Journal Description
Technologies
Technologies
is an international, peer-reviewed, open access journal singularly focusing on emerging scientific and technological trends and is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within ESCI (Web of Science), Scopus, Inspec, INSPIRE, and other databases.
- Journal Rank: CiteScore - Q1 (Computer Science (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.7 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.6 (2022);
5-Year Impact Factor:
3.1 (2022)
Latest Articles
Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms
Technologies 2024, 12(6), 82; https://doi.org/10.3390/technologies12060082 (registering DOI) - 3 Jun 2024
Abstract
Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping
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Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs.
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(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)
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Open AccessReview
A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions
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Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi and Mohammad N. Alanazi
Technologies 2024, 12(6), 81; https://doi.org/10.3390/technologies12060081 (registering DOI) - 3 Jun 2024
Abstract
Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions
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Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.
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(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)
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Open AccessArticle
Applications of Brain Wave Classification for Controlling an Intelligent Wheelchair
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Maria Carolina Avelar, Patricia Almeida, Brigida Monica Faria and Luis Paulo Reis
Technologies 2024, 12(6), 80; https://doi.org/10.3390/technologies12060080 (registering DOI) - 3 Jun 2024
Abstract
The independence and autonomy of both elderly and disabled people have been a growing concern in today’s society. Therefore, wheelchairs have proven to be fundamental for the movement of these people with physical disabilities in the lower limbs, paralysis, or other type of
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The independence and autonomy of both elderly and disabled people have been a growing concern in today’s society. Therefore, wheelchairs have proven to be fundamental for the movement of these people with physical disabilities in the lower limbs, paralysis, or other type of restrictive diseases. Various adapted sensors can be employed in order to facilitate the wheelchair’s driving experience. This work develops the proof concept of a brain–computer interface (BCI), whose ultimate final goal will be to control an intelligent wheelchair. An event-related (de)synchronization neuro-mechanism will be used, since it corresponds to a synchronization, or desynchronization, in the mu and beta brain rhythms, during the execution, preparation, or imagination of motor actions. Two datasets were used for algorithm development: one from the IV competition of BCIs (A), acquired through twenty-two Ag/AgCl electrodes and encompassing motor imagery of the right and left hands, and feet; and the other (B) was obtained in the laboratory using an Emotiv EPOC headset, also with the same motor imaginary. Regarding feature extraction, several approaches were tested: namely, two versions of the signal’s power spectral density, followed by a filter bank version; the use of respective frequency coefficients; and, finally, two versions of the known method filter bank common spatial pattern (FBCSP). Concerning the results from the second version of FBCSP, dataset A presented an F1-score of 0.797 and a rather low false positive rate of 0.150. Moreover, the correspondent average kappa score reached the value of 0.693, which is in the same order of magnitude as 0.57, obtained by the competition. Regarding dataset B, the average value of the F1-score was 0.651, followed by a kappa score of 0.447, and a false positive rate of 0.471. However, it should be noted that some subjects from this dataset presented F1-scores of 0.747 and 0.911, suggesting that the movement imagery (MI) aptness of different users may influence their performance. In conclusion, it is possible to obtain promising results, using an architecture for a real-time application.
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(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence)
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Open AccessArticle
Comparison of a Custom-Made Inexpensive Air Permeability Tester with a Standardized Measurement Instrument
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Dietrich Spädt, Niclas Richter, Cornelia Golle, Andrea Ehrmann and Lilia Sabantina
Technologies 2024, 12(6), 79; https://doi.org/10.3390/technologies12060079 (registering DOI) - 2 Jun 2024
Abstract
The air permeability of a textile fabric belongs to the parameters which characterize its potential applications as garments, filters, airbags, etc. Calculating the air permeability is complicated due to its dependence on many other fabric parameters, such as porosity, thickness, weaving parameters and
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The air permeability of a textile fabric belongs to the parameters which characterize its potential applications as garments, filters, airbags, etc. Calculating the air permeability is complicated due to its dependence on many other fabric parameters, such as porosity, thickness, weaving parameters and others, which is why the air permeability is usually measured. Standardized measurement instruments according to EN ISO 9237, however, are expensive and complex, prohibiting small companies or many universities from using them. This is why a simpler and inexpensive test instrument was suggested in a previous paper. Here, we show correlations between the results of the standardized and the custom-made instrument and verify this correlation using fluid dynamics calculations.
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(This article belongs to the Section Innovations in Materials Processing)
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Open AccessArticle
Smart Energy Systems Based on Next-Generation Power Electronic Devices
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Nikolay Hinov
Technologies 2024, 12(6), 78; https://doi.org/10.3390/technologies12060078 (registering DOI) - 1 Jun 2024
Abstract
Power electronics plays a key role in the management and conversion of electrical energy in a variety of applications, including the use of renewable energy sources such as solar, wind and hydrogen energy, as well as in electric vehicles, industrial technologies, homes and
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Power electronics plays a key role in the management and conversion of electrical energy in a variety of applications, including the use of renewable energy sources such as solar, wind and hydrogen energy, as well as in electric vehicles, industrial technologies, homes and smart grids. These technologies are essential for the successful implementation of the green transition, as they help reduce carbon emissions and promote the production and consumption of cleaner and more sustainable energy. The present work presents a new generation of power electronic devices and systems, which includes the following main aspects: advances in semiconductor technologies, such as the use of silicon carbide (SiC) and gallium nitride (GaN); nanomaterials for the realization of magnetic components; using a modular principle to construct power electronic devices; applying artificial intelligence techniques to device lifecycle design; and the environmental aspects of design. The new materials allow the devices to operate at higher voltages, temperatures and frequencies, making them ideal for high-power applications and high-frequency operation. In addition, the development of integrated and modular power electronic systems that combine energy management, diagnostics and communication capabilities contributes to the more intelligent and efficient management of energy resources. This includes integration with the Internet of Things (IoT) and artificial intelligence (AI) for automated task solving and work optimization.
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(This article belongs to the Special Issue Smart Systems (SmaSys2023))
Open AccessArticle
Deep Learning Approaches for Water Stress Forecasting in Arboriculture Using Time Series of Remote Sensing Images: Comparative Study between ConvLSTM and CNN-LSTM Models
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Ismail Bounoua, Youssef Saidi, Reda Yaagoubi and Mourad Bouziani
Technologies 2024, 12(6), 77; https://doi.org/10.3390/technologies12060077 (registering DOI) - 1 Jun 2024
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Irrigation is crucial for crop cultivation and productivity. However, traditional methods often waste water and energy due to neglecting soil and crop variations, leading to inefficient water distribution and potential crop water stress. The crop water stress index (CWSI) has become a widely
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Irrigation is crucial for crop cultivation and productivity. However, traditional methods often waste water and energy due to neglecting soil and crop variations, leading to inefficient water distribution and potential crop water stress. The crop water stress index (CWSI) has become a widely accepted index for assessing plant water status. However, it is necessary to forecast the plant water stress to estimate the quantity of water to irrigate. Deep learning (DL) models for water stress forecasting have gained prominence in irrigation management to address these needs. In this paper, we present a comparative study between two deep learning models, ConvLSTM and CNN-LSTM, for water stress forecasting using remote sensing data. While these DL architectures have been previously proposed and studied in various applications, our novelty lies in studying their effectiveness in the field of water stress forecasting using time series of remote sensing images. The proposed methodology involves meticulous preparation of time series data, where we calculate the crop water stress index (CWSI) using Landsat 8 satellite imagery through Google Earth Engine. Subsequently, we implemented and fine-tuned the hyperparameters of the ConvLSTM and CNN-LSTM models. The same processes of model compilation, optimization of hyperparameters, and model training were applied for the two architectures. A citrus farm in Morocco was chosen as a case study. The analysis of the results reveals that the CNN-LSTM model excels over the ConvLSTM model for long sequences (nine images) with an RMSE of 0.119 and 0.123, respectively, while ConvLSTM provides better results for short sequences (three images) than CNN-LSTM with an RMSE of 0.153 and 0.187, respectively.
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Vertical Balance of an Autonomous Two-Wheeled Single-Track Electric Vehicle
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David Rodríguez-Rosa, Andrea Martín-Parra, Andrés García-Vanegas, Francisco Moya-Fernández, Ismael Payo-Gutiérrez and Fernando J. Castillo-García
Technologies 2024, 12(6), 76; https://doi.org/10.3390/technologies12060076 - 28 May 2024
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In the dynamic landscape of autonomous transport, the integration of intelligent transport systems and embedded control technology is pivotal. While strides have been made in the development of autonomous agents and multi-agent systems, the unique challenges posed by two-wheeled vehicles remain largely unaddressed.
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In the dynamic landscape of autonomous transport, the integration of intelligent transport systems and embedded control technology is pivotal. While strides have been made in the development of autonomous agents and multi-agent systems, the unique challenges posed by two-wheeled vehicles remain largely unaddressed. Dedicated control strategies for these vehicles have yet to be developed. The vertical balance of an autonomous two-wheeled single-track vehicle is a challenge for engineering. This type of vehicle is unstable and its dynamic behaviour changes with the forward velocity. We designed a scheduled-gain proportional–integral controller that adapts its gains to the forward velocity, maintaining the vertical balance of the vehicle by means of the steering front-wheel angle. The control law was tested with a prototype designed by the authors under different scenarios, smooth and uneven floors, maintaining the vertical balance in all cases.
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Open AccessArticle
Intelligent Cane for Assisting the Visually Impaired
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Claudiu-Eugen Panazan and Eva-Henrietta Dulf
Technologies 2024, 12(6), 75; https://doi.org/10.3390/technologies12060075 - 27 May 2024
Abstract
Those with visual impairments, including complete blindness or partial sight loss, constitute a significant global population. According to estimates by the World Health Organization (WHO), there are at least 2.2 billion people worldwide who have near or distance vision disorders. Addressing their needs
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Those with visual impairments, including complete blindness or partial sight loss, constitute a significant global population. According to estimates by the World Health Organization (WHO), there are at least 2.2 billion people worldwide who have near or distance vision disorders. Addressing their needs is crucial. Introducing a smart cane tailored for the blind can greatly improve their daily lives. This paper introduces a significant technical innovation, presenting a smart cane equipped with dual ultrasonic sensors for obstacle detection, catering to the visually impaired. The primary focus is on developing a versatile device capable of operating in diverse conditions, ensuring efficient obstacle alerts. The strategic placement of ultrasonic sensors facilitates the emission and measurement of high-frequency sound waves, calculating obstacle distances and assessing potential threats to the user. Addressing various obstacle types, two ultrasonic sensors handle overhead and ground-level barriers, ensuring precise warnings. With a detection range spanning 2 to 400 cm, the device provides timely information for user reaction. Dual alert methods, including vibrations and audio signals, offer flexibility to users, controlled through intuitive switches. Additionally, a Bluetooth-connected mobile app enhances functionality, activating audio alerts if the cane is misplaced or too distant. Cost-effective implementation enhances accessibility, supporting a broader user base. This innovative smart cane not only represents a technical achievement but also significantly improves the quality of life for visually impaired individuals, emphasizing the social impact of technology. The research underscores the importance of technological research in addressing societal challenges and highlights the need for solutions that positively impact vulnerable communities, shaping future directions in research and technological development.
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(This article belongs to the Section Assistive Technologies)
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Open AccessArticle
Effect of Oscillating Area on Generating Microbubbles from Hollow Ultrasonic Horn
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Kodai Hasegawa, Nobuhiro Yabuki and Toshinori Makuta
Technologies 2024, 12(6), 74; https://doi.org/10.3390/technologies12060074 - 25 May 2024
Abstract
Microbubbles, which are tiny bubbles with a diameter of less than 100 µm, have been attracting attention in recent years. Conventional methods of microbubble generation using porous material and swirling flows have problems such as large equipment size and non-uniform bubble generation. Therefore,
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Microbubbles, which are tiny bubbles with a diameter of less than 100 µm, have been attracting attention in recent years. Conventional methods of microbubble generation using porous material and swirling flows have problems such as large equipment size and non-uniform bubble generation. Therefore, we have been developing a hollow ultrasonic horn with an internal flow path as a microbubble-generating device. By supplying gas and ultrasonic waves simultaneously, the gas–liquid interface is violently disturbed to generate microbubbles. Although this device can generate microbubbles even in highly viscous fluids and high-temperature fluids such as molten metals, it has the problem of generating many relatively large bubbles of 1 mm or more. Since the generation of a large amount of microbubbles in a short period of time is required to realize actual applications in agriculture, aquaculture, and medicine, conventional research has tried to solve this problem by increasing the amplitude of the ultrasonic oscillation. However, it is difficult to further increase the amplitude due to the structural reasons of the horn and the behavior of bubbles at the horn tip; therefore, the oscillating area of the tip of the horn, which had not received attention before, was enlarged by a factor of 2.94 times to facilitate the ultrasonic wave transmission to the bubbles, and the effect of this was investigated. As a result, a large number of gases were miniaturized, especially at high gas flow rates, leading to an increase in the amount of microbubbles generated.
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(This article belongs to the Special Issue Smart Systems (SmaSys2023))
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Gamified VR Storytelling for Cultural Tourism Using 3D Reconstructions, Virtual Humans, and 360° Videos
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Emmanouil Kontogiorgakis, Emmanouil Zidianakis, Eirini Kontaki, Nikolaos Partarakis, Constantina Manoli, Stavroula Ntoa and Constantine Stephanidis
Technologies 2024, 12(6), 73; https://doi.org/10.3390/technologies12060073 - 22 May 2024
Abstract
This work addresses the lack of methodologies for the seamless integration of 360° videos, 3D digitized artifacts, and virtual human agents within a virtual reality environment. The proposed methodology is showcased in the context of a tour guide application and centers around the
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This work addresses the lack of methodologies for the seamless integration of 360° videos, 3D digitized artifacts, and virtual human agents within a virtual reality environment. The proposed methodology is showcased in the context of a tour guide application and centers around the innovative use of a central hub, metaphorically linking users to various historical locations. Leveraging a treasure hunt metaphor and a storytelling approach, this combination of digital structures is capable of building an exploratory learning experience. Virtual human agents contribute to the scenario by offering personalized narratives and educational content, contributing to an enriched cultural heritage journey. Key contributions of this research include the exploration of the symbolic use of the central hub, the application of a gamified approach through the treasure hunt metaphor, and the seamless integration of various technologies to enhance user engagement. This work contributes to the understanding of context-specific cultural heritage applications and their potential impact on cultural tourism. The output of this research work is the reusable methodology and its demonstration in the implemented showcase application that was assessed by a heuristic evaluation.
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(This article belongs to the Section Information and Communication Technologies)
Open AccessReview
A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education
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Haseeb Ali Khan, Sonain Jamil, Md. Jalil Piran, Oh-Jin Kwon and Jong-Weon Lee
Technologies 2024, 12(5), 72; https://doi.org/10.3390/technologies12050072 - 19 May 2024
Abstract
Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and
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Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and gestures from kindergarten through university. The purpose of this survey is to provide an overview of various ways in which ML techniques can be applied within the field of AR within education. The first step is to describe the background of AR. In the next step, we discuss the ML models that are used in AR education applications. Additionally, we discuss how ML is used in AR. Each subgroup’s challenges and solutions can be identified by analyzing these frameworks. In addition, we outline several research gaps and future research directions in ML-based AR frameworks for education.
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(This article belongs to the Topic Emerging AI+X Technologies including Selected Papers from ICGHIT 2024)
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Analysis, Evaluation, and Future Directions on Multimodal Deception Detection
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Arianna D’Ulizia, Alessia D’Andrea, Patrizia Grifoni and Fernando Ferri
Technologies 2024, 12(5), 71; https://doi.org/10.3390/technologies12050071 - 18 May 2024
Abstract
Multimodal deception detection has received increasing attention from the scientific community in recent years, mainly due to growing ethical and security issues, as well as the growing use of digital media. A great number of deception detection methods have been proposed in several
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Multimodal deception detection has received increasing attention from the scientific community in recent years, mainly due to growing ethical and security issues, as well as the growing use of digital media. A great number of deception detection methods have been proposed in several domains, such as political elections, security contexts, and job interviews. However, a systematic analysis of the current situation and the evaluation and future directions of deception detection based on cues coming from multiple modalities seems to be lacking. This paper, starting from a description of methods and metrics used for the analysis and evaluation of multimodal deception detection on video, provides a vision of future directions in this field. For the analysis, the PRISMA recommendations are followed, which allow the collection and synthesis of all the available research on the topic and the extraction of information on the multimodal features, the fusion methods, the classification approaches, the evaluation datasets, and metrics. The results of this analysis contribute to the assessment of the state of the art and the evaluation of evidence on important research questions in multimodal deceptive deception. Moreover, they provide guidance on future research in the field.
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(This article belongs to the Section Information and Communication Technologies)
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Open AccessArticle
Speckle Plethysmograph-Based Blood Pressure Assessment
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Floranne T. Ellington, Anh Nguyen, Mao-Hsiang Huang, Tai Le, Bernard Choi and Hung Cao
Technologies 2024, 12(5), 70; https://doi.org/10.3390/technologies12050070 - 18 May 2024
Abstract
Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse
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Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse arrival time (PAT), the time difference between the proximal and distal signal peaks. The most widely employed pairing involves electrocardiography (ECG) and photoplethysmography (PPG). Possessing similar characteristics in terms of measuring blood flow changes, a recently investigated optical signal known as speckleplethysmography (SPG) showed its stability and high signal-to-noise ratio compared with PPG. Thus, SPG is a potential surrogate to pair with ECG for CNBP estimation. The present study aims to unlock the untapped potential of SPG as a signal for non-invasive blood pressure monitoring based on PAT. To ascertain SPG’s capabilities, eight subjects were enrolled in multiple recording sessions. A third-party device was employed for ECG and PPG measurements, while a commercial device served as the reference for arterial blood pressure (ABP). SPG measurements were obtained using a prototype smartphone-based system. Following the completion of three scenarios—sitting, walking, and running—the subjects’ signals and ABP were recorded to investigate the predictive capacity of systolic blood pressure. The collected data were processed and prepared for machine learning models, including support vector regression and decision tree regression. The models’ effectiveness was evaluated using root-mean-square error and mean absolute percentage error. In most instances, predictions utilizing exhibited comparable or superior performance to (i.e., SPG Rest ± 12.4 mmHg vs. PPG Rest ± 13.7 mmHg for RSME, and SPG 8% vs. PPG 9% for MAPE). Furthermore, incorporating an additional feature, namely the previous SBP value, resulted in reduced prediction errors for both signals in multiple model configurations (i.e., SPG Rest ± 12.4 mmHg to ±3.7 mmHg for RSME, and SPG Rest 8% to 3% for MAPE). These preliminary tests of SPG underscore the remarkable potential of this novel signal in PAT-based blood pressure predictions. Subsequent studies involving a larger cohort of test subjects and advancements in the SPG acquisition system hold promise for further improving the effectiveness of this newly explored signal in blood pressure monitoring.
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(This article belongs to the Topic Smart Healthcare: Technologies and Applications)
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Open AccessArticle
Evaluating a Controlled Electromagnetic Launcher for Safe Remote Drug Delivery
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John LaRocco, Qudsia Tahmina and John Simonis
Technologies 2024, 12(5), 69; https://doi.org/10.3390/technologies12050069 - 17 May 2024
Abstract
Biologists and veterinarians rely on dart projectors to inject animals with drugs, take biopsies from specimens, or inject tracking chips. Firearms, air guns, and other launchers are limited in their ability to precisely control the kinetic energy of a projectile, which can injure
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Biologists and veterinarians rely on dart projectors to inject animals with drugs, take biopsies from specimens, or inject tracking chips. Firearms, air guns, and other launchers are limited in their ability to precisely control the kinetic energy of a projectile, which can injure the animal if too high. In order to improve the safety of remote drug delivery, a lidar-modulated electromagnetic launcher and a soft drug delivery dart were prototyped. A single-stage revolver coilgun and soft dart were designed and tested at distances up to 8 m. With a coil efficiency of 2.25%, the launcher could consistently deliver a projectile at a controlled kinetic energy of 1.00 ± 0.006 J and an uncontrolled kinetic energy of 2.66 ± 0.076 J. Although modifications to charging time, sensors, and electronics could improve performance, our launcher performed at the required level at the necessary distances. The precision achieved with commercial components enables many other applications, from law enforcement to manufacturing.
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(This article belongs to the Section Manufacturing Technology)
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Open AccessArticle
Application and Challenges of the Technology Acceptance Model in Elderly Healthcare: Insights from ChatGPT
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Sang Dol Kim
Technologies 2024, 12(5), 68; https://doi.org/10.3390/technologies12050068 - 13 May 2024
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The Technology Acceptance Model (TAM) plays a pivotal role in elderly healthcare, serving as a theoretical framework. This study aimed to identify TAM’s core components, practical applications, challenges arising from its applications, and propose countermeasures in elderly healthcare. This descriptive study was conducted
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The Technology Acceptance Model (TAM) plays a pivotal role in elderly healthcare, serving as a theoretical framework. This study aimed to identify TAM’s core components, practical applications, challenges arising from its applications, and propose countermeasures in elderly healthcare. This descriptive study was conducted by utilizing OpenAI’s ChatGPT, with an access date of 10 January 2024. The three open-ended questions administered to ChatGPT and its responses were collected and qualitatively evaluated for reliability through previous studies. The core components of TAMs were identified as perceived usefulness, perceived ease of use, attitude toward use, behavioral intention to use, subjective norms, image, and facilitating conditions. TAM’s application areas span various technologies in elderly healthcare, such as telehealth, wearable devices, mobile health apps, and more. Challenges arising from TAM applications include technological literacy barriers, digital divide concerns, privacy and security apprehensions, resistance to change, limited awareness and information, health conditions and cognitive impairment, trust and reliability concerns, a lack of tailored interventions, overcoming age stereotypes, and integration with traditional healthcare. In conclusion, customized interventions are crucial for successful tech acceptance among the elderly population. The findings of this study are expected to enhance understanding of elderly healthcare and technology adoption, with insights gained through natural language processing models like ChatGPT anticipated to provide a fresh perspective.
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Open AccessArticle
Study of an LLC Converter for Thermoelectric Waste Heat Recovery Integration in Shipboard Microgrids
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Nick Rigogiannis, Ioannis Roussos, Christos Pechlivanis, Ioannis Bogatsis, Anastasios Kyritsis, Nick Papanikolaou and Michael Loupis
Technologies 2024, 12(5), 67; https://doi.org/10.3390/technologies12050067 - 11 May 2024
Abstract
Static waste heat recovery, by means of thermoelectric generator (TEG) modules, constitutes a fast-growing energy harvesting technology on the way towards greener transportation. Many commercial solutions are already available for small internal combustion engine (ICE) vehicles, whereas further development and cost reductions of
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Static waste heat recovery, by means of thermoelectric generator (TEG) modules, constitutes a fast-growing energy harvesting technology on the way towards greener transportation. Many commercial solutions are already available for small internal combustion engine (ICE) vehicles, whereas further development and cost reductions of TEG devices expand their applicability at higher-power transportation means (i.e., ships and aircrafts). In this light, the integration of waste heat recovery based on TEG modules in a shipboard distribution network is studied in this work. Several voltage step-up techniques are considered, whereas the most suitable ones are assessed via the LTspice simulation platform. The design procedure of the selected LLC resonant converter is presented and analyzed in detail. Furthermore, a flexible control strategy is proposed, capable of either output voltage regulation (constant voltage) or maximum power point tracking (MPPT), according to the application demands. Finally, both simulations and experiments (on a suitable laboratory testbench) are performed. The obtained measurements indicate the high efficiency that can be achieved with the LLC converter for a wide operating area as well as the functionality and adequate performance of the control scheme in both operating conditions.
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(This article belongs to the Special Issue MOCAST 2023)
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Converging Artificial Intelligence and Quantum Technologies: Accelerated Growth Effects in Technological Evolution
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Mario Coccia
Technologies 2024, 12(5), 66; https://doi.org/10.3390/technologies12050066 - 10 May 2024
Abstract
One of the fundamental problems in the field of technological studies is to clarify the drivers and dynamics of technological evolution for sustaining industrial and economic change. This study confronts the problem by analyzing the converging technologies to explain effects on the evolutionary
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One of the fundamental problems in the field of technological studies is to clarify the drivers and dynamics of technological evolution for sustaining industrial and economic change. This study confronts the problem by analyzing the converging technologies to explain effects on the evolutionary dynamics over time. This paper focuses on technological interaction between artificial intelligence and quantum technologies using a technometric model of technological evolution based on scientific and technological information (publications and patents). Findings show that quantum technology has a growth rate of 1.07, artificial intelligence technology has a rate of growth of 1.37, whereas the technological interaction of converging quantum and artificial intelligence technologies has an accelerated rate of growth of 1.58, higher than trends of these technologies taken individually. These findings suggest that technological interaction is one of the fundamental determinants in the rapid evolution of path-breaking technologies and disruptive innovations. The deductive implications of results about the effects of converging technologies are: (a) accelerated evolutionary growth; (b) a disproportionate (allometric) growth of patents driven by publications supporting a fast technological evolution. Our results support policy and managerial implications for the decision making of policymakers, technology analysts, and R&D managers that can direct R&D investments towards fruitful inter-relationships between radical technologies to foster scientific and technological change with positive societal and economic impcats.
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(This article belongs to the Section Quantum Technologies)
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Open AccessArticle
Fluorine-Free Single-Component Polyelectrolyte of Poly(ethylene glycol) Bearing Lithium Methanesulfonylsulfonimide Terminal Groups: Effect of Structural Variance on Ionic Conductivity
by
Bungo Ochiai, Koki Hirabayashi, Yudai Fujii and Yoshimasa Matsumura
Technologies 2024, 12(5), 65; https://doi.org/10.3390/technologies12050065 - 9 May 2024
Abstract
Fluorine-free single-component polyelectrolytes were developed via the hybridization of lithium methanesulfonylsulfonimide (LiMSSI) moieties to poly(ethylene glycol) (PEG) derivatives with different morphologies, and the relationship between the structure and its ionic conductivity was investigated. The PEG-LiMSSI derivatives with one, two, and three LiMSSI end
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Fluorine-free single-component polyelectrolytes were developed via the hybridization of lithium methanesulfonylsulfonimide (LiMSSI) moieties to poly(ethylene glycol) (PEG) derivatives with different morphologies, and the relationship between the structure and its ionic conductivity was investigated. The PEG-LiMSSI derivatives with one, two, and three LiMSSI end groups were prepared via the concomitant Michael-type addition and lithiation of PEGs and N-methanesulfonylvinylsulfonimide. The ionic conductivity at 60 °C ranged from 1.8 × 10−7 to 2.0 × 10−4 S/cm. PEG-LiMSSI derivatives with one LiMSSI terminus and with two LiMSSI termini at both ends show higher ionic conductivity, that is as good as fluorine-free single-component polyelectrolytes, than that with two LiMSSI termini at one end and that with three LiMSSI termini.
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(This article belongs to the Special Issue Smart Systems (SmaSys2023))
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Open AccessFeature PaperReview
Atomic Quantum Technologies for Quantum Matter and Fundamental Physics Applications
by
Jorge Yago Malo, Luca Lepori, Laura Gentini and Maria Luisa (Marilù) Chiofalo
Technologies 2024, 12(5), 64; https://doi.org/10.3390/technologies12050064 - 7 May 2024
Abstract
Physics is living an era of unprecedented cross-fertilization among the different areas of science. In this perspective review, we discuss the manifold impact that state-of-the-art cold and ultracold-atomic platforms can have in fundamental and applied science through the development of platforms for quantum
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Physics is living an era of unprecedented cross-fertilization among the different areas of science. In this perspective review, we discuss the manifold impact that state-of-the-art cold and ultracold-atomic platforms can have in fundamental and applied science through the development of platforms for quantum simulation, computation, metrology and sensing. We illustrate how the engineering of table-top experiments with atom technologies is engendering applications to understand problems in condensed matter and fundamental physics, cosmology and astrophysics, unveil foundational aspects of quantum mechanics, and advance quantum chemistry and the emerging field of quantum biology. In this journey, we take the perspective of two main approaches, i.e., creating quantum analogues and building quantum simulators, highlighting that independently of the ultimate goal of a universal quantum computer to be met, the remarkable transformative effects of these achievements remain unchanged. We wish to convey three main messages. First, this atom-based quantum technology enterprise is signing a new era in the way quantum technologies are used for fundamental science, even beyond the advancement of knowledge, which is characterised by truly cross-disciplinary research, extended interplay between theoretical and experimental thinking, and intersectoral approach. Second, quantum many-body physics is unavoidably taking center stage in frontier’s science. Third, quantum science and technology progress will have capillary impact on society, meaning this effect is not confined to isolated or highly specialized areas of knowledge, but is expected to reach and have a pervasive influence on a broad range of society aspects: while this happens, the adoption of a responsible research and innovation approach to quantum technologies is mandatory, to accompany citizens in building awareness and future scaffolding. Following on all the above reflections, this perspective review is thus aimed at scientists active or interested in interdisciplinary research, providing the reader with an overview of the current status of these wide fields of research where cold and ultracold-atomic platforms play a vital role in their description and simulation.
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(This article belongs to the Section Quantum Technologies)
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Open AccessArticle
Hunting Search Algorithm-Based Adaptive Fuzzy Tracking Controller for an Aero-Pendulum
by
Ricardo Rojas-Galván, José R. García-Martínez, Edson E. Cruz-Miguel, Omar A. Barra-Vázquez, Luis F. Olmedo-García and Juvenal Rodríguez-Reséndiz
Technologies 2024, 12(5), 63; https://doi.org/10.3390/technologies12050063 - 4 May 2024
Abstract
The aero-pendulum is a non-linear system used broadly to develop and test new controller strategies. This paper presents a new methodology for an adaptive PID fuzzy-based tracking controller using a Hunting Search (HuS) algorithm. The HuS algorithm computes the parameters of the membership
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The aero-pendulum is a non-linear system used broadly to develop and test new controller strategies. This paper presents a new methodology for an adaptive PID fuzzy-based tracking controller using a Hunting Search (HuS) algorithm. The HuS algorithm computes the parameters of the membership functions of the fuzzification stage. As a novelty, the algorithm guarantees the overlap of the membership functions to ensure that all the functions are interconnected, generating new hunters to search for better solutions in the overlapping area. For the defuzzification stage, the HuS algorithm sets the singletons in optimal positions to evaluate the controller response using the centroid method. To probe the robustness of the methodology, the PID fuzzy controller algorithm is implemented in an embedded system to track the angular position of an aero-pendulum test bench. The results show that the adaptive PID fuzzy controller proposed presents root mean square error values of 0.42, 0.40, and 0.49 for 80, 90, and 100 degrees, respectively.
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(This article belongs to the Special Issue Smart Systems (SmaSys2023))
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