Technologies

15 pages, 766 KiB

Open AccessReview

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

by Ravichandra Madanu, Maysam F. Abbod, Fu-Jung Hsiao, Wei-Ta Chen and Jiann-Shing Shieh

Technologies 2022, 10(3), 74; https://doi.org/10.3390/technologies10030074 - 14 Jun 2022

Cited by 8 | Viewed by 4387

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)

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35 pages, 4322 KiB

Open AccessArticle

Solving Dual-Channel Supply Chain Pricing Strategy Problem with Multi-Level Programming Based on Improved Simplified Swarm Optimization

by Wei-Chang Yeh, Zhenyao Liu, Yu-Cheng Yang and Shi-Yi Tan

Technologies 2022, 10(3), 73; https://doi.org/10.3390/technologies10030073 - 11 Jun 2022

Cited by 5 | Viewed by 2560

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)

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13 pages, 2091 KiB

Open AccessArticle

Two-Step Validation of a New Wireless Inertial Sensor System: Application in the Squat Motion

by Mathias Blandeau, Romain Guichard, Rémy Hubaut and Sébastien Leteneur

Technologies 2022, 10(3), 72; https://doi.org/10.3390/technologies10030072 - 09 Jun 2022

Cited by 5 | Viewed by 2255

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 [...] Read more.

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. Full article

(This article belongs to the Section Assistive Technologies)

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10 pages, 28371 KiB

Open AccessArticle

An Application of Artificial Neural Networks to Estimate the Performance of High-Energy Laser Weapons in Maritime Environments

by Antonios Lionis, Andreas Tsigopoulos and Keith Cohn

Technologies 2022, 10(3), 71; https://doi.org/10.3390/technologies10030071 - 08 Jun 2022

Cited by 4 | Viewed by 2663

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 [...] Read more.

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). Full article

(This article belongs to the Section Environmental Technology)

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23 pages, 27759 KiB

Open AccessArticle

Numerical Simulation and Optimization of Microwave Heating Effect on Coal Seam Permeability Enhancement

by Ali Jebelli, Arezoo Mahabadi and Rafiq Ahmad

Technologies 2022, 10(3), 70; https://doi.org/10.3390/technologies10030070 - 06 Jun 2022

Cited by 4 | Viewed by 2081

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue New Advances in Microwave Technologies and Its Applications)

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8 pages, 2145 KiB

Open AccessArticle

Patterns Simulations Using Gibbs/MRF Auto-Poisson Models

by Stelios Zimeras

Technologies 2022, 10(3), 69; https://doi.org/10.3390/technologies10030069 - 06 Jun 2022

Viewed by 1644

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)

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19 pages, 442 KiB

Open AccessReview

Supporting Newsrooms with Journalistic Knowledge Graph Platforms: Current State and Future Directions

by Marc Gallofré Ocaña and Andreas L. Opdahl

Technologies 2022, 10(3), 68; https://doi.org/10.3390/technologies10030068 - 31 May 2022

Cited by 2 | Viewed by 2682

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 [...] Read more.

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. Full article

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

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7 pages, 1174 KiB

Open AccessCommunication

An a Priori Discussion of the Fill Front Stability in Semisolid Casting

by Anders E. W. Jarfors, Qing Zhang and Stefan Jonsson

Technologies 2022, 10(3), 67; https://doi.org/10.3390/technologies10030067 - 30 May 2022

Viewed by 3240

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)

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12 pages, 11814 KiB

Open AccessArticle

Electrospinning for the Modification of 3D Objects for the Potential Use in Tissue Engineering

by Laura Bauer, Lisa Brandstäter, Mika Letmate, Manasi Palachandran, Fynn Ole Wadehn, Carlotta Wolfschmidt, Timo Grothe, Uwe Güth and Andrea Ehrmann

Technologies 2022, 10(3), 66; https://doi.org/10.3390/technologies10030066 - 29 May 2022

Cited by 3 | Viewed by 2238

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)

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10 pages, 372 KiB

Open AccessArticle

Specific Electronic Platform to Test the Influence of Hypervisors on the Performance of Embedded Systems

by Jaime Jiménez, Leire Muguira, Unai Bidarte, Alejandro Largacha and Jesús Lázaro

Technologies 2022, 10(3), 65; https://doi.org/10.3390/technologies10030065 - 24 May 2022

Viewed by 2114

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, [...] Read more.

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. Full article

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12 pages, 2388 KiB

Open AccessArticle

Study of Joint Symmetry in Gait Evolution for Quadrupedal Robots Using a Neural Network

by Zainullah Khan, Farhat Naseer, Yousuf Khan, Muhammad Bilal and Muhammad A. Butt

Technologies 2022, 10(3), 64; https://doi.org/10.3390/technologies10030064 - 22 May 2022

Cited by 3 | Viewed by 2310

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)

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18 pages, 3523 KiB

Open AccessArticle

STAMINA: Bioinformatics Platform for Monitoring and Mitigating Pandemic Outbreaks

by 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 and Luca Rossi Show full author list Hide full author list

Technologies 2022, 10(3), 63; https://doi.org/10.3390/technologies10030063 - 17 May 2022

Cited by 2 | Viewed by 3003

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 [...] Read more.

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. Full article

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

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13 pages, 11230 KiB

Open AccessEditor’s ChoiceArticle

Application of 3D Virtual Prototyping Technology to the Integration of Wearable Antennas into Fashion Garments

by Evridiki Papachristou and Hristos T. Anastassiu

Technologies 2022, 10(3), 62; https://doi.org/10.3390/technologies10030062 - 17 May 2022

Cited by 5 | Viewed by 4058

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)

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20 pages, 7415 KiB

Open AccessEditor’s ChoiceArticle

Application of Multi-Channel Convolutional Neural Network to Improve DEM Data in Urban Cities

by Ngoc Son Nguyen, Dong Eon Kim, Yilin Jia, Srivatsan V. Raghavan and Shie Yui Liong

Technologies 2022, 10(3), 61; https://doi.org/10.3390/technologies10030061 - 13 May 2022

Cited by 4 | Viewed by 2735

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 [...] Read more.

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. Full article

(This article belongs to the Topic Advances in the Sustainability Assessment of Building and Infrastructure Projects)

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17 pages, 2911 KiB

Open AccessReview

Advanced Security Framework for Internet of Things (IoT)

by Abid Ali, Abdul Mateen, Abdul Hanan and Farhan Amin

Technologies 2022, 10(3), 60; https://doi.org/10.3390/technologies10030060 - 12 May 2022

Cited by 12 | Viewed by 4773

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)

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14 pages, 3829 KiB

Open AccessEditor’s ChoiceArticle

Continuous Emotion Recognition for Long-Term Behavior Modeling through Recurrent Neural Networks

by Ioannis Kansizoglou, Evangelos Misirlis, Konstantinos Tsintotas and Antonios Gasteratos

Technologies 2022, 10(3), 59; https://doi.org/10.3390/technologies10030059 - 12 May 2022

Cited by 26 | Viewed by 3535

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 [...] Read more.

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. Full article

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

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12 pages, 2631 KiB

Open AccessArticle

Study of Structural, Strength, and Thermophysical Properties of Li_2+4xZr₄_−xO₃ Ceramics

by Artem L. Kozlovskiy, Bauyrzhan Abyshev, Dmitriy I. Shlimas and Maxim V. Zdorovets

Technologies 2022, 10(3), 58; https://doi.org/10.3390/technologies10030058 - 10 May 2022

Cited by 1 | Viewed by 3277

Abstract

The work is devoted to the study of technology that can be used to obtain lithium-containing ceramics of the Li_2+4xZr₄_−xO₃ type using the method of solid-phase synthesis combined with thermal annealing at a temperature of 1500 °C. [...] Read more.

The work is devoted to the study of technology that can be used to obtain lithium-containing ceramics of the Li_2+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%. Full article

(This article belongs to the Special Issue Commemorative Issue in Honor of Prof. Dr. Manoj Gupta on the Occasion of 60th Birthday)

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15 pages, 2999 KiB

Open AccessEditor’s ChoiceArticle

A Comparative Analysis on Suicidal Ideation Detection Using NLP, Machine, and Deep Learning

by Rezaul Haque, Naimul Islam, Maidul Islam and Md Manjurul Ahsan

Technologies 2022, 10(3), 57; https://doi.org/10.3390/technologies10030057 - 29 Apr 2022

Cited by 29 | Viewed by 6721

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 10th Anniversary of Technologies—Recent Advances and Perspectives)

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10 pages, 1428 KiB

Open AccessEditor’s ChoiceArticle

Electronic Structure Calculation of Cr³⁺ and Fe³⁺ in Phosphor Host Materials Based on Relaxed Structures by Molecular Dynamics Simulation

by Joichiro Ichikawa, Hiroko Kominami, Kazuhiko Hara, Masato Kakihana and Yuta Matsushima

Technologies 2022, 10(3), 56; https://doi.org/10.3390/technologies10030056 - 27 Apr 2022

Cited by 3 | Viewed by 2203

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, [...] Read more.

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. Full article

(This article belongs to the Special Issue Smart Systems (SmaSys2021))

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11 pages, 419 KiB

Open AccessArticle

Time Sensitive Networking Protocol Implementation for Linux End Equipment

by Jesús Lázaro, Jimena Cabrejas, Aitzol Zuloaga, Leire Muguira and Jaime Jiménez

Technologies 2022, 10(3), 55; https://doi.org/10.3390/technologies10030055 - 22 Apr 2022

Cited by 2 | Viewed by 4414

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 [...] Read more.

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. Full article

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31 pages, 15970 KiB

Open AccessEditor’s ChoiceArticle

Reliable Ultrasonic Obstacle Recognition for Outdoor Blind Navigation

by Apostolos Meliones, Costas Filios and Jairo Llorente

Technologies 2022, 10(3), 54; https://doi.org/10.3390/technologies10030054 - 21 Apr 2022

Cited by 13 | Viewed by 4404

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 [...] Read more.

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. Full article

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

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Technologies, Volume 10, Issue 3 (June 2022) – 21 articles

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