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ISSN (Online) 2227-7080
Published by MDPI Homepage  [246 journals]
  • Technologies, Vol. 10, Pages 110: The Impossible, the Unlikely, and the
           Probable Nudges: A Classification for the Design of Your Next Nudge

    • Authors: Randi Karlsen, Anders Andersen
      First page: 110
      Abstract: Nudging provides a way to gently influence people to change behavior towards a desired goal, e.g., by moving towards a healthier or more environmentally friendly lifestyle. Personalized and context-aware digital nudging (named smart nudging) can be a powerful tool for efficient nudging by tailoring nudges to the current situation of each individual user. However, designing smart nudges is challenging, as different users may need different supports to improve their behavior. Determining the next nudge for a specific user must be done based on the user’s current situation, abilities, and potential for improvement. In this paper, we focus on the challenge of designing the next nudge by presenting a novel classification of nudges that distinguishes between (i) nudges that are impossible for the user to follow, (ii) nudges that are unlikely to be followed, and (iii) probable nudges that the user can follow. The classification is tailored to individual users based on user profiles, current situations, and knowledge of previous behaviors. This paper describes steps in the nudge design process and a novel set of principles for designing smart nudges.
      Citation: Technologies
      PubDate: 2022-10-22
      DOI: 10.3390/technologies10060110
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 111: Variance-Based Sensitivity Analysis of
           Fitting Parameters to Impact on Cycling Durability of Polymer Electrolyte
           Fuel Cells

    • Authors: Victor A. Kovtunenko
      First page: 111
      Abstract: Degradation of a catalyst layer in polymer electrolyte membrane fuel cells is considered, which is caused by electrochemical reactions of the platinum ion dissolution and oxide coverage. An accelerated stress test is applied, where the electric potential cycling is given by a non-symmetric square profile. Computer simulations of the underlying one-dimensional Holby–Morgan model predict durability of the fuel cell operating. A sensitivity analysis based on the variance quantifies how loss of the platinum mass subjected to the degradation is impacted by the variation of fitting parameters in the model.
      Citation: Technologies
      PubDate: 2022-10-28
      DOI: 10.3390/technologies10060111
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 112: Fabrication and Characterization of
           SnCl2- and CuBr-Added Perovskite Photovoltaic Devices

    • Authors: Yugo Asakawa, Takeo Oku, Masashi Kido, Atsushi Suzuki, Riku Okumura, Masanobu Okita, Sakiko Fukunishi, Tomoharu Tachikawa, Tomoya Hasegawa
      First page: 112
      Abstract: Perovskite photovoltaic devices added with tin (Sn) dichloride and copper (Cu) bromide were fabricated and characterized. The thin film devices were prepared by an ordinary spin-coating technique using an air blowing method in ambient air. A decaphenylcyclopentasilane layer was coated at the surface of perovskite layer and annealed at a high temperature of 190 °C. Conversion efficiencies and short-circuit current densities were improved for devices added with Sn and Cu compared with the standard devices. The energy gap of the perovskite crystal decreased through the Sn addition, which was also confirmed by first-principles calculations.
      Citation: Technologies
      PubDate: 2022-10-28
      DOI: 10.3390/technologies10060112
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 113: Modular Multi-Input DC/DC Converter for
           EV Fast Charging

    • Authors: Hossam A. Gabbar, Abdalrahman Elshora
      First page: 113
      Abstract: In this paper, a modular multi-input, single output DC/DC converter is proposed to enhance the energy management of a fast-charging station for electric vehicles (EVs). The proposed bidirectional converter can work in different modes of operation with fewer components and a modular design to extend the input power sources and increase the charging power rate. The converter has several merits compared to the conventional converters, such as centralizing the control, reducing power devices, and reducing power conversion stages. By using MATLAB/Simulink, the converter was tested in many operation modes and was used to charge a Nissan Leaf EV’s battery (350 V, 60 Ah) from hybrid sources simultaneously and individually in power up to (17 kW). In addition, it was tested on a hardware scale at a low power rate (100 W) for the validation of the simulation work and the topology concept. In addition, its different losses and efficiency were calculated during the different operation modes.
      Citation: Technologies
      PubDate: 2022-11-07
      DOI: 10.3390/technologies10060113
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 114: Open-Source
           Photovoltaic—Electrical Vehicle Carport Designs

    • Authors: Nicholas Vandewetering, Koami Soulemane Hayibo, Joshua M. Pearce
      First page: 114
      Abstract: Solar powering the increasing fleet of electrical vehicles (EV) demands more surface area than may be available for photovoltaic (PV)-powered buildings. Parking lot solar canopies can provide the needed area to charge EVs but are substantially costlier than roof- or ground-mounted PV systems. To provide a low-cost PV parking lot canopy to supply EV charging, in this study, we provide a full mechanical and economic analysis of three novel PV canopy systems: (1) an exclusively wood, single-parking-spot spanning system, (2) a wood and aluminum double-parking-spot spanning system, and (3) a wood and aluminum cantilevered system for curbside parking. All three systems can be scaled to any amount of EV parking spots. The complete designs and bill of materials (BOM) of the canopies are provided, along with basic instructions, and are released with an open-source license that will enable anyone to fabricate them. Analysis results indicate that single-span systems provide cost savings of 82–85%, double-span systems save 43–50%, and cantilevered systems save 31–40%. In the first year of operation, PV canopies can provide 157% of the energy needed to charge the least efficient EV currently on the market if it is driven the average driving distance in London, ON, Canada.
      Citation: Technologies
      PubDate: 2022-11-07
      DOI: 10.3390/technologies10060114
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 115: Modelling the Trust Value for Human
           Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems

    • Authors: Chin-Teng Lin, Hsiu-Yu Fan, Yu-Cheng Chang, Liang Ou, Jia Liu, Yu-Kai Wang, Tzyy-Ping Jung
      First page: 115
      Abstract: The modelling of trust values on agents is broadly considered fundamental for decision-making in human-autonomous teaming (HAT) systems. Compared to the evaluation of trust values for robotic agents, estimating human trust is more challenging due to trust miscalibration issues, including undertrust and overtrust problems. From a subjective perception, human trust could be altered along with dynamic human cognitive states, which makes trust values hard to calibrate properly. Thus, in an attempt to capture the dynamics of human trust, the present study evaluated the dynamic nature of trust for human agents through real-time multievidence measures, including human states of attention, stress and perception abilities. The proposed multievidence human trust model applied an adaptive fusion method based on fuzzy reinforcement learning to fuse multievidence from eye trackers, heart rate monitors and human awareness. In addition, fuzzy reinforcement learning was applied to generate rewards via a fuzzy logic inference process that has tolerance for uncertainty in human physiological signals. The results of robot simulation suggest that the proposed trust model can generate reliable human trust values based on real-time cognitive states in the process of ongoing tasks. Moreover, the human-autonomous team with the proposed trust model improved the system efficiency by over 50% compared to the team with only autonomous agents. These results may demonstrate that the proposed model could provide insight into the real-time adaptation of HAT systems based on human states and, thus, might help develop new ways to enhance future HAT systems better.
      Citation: Technologies
      PubDate: 2022-11-08
      DOI: 10.3390/technologies10060115
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 116: Electrical Discharge Machining of Al2O3
           Using Copper Tape and TiO2 Powder-Mixed Water Medium

    • Authors: Sergey N. Grigoriev, Anna A. Okunkova, Marina A. Volosova, Khaled Hamdy, Alexander S. Metel
      First page: 116
      Abstract: Aluminum-based ceramics are used in industry to produce cutting tools that resist extreme mechanical and thermal load conditions during the machining of Ni-based and high-entropy alloys. There is wide field of application also in the aerospace industry. Microtexturing of cutting ceramics reduces contact loads and wear of cutting tools. However, most of the published works are related to the electrical discharge machining of alumina in hydrocarbons, which creates risks for the personnel and equipment due to the formation of chemically unstable dielectric carbides (methanide Al3C4 and acetylenide Al2(C2)3). An alternative approach for wire electrical discharge machining Al2O3 in the water-based dielectric medium using copper tape of 40 µm thickness and TiO2 powder suspension was proposed for the first time. The performance was evaluated by calculating the material removal rate for various combinations of pulse frequency and TiO2 powder concentration. The obtained kerf of 54.16 ± 0.05 µm in depth demonstrated an increasing efficiency of more than 1.5 times with the closest analogs for the workpiece thickness up to 5 mm in height. The comparison of the performance (0.0083–0.0084 mm3/s) with the closest analogs shows that the results may correlate with the electrical properties of the assisting materials.
      Citation: Technologies
      PubDate: 2022-11-11
      DOI: 10.3390/technologies10060116
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 117: Towards a Modern Learning Organization:
           Human-Centered Digitalization of Lessons Learned Management for Complex
           Systems Development Projects

    • Authors: Yangyang Zhao, Henrik Jensen
      First page: 117
      Abstract: The importance of learning from experience is incontrovertible; however, little is studied regarding the digitalization of in- and inter-project lessons learned in modern organizational practices. As a critical part of organizational knowledge, lessons learned are known to help organizations adapt to the ever-changing world via the complex systems development projects they use to capitalize on and to develop their competitive advantage. In this paper, we introduce the concept of human-centered digitalization for this unique type of organizational knowledge and explain why this approach to managing lessons learned for complex systems development projects is necessary. Drawing from design thinking and systems thinking theories, we further outline the design principles for guiding actions and provide a case study of their implementation in automated systems projects for maritime industries.
      Citation: Technologies
      PubDate: 2022-11-16
      DOI: 10.3390/technologies10060117
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 118: Friction Stir Welding of Ti-6Al-4V Using
           a Liquid-Cooled Nickel Superalloy Tool

    • Authors: Sergei Tarasov, Alihan Amirov, Andrey Chumaevskiy, Nikolay Savchenko, Valery E. Rubtsov, Aleksey Ivanov, Evgeniy Moskvichev, Evgeny Kolubaev
      First page: 118
      Abstract: Friction stir welding (FSW) of titanium alloy was carried out using liquid cooling of the FSW tool made of heat-resistant nickel superalloy. Cooling of the nickel superalloy tool was performed by means of circulating water inside the tool. The FSW joints were characterized by microstructures and mechanical strength. The mechanical strength of the joints was higher than that of the base metal.
      Citation: Technologies
      PubDate: 2022-11-18
      DOI: 10.3390/technologies10060118
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 119: Infrared Thermal Imaging and Artificial
           Neural Networks to Screen for Wrist Fractures in Pediatrics

    • Authors: Olamilekan Shobayo, Reza Saatchi, Shammi Ramlakhan
      First page: 119
      Abstract: Paediatric wrist fractures are commonly seen injuries at emergency departments. Around 50% of the X-rays taken to identify these injuries indicate no fracture. The aim of this study was to develop a model using infrared thermal imaging (IRTI) data and multilayer perceptron (MLP) neural networks as a screening tool to assist clinicians in deciding which patients require X-ray imaging to diagnose a fracture. Forty participants with wrist injury (19 with a fracture, 21 without, X-ray confirmed), mean age 10.50 years, were included. IRTI of both wrists was performed with the contralateral as reference. The injured wrist region of interest (ROI) was segmented and represented by the means of cells of 10 × 10 pixels. The fifty largest means were selected, the mean temperature of the contralateral ROI was subtracted, and they were expressed by their standard deviation, kurtosis, and interquartile range for MLP processing. Training and test files were created, consisting of randomly split 2/3 and 1/3 of the participants, respectively. To avoid bias of participant inclusion in the two files, the experiments were repeated 100 times, and the MLP outputs were averaged. The model’s sensitivity and specificity were 84.2% and 71.4%, respectively. Further work involves a larger sample size, adults, and other bone fractures.
      Citation: Technologies
      PubDate: 2022-11-22
      DOI: 10.3390/technologies10060119
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 120: Human-in-Loop Decision-Making and
           Autonomy: Lessons Learnt from the Aviation Industry Transferred to
           Cyber-Physical Systems

    • Authors: Chara Makri, Didem Gürdür Broo, Andy Neely
      First page: 120
      Abstract: In this study, we reviewed aircraft accidents in order to understand how autonomy and safety has been managed in the aviation industry, with the aim of transferring our findings to autonomous cyber-physical systems (CPSs) in general. Through the qualitative analysis of 26 reports of aircraft accidents that took place from 2016 to 2022, we identified the most common contributing factors and the actors involved in aircraft accidents. We found that accidents were rarely the result of a single event or actor, with the most common contributing factor being non-adherence to standard operating procedures (SOPs). Considering that the aviation industry has had decades to perfect their SOPs, it is important for CPSs not only to consider the actors and causes that may contribute to safety-related issues, but also to consider well-defined reporting practices, as well as the different levels of mechanisms checked by diverse stakeholders, in order to minimise the cascading nature of such events to improve safety. In addition to proposing a new definition of safety, in this study we suggest reviewing high-reliability organisations to offer further insights as part of future research on CPS safety.
      Citation: Technologies
      PubDate: 2022-11-24
      DOI: 10.3390/technologies10060120
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 121: Simulation Analysis of Signal
           Conditioning Circuits for Plants’ Electrical Signals

    • Authors: Mirella Carneiro, Victor Oliveira, Fernanda Oliveira, Marco Teixeira, Milena Pinto
      First page: 121
      Abstract: Electrical signals are generated and transmitted through plants in response to stimuli caused by external environment factors, such as touching, luminosity, and leaf burning. By analyzing a specific plant’s electrical responses, it is possible to interpret the impact of external aspects in the plasma membrane potential and, thus, determine the cause of the electrical signal. Moreover, these signals permit the whole plant structure to be informed almost instantaneously. This work presents a brief discussion of plants electrophysiology theory and low-cost signal conditioning circuits, which are necessary for the acquisition of plants’ electrical signals. Two signal conditioning circuits, which must be chosen depending on the signal to be measured, are explained in detail and electrical simulation results, performed in OrCAD Capture Software are presented. Furthermore, Monte Carlo simulations were performed to evaluate the impact of components variations on the accuracy and efficiency of the signal conditioning circuits. Those simulations showed that, even after possible component variations, the filters’ cut-off frequencies had at most 4% variation from the mean.
      Citation: Technologies
      PubDate: 2022-11-25
      DOI: 10.3390/technologies10060121
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 122: Comparison of the Predictive Performance
           of Medical Coding Diagnosis Classification Systems

    • Authors: Dimitrios Zikos, Nailya DeLellis
      First page: 122
      Abstract: Health analytics frequently involve tasks to predict outcomes of care. A foundational predictor of clinical outcomes is the medical diagnosis (Dx). The most used expression of medical Dx is the International Classification of Diseases (ICD-10-CM). Since ICD-10-CM includes >70,000 codes, it is computationally expensive and slow to train models with. Alternative lower-dimensionality alternatives include clinical classification software (CCS) and diagnosis-related groups (MS-DRGs). This study compared the predictive power of these alternatives against ICD-10-CM for two outcomes of hospital care: inpatient mortality and length of stay (LOS). Naïve Bayes (NB) and Random Forests models were created for each Dx system to examine their predictive performance for inpatient mortality, and Multiple Linear Regression models for the continuous LOS variable. The MS-DRGs performed highest for both outcomes, even outperforming ICD-10-CM. The admitting ICD-10-CM codes were, surprisingly, not underperformed by the primary ICD-10-CM Dxs. The CCS system, although having a much lower dimensionality than ICD-10-CM, has only slightly lower performance while the refined version of CCS only slightly outperformed the old CCS. Random Forests outperformed NB for MS-DRG, and ICD-10-CM, by a large margin. Results can provide insights to understand the compromise from using lower-dimensionality representations in clinical outcome studies.
      Citation: Technologies
      PubDate: 2022-11-28
      DOI: 10.3390/technologies10060122
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 123: HADD: High-Accuracy Detection of
           Depressed Mood

    • Authors: Yu Liu, Kyoung-Don Kang, Mi Jin Doe
      First page: 123
      Abstract: Depression is a serious mood disorder that is under-recognized and under-treated. Recent advances in mobile/wearable technology and ML (machine learning) have provided opportunities to detect the depressed moods of participants in their daily lives with their consent. To support high-accuracy, ubiquitous detection of depressed mood, we propose HADD, which provides new capabilities. First, HADD supports multimodal data analysis in order to enhance the accuracy of ubiquitous depressed mood detection by analyzing not only objective sensor data, but also subjective EMA (ecological momentary assessment) data collected by using mobile devices. In addition, HADD improves upon the accuracy of state-of-the-art ML algorithms for depressed mood detection via effective feature selection, data augmentation, and two-stage outlier detection. In our evaluation, HADD significantly enhanced the accuracy of a comprehensive set of ML models for depressed mood detection.
      Citation: Technologies
      PubDate: 2022-11-29
      DOI: 10.3390/technologies10060123
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 124: Methods of Material and Surface Analysis
           for the Evaluation of Failure Modes for Electrical Connectors

    • Authors: Philipp Kolmer, Abhay Shukla, Jian Song
      First page: 124
      Abstract: The development of autonomous vehicles and the integration of new information and communication technologies are making the reliability of electrical systems and components in modern vehicles increasingly important. Electrical connectors are a crucial component in an electrical on-board system. They are exposed to a wide variety of influences by the environment and operating conditions. Thus, the degradation of electrical connectors can occur. Material and surface analysis methods are the tools used to analyze the degradation mechanisms in connectors after lifetime tests, as well as in field operations. Within the framework of this study, a wide variety of methods from the analytical scope are presented and discussed. The connector surfaces degraded by different failure mechanisms are analyzed using various material and surface analysis methods. The quality and the nature of the analyses results obtained from various analysis methods are compared. Also, this study deals with the benefits and limitations, as well as the effort and the specific challenges of different material and surface analytical methods for the evaluation of failure mechanisms from the point of view of a material and surface analyst.
      Citation: Technologies
      PubDate: 2022-11-29
      DOI: 10.3390/technologies10060124
      Issue No: Vol. 10, No. 6 (2022)
  • Technologies, Vol. 10, Pages 98: Digitization of Manufacturing Processes:
           From Sensing to Twining

    • Authors: Panagiotis Stavropoulos
      First page: 98
      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 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.
      Citation: Technologies
      PubDate: 2022-08-30
      DOI: 10.3390/technologies10050098
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 99: Selected Techniques for Cutting SOx
           Emissions in Maritime Industry

    • Authors: Christos Papadopoulos, Marios Kourtelesis, Anastasia Maria Moschovi, Konstantinos Miltiadis Sakkas, Iakovos Yakoumis
      First page: 99
      Abstract: Burning fuels with high sulfur content leads to SOx 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 NOx, 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 DeSOx solution, which still allows them to use the cheapest heavy fuel oil. The current work reviews the state-of-the-art DeSOx solutions both for the maritime and land-based sector. Next, it proposes an alternative cheaper and environmentally friendly DeSOx 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.
      Citation: Technologies
      PubDate: 2022-08-30
      DOI: 10.3390/technologies10050099
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 100: Application of Blockchain Technology in
           Environmental Health: Literature Review and Prospect of Visualization
           Based on CiteSpace

    • Authors: Meiwen Guo, Zhenheng Huang, Liang Wu, Cheng Ling Tan, Jianping Peng, Xingcheng Guo, Hong Chen
      First page: 100
      Abstract: Blockchain technology and its applications have recently become a research hotspot. Its three core technologies, distributed ledger, smart contract, and consensus mechanism, provide trust-enhancing features such as tamper-proof records, full traceability, and data decentralization for a wide range of applications. This paper investigates the use of blockchain technology in environmental health. It investigates indicators such as the number of articles published, author collaboration network, research institution network, and keyword co-occurrence in this field between 2014 and 2021. It describes and analyzes the development and connotations of these indicators. Many scholars have conducted in-depth studies on blockchain in various areas. Still, there are few cross-over studies on environmental health and a lack of cross-over studies on technology application in multiple fields. The current study investigates the evolution of research on the application of blockchain technology in environmental health, as well as potential development patterns and research trends, to provide a theoretical foundation for the application and sustainability of blockchain technology in this field.
      Citation: Technologies
      PubDate: 2022-09-07
      DOI: 10.3390/technologies10050100
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 101: Solar Energy Management Using a
           V-Groove: An Approach Based on a Multiple Optical Path Algorithm

    • Authors: Fadel Kawtharani, Bruno Serio, Geraldine Guida, Patrice Twardowski, Mohammad Hammoud
      First page: 101
      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 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.
      Citation: Technologies
      PubDate: 2022-09-12
      DOI: 10.3390/technologies10050101
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 102: A Machine-Learning-Based Approach to
           Critical Geometrical Feature Identification and Segmentation in Additive

    • Authors: Alexandre Staub, Lucas Brunner, Adriaan B. Spierings, Konrad Wegener
      First page: 102
      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 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.
      Citation: Technologies
      PubDate: 2022-09-16
      DOI: 10.3390/technologies10050102
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 103: Design and Implementation of an
           Anthropomorphic Robotic Arm Prosthesis

    • Authors: Valentina A. Yurova, Gleb Velikoborets, Andrei Vladyko
      First page: 103
      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 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.
      Citation: Technologies
      PubDate: 2022-09-21
      DOI: 10.3390/technologies10050103
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 104: Exploration of Educational Possibilities
           by Four Metaverse Types in Physical Education

    • Authors: Ji-Eun Yu
      First page: 104
      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 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.
      Citation: Technologies
      PubDate: 2022-09-23
      DOI: 10.3390/technologies10050104
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 105: Deep Neural Network for Lung Image
           Segmentation on Chest X-ray

    • Authors: Mahesh Chavan, Vijayakumar Varadarajan, Shilpa Gite, Ketan Kotecha
      First page: 105
      Abstract: COVID-19 patients require effective diagnostic methods, which are currently in short supply. In this study, we explained how to accurately identify the lung regions on the X-ray scans of such people’s lungs. Images from X-rays or CT scans are critical in the healthcare business. Image data categorization and segmentation algorithms have been developed to help doctors save time and reduce manual errors during the diagnosis. Over time, CNNs have consistently outperformed other image segmentation algorithms. Various architectures are presently based on CNNs such as ResNet, U-Net, VGG-16, etc. This paper merged the U-Net image segmentation and ResNet feature extraction networks to construct the ResUNet++ network. The paper’s novelty lies in the detailed discussion and implementation of the ResUNet++ architecture in lung image segmentation. In this research paper, we compared the ResUNet++ architecture with two other popular segmentation architectures. The ResNet residual block helps us in lowering the feature reduction issues. ResUNet++ performed well compared with the UNet and ResNet architectures by achieving high evaluation scores with the validation dice coefficient (96.36%), validation mean IoU (94.17%), and validation binary accuracy (98.07%). The novelty of this research paper lies in a detailed discussion of the UNet and ResUNet architectures and the implementation of ResUNet++ in lung images. As per our knowledge, until now, the ResUNet++ architecture has not been performed on lung image segmentation. We ran both the UNet and ResNet models for the same amount of epochs and found that the ResUNet++ architecture achieved higher accuracy with fewer epochs. In addition, the ResUNet model gave us higher accuracy (94%) than the UNet model (92%).
      Citation: Technologies
      PubDate: 2022-09-30
      DOI: 10.3390/technologies10050105
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 106: Data Model Design to Support Data-Driven
           IT Governance Implementation

    • Authors: Vittoria Biagi, Angela Russo
      First page: 106
      Abstract: Organizations must quickly adapt their processes to understand the dynamic nature of modern business environments. As highlighted in the literature, centralized governance supports decision-making and performance measurement processes in technology companies. For this reason, a reliable decision-making system with an integrated data model that enables the rapid collection and transformation of data stored in heterogeneous and different sources is needed. Therefore, this paper proposes the design of a data model to implement data-driven governance through a literature review of adopted approaches. The lack of a standardized procedure and a disconnection between theoretical frameworks and practical application has emerged. This paper documented the suggested approach following these steps: (i) mapping of monitoring requirements to the data structure, (ii) documentation of ER diagram design, and (iii) reporting dashboards used for monitoring and reporting. The paper helped fill the gaps highlighted in the literature by supporting the design and development of a DWH data model coupled with a BI system. The application prototype shows benefits for top management, particularly those responsible for governance and operations, especially for risk monitoring, audit compliance, communication, knowledge sharing on strategic areas of the company, and identification and implementation of performance improvements and optimizations.
      Citation: Technologies
      PubDate: 2022-10-08
      DOI: 10.3390/technologies10050106
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 107: Evaluation Based on the Distance from
           the Average Solution Approach: A Derivative Model for Evaluating and
           Selecting a Construction Manager

    • Authors: Phuong Thanh Phan, Phong Thanh Nguyen
      First page: 107
      Abstract: In the current market of integration and globalization, the competition between engineering and construction companies is increasing. Construction contractors can improve their competitiveness by evaluating and selecting qualified personnel for the construction engineering manager position for their company’s civil engineering projects. However, most personnel evaluation and selection models in the construction industry rely on qualitative techniques, which leads to unsuitable decisions. To overcome this problem, this paper presents evaluation criteria and proposes a new model for selecting construction managers based on the evaluation based on the distance from the average solution approach (EDASA). The research results showed that EDASA has many strengths, such as solving the problem faster when the number of evaluation criteria or the number of alternatives is increased.
      Citation: Technologies
      PubDate: 2022-10-14
      DOI: 10.3390/technologies10050107
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 108: Thermal Inkjet Printing: Prospects and
           Applications in the Development of Medicine

    • Authors: Md Jasim Uddin, Jasmin Hassan, Dennis Douroumis
      First page: 108
      Abstract: 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.
      Citation: Technologies
      PubDate: 2022-10-21
      DOI: 10.3390/technologies10050108
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 109: Production Technologies, Regulatory
           Parameters, and Quality Control of Vaccine Vectors for Veterinary Use

    • Authors: Raquel de M. Barbosa, Amélia M. Silva, Classius F. da Silva, Juliana C. Cardoso, Patricia Severino, Lyghia M. A. Meirelles, Arnobio A. da Silva-Junior, César Viseras, Joel Fonseca, Eliana B. Souto
      First page: 109
      Abstract: This paper presents a comprehensive review of the main types of vaccines approaching production technology, regulatory parameters, and the quality control of vaccines. Bioinformatic tools and computational strategies have been used in the research and development of new pharmaceutical products, reducing the time between supposed pharmaceutical product candidates (R&D steps) and final products (to be marketed). In fact, in the reverse vaccinology field, in silico studies can be very useful in identifying possible vaccine targets from databases. In addition, in some cases (subunit or RNA/ DNA vaccines), the in silico approach permits: (I) the evaluation of protein immunogenicity through the prediction of epitopes, (II) the potential adverse effects of antigens through the projection of similarity to host proteins, (III) toxicity and (IV) allergenicity, contributing to obtaining safe, effective, stable, and economical vaccines for existing and emerging infectious pathogens. Additionally, the rapid growth of emerging infectious diseases in recent years should be considered a driving force for developing and implementing new vaccines and reassessing vaccine schedules in companion animals, food animals, and wildlife disease control. Comprehensive and well-planned vaccination schedules are effective strategies to prevent and treat infectious diseases.
      Citation: Technologies
      PubDate: 2022-10-21
      DOI: 10.3390/technologies10050109
      Issue No: Vol. 10, No. 5 (2022)
  • Technologies, Vol. 10, Pages 75: Determination of
           “Neutral”–“Pleasure”, and
           “Pleasure”–“Pain” Affective State Distances
           by Using AI Image Analysis of Facial Expressions

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

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

    • Authors: Manoj Gupta
      First page: 77
      Abstract: In the area of Materials Science and Engineering, the tetrahedron comprising of processing, microstructure, properties and performance as four vertex corners is always key to develop new materials and to convert them to a useful shape for end application with the best properties possible [...]
      Citation: Technologies
      PubDate: 2022-06-24
      DOI: 10.3390/technologies10040077
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 78: Exciting of Strong Electrostatic Fields
           and Electromagnetic Resonators at the Plasma Boundary by a Power
           Electromagnetic Beam

    • Authors: O. M. Gradov
      First page: 78
      Abstract: The interaction of an electromagnetic beam with a sharp boundary of a dense cold semi-limited plasma was considered in the case of a normal wave incidence on the plasma surface. The possibility of the appearance of an electrostatic field outside the plasma was revealed, the intensity of which decreased according to the power law with a distance from the plasma and the center of the beam. It was possible to form cavities with a reduced electron density, being each electromagnetic resonators, which probed deeply into the dense plasma and couldexist in a stable state for a long period.
      Citation: Technologies
      PubDate: 2022-06-29
      DOI: 10.3390/technologies10040078
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 79: Evaluation of Machine Learning Algorithms
           for Classification of EEG Signals

    • Authors: 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, Everardo Inzunza-González
      First page: 79
      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 (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.
      Citation: Technologies
      PubDate: 2022-06-30
      DOI: 10.3390/technologies10040079
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 80: Laboratory Study on the Water-Soluble
           Polymer as a Self-Curing Compound for Cement Concrete Roads in Ethiopia

    • Authors: Makendran Chandrakasu, Karunanidhi Suthandhiram, Shiferaw Garoma, Bekesha Merea, Balaguru Sethuraman
      First page: 80
      Abstract: In this paper, the self-curing process was considered and found to be a better alternative to the conventional curing process for concrete structures in Ethiopia. It is well known that water plays a significant role in the curing process of preparing concrete in the construction industry. A good quality water is required for the conventional curing process, but that is scarce in Ethiopia. Curing concrete for bridges and roads is difficult in Ethiopia due to the poor quality and scarcity of water. In this study, Polyethylene Glycol (PEG) 600, a self-curing process, is considered as an alternative. Using the M40 Grade mix, four different percentages of PEG-600, 0.0, 0.5, 1.0, and 1.5 of cement weight, were studied, and the specimens were tested. Here, M40 grade stands for “a concrete mix with a characteristic compressive strength of 40 N/mm2, i.e., 40 Newton per square millimeter”. Additionally, the mechanical strengths and properties of both conventional and self-cured processed concretes were calculated and compared. The present investigation concludes that PEG 600 offers significant results for self-curing concrete. The study procedure, results, and recommendations are presented in the text of the paper.
      Citation: Technologies
      PubDate: 2022-07-05
      DOI: 10.3390/technologies10040080
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 81: Distribution Path Optimization by an
           Improved Genetic Algorithm Combined with a Divide-and-Conquer Strategy

    • Authors: Jiaqi Li, Yun Wang, Ke-Lin Du
      First page: 81
      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 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.
      Citation: Technologies
      PubDate: 2022-07-05
      DOI: 10.3390/technologies10040081
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 82: Accurate Numerical Treatment on a
           Stochastic SIR Epidemic Model with Optimal Control Strategy

    • Authors: Indranil Ghosh, Muhammad Mahbubur Rashid, Pallabi Ghosh, Shukranul Mawa, Rupal Roy, Md Manjurul Ahsan, Kishor Datta Gupta
      First page: 82
      Abstract: In this paper, a numerical study has been undertaken on the susceptible-infected-recovered (SIR) epidemic model that encompasses the mechanisms of the evolution of disease transmission; a prophylactic vaccination strategy in the susceptible populations, depending on the infective individuals. We furnish numerical and graphical simulation combined with explicit series solutions of the proposed model using the New Iterative Method (NIM) and Modified New Iterative Method (MNIM). The analytic-numeric New Iterative Method failed to deliver accurate solution for the large time domain. A new reliable algorithm based on NIM, the coupling of the Laplace transforms, and the New Iterative method is called Modified New Iterative Method (MNIM) which is presented to enhance the validity domain of NIM techniques. The convergence analysis of the MNIM has also been illustrated. The simulation results show that the vaccination strategy can slow down the spread of the epidemic rapidly. Numerical results illustrate the excellent performance of the MNIM and show that the modified method is much more accurate than the NIM.
      Citation: Technologies
      PubDate: 2022-07-06
      DOI: 10.3390/technologies10040082
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 83: Demonstration of Resilient Microgrid with
           Real-Time Co-Simulation and Programmable Loads

    • Authors: Hossam A. Gabbar, Yasser Elsayed, Manir Isham, Abdalrahman Elshora, Abu Bakar Siddique, Otavio Lopes Alves Esteves
      First page: 83
      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 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.
      Citation: Technologies
      PubDate: 2022-07-12
      DOI: 10.3390/technologies10040083
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 84: Optimization of the Pick-Up and Delivery
           Technology in a Selected Company: A Case Study

    • Authors: Ondrej Stopka, Patrik Gross, Jan Pečman, Jiří Hanzl, Mária Stopková, Martin Jurkovič
      First page: 84
      Abstract: This article deals with pick-up and delivery activities in a selected company that focuses on the distribution of products in the gastronomic sector of the market and suggests how to make the present approach more efficient. The introductory part of the article clarifies the meanings of basic concepts related to the issue of optimizing the logistics processes in the company. The crucial goal is to analyze the existing pick-up and delivery technology and then, in the application part of the article, to propose adequate measures in the context of streamlining these activities with their technical and economic evaluation. An analysis of current delivery routes, which are used for the distribution of gastronomic products, is first performed. Thereafter, the routes are optimized with the aim of minimizing the total distance traveled by using the Operations Research methods, namely: the Hungarian method, Vogel approximation method, nearest neighbor method and the Routin route planner which is based on a principle of the Greedy algorithm. At the end of the article, a technical and economical evaluation of the findings is discussed, wherein the individual results of optimization through selected methods are first compared and then, new optimized routes are selected.
      Citation: Technologies
      PubDate: 2022-07-14
      DOI: 10.3390/technologies10040084
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 85: Efficient Supervised Machine Learning
           Network for Non-Intrusive Load Monitoring

    • Authors: Muhammad Usman Hadi, Nik Hazmi Nik Suhaimi, Abdul Basit
      First page: 85
      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 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.
      Citation: Technologies
      PubDate: 2022-07-16
      DOI: 10.3390/technologies10040085
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 86: Analysis and Hardware Architecture on
           FPGA of a Robust Audio Fingerprinting Method Using SSM

    • Authors: Ignacio Algredo-Badillo, Brenda Sánchez-Juárez, Kelsey A. Ramírez-Gutiérrez, Claudia Feregrino-Uribe, Francisco López-Huerta, Johan J. Estrada-López
      First page: 86
      Abstract: The significant volume of sharing of digital media has recently increased due to the pandemic, raising the number of unauthorized uses of these media, such as emerging unauthorized copies, forgery, the lack of copyright, and electronic fraud, among others. In particular, several applications integrate services or products such as music distribution, content management, audiobooks, streaming, and so on, which require users to demonstrate and guarantee their audio ownership. The use of acoustic fingerprint technology has emerged as a solution that is widely used to secure audio applications. This technique extracts and analyzes certain information that identifies the inherent properties of a partial or complete audio file. In this paper, we introduce two audio fingerprinting hardware architectures with a feature extraction system based on spectrogram saliency maps (SSM) and a brute-force search. The first of these conducts a search in 33 saliency maps of 32 × 32 pixels in size. After analyzing the first algorithm, a second architecture is proposed, in which the saliency map is reduced to 27 × 25 pixels, requiring 75.67% fewer hardware resources, lowering the power consumption by 64.58%, and improving the efficiency by 3.19 times via a throughput reduction of 22.29%.
      Citation: Technologies
      PubDate: 2022-07-19
      DOI: 10.3390/technologies10040086
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 87: MOCAST 2021

    • Authors: Spyridon Nikolaidis, Rodrigo Picos
      First page: 87
      Abstract: The International Conference on Modern Circuits and Systems Technologies (MOCAST) was first launched in 2012 inside the framework of a European Project (JEWEL) [...]
      Citation: Technologies
      PubDate: 2022-07-20
      DOI: 10.3390/technologies10040087
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 88: Entropy as a Transitional In-Game

    • Authors: Angelos Papavlasopoulos, Agnes Papadopoulou, Andreas Floros, Andreas Giannakoulopoulos
      First page: 88
      Abstract: The aim of this research is to discover the bond of entropy and the experience of video game immersion, using an Interpretative Phenomenological Analysis (IPA) to interpret the immersive experiences of players and how this bond of entropy and immersion could be transferred on other immersive technologies. The experiment was conducted on a selection of low-entropy scenes in three video games belonging to the genre of interactive drama. Six players were selected as the sample group for this research, based on their playthrough experiences of the games Heavy Rain (2010), Until Dawn (2015) and Dark Pictures Anthology: Man of Medan (2019) on the PlayStation platform. By monitoring the levels of entropy and immersion during their playthroughs, this research explores the potential of transferring immersion through the use of entropy from digital games to other immersive technologies. According to the research highlights and through data interpretation, entropy is found to be immensely influential upon achieving and maintaining narrative, physical and emotional immersion, and its effect could be further applied to other immersive technologies sharing a common ground with digital games, which features are further examined in finer detail in the current research.
      Citation: Technologies
      PubDate: 2022-07-20
      DOI: 10.3390/technologies10040088
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 89: Extraction and Characterization of
           β-Viginin Protein Hydrolysates from Cowpea Flour as a New
           Manufacturing Active Ingredient

    • Authors: Taline S. Almeida, Caio A. da Cruz Souza, Mariana B. de Cerqueira e Silva, Fabiana P. R. Batista, Ederlan S. Ferreira, André L. S. Santos, Laura N. Silva, Carlisson R. Melo, Cristiane Bani, M. Lucia Bianconi, Juliana C. Cardoso, Ricardo L. C. de Albuquerque-Júnior, Raquel de Melo Barbosa, Matheus M. Pereira, Eliana B. Souto, Cleide M. F. Soares, Patrícia Severino
      First page: 89
      Abstract: The increased mortality rates associated with antibiotic resistance has become a significant public health problem worldwide. Living beings produce a variety of endogenous compounds to defend themselves against exogenous pathogens. The knowledge of these endogenous compounds may contribute to the development of improved bioactive ingredients with antimicrobial properties, useful against conventional antibiotic resistance. Cowpea is an herbaceous legume of great interest due to its high protein content and high productivity rates. The study of genetic homology of vicillin (7S) from cowpea (Vigna unguiculata L.) with vicilins from soybean and other beans, such as adzuki, in addition to the need for further studies about potential biological activities of this vegetable, led us to seek the isolation of the vicilin fraction from cowpea and to evaluate the potential in vitro inhibitory action of pathogenic microorganisms. The cowpea beta viginin protein was isolated, characterized, and hydrolyzed in silico and in vitro by two enzymes, namely, pepsin and chymotrypsin. The antimicrobial activity of the protein hydrolysate fractions of cowpea flour was evaluated against Staphylococcus aureus and Pseudomonas aeruginosa, confirming the potential use of the peptides as innovative antimicrobial agents.
      Citation: Technologies
      PubDate: 2022-07-21
      DOI: 10.3390/technologies10040089
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 90: Multimodal Semantic Segmentation in
           Autonomous Driving: A Review of Current Approaches and Future Perspectives

    • Authors: Giulia Rizzoli, Francesco Barbato, Pietro Zanuttigh
      First page: 90
      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 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.
      Citation: Technologies
      PubDate: 2022-07-25
      DOI: 10.3390/technologies10040090
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 91: Triple-Band Implantable Antenna Design
           for Biotelemetry Applications in MICS/ISM/Wi-Fi/Bluetooth Bands

    • Authors: Kaveh Shahverdi, Soheil Hashemi, Sadaf Sarafan, Hung Cao
      First page: 91
      Abstract: Our objective is to design triple-band implantable antennas with wide bandwidths and appropriate sizes for biomedical applications. The targeted design frequencies are 400 MHz, 2.4 GHz, and the new Wi-Fi band of 5.7 GHz. Three triple-band antennas with bandwidth improvements are presented to insure all-time data connection. The proposed triple-band implantable antennas benefit from combining long-distance data transfer at lower frequency bands and a higher effective bandwidth, and high-speed communications at higher frequency bands, which will have flexibility for a variety of applications. A comprehensive explanation of the design procedure to achieve multiple-band implantable antennas is provided. Furthermore, miniaturization techniques are utilized to design antennas in compact sizes suitable for biomedical applications. In this paper, three-layer structures including skin, fat, and muscle are used for the designs, then antennas are placed in the chest, neck, head, and hand of different human voxels to compare antennas’ performance. Additionally, normal and overweight human effects on antenna performance were compared. Antennas have 2 to 6 dBi directivity for telemetry usage, and they are designed to satisfy the absorption limit for the human body to keep the Specific Absorption Rate (SAR) averaged over 1 g of tissue less than 1.6 W/kg and over 10 g of tissue less than 2 W/kg, according to IEEE standard. The antennas include fractal, meandered, and comb types with sizes of 1.4 mm × 10 mm × 10 mm, 3.04 mm × 10 mm × 17.25 mm, and 1.4 mm × 12 mm × 12 mm, respectively. The designed antenna showed an impedance bandwidth of 53 MHz to 120 MHz, 90 MHz to 320 MHz, and 300 MHz to 1200 MHz at the three bands. The meandered antenna was selected for validation of simulations, and its S parameters were measured in the equivalent liquid phantom of body tissues.
      Citation: Technologies
      PubDate: 2022-08-04
      DOI: 10.3390/technologies10040091
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 92: Business Intelligence’s
           Self-Service Tools Evaluation

    • Authors: Jordina Orcajo Hernández, Pau Fonseca i Casas
      First page: 92
      Abstract: The software selection process in the context of a big company is not an easy task. In the Business Intelligence area, this decision is critical, since the resources needed to implement the tool are huge and imply the participation of all organization actors. We propose to adopt the systemic quality model to perform a neutral comparison between four business intelligence self-service tools. To assess the quality, we consider eight characteristics and eighty-two metrics. We built a methodology to evaluate self-service BI tools, adapting the systemic quality model. As an example, we evaluated four tools that were selected from all business intelligence platforms, following a rigorous methodology. Through the assessment, we obtained two tools with the maximum quality level. To obtain the differences between them, we were more restrictive increasing the level of satisfaction. Finally, we got a unique tool with the maximum quality level, while the other one was rejected according to the rules established in the methodology. The methodology works well for this type of software, helping in the detailed analysis and neutral selection of the final software to be used for the implementation.
      Citation: Technologies
      PubDate: 2022-08-10
      DOI: 10.3390/technologies10040092
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 93: Spectrometry of the Urban Lightscape

    • Authors: Christopher Small
      First page: 93
      Abstract: NASA’s Gateway to Astronaut Photography of Earth contains over 30,000 photos of ~2500 cataloged urban lightscapes (anthropogenic night light) taken from the International Space Station. A subset of over 100 of these multispectral DSLR photos are of sufficient spatial resolution, sharpness and exposure to be potentially useful for broadband spectral characterization of urban lightscapes. Spectral characterization of multiple urban lightscapes can provide a basis for quantifying intra and interurban variability in night light brightness, color and extent, as well as the potential for change analyses. A comparative analysis of simulated atmospheric transmissivity from the MODTRAN radiative transfer model indicates that the spectral slopes of transmissivity spectra are relatively insensitive model atmospheres, with variations in atmospheric path length and aerosol optical depth primarily affecting the bias of the spectrum rather than the slope. A mosaic of 18 intercalibrated, transmissivity-compensated RGB photos renders a spectral feature space bounded by four clearly defined spectral endmembers corresponding to white, yellow and red light sources, with brightness modulated by a dark background endmember. These four spectral endmembers form the basis of a linear spectral mixture model which can be inverted to provide estimates of the areal fraction of each endmember present within every pixel field of view. The resulting spectral feature spaces consistently show two distinct mixing trends extending from the dark endmember to flat spectrum (white–yellow) and warm spectrum (orange) sources. The distribution of illuminated pixels is strongly skewed toward a lower luminance background of warm spectrum street lighting with brighter lights, generally corresponding to point sources and major thoroughfares.
      Citation: Technologies
      PubDate: 2022-08-13
      DOI: 10.3390/technologies10040093
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 94: The History of Computing in Iran
           (Persia)—Since the Achaemenid Empire

    • Authors: Arash Heidari, Nima Jafari Navimipour, Mehmet Unal
      First page: 94
      Abstract: Persia was the early name for the territory that is currently recognized as Iran. Iran’s proud history starts with the Achaemenid Empire, which began in the 6th century BCE (c. 550). The Iranians provided numerous innovative ideas in breakthroughs and technologies that are often taken for granted today or whose origins are mostly unknown from the Achaemenid Empire’s early days. To recognize the history of computing systems in Iran, we must pay attention to everything that can perform computing. Because of Iran’s historical position in the ancient ages, studying the history of computing in this country is an exciting subject. The history of computing in Iran started very far from the digital systems of the 20th millennium. The Achaemenid Empire can be mentioned as the first recorded sign of using computing systems in Persia. The history of computing in Iran started with the invention of mathematical theories and methods for performing simple calculations. This paper also attempts to shed light on Persia’s computing heritage elements, dating back to 550 BC. We look at both the ancient and current periods of computing. In the ancient section, we will go through the history of computing in the Achaemenid Empire, followed by a description of the tools used for calculations. Additionally, the transition to the Internet era, the formation of a computer-related educational system, the evolution of data networks, the growth of the software and hardware industry, cloud computing, and the Internet of Things (IoT) are all discussed in the modern section. We highlighted the findings in each period that involve vital sparks of computing evolution, such as the gradual growth of computing in Persia from its early stages to the present. The findings indicate that the development of computing and related technologies has been rapidly accelerating recently.
      Citation: Technologies
      PubDate: 2022-08-15
      DOI: 10.3390/technologies10040094
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 95: 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

    • Authors: Saeid Saeidi Aminabadi, Atae Jafari-Tabrizi, Dieter Paul Gruber, Gerald Berger-Weber, Walter Friesenbichler
      First page: 95
      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 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.
      Citation: Technologies
      PubDate: 2022-08-17
      DOI: 10.3390/technologies10040095
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 96: Synthetic Micro/Nanomotors for Drug

    • Authors: Eduardo Guzmán, Armando Maestro
      First page: 96
      Abstract: Synthetic micro/nanomotors (MNMs) are human-made machines characterized by their capacity for undergoing self-propelled motion as a result of the consumption of chemical energy obtained from specific chemical or biochemical reactions, or as a response to an external actuation driven by a physical stimulus. This has fostered the exploitation of MNMs for facing different biomedical challenges, including drug delivery. In fact, MNMs are superior systems for an efficient delivery of drugs, offering several advantages in relation to conventional carriers. For instance, the self-propulsion ability of micro/nanomotors makes possible an easier transport of drugs to specific targets in comparison to the conventional distribution by passive carriers circulating within the blood, which enhances the drug bioavailability in tissues. Despite the promising avenues opened by the use of synthetic micro/nanomotors in drug delivery applications, the development of systems for in vivo uses requires further studies to ensure a suitable biocompatibility and biodegradability of the fabricated engines. This is essential for guaranteeing the safety of synthetic MNMs and patient convenience. This review provides an updated perspective to the potential applications of synthetic micro/nanomotors in drug delivery. Moreover, the most fundamental aspects related to the performance of synthetic MNMs and their biosafety are also discussed.
      Citation: Technologies
      PubDate: 2022-08-17
      DOI: 10.3390/technologies10040096
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 97: Research on a Vehicle Recognition Method
           Based on Radar and Camera Information Fusion

    • Authors: Fang Ding, Bo Wang, Qianbin Zhang, Aiguo Wang
      First page: 97
      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 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.
      Citation: Technologies
      PubDate: 2022-08-22
      DOI: 10.3390/technologies10040097
      Issue No: Vol. 10, No. 4 (2022)
  • Technologies, Vol. 10, Pages 54: Reliable Ultrasonic Obstacle Recognition
           for Outdoor Blind Navigation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    • Authors: Joshua M. Pearce
      First page: 53
      Abstract: Free and open-source hardware (FOSH) development has been shown to increase innovation and reduce economic costs. This article reviews the opportunity to use FOSH as a sanction to undercut imports and exports from a target criminal country. A formal methodology is presented for selecting strategic national investments in FOSH development to improve both national security and global safety. In this methodology, first the target country that is threatening national security or safety is identified. Next, the top imports from the target country as well as potentially other importing countries (allies) are quantified. Hardware is identified that could undercut imports/exports from the target country. Finally, methods to support the FOSH development are enumerated to support production in a commons-based peer production strategy. To demonstrate how this theoretical method works in practice, it is applied as a case study to a current criminal military aggressor nation, who is also a fossil-fuel exporter. The results show that there are numerous existing FOSH and opportunities to develop new FOSH for energy conservation and renewable energy to reduce fossil-fuel-energy demand. Widespread deployment would reduce the concomitant pollution, human health impacts, and environmental desecration as well as cut financing of military operations.
      Citation: Technologies
      PubDate: 2022-04-18
      DOI: 10.3390/technologies10020053
      Issue No: Vol. 10, No. 2 (2022)
  • Technologies, Vol. 10, Pages 25: User-Centric Design Methodology for
           mHealth Apps. The PainApp Paradigm for Chronic Pain

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

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

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

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

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

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

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

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

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

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

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

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