Technologies

22 pages, 8308 KiB

Open AccessArticle

Virtual Teleoperation System for Mobile Manipulator Robots Focused on Object Transport and Manipulation

by Fernando J. Pantusin, Christian P. Carvajal, Jessica S. Ortiz and Víctor H. Andaluz

Technologies 2024, 12(9), 146; https://doi.org/10.3390/technologies12090146 (registering DOI) - 31 Aug 2024

This work describes the development of a tool for the teleoperation of robots. The tool is developed in a virtual environment using the Unity graphics engine. For the development of the application, a kinematic model and a dynamic model of a mobile manipulator [...] Read more.

This work describes the development of a tool for the teleoperation of robots. The tool is developed in a virtual environment using the Unity graphics engine. For the development of the application, a kinematic model and a dynamic model of a mobile manipulator are used. The mobile manipulator robot consists of an omnidirectional platform and an anthropomorphic robotic arm with 4 degrees of freedom (4DOF). The model is essential to emulate the movements of the robot and to facilitate the immersion in the virtual environment. In addition, the control algorithms are established and developed in MATLAB 2020 software, which improves the acquisition of knowledge to teleoperate robots and execute tasks of manipulation and transport of objects. This methodology offers a cheaper and safer alternative to real physical systems, as it reduces both the costs and risks associated with using a real robot for training. Full article

(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)

20 pages, 1102 KiB

Open AccessArticle

Training Artificial Neural Networks to Detect Multiple Sclerosis Lesions Using Granulometric Data from Preprocessed Magnetic Resonance Images with Morphological Transformations

by Edgar Rafael Ponce de Leon-Sanchez, Jorge Domingo Mendiola-Santibañez, Omar Arturo Dominguez-Ramirez, Ana Marcela Herrera-Navarro, Alberto Vazquez-Cervantes, Hugo Jimenez-Hernandez, Diana Margarita Cordova-Esparza, María de los Angeles Cuán Hernández and Horacio Senties-Madrid

Technologies 2024, 12(9), 145; https://doi.org/10.3390/technologies12090145 (registering DOI) - 31 Aug 2024

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Abstract

The symptoms of multiple sclerosis (MS) are determined by the location of demyelinating lesions in the white matter of the brain and spinal cord. Currently, magnetic resonance imaging (MRI) is the most common tool used for diagnosing MS, understanding the course of the [...] Read more.

The symptoms of multiple sclerosis (MS) are determined by the location of demyelinating lesions in the white matter of the brain and spinal cord. Currently, magnetic resonance imaging (MRI) is the most common tool used for diagnosing MS, understanding the course of the disease, and analyzing the effects of treatments. However, undesirable components may appear during the generation of MRI scans, such as noise or intensity variations. Mathematical morphology (MM) is a powerful image analysis technique that helps to filter the image and extract relevant structures. Granulometry is an image measurement tool for measuring MM that determines the size distribution of objects in an image without explicitly segmenting each object. While several methods have been proposed for the automatic segmentation of MS lesions in MRI scans, in some cases, only simple data preprocessing, such as image resizing to standardize the input dimensions, has been performed before the algorithm training. Therefore, this paper proposes an MRI preprocessing algorithm capable of performing elementary morphological transformations in brain images of MS patients and healthy individuals in order to delete undesirable components and extract the relevant structures such as MS lesions. Also, the algorithm computes the granulometry in MRI scans to describe the size qualities of lesions. Using this algorithm, we trained two artificial neural networks (ANNs) to predict MS diagnoses. By computing the differences in granulometry measurements between an image with MS lesions and a reference image (without lesions), we determined the size characterization of the lesions. Then, the ANNs were evaluated with the validation set, and the performance results (test accuracy = 0.9753; cross-entropy loss = 0.0247) show that the proposed algorithm can support specialists in making decisions to diagnose MS and estimating the disease progress based on granulometry values. Full article

(This article belongs to the Special Issue Medical Imaging & Image Processing III)

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14 pages, 1469 KiB

Open AccessArticle

Fault Detection of Wheelset Bearings through Vibration-Sound Fusion Data Based on Grey Wolf Optimizer and Support Vector Machine

by Tianhao Wang, Hongying Meng, Fan Zhang and Rui Qin

Technologies 2024, 12(9), 144; https://doi.org/10.3390/technologies12090144 - 28 Aug 2024

Viewed by 329

Abstract

This study aims to detect faults in wheelset bearings by analyzing vibration-sound fusion data, proposing a novel method based on Grey Wolf Optimizer (GWO) and Support Vector Machine (SVM). Wheelset bearings play a vital role in transportation. However, malfunctions in the bearing might [...] Read more.

This study aims to detect faults in wheelset bearings by analyzing vibration-sound fusion data, proposing a novel method based on Grey Wolf Optimizer (GWO) and Support Vector Machine (SVM). Wheelset bearings play a vital role in transportation. However, malfunctions in the bearing might result in extensive periods of inactivity and maintenance, disrupting supply chains, increasing operational costs, and causing delays that affect both businesses and consumers. Fast fault identification is crucial for minimizing maintenance expenses. In this paper, we proposed a new integration of GWO for optimizing SVM hyperparameters, specifically tailored for handling sound-vibration signals in fault detection. We have developed a new fault detection method that efficiently processes fusion data and performs rapid analysis and prediction within 0.0027 milliseconds per data segment, achieving a test accuracy of 98.3%. Compared to the SVM and neural network models built in MATLAB, the proposed method demonstrates superior detection performance. Overall, the GWO-SVM-based method proposed in this study shows significant advantages in fault detection of wheelset bearing vibrations, providing an efficient and reliable solution that is expected to reduce maintenance costs and improve the operational efficiency and reliability of equipment. Full article

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

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34 pages, 4573 KiB

Open AccessReview

Revolutionary Integration of Artificial Intelligence with Meta-Optics-Focus on Metalenses for Imaging

by Nikolay L. Kazanskiy, Svetlana N. Khonina, Ivan V. Oseledets, Artem V. Nikonorov and Muhammad A. Butt

Technologies 2024, 12(9), 143; https://doi.org/10.3390/technologies12090143 - 28 Aug 2024

Viewed by 549

Abstract

Artificial intelligence (AI) significantly enhances the development of Meta-Optics (MOs), which encompasses advanced optical components like metalenses and metasurfaces designed to manipulate light at the nanoscale. The intricate design of these components requires sophisticated modeling and optimization to achieve precise control over light [...] Read more.

Artificial intelligence (AI) significantly enhances the development of Meta-Optics (MOs), which encompasses advanced optical components like metalenses and metasurfaces designed to manipulate light at the nanoscale. The intricate design of these components requires sophisticated modeling and optimization to achieve precise control over light behavior, tasks for which AI is exceptionally well-suited. Machine learning (ML) algorithms can analyze extensive datasets and simulate numerous design variations to identify the most effective configurations, drastically speeding up the development process. AI also enables adaptive MOs that can dynamically adjust to changing imaging conditions, improving performance in real-time. This results in superior image quality, higher resolution, and new functionalities across various applications, including microscopy, medical diagnostics, and consumer electronics. The combination of AI with MOs thus epitomizes a transformative advancement, pushing the boundaries of what is possible in imaging technology. In this review, we explored the latest advancements in AI-powered metalenses for imaging applications. Full article

(This article belongs to the Collection Review Papers Collection for Advanced Technologies)

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17 pages, 5043 KiB

Open AccessArticle

Enhancing Diagnostic Accuracy for Skin Cancer and COVID-19 Detection: A Comparative Study Using a Stacked Ensemble Method

by Hafza Qayyum, Syed Tahir Hussain Rizvi, Muddasar Naeem, Umamah bint Khalid, Musarat Abbas and Antonio Coronato

Technologies 2024, 12(9), 142; https://doi.org/10.3390/technologies12090142 - 27 Aug 2024

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Abstract

In recent years, COVID-19 and skin cancer have become two prevalent illnesses with severe consequences if untreated. This research represents a significant step toward leveraging machine learning (ML) and ensemble techniques to improve the accuracy and efficiency of medical image diagnosis for critical [...] Read more.

In recent years, COVID-19 and skin cancer have become two prevalent illnesses with severe consequences if untreated. This research represents a significant step toward leveraging machine learning (ML) and ensemble techniques to improve the accuracy and efficiency of medical image diagnosis for critical diseases such as COVID-19 (grayscale images) and skin cancer (RGB images). In this paper, a stacked ensemble learning approach is proposed to enhance the precision and effectiveness of diagnosis of both COVID-19 and skin cancer. The proposed method combines pretrained models of convolutional neural networks (CNNs) including ResNet101, DenseNet121, and VGG16 for feature extraction of grayscale (COVID-19) and RGB (skin cancer) images. The performance of the model is evaluated using both individual CNNs and a combination of feature vectors generated from ResNet101, DenseNet121, and VGG16 architectures. The feature vectors obtained through transfer learning are then fed into base-learner models consisting of five different ML algorithms. In the final step, the predictions from the base-learner models, the ensemble validation dataset, and the feature vectors extracted from neural networks are assembled and applied as input for the meta-learner model to obtain final predictions. The performance metrics of the stacked ensemble model show high accuracy for COVID-19 diagnosis and intermediate accuracy for skin cancer. Full article

(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)

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20 pages, 5286 KiB

Open AccessArticle

Wireless Ranging by Evaluating Received Signal Strength of UWB Chaotic Radio Pulses: Effects of Signal Propagation Conditions

by Elena V. Efremova and Lev V. Kuzmin

Technologies 2024, 12(9), 141; https://doi.org/10.3390/technologies12090141 - 25 Aug 2024

Viewed by 393

Abstract

Ultra-wideband radio signals have been the subject of study for several decades. They are used to solve problems of communications and ranging. Measuring the strength (power) of a radio signal is a technically simple way to estimate the distance between the emitter and [...] Read more.

Ultra-wideband radio signals have been the subject of study for several decades. They are used to solve problems of communications and ranging. Measuring the strength (power) of a radio signal is a technically simple way to estimate the distance between the emitter and the receiver of the signal. However, the conditions of signal propagation have a significant impact on the power of the received signal. This work is relevant because chaotic radio pulses are a relatively new type of carrier in wireless technologies, and actual knowledge about the change in signal power in different types of premises is relatively small, so such a study is necessary. In this paper, we study the variation in signal power with distance for chaotic ultra-wideband radio pulses under various propagation conditions. Using experimental measurements in several outdoor (field, roadside) and indoor (corridors, conference room, office) environments, we investigate the effect of propagation conditions on ultra-wideband chaotic radio signals and determine the limits within which the dependence of the calculated power on distance can be approximated by a power law. For this purpose, the results of experimental measurements of the received signal power (a total of about 17.5 M values) were accumulated and analyzed. The accuracy of distance measurement that can be achieved in different conditions is compared and analyzed. It was found that for a 9.5 dBm signal, the range of distances at which the average accuracy is only 15–50 cm when using a power law is 5–7 m indoors and 10–15 m outdoors. Full article

(This article belongs to the Special Issue Perpetual Sensor Nodes for Sustainable Wireless Network Applications)

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23 pages, 6929 KiB

Open AccessArticle

IoT Energy Management System Based on a Wireless Sensor/Actuator Network

by Omar Arzate-Rivas, Víctor Sámano-Ortega, Juan Martínez-Nolasco, Mauro Santoyo-Mora, Coral Martínez-Nolasco and Roxana De León-Lomelí

Technologies 2024, 12(9), 140; https://doi.org/10.3390/technologies12090140 - 24 Aug 2024

Viewed by 755

Abstract

The use of DC microgrids (DC-µGs) offers a variety of environmental benefits; albeit, a successful implementation depends on the implementation of an Energy Management System (EMS). An EMS is broadly implemented with a hierarchical and centralized structure, where the communications layer presents as [...] Read more.

The use of DC microgrids (DC-µGs) offers a variety of environmental benefits; albeit, a successful implementation depends on the implementation of an Energy Management System (EMS). An EMS is broadly implemented with a hierarchical and centralized structure, where the communications layer presents as a key element of the system to achieve a successful operation. Additionally, the relatively low cost of wireless communication technologies and the advantages offered by remote monitoring have promoted the inclusion of the Internet of Things (IoT) and Wireless Sensor and Actuator Network (WSAN) technologies in the energy sector. In this article is presented the development of an IoT EMS based on a WSAN (IoT-EMS-WSAN) for the management of a DC-µG. The proposed EMS is composed of a WiFi-based WSAN that is interconnected to a DC-µG, a cloud server, and a User Web App. The proposed system was compared to a conventional EMS with a high latency wired communication layer. In comparison to the conventional EMS, the IoT-EMS-WSAN increased the updating time from 100 ms to 1200 ms; also, the bus of the DC-µG maintained its stability even though its variations increased; finally, the DC bus responded to an energy-outage scenario with a recovery time of 1 s instead of 150 ms, as seen with the conventional EMS. Despite the reduced latency, the developed IoT-EMS-WSAN was demonstrated to be a reliable tool for the management, monitoring, and remote controlling of a DC-µG. Full article

(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)

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Technologies, Volume 12, Issue 9 (September 2024) – 7 articles

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