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ISSN (Online) 2227-7080
Published by MDPI Homepage  [84 journals]
  • Technologies, Vol. 10, Pages 75: Determination of
           “Neutral”–“Pleasure”, and
           “Pleasure”–“Pain” Affective State Distances
           by Using AI Image Analysis of Facial Expressions

    • Authors: Hermann Prossinger, Tomáš Hladký, Silvia Boschetti, Daniel Říha, Jakub Binter
      First page: 75
      Abstract: (1) Background: In addition to verbalizations, facial expressions advertise one’s affective state. There is an ongoing debate concerning the communicative value of the facial expressions of pain and of pleasure, and to what extent humans can distinguish between these. We introduce a novel method of analysis by replacing human ratings with outputs from image analysis software. (2) Methods: We use image analysis software to extract feature vectors of the facial expressions neutral, pain, and pleasure displayed by 20 actresses. We dimension-reduced these feature vectors, used singular value decomposition to eliminate noise, and then used hierarchical agglomerative clustering to detect patterns. (3) Results: The vector norms for pain–pleasure were rarely less than the distances pain–neutral and pleasure–neutral. The pain–pleasure distances were Weibull-distributed and noise contributed 10% to the signal. The noise-free distances clustered in four clusters and two isolates. (4) Conclusions: AI methods of image recognition are superior to human abilities in distinguishing between facial expressions of pain and pleasure. Statistical methods and hierarchical clustering offer possible explanations as to why humans fail. The reliability of commercial software, which attempts to identify facial expressions of affective states, can be improved by using the results of our analyses.
      Citation: Technologies
      PubDate: 2022-06-22
      DOI: 10.3390/technologies10040075
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 76: Proof-of-Concept Study of the Use of
           Accelerometry to Quantify Knee Joint Movement and Assist with the
           Diagnosis of Juvenile Idiopathic Arthritis

    • Authors: Amelia Jane Garner, Reza Saatchi, Oliver Ward, Harriet Nwaizu, Daniel Philip Hawley
      First page: 76
      Abstract: Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in childhood. Seven children and young people (CYP) with a diagnosis of JIA and suspected active arthritis of a single knee joint were recruited for this proof-of-concept study. The presence of active arthritis was confirmed by clinical examination. Four tri-axial accelerometers were integrated individually in elastic bands and placed above and below each knee. Participants performed ten periodic flexion-extensions of each knee joint while lying down, followed by walking ten meters in a straight path. The contralateral (non-inflamed) knee joint acted as a control. Accelerometry data were concordant with the results of clinical examination in six out of the seven patients recruited. There was a significant difference between the accelerometry measured range of movement (ROM, p-value = 0.032) of the knees with active arthritis and the healthy contralateral knees during flexion-extension. No statistically significant difference was identified between the ROM of the knee joints with active arthritis and healthy knee joints during the walking test. The study demonstrated that accelerometry may help in differentiating between healthy knee joints and those with active arthritis; however, further research is required to confirm these findings.
      Citation: Technologies
      PubDate: 2022-06-23
      DOI: 10.3390/technologies10040076
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 77: Editorial for the Special Issue
           “Reviews and Advances in Materials Processing”

    • Authors: Manoj Gupta
      First page: 77
      Abstract: In the area of Materials Science and Engineering, the tetrahedron comprising of processing, microstructure, properties and performance as four vertex corners is always key to develop new materials and to convert them to a useful shape for end application with the best properties possible [...]
      Citation: Technologies
      PubDate: 2022-06-24
      DOI: 10.3390/technologies10040077
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 54: Reliable Ultrasonic Obstacle Recognition
           for Outdoor Blind Navigation

    • Authors: Apostolos Meliones, Costas Filios, Jairo Llorente
      First page: 54
      Abstract: A reliable state-of-the-art obstacle detection algorithm is proposed for a mobile application that will analyze in real time the data received by an external sonar device and decide the need to audibly warn the blind person about near field obstacles. The proposed algorithm can equip an orientation and navigation device that allows the blind person to walk safely autonomously outdoors. The smartphone application and the microelectronic external device will serve as a wearable that will help the safe outdoor navigation and guidance of blind people. The external device will collect information using an ultrasonic sensor and a GPS module. Its main objective is to detect the existence of obstacles in the path of the user and to provide information, through oral instructions, about the distance at which it is located, its size and its potential motion and to advise how it could be avoided. Subsequently, the blind can feel more confident, detecting obstacles via hearing before sensing them with the walking cane, including hazardous obstacles that cannot be sensed at the ground level. Besides presenting the micro-servo-motor ultrasonic obstacle detection algorithm, the paper also presents the external microelectronic device integrating the sonar module, the impulse noise filtering implementation, the power budget of the sonar module and the system evaluation. The presented work is an integral part of a state-of-the-art outdoor blind navigation smartphone application implemented in the MANTO project.
      Citation: Technologies
      PubDate: 2022-04-21
      DOI: 10.3390/technologies10030054
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 55: Time Sensitive Networking Protocol
           Implementation for Linux End Equipment

    • Authors: Jesús Lázaro, Jimena Cabrejas, Aitzol Zuloaga, Leire Muguira, Jaime Jiménez
      First page: 55
      Abstract: By bringing industrial-grade robustness and reliability to Ethernet, Time Sensitive Networking (TSN) offers an IEEE standard communication technology that enables interoperability between standard-conformant industrial devices from any vendor. It also eliminates the need for physical separation of critical and non-critical communication networks, which allows a direct exchange of data between operation centers and companies, a concept at the heart of the Industrial Internet of Things (IIoT). This article describes creating an end-to-end TSN network using specialized PCI Express (PCIe) cards and two final Linux endpoints. For this purpose, the two primary standards of TSN, IEEE 802.1AS (regarding clock synchronization), and IEEE 802.1Qbv (regarding time scheduled traffic) have been implemented in Linux equipment as well as a configuration and monitoring system.
      Citation: Technologies
      PubDate: 2022-04-22
      DOI: 10.3390/technologies10030055
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 56: Electronic Structure Calculation of Cr3+
           and Fe3+ in Phosphor Host Materials Based on Relaxed Structures by
           Molecular Dynamics Simulation

    • Authors: Joichiro Ichikawa, Hiroko Kominami, Kazuhiko Hara, Masato Kakihana, Yuta Matsushima
      First page: 56
      Abstract: The electronic structures of the luminescent center ions Cr3+ and Fe3+ in the deep red phosphors LiAl5O8:Cr3+, α-Al2O3:Cr3+, and γ-LiAlO2:Fe3+ were calculated by the DV-Xα method, in which the local distortion induced by the replacement of Al3+ sites in the host crystals by the luminescent center ions was reproduced by classical molecular dynamics (MD) simulation. The MD simulations based on classical dynamics allowed for the handling of more than 1000 atoms for the lattice relaxation calculations, which was advantageous to simulate situations in which a small number of foreign atoms (ions) were dispersed in the host lattice as in phosphors, even when typical periodic boundary conditions were applied. The relaxed lattices obtained after MD indicated that the coordination polyhedra around Cr3+ and Fe3+ expanded in accordance with the size difference between the luminescent center ions and Al3+ in the host crystals. The overall profiles of the partial density of states (p-DOSs) of the isolated Cr3+ and Fe3+ 3d orbitals were not significantly affected by the lattice relaxation, whereas the widths of the energy splitting of the 3d orbitals were reduced. The electronic structure calculations for Fe–Fe pairs in γ-LiAlO2 showed that the antiferromagnetic interactions with antiparallel electron spins between the Fe3+ ions were preferred, especially when the Fe–Fe pair was on the first-nearest neighboring cation sites.
      Citation: Technologies
      PubDate: 2022-04-27
      DOI: 10.3390/technologies10030056
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 57: A Comparative Analysis on Suicidal
           Ideation Detection Using NLP, Machine, and Deep Learning

    • Authors: Rezaul Haque, Naimul Islam, Maidul Islam, Md Manjurul Ahsan
      First page: 57
      Abstract: Social networks are essential resources to obtain information about people’s opinions and feelings towards various issues as they share their views with their friends and family. Suicidal ideation detection via online social network analysis has emerged as an essential research topic with significant difficulties in the fields of NLP and psychology in recent years. With the proper exploitation of the information in social media, the complicated early symptoms of suicidal ideations can be discovered and hence, it can save many lives. This study offers a comparative analysis of multiple machine learning and deep learning models to identify suicidal thoughts from the social media platform Twitter. The principal purpose of our research is to achieve better model performance than prior research works to recognize early indications with high accuracy and avoid suicide attempts. We applied text pre-processing and feature extraction approaches such as CountVectorizer and word embedding, and trained several machine learning and deep learning models for such a goal. Experiments were conducted on a dataset of 49,178 instances retrieved from live tweets by 18 suicidal and non-suicidal keywords using Python Tweepy API. Our experimental findings reveal that the RF model can achieve the highest classification score among machine learning algorithms, with an accuracy of 93% and an F1 score of 0.92. However, training the deep learning classifiers with word embedding increases the performance of ML models, where the BiLSTM model reaches an accuracy of 93.6% and a 0.93 F1 score.
      Citation: Technologies
      PubDate: 2022-04-29
      DOI: 10.3390/technologies10030057
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 58: Study of Structural, Strength, and
           Thermophysical Properties of Li2+4xZr4−xO3 Ceramics

    • Authors: Artem L. Kozlovskiy, Bauyrzhan Abyshev, Dmitriy I. Shlimas, Maxim V. Zdorovets
      First page: 58
      Abstract: The work is devoted to the study of technology that can be used to obtain lithium-containing ceramics of the Li2+4xZr4−xO3 type using the method of solid-phase synthesis combined with thermal annealing at a temperature of 1500 °C. A distinctive feature of this work is the preparation of pure Li2ZrO3 ceramics with a high structural ordering degree (more than 88%) and density (95–97% of the theoretical density). During the study, it was found that a change in the content of initial components for synthesis does not lead to the formation of new phase inclusions; however, an increase in the LiClO4·3H2O and ZrO2 components leads to changes in the size of crystallites and dislocation density, which lead to the strengthening of ceramics to external mechanical influences. The results of the measurements of thermophysical characteristics made it possible to establish that the compaction of ceramics and a decrease in porosity lead to an increase in the thermal conductivity coefficient of 3–7%.
      Citation: Technologies
      PubDate: 2022-05-10
      DOI: 10.3390/technologies10030058
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 59: Continuous Emotion Recognition for
           Long-Term Behavior Modeling through Recurrent Neural Networks

    • Authors: Ioannis Kansizoglou, Evangelos Misirlis, Konstantinos Tsintotas, Antonios Gasteratos
      First page: 59
      Abstract: One’s internal state is mainly communicated through nonverbal cues, such as facial expressions, gestures and tone of voice, which in turn shape the corresponding emotional state. Hence, emotions can be effectively used, in the long term, to form an opinion of an individual’s overall personality. The latter can be capitalized on in many human–robot interaction (HRI) scenarios, such as in the case of an assisted-living robotic platform, where a human’s mood may entail the adaptation of a robot’s actions. To that end, we introduce a novel approach that gradually maps and learns the personality of a human, by conceiving and tracking the individual’s emotional variations throughout their interaction. The proposed system extracts the facial landmarks of the subject, which are used to train a suitably designed deep recurrent neural network architecture. The above architecture is responsible for estimating the two continuous coefficients of emotion, i.e., arousal and valence, following the broadly known Russell’s model. Finally, a user-friendly dashboard is created, presenting both the momentary and the long-term fluctuations of a subject’s emotional state. Therefore, we propose a handy tool for HRI scenarios, where robot’s activity adaptation is needed for enhanced interaction performance and safety.
      Citation: Technologies
      PubDate: 2022-05-12
      DOI: 10.3390/technologies10030059
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 60: Advanced Security Framework for Internet
           of Things (IoT)

    • Authors: Abid Ali, Abdul Mateen, Abdul Hanan, Farhan Amin
      First page: 60
      Abstract: The stimulus to carry out this research was to identify and propose a secure framework for the Internet of Things (IoT). Due to the massive accessibility and interconnection of IoT devices, systems are at risk of being exploited by hackers. Therefore, there is a need to find an advanced security framework that covers data security, data confidentiality, and data integrity issues. The study uses a systematic literature review (SLR) technique and complete substantive literature is reviewed to find out the constructs and themes in the existing literature. We performed it in four steps, which were inclusion, eligibility, screening, and identification. We reviewed around 568 articles from well-reputable journals, and after exclusion, 260 articles and 54 reports were analyzed. We performed an analysis using MAXQDA in which the nodes and themes were first identified. After the classification, a qualitative model was generated using MAXQDA. The proposed model is supported by the literature so it will be useful for the IT managers, developers, and the users of IoT.
      Citation: Technologies
      PubDate: 2022-05-12
      DOI: 10.3390/technologies10030060
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 61: Application of Multi-Channel
           Convolutional Neural Network to Improve DEM Data in Urban Cities

    • Authors: Ngoc Son Nguyen, Dong Eon Kim, Yilin Jia, Srivatsan V. Raghavan, Shie Yui Liong
      First page: 61
      Abstract: A digital elevation model (DEM) represents the topographic surface of the Earth and is an indispensable source of data in many applications, such as flood modeling, infrastructure design and land management. DEM data at high spatial resolution and high accuracy of elevation data are not only costly and time-consuming to acquire but also often confidential. In this paper, we explore a cost-effective approach to derive good quality DEM data by applying a multi-channel convolutional neural network (CNN) to enhance free resources of available DEM data. Shuttle Radar Topography Mission (SRTM) data, multi-spectral imaging Sentinel-2, as well as Google satellite imagery were used as inputs to the CNN model. The CNN model was first trained using high-quality reference DEM data in a dense urban city—Nice, France—then validated on another site in Nice and finally tested in the Orchard Road area (Singapore), which is also an equally dense urban area in Singapore. The CNN model not only shows an impressive reduction in the root mean square error (RMSE) of 50% at validation site in Nice and 30% at the test site in Singapore, but also results in much clearer profiles of the land surface than input SRTM data. A comparison between CNN performance and that of an earlier conducted study using artificial neural networks (ANN) was conducted as well. The comparison within this limited study shows that CNN yields a more accurate DEM.
      Citation: Technologies
      PubDate: 2022-05-13
      DOI: 10.3390/technologies10030061
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 62: Application of 3D Virtual Prototyping
           Technology to the Integration of Wearable Antennas into Fashion Garments

    • Authors: Evridiki Papachristou, Hristos T. Anastassiu
      First page: 62
      Abstract: A very large number of scientific papers have been published in the literature on wearable antennas of several types, structure and functionality. The main focus is always antenna efficiency from an engineering point of view. However, antenna integration into actual, realistic garments is seldom addressed. In this paper, 2D pattern and 3D virtual prototyping technology is utilized to develop regular clothing, available in the market, in which wearable antennas are incorporated in an automated manner, reducing the chances of compromising the garment elegance or comfort. The functionality of various commercial software modules is described, and particular design examples are implemented, proving the efficiency of the procedure and leading the way for more complex configurations.
      Citation: Technologies
      PubDate: 2022-05-17
      DOI: 10.3390/technologies10030062
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 63: STAMINA: Bioinformatics Platform for
           Monitoring and Mitigating Pandemic Outbreaks

    • Authors: Nikolaos Bakalos, Maria Kaselimi, Nikolaos Doulamis, Anastasios Doulamis, Dimitrios Kalogeras, Mathaios Bimpas, Agapi Davradou, Aggeliki Vlachostergiou, Anaxagoras Fotopoulos, Maria Plakia, Alexandros Karalis, Sofia Tsekeridou, Themistoklis Anagnostopoulos, Angela Maria Despotopoulou, Ilaria Bonavita, Katrina Petersen, Leonidas Pelepes, Lefteris Voumvourakis, Anastasia Anagnostou, Derek Groen, Kate Mintram, Arindam Saha, Simon J. E. Taylor, Charon van der Ham, Patrick Kaleta, Dražen Ignjatović, Luca Rossi
      First page: 63
      Abstract: This paper presents the components and integrated outcome of a system that aims to achieve early detection, monitoring and mitigation of pandemic outbreaks. The architecture of the platform aims at providing a number of pandemic-response-related services, on a modular basis, that allows for the easy customization of the platform to address user’s needs per case. This customization is achieved through its ability to deploy only the necessary, loosely coupled services and tools for each case, and by providing a common authentication, data storage and data exchange infrastructure. This way, the platform can provide the necessary services without the burden of additional services that are not of use in the current deployment (e.g., predictive models for pathogens that are not endemic to the deployment area). All the decisions taken for the communication and integration of the tools that compose the platform adhere to this basic principle. The tools presented here as well as their integration is part of the project STAMINA.
      Citation: Technologies
      PubDate: 2022-05-17
      DOI: 10.3390/technologies10030063
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 64: Study of Joint Symmetry in Gait Evolution
           for Quadrupedal Robots Using a Neural Network

    • Authors: Zainullah Khan, Farhat Naseer, Yousuf Khan, Muhammad Bilal, Muhammad A. Butt
      First page: 64
      Abstract: Bio-inspired legged robots have the potential to traverse uneven terrains in a very efficient way. The effectiveness of the robot gait depends on the joint symmetry of the robot; variations in joint symmetries can result in different types of gaits suitable for different scenarios. In the literature, symmetric and asymmetric gaits have been synthesized for legged robots; however, no relation between the gait effectiveness and joint symmetry has been studied. In this research work, the effect of joint symmetry on the robot gait is studied. To test the suggested algorithm, spider-like robot morphology was created in a simulator. The simulation environment was set to a flat surface where the robots could be tested. The simulations were performed on the PyroSim software platform, a physics engine built on top of the Open Dynamics Engine. The quadrupedal robot was created with eight joints, and it is controlled using an artificial neural network. The artificial neural network was optimized using a genetic algorithm. Different robot symmetries were tested, i.e., diagonal joint symmetry, diagonal joint reverse symmetry, adjacent joint symmetry, adjacent joint reverse symmetry and random joint symmetry or joint asymmetry. The robot controllers for each joint symmetry were evolved for a set number of generations and the robot controllers were evaluated using a fitness function that we designed. Our results showed that symmetry in joint movement could help in generating optimal gaits for our test terrain, and joint symmetry produced gaits that were already present in nature. Moreover, our results also showed that certain joint symmetries tended to perform better than others in terms of stability, speed, and distance traveled.
      Citation: Technologies
      PubDate: 2022-05-22
      DOI: 10.3390/technologies10030064
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 65: Specific Electronic Platform to Test the
           Influence of Hypervisors on the Performance of Embedded Systems

    • Authors: Jaime Jiménez, Leire Muguira, Unai Bidarte, Alejandro Largacha, Jesús Lázaro
      First page: 65
      Abstract: Some complex digital circuits must host various operating systems in a single electronic platform to make real-time and not-real-time tasks compatible or assign different priorities to current applications. For this purpose, some hardware–software techniques—called virtualization—must be integrated to run the operating systems independently, as isolated in different processors: virtual machines. These are monitored and managed by a software tool named hypervisor, which is in charge of allowing each operating system to take control of the hardware resources. Therefore, the hypervisor determines the effectiveness of the system when reacting to events. To measure, estimate or compare the performance of different ways to configure the virtualization, our research team has designed and implemented a specific testbench: an electronic system, based on a complex System on Chip with a processing system and programmable logic, to configure the hardware–software partition and show merit figures, to evaluate the performance of the different options, a field that has received insufficient attention so far. In this way, the fabric of the Field Programmable Gate Array (FPGA) can be exploited for measurements and instrumentation. The platform has been validated with two hypervisors, Xen and Jailhouse, in a multiprocessor System-on-Chip, by executing real-time operating systems and application programs in different contexts.
      Citation: Technologies
      PubDate: 2022-05-24
      DOI: 10.3390/technologies10030065
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 66: Electrospinning for the Modification of
           3D Objects for the Potential Use in Tissue Engineering

    • Authors: Laura Bauer, Lisa Brandstäter, Mika Letmate, Manasi Palachandran, Fynn Ole Wadehn, Carlotta Wolfschmidt, Timo Grothe, Uwe Güth, Andrea Ehrmann
      First page: 66
      Abstract: Electrospinning is often investigated for biotechnological applications, such as tissue engineering and cell growth in general. In many cases, three-dimensional scaffolds would be advantageous to prepare tissues in a desired shape. Some studies thus investigated 3D-printed scaffolds decorated with electrospun nanofibers. Here, we report on the influence of 3D-printed substrates on fiber orientation and diameter of a nanofiber mat, directly electrospun on conductive and isolating 3D-printed objects, and show the effect of shadowing, taking 3D-printed ears with electrospun nanofiber mats as an example for potential and direct application in tissue engineering in general.
      Citation: Technologies
      PubDate: 2022-05-29
      DOI: 10.3390/technologies10030066
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 67: An a Priori Discussion of the Fill Front
           Stability in Semisolid Casting

    • Authors: Anders E. W. Jarfors, Qing Zhang, Stefan Jonsson
      First page: 67
      Abstract: Metal casting is an industrially important manufacturing process offering a superior combination of design flexibility, productivity and cost-effectiveness, but has limitations due to filling related defects. Several semisolid casting processes are available capable of casting at a range of solid fractions to overcome this. The current communication aims to review the filling front behaviour and give a new perspective to the gate design in semisolid processing compared to conventional high-pressure die-casting. It is shown that solid fraction and gate widths are critical to avoid instability and spraying.
      Citation: Technologies
      PubDate: 2022-05-30
      DOI: 10.3390/technologies10030067
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 68: Supporting Newsrooms with Journalistic
           Knowledge Graph Platforms: Current State and Future Directions

    • Authors: Marc Gallofré Ocaña, Andreas L. Opdahl
      First page: 68
      Abstract: Increasing competition and loss of revenues force newsrooms to explore new digital solutions. The new solutions employ artificial intelligence and big data techniques such as machine learning and knowledge graphs to manage and support the knowledge work needed in all stages of news production. The result is an emerging type of intelligent information system we have called the Journalistic Knowledge Platform (JKP). In this paper, we analyse for the first time knowledge graph-based JKPs in research and practice. We focus on their current state, challenges, opportunities and future directions. Our analysis is based on 14 platforms reported in research carried out in collaboration with news organisations and industry partners and our experiences with developing knowledge graph-based JKPs along with an industry partner. We found that: (a) the most central contribution of JKPs so far is to automate metadata annotation and monitoring tasks; (b) they also increasingly contribute to improving background information and content analysis, speeding-up newsroom workflows and providing newsworthy insights; (c) future JKPs need better mechanisms to extract information from textual and multimedia news items; (d) JKPs can provide a digitalisation path towards reduced production costs and improved information quality while adapting the current workflows of newsrooms to new forms of journalism and readers’ demands.
      Citation: Technologies
      PubDate: 2022-05-31
      DOI: 10.3390/technologies10030068
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 69: Patterns Simulations Using Gibbs/MRF
           Auto-Poisson Models

    • Authors: Stelios Zimeras
      First page: 69
      Abstract: Pattern analysis is the process where characteristics of big data can be recognized using specific methods. Recognition of the data, especially images, can be achieved by applying spatial models, explaining the neighborhood structure of the patterns. These models can be introduced by Markov random field (MRF) models where conditional distribution of the pixels may be defined by a specific distribution. Various spatial models could be introduced, explaining the real patterns of the data; one class of these models is based on the Poisson distribution, called auto-Poisson models. The main advantage of these models is the consideration of the local characteristics of the image. Based on the local analysis, various patterns can be introduced and models that better explain the real data can be estimated, using advanced statistical techniques like Monte Carlo Markov Chains methods. These methods are based on simulations where the proposed distribution must converge to the original (final) one. In this work, an analysis of a MRF model under Poisson distribution would be defined and simulations would be illustrated based on Monte Carlo Markov Chains (MCMC) process like Gibbs sampler. Results would be illustrated using simulated and real patterns data.
      Citation: Technologies
      PubDate: 2022-06-06
      DOI: 10.3390/technologies10030069
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 70: Numerical Simulation and Optimization of
           Microwave Heating Effect on Coal Seam Permeability Enhancement

    • Authors: Ali Jebelli, Arezoo Mahabadi, Rafiq Ahmad
      First page: 70
      Abstract: In coal mining operations, coalbed methane is one of the potential hazards that must be extracted to prevent an explosion of the accumulated gas and environmental pollution. One of the mechanisms is using microwave irradiation so that the thermal stress caused by microwave heating generates fractures. In this research, we investigated the most important parameters affecting the electric and thermal fields’ distribution in coal in order to identify the effective parameters that achieve the highest temperature increase rate and to reach the highest impact and efficiency of the system with the least amount of consumed energy. In this paper, using Maxwell equations, heat transfer, mass transfer and coupling them by COMSOL, we have simulated the radiation of electromagnetic field and heat in the cavity and coal, and we have also shown the temperature dispersion inside the coal. The parameters studied included the amount of coal moisture (type of coal), operating frequency, input power and heating time, location of the waveguide, the size of the waveguide and the location of the coal, and finally the parameters were re-examined in a secondary standard cavity to separate the parameters related to the size of the environment and the cavity from the independent parameters. The results of this study show that the most effective parameter on the electric and thermal fields’ distribution within coal is the size of the resonance chamber. Additionally, the results show that the moisture of 5%, the highest input power and cutoff frequency close to the operating frequency cause the highest average temperature inside the coal, but many parameters such as operating frequency, waveguide location and coal location should be selected depending on the chamber size.
      Citation: Technologies
      PubDate: 2022-06-06
      DOI: 10.3390/technologies10030070
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 71: An Application of Artificial Neural
           Networks to Estimate the Performance of High-Energy Laser Weapons in
           Maritime Environments

    • Authors: Antonios Lionis, Andreas Tsigopoulos, Keith Cohn
      First page: 71
      Abstract: Efforts to develop high-energy laser (HEL) weapons that are capable of being integrated and operated aboard naval platforms have gained an increased interest, partially due to the proliferation of various kinds of unmanned systems that pose a critical asymmetric threat to them, both operationally and financially. HEL weapons allow for an unconstrained depth of magazine and cost exchange ratio, both of which are essential characteristics to effectively oppose small unmanned systems, compared to their kinetic weapons counterparts. However, HEL performance is heavily affected by atmospheric conditions between the weapon and the target; therefore, the more precise and accurate the atmospheric characterization, the more accurate the performance estimation of the HEL weapon. To that end, the Directed Energy Group of the Naval Postgraduate School (NPS) is conducting experimental, theoretical and computational research on the effects of atmospheric conditions on HEL weapon efficacy. This paper proposes a new approach to the NPS laser performance code scheme, which leverages artificial neural networks (ANNs) for the prediction of optical turbulence strength. This improvement could allow for near real-time and location-independent HEL weapon performance estimation. Two experimental datasets, which were obtained from the NPS facilities, were utilized to perform regression modeling using an ANN, which achieved a decent fit (R2 = 0.75 for the first dataset and R2 = 0.78 for the second dataset).
      Citation: Technologies
      PubDate: 2022-06-08
      DOI: 10.3390/technologies10030071
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 72: Two-Step Validation of a New Wireless
           Inertial Sensor System: Application in the Squat Motion

    • Authors: Mathias Blandeau, Romain Guichard, Rémy Hubaut, Sébastien Leteneur
      First page: 72
      Abstract: The use of Inertial Measurement Units (IMUs) can provide embedded motion data to improve clinical application. The objective of this study was to validate a newly designed IMU system. The validation is provided through two main methods, a classical sensor validation achieved on a six-degrees-of-freedom hexapod platform with controlled linear and rotation motions and a functional validation on subjects performing squats with segmental angle measurement. The kinematics of the sensors were measured by using an optoelectronic reference system (VICON) and then compared to the orientation and raw data of the IMUs. Bland–Altman plots and Lin’s concordance correlation coefficient were computed to assess the kinematic parameter errors between the IMUs and VICON system. The results showed suitable precision of the IMU system for linear, rotation and squat motions.
      Citation: Technologies
      PubDate: 2022-06-09
      DOI: 10.3390/technologies10030072
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 73: Solving Dual-Channel Supply Chain Pricing
           Strategy Problem with Multi-Level Programming Based on Improved Simplified
           Swarm Optimization

    • Authors: Wei-Chang Yeh, Zhenyao Liu, Yu-Cheng Yang, Shi-Yi Tan
      First page: 73
      Abstract: With the evolution of the Internet and the introduction of third-party platforms, a diversified supply chain has gradually emerged. In contrast to the traditional single sales channel, companies can also increase their revenue by selling through multiple channels, such as dual-channel sales: adding a sales channel for direct sales through online third-party platforms. However, due to the complexity of the supply chain structure, previous studies have rarely discussed and analyzed the capital-constrained dual-channel supply chain model, which is more relevant to the actual situation. To solve more complex and realistic supply chain decision problems, this paper uses the concept of game theory to describe the pricing negotiation procedures among the capital-constrained manufacturers and other parties in the dual-channel supply chain by applying the Stackelberg game theory to describe the supply chain structure as a hierarchical multi-level mathematical model to solve the optimal pricing strategy for different financing options to achieve the common benefit of the supply chain. In this study, we propose a Multi-level Improved Simplified Swarm Optimization (MLiSSO) method, which uses the improved, simplified swarm optimization (iSSO) for the Multi-level Programming Problem (MLPP). It is applied to this pricing strategy model of the supply chain and experiments with three related MLPPs in the past studies to verify the effectiveness of the method. The results show that the MLiSSO algorithm is effective, qualitative, and stable and can be used to solve the pricing strategy problem for supply chain models; furthermore, the algorithm can also be applied to other MLPPs.
      Citation: Technologies
      PubDate: 2022-06-11
      DOI: 10.3390/technologies10030073
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 74: Explainable AI (XAI) Applied in Machine
           Learning for Pain Modeling: A Review

    • Authors: Ravichandra Madanu, Maysam F. Abbod, Fu-Jung Hsiao, Wei-Ta Chen, Jiann-Shing Shieh
      First page: 74
      Abstract: Pain is a complex term that describes various sensations that create discomfort in various ways or types inside the human body. Generally, pain has consequences that range from mild to severe in different organs of the body and will depend on the way it is caused, which could be an injury, illness or medical procedures including testing, surgeries or therapies, etc. With recent advances in artificial-intelligence (AI) systems associated in biomedical and healthcare settings, the contiguity of physician, clinician and patient has shortened. AI, however, has more scope to interpret the pain associated in patients with various conditions by using any physiological or behavioral changes. Facial expressions are considered to give much information that relates with emotions and pain, so clinicians consider these changes with high importance for assessing pain. This has been achieved in recent times with different machine-learning and deep-learning models. To accentuate the future scope and importance of AI in medical field, this study reviews the explainable AI (XAI) as increased attention is given to an automatic assessment of pain. This review discusses how these approaches are applied for different pain types.
      Citation: Technologies
      PubDate: 2022-06-14
      DOI: 10.3390/technologies10030074
      Issue No: Vol. 10, No. 3 (2022)
  • Technologies, Vol. 10, Pages 37: Lightweight Neural Network for COVID-19
           Detection from Chest X-ray Images Implemented on an Embedded System

    • Authors: Theodora Sanida, Argyrios Sideris, Dimitris Tsiktsiris, Minas Dasygenis
      First page: 37
      Abstract: At the end of 2019, a severe public health threat named coronavirus disease (COVID-19) spread rapidly worldwide. After two years, this coronavirus still spreads at a fast rate. Due to its rapid spread, the immediate and rapid diagnosis of COVID-19 is of utmost importance. In the global fight against this virus, chest X-rays are essential in evaluating infected patients. Thus, various technologies that enable rapid detection of COVID-19 can offer high detection accuracy to health professionals to make the right decisions. The latest emerging deep-learning (DL) technology enhances the power of medical imaging tools by providing high-performance classifiers in X-ray detection, and thus various researchers are trying to use it with limited success. Here, we propose a robust, lightweight network where excellent classification results can diagnose COVID-19 by evaluating chest X-rays. The experimental results showed that the modified architecture of the model we propose achieved very high classification performance in terms of accuracy, precision, recall, and f1-score for four classes (COVID-19, normal, viral pneumonia and lung opacity) of 21.165 chest X-ray images, and at the same time meeting real-time constraints, in a low-power embedded system. Finally, our work is the first to propose such an optimized model for a low-power embedded system with increased detection accuracy.
      Citation: Technologies
      PubDate: 2022-02-25
      DOI: 10.3390/technologies10020037
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 38: Parasitic Coupling in 3D Sequential
           Integration: The Example of a Two-Layer 3D Pixel

    • Authors: Petros Sideris, Arnaud Peizerat, Perrine Batude, Gilles Sicard, Christoforos Theodorou
      First page: 38
      Abstract: In this paper, we present a thorough analysis of parasitic coupling effects between different electrodes for a 3D Sequential Integration circuit example comprising stacked devices. More specifically, this study is performed for a Back-Side Illuminated, 4T–APS, 3D Sequential Integration pixel with both its photodiode and Transfer Gate at the bottom tier and the other parts of the circuit on the top tier. The effects of voltage bias and 3D inter-tier contacts are studied by using TCAD simulations. Coupling-induced electrical parameter variations are compared against variations due to temperature change, revealing that these two effects can cause similar levels of readout error for the top-tier readout circuit. On the bright side, we also demonstrate that in the case of a rolling shutter pixel readout, the coupling effect becomes nearly negligible. Therefore, we estimate that the presence of an inter-tier ground plane, normally used for electrical isolation, is not strictly mandatory for Monolithic 3D pixels.
      Citation: Technologies
      PubDate: 2022-02-28
      DOI: 10.3390/technologies10020038
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 39: A Switched Capacitor Memristor Emulator
           Using Stochastic Computing

    • Authors: Carola de Benito, Oscar Camps, Mohamad Moner Al Chawa, Stavros G. Stavrinides, Rodrigo Picos
      First page: 39
      Abstract: Due to the increased use of memristors and their many applications, the use of emulators has grown in parallel to avoid some of the difficulties presented by real devices, such as variability and reliability. In this paper, we present a memristive emulator designed using a switched capacitor (SC), that is, an analog component/block and a control part or block implemented using stochastic computing (SCo) and therefore fully digital. Our design is thus a mixed signal circuit. Memristor equations are implemented using stochastic computing to generate the control signals necessary to work with the controllable resistor implemented as a switched capacitor.
      Citation: Technologies
      PubDate: 2022-03-02
      DOI: 10.3390/technologies10020039
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 40: An Optimized Enhanced Phase Locked Loop
           Controller for a Hybrid System

    • Authors: Amritha Kodakkal, Rajagopal Veramalla, Narasimha Raju Kuthuri, Surender Reddy Salkuti
      First page: 40
      Abstract: The use of renewable energy sources is the need of the hour, but the highly intermittent nature of the wind and solar energies demands an efficient controller be connected with the system. This paper proposes an adept control algorithm for an isolated system connected with renewable energy sources. The system under consideration is a hybrid power system with a wind power harnessing unit associated with a solar energy module. A controller that works with enhanced phase locked loop (EPLL) algorithm is provided to maintain the quality of power at the load side and ensure that the source current is not affected during the load fluctuations. EPLL is very simple, precise, stable, and highly efficient in maintaining power quality. The double-frequency error which is the drawback of standard phase locked loop is eliminated in EPLL. Optimization techniques are used here to tune the values of the PI controller gains in the controlling algorithm. Tuning of the controller is an important process, as the gains of the controllers decide the quality of the output. The system is designed using MATLAB/SIMULINK. Codes are written in MATLAB for the optimization. Out of the three different optimization techniques applied, the salp swarm algorithm is found to give the most suitable gain values for the proposed system. Solar power generation is made more efficient by implementing maximum power point tracking. Perturb and observe is the method adopted for MPPT.
      Citation: Technologies
      PubDate: 2022-03-11
      DOI: 10.3390/technologies10020040
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 41: MINA: A Robotic Assistant for Hospital
           Fetching Tasks

    • Authors: Harish Ram Nambiappan, Stephanie Arevalo Arboleda, Cody Lee Lundberg, Maria Kyrarini, Fillia Makedon, Nicholas Gans
      First page: 41
      Abstract: In this paper, a robotic Multitasking Intelligent Nurse Aid (MINA) is proposed to assist nurses with everyday object fetching tasks. MINA consists of a manipulator arm on an omni-directional mobile base. Before the operation, an augmented reality interface was used to place waypoints. Waypoints can indicate the location of a patient, supply shelf, and other locations of interest. When commanded to retrieve an object, MINA uses simultaneous localization and mapping to map its environment and navigate to the supply shelf waypoint. At the shelf, MINA builds a 3D point cloud representation of the shelf and searches for barcodes to identify and localize the object it was sent to retrieve. Upon grasping the object, it returns to the user. Collision avoidance is incorporated during the mobile navigation and grasping tasks. We performed experiments to evaluate MINA’s efficacy including with obstacles along the path. The experimental results showed that MINA can repeatedly navigate to the specified waypoints and successfully perform the grasping and retrieval task.
      Citation: Technologies
      PubDate: 2022-03-12
      DOI: 10.3390/technologies10020041
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 42: Detection of Physical Strain and Fatigue
           in Industrial Environments Using Visual and Non-Visual Low-Cost Sensors

    • Authors: Konstantinos Papoutsakis, George Papadopoulos, Michail Maniadakis, Thodoris Papadopoulos, Manolis Lourakis, Maria Pateraki, Iraklis Varlamis
      First page: 42
      Abstract: The detection and prevention of workers’ body straining postures and other stressing conditions within the work environment, supports establishing occupational safety and promoting well being and sustainability at work. Developed methods towards this aim typically rely on combining highly ergonomic workplaces and expensive monitoring mechanisms including wearable devices. In this work, we demonstrate how the input from low-cost sensors, specifically, passive camera sensors installed in a real manufacturing workplace, and smartwatches used by the workers can provide useful feedback on the workers’ conditions and can yield key indicators for the prevention of work-related musculo-skeletal disorders (WMSD) and physical fatigue. To this end, we study the ability to assess the risk for physical strain of workers online during work activities based on the classification of ergonomically sub-optimal working postures using visual information, the correlation and fusion of these estimations with synchronous worker heart rate data, as well as the prediction of near-future heart rate using deep learning-based techniques. Moreover, a new multi-modal dataset of video and heart rate data captured in a real manufacturing workplace during car door assembly activities is introduced. The experimental results show the efficiency of the proposed approach that exceeds 70% of classification rate based on the F1 score measure using a set of over 300 annotated video clips of real line workers during work activities. In addition a time lagging correlation between the estimated ergonomic risks for physical strain and high heart rate was assessed using a larger dataset of synchronous visual and heart rate data sequences. The statistical analysis revealed that imposing increased strain to body parts will results in an increase to the heart rate after 100–120 s. This finding is used to improve the short term forecasting of worker’s cardiovascular activity for the next 10 to 30 s by fusing the heart rate data with the estimated ergonomic risks for physical strain and ultimately to train better predictive models for worker fatigue.
      Citation: Technologies
      PubDate: 2022-03-16
      DOI: 10.3390/technologies10020042
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 43: A Survey on GAN-Based Data Augmentation
           for Hand Pose Estimation Problem

    • Authors: Farnaz Farahanipad, Mohammad Rezaei, Mohammad Sadegh Nasr, Farhad Kamangar, Vassilis Athitsos
      First page: 43
      Abstract: Deep learning solutions for hand pose estimation are now very reliant on comprehensive datasets covering diverse camera perspectives, lighting conditions, shapes, and pose variations. While acquiring such datasets is a challenging task, several studies circumvent this problem by exploiting synthetic data, but this does not guarantee that they will work well in real situations mainly due to the gap between the distribution of synthetic and real data. One recent popular solution to the domain shift problem is learning the mapping function between different domains through generative adversarial networks. In this study, we present a comprehensive study on effective hand pose estimation approaches, which are comprised of the leveraged generative adversarial network (GAN), providing a comprehensive training dataset with different modalities. Benefiting from GAN, these algorithms can augment data to a variety of hand shapes and poses where data manipulation is intuitively controlled and greatly realistic. Next, we present related hand pose datasets and performance comparison of some of these methods for the hand pose estimation problem. The quantitative and qualitative results indicate that the state-of-the-art hand pose estimators can be greatly improved with the aid of the training data generated by these GAN-based data augmentation methods. These methods are able to beat the baseline approaches with better visual quality and higher values in most of the metrics (PCK and ME) on both the STB and NYU datasets. Finally, in conclusion, the limitation of the current methods and future directions are discussed.
      Citation: Technologies
      PubDate: 2022-03-21
      DOI: 10.3390/technologies10020043
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 44: Negotiating Learning Goals with Your
           Future Learning-Self

    • Authors: Konstantinos Tsiakas, Deborah Cnossen, Timothy H. C. Muyrers, Danique R. C. Stappers, Romain H. A. Toebosch, Emilia I. Barakova
      First page: 44
      Abstract: This paper discusses the challenges towards designing an educational avatar which visualizes the future learning-self of a student in order to promote their self-regulated learning skills. More specifically, the avatar follows a negotiation-based interaction with the student during the goal-setting process of self-regulated learning. The goal of the avatar is to help the student get insights of their possible future learning-self based on their daily goals. Our approach utilizes a Recurrent Neural Network as the underlying prediction model for expected learning outcomes and goal feasibility. In this paper, we present our ongoing work and design process towards an explainable and personalized educational avatar, focusing both on the avatar design and the human-algorithm interactions.
      Citation: Technologies
      PubDate: 2022-03-22
      DOI: 10.3390/technologies10020044
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 45: Material Design for Enhancing Properties
           of 3D Printed Polymer Composites for Target Applications

    • Authors: Vinita V. Shinde, Yuyang Wang, Md Fahim Salek, Maria L. Auad, Lauren E. Beckingham, Bryan S. Beckingham
      First page: 45
      Abstract: Polymer composites are becoming an important class of materials for a diversified range of industrial applications due to their unique characteristics and natural and synthetic reinforcements. Traditional methods of polymer composite fabrication require machining, manual labor, and increased costs. Therefore, 3D printing technologies have come to the forefront of scientific, industrial, and public attention for customized manufacturing of composite parts having a high degree of control over design, processing parameters, and time. However, poor interfacial adhesion between 3D printed layers can lead to material failure, and therefore, researchers are trying to improve material functionality and extend material lifetime with the addition of reinforcements and self-healing capability. This review provides insights on different materials used for 3D printing of polymer composites to enhance mechanical properties and improve service life of polymer materials. Moreover, 3D printing of flexible energy-storage devices (FESD), including batteries, supercapacitors, and soft robotics using soft materials (polymers), is discussed as well as the application of 3D printing as a platform for bioengineering and earth science applications by using a variety of polymer materials, all of which have great potential for improving future conditions for humanity and planet Earth.
      Citation: Technologies
      PubDate: 2022-03-23
      DOI: 10.3390/technologies10020045
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 46: Efficiently Mitigating Face-Swap-Attacks:
           Compressed-PRNU Verification with Sub-Zones

    • Authors: Ali Hassani, Hafiz Malik, Jon Diedrich
      First page: 46
      Abstract: Face-swap-attacks (FSAs) are a new threat to face recognition systems. FSAs are essentially imperceptible replay-attacks using an injection device and generative networks. By placing the device between the camera and computer device, attackers can present any face as desired. This is particularly potent as it also maintains liveliness features, as it is a sophisticated alternation of a real person, and as it can go undetected by traditional anti-spoofing methods. To address FSAs, this research proposes a noise-verification framework. Even the best generative networks today leave alteration traces in the photo-response noise profile; these are detected by doing a comparison of challenge images against the camera enrollment. This research also introduces compression and sub-zone analysis for efficiency. Benchmarking with open-source tampering-detection algorithms shows the proposed compressed-PRNU verification robustly verifies facial-image authenticity while being significantly faster. This demonstrates a novel efficiency for mitigating face-swap-attacks, including denial-of-service attacks.
      Citation: Technologies
      PubDate: 2022-03-27
      DOI: 10.3390/technologies10020046
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 47: Fall Detection Using Multi-Property
           Spatiotemporal Autoencoders in Maritime Environments

    • Authors: Iason Katsamenis, Nikolaos Bakalos, Eleni Eirini Karolou, Anastasios Doulamis, Nikolaos Doulamis
      First page: 47
      Abstract: Man overboard is an emergency in which fast and efficient detection of the critical event is the key factor for the recovery of the victim. Its severity urges the utilization of intelligent video surveillance systems that monitor the ship’s perimeter in real time and trigger the relative alarms that initiate the rescue mission. In terms of deep learning analysis, since man overboard incidents occur rarely, they present a severe class imbalance problem, and thus, supervised classification methods are not suitable. To tackle this obstacle, we follow an alternative philosophy and present a novel deep learning framework that formulates man overboard identification as an anomaly detection task. The proposed system, in the absence of training data, utilizes a multi-property spatiotemporal convolutional autoencoder that is trained only on the normal situation. We explore the use of RGB video sequences to extract specific properties of the scene, such as gradient and saliency, and utilize the autoencoders to detect anomalies. To the best of our knowledge, this is the first time that man overboard detection is made in a fully unsupervised manner while jointly learning the spatiotemporal features from RGB video streams. The algorithm achieved 97.30% accuracy and a 96.01% F1-score, surpassing the other state-of-the-art approaches significantly.
      Citation: Technologies
      PubDate: 2022-03-29
      DOI: 10.3390/technologies10020047
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 48: Verifiable Surface Disinfection Using
           Ultraviolet Light with a Mobile Manipulation Robot

    • Authors: Alan G. Sanchez, William D. Smart
      First page: 48
      Abstract: Robots are being increasingly used in the fight against highly-infectious diseases such as the Novel Coronavirus (SARS-CoV-2). By using robots in place of human health care workers in disinfection tasks, we can reduce the exposure of these workers to the virus and, as a result, often dramatically reduce their risk of infection. Since healthcare workers are often disproportionately affected by large-scale infectious disease outbreaks, this risk reduction can profoundly affect our ability to fight these outbreaks. Many robots currently available for disinfection, however, are little more than mobile platforms for ultraviolet lights, do not allow fine-grained control over how the disinfection is performed, and do not allow verification that it was done as the human supervisor intended. In this paper, we present a semi-autonomous system, originally designed for the disinfection of surfaces in the context of Ebola Virus Disease (EVD) that allows a human supervisor to direct an autonomous robot to disinfect contaminated surfaces to a desired level, and to subsequently verify that this disinfection has taken place. We describe the overall system, the user interface, how our calibration and modeling allows for reliable disinfection, and offer directions for future work to address open space disinfection tasks.
      Citation: Technologies
      PubDate: 2022-03-29
      DOI: 10.3390/technologies10020048
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 49: Vacuum UV (VUV) Photo-Oxidation of
           Polyethersulfone (PES)

    • Authors: Sarah Oakes, Ryan Keeley, Hunter Heineman, Tom Allston, Joel Shertok, Michael Mehan, Gregory K. Thompson, Gerald A. Takacs
      First page: 49
      Abstract: International need for water quality is placing a high demand on separation technology to develop advanced oxidative processes for polyethersulfone (PES) membranes to help improve water purification. Therefore, VUV photo-oxidation with a low pressure Ar plasma was studied to improve the hydrophilicity of PES by flowing oxygen over the surface during treatment. X-ray photoelectron spectroscopy (XPS) detected a decrease in the C at% (4.4 ± 1.7 at%), increase in O at% (3.7 ± 1.0 at%), and a constant S at% (5.4 ± 0.2 at%). Curve fitting of the XPS spectra showed a decrease in sp2 C-C aromatic group bonding, and an increase in C-O, C-S, O=C-OH, sulphonate (-SO3) and sulphate (-SO4) functional groups with treatment time. The water contact angle decreased from 71.9° for untreated PES down to a saturation level of 41.9° with treatment. Since scanning electron microscopy (SEM) showed no major changes in surface roughness, the increase in hydrophilicity was mainly due to oxidation of the surface. Washing the VUV photo-oxidized PES samples with water or ethanol increased the water contact angle saturation level up to 66° indicating the formation of a weak boundary layer.
      Citation: Technologies
      PubDate: 2022-03-30
      DOI: 10.3390/technologies10020049
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 50: The NESTORE e-Coach: Designing a
           Multi-Domain Pathway to Well-Being in Older Age

    • Authors: Leonardo Angelini, Mira El Kamali, Elena Mugellini, Omar Abou Khaled, Christina Röcke, Simone Porcelli, Alfonso Mastropietro, Giovanna Rizzo, Noemi Boqué, Josep Maria del Bas, Filippo Palumbo, Michele Girolami, Antonino Crivello, Canan Ziylan, Paula Subías-Beltrán, Silvia Orte, Carlo Emilio Standoli, Laura Fernandez Maldonado, Maurizio Caon, Martin Sykora, Suzanne Elayan, Sabrina Guye, Giuseppe Andreoni
      First page: 50
      Abstract: This article describes the coaching strategies of the NESTORE e-coach, a virtual coach for promoting healthier lifestyles in older age. The novelty of the NESTORE project is the definition of a multi-domain personalized pathway where the e-coach accompanies the user throughout different structured and non-structured coaching activities and recommendations. The article also presents the design process of the coaching strategies, carried out including older adults from four European countries and experts from the different health domains, and the results of the tests carried out with 60 older adults in Italy, Spain and The Netherlands.
      Citation: Technologies
      PubDate: 2022-04-01
      DOI: 10.3390/technologies10020050
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 51: Rough-Set-Theory-Based Classification
           with Optimized k-Means Discretization

    • Authors: Teguh Handjojo Dwiputranto, Noor Akhmad Setiawan, Teguh Bharata Adji
      First page: 51
      Abstract: The discretization of continuous attributes in a dataset is an essential step before the Rough-Set-Theory (RST)-based classification process is applied. There are many methods for discretization, but not many of them have linked the RST instruments from the beginning of the discretization process. The objective of this research is to propose a method to improve the accuracy and reliability of the RST-based classifier model by involving RST instruments at the beginning of the discretization process. In the proposed method, a k-means-based discretization method optimized with a genetic algorithm (GA) was introduced. Four datasets taken from UCI were selected to test the performance of the proposed method. The evaluation of the proposed discretization technique for RST-based classification is performed by comparing it to other discretization methods, i.e., equal-frequency and entropy-based. The performance comparison among these methods is measured by the number of bins and rules generated and by its accuracy, precision, and recall. A Friedman test continued with post hoc analysis is also applied to measure the significance of the difference in performance. The experimental results indicate that, in general, the performance of the proposed discretization method is significantly better than the other compared methods.
      Citation: Technologies
      PubDate: 2022-04-08
      DOI: 10.3390/technologies10020051
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 52: Flow Stress Description Characteristics
           of Some Constitutive Models at Wide Strain Rates and Temperatures

    • Authors: Hyunho Shin, Yongwon Ju, Min Kuk Choi, Dong Ho Ha
      First page: 52
      Abstract: The commonly employed mathematical functions in constitutive models, such as the strain hardening/softening model, strain-rate hardening factor, and temperature-softening factor, are reviewed, and their prediction characteristics are illustrated. The results may assist one (i) to better understand the behavior of the constitutive model that employs a given mathematical function; (ii) to find the reason for deficiencies, if any, of an existing constitutive model; (iii) to avoid employing an inappropriate mathematical function in future constitutive models. This study subsequently illustrates the flow stress description characteristics of twelve constitutive models at wide strain rates (from 10−6 to 106 s−1) and temperatures (from absolute to melting temperatures) using the material parameters presented in the original studies. The phenomenological models considered herein include the Johnson–Cook, Shin–Kim, Lin–Wagoner, Sung–Kim–Wagoner, Khan–Huang–Liang, and Rusinek–Klepaczko models. The physically based models considered are the Zerilli–Armstrong, Voyiadjis–Abed, Testa et al., Steinberg et al., Preston–Tonks–Wallace, and Follansbee–Kocks models. The illustrations of the behavior of the foregoing constitutive models may be informative in (i) selecting an appropriate constitutive model; (ii) understanding and interpreting simulation results obtained using a given constitutive model; (iii) finding a reference material to develop future constitutive models.
      Citation: Technologies
      PubDate: 2022-04-11
      DOI: 10.3390/technologies10020052
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 53: Strategic Investment in Open Hardware for
           National Security

    • Authors: Joshua M. Pearce
      First page: 53
      Abstract: Free and open-source hardware (FOSH) development has been shown to increase innovation and reduce economic costs. This article reviews the opportunity to use FOSH as a sanction to undercut imports and exports from a target criminal country. A formal methodology is presented for selecting strategic national investments in FOSH development to improve both national security and global safety. In this methodology, first the target country that is threatening national security or safety is identified. Next, the top imports from the target country as well as potentially other importing countries (allies) are quantified. Hardware is identified that could undercut imports/exports from the target country. Finally, methods to support the FOSH development are enumerated to support production in a commons-based peer production strategy. To demonstrate how this theoretical method works in practice, it is applied as a case study to a current criminal military aggressor nation, who is also a fossil-fuel exporter. The results show that there are numerous existing FOSH and opportunities to develop new FOSH for energy conservation and renewable energy to reduce fossil-fuel-energy demand. Widespread deployment would reduce the concomitant pollution, human health impacts, and environmental desecration as well as cut financing of military operations.
      Citation: Technologies
      PubDate: 2022-04-18
      DOI: 10.3390/technologies10020053
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 4: Does One Size Fit All' A Case Study to
           Discuss Findings of an Augmented Hands-Free Robot Teleoperation Concept
           for People with and without Motor Disabilities

    • Authors: Stephanie Arévalo Arboleda, Marvin Becker, Jens Gerken
      First page: 4
      Abstract: Hands-free robot teleoperation and augmented reality have the potential to create an inclusive environment for people with motor disabilities. It may allow them to teleoperate robotic arms to manipulate objects. However, the experiences evoked by the same teleoperation concept and augmented reality can vary significantly for people with motor disabilities compared to those without disabilities. In this paper, we report the experiences of Miss L., a person with multiple sclerosis, when teleoperating a robotic arm in a hands-free multimodal manner using a virtual menu and visual hints presented through the Microsoft HoloLens 2. We discuss our findings and compare her experiences to those of people without disabilities using the same teleoperation concept. Additionally, we present three learning points from comparing these experiences: a re-evaluation of the metrics used to measure performance, being aware of the bias, and considering variability in abilities, which evokes different experiences. We consider these learning points can be extrapolated to carrying human–robot interaction evaluations with mixed groups of participants with and without disabilities.
      Citation: Technologies
      PubDate: 2022-01-06
      DOI: 10.3390/technologies10010004
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 5: Traffic Flow Prediction for Smart Traffic
           Lights Using Machine Learning Algorithms

    • Authors: Alfonso Navarro-Espinoza, Oscar Roberto López-Bonilla, Enrique Efrén García-Guerrero, Esteban Tlelo-Cuautle, Didier López-Mancilla, Carlos Hernández-Mejía, Everardo Inzunza-González
      First page: 5
      Abstract: Nowadays, many cities have problems with traffic congestion at certain peak hours, which produces more pollution, noise and stress for citizens. Neural networks (NN) and machine-learning (ML) approaches are increasingly used to solve real-world problems, overcoming analytical and statistical methods, due to their ability to deal with dynamic behavior over time and with a large number of parameters in massive data. In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow. Therefore, this work only focuses on traffic flow prediction. Two public datasets are used to train, validate and test the proposed ML and DL models. The first one contains the number of vehicles sampled every five minutes at six intersections for 56 days using different sensors. For this research, four of the six intersections are used to train the ML and DL models. The Multilayer Perceptron Neural Network (MLP-NN) obtained better results (R-Squared and EV score of 0.93) and took less training time, followed closely by Gradient Boosting then Recurrent Neural Networks (RNNs), with good metrics results but the longer training time, and finally Random Forest, Linear Regression and Stochastic Gradient. All ML and DL algorithms scored good performance metrics, indicating that they are feasible for implementation on smart traffic light controllers.
      Citation: Technologies
      PubDate: 2022-01-10
      DOI: 10.3390/technologies10010005
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 6: A Simplified Tantalum Oxide Memristor
           Model, Parameters Estimation and Application in Memory Crossbars

    • Authors: Valeri Mladenov, Stoyan Kirilov
      First page: 6
      Abstract: In this paper, an improved and simplified modification of a tantalum oxide memristor model is presented. The proposed model is applied and analyzed in hybrid and passive memory crossbars in LTSPICE environment and is based on the standard Ta2O5 memristor model proposed by Hewlett–Packard. The discussed modified model has several main enhancements—inclusion of a simplified window function, improvement of its effectiveness by the use of a simple expression for the i–v relationship, and replacement of the classical Heaviside step function with a differentiable and flat step-like function. The optimal values of coefficients of the tantalum oxide memristor model are derived by comparison of experimental current–voltage relationships and by using a procedure for parameter estimation. A simplified LTSPICE library model, correspondent to the analyzed tantalum oxide memristor, is created in accordance with the considered mathematical model. The improved and altered Ta2O5 memristor model is tested and simulated in hybrid and passive memory crossbars for a state near to a hard-switching operation. After a comparison of several of the best existing memristor models, the main pros of the proposed memristor model are highlighted—its improved implementation, better operating rate, and good switching properties.
      Citation: Technologies
      PubDate: 2022-01-10
      DOI: 10.3390/technologies10010006
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 7: A Simulated Environment for Robot Vision

    • Authors: Christos Sevastopoulos, Stasinos Konstantopoulos, Keshav Balaji, Mohammad Zaki Zadeh, Fillia Makedon
      First page: 7
      Abstract: Training on simulation data has proven invaluable in applying machine learning in robotics. However, when looking at robot vision in particular, simulated images cannot be directly used no matter how realistic the image rendering is, as many physical parameters (temperature, humidity, wear-and-tear in time) vary and affect texture and lighting in ways that cannot be encoded in the simulation. In this article we propose a different approach for extracting value from simulated environments: although neither of the trained models can be used nor are any evaluation scores expected to be the same on simulated and physical data, the conclusions drawn from simulated experiments might be valid. If this is the case, then simulated environments can be used in early-stage experimentation with different network architectures and features. This will expedite the early development phase before moving to (harder to conduct) physical experiments in order to evaluate the most promising approaches. In order to test this idea we created two simulated environments for the Unity engine, acquired simulated visual datasets, and used them to reproduce experiments originally carried out in a physical environment. The comparison of the conclusions drawn in the physical and the simulated experiments is promising regarding the validity of our approach.
      Citation: Technologies
      PubDate: 2022-01-12
      DOI: 10.3390/technologies10010007
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 8: Assistive Technologies for Supporting the
           Wellbeing of Older Adults

    • Authors: Ioanna Dratsiou, Annita Varella, Evangelia Romanopoulou, Oscar Villacañas, Sara Cooper, Pavlos Isaris, Manex Serras, Luis Unzueta, Tatiana Silva, Alexia Zurkuhlen, Malcolm MacLachlan, Panagiotis D. Bamidis
      First page: 8
      Abstract: As people age, they are more likely to develop multiple chronic diseases and experience a decline in some of their physical and cognitive functions, leading to the decrease in their ability to live independently. Innovative technology-based interventions tailored to older adults’ functional levels and focused on healthy lifestyles are considered imperative. This work proposed a framework of active and healthy ageing through the integration of a broad spectrum of digital solutions into an open Pan-European technological platform in the context of the SHAPES project, an EU-funded innovation action. In conclusion, the SHAPES project can potentially engage older adults in a holistic technological ecosystem and, therefore, facilitate the maintenance of a high-quality standard of life.
      Citation: Technologies
      PubDate: 2022-01-14
      DOI: 10.3390/technologies10010008
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 9: 3D Scanning/Printing: A Technological
           Stride in Sculpture

    • Authors: G.-Fivos Sargentis, Evangelia Frangedaki, Michalis Chiotinis, Demetris Koutsoyiannis, Stephanos Camarinopoulos, Alexios Camarinopoulos, Nikos D. Lagaros
      First page: 9
      Abstract: The creation of innovative tools, objects and artifacts that introduce abstract ideas in the real world is a necessary step for the evolution process and characterize the creative capacity of civilization. Sculpture is based on the available technology for its creation process and is strongly related to the level of technological sophistication of each era. This paper analyzes the evolution of basic sculpture techniques (carving, lost-wax casting and 3D scanning/printing), and their importance as a culture footprint. It also presents and evaluates the added creative capacities of each technological step and the different methods of 3D scanning/printing concerning sculpture. It is also an attempt to define the term “material poetics”, which is connected to sculpture artifacts. We conclude that 3D scanning/printing is an important sign of civilization, although artifacts lose a part of material poetics with additive manufacturing. Subsequently, there are various causes of the destruction of sculptures, leaving a hole in the history of art. Finally, this paper showcases the importance of 3D scanning/printing in salvaging cultural heritage, as it has radically altered the way we “backup” objects.
      Citation: Technologies
      PubDate: 2022-01-14
      DOI: 10.3390/technologies10010009
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 10: Enhanced Air Stability of Perovskite
           Quantum Dots by Manganese Passivation

    • Authors: Ryota Sato, Kazuki Umemoto, Satoshi Asakura, Akito Masuhara
      First page: 10
      Abstract: Organic-inorganic perovskite quantum dots (PeQDs) have attracted attention due to their excellent optical properties, e.g., high photoluminescence quantum yields (PLQYs; >70%), a narrow full width at half maximum (FWHM; 25 nm or less), and color tunability adjusted by the halide components in an entire tunability (from 450 nm to 730 nm). On the other hand, PeQD stability against air, humidity, and thermal conditions has still not been enough, which disturbs their application. To overcome these issues, with just a focus on the air stability, Mn2+ ion passivated perovskite quantum dots (Mn/MAPbBr3 QDs) were prepared. Mn2+ could be expected to contract the passivating layer against the air condition because the Mn2+ ion was changed to the oxidized Mn on PeQDs under the air conditions. In this research, Mn/MAPbBr3 QDs were successfully prepared by ligand-assisted reprecipitation (LARP) methods. Surprisingly, Mn/MAPbBr3 QD films showed more than double PLQY stability over 4 months compared with pure MAPbBr3 ones against the air, which suggested that oxidized Mn worked as a passivating layer. Improving the PeQD stability is significantly critical for their application.
      Citation: Technologies
      PubDate: 2022-01-16
      DOI: 10.3390/technologies10010010
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 11: Water-Assisted Perovskite Quantum Dots
           with High Optical Properties

    • Authors: Masaaki Yokoyama, Ryota Sato, Junya Enomoto, Naoaki Oshita, Taisei Kimura, Keisuke Kikuchi, Satoshi Asakura, Kazuki Umemoto, Akito Masuhara
      First page: 11
      Abstract: Lead halide perovskite quantum dots (PeQDs) have excellent optical properties, such as narrow emission spectra (FWHM: 18–30 nm), a tunable bandgap (λPL: 420–780 nm), and excellent photoluminescence quantum yields (PLQYs: >90%). PeQDs are known as a material that is easily decomposed when exposed to water in the atmosphere, resulting in causing PeQDs to lower performance. On the other hand, according to the recent reports, adding water after preparing the PeQD dispersion decomposed the PeQD surface defects, resulting in improving their PLQY. Namely, controlling the amount of assisting water during the preparation of the PeQDs is a significantly critical factor to determining their optical properties and device applications. In this paper, our research group discovered the novel effects of the small amount of water to their optical properties when preparing the PeQDs. According to the TEM Images, the PeQDs particle size was clearly increased after water-assisting. In addition, XPS measurement showed that the ratio of Br/Pb achieved to be close to three. Namely, by passivating the surface defect using Ostwald ripening, the prepared PeQDs achieved a high PLQY of over 95%.
      Citation: Technologies
      PubDate: 2022-01-17
      DOI: 10.3390/technologies10010011
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 12: A Review of Efficient Real-Time Decision
           Making in the Internet of Things

    • Authors: Kyoung-Don Kang
      First page: 12
      Abstract: Emerging applications of IoT (the Internet of Things), such as smart transportation, health, and energy, are envisioned to greatly enhance the societal infrastructure and quality of life of individuals. In such innovative IoT applications, cost-efficient real-time decision-making is critical to facilitate, for example, effective transportation management and healthcare. In this paper, we formally define real-time decision tasks in IoT, review cutting-edge approaches that aim to efficiently schedule real-time decision tasks to meet their timing and data freshness constraints, review state-of-the-art approaches for efficient sensor data analytics in IoT, and discuss future research directions.
      Citation: Technologies
      PubDate: 2022-01-19
      DOI: 10.3390/technologies10010012
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 13: IoT Framework for Measurement and
           Precision Agriculture: Predicting the Crop Using Machine Learning

    • Authors: Kalaiselvi Bakthavatchalam, Balaguru Karthik, Vijayan Thiruvengadam, Sriram Muthal, Deepa Jose, Ketan Kotecha, Vijayakumar Varadarajan
      First page: 13
      Abstract: IoT architectures facilitate us to generate data for large and remote agriculture areas and the same can be utilized for Crop predictions using this machine learning algorithm. Recommendations are based on the following N, P, K, pH, Temperature, Humidity, and Rainfall these attributes decide the crop to be recommended. The data set has 2200 instances and 8 attributes. Nearly 22 different crops are recommended for a different combination of 8 attributes. Using the supervised learning method, the optimum model is attained using selected machine learning algorithms in WEKA. The Machine learning algorithm selected for classifying is multilayer perceptron rules-based classifier JRip, and decision table classifier. The main objective of this case study is to end up with a model which predicts the high yield crop and precision agriculture. The proposed system modeling incorporates the trending technology, IoT, and Agriculture needy measurements. The performance assessed by the selected classifiers is 98.2273%, the Weighted average Receiver Operator Characteristics is 1 with the maximum time taken to build the model being 8.05 s.
      Citation: Technologies
      PubDate: 2022-01-20
      DOI: 10.3390/technologies10010013
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 14: Efficient Stochastic Computing FIR
           Filtering Using Sigma-Delta Modulated Signals

    • Authors: Nikos Temenos, Anastasis Vlachos, Paul P. Sotiriadis
      First page: 14
      Abstract: This work presents a soft-filtering digital signal processing architecture based on sigma-delta modulators and stochastic computing. A sigma-delta modulator converts the input high-resolution signal to a single-bit stream enabling filtering structures to be realized using stochastic computing’s negligible-area multipliers. Simulation in the spectral domain demonstrates the filter’s proper operation and its roll-off behavior, as well as the signal-to-noise ratio improvement using the sigma-delta modulator, compared to typical stochastic computing filter realizations. The proposed architecture’s hardware advantages are showcased with synthesis results for two FIR filters using FPGA and synopsys tools, while comparisons with standard stochastic computing-based hardware realizations, as well as with conventional binary ones, demonstrate its efficacy.
      Citation: Technologies
      PubDate: 2022-01-20
      DOI: 10.3390/technologies10010014
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 15: Self-Organizing and Self-Explaining
           Pervasive Environments by Connecting Smart Objects and Applications

    • Authors: Börge Kordts, Bennet Gerlach, Andreas Schrader
      First page: 15
      Abstract: In the past decade, pervasive environments have progressed from promising research concepts to available products present in our everyday lives. By connecting multiple smart objects, device ensembles can be formed to assist users in performing tasks. Furthermore, smart objects can be used to control applications, that, in turn, can be used to control other smart objects. As manual configuration is often time-consuming, an automatic connection of the components may present a useful tool, which should take various aspects into account. While dynamically connecting these components allows for solutions tailored to the needs and respective tasks of a user, it obfuscates the handling and ultimately may decrease usability. Self-descriptions have been proposed to overcome this issue for ensembles of smart objects. For a more extensive approach, descriptions of applications in pervasive environments need to be addressed as well. Based on previous research in the context of self-explainability of smart objects, we propose a description language as well as a framework to support self-explaining ambient applications (applications that are used within smart environments). The framework can be used to manually or automatically connect smart objects as well as ambient applications and to realize self-explainability for these interconnected device and application ensembles.
      Citation: Technologies
      PubDate: 2022-01-24
      DOI: 10.3390/technologies10010015
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 16: Novel Benes Network Routing Algorithm and
           Hardware Implementation

    • Authors: Dimitris Nikolaidis, Panos Groumas, Christos Kouloumentas, Hercules Avramopoulos
      First page: 16
      Abstract: Benes/Clos networks constitute a particularly important part of interconnection networks and have been used in numerous areas, such as multi-processor systems, data centers and on-chip networks. They have also attracted great interest in the field of optical communications due to the increasing popularity of optical switches based on these architectures. There are numerous algorithms aimed at routing these types of networks, with varying degrees of utility. Linear algorithms, such as Sun Tsu and Opferman, were historically the first attempt to standardize the routing procedure of this types of networks. They require matrix-based calculations, which are very demanding in terms of resources and in some cases involve backtracking, which impairs their efficiency. Parallel solutions, such as Lee’s algorithm, were introduced later and provide a different answer that satisfy the requirements of high-performance networks. They are, however, extremely complex and demand even more resources. In both cases, hardware implementations reflect their algorithmic characteristics. In this paper, we attempt to design an algorithm that is simple enough to be implemented on a small field programmable gate array board while simultaneously efficient enough to be used in practical scenarios. The design itself is of a generic nature; therefore, its behavior across different sizes (8 × 8,16 × 16,32 × 32,64 × 64) is examined. The platform of implementation is a medium range FPGA specifically selected to represent the average hardware prototyping device. In the end, an overview of the algorithm’s imprint on the device is presented alongside other approaches, which include both hard and soft computing techniques.
      Citation: Technologies
      PubDate: 2022-01-25
      DOI: 10.3390/technologies10010016
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 17: Stacking-Based Ensemble Learning Method
           for Multi-Spectral Image Classification

    • Authors: Tagel Aboneh, Abebe Rorissa, Ramasamy Srinivasagan
      First page: 17
      Abstract: Higher dimensionality, Hughes phenomenon, spatial resolution of image data, and presence of mixed pixels are the main challenges in a multi-spectral image classification process. Most of the classical machine learning algorithms suffer from scoring optimal classification performance over multi-spectral image data. In this study, we propose stack-based ensemble-based learning approach to optimize image classification performance. In addition, we integrate the proposed ensemble learning with XGBoost method to further improve its classification accuracy. To conduct the experiment, the Landsat image data has been acquired from Bishoftu town located in the Oromia region of Ethiopia. The current study’s main objective was to assess the performance of land cover and land use analysis using multi-spectral image data. Results from our experiment indicate that, the proposed ensemble learning method outperforms any strong base classifiers with 99.96% classification performance accuracy.
      Citation: Technologies
      PubDate: 2022-01-26
      DOI: 10.3390/technologies10010017
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 18: Acknowledgment to Reviewers of
           Technologies in 2021

    • Authors: Technologies Editorial Office Technologies Editorial Office
      First page: 18
      Abstract: Rigorous peer-reviews are the basis of high-quality academic publishing [...]
      Citation: Technologies
      PubDate: 2022-01-28
      DOI: 10.3390/technologies10010018
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 19: On the Exploration of Automatic Building
           Extraction from RGB Satellite Images Using Deep Learning Architectures
           Based on U-Net

    • Authors: Anastasios Temenos, Nikos Temenos, Anastasios Doulamis, Nikolaos Doulamis
      First page: 19
      Abstract: Detecting and localizing buildings is of primary importance in urban planning tasks. Automating the building extraction process, however, has become attractive given the dominance of Convolutional Neural Networks (CNNs) in image classification tasks. In this work, we explore the effectiveness of the CNN-based architecture U-Net and its variations, namely, the Residual U-Net, the Attention U-Net, and the Attention Residual U-Net, in automatic building extraction. We showcase their robustness in feature extraction and information processing using exclusively RGB images, as they are a low-cost alternative to multi-spectral and LiDAR ones, selected from the SpaceNet 1 dataset. The experimental results show that U-Net achieves a 91.9% accuracy, whereas introducing residual blocks, attention gates, or a combination of both improves the accuracy of the vanilla U-Net to 93.6%, 94.0%, and 93.7%, respectively. Finally, the comparison between U-Net architectures and typical deep learning approaches from the literature highlights their increased performance in accurate building localization around corners and edges.
      Citation: Technologies
      PubDate: 2022-01-29
      DOI: 10.3390/technologies10010019
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 20: Results of Preliminary Studies on the
           Perception of the Relationships between Objects Presented in a Cartesian

    • Authors: Ira Woodring, Charles Owen
      First page: 20
      Abstract: Visualizations often use the paradigm of a Cartesian space for the presentation of objects and information. Unified Modeling Language (UML) is a visual language used to describe relationships in processes and systems and is heavily used in computer science and software engineering. Visualizations are a powerful development tool, but are not necessarily accessible to all users, as individuals may differ in their level of visual ability or perceptual biases. Sonfication methods can be used to supplement or, in some cases, replace visual models. This paper describes two studies created to determine the ability of users to perceive relationships between objects in a Cartesian space when presented in a sonified form. Results from this study will be used to guide the creation of sonified UML software.
      Citation: Technologies
      PubDate: 2022-01-30
      DOI: 10.3390/technologies10010020
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 21: A Lightweight Messaging Protocol for
           Internet of Things Devices

    • Authors: Justice Owusu Agyemang, Jerry John Kponyo, James Dzisi Gadze, Henry Nunoo-Mensah, Dantong Yu
      First page: 21
      Abstract: The move towards intelligent systems has led to the evolution of IoT. This technological leap has over the past few years introduced significant improvements to various aspects of the human environment, such as health, commerce, transport, etc. IoT is data-centric; hence, it is required that the underlying protocols are scalable and sufficient to support the vast D2D communication. Several application layer protocols are being used for M2M communication protocols such as CoAP, MQTT, etc. Even though these messaging protocols have been designed for M2M communication, they are still not optimal for communications where message size and overhead are of much concern. This research paper presents a Lightweight Messaging Protocol (LiMP), which is a minified version of CoAP. We present a detailed protocol stack of the proposed messaging protocol and also perform a benchmark analysis of the protocol on some IoT devices. The proposed minified protocol achieves minimal overhead (a header size of 2 bytes) and has faster point-to-point communication from the benchmark analysis; for communication over LAN, the LiMP-TCP outperformed the CoAP-TCP by an average of 21% whereas that of LiMP-UDP was over 37%. For a device to remote server communication, LiMP outperformed CoAP by an average of 15%.
      Citation: Technologies
      PubDate: 2022-01-29
      DOI: 10.3390/technologies10010021
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 22: An Affordable Upper-Limb Exoskeleton
           Concept for Rehabilitation Applications

    • Authors: Emanuele Palazzi, Luca Luzi, Eldison Dimo, Andrea Calanca
      First page: 22
      Abstract: In recent decades, many researchers have focused on the design and development of exoskeletons. Several strategies have been proposed to develop increasingly more efficient and biomimetic mechanisms. However, existing exoskeletons tend to be expensive and only available for a few people. This paper introduces a new gravity-balanced upper-limb exoskeleton suited for rehabilitation applications and designed with the main objective of reducing the cost of the components and materials. Regarding mechanics, the proposed design significantly reduces the motor torque requirements, because a high cost is usually associated with high-torque actuation. Regarding the electronics, we aim to exploit the microprocessor peripherals to obtain parallel and real-time execution of communication and control tasks without relying on expensive RTOSs. Regarding sensing, we avoid the use of expensive force sensors. Advanced control and rehabilitation features are implemented, and an intuitive user interface is developed. To experimentally validate the functionality of the proposed exoskeleton, a rehabilitation exercise in the form of a pick-and-place task is considered. Experimentally, peak torques are reduced by 89% for the shoulder and by 84% for the elbow.
      Citation: Technologies
      PubDate: 2022-01-30
      DOI: 10.3390/technologies10010022
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 23: Analysis of the Impact of Electrical and
           Timing Masking on Soft Error Rate Estimation in VLSI Circuits

    • Authors: Pelopidas Tsoumanis, Georgios Ioannis Paliaroutis, Nestor Evmorfopoulos, George Stamoulis
      First page: 23
      Abstract: Due to continuous CMOS technology downscaling, Integrated Circuits (ICs) have become more susceptible to radiation-induced hazards such as soft errors. Thus, to design radiation-hardened and reliable ICs, the Soft Error Rate (SER) estimation constitutes an essential procedure. An accurate SER evaluation is provided based on a SPICE-oriented electrical masking analysis, combined with a TCAD characterization process. Furthermore, the proposed work analyzes the effect of a Static Timing Analysis (STA) methodology and the actual interconnection delay on SER evaluation. An analysis of the generated Single Event Multiple Transients (SEMTs) and the circuit operating frequency that are related to the SER estimation is also discussed. Various benchmarks, synthesized utilizing a 45 nm and 15 nm technology, are employed, and the experimental results demonstrate the SER variation as the device node scales down.
      Citation: Technologies
      PubDate: 2022-01-31
      DOI: 10.3390/technologies10010023
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 24: Improving Effectiveness of a Coaching
           System through Preference Learning

    • Authors: Martin Žnidaršič, Aljaž Osojnik, Peter Rupnik, Bernard Ženko
      First page: 24
      Abstract: The paper describes an approach for indirect data-based assessment and use of user preferences in an unobtrusive sensor-based coaching system with the aim of improving coaching effectiveness. The preference assessments are used to adapt the reasoning components of the coaching system in a way to better align with the preferences of its users. User preferences are learned based on data that describe user feedback as reported for different coaching messages that were received by the users. The preferences are not learned directly, but are assessed through a proxy—classifications or probabilities of positive feedback as assigned by a predictive machine learned model of user feedback. The motivation and aim of such an indirect approach is to allow for preference estimation without burdening the users with interactive preference elicitation processes. A brief description of the coaching setting is provided in the paper, before the approach for preference assessment is described and illustrated on a real-world example obtained during the testing of the coaching system with elderly users.
      Citation: Technologies
      PubDate: 2022-01-31
      DOI: 10.3390/technologies10010024
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 25: User-Centric Design Methodology for
           mHealth Apps. The PainApp Paradigm for Chronic Pain

    • Authors: Yiannis Koumpouros
      First page: 25
      Abstract: The paper presents a user-centric methodology in order to design successful mobile health (mHealth) applications. In addition to the theoretical background, such an example is presented with an application targeting chronic pain. The pain domain was decided due to its significance in many aspects: its complexity, dispersion in the population, the financial burden it causes, etc. The paper presents a step-by-step plan in order to build mobile health applications. Participatory design and interdisciplinarity are only some of the critical issues towards the desired result. In the given example (development of the PainApp), a participatory design was followed with a team of seventeen stakeholders that drove the design and development phases. Three physicians, one behavioral scientist, three IT and UX experts, and ten patients collaborated together to develop the final solution. The several features implemented in the PainApp solution are presented in details. The application is threefold: it supports the management, reporting, and treatment effectiveness monitoring. The paper is giving details on the methodological approach while presenting insights on the actual plan and the steps followed for having a patient-centric solution. Key success factors and barriers to mobile health applications that support the need for such an approach are also presented.
      Citation: Technologies
      PubDate: 2022-01-31
      DOI: 10.3390/technologies10010025
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 26: Reliable IoT-Based Monitoring and Control
           of Hydroponic Systems

    • Authors: Konstantinos Tatas, Ahmad Al-Zoubi, Nicholas Christofides, Chrysostomos Zannettis, Michael Chrysostomou, Stavros Panteli, Anthony Antoniou
      First page: 26
      Abstract: This paper presents the design and implementation of iPONICS: an intelligent, low-cost IoT-based control and monitoring system for hydroponics greenhouses. The system is based on three types of sensor nodes. The main (master) node is responsible for controlling the pump, monitoring the quality of the water in the greenhouse and aggregating and transmitting the data from the slave nodes. Environment sensing slave nodes monitor the ambient conditions in the greenhouse and transmit the data to the main node. Security nodes monitor activity (movement in the area). The system monitors water quality and greenhouse temperature and humidity, ensuring that crops grow under optimal conditions according to hydroponics guidelines. Remote monitoring for the greenhouse keepers is facilitated by monitoring these parameters via connecting to a website. An innovative fuzzy inference engine determines the plant irrigation duration. The system is optimized for low power consumption in order to facilitate off-grid operation. Preliminary reliability analysis indicates that the system can tolerate various transient faults without requiring intervention.
      Citation: Technologies
      PubDate: 2022-02-02
      DOI: 10.3390/technologies10010026
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 27: Performance Analysis of 2D and 3D
           Bufferless NoCs Using Markov Chain Models

    • Authors: Konstantinos Tatas
      First page: 27
      Abstract: Performance analysis and design space exploration of bufferless Networks-on-Chip is done mainly through time-consuming cycle-accurate simulation, due to the chaotic nature of packet deflections, which have thus far prevented the development of an accurate analytical model. In order to raise the level of abstraction as well as capture the inherently probabilistic behavior of deflection routing, this paper presents a methodology for employing Markov chain models in the analysis of the behavior of bufferless Networks-on-Chip. A formal way of describing a bufferless NoC topology as a set of discrete-time Markov chains is presented. It is demonstrated that by combining this description with the network average distance, it is possible to obtain the expectation of the number of hops between any pair of nodes in the network as a function of the flit deflection probability. Comparisons between the proposed model and cycle-accurate simulation demonstrate the accuracy achieved by the model, with negligible computational cost. The useful range of the proposed model is quantified, demonstrating that it has an error of less than 10% for a significant proportion (between 33 and 75%) of the injection rate range below saturation. Finally, a simple equation for comparing mesh topologies with a “back-of-the-envelope” calculation is introduced.
      Citation: Technologies
      PubDate: 2022-02-02
      DOI: 10.3390/technologies10010027
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 28: Visible Light Communications for Internet
           of Things: Prospects and Approaches, Challenges, Solutions and Future

    • Authors: Stephen S. Oyewobi, Karim Djouani, Anish Matthew Kurien
      First page: 28
      Abstract: Visible light communications (VLC) is an emerging and promising concept that is capable of solving the major challenges of 5G and Internet of Things (IoT) communication systems. Moreover, due to the usage of light-emitting diodes (LEDs) in almost every aspect of our daily life VLC is providing massive connectivity for various types of massive IoT communications ranging from machine-to-machine, vehicle-to-infrastructure, infrastructure-to-vehicle, chip-to-chip as well as device-to-device. In this paper, we undertake a comprehensive review of the prospects of implementing VLC for IoT. Moreover, we investigate existing and proposed approaches implemented in the application of VLC for IoT. Additionally, we look at the challenges faced in applying VLC for IoT and offer solutions where applicable. Then, we identify future research directions in the implementation of VLC for IoT.
      Citation: Technologies
      PubDate: 2022-02-05
      DOI: 10.3390/technologies10010028
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 29: On the In-Die Conditions and Process
           Parameter Settings in Indirect Squeeze Casting

    • Authors: Anders E. W. Jarfors, Andong Du, Jie Zhou, Jinchuan Zheng, Gegang Yu
      First page: 29
      Abstract: The current study investigated the relationship between the process settings and in-die conditions to understand the transitions between the different filling stages and the final pressure settings in indirect squeeze casting. A pressure sensor was placed in the die cavity to indirectly measure the evolution of pressure over time and monitor the filling process to study the in-die conditions. The pressure–time profile was analysed, and the maximum pressure and acceleration of the pressure were investigated empirically. The main conclusion of this paper is that the use of increasing intensification pressures is positive for the casting soundness. However, it must be stressed that there is a strong effect from the intensification pressure on the acceleration that has a far more reaching influence than the actual speed setting. A direct practical outcome is that a high intensification pressure has a more substantial effect than the second stage fill speed. This translates directly to a possibility of reducing the second stage fill speed to stabilise the fill front. Furthermore, this also pinpoints the need for improvements in hydraulics system designs to decouple the intensification pressure from the filling piston motion control.
      Citation: Technologies
      PubDate: 2022-02-11
      DOI: 10.3390/technologies10010029
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 30: Adapt or Perish' Exploring the
           Effectiveness of Adaptive DoF Control Interaction Methods for Assistive
           Robot Arms

    • Authors: Kirill Kronhardt, Stephan Rübner, Max Pascher, Felix Ferdinand Goldau, Udo Frese, Jens Gerken
      First page: 30
      Abstract: Robot arms are one of many assistive technologies used by people with motor impairments. Assistive robot arms can allow people to perform activities of daily living (ADL) involving grasping and manipulating objects in their environment without the assistance of caregivers. Suitable input devices (e.g., joysticks) mostly have two Degrees of Freedom (DoF), while most assistive robot arms have six or more. This results in time-consuming and cognitively demanding mode switches to change the mapping of DoFs to control the robot. One option to decrease the difficulty of controlling a high-DoF assistive robot arm using a low-DoF input device is to assign different combinations of movement-DoFs to the device’s input DoFs depending on the current situation (adaptive control). To explore this method of control, we designed two adaptive control methods for a realistic virtual 3D environment. We evaluated our methods against a commonly used non-adaptive control method that requires the user to switch controls manually. This was conducted in a simulated remote study that used Virtual Reality and involved 39 non-disabled participants. Our results show that the number of mode switches necessary to complete a simple pick-and-place task decreases significantly when using an adaptive control type. In contrast, the task completion time and workload stay the same. A thematic analysis of qualitative feedback of our participants suggests that a longer period of training could further improve the performance of adaptive control methods.
      Citation: Technologies
      PubDate: 2022-02-14
      DOI: 10.3390/technologies10010030
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 31: Insights on the Effect and Experience of
           a Diet-Tracking Application for Older Adults in a Diet Trial

    • Authors: Laura M. van der Lubbe, Michel C. A. Klein, Marjolein Visser, Hanneke A. H. Wijnhoven, Ilse Reinders
      First page: 31
      Abstract: With an ageing population, healthy ageing becomes more important. Healthy nutrition is part of this process and can be supported in many ways. The PROMISS trial studies the effect of increasing protein intake in older adults on their physical functioning. Within this trial, a sub-study was performed, researching the added effect of using a diet-tracking app enhanced with persuasive and (optional) gamification techniques. The goal was to see how older adult participants received such technology within their diet program. There were 48 participants included in this sub-study, of which 36 completed the study period of 6 months. Our results on adherence and user evaluation show that a dedicated app used within the PROMISS trial is a feasible way to engage older adults in diet tracking. On average, participants used the app 83% of the days, during a period of on average 133 days. User-friendliness was evaluated with an average score of 4.86 (out of 7), and experienced effectiveness was evaluated with an average score of 4.57 (out of 7). However, no effect of the technology on protein intake was found. The added gamification elements did not have a different effect compared with the version without those elements. However, some participants did like the added gamification elements, and it can thus be nice to add them as additional features for participants that like them. This article also studies whether personal characteristics correlate with any of the other results. Although some significant results were found, this does not give a clear view on which types of participants like or benefit from this technology.
      Citation: Technologies
      PubDate: 2022-02-16
      DOI: 10.3390/technologies10010031
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 32: Mechanical Properties of Sustainable
           Metal Matrix Composites: A Review on the Role of Green Reinforcements and
           Processing Methods

    • Authors: Sankaranarayanan Seetharaman, Jayalakshmi Subramanian, Ramachandra Arvind Singh, Wai Leong Eugene Wong, Mui Ling Sharon Nai, Manoj Gupta
      First page: 32
      Abstract: Growing concerns like depleting mineral resources, increased materials wastage, and structural light-weighting requirements due to emission control regulations drive the development of sustainable metal matrix composites. Al and Mg based alloys with relatively lower melting temperatures qualify for recycling applications and hence are considered as the matrix material for developing sustainable composites. The recent trend also explores various industrial by-products and agricultural wastes as green reinforcements, and this article presents insights on the properties of Al and Mg based sustainable metal matrix composites with special emphasis on green reinforcements and processing methods.
      Citation: Technologies
      PubDate: 2022-02-16
      DOI: 10.3390/technologies10010032
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 33: Self-Supervised Human Activity
           Representation for Embodied Cognition Assessment

    • Authors: Mohammad Zaki Zadeh, Ashwin Ramesh Babu, Ashish Jaiswal, Fillia Makedon
      First page: 33
      Abstract: Physical activities, according to the embodied cognition theory, are an important manifestation of cognitive functions. As a result, in this paper, the Activate Test of Embodied Cognition (ATEC) system is proposed to assess various cognitive measures. It consists of physical exercises with different variations and difficulty levels designed to provide assessment of executive and motor functions. This work focuses on obtaining human activity representation from recorded videos of ATEC tasks in order to automatically assess embodied cognition performance. A self-supervised approach is employed in this work that can exploit a small set of annotated data to obtain an effective human activity representation. The performance of different self-supervised approaches along with a supervised method are investigated for automated cognitive assessment of children performing ATEC tasks. The results show that the supervised learning approach performance decreases as the training set becomes smaller, whereas the self-supervised methods maintain their performance by taking advantage of unlabeled data.
      Citation: Technologies
      PubDate: 2022-02-17
      DOI: 10.3390/technologies10010033
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 34: Editorial for the Special Issue
           “Advances in Multiscale and Multifield Solid Material

    • Authors: Raffaella Rizzoni, Frédéric Lebon, Serge Dumont, Michele Serpilli
      First page: 34
      Abstract: Interfaces play an essential role in determining the mechanical properties and the structural integrity of a wide variety of technological materials [...]
      Citation: Technologies
      PubDate: 2022-02-18
      DOI: 10.3390/technologies10010034
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 35: Sovereign Digital Consent through Privacy
           Impact Quantification and Dynamic Consent

    • Authors: Arno Appenzeller, Marina Hornung, Thomas Kadow, Erik Krempel, Jürgen Beyerer
      First page: 35
      Abstract: Digitization is becoming more and more important in the medical sector. Through electronic health records and the growing amount of digital data of patients available, big data research finds an increasing amount of use cases. The rising amount of data and the imposing privacy risks can be overwhelming for patients, so they can have the feeling of being out of control of their data. Several previous studies on digital consent have tried to solve this problem and empower the patient. However, there are no complete solution for the arising questions yet. This paper presents the concept of Sovereign Digital Consent by the combination of a consent privacy impact quantification and a technology for proactive sovereign consent. The privacy impact quantification supports the patient to comprehend the potential risk when sharing the data and considers the personal preferences regarding acceptance for a research project. The proactive dynamic consent implementation provides an implementation for fine granular digital consent, using medical data categorization terminology. This gives patients the ability to control their consent decisions dynamically and is research friendly through the automatic enforcement of the patients’ consent decision. Both technologies are evaluated and implemented in a prototypical application. With the combination of those technologies, a promising step towards patient empowerment through Sovereign Digital Consent can be made.
      Citation: Technologies
      PubDate: 2022-02-21
      DOI: 10.3390/technologies10010035
      Issue No: Vol. 10, No. 1 (2022)
  • Technologies, Vol. 10, Pages 36: Effective Current Pre-Amplifiers for
           Visible Light Communication (VLC) Receivers

    • Authors: Simon-Ilias Poulis, Georgios Papatheodorou, Christoforos Papaioannou, Yiorgos Sfikas, Marina E. Plissiti, Aristides Efthymiou, John Liaperdos, Yiorgos Tsiatouhas
      First page: 36
      Abstract: Visible light communication (VLC) is an upcoming wireless communication technology. In a VLC system, signal integrity under low illumination intensity and high transmission frequencies are of great importance. Towards this direction, the performance of the analog front end (AFE) sub-system either at the side of the transmitter or the receiver is crucial. However, little research on the AFE of the receiver is reported in the open literature. Aiming to enhance signal integrity, three pre-amplification topologies for the VLC receiver AFE are presented and compared in this paper. All three use bipolar transistors (BJT): the first consists of a single BJT, the second of a double BJT in cascade connection, and the third of a double BJT in Darlington-like connection. In order to validate the performance characteristics of the three topologies, simulation results are provided with respect to the light illumination intensity, the data transmission frequency and the power consumption. According to these simulations, the third topology is characterized by higher data transmission frequencies, lower illuminance intensity and lower power consumption per MHz of operation.
      Citation: Technologies
      PubDate: 2022-02-21
      DOI: 10.3390/technologies10010036
      Issue No: Vol. 10, No. 1 (2022)
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