Technologies

29 pages, 4740 KiB

Open AccessEditor’s ChoiceReview

Thermal Inkjet Printing: Prospects and Applications in the Development of Medicine

by Md Jasim Uddin, Jasmin Hassan and Dennis Douroumis

Technologies 2022, 10(5), 108; https://doi.org/10.3390/technologies10050108 - 21 Oct 2022

Cited by 12 | Viewed by 10637

Over the last 10 years, inkjet printing technologies have advanced significantly and found several applications in the pharmaceutical and biomedical sector. Thermal inkjet printing is one of the most widely used techniques due to its versatility in the development of bioinks for cell [...] Read more.

Over the last 10 years, inkjet printing technologies have advanced significantly and found several applications in the pharmaceutical and biomedical sector. Thermal inkjet printing is one of the most widely used techniques due to its versatility in the development of bioinks for cell printing or biosensors and the potential to fabricate personalized medications of various forms such as films and tablets. In this review, we provide a comprehensive discussion of the principles of inkjet printing technologies highlighting their advantages and limitations. Furthermore, the review covers a wide range of case studies and applications for precision medicine. Full article

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

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11 pages, 2490 KiB

Open AccessEditor’s ChoiceReview

Exploration of Educational Possibilities by Four Metaverse Types in Physical Education

by Ji-Eun Yu

Technologies 2022, 10(5), 104; https://doi.org/10.3390/technologies10050104 - 23 Sep 2022

Cited by 36 | Viewed by 8395

Abstract

The metaverse has been evolving the internet-based education represented by e-learning. Metaverse technology is currently being developed as a platform centered on content-based information industries. It can be classified into four categories: augmented reality, lifelogging, mirror worlds, and virtual worlds. Although current research [...] Read more.

The metaverse has been evolving the internet-based education represented by e-learning. Metaverse technology is currently being developed as a platform centered on content-based information industries. It can be classified into four categories: augmented reality, lifelogging, mirror worlds, and virtual worlds. Although current research finds that the potential of the metaverse is not small in the education world, and metaverse technology is already being used in the sports world, concrete applications have not been investigated. The main aims of this study, which started with this purpose, can be summarized as follows. The metaverse environment is still in its rudimentary stage, and its use related to physical education subjects is only at the game level. In the future, the utilization of the metaverse by physical education subjects will be possible in universities only when more specialized technology is grafted into various sports. Ultimately, this study contributes to expanding the scope and depth of follow-up research by offering basic data showing the direction of metaverse-based physical education. Full article

(This article belongs to the Special Issue Human-Centered Cyber-Physical Systems)

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13 pages, 27820 KiB

Open AccessEditor’s ChoiceArticle

Design and Implementation of an Anthropomorphic Robotic Arm Prosthesis

by Valentina A. Yurova, Gleb Velikoborets and Andrei Vladyko

Technologies 2022, 10(5), 103; https://doi.org/10.3390/technologies10050103 - 21 Sep 2022

Cited by 5 | Viewed by 4557

Abstract

The development and manufacture of prosthetic limbs is one of the important tendencies of the development of medical techniques. Taking into account the development of modern electronic technology and automated systems and its mobility and compactness, the actual task is to create a [...] Read more.

The development and manufacture of prosthetic limbs is one of the important tendencies of the development of medical techniques. Taking into account the development of modern electronic technology and automated systems and its mobility and compactness, the actual task is to create a prosthesis that will be close to a fully functioning human limb in its anthropomorphic properties and will be capable of reproducing its basic actions with a high accuracy. The paper analyzes the main directions in the development of a control system for electronic limb prostheses. The description and results of the practical implementation of a prototype of an anthropomorphic prosthetic arm and its control system are presented in the paper. We developed an anthropomorphic multi-finger artificial hand for utilization in robotic research and teaching applications. The designed robotic hand is a low-cost alternative to other known 3D printed robotic hands and has 21 degrees of freedom—4 degrees of freedom for each finger, 3 degrees for the thumb and 2 degrees responsible for the position of the robotic hand in space. The open-source mechanical design of the presented robotic arm has mass-dimensional and motor parameters close to the human hand, with the possibility of autonomous battery operation, the ability to connect different control systems, such as from a computer, an electroencephalograph, a touch glove. Full article

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

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9 pages, 1928 KiB

Open AccessEditor’s ChoiceArticle

A Machine-Learning-Based Approach to Critical Geometrical Feature Identification and Segmentation in Additive Manufacturing

by Alexandre Staub, Lucas Brunner, Adriaan B. Spierings and Konrad Wegener

Technologies 2022, 10(5), 102; https://doi.org/10.3390/technologies10050102 - 16 Sep 2022

Cited by 2 | Viewed by 1894

Abstract

Additive manufacturing (AM) processes offer a good opportunity to manufacture three- dimensional objects using various materials. However, many of the processes, notably laser Powder bed fusion, face limitations in manufacturing specific geometrical features due to their physical constraints, such as the thermal conductivity [...] Read more.

Additive manufacturing (AM) processes offer a good opportunity to manufacture three- dimensional objects using various materials. However, many of the processes, notably laser Powder bed fusion, face limitations in manufacturing specific geometrical features due to their physical constraints, such as the thermal conductivity of the surrounding medium, the internal stresses, and the warpage or weight of the part being manufactured. This work investigates the opportunity to use machine learning algorithms in order to identify hard-to-manufacture geometrical features. The segmentation of these features from the main body of the part permits the application of different manufacturing strategies to improve the overall manufacturability. After selecting features that are particularly problematic during laser powder bed fusion using stainless steel, an algorithm is trained using simple geometries, which permits the identification of hard-to-manufacture features on new parts with a success rate of 88%, showing the potential of this approach. Full article

(This article belongs to the Special Issue Advanced Processing Technologies of Innovative Materials)

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14 pages, 5549 KiB

Open AccessEditor’s ChoiceArticle

Solar Energy Management Using a V-Groove: An Approach Based on a Multiple Optical Path Algorithm

by Fadel Kawtharani, Bruno Serio, Geraldine Guida, Patrice Twardowski and Mohammad Hammoud

Technologies 2022, 10(5), 101; https://doi.org/10.3390/technologies10050101 - 12 Sep 2022

Viewed by 1613

Abstract

Angular and spectral separations of thermal radiation have become a key challenge in solar concentration or thermal management of sources radiating at extremely high or low temperatures. Reflections obtained from electromagnetic theory in an open cavity geometry increase the emission and absorption compared [...] Read more.

Angular and spectral separations of thermal radiation have become a key challenge in solar concentration or thermal management of sources radiating at extremely high or low temperatures. Reflections obtained from electromagnetic theory in an open cavity geometry increase the emission and absorption compared to a flat surface due to the cavity effect. In this paper, in order to obtain the directional emission of geometric surfaces (V-Grooves) using ray tracing and studying the propagation of light, a new algorithm is developed. The numerical simulations take into account the materials properties of both facets of the V-shape, thus simulating an original asymmetric and a multilayer V-shape and providing a very interesting directive thermal emission behavior. We evaluated the emission behavior from the reflection and emission coefficients of different rays at different angles for different parameters (materials properties, wavelength, and geometry). The simulations of a V-groove showed that due to the different reflections occurring inside an aluminum V-cavity with an aperture angle of 28°, the emissivity was well enhanced by 86% in the normal direction compared to a flat surface made of the same material. Moreover, using the original asymmetric opposite-sided materials (Al and Si) in a V- groove, it was possible to separate and control the emission by focusing the radiation or directing different spectral bands in different directions. Full article

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21 pages, 1475 KiB

Open AccessEditor’s ChoiceReview

Selected Techniques for Cutting SO_x Emissions in Maritime Industry

by Christos Papadopoulos, Marios Kourtelesis, Anastasia Maria Moschovi, Konstantinos Miltiadis Sakkas and Iakovos Yakoumis

Technologies 2022, 10(5), 99; https://doi.org/10.3390/technologies10050099 - 30 Aug 2022

Cited by 7 | Viewed by 4186

Abstract

Burning fuels with high sulfur content leads to SO_x emissions, especially SO₂, which leads to various environmental and health problems. The maritime sector is responsible for 13% of the global anthropogenic emissions of SO₂. Thus, the International Maritime [...] Read more.

Burning fuels with high sulfur content leads to SO_x emissions, especially SO₂, which leads to various environmental and health problems. The maritime sector is responsible for 13% of the global anthropogenic emissions of SO₂. Thus, the International Maritime Organization (IMO) has issued a protocol, known as MARPOL Annex VI, which aims to further limit SO₂ emissions derived from ships along with NO_x, particulate matter and volatile organic compound emissions. This has led ship owners and operators to choose between more expensive fuels with low sulfur content or to apply a DeSO_x solution, which still allows them to use the cheapest heavy fuel oil. The current work reviews the state-of-the-art DeSO_x solutions both for the maritime and land-based sector. Next, it proposes an alternative cheaper and environmentally friendly DeSO_x solution based on the selective reduction of SO₂ to elemental sulfur by utilizing a catalytic converter based on metal oxides, similar to the ones used in the automotive industry. Finally, it reviews the most promising metal oxide catalysts reported in the literature for the selective reduction of SO₂ towards elemental sulfur. Full article

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

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30 pages, 8357 KiB

Open AccessEditor’s ChoiceArticle

Digitization of Manufacturing Processes: From Sensing to Twining

by Panagiotis Stavropoulos

Technologies 2022, 10(5), 98; https://doi.org/10.3390/technologies10050098 - 30 Aug 2022

Cited by 9 | Viewed by 3134

Abstract

Zero-defect manufacturing and flexibility in production lines is driven from accurate Digital Twins (DT) which monitor, understand, and predict the behavior of a manufacturing process under different conditions while also adapting to them by deciding the right course of action in time intervals [...] Read more.

Zero-defect manufacturing and flexibility in production lines is driven from accurate Digital Twins (DT) which monitor, understand, and predict the behavior of a manufacturing process under different conditions while also adapting to them by deciding the right course of action in time intervals relevant to the captured phenomenon. During the exploration of the alternative approaches for the development of process twins, significant efforts should be made for the selection of acquisition devices and signal-processing techniques to extract meaningful information from the studied process. As such, in Industry 4.0 era, machine tools are equipped with embedded sensors that give feedback related to the process efficiency and machine health, while additional sensors are installed to capture process-related phenomena, feeding simulation tools and decision-making algorithms. Although the maturity level of some process mechanisms facilitates the representation of the physical world with the aid of physics-based models, data-driven models are proposed for complex phenomena and non-mature processes. This paper introduces the components of Digital Twin and gives emphasis on the steps that are required to transform obtained data into meaningful information that will be used in a Digital Twin. The introduced steps are identified in a case study from the milling process. Full article

(This article belongs to the Special Issue Advances and Innovations in Manufacturing Technologies)

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15 pages, 10110 KiB

Open AccessEditor’s ChoiceArticle

Research on a Vehicle Recognition Method Based on Radar and Camera Information Fusion

by Fang Ding, Bo Wang, Qianbin Zhang and Aiguo Wang

Technologies 2022, 10(4), 97; https://doi.org/10.3390/technologies10040097 - 22 Aug 2022

Cited by 1 | Viewed by 1788

Abstract

To improve the accuracy and real-time performance of vehicle recognition in an advanced driving-assistance system (ADAS), a vehicle recognition method based on radar and camera information fusion is proposed. Firstly, the millimeter-wave radar and camera are calibrated jointly, the radar recognition information is [...] Read more.

To improve the accuracy and real-time performance of vehicle recognition in an advanced driving-assistance system (ADAS), a vehicle recognition method based on radar and camera information fusion is proposed. Firstly, the millimeter-wave radar and camera are calibrated jointly, the radar recognition information is mapped on the camera image, and the region of interest is established. Then, based on operator edge detection, global threshold binarization is performed on the image of the region of interest (ROI) to obtain the contour information of the vehicle in front, and Hough transform is used to fit the vehicle contour edge straight line. Finally, a sliding window is established according to the symmetry characteristics of the fitting line, which can find the vehicle region with the highest symmetry and complete the identification of the vehicle. The experimental results show that compared to the original recognition region of the radar, the mean square error of this algorithm is reduced by 13.4 and the single frame detection time is reduced to 28 ms. It is proven that the algorithm has better accuracy and a faster detection rate, and it can solve the problem of an inaccurate recognition region caused by radar error. Full article

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19 pages, 6708 KiB

Open AccessEditor’s ChoiceArticle

An Automatic, Contactless, High-Precision, High-Speed Measurement System to Provide In-Line, As-Molded Three-Dimensional Measurements of a Curved-Shape Injection-Molded Part

by Saeid Saeidi Aminabadi, Atae Jafari-Tabrizi, Dieter Paul Gruber, Gerald Berger-Weber and Walter Friesenbichler

Technologies 2022, 10(4), 95; https://doi.org/10.3390/technologies10040095 - 17 Aug 2022

Cited by 2 | Viewed by 2122

Abstract

In the manufacturing of injection-molded plastic parts, it is essential to perform a non-destructive (and, in some applications, contactless) three-dimensional measurement and surface inspection of the injection-molded part to monitor the part quality. The measurement method depends strongly on the shape and the [...] Read more.

In the manufacturing of injection-molded plastic parts, it is essential to perform a non-destructive (and, in some applications, contactless) three-dimensional measurement and surface inspection of the injection-molded part to monitor the part quality. The measurement method depends strongly on the shape and the optical properties of the part. In this study, a high-precision (±5 µm) and high-speed system (total of 24 s for a complete part dimensional measurement) was developed to measure the dimensions of a piano-black injection-molded part. This measurement should be done in real time and close to the part’s production time to evaluate the quality of the produced parts for future online, closed-loop, and predictive quality control. Therefore, a novel contactless, three-dimensional measurement system using a multicolor confocal sensor was designed and manufactured, taking into account the nominal curved shape and the glossy black surface properties of the part. This system includes one linear and one cylindrical moving axis, as well as one confocal optical sensor for radial R-direction measurements. A 6 DOF (degrees of freedom) robot handles the part between the injection molding machine and the measurement system. An IPC coordinates the communications and system movements over the OPC UA communication network protocol. For validation, several repeatability tests were performed at various speeds and directions. The results were compared using signal similarity methods, such as MSE, SSID, and RMS difference. The repeatability of the system in all directions was found to be in the range of ±5 µm for the desired speed range (less than 60 mm/s–60 degrees/s). However, the error increases up to ±10 µm due to the fixture and the suction force effect. Full article

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

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29 pages, 10117 KiB

Open AccessEditor’s ChoiceReview

Multimodal Semantic Segmentation in Autonomous Driving: A Review of Current Approaches and Future Perspectives

by Giulia Rizzoli, Francesco Barbato and Pietro Zanuttigh

Technologies 2022, 10(4), 90; https://doi.org/10.3390/technologies10040090 - 25 Jul 2022

Cited by 14 | Viewed by 6174

Abstract

The perception of the surrounding environment is a key requirement for autonomous driving systems, yet the computation of an accurate semantic representation of the scene starting from RGB information alone is very challenging. In particular, the lack of geometric information and the strong [...] Read more.

The perception of the surrounding environment is a key requirement for autonomous driving systems, yet the computation of an accurate semantic representation of the scene starting from RGB information alone is very challenging. In particular, the lack of geometric information and the strong dependence on weather and illumination conditions introduce critical challenges for approaches tackling this task. For this reason, most autonomous cars exploit a variety of sensors, including color, depth or thermal cameras, LiDARs, and RADARs. How to efficiently combine all these sources of information to compute an accurate semantic description of the scene is still an unsolved task, leading to an active research field. In this survey, we start by presenting the most commonly employed acquisition setups and datasets. Then we review several different deep learning architectures for multimodal semantic segmentation. We will discuss the various techniques to combine color, depth, LiDAR, and other modalities of data at different stages of the learning architectures, and we will show how smart fusion strategies allow us to improve performances with respect to the exploitation of a single source of information. Full article

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

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18 pages, 4518 KiB

Open AccessEditor’s ChoiceArticle

Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring

by Muhammad Usman Hadi, Nik Hazmi Nik Suhaimi and Abdul Basit

Technologies 2022, 10(4), 85; https://doi.org/10.3390/technologies10040085 - 16 Jul 2022

Cited by 5 | Viewed by 2329

Abstract

From a single meter that measures the entire home’s electrical demand, energy disaggregation calculates appliance-by-appliance electricity consumption. Non-intrusive load monitoring (NILM), also known as energy disaggregation, tries to decompose aggregated energy consumption data and estimate each appliance’s contribution. Recently, methodologies based on Artificial [...] Read more.

From a single meter that measures the entire home’s electrical demand, energy disaggregation calculates appliance-by-appliance electricity consumption. Non-intrusive load monitoring (NILM), also known as energy disaggregation, tries to decompose aggregated energy consumption data and estimate each appliance’s contribution. Recently, methodologies based on Artificial Intelligence (AI) have been proposed commonly used in these models, which can be expensive to run on a server or prohibitive when the target device has limited capabilities. AI-based models are typically computationally expensive and require a lot of storage. It is not easy to reduce the computing cost and size of a neural network without sacrificing performance. This study proposed an efficient non-parametric supervised machine learning network (ENSML) architecture with a smaller size, and a quick inference time without sacrificing performance. The proposed architecture can maximise energy disaggregation performance and predict new observations based on past ones. The results showed that employing the ENSML model considerably increased the accuracy of energy prediction in 99 percent of cases. Full article

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

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23 pages, 4284 KiB

Open AccessEditor’s ChoiceArticle

Demonstration of Resilient Microgrid with Real-Time Co-Simulation and Programmable Loads

by Hossam A. Gabbar, Yasser Elsayed, Manir Isham, Abdalrahman Elshora, Abu Bakar Siddique and Otavio Lopes Alves Esteves

Technologies 2022, 10(4), 83; https://doi.org/10.3390/technologies10040083 - 12 Jul 2022

Cited by 2 | Viewed by 2642

Abstract

In recent years, the foment for sustainable and reliable micro energy grid (MEG) systems has increased significantly, aiming mainly to reduce the dependency on fossil fuels, provide low-cost clean energy, lighten the burden, and increase the stability and reliability of the regional electrical [...] Read more.

In recent years, the foment for sustainable and reliable micro energy grid (MEG) systems has increased significantly, aiming mainly to reduce the dependency on fossil fuels, provide low-cost clean energy, lighten the burden, and increase the stability and reliability of the regional electrical grid by having interconnected and centralized clean energy sources, and ensure energy resilience for the population. A resilient energy system typically consists of a system able to control the energy flow effectively by backing up the intermittent output of renewable sources, reducing the effects of the peak demand on the grid side, considering the impact on dispatch and reliability, and providing resilient features to ensure minimum operation interruptions. This paper aims to demonstrate a real-time simulation of a microgrid capable of predicting and ensuring energy lines run correctly to prevent or shorten outages on the grid when it is subject to different disturbances by using energy management with a fail-safe operation and redundant control. In addition, it presents optimized energy solutions to enhance the situational awareness of energy grid operators based on a graphical and interactive user interface. To expand the MEG’s capability, the setup integrates real implemented hardware components with the emulated components based on real-time simulation using OPAL-RT OP4510. Most hardware components are implemented in the lab to be modular, expandable, and flexible for various test scenarios, including fault imitation. They include but are not limited to the power converter, inverter, battery charger controller, relay drivers, programmable AC and DC loads, PLC, and microcontroller-based controller. In addition, the real-time simulation offers a great variety of power sources and energy storage such as wind turbine emulators and flywheels in addition to the physical sources such as solar panels, supercapacitors, and battery packs. Full article

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

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14 pages, 1826 KiB

Open AccessEditor’s ChoiceArticle

Distribution Path Optimization by an Improved Genetic Algorithm Combined with a Divide-and-Conquer Strategy

by Jiaqi Li, Yun Wang and Ke-Lin Du

Technologies 2022, 10(4), 81; https://doi.org/10.3390/technologies10040081 - 05 Jul 2022

Cited by 1 | Viewed by 2153

Abstract

The multivehicle routing problem (MVRP) is a variation of the classical vehicle routing problem (VRP). The MVRP is to find a set of routes by multiple vehicles that serve multiple customers at a minimal total cost while the travelling-time delay due to traffic [...] Read more.

The multivehicle routing problem (MVRP) is a variation of the classical vehicle routing problem (VRP). The MVRP is to find a set of routes by multiple vehicles that serve multiple customers at a minimal total cost while the travelling-time delay due to traffic congestion is tolerated. It is an NP problem and is conventionally solved by metaheuristics such as evolutionary algorithms. For the MVRP in a distribution network, we propose an optimal distribution path optimization method that is composed of a distribution sequence search stage and a distribution path search stage that exploits a divide-and-conquer strategy, inspired by the idea of dynamic programming. Several optimization objectives subject to constraints are defined. The search for the optimal solution of the number of distribution vehicles, distribution sequence, and path is implemented by using an improved genetic algorithm (GA), which is characterized by an operation for preprocessing infeasible solutions, an elitist’s strategy, a sequence-related two-point crossover operator, and a reversion mutation operator. The improved GA outperforms the simple GA in terms of total cost, route topology, and route feasibility. The proposed method can help to reduce costs and increase efficiency for logistics and transportation enterprises and can also be used for flow-shop scheduling by manufacturing enterprises. Full article

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

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22 pages, 66210 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

by Francisco Javier Ramírez-Arias, Enrique Efren García-Guerrero, Esteban Tlelo-Cuautle, Juan Miguel Colores-Vargas, Eloisa García-Canseco, Oscar Roberto López-Bonilla, Gilberto Manuel Galindo-Aldana and Everardo Inzunza-González

Technologies 2022, 10(4), 79; https://doi.org/10.3390/technologies10040079 - 30 Jun 2022

Cited by 13 | Viewed by 6383

Abstract

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes [...] Read more.

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices. Full article

(This article belongs to the Special Issue Image and Signal Processing)

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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 4065

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 2737

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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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 3539

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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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 6730

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 2206

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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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 4413

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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18 pages, 2046 KiB

Open AccessEditor’s ChoiceReview

Strategic Investment in Open Hardware for National Security

by Joshua M. Pearce

Technologies 2022, 10(2), 53; https://doi.org/10.3390/technologies10020053 - 18 Apr 2022

Cited by 6 | Viewed by 3747

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 [...] Read more.

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. Full article

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

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44 pages, 6552 KiB

Open AccessEditor’s ChoiceReview

Flow Stress Description Characteristics of Some Constitutive Models at Wide Strain Rates and Temperatures

by Hyunho Shin, Yongwon Ju, Min Kuk Choi and Dong Ho Ha

Technologies 2022, 10(2), 52; https://doi.org/10.3390/technologies10020052 - 11 Apr 2022

Cited by 13 | Viewed by 4644

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 [...] Read more.

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. Full article

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28 pages, 3361 KiB

Open AccessEditor’s ChoiceArticle

The NESTORE e-Coach: Designing a Multi-Domain Pathway to Well-Being in Older Age

by 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 and Giuseppe Andreoni Show full author list Hide full author list

Technologies 2022, 10(2), 50; https://doi.org/10.3390/technologies10020050 - 01 Apr 2022

Cited by 5 | Viewed by 3416

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 [...] Read more.

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. Full article

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

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14 pages, 29620 KiB

Open AccessEditor’s ChoiceArticle

Verifiable Surface Disinfection Using Ultraviolet Light with a Mobile Manipulation Robot

by Alan G. Sanchez and William D. Smart

Technologies 2022, 10(2), 48; https://doi.org/10.3390/technologies10020048 - 29 Mar 2022

Cited by 3 | Viewed by 2637

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 [...] Read more.

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. Full article

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

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18 pages, 3213 KiB

Open AccessEditor’s ChoiceArticle

Efficiently Mitigating Face-Swap-Attacks: Compressed-PRNU Verification with Sub-Zones

by Ali Hassani, Hafiz Malik and Jon Diedrich

Technologies 2022, 10(2), 46; https://doi.org/10.3390/technologies10020046 - 27 Mar 2022

Cited by 2 | Viewed by 2917

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue The Applications and Challenges of Cybersecurity in Science, Healthcare and Medicine)

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31 pages, 7448 KiB

Open AccessEditor’s ChoiceReview

Material Design for Enhancing Properties of 3D Printed Polymer Composites for Target Applications

by Vinita V. Shinde, Yuyang Wang, Md Fahim Salek, Maria L. Auad, Lauren E. Beckingham and Bryan S. Beckingham

Technologies 2022, 10(2), 45; https://doi.org/10.3390/technologies10020045 - 23 Mar 2022

Cited by 10 | Viewed by 4997

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 3D Printing and Additive Manufacturing: Principles and Applications)

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30 pages, 5249 KiB

Open AccessEditor’s ChoiceArticle

Detection of Physical Strain and Fatigue in Industrial Environments Using Visual and Non-Visual Low-Cost Sensors

by Konstantinos Papoutsakis, George Papadopoulos, Michail Maniadakis, Thodoris Papadopoulos, Manolis Lourakis, Maria Pateraki and Iraklis Varlamis

Technologies 2022, 10(2), 42; https://doi.org/10.3390/technologies10020042 - 16 Mar 2022

Cited by 5 | Viewed by 4840

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 [...] Read more.

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. Full article

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

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18 pages, 36905 KiB

Open AccessEditor’s ChoiceArticle

MINA: A Robotic Assistant for Hospital Fetching Tasks

by Harish Ram Nambiappan, Stephanie Arevalo Arboleda, Cody Lee Lundberg, Maria Kyrarini, Fillia Makedon and Nicholas Gans

Technologies 2022, 10(2), 41; https://doi.org/10.3390/technologies10020041 - 12 Mar 2022

Cited by 4 | Viewed by 4124

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. [...] Read more.

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. Full article

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

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18 pages, 4030 KiB

Open AccessEditor’s ChoiceArticle

An Optimized Enhanced Phase Locked Loop Controller for a Hybrid System

by Amritha Kodakkal, Rajagopal Veramalla, Narasimha Raju Kuthuri and Surender Reddy Salkuti

Technologies 2022, 10(2), 40; https://doi.org/10.3390/technologies10020040 - 11 Mar 2022

Cited by 4 | Viewed by 2943

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 [...] Read more.

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. Full article

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

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12 pages, 3271 KiB

Open AccessEditor’s ChoiceArticle

Parasitic Coupling in 3D Sequential Integration: The Example of a Two-Layer 3D Pixel

by Petros Sideris, Arnaud Peizerat, Perrine Batude, Gilles Sicard and Christoforos Theodorou

Technologies 2022, 10(2), 38; https://doi.org/10.3390/technologies10020038 - 28 Feb 2022

Cited by 1 | Viewed by 3752

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue MOCAST 2021)

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13 pages, 1030 KiB

Open AccessEditor’s ChoiceArticle

Lightweight Neural Network for COVID-19 Detection from Chest X-ray Images Implemented on an Embedded System

by Theodora Sanida, Argyrios Sideris, Dimitris Tsiktsiris and Minas Dasygenis

Technologies 2022, 10(2), 37; https://doi.org/10.3390/technologies10020037 - 25 Feb 2022

Cited by 16 | Viewed by 4819

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue MOCAST 2021)

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24 pages, 1663 KiB

Open AccessEditor’s ChoiceArticle

Sovereign Digital Consent through Privacy Impact Quantification and Dynamic Consent

by Arno Appenzeller, Marina Hornung, Thomas Kadow, Erik Krempel and Jürgen Beyerer

Technologies 2022, 10(1), 35; https://doi.org/10.3390/technologies10010035 - 21 Feb 2022

Cited by 3 | Viewed by 2816

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 [...] Read more.

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. Full article

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

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27 pages, 3415 KiB

Open AccessEditor’s ChoiceReview

Mechanical Properties of Sustainable Metal Matrix Composites: A Review on the Role of Green Reinforcements and Processing Methods

by Sankaranarayanan Seetharaman, Jayalakshmi Subramanian, Ramachandra Arvind Singh, Wai Leong Eugene Wong, Mui Ling Sharon Nai and Manoj Gupta

Technologies 2022, 10(1), 32; https://doi.org/10.3390/technologies10010032 - 16 Feb 2022

Cited by 16 | Viewed by 5753

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 [...] Read more.

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. 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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18 pages, 1305 KiB

Open AccessEditor’s ChoiceArticle

Visible Light Communications for Internet of Things: Prospects and Approaches, Challenges, Solutions and Future Directions

by Stephen S. Oyewobi, Karim Djouani and Anish Matthew Kurien

Technologies 2022, 10(1), 28; https://doi.org/10.3390/technologies10010028 - 05 Feb 2022

Cited by 36 | Viewed by 5809

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 [...] Read more.

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. Full article

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16 pages, 2472 KiB

Open AccessEditor’s ChoiceArticle

Performance Analysis of 2D and 3D Bufferless NoCs Using Markov Chain Models

by Konstantinos Tatas

Technologies 2022, 10(1), 27; https://doi.org/10.3390/technologies10010027 - 02 Feb 2022

Cited by 1 | Viewed by 2074

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue MOCAST 2021)

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18 pages, 5237 KiB

Open AccessEditor’s ChoiceArticle

Reliable IoT-Based Monitoring and Control of Hydroponic Systems

by Konstantinos Tatas, Ahmad Al-Zoubi, Nicholas Christofides, Chrysostomos Zannettis, Michael Chrysostomou, Stavros Panteli and Anthony Antoniou

Technologies 2022, 10(1), 26; https://doi.org/10.3390/technologies10010026 - 02 Feb 2022

Cited by 21 | Viewed by 11095

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue MOCAST 2021)

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17 pages, 2806 KiB

Open AccessEditor’s ChoiceArticle

User-Centric Design Methodology for mHealth Apps: The PainApp Paradigm for Chronic Pain

by Yiannis Koumpouros

Technologies 2022, 10(1), 25; https://doi.org/10.3390/technologies10010025 - 31 Jan 2022

Cited by 5 | Viewed by 4112

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 [...] Read more.

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. Full article

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

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10 pages, 1457 KiB

Open AccessEditor’s ChoiceArticle

Results of Preliminary Studies on the Perception of the Relationships between Objects Presented in a Cartesian Space

by Ira Woodring and Charles Owen

Technologies 2022, 10(1), 20; https://doi.org/10.3390/technologies10010020 - 30 Jan 2022

Viewed by 1887

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 [...] Read more.

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. Full article

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

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14 pages, 2847 KiB

Open AccessEditor’s ChoiceArticle

On the Exploration of Automatic Building Extraction from RGB Satellite Images Using Deep Learning Architectures Based on U-Net

by Anastasios Temenos, Nikos Temenos, Anastasios Doulamis and Nikolaos Doulamis

Technologies 2022, 10(1), 19; https://doi.org/10.3390/technologies10010019 - 29 Jan 2022

Cited by 9 | Viewed by 3358

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 [...] Read more.

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. Full article

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

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18 pages, 1780 KiB

Open AccessEditor’s ChoiceArticle

Stacking-Based Ensemble Learning Method for Multi-Spectral Image Classification

by Tagel Aboneh, Abebe Rorissa and Ramasamy Srinivasagan

Technologies 2022, 10(1), 17; https://doi.org/10.3390/technologies10010017 - 26 Jan 2022

Cited by 18 | Viewed by 4182

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Multimedia Indexing and Retrieval)

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23 pages, 5508 KiB

Open AccessEditor’s ChoiceArticle

Novel Benes Network Routing Algorithm and Hardware Implementation

by Dimitris Nikolaidis, Panos Groumas, Christos Kouloumentas and Hercules Avramopoulos

Technologies 2022, 10(1), 16; https://doi.org/10.3390/technologies10010016 - 25 Jan 2022

Cited by 4 | Viewed by 5738

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 [...] Read more.

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. Full article

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

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12 pages, 1985 KiB

Open AccessEditor’s ChoiceArticle

IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms

by Kalaiselvi Bakthavatchalam, Balaguru Karthik, Vijayan Thiruvengadam, Sriram Muthal, Deepa Jose, Ketan Kotecha and Vijayakumar Varadarajan

Technologies 2022, 10(1), 13; https://doi.org/10.3390/technologies10010013 - 20 Jan 2022

Cited by 31 | Viewed by 6791

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 [...] Read more.

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. Full article

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15 pages, 782 KiB

Open AccessEditor’s ChoiceArticle

Efficient Stochastic Computing FIR Filtering Using Sigma-Delta Modulated Signals

by Nikos Temenos, Anastasis Vlachos and Paul P. Sotiriadis

Technologies 2022, 10(1), 14; https://doi.org/10.3390/technologies10010014 - 20 Jan 2022

Cited by 5 | Viewed by 2301

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue MOCAST 2021)

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15 pages, 332 KiB

Open AccessEditor’s ChoiceReview

A Review of Efficient Real-Time Decision Making in the Internet of Things

by Kyoung-Don Kang

Technologies 2022, 10(1), 12; https://doi.org/10.3390/technologies10010012 - 19 Jan 2022

Cited by 3 | Viewed by 3348

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, [...] Read more.

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. Full article

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

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17 pages, 4039 KiB

Open AccessEditor’s ChoiceArticle

Assistive Technologies for Supporting the Wellbeing of Older Adults

by Ioanna Dratsiou, Annita Varella, Evangelia Romanopoulou, Oscar Villacañas, Sara Cooper, Pavlos Isaris, Manex Serras, Luis Unzueta, Tatiana Silva, Alexia Zurkuhlen, Malcolm MacLachlan and Panagiotis D. Bamidis

Technologies 2022, 10(1), 8; https://doi.org/10.3390/technologies10010008 - 14 Jan 2022

Cited by 9 | Viewed by 4186

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 [...] Read more.

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. Full article

(This article belongs to the Section Assistive Technologies)

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22 pages, 13186 KiB

Open AccessEditor’s ChoiceReview

3D Scanning/Printing: A Technological Stride in Sculpture

by G.-Fivos Sargentis, Evangelia Frangedaki, Michalis Chiotinis, Demetris Koutsoyiannis, Stephanos Camarinopoulos, Alexios Camarinopoulos and Nikos D. Lagaros

Technologies 2022, 10(1), 9; https://doi.org/10.3390/technologies10010009 - 14 Jan 2022

Cited by 5 | Viewed by 5560

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 [...] Read more.

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. Full article

(This article belongs to the Section Manufacturing Technology)

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10 pages, 4927 KiB

Open AccessEditor’s ChoiceArticle

A Simulated Environment for Robot Vision Experiments

by Christos Sevastopoulos, Stasinos Konstantopoulos, Keshav Balaji, Mohammad Zaki Zadeh and Fillia Makedon

Technologies 2022, 10(1), 7; https://doi.org/10.3390/technologies10010007 - 12 Jan 2022

Cited by 5 | Viewed by 2625

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 [...] Read more.

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. Full article

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

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11 pages, 2094 KiB

Open AccessEditor’s ChoiceArticle

Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms

by Alfonso Navarro-Espinoza, Oscar Roberto López-Bonilla, Enrique Efrén García-Guerrero, Esteban Tlelo-Cuautle, Didier López-Mancilla, Carlos Hernández-Mejía and Everardo Inzunza-González

Technologies 2022, 10(1), 5; https://doi.org/10.3390/technologies10010005 - 10 Jan 2022

Cited by 36 | Viewed by 15515

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 [...] Read more.

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. Full article

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

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16 pages, 2920 KiB

Open AccessEditor’s ChoiceArticle

A Simplified Tantalum Oxide Memristor Model, Parameters Estimation and Application in Memory Crossbars

by Valeri Mladenov and Stoyan Kirilov

Technologies 2022, 10(1), 6; https://doi.org/10.3390/technologies10010006 - 10 Jan 2022

Cited by 4 | Viewed by 2864

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue MOCAST 2021)

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12 pages, 944 KiB

Open AccessEditor’s ChoiceArticle

Artwork Style Recognition Using Vision Transformers and MLP Mixer

by Lazaros Alexios Iliadis, Spyridon Nikolaidis, Panagiotis Sarigiannidis, Shaohua Wan and Sotirios K. Goudos

Technologies 2022, 10(1), 2; https://doi.org/10.3390/technologies10010002 - 28 Dec 2021

Cited by 5 | Viewed by 3112

Abstract

Through the extensive study of transformers, attention mechanisms have emerged as potentially more powerful than sequential recurrent processing and convolution. In this realm, Vision Transformers have gained much research interest, since their architecture changes the dominant paradigm in Computer Vision. An interesting and [...] Read more.

Through the extensive study of transformers, attention mechanisms have emerged as potentially more powerful than sequential recurrent processing and convolution. In this realm, Vision Transformers have gained much research interest, since their architecture changes the dominant paradigm in Computer Vision. An interesting and difficult task in this field is the classification of artwork styles, since the artistic style of a painting is a descriptor that captures rich information about the painting. In this paper, two different Deep Learning architectures—Vision Transformer and MLP Mixer (Multi-layer Perceptron Mixer)—are trained from scratch in the task of artwork style recognition, achieving over 39% prediction accuracy for 21 style classes on the WikiArt paintings dataset. In addition, a comparative study between the most common optimizers was conducted obtaining useful information for future studies. Full article

(This article belongs to the Special Issue MOCAST 2021)

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