Technologies最新文献

{"title":"A Survey of the Diagnosis of Peripheral Neuropathy Using Intelligent and Wearable Systems","authors":"Muhammad Talha, Maria Kyrarini, E. Buriro","doi":"10.3390/technologies11060163","DOIUrl":"https://doi.org/10.3390/technologies11060163","url":null,"abstract":"In recent years, the usage of wearable systems in healthcare has gained much attention, as they can be easily worn by the subject and provide a continuous source of data required for the tracking and diagnosis of multiple kinds of abnormalities or diseases in the human body. Wearable systems can be made useful in improving a patient’s quality of life and at the same time reducing the overall cost of caring for individuals including the elderly. In this survey paper, the recent research in the development of intelligent wearable systems for the diagnosis of peripheral neuropathy is discussed. The paper provides detailed information about recent techniques based on different wearable sensors for the diagnosis of peripheral neuropathy including experimental protocols, biomarkers, and other specifications and parameters such as the type of signals and data processing methods, locations of sensors, the scales and tests used in the study, and the scope of the study. It also highlights challenges that are still present in order to make wearable devices more effective in the diagnosis of peripheral neuropathy in clinical settings.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139262850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Curve-Fitting Correction Method for the Nonlinear Dimming Response of Tunable SSL Devices","authors":"Rugved Kore, D. Durmus","doi":"10.3390/technologies11060162","DOIUrl":"https://doi.org/10.3390/technologies11060162","url":null,"abstract":"Solid-state lighting (SSL) devices are ubiquitous in several markets, including architectural, automotive, healthcare, heritage conservation, and entertainment lighting. Fine control of the LED light output is crucial for applications where spectral precision is required, but dimming LEDs can cause a nonlinear response in its output, shifting the chromaticity. The nonlinear response of a multi-color LEDs can be corrected by curve-fitting the measured data to input dimming controls. In this study, the spectral output of an RGB LED projector was corrected using polynomial curve fitting. The accuracy of four different measurement methods was compared in order to find the optimal correction approach in terms of the time and effort needed to perform measurements. The results suggest that the curve fitting of very high-resolution dimming steps (n = 125) significantly decreased the chromaticity shifts between measured (actual) and corrected spectra. The effect size between approaches indicates that the curve-fitting of the high-resolution approach (n = 23) performs equally well as at very high resolution (n = 125). The curve-fitting correction can be used as an alternative approach or in addition to existing methods, such as the closed-loop correction. The curve fitting method can be applied to any tunable multi-color LED lighting system to correct the nonlinear dimming response.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Speaker Profiling Based on the Short-Term Acoustic Features of Vowels","authors":"M. Humayun, Junaid Shuja, P. E. Abas","doi":"10.3390/technologies11050119","DOIUrl":"https://doi.org/10.3390/technologies11050119","url":null,"abstract":"Speech samples can provide valuable information regarding speaker characteristics, including their social backgrounds. Accent variations with speaker backgrounds reflect corresponding acoustic features of speech, and these acoustic variations can be analyzed to assist in tracking down criminals from speech samples available as forensic evidence. Speech accent identification has recently received significant consideration in the speech forensics research community. However, most works have utilized long-term temporal modelling of acoustic features for accent classification and disregarded the stationary acoustic characteristics of particular phoneme articulations. This paper analyzes short-term acoustic features extracted from a central time window of English vowel speech segments for accent discrimination. Various feature computation techniques have been compared for the accent classification task. It has been found that using spectral features as an input gives better performance than using cepstral features, with the lower filters contributing more significantly to the classification task. Moreover, detailed analysis has been presented for time window durations and frequency bin resolution to compute short-term spectral features concerning accent discrimination. Using longer time durations generally requires higher frequency resolution to optimize classification performance. These results are significant, as they show the benefits of using spectral features for speaker profiling despite the popularity of cepstral features for other speech-related tasks.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88598051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hypertuned Lightweight and Scalable LSTM Model for Hybrid Network Intrusion Detection","authors":"Aysha Bibi, Gabriel Avelino Sampedro, Ahmad S. Almadhor, Abdul Rehman Javed, Tai-hoon Kim","doi":"10.3390/technologies11050121","DOIUrl":"https://doi.org/10.3390/technologies11050121","url":null,"abstract":"Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial Intelligence (AI)-based solutions into historical methods. However, AI-driven approaches often encounter challenges, including lower detection rates and the complexity of feature engineering requirements. Finding solutions to overcome these hurdles is critical for enhancing the effectiveness of intrusion detection systems. This research paper introduces a deep learning-based approach for network intrusion detection to overcome these challenges. The proposed approach utilizes various classification algorithms, including the AutoEncoder (AE), Long-short-term-memory (LSTM), Multi-Layer Perceptron (MLP), Linear Support Vector Machine (L-SVM), Quantum Support Vector Machine (Q-SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). To validate the effectiveness of the proposed approach, three datasets, namely IOT23, CICIDS2017, and NSL KDD, are used for experimentation. The results demonstrate impressive accuracy, particularly with the LSTM algorithm, achieving a 97.7% accuracy rate on the NSL KDD dataset, 99% accuracy rate on the CICIDS2017 dataset, and 98.7% accuracy on the IOT23 dataset. These findings highlight the potential of deep learning algorithms in enhancing network intrusion detection. By providing network administrators with robust security measures for accurate and timely intrusion detection, the proposed approach contributes to network safety and helps mitigate the impact of network attacks.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82722077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Simple Interactive Technology to Help People with Intellectual and Visual Disabilities Exercise Functional Physical Responses: A Case Series Study","authors":"G. Lancioni, Gloria Alberti, Chiara Filippini, Valeria Chiariello, Nirbhay N. Singh, M. O'Reilly, J. Sigafoos","doi":"10.3390/technologies11050120","DOIUrl":"https://doi.org/10.3390/technologies11050120","url":null,"abstract":"The study assessed a new interactive technology system for helping six people with intellectual and visual disabilities exercise relevant physical responses embedded within a fairly straightforward activity (i.e., placing objects in containers). Activity responses consisted of the participants taking objects from the floor or a low shelf and placing those objects in a container high up in front of them (thus bending their body and legs and stretching their arms and hands). The technology involved a portable computer, a webcam, and three mini speakers whose basic functions included monitoring the participants’ responses, delivering preferred stimulation contingent on the responses and verbal encouragements/prompts for lack of responses, and assisting in data recording. The study was conducted following a non-concurrent multiple baseline design across participants. During baseline (i.e., when the system was used only for data recording), the participants’ mean frequency of responses per session varied between zero and nearly 12. During intervention (i.e., when the system was fully working), the participants’ mean frequency of responses per session increased to between about 34 and 59. Mean session duration varied between nearly 10 and over 14 min. The new system may be a valuable tool for supporting relevant physical activity engagement in people with intellectual and multiple disabilities.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75959061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Influence of Technological Factors and Polar Molecules on the Structure of Fibrillar Matrices Based on Ultrafine Poly-3-hydroxybutyrate Fibers Obtained via Electrospinning","authors":"A. A. Olkhov, P. Tyubaeva, Yu. N. Zernova, Valery S. Markin, Regina Kosenko, Anna G. Filatova, Kristina G. Gasparyan, A. Iordanskii","doi":"10.3390/technologies11050118","DOIUrl":"https://doi.org/10.3390/technologies11050118","url":null,"abstract":"The article examines the regularities of structure formation of ultrafine fibers based on poly-3-hydroxybutyrat under the influence of technological (electrical conductivity, viscosity), molecular (molecular weight), and external factors (low-molecular and nanodispersed substances of different chemical natures). Systems with polar substances are characterized by the presence of intermolecular interactions and the formation of a more perfect crystalline fiber structure. Changes in technological and molecular characteristics affect the fiber formation process, resulting in alterations in the morphology of the nonwoven fabric, fiber geometry, and supramolecular fiber structure. Polymer molecular weight, electrical conductivity, and solution viscosity influence fiber formation and fiber diameter. The fiber structure is heterogeneous, consisting of both crystalline and non-equilibrium amorphous phases. This article shows that with an increase in the molecular weight and concentration of the polymer, the diameter of the fiber increases. At the same time, the increase in the productivity of the electrospinning process does not affect the fiber geometry. The chemical structure of the solvent and the concentration of polar substances play a decisive role in the formation of fibers of even geometry. As the polarity of the solvent increases, the intermolecular interaction with the polar groups of poly-3-hydroxybutyrate increases. As a result of this interaction, the crystallites are improved, and the amorphous phase of the polymer is compacted. The action of polar molecules on the polymer is similar to the action of polar nanoparticles. They increase crystallinity via a nucleation mechanism. This is significant in the development of matrix-fibrillar systems for drug delivery, bioactive substances, antiseptics, tissue engineering constructs, tissue engineering scaffolds, artificial biodegradable implants, sorbents, and other applications.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80206286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Connected and Automated Vehicles: Infrastructure, Applications, Security, Critical Challenges, and Future Aspects","authors":"Memoona Sadaf, Zafar Iqbal, Abdul Rehman Javed, Irum Saba, M. Krichen, Sajid Majeed, Arooj Raza","doi":"10.3390/technologies11050117","DOIUrl":"https://doi.org/10.3390/technologies11050117","url":null,"abstract":"Autonomous vehicles (AV) are game-changing innovations that promise a safer, more convenient, and environmentally friendly mode of transportation than traditional vehicles. Therefore, understanding AV technologies and their impact on society is critical as we continue this revolutionary journey. Generally, there needs to be a detailed study available to assist a researcher in understanding AV and its challenges. This research presents a comprehensive survey encompassing various aspects of AVs, such as public adoption, driverless city planning, traffic management, environmental impact, public health, social implications, international standards, safety, and security. Furthermore, it presents emerging technologies such as artificial intelligence (AI), integration of cloud computing, and solar power usage in automated vehicles. It also presents forensics approaches, tools used, standards involved, and challenges associated with conducting digital forensics in the context of autonomous vehicles. Moreover, this research provides an overview of cyber attacks affecting autonomous vehicles, attack management, traditional security devices, threat modeling, authentication schemes, over-the-air updates, zero-trust architectures, data privacy, and the corresponding defensive strategies to mitigate such risks. It also presents international standards, guidelines, and best practices for AVs. Finally, it outlines the future directions of AVs and the challenges that must be addressed to achieve widespread adoption.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79279431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica Dataset","authors":"Eric Hitimana, Omar Janvier Sinayobye, J. C. Ufitinema, Jane Mukamugema, Peter Rwibasira, Theoneste Murangira, Emmanuel Masabo, Lucy Cherono Chepkwony, Marie Cynthia Abijuru Kamikazi, Jeanne Aline Ukundiwabo Uwera, S. M. Mvuyekure, Gaurav Bajpai, Jackson Ngabonziza","doi":"10.3390/technologies11050116","DOIUrl":"https://doi.org/10.3390/technologies11050116","url":null,"abstract":"Rwandan coffee holds significant importance and immense value within the realm of agriculture, serving as a vital and valuable commodity. Additionally, coffee plays a pivotal role in generating foreign exchange for numerous developing nations. However, the coffee plant is vulnerable to pests and diseases weakening production. Farmers in cooperation with experts use manual methods to detect diseases resulting in human errors. With the rapid improvements in deep learning methods, it is possible to detect and recognize plan diseases to support crop yield improvement. Therefore, it is an essential task to develop an efficient method for intelligently detecting, identifying, and predicting coffee leaf diseases. This study aims to build the Rwandan coffee plant dataset, with the occurrence of coffee rust, miner, and red spider mites identified to be the most popular due to their geographical situations. From the collected coffee leaves dataset of 37,939 images, the preprocessing, along with modeling used five deep learning models such as InceptionV3, ResNet50, Xception, VGG16, and DenseNet. The training, validation, and testing ratio is 80%, 10%, and 10%, respectively, with a maximum of 10 epochs. The comparative analysis of the models’ performances was investigated to select the best for future portable use. The experiment proved the DenseNet model to be the best with an accuracy of 99.57%. The efficiency of the suggested method is validated through an unbiased evaluation when compared to existing approaches with different metrics.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90731339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Deep Learning-Based Data-Centric Approach for Autism Spectrum Disorder Diagnosis from Facial Images Using Explainable AI","authors":"Mohammad Shafiul Alam, Muhammad Mahbubur Rashid, Ahmed Rimaz Faizabadi, Hasan Firdaus Mohd Zaki, Tasfiq E. Alam, Md Shahin Ali, Kishor Datta Gupta, M. Ahsan","doi":"10.3390/technologies11050115","DOIUrl":"https://doi.org/10.3390/technologies11050115","url":null,"abstract":"The research describes an effective deep learning-based, data-centric approach for diagnosing autism spectrum disorder from facial images. To classify ASD and non-ASD subjects, this method requires training a convolutional neural network using the facial image dataset. As a part of the data-centric approach, this research applies pre-processing and synthesizing of the training dataset. The trained model is subsequently evaluated on an independent test set in order to assess the performance matrices of various data-centric approaches. The results reveal that the proposed method that simultaneously applies the pre-processing and augmentation approach on the training dataset outperforms the recent works, achieving excellent 98.9% prediction accuracy, sensitivity, and specificity while having 99.9% AUC. This work enhances the clarity and comprehensibility of the algorithm by integrating explainable AI techniques, providing clinicians with valuable and interpretable insights into the decision-making process of the ASD diagnosis model.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83222271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model","authors":"Rongbin Yao, Peng Qi, Dezheng Hua, Xu Zhang, He Lu, Xinhua Liu","doi":"10.3390/technologies11050114","DOIUrl":"https://doi.org/10.3390/technologies11050114","url":null,"abstract":"As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine’s intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and mAP50 of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90460368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}