Decoding Imagined, Heard, and Spoken Speech: Classification and Regression of EEG Using a 14-Channel Dry-Contact Mobile Headset

Author(s):  
Jonathan Clayton ◽  
Scott Wellington ◽  
Cassia Valentini-Botinhao ◽  
Oliver Watts
Friction ◽  
2021 ◽  
Author(s):  
Zongzheng Wang ◽  
Wei Pu ◽  
Xin Pei ◽  
Wei Cao

AbstractExisting studies primarily focus on stiffness and damping under full-film lubrication or dry contact conditions. However, most lubricated transmission components operate in the mixed lubrication region, indicating that both the asperity contact and film lubrication exist on the rubbing surfaces. Herein, a novel method is proposed to evaluate the time-varying contact stiffness and damping of spiral bevel gears under transient mixed lubrication conditions. This method is sufficiently robust for addressing any mixed lubrication state regardless of the severity of the asperity contact. Based on this method, the transient mixed contact stiffness and damping of spiral bevel gears are investigated systematically. The results show a significant difference between the transient mixed contact stiffness and damping and the results from Hertz (dry) contact. In addition, the roughness significantly changes the contact stiffness and damping, indicating the importance of film lubrication and asperity contact. The transient mixed contact stiffness and damping change significantly along the meshing path from an engaging-in to an engaging-out point, and both of them are affected by the applied torque and rotational speed. In addition, the middle contact path is recommended because of its comprehensive high stiffness and damping, which maintained the stability of spiral bevel gear transmission.


Author(s):  
Asma M. Naim ◽  
Kithmin Wickramasinghe ◽  
Ashwin De Silva ◽  
Malsha V. Perera ◽  
Thilina Dulantha Lalitharatne ◽  
...  

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.


2021 ◽  
pp. 073428292110259
Author(s):  
Brittany A. Dale ◽  
W. Holmes Finch ◽  
Kassie A. R. Shellabarger ◽  
Andrew Davis

The Wechsler Intelligence Scales for Children (WISC) are the most widely used instrument in assessing cognitive ability, especially with children with autism spectrum disorder (ASD). Previous literature on the WISC has demonstrated a divergent pattern of performance on the WISC for children ASD compared to their typically developing peers; however, there is a lack of research concerning the most recent iteration, the Wechsler Intelligence Scale for Children, Fifth Edition (WISC-V). Due to the distinctive changes made to the WISC-V, we sought to identify the pattern of performance of children with ASD on the WISC-V using a classification and regression (CART) analysis. The current study used the standardization sample data of the WISC-V obtained from NCS Pearson, Inc. Sixty-two children diagnosed with ASD, along with their demographically matched controls, comprised the sample. Results revealed the Comprehension and Letter-Number Sequencing subtests were the most important factors in predicting group membership for children with ASD with an accompanying language impairment. Children with ASD without an accompanying language impairment, however, were difficult to distinguish from matched controls through the CART analysis. Results suggest school psychologists and other clinicians should administer all primary and supplemental subtests of the WISC-V as part of a comprehensive assessment of ASD.


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