating the coexistence of NTN satellite systems with mobile satellite networks such as Omnispace and Lyra, and an interference analysis in the L-band between NTN satellites and the mobile satellite network Inmarsat. This study simulates an NTN satellite network with typical characteristics defined by 3GPP and ITU-R for the n255 and n256 bands. Furthermore, it provides calculations illustrating the signal-to-noise ratio degradation of low-Earth-orbit (LEO), medium-Earth-orbit (MEO), and geostationary-Earth-orbit (GEO) satellite networks operating in the L-band and S-band when exposed to interference from NTN satellites.
      Citation: Technologies
      PubDate: 2023-08-10
      DOI: 10.3390/technologies11040110
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 111: Image Denoising Using Hybrid Deep
           Learning Approach and Self-Improved Orca Predation Algorithm

    • Authors: Rusul Sabah Jebur, Mohd Hazli Bin Mohamed Zabil, Dalal Abdulmohsin Hammood, Lim Kok Cheng, Ali Al-Naji
      First page: 111
      Abstract: Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of-the-art denoising methods, including traditional algorithms and deep learning-based techniques, are conducted, focusing on denoising effectiveness, computational efficiency, and preservation of image details. The proposed approach demonstrates superior performance in all aspects, highlighting its potential as a promising solution for image-denoising tasks. Implemented in Python, the hybrid model showcases the benefits of combining Bi-LSTM, optimized CNN, and SI-OPA for advanced image-denoising applications.
      Citation: Technologies
      PubDate: 2023-08-12
      DOI: 10.3390/technologies11040111
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 112: Tendency on the Application of
           Drill-Down Analysis in Scientific Studies: A Systematic Review

    • Authors: Victor Hugo Silva-Blancas, José Manuel Álvarez-Alvarado, Ana Marcela Herrera-Navarro, Juvenal Rodríguez-Reséndiz
      First page: 112
      Abstract: With the fact that new server technologies are coming to market, it is necessary to update or create new methodologies for data analysis and exploitation. Applied methodologies go from decision tree categorization to artificial neural networks (ANN) usage, which implement artificial intelligence (AI) for decision making. One of the least used strategies is drill-down analysis (DD), belonging to the decision trees subcategory, which because of not having AI resources has lost interest among researchers. However, its easy implementation makes it a suitable tool for database processing systems. This research has developed a systematic review to understand the prospective of DD analysis on scientific literature in order to establish a knowledge platform and establish if it is convenient to drive it to integration with superior methodologies, as it would be those based on ANN, and produce a better diagnosis in future works. A total of 80 scientific articles were reviewed from 1997 to 2023, showing a high frequency in 2021 and experimental as the predominant methodology. From a total of 100 problems solved, 42% were using the experimental methodology, 34% descriptive, 17% comparative, and just 7% post facto. We detected 14 unsolved problems, from which 50% fall in the experimental area. At the same time, by study type, methodologies included correlation studies, processes, decision trees, plain queries, granularity, and labeling. It was observed that just one work focuses on mathematics, which reduces new knowledge production expectations. Additionally, just one work manifested ANN usage.
      Citation: Technologies
      PubDate: 2023-08-13
      DOI: 10.3390/technologies11040112
      Issue No: Vol. 11, No. 4 (2023)
  • Technologies, Vol. 11, Pages 63: Miniaturized Compact Reconfigurable
           Half-Mode SIW Phase Shifter with PIN Diodes

    • Authors: Franky Dakam Wappi, Bilel Mnasri, Alireza Ghayekhloo, Larbi Talbi, Halim Boutayeb
      First page: 63
      Abstract: In this work, a novel electrically reconfigurable phase shifter based on a half-mode substrate integrated waveguide (HM-SIW) is proposed. SIW is a guided transmission line topology, and by using half-mode excitation, a smaller size can be achieved. Phase shifters are electronic devices that change the phase of transmission for a wide range of applications, including inverse scattering and sensing. The tunability of PIN diodes is applied here to achieve a reconfigurable design. The proposed single-layer structure does not require extra wiring layers for the bias circuit on the suggested printed circuit board. Its principle consists in the integration, in the HM-SIW, of three parallel lines, each connecting the edge of the HM-SIW and linked to a PIN diode and a radial stub. Here we present the results of measurements for a frequency band from 4.5 to 7 GHz that demonstrate how the experiment agrees with simulations. Insertion loss was less than −10 dB, and port coupling was less than −2 dB for both simulation and measurement solutions. The proposed half-mode structure is around half the size of a typical SIW line. With the proposed design, the seven states of the PIN diodes can be validated (ON and OFF), with a wide band adaptation and a relatively constant phase difference across a broad frequency range (44%). A key benefit of the proposed design for a microwave component is the reduction of extra biasing layers for the PIN diodes. This is in addition to the reduced size of the transmission line compared to a commercial SIW. In the annexed section, simulation software is used for a more comprehensive analysis involving more phase shift values and parametric studies.
      Citation: Technologies
      PubDate: 2023-04-23
      DOI: 10.3390/technologies11030063
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 64: Towards Safe Visual Navigation of a
           Wheelchair Using Landmark Detection

    • Authors: Christos Sevastopoulos, Mohammad Zaki Zadeh, Michail Theofanidis, Sneh Acharya, Nishi Patel, Fillia Makedon
      First page: 64
      Abstract: This article presents a method for extracting high-level semantic information through successful landmark detection using 2D RGB images. In particular, the focus is placed on the presence of particular labels (open path, humans, staircase, doorways, obstacles) in the encountered scene, which can be a fundamental source of information enhancing scene understanding and paving the path towards the safe navigation of the mobile unit. Experiments are conducted using a manual wheelchair to gather image instances from four indoor academic environments consisting of multiple labels. Afterwards, the fine-tuning of a pretrained vision transformer (ViT) is conducted, and the performance is evaluated through an ablation study versus well-established state-of-the-art deep architectures for image classification such as ResNet. Results show that the fine-tuned ViT outperforms all other deep convolutional architectures while achieving satisfactory levels of generalization.
      Citation: Technologies
      PubDate: 2023-04-25
      DOI: 10.3390/technologies11030064
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 65: Digital Interaction with Physical Museum

    • Authors: Andreas Pattakos, Emmanouil Zidianakis, Michalis Sifakis, Michalis Roulios, Nikolaos Partarakis, Constantine Stephanidis
      First page: 65
      Abstract: In the digital information world, visualizing information in public spaces has been implemented in various formats and for application contexts such as advertisement, useful information provision, and provision of critical information in the cases of accidents, natural disasters, etc. Among the different types of information displays, in this research work, the focus is given to the ones that extend the experience of people visiting cultural heritage institutions. To this end, the design and implementation of an interactive display case that aims to overcome the “non-touch policy” of museums are presented. This novel display allows visitors to get engaged with artifacts and information through touch-based interaction with the ambition to extend the target audience and impact of museum content. The conducted study demonstrates that the interactive display case is an effective solution for providing relevant information to visitors, enhancing their engagement with exhibits, and improving their overall experience. The proposed solution is user-friendly, engaging, and informative, making it ideal for museums and other public exhibit spaces.
      Citation: Technologies
      PubDate: 2023-04-25
      DOI: 10.3390/technologies11030065
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 66: A Deeper Look into Exercise Intensity
           Tracking through Mobile Applications: A Brief Report

    • Authors: Alexie Elder, Gabriel Guillen, Rebecca Isip, Ruben Zepeda, Zakkoyya H. Lewis
      First page: 66
      Abstract: Mobile fitness applications (apps) allow for time-efficient opportunities for physical activity. Current research suggests that fitness apps do not accurately comply with the frequency, intensity, time, and type (FITT) principle. FITT is an important principle in exercise prescription as it applies scientific evidence to improve the quality of exercise. Based on app assessment using the Fitness Apps Scoring Instrument, most fitness apps adequately address FITT in their exercise plans. In particular, fitness apps do not adequately adhere to the FITT intensity guidelines. Many apps allow the users to track their heart rate as a method of assessing their exercise intensity, but few use that information to provide real-time feedback on the intensity of the workout. For app users, awareness and education of intensity standards should be put forth in coordination with exercise professionals, rather than relying on apps alone.
      Citation: Technologies
      PubDate: 2023-05-01
      DOI: 10.3390/technologies11030066
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 67: Identifying Growth Patterns in Arid-Zone
           Onion Crops (Allium Cepa) Using Digital Image Processing

    • Authors: David Duarte-Correa, Juvenal Rodríguez-Reséndiz, Germán Díaz-Flórez, Carlos Alberto Olvera-Olvera, José M. Álvarez-Alvarado
      First page: 67
      Abstract: The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on onion crops and the challenges presented throughout its phenological cycle. Unmanned aerial vehicles (UAVs) and digital image processing were used to monitor the crop and identify patterns such as humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 tons/ha. However, only 25.00 tons/ha were reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being medium-sized. Approximately 20% of the production was lost due to blight and unfavorable weather conditions.
      Citation: Technologies
      PubDate: 2023-05-10
      DOI: 10.3390/technologies11030067
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 68: Preprocessing Selection for Deep Learning
           Classification of Arrhythmia Using ECG Time-Frequency Representations

    • Authors: Rafael Holanda, Rodrigo Monteiro, Carmelo Bastos-Filho
      First page: 68
      Abstract: The trend of using deep learning techniques to classify arbitrary tasks has grown significantly in the last decade. Such techniques in the background provide a stack of non-linear functions to solve tasks that cannot be solved in a linear manner. Naturally, deep learning models can always solve almost any problem with the right amount of functional parameters. However, with the right set of preprocessing techniques, these models might become much more accessible by negating the need for a large set of model parameters and the concomitant computational costs that accompany the need for many parameters. This paper studies the effects of such preprocessing techniques, and is focused, more specifically, on the resulting learning representations, so as to classify the arrhythmia task provided by the ECG MIT-BIH signal dataset. The types of noise we filter out from such signals are the Baseline Wander (BW) and the Powerline Interference (PLI). The learning representations we use as input to a Convolutional Neural Network (CNN) model are the spectrograms extracted by the Short-time Fourier Transform (STFT) and the scalograms extracted by the Continuous Wavelet Transform (CWT). These features are extracted using different parameter values, such as the window size of the Fourier Transform and the number of scales from the mother wavelet. We highlight that the noise with the most significant influence on a CNN’s classification performance is the BW noise. The most accurate classification performance was achieved using the 64 wavelet scales scalogram with the Mexican Hat and with only the BW noise suppressed. The deployed CNN has less than 90k parameters and achieved an average F1-Score of 90.11%.
      Citation: Technologies
      PubDate: 2023-05-11
      DOI: 10.3390/technologies11030068
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 69: Corrosion Resistance of Steel S355MC in
           Crude Glycerol

    • Authors: Marián Palcut, Žaneta Gerhátová, Patrik Šulhánek, Peter Gogola
      First page: 69
      Abstract: Corrosion is the degradation of materials in oxidizing environments. In aqueous solutions, it is initiated by the surface reaction of the metallic material with the surrounding electrolyte. The corrosion rate of metals can be significantly reduced by the presence of organic compounds. Crude glycerol is an organic by-product of biodiesel, soap, and fatty acid production. It is produced in substantial amounts through transesterification. Crude glycerol contains several impurities and has low economic value. Its disposal in the environment is unwanted and potential applications need to be explored. In the present short communication, steel corrosion in crude glycerol has been investigated for the first time. The corrosion behavior of low-alloy structural steel S355MC in non-purified crude glycerol was studied by electrochemical methods. The results were compared with the use of tap water. The open-circuit potential (OCP) of S355MC in crude glycerol was more negative compared with that of tap water. The OCP was stable over time, indicating the rapid passivation of the steel substrate. The corrosion resistance was further studied by electrode polarization. On the polarization curve of S355MC in crude glycerol, a wide passivation region was found. Furthermore, the corrosion rate was 2.2 times smaller compared with that of tap water. The surface exposed to tap water was significantly degraded by red rust. The surface of S355MC after exposure to crude glycerol, on the other hand, was less affected by corrosion and covered with a protective layer. The results demonstrate a significant corrosion-inhibiting activity of crude glycerol that could be utilized in various technologies.
      Citation: Technologies
      PubDate: 2023-05-21
      DOI: 10.3390/technologies11030069
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 70: Utilization of Artificial Neural Networks
           for Precise Electrical Load Prediction

    • Authors: Christos Pavlatos, Evangelos Makris, Georgios Fotis, Vasiliki Vita, Valeri Mladenov
      First page: 70
      Abstract: In the energy-planning sector, the precise prediction of electrical load is a critical matter for the functional operation of power systems and the efficient management of markets. Numerous forecasting platforms have been proposed in the literature to tackle this issue. This paper introduces an effective framework, coded in Python, that can forecast future electrical load based on hourly or daily load inputs. The framework utilizes a recurrent neural network model, consisting of two simpleRNN layers and a dense layer, and adopts the Adam optimizer and tanh loss function during the training process. Depending on the size of the input dataset, the proposed system can handle both short-term and medium-term load-forecasting categories. The network was extensively tested using multiple datasets, and the results were found to be highly promising. All variations of the network were able to capture the underlying patterns and achieved a small test error in terms of root mean square error and mean absolute error. Notably, the proposed framework outperformed more complex neural networks, with a root mean square error of 0.033, indicating a high degree of accuracy in predicting future load, due to its ability to capture data patterns and trends.
      Citation: Technologies
      PubDate: 2023-05-26
      DOI: 10.3390/technologies11030070
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 71: Validation of Aerobic Capacity (VO2max)
           and Lactate Threshold in Wearable Technology for Athletic Populations

    • Authors: Bryson Carrier, Macy M. Helm, Kyle Cruz, Brenna Barrios, James W. Navalta
      First page: 71
      Abstract: As wearable technology (WT) has evolved, devices have developed the ability to track a range of physiological variables. These include maximal aerobic capacity (VO2max) and lactate threshold (LT). With WT quickly growing in popularity, independent evaluation of these devices is important to determine the appropriate use-cases for the devices. Therefore, the purpose of this study was to determine the validity of WT in producing estimates of VO2max and LT in athletic populations. METHODS: 21 participants completed laboratory LT and VO2max testing, as well as an outdoor testing session guided by the WT being tested (Garmin fēnix 6® watch and accompanying heart rate monitor). Statistical analysis was completed, using hypothesis testing (ANOVA, t-test), correlation analysis (Pearson’s r, Lin’s Concordance Correlation [CCC]), error analysis (mean absolute percentage error [MAPE]), equivalence testing (TOST test), and bias assessment (Bland–Altman analysis). RESULTS: The Garmin watch was found to have acceptable agreement for VO2max when compared to the 1 min averaged values (MAPE = 6.85%, CCC = 0.7) and for LT and the onset of blood lactate accumulation (OBLA), (MAPE = 7.52%, CCC = 0.79; MAPE = 8.20%, CCC = 0.74, respectively). Therefore, the Garmin fēnix 6® produces accurate measurements of VO2max and LT in athletic populations and can be used to make training decisions among athletes.
      Citation: Technologies
      PubDate: 2023-05-26
      DOI: 10.3390/technologies11030071
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 72: Jordan Canonical Form for Solving the
           Fault Diagnosis and Estimation Problems

    • Authors: Oleg Sergiyenko, Alexey Zhirabok, Paolo Mercorelli, Alexander Zuev, Vladimir Filaretov, Vera Tyrsa
      First page: 72
      Abstract: The suggested methods for solving fault diagnosis and estimation problems are based on the use of the Jordan canonical form. The diagnostic observer, virtual sensor, interval, and sliding mode observer design problems are considered. Algorithms have been developed to solve these problems for both linear and nonlinear systems, considering the presence of external disturbances and measurement noise. It has been shown that the Jordan canonical form allows reducing the dimensions of interval observers and virtual sensors, thus simplifying the design process in comparison to the identification canonical form. The theoretical results are illustrated through examples.
      Citation: Technologies
      PubDate: 2023-06-03
      DOI: 10.3390/technologies11030072
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 73: Cross-Tier Interference Mitigation for
           RIS-Assisted Heterogeneous Networks

    • Authors: Abdel Nasser Soumana Hamadou, Ciira wa Maina, Moussa Moindze Soidridine
      First page: 73
      Abstract: With the development of the next generation of mobile networks, new research challenges have emerged, and new technologies have been proposed to address them. On the other hand, reconfigurable intelligent surface (RIS) technology is being investigated for partially controlling wireless channels. RIS is a promising technology for improving signal quality by controlling the scattering of electromagnetic waves in a nearly passive manner. Heterogeneous networks (HetNets) are another promising technology that is designed to meet the capacity requirements of the network. RIS technology can be used to improve system performance in the context of HetNets. This study investigates the applications of reconfigurable intelligent surfaces (RISs) in heterogeneous downlink networks (HetNets). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUEs). In this paper, we utilise RIS to mitigate cross-tier interference in a HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider RIS-assisted heterogeneous networks consisting of multiple SBS nodes and MUEs that utilise both direct paths and reflected paths. Therefore, the aim of this study is to maximise the sum rate of all MUEs by jointly optimising the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficient-based optimisation (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts.
      Citation: Technologies
      PubDate: 2023-06-09
      DOI: 10.3390/technologies11030073
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 74: Radiation Dose Tracking in Computed
           Tomography Using Data Visualization

    • Authors: Reem Alotaibi, Felwa Abukhodair
      First page: 74
      Abstract: Radiation dose tracking is becoming very important due to the popularity of computerized tomography (CT) scans. One of the challenges of radiation dose tracking is that there are several variables that affect the dose from the patient side, machine side, and procedures side. Although some tracking software programs exists, they are based on static analysis and cause integration errors due to the heterogeneity of Hospital Information Systems (HISs) and prevent users from obtaining accurate answers to their questions. In this paper, a visual analytic approach is utilized to track radiation dose data from computed tomography (CT) through the use of Tableau data visualization software. The web solution is evaluated in real-life scenarios by domain experts. The results show that the visual analytics approach improves the tracking process, as users completed the tasks with a 100% success rate. The process increased user satisfaction and also provided invaluable insight into the analytical process.
      Citation: Technologies
      PubDate: 2023-06-10
      DOI: 10.3390/technologies11030074
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 75: An Efficient Smart Pharmaceutical
           Packaging Technology Framework to Assess the Quality of Returned
           Medication through Non-Intrusively Recording Storage Conditions after

    • Authors: James Gerrans, Parastou Donyai, Katherine Finlay, R. Simon Sherratt
      First page: 75
      Abstract: Medicine waste is a global issue, with economic, environmental, and social consequences that are only predicted to worsen. A structured review of the literature on medicine reuse revealed that there is a lack of technological applications addressing the key concerns raised by pharmaceutical stakeholders on the safety and feasibility of redispensing medication. A basis and guidelines for solutions aiming at enabling medicine reuse were devised by exploring a conceptual model of a Circular Pharmaceutical Supply Chain (CPSC), discussing concerns raised within the literature and identifying methods to influence the public and pharmaceutical companies. SPaRAS, a novel system to validate the storage conditions and streamline the assessment of returned medicines, is proposed. The Smart Packaging System (SPS) will record the storage conditions of medication while in patient care. The companion Returns Assessment System (RAS) will efficiently communicate with the SPS through RFID, configure the sensors within the SPS to the needs of its assigned medicine and assess the returns against tailored eligibility criteria. The increased safety and efficiency provided by SPaRAS addresses the concerns of large pharmaceutical companies and the public, offering a method to reuse previously owned medication and reduce the effects of unnecessary medicine waste.
      Citation: Technologies
      PubDate: 2023-06-10
      DOI: 10.3390/technologies11030075
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 76: Smartphone Security and Privacy: A Survey
           on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks,
           and Defenses

    • Authors: Zia Muhammad, Zahid Anwar, Abdul Rehman Javed, Bilal Saleem, Sidra Abbas, Thippa Reddy Gadekallu
      First page: 76
      Abstract: There is an exponential rise in the use of smartphones in government and private institutions due to business dependencies such as communication, virtual meetings, and access to global information. These smartphones are an attractive target for cybercriminals and are one of the leading causes of cyber espionage and sabotage. A large number of sophisticated malware attacks as well as advanced persistent threats (APTs) have been launched on smartphone users. These attacks are becoming significantly more complex, sophisticated, persistent, and undetected for extended periods. Traditionally, devices are targeted by exploiting a vulnerability in the operating system (OS) or device sensors. Nevertheless, there is a rise in APTs, side-channel attacks, sensor-based attacks, and attacks launched through the Google Play Store. Previous research contributions have lacked contemporary threats, and some have proven ineffective against the latest variants of the mobile operating system. In this paper, we conducted an extensive survey of papers over the last 15 years (2009–2023), covering vulnerabilities, contemporary threats, and corresponding defenses. The research highlights APTs, classifies malware variants, defines how sensors are exploited, visualizes multiple ways that side-channel attacks are launched, and provides a comprehensive list of malware families that spread through the Google Play Store. In addition, the research provides details on threat defense solutions, such as malware detection tools and techniques presented in the last decade. Finally, it highlights open issues and identifies the research gap that needs to be addressed to meet the challenges of next-generation smartphones.
      Citation: Technologies
      PubDate: 2023-06-12
      DOI: 10.3390/technologies11030076
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 77: Injectable Hydrated Calcium Phosphate
           Bone-like Paste: Synthesis, In Vitro, and In Vivo Biocompatibility

    • Authors: Anastasia Yu. Teterina, Vladislav V. Minaychev, Polina V. Smirnova, Margarita I. Kobiakova, Igor V. Smirnov, Roman S. Fadeev, Alexey A. Egorov, Artem A. Ashmarin, Kira V. Pyatina, Anatoliy S. Senotov, Irina S. Fadeeva, Vladimir S. Komlev
      First page: 77
      Abstract: The injectable hydrated calcium phosphate bone-like paste (hCPP) was developed with suitable rheological characteristics, enabling unhindered injection through standard 23G needles. In vitro assays showed the cytocompatibility of hCPP with mesenchymal embryonic C3H10T1/2 cell cultures. The hCPP was composed of aggregated micro-sized particles with sphere-like shapes and low crystallinity. The ability of hCPP particles to adsorb serum proteins (FBS) was investigated. The hCPP demonstrated high protein adsorption capacity, indicating its potential in various biomedical applications. The results of the in vivo assay upon subcutaneous injection in Wistar rats indicated nontoxicity and biocompatibility of experimental hCPP, as well as gradual resorption of hCPP, comparable to the period of bone regeneration. The data obtained are of great interest for the development of commercial highly effective osteoplastic materials for bone tissue regeneration and augmentation.
      Citation: Technologies
      PubDate: 2023-06-15
      DOI: 10.3390/technologies11030077
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 78: Two Fe-Zr-B-Cu Nanocrystalline Magnetic
           Alloys Produced by Mechanical Alloying Technique

    • Authors: Jason Daza, Wael Ben Mbarek, Lluisa Escoda, Joan Saurina, Joan-Josep Suñol
      First page: 78
      Abstract: Fe-rich soft magnetic alloys are candidates for applications as magnetic sensors and actuators. Spring magnets can be obtained when these alloys are added to hard magnetic compounds. In this work, two nanocrystalline Fe-Zr-B-Cu alloys are produced by mechanical alloying, MA. The increase in boron content favours the reduction of the crystalline size. Thermal analysis (by differential scanning calorimetry) shows that, in the temperature range compressed between 450 and 650 K, wide exothermic processes take place, which are associated with the relaxation of the tensions of the alloys produced by MA. At high temperatures, a main crystallisation peak is found. A Kissinger and an isoconversional method were used to determine the apparent activation of the exothermic processes. The values are compared with those found in the scientific literature. Likewise, adapted thermogravimetry allowed for the determination of the Curie temperature. The functional response has been analysed by hysteresis loop cycles. According to the composition, the decrease of the Fe/B ratio diminishes the soft magnetic behaviour.
      Citation: Technologies
      PubDate: 2023-06-16
      DOI: 10.3390/technologies11030078
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 79: Training Impulse and Its Impact on Load
           Management in Collegiate and Professional Soccer Players

    • Authors: Clinton Gardner, James W. Navalta, Bryson Carrier, Charli Aguilar, Jorge Perdomo Rodriguez
      First page: 79
      Abstract: Methods: Training impulse (TRIMP) is obtained through wearable technology and plays a direct role on the load management of soccer players. It is important to understand TRIMP to best prepare athletes for competition. A systematic search for articles was conducted using Google Scholar, with papers screened and extracted by five reviewers. The inclusion criteria were: the study was focused on collegiate or professional soccer, the use of training impulse (TRIMP), and the use of wearable technology to measure TRIMP. Of 10,100 papers, 10,090 articles were excluded through the systematic review process. Ten papers were selected for final review and grouped based on (1) training vs. match (N = 8/10), (2) preseason vs. in-season (N = 3/10), and (3) positional comparison (N = 3/10). Wearable technologies mainly track physical metrics (N = 10/10). Higher TRIMP data were noted in starters than reserves throughout the season in matches and slightly lower TRIMP for starters vs. reserves during training. TRIMP data change throughout the season, being higher in preseason phases compared to early-season, mid-season, and late-season phases. These findings help highlight the benefits of TRIMP in managing internal player load in soccer. Future research should focus on utilizing wearable-derived TRIMP and the impact on player performance metrics, and how TRIMP data vary across different positions in soccer.
      Citation: Technologies
      PubDate: 2023-06-17
      DOI: 10.3390/technologies11030079
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 80: Analysis of Redundancy Techniques for
           Electronics Design—Case Study of Digital Image Processing

    • Authors: Padmanabhan Balasubramanian
      First page: 80
      Abstract: Electronic circuits/systems operating in harsh environments such as space are likely to experience faults or failures due to the impact of high-energy radiation. Given this, to overcome any faults or failures, redundancy is usually employed as a hardening-by-design approach. Moreover, low power and a small silicon footprint are also important considerations for space electronics since these translate into better energy efficiency, less system weight, and less cost. Therefore, the fault-tolerant design of electronic circuits and systems should go hand in hand with the optimization of design metrics, especially for resource-constrained electronics such as those used in space systems. A single circuit or system (also called a simplex implementation) is not fault-tolerant as it may become a single point of failure and is not used for a space application. As an alternative, a triple modular redundancy (TMR) implementation, which uses three identical copies of a circuit or system and a voter to perform majority voting of the circuits and systems outputs, may be used. However, in comparison with a simplex implementation, a TMR implementation consumes about 200% more area and dissipates 200% more power when circuits or systems are triplicated. To mitigate the area and power overheads of a TMR implementation compared to a simplex implementation, researchers have suggested alternative redundancy approaches such as selective TMR (STMR) insertion, partially approximate TMR (PATMR), fully approximate TMR (FATMR), and majority voting-based reduced precision redundancy (VRPR). Among these, VRPR appears to be promising, especially for inherently error-tolerant applications such as digital image/video/audio processing, which is relevant to space systems. However, the alternative redundancy approaches mentioned are unlikely to be suitable for the implementation of control logic. In this work, we analyze various redundancy approaches and evaluate the performance of TMR and VRPR for a digital image processing application. We provide MATLAB-based image processing results corresponding to TMR and VRPR and physical implementation results of functional units based on TMR and VRPR using a 28-nm CMOS technology.
      Citation: Technologies
      PubDate: 2023-06-19
      DOI: 10.3390/technologies11030080
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 81: An Extensive Critique on Smart Grid
           Technologies: Recent Advancements, Key Challenges, and Future Directions

    • Authors: Sonam Dorji, Albert Alexander Stonier, Geno Peter, Ramya Kuppusamy, Yuvaraja Teekaraman
      First page: 81
      Abstract: Given the various aspects of climate change and the growing demand for energy, energy efficiency and environmental protection have become major concerns worldwide. If not taken care of, energy demand will become unmanageable due to technological growth in cities and nations. The solution to the global energy crisis could be an advanced two-way digital power flow system that is capable of self-healing, interoperability, and predicting conditions under various uncertainties and is equipped with cyber protections against malicious attacks. The smart grid enables the integration of renewable energy sources such as solar, wind, and energy storage into the grid. Therefore, the perception of the smart grid and the weight given to it by researchers and policymakers are of utmost importance. In this paper, the studies of many researchers on smart grids are examined in detail. Based on the literature review, various principles of smart grids, the development of smart grids, functionality of smart grids, technologies of smart grids with their characteristics, communication of smart grids, problems in the implementation of smart grids, and possible future studies proposed by various researchers have been presented.
      Citation: Technologies
      PubDate: 2023-06-19
      DOI: 10.3390/technologies11030081
      Issue No: Vol. 11, No. 3 (2023)
  • Technologies, Vol. 11, Pages 33: On the Sliding Mode Control Applied to a
           DC-DC Buck Converter

    • Authors: Sandra Huerta-Moro, Oscar Martínez-Fuentes, Victor Rodolfo Gonzalez-Diaz, Esteban Tlelo-Cuautle
      First page: 33
      Abstract: This work shows the voltage regulation of a DC–DC buck converter by applying sliding mode control using three different cases of sliding surfaces. The DC–DC buck converter is modeled by ordinary differential equations (ODEs) that are solved by applying numerical methods. The ODEs describe two state variables that are associated to the capacitor voltage and the inductor current. The state variable associated to voltage is regulated by applying two well-known sliding surfaces and a third one that is introduced herein to improve the response of the sliding mode control. The stability of the proposed sliding surface is verified by using a Lyapunov theorem to guarantee closed-loop stability. Finally, simulation results show the improvement of voltage regulation when applying the proposed sliding surface compared to already reported approaches.
      Citation: Technologies
      PubDate: 2023-02-23
      DOI: 10.3390/technologies11020033
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 34: Dual-Band Rectifier Circuit Design for
           IoT Communication in 5G Systems

    • Authors: Ioannis D. Bougas, Maria S. Papadopoulou, Achilles D. Boursianis, Spyridon Nikolaidis, Sotirios K. Goudos
      First page: 34
      Abstract: Radio-frequency (RF) energy harvesting (EH) is emerging as a reliable and constantly available free energy source. The primary factor determining whether this energy can be utilized is how efficiently it can be collected. In this work, an RF EH system is presented. More particularly, we designed a dual-band RF to DC rectifier circuit at sub-6 GHz in the 5G bands, able to supply low-power sensors and microcontrollers used in agriculture, the military, or health services. The system operates at 3.5 GHz and 5 GHz in the 5G cellular network’s frequency band FR1. Numerical results reveal that the system provides maximum power conversion efficiency (PCE) equal to 53% when the output load (sensor or microcontroller) is 1.74 kΩ and the input power is 12 dBm.
      Citation: Technologies
      PubDate: 2023-02-24
      DOI: 10.3390/technologies11020034
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 35: A Layer-Wise Coupled Thermo-Elastic Shell
           Model for Three-Dimensional Stress Analysis of Functionally Graded
           Material Structures

    • Authors: Salvatore Brischetto, Domenico Cesare, Roberto Torre
      First page: 35
      Abstract: In this work, a coupled 3D thermo-elastic shell model is presented. The primary variables are the scalar sovra-temperature and the displacement vector. This model allows for the thermal stress analysis of one-layered and sandwich plates and shells embedding Functionally Graded Material (FGM) layers. The 3D equilibrium equations and the 3D Fourier heat conduction equation for spherical shells are put together into a set of four coupled equations. They automatically degenerate in those for simpler geometries thanks to proper considerations about the radii of curvature and the use of orthogonal mixed curvilinear coordinates α, β, and z. The obtained partial differential governing the equations along the thickness direction are solved using the exponential matrix method. The closed form solution is possible assuming simply supported boundary conditions and proper harmonic forms for all the unknowns. The sovra-temperature amplitudes are directly imposed at the outer surfaces for each geometry in steady-state conditions. The effects of the thermal environment are related to the sovra-temperature profiles through the thickness. The static responses are evaluated in terms of displacements and stresses. After a proper and global preliminary validation, new cases are presented for different thickness ratios, geometries, and temperature values at the external surfaces. The considered FGM is metallic at the bottom and ceramic at the top. This FGM layer can be embedded in a sandwich configuration or in a one-layered configuration. This new fully coupled thermo-elastic model provides results that are coincident with the results proposed by the uncoupled thermo-elastic model that separately solves the 3D Fourier heat conduction equation. The differences are always less than 0.5% for each investigated displacement, temperature, and stress component. The differences between the present 3D full coupled model and the the advantages of this new model are clearly shown. Both the thickness layer and material layer effects are directly included in all the conducted coupled thermal stress analyses.
      Citation: Technologies
      PubDate: 2023-02-24
      DOI: 10.3390/technologies11020035
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 36: Reconstruction of Industrial and
           Historical Heritage for Cultural Enrichment Using Virtual and Augmented

    • Authors: Lukas Paulauskas, Andrius Paulauskas, Tomas Blažauskas, Robertas Damaševičius, Rytis Maskeliūnas
      First page: 36
      Abstract: Because of its benefits in providing an engaging and mobile environment, virtual reality (VR) has recently been rapidly adopted and integrated in education and professional training. Augmented reality (AR) is the integration of VR with the real world, where the real world provides context and the virtual world provides or reconstructs missing information. Mixed reality (MR) is the blending of virtual and physical reality environments allowing users to interact with both digital and physical objects at the same time. In recent years, technology for creating reality-based 3D models has advanced and spread across a diverse range of applications and research fields. The purpose of this paper is to design, develop, and test VR for kinaesthetic distance learning in a museum setting. A VR training program has been developed in which learners can select and perform pre-made scenarios in a virtual environment. The interaction in the program is based on kinaesthetic learning characteristics. Scenarios with VR controls simulate physical interaction with objects in a virtual environment for learners. Learners can grasp and lift objects to complete scenario tasks. There are also simulated devices in the virtual environment that learners can use to perform various actions. The study’s goal was to compare the effectiveness of the developed VR educational program to that of other types of educational material. Our innovation is the development of a system for combining their 3D visuals with rendering capable of providing a mobile VR experience for effective heritage enhancement.
      Citation: Technologies
      PubDate: 2023-02-25
      DOI: 10.3390/technologies11020036
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 37: The Use of Domain-Specific Languages for
           Visual Analytics: A Systematic Literature Review

    • Authors: Alireza Khakpour, Ricardo Colomo-Palacios, Antonio Martini, Mary Sánchez-Gordón
      First page: 37
      Abstract: Visual Analytics (VA) is a multidisciplinary field that requires various skills including but not limited to data analytics, visualizations, and the corresponding domain knowledge. Recently, many studies proposed creating and using Domain-Specific Languages (DSLs) for VA in order to abstract complexities and assist designers in developing better VAs for different data domains. However, development methods and types of DSLs vary for different applications and objectives. In this study, we conducted a systematic literature review to overview DSL methods and their intended applications for VA systems. Moreover, the review outlines the benefits and limitations of each of these methods. The aim is to provide decision support for both the research and development communities to choose the most compatible approach for their application. We think the communication of this research delivers a broad figure of previous relevant research and assists with the transfer and adaptation of the results to other domains.
      Citation: Technologies
      PubDate: 2023-03-02
      DOI: 10.3390/technologies11020037
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 38: Aging Mechanism and Models of
           Supercapacitors: A Review

    • Authors: Ma, Yang, Riaz, Wang, Wang
      First page: 38
      Abstract: Electrochemical supercapacitors are a promising type of energy storage device with broad application prospects. Developing an accurate model to reflect their actual working characteristics is of great research significance for rational utilization, performance optimization, and system simulation of supercapacitors. This paper presents the fundamental working principle and applications of supercapacitors, analyzes their aging mechanism, summarizes existing supercapacitor models, and evaluates the characteristics and application scope of each model. By examining the current state and limitations of supercapacitor modeling research, this paper identifies future development trends and research focuses in this area.
      Citation: Technologies
      PubDate: 2023-03-03
      DOI: 10.3390/technologies11020038
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 39: Non-Contact In-Vehicle Occupant
           Monitoring System Based on Point Clouds from FMCW Radar

    • Authors: Yixuan Chen, Yunlong Luo, Jianhua Ma, Alex Qi, Runhe Huang, Francesco De Paulis, Yihong Qi
      First page: 39
      Abstract: In order to reduce the probability of automobile safety incidents, the in-vehicle occupant monitoring is indispensable. However, occupant monitoring using frequency-modulated continuous wave (FMCW) radar can be challenging due to the interference from passengers’ posture, movement, and the presence of multiple people. This paper proposes an improved method for generating point clouds using FMCW radar. The approach involves point cloud clustering, post-processing operations such as segmentation, merging, and filtering of the clustered point cloud to match the actual in-vehicle environment, and a state machine combination step. Experimental results show that the proposed method can achieve high recognition accuracy in scenarios with multiple passengers who are moving and sitting in a relaxed manner.
      Citation: Technologies
      PubDate: 2023-03-13
      DOI: 10.3390/technologies11020039
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 40: A Review of Deep Transfer Learning and
           Recent Advancements

    • Authors: Mohammadreza Iman, Hamid Reza Arabnia, Khaled Rasheed
      First page: 40
      Abstract: Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two significant constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such reliance and costs by reusing obtained knowledge from a source data/task in training on a target data/task. Most applied DTL techniques are network/model-based approaches. These methods reduce the dependency of deep learning models on extensive training data and drastically decrease training costs. Moreover, the training cost reduction makes DTL viable on edge devices with limited resources. Like any new advancement, DTL methods have their own limitations, and a successful transfer depends on specific adjustments and strategies for different scenarios. This paper reviews the concept, definition, and taxonomy of deep transfer learning and well-known methods. It investigates the DTL approaches by reviewing applied DTL techniques in the past five years and a couple of experimental analyses of DTLs to discover the best practice for using DTL in different scenarios. Moreover, the limitations of DTLs (catastrophic forgetting dilemma and overly biased pre-trained models) are discussed, along with possible solutions and research trends.
      Citation: Technologies
      PubDate: 2023-03-14
      DOI: 10.3390/technologies11020040
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 41: Comparative Effect of the Type of a
           Pulsed Discharge on the Ionic Speciation of Plasma-Activated Water

    • Authors: Victor Panarin, Eduard Sosnin, Andrey Ryabov, Victor Skakun, Sergey Kudryashov, Dmitry Sorokin
      First page: 41
      Abstract: The comparison of ion concentrations, pH index, and conductivity in distilled and ground water after exposure to low-temperature plasma formed by barrier and bubble discharges is performed. It has been found that in the case of groundwater, the best performance for the production of NO3− anions is provided by the discharge inside the gas bubbles. For distilled water, the barrier discharge in air, followed by saturation of water with plasma products, is the most suitable from this point of view. In both treatments, the maximum energy input into the stock solution is ensured. After 10 min treatment of ground water, the pH index increases and then it decreases. The obtained numerical indicators make it possible to understand in which tasks the indicated treatment modes should be used, their comparative advantages, and disadvantages. From the point of view of energy consumption for obtaining approximately equal (in order of magnitude) amounts of NO3− anions, both types of discharge treatment are suitable. The research results point to a fairly simple way to convert salts (calcium carbonates) from an insoluble form to soluble one. Namely, when interacting with NO3− anions, insoluble carbonates pass into soluble nitrates.
      Citation: Technologies
      PubDate: 2023-03-14
      DOI: 10.3390/technologies11020041
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 42: Developments and Applications of
           Artificial Intelligence in Music Education

    • Authors: Xiaofei Yu, Ning Ma, Lei Zheng, Licheng Wang, Kai Wang
      First page: 42
      Abstract: With the continuous developments of information technology, advanced computer technology and information technology have been promoted and used in the field of music. As one of the products of the rapid development of information technology, Artificial Intelligence (AI) involves many interdisciplinary subjects, adding new elements to music education. By analyzing the advantages of AI in music education, this paper systematically summarizes the application of AI in music education and discusses the development prospects of AI in music education. With the aid of AI, the combination of intelligent technology and on-site teaching solves the lack of individuation in the traditional mode and enhances students’ interest in learning.
      Citation: Technologies
      PubDate: 2023-03-16
      DOI: 10.3390/technologies11020042
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 43: Matching Assistive Technology,
           Telerehabilitation, and Virtual Reality to Promote Cognitive
           Rehabilitation and Communication Skills in Neurological Populations: A
           Perspective Proposal

    • Authors: Fabrizio Stasolla, Antonella Lopez, Khalida Akbar, Leonarda Anna Vinci, Maria Cusano
      First page: 43
      Abstract: Neurological populations (NP) commonly experience several impairments. Beside motor and sensorial delays, communication and intellectual disabilities are included. The COVID-19 pandemic has suddenly exacerbated their clinical conditions due to lockdown, quarantine, and social distancing preventive measures. Healthcare services unavailability has negatively impacted NP clinical conditions, partially mitigated by vaccine diffusion. One way to overcome this issue is the use of technology-aided interventions for both assessment and rehabilitative purposes. Assistive technology-based interventions, telerehabilitation, and virtual reality setups have been widely adopted to help individuals with neurological damages or injuries. Nevertheless, to the best of our knowledge, their matching (i.e., combination or integration) has rarely been investigated. The main objectives of the current position paper were (a) to provide the reader with a perspective proposal on the matching of the three aforementioned technological solutions, (b) to outline a concise background on the use of technology-aided solutions, (c) to argue on the effectiveness and the suitability of technology-mediated programs, and (d) to postulate an integrative proposal to support cognitive rehabilitation including assistive technology, telerehabilitation, and virtual reality. Practical implications for both research and practice are critically discussed.
      Citation: Technologies
      PubDate: 2023-03-16
      DOI: 10.3390/technologies11020043
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 44: How to Bell the Cat' A Theoretical Review
           of Generative Artificial Intelligence towards Digital Disruption in All
           Walks of Life

    • Authors: Subhra Mondal, Subhankar Das, Vasiliki G. Vrana
      First page: 44
      Abstract: Generative Artificial Intelligence (GAI) has brought revolutionary changes to the world, enabling businesses to create new experiences by combining virtual and physical worlds. As the use of GAI grows along with the Metaverse, it is explored by academics, researchers, and industry communities for its endless possibilities. From ChatGPT by OpenAI to Bard AI by Google, GAI is a leading technology in physical and virtual business platforms. This paper focuses on GAI’s economic and societal impact and the challenges it poses. Businesses must rethink their operations and strategies to create hybrid physical and virtual experiences using GAI. This study proposes a framework that can help business managers develop effective strategies to enhance their operations. It analyzes the initial applications of GAI in multiple sectors to promote the development of future customer solutions and explores how GAI can help businesses create new value propositions and experiences for their customers, and the possibilities of digital communication and information technology. A research agenda is proposed for developing GAI for business management to enhance organizational efficiency. The results highlight a healthy conversation on the potential of GAI in various business sectors to improve customer experience.
      Citation: Technologies
      PubDate: 2023-03-17
      DOI: 10.3390/technologies11020044
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 45: A Gas Leakage Detection Device Based on
           the Technology of TinyML †

    • Authors: Vasileios Tsoukas, Anargyros Gkogkidis, Eleni Boumpa, Stefanos Papafotikas, Athanasios Kakarountas
      First page: 45
      Abstract: Internet of Things devices are frequently used as consumer devices to provide digital solutions, such as smart lighting and digital voice-activated assistants, but they are also employed to alert residents in the instance of an emergency. Given the increasingly costly nature of present neural network systems, it is necessary to transport information to the cloud for intelligent machine analysis. TinyML is a potential technology that has been presented by the research world for building fully independent and safe devices that can gather, analyze, and produce data, without transferring it to distant organizations. This paper describes a gas leakage detection system based on TinyML. The proposed solution can be programmed to identify anomalies and warn occupants via the utilization of the BLE technology, in addition to an incorporated LCD screen. Experiments have been employed to show and assess two distinct test situations. For the first occasion, the smoke detection test case, the system earned an F1-Score of 0.77, whereas the F1-Score for the ammonia test case was 0.70.
      Citation: Technologies
      PubDate: 2023-03-22
      DOI: 10.3390/technologies11020045
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 46: GDAL and PROJ Libraries Integrated with
           GRASS GIS for Terrain Modelling of the Georeferenced Raster Image

    • Authors: Polina Lemenkova, Olivier Debeir
      First page: 46
      Abstract: Libraries with pre-written codes optimize the workflow in cartography and reduce labour intensive data processing by iteratively applying scripts to implementing mapping tasks. Most existing Geographic Information System (GIS) approaches are based on traditional software with a graphical user’s interface which significantly limits their performance. Although plugins are proposed to improve the functionality of many GIS programs, they are usually ad hoc in finding specific mapping solutions, e.g., cartographic projections and data conversion. We address this limitation by applying the principled approach of Geospatial Data Abstraction Library (GDAL), library for conversions between cartographic projections (PROJ) and Geographic Resources Analysis Support System (GRASS) GIS for geospatial data processing and morphometric analysis. This research presents topographic analysis of the dataset using scripting methods which include several tools: (1) GDAL, a translator library for raster and vector geospatial data formats used for converting Earth Global Relief Model (ETOPO1) GeoTIFF in XY Cartesian coordinates into World Geodetic System 1984 (WGS84) by the ‘gdalwarp’ utility; (2) PROJ projection transformation library used for converting ETOPO1 WGS84 grid to cartographic projections (Cassini–Soldner equirectangular, Equal Area Cylindrical, Two-Point Equidistant Azimuthal, and Oblique Mercator); and (3) GRASS GIS by sequential use of the following modules: r.info, d.mon, d.rast, r.colors, d.rast.leg, d.legend, d.northarrow, d.grid, d.text, g.region, and r.contour. The depth frequency was analysed by the module ‘d.histogram’. The proposed approach provided a systematic way for morphometric measuring of topographic data and combine the advantages of the GDAL, PROJ, and GRASS GIS tools that include the informativeness, effectiveness, and representativeness in spatial data processing. The morphometric analysis included the computed slope, aspect, profile, and tangential curvature of the study area. The data analysis revealed the distribution pattern in topographic data: 24% of data with elevations below 400 m, 13% of data with depths −5000 to −6000 m, 4% of depths have values −3000 to −4000 m, the least frequent data (−6000 to 7000 m) <1%, 2% of depths have values −2000 to 3000 m in the basin, while other values are distributed proportionally. Further, by incorporating the generic coordinate transformation software library PROJ, the raster grid was transformed into various cartographic projections to demonstrate distortions in shape and area. Scripting techniques of GRASS GIS are demonstrated for applications in topographic modelling and raster data processing. The GRASS GIS shows the effectiveness for mapping and visualization, compatibility with libraries (GDAL, PROJ), technical flexibility in combining Graphical User Interface (GUI), and command-line data processing. The research contributes to the technical cartographic development.
      Citation: Technologies
      PubDate: 2023-03-22
      DOI: 10.3390/technologies11020046
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 47: Mobilenetv2_CA Lightweight Object
           Detection Network in Autonomous Driving

    • Authors: Peicheng Shi, Long Li, Heng Qi, Aixi Yang
      First page: 47
      Abstract: A lightweight network target detection algorithm was proposed, based on MobileNetv2_CA, focusing on the problem of high complexity, a large number of parameters, and the missed detection of small targets in the target detection network based on candidate regions and regression methods in autonomous driving scenarios. First, Mosaic image enhancement technology is used in the data pre-processing stage to enhance the feature extraction of small target scenes and complex scenes; second, the Coordinate Attention (CA) mechanism is embedded into the Mobilenetv2 backbone feature extraction network, combined with the PANet and Yolo detection heads for multi-scale feature fusion; finally, a Lightweight Object Detection Network is built. The experimental test results show that the designed network obtained the highest average detection accuracy of 81.43% on the Voc2007 + 2012 dataset, and obtained the highest average detection accuracy of 85.07% and a detection speed of 31.84 FPS on the KITTI dataset. The total amount of network parameters is only 39.5 M. This is beneficial to the engineering application of MobileNetv2 network in automatic driving.
      Citation: Technologies
      PubDate: 2023-03-23
      DOI: 10.3390/technologies11020047
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 48: PHB/PEG Nanofiber Mat Obtained by
           Electrospinning and Their Performances

    • Authors: Nguyen Hong Thanh, Roman Olekhnovich, Vera Sitnikova, Arina Kremleva, Petr Snetkov, Mayya Uspenskaya
      First page: 48
      Abstract: In this work, a nanofiber mat based on PHB/PEG with various PEG contents was obtained by electrospinning process. The thermal and mechanical properties of the PHB/PEG nanofiber mat were investigated. In addition, PHB/PEG nanofiber mats were characterized by Fourier transforms infrared spectroscopy (FTIR), differential scanning calorimetry, thermogravimetric analysis, X-ray diffraction, and water contact angle measurement. It was shown that, by increasing the PEG contents from 1 to 4%, the average diameter of PHB nanofibers decreased from 1177 nm to 1101 nm, corresponding to 2% PEG, then the diameter of the fiber increased again from 1101 nm to 1136 nm, corresponding to 4% PEG. Tensile strength increased from 3.6 MPa to 4.4 MPa, then decreased from 4.4 MPa to 2.9 MPa. Thermogravimetric analysis showed a difference in the process of thermal degradation of nanofiber mats. The degree of crystallinity measured by XRD and DSC methods gives different values at some points. The results demonstrated that adding PEG improved the mechanical properties, hydrophobicity, porosity, and thermal stability of the PHB fiber mat, which showed that the PHB/PEG nanofiber mat has great potential for air filtration or water filtration.
      Citation: Technologies
      PubDate: 2023-03-24
      DOI: 10.3390/technologies11020048
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 49: Image-Based Quantification of Color and
           Its Machine Vision and Offline Applications

    • Authors: Woo Sik Yoo, Kitaek Kang, Jung Gon Kim, Yeongsik Yoo
      First page: 49
      Abstract: Image-based colorimetry has been gaining relevance due to the wide availability of smart phones with image sensors and increasing computational power. The low cost and portable designs with user-friendly interfaces, and their compatibility with data acquisition and processing, are very attractive for interdisciplinary applications from art, the fashion industry, food science, medical science, oriental medicine, agriculture, geology, chemistry, biology, material science, environmental engineering, and many other applications. This work describes the image-based quantification of color and its machine vision and offline applications in interdisciplinary fields using specifically developed image analysis software. Examples of color information extraction from a single pixel to predetermined sizes/shapes of areas, including customized regions of interest (ROIs) from various digital images of dyed T-shirts, tongues, and assays, are demonstrated. Corresponding RGB, HSV, CIELAB, Munsell color, and hexadecimal color codes, from a single pixel to ROIs, are extracted for machine vision and offline applications in various fields. Histograms and statistical analyses of colors from a single pixel to ROIs are successfully demonstrated. Reliable image-based quantification of color, in a wide range of potential applications, is proposed and the validity is verified using color quantification examples in various fields of applications. The objectivity of color-based diagnosis, judgment and control can be significantly improved by the image-based quantification of color proposed in this study.
      Citation: Technologies
      PubDate: 2023-03-29
      DOI: 10.3390/technologies11020049
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 50: Forecasting by Combining Chaotic PSO and
           Automated LSSVR

    • Authors: Wei-Chang Yeh, Wenbo Zhu
      First page: 50
      Abstract: An automatic least square support vector regression (LSSVR) optimization method that uses mixed kernel chaotic particle swarm optimization (CPSO) to handle regression issues has been provided. The LSSVR model is composed of three components. The position of the particles (solution) in a chaotic sequence with good randomness and ergodicity of the initial characteristics is taken into consideration in the first section. The binary particle swarm optimization (PSO) used to choose potential input characteristic combinations makes up the second section. The final step involves using a chaotic search to narrow down the set of potential input characteristics before combining the PSO-optimized parameters to create CP-LSSVR. The CP-LSSVR is used to forecast the impressive datasets testing targets obtained from the UCI dataset for purposes of illustration and evaluation. The results suggest CP-LSSVR has a good predictive capability discussed in this paper and can build a projected model utilizing a limited number of characteristics.
      Citation: Technologies
      PubDate: 2023-03-30
      DOI: 10.3390/technologies11020050
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 51: HAIS: Highways Automated-Inspection

    • Authors: Hossam A. Gabbar, Abderrazak Chahid, Manir U. Isham, Shashwat Grover, Karan Pal Singh, Khalid Elgazzar, Ahmad Mousa, Hossameldin Ouda
      First page: 51
      Abstract: A smart city is a trending concept describing a new generation of cities operated intelligently with minimal human interaction. It promotes energy sustainability, minimal environmental impact, and better governance. In transportation, the highway infrastructure will enhance the driver’s safety by remotely monitoring traffic, road conditions, and potential hazardous incidents, such as accidents, floods, or snow storms. In addition, it facilitates the integration of future cutting-edge technologies, such as self-driving vehicles. This paper presents a general introduction to a smart monitoring system for automated real-time road condition inspection. The proposed solution includes hardware devices/nodes and software applications for data processing: road condition inspection using hybrid algorithms based on digital signal processing and artificial intelligence technologies. The proposed system has an interactive web interface for real-time data sharing and the monitoring, visualization, and management of inspection reports which can improve the maintenance process assistance.
      Citation: Technologies
      PubDate: 2023-04-01
      DOI: 10.3390/technologies11020051
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 52: Anisotropy Analysis of the Permeation
           Behavior in Carbon Dioxide-Assisted Polymer Compression Porous Products

    • Authors: Takafumi Aizawa
      First page: 52
      Abstract: The carbon dioxide-assisted polymer compression method is used to create porous polymer products with laminated fiber sheets that are crimped in the presence of carbon dioxide. In this method, fibers are oriented in the sheet-spread direction, and the intersections of the upper and lower fibers are crimped, leading to several intersections within the porous product. This type of orientation in a porous material is anisotropic. A dye solution was injected via a syringe into a compression product made of poly(ethylene terephthalate) nonwoven fabric with an average fiber diameter of 8 μm. The anisotropy of permeation was evaluated using the aspect ratio of the vertical and horizontal permeation distances of a permeation area. The aspect ratio decreased monotonically with decreasing porosity; it was 2.73 for the 80-ply laminated product with a porosity of 0.63 and 2.33 for the 160-ply laminated product with a porosity of 0.25. A three-dimensional structural analysis using X-ray computed tomography revealed that as the compression ratio increased, the fiber-to-fiber connection increased due to the increase in adhesion points, resulting in decreased anisotropy of permeation. The anisotropy of permeation is essential data for analyzing the sustained release behavior of drug-loaded tablets for future fabrication.
      Citation: Technologies
      PubDate: 2023-04-03
      DOI: 10.3390/technologies11020052
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 53: Visual Performance and
           Perceptual–Motor Skills of Late Preterm Children and Healthy
           Controls Using the TVPS-3rd and VMI-6th Editions

    • Authors: Danjela Ibrahimi, Jorge D. Mendiola Santibañez, Juvenal Rodríguez-Reséndiz
      First page: 53
      Abstract: Background: The visual system is key to the learning process, preterm births are commonly followed by visual dysfunctions and other neurological conditions. Objective: to measure, analyze and compare the visual efficacy, visual–perceptual, and visual–motor skills of 20 late preterm children (34–36 weeks) born by caesarean section and appropriate weight for gestational age with 20 healthy controls born at full term by natural birth, age 5 to 12 years, from Querétaro, México. Methods: This was an observational, transverse, and prospective study. Parametric and non-parametric tests were performed using the SPSS 25.0. The visual acuity at distance and near, the phoria state, and the degree of stereopsis were analyzed. The Test of Visual-Perceptual Skills, Third Edition, was used to assess the overall performance, basic, sequencing, and complex processes. Fine motor skills were evaluated using the Visual–Motor Integration Test of Beery, Sixth Edition. Results: Visual acuity at distance and near (p<0.001), stereopsis (p<0.001), and the amount of exophoria at distance (p=0.01) showed statistically significant differences between the groups. The overall performance (p=0.006), basic processes (p=0.001), sequencing processes (p=0.02), and General and Motor VMI (p<0.001 and 0.002, respectively) presented lower values in children born preterm. Conclusion: This research showed that even late preterm children present visual deficiencies and are at risk of delays on perceptual–motor skills. Early evaluation of their visual and motor abilities should be considered in order to help improve their cognitive functioning.
      Citation: Technologies
      PubDate: 2023-04-04
      DOI: 10.3390/technologies11020053
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 54: Medical Imaging and Image Processing

    • Authors: Yudong Zhang, Zhengchao Dong
      First page: 54
      Abstract: Medical imaging (MI) [...]
      Citation: Technologies
      PubDate: 2023-04-05
      DOI: 10.3390/technologies11020054
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 55: A Novel Methodology for Human Kinematics
           Motion Detection Based on Smartphones Sensor Data Using Artificial

    • Authors: Ali Raza, Mohammad Rustom Al Nasar, Essam Said Hanandeh, Raed Abu Zitar, Ahmad Yacoub Nasereddin, Laith Abualigah
      First page: 55
      Abstract: Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.
      Citation: Technologies
      PubDate: 2023-04-11
      DOI: 10.3390/technologies11020055
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 56: Examination of Polymer Blends by AFM
           Phase Images

    • Authors: Enrico Werner, Uwe Güth, Bennet Brockhagen, Christoph Döpke, Andrea Ehrmann
      First page: 56
      Abstract: Atomic force microscopy (AFM) belongs to the high-resolution surface morphology investigation methods. Since it can, in many cases, be applied in air, samples can more easily be inspected than by a scanning electron microscope (SEM). In addition, several special modes exist which enable examination of the mechanical and other physical parameters of the specimen, such as friction, adhesion between tip and sample, elastic modulus, etc. In tapping mode, e.g., phase imaging can be used to qualitatively distinguish between different materials on the surface. This is especially interesting for polymers, for which the evaluation by energy-dispersive X-ray spectroscopy (EDS) is mostly irrelevant. Here we give an overview of phase imaging experiments on different filaments used for 3D printing by fused deposition modeling (FDM). Furthermore, the acrylonitrile butadiene styrene (ABS), especially different poly(lactide acids) (PLAs) with special features, such as thermochromic or photochromic properties, are investigated and compared with SEM images.
      Citation: Technologies
      PubDate: 2023-04-12
      DOI: 10.3390/technologies11020056
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 57: Possibilities of Using Bluetooth Low
           Energy Beacon Technology to Locate Objects Internally: A Case Study

    • Authors: Jan Ližbetin, Jan Pečman
      First page: 57
      Abstract: The developments that are occurring in relation to Industry 4.0 are making it possible to automate a huge number of production activities. Automation includes the possibility of automatically identifying individual elements of a system. One of the options for doing this involves the use of Bluetooth Low Energy technology. The system’s advantages lie in its wide availability, economic simplicity, ability to design individual system elements, and overall system architecture. The system applied in the case study presented in this article consisted of beacons from Accent Systems and identification gateways based on the Raspberry Pi Zero W device. During several hours of testing, the functionality and reliability of all system components was demonstrated. The measurements showed that the system was able to determine the distance from a gate in line of sight with 94% accuracy. With regards to indirect visibility, when a metal crate was used to shield the beacon from the gateway, the system was able to determine the exact distance only 22% of the time. However, the variance between the actual and measured values was found to be small, therefore proving sufficient for most use cases. The major advantage of Bluetooth Low Energy beacons, and Bluetooth technology in general, is its massive ubiquity in the market. Since the Bluetooth module is part of every smartphone, this system can be made available to a wide range of users.
      Citation: Technologies
      PubDate: 2023-04-12
      DOI: 10.3390/technologies11020057
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 58: Computational Investigation of a Tibial
           Implant Using Topology Optimization and Finite Element Analysis

    • Authors: Nikolaos Kladovasilakis, Theologos Bountourelis, Konstantinos Tsongas, Dimitrios Tzetzis
      First page: 58
      Abstract: Additive manufacturing methods enable the rapid fabrication of fully functional customized objects with complex geometry and lift the limitations of traditional manufacturing techniques, such as machining. Therefore, the structural optimization of parts has concentrated increased scientific interest and more especially for topology optimization (TO) processes. In this paper, the working principles and the two approaches of the TO procedures were analyzed along with an investigation and a comparative study of a novel case study for the TO processes of a tibial implant designed for additive manufacturing (DfAM). In detail, the case study focused on the TO of a tibial implant for knee replacement surgery in order to improve the overall design and enhance its efficiency and the rehabilitation process. An initial design of a customized tibial implant was developed utilizing reserve engineering procedures with DICOM files from a CT scan machine. The mechanical performance of the designed implant was examined via finite element analyses (FEA) under realistic static loads. The TO was conducted with two distinct approaches, namely density-based and discrete-based, to compare them and lead to the best approach for biomechanical applications. The overall performance of each approach was evaluated through FEA, and its contribution to the final mass reduction was measured. Through this study, the maximum reduction in the implant’s mass was achieved by maintaining the mechanical performance at the desired levels and the best approach was pointed out. To conclude, with the discrete-based approach, a mass reduction of around 45% was achieved, almost double of the density-based approach, offering on the part physical properties which provide comprehensive advantages for biomechanical application.
      Citation: Technologies
      PubDate: 2023-04-13
      DOI: 10.3390/technologies11020058
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 59: Photovoltaic Inverter Reliability Study
           through SiC Switches Redundant Structures

    • Authors: Ignacio Villanueva, Nimrod Vázquez, Joaquín Vaquero, Claudia Hernández, Héctor López-Tapia, Rene Osorio-Sánchez
      First page: 59
      Abstract: Reliability is a very important issue in power electronics; however, sometimes it is not considered, studied, or analyzed. At present, renewables have become more popular, and more complex setups are required to drive this type of system. In the specific case of inverters in photovoltaic systems, the user’s safety, quality, reliability, and the system’s useful life must be guaranteed. In this paper, the reliability of a full bridge inverter is predicted by calculating metrics such as failure rates and Mean Time Between Failures. Reliability is obtained using different types of structures for SiC MOSFETs: serial systems, active parallel redundant systems, and passive parallel redundant systems. Finally, the reliability study shows that a system with a passive parallel redundant structure is more reliable and has a higher useful life compared to the other structures.
      Citation: Technologies
      PubDate: 2023-04-14
      DOI: 10.3390/technologies11020059
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 60: Research on Outdoor Mobile Music Speaker
           Battery Management Algorithm Based on Dynamic Redundancy

    • Authors: Xiaofei Yu, Yanke Li, Xiaonan Li, Licheng Wang, Kai Wang
      First page: 60
      Abstract: In terms of the battery management system of a mobile music speaker, reliability optimization has always been an important topic. This paper proposes a new dynamic redundant battery management algorithm based on the existing fault-tolerant structure of a lithium battery pack. The internal configuration is adjusted according to the SOC of each battery, and the power supply battery is dynamically allocated. This paper selects four batteries to experiment on with two different algorithms. The simulation results show that compared with the traditional battery management algorithm, the dynamic redundant battery management algorithm extends the battery pack working time by 18.75%, and the energy utilization rate of B1 and B4 increases by 96.0% and 99.8%, respectively. This proves that the dynamic redundant battery management algorithm can effectively extend battery working time and improve energy utilization.
      Citation: Technologies
      PubDate: 2023-04-18
      DOI: 10.3390/technologies11020060
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 61: An Improved Photovoltaic Module Array
           Global Maximum Power Tracker Combining a Genetic Algorithm and Ant Colony

    • Authors: Kuo-Hua Huang, Kuei-Hsiang Chao, Ting-Wei Lee
      First page: 61
      Abstract: In this paper, a hybrid optimization controller that combines a genetic algorithm (GA) and ant colony optimization (ACO) called GA-ACO algorithm is proposed. It is applied to a photovoltaic module array (PVMA) to carry out maximum power point tracking (MPPT). This way, under the condition that the PVMA is partially shaded and that multiple peaks are produced in the power-voltage (P-V) characteristic curve, the system can still operate at the global maximum power point (GMPP). This solves the problem seen in general traditional MPPT controllers where the PVMA works at the local maximum power point (LMPP). The improved MPPT controller that combines GA and ACO uses the slope of the P-V characteristic curve at the PVMA work point to dynamically adjust the iteration parameters of ACO. The simulation results prove that the improved GA-ACO MPPT controller is able to quickly track GMPP when the output P-V characteristic curve of PVMA shows the phenomenon of multiple peaks. Comparing the time required for tracking to MPP with different MPPT approaches for the PVMA under five different shading levels, it was observed that the improved GA-ACO algorithm requires 19.5~35.9% (average 29.2%) fewer iterations to complete tracking than the mentioned GA-ACO algorithm. Compared with the ACO algorithm, it requires 74.9~79.7% (average 78.2%) fewer iterations, and 75.0~92.5% (average 81.0%) fewer than the conventional P&O method. Therefore, it is proved that by selecting properly adjusted values of the Pheromone evaporation rate and the Gaussian standard deviation of the proposed GA-ACO algorithm based on the slope scope of the P-V characteristic curves, a better response performance of MPPT is obtained.
      Citation: Technologies
      PubDate: 2023-04-20
      DOI: 10.3390/technologies11020061
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 62: Geographical Dependence of Open Hardware
           Optimization: Case Study of Solar Photovoltaic Racking

    • Authors: Shafquat Rana, Nicholas Vandewetering, Jadyn Powell, Jonathan Álvarez Ariza, Joshua M. Pearce
      First page: 62
      Abstract: Open-source technological development is well-known for rapid innovation and providing opportunities to reduce costs and thus increase accessibility for a wide range of products. This is done through distributed manufacturing, in which products are produced close to end users. There is anecdotal evidence that these opportunities are heavily geographically dependent, with some locations unable to acquire components to build open hardware at accessible prices because of trade restrictions, tariffs, taxes, or market availability. Supply chain disruptions during the COVID-19 pandemic exacerbated this and forced designers to pivot towards a la carte-style design frameworks for critical system components. To further develop this phenomenon, a case study of free and open-source solar photovoltaic (PV) racking systems is provided. Two similar open-source designs made from different materials are compared in terms of capital costs for their detailed bill of materials throughout ten locations in North, Central and South America. The differences in economic optimization showed that the costs of wood-based racks were superior in North America and in some South American countries, while metal was less costly in Central and South America. The results make it clear that open hardware designs would be best to allow for local optimization based on material availability in all designs.
      Citation: Technologies
      PubDate: 2023-04-21
      DOI: 10.3390/technologies11020062
      Issue No: Vol. 11, No. 2 (2023)
  • Technologies, Vol. 11, Pages 32: Identifying Historic Buildings over Time
           through Image Matching

    • Authors: Kyriaki A. Tychola, Stamatis Chatzistamatis, Eleni Vrochidou, George E. Tsekouras, George A. Papakostas
      First page: 32
      Abstract: The buildings in a city are of great importance. Certain historic buildings are landmarks and indicate the city’s architecture and culture. The buildings over time undergo changes because of various factors, such as structural changes, natural disaster damages, and aesthetic interventions. The form of buildings in each period is perceived and understood by people of each generation, through photography. Nevertheless, each photograph has its own characteristics depending on the camera (analog or digital) used for capturing it. Any photo, even depicting the same object, is impossible to capture in the same way in terms of illumination, viewing angle, and scale. Hence, to study two or more photographs depicting the same object, first they should be identified and then properly matched. Nowadays, computer vision contributes to this process by providing useful tools. In particular, for this purpose, several feature detection and description algorithms of homologous points have been developed. In this study, the identification of historic buildings over time through feature correspondence techniques and methods is investigated. Especially, photographs from landmarks of Drama city, in Greece, on different dates and conditions (weather, light, rotation, scale, etc.), were gathered and experiments on 2D pairs of images, implementing traditional feature detectors and descriptors algorithms, such as SIFT, ORB, and BRISK, were carried out. This study aims to evaluate the feature matching procedure focusing on both the algorithms’ performance (accuracy, efficiency, and robustness) and the identification of the buildings. SIFT and BRISK are the most accurate algorithms while ORB and BRISK are the most efficient.
      Citation: Technologies
      PubDate: 2023-02-17
      DOI: 10.3390/technologies11010032
      Issue No: Vol. 11, No. 1 (2023)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762

Your IP address:
Home (Search)
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-