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Google Glass Indirect Ophthalmoscopy.
J Mob Technol Med
January 2015
Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Background: Google Glass is a wearable, head-mounted computer with display, photographic and videographic imaging capability, and connectivity to other devices through Wi-Fi and Bluetooth signaling.
Aims: To describe for the first time the use of Google Glass for use in indirect ophthalmoscopy and modification techniques to assist with its use.
Methods: A lightweight, portable light source was installed above the Glass aperture, a small tissue paper used to diffuse the light, and the arm of the headset was taped to the examiner's glasses in order to bring the display into the right eye's central visual field.
Development of a vegetation canopy reflectance sensor and its diurnal applicability under clear sky conditions.
Front Plant Sci
January 2025
Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Huaiyin Normal University, Huai'an, China.
The spectral reflectance provides valuable information regarding vegetation growth and plays an important role in agriculture, forestry, and grassland management. In this study, a small, portable vegetation canopy reflectance (VCR) sensor that can operate throughout the day was developed. The sensor includes two optical bands at 710 nm and 870 nm, with the light separated by filters, and has a field of view of 28°.
View Article and Find Full Text PDFSingle-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM.
Front Hum Neurosci
January 2025
Student Affairs Office, Guilin Normal College, Guilin, China.
Introduction: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.
View Article and Find Full Text PDFClassification and Prediction of VOCs Using an IoT-Enabled Electronic Nose System-Based Lab Prototype for Breath Sensing Applications.
ACS Sens
January 2025
Interdisciplinary Research Division Smart HealthCare, Indian Institute of Technology Jodhpur, Jodhpur 342030, India.
Electronic nose (e-nose) systems are well known in breath analysis because they combine breath printing with advanced and intelligent machine learning (ML) algorithms. This work demonstrates development of an e-nose system comprising gas sensors exposed to six different volatile organic compounds (VOCs). The change in the voltage of the sensors was recorded and analyzed through ML algorithms to achieve selectivity and predict the VOCs.
View Article and Find Full Text PDFUltraviolet radiation vs air filtration to mitigate virus laden aerosol in an occupied clinical room.
J Hazard Mater
January 2025
Monash Lung, Sleep, Allergy and Immunology, Monash Health, Melbourne, VIC, Australia; School of Clinical Sciences, Monash University, Melbourne, VIC, Australia; Monash Partners - Epworth, Melbourne, VIC, Australia.
Mitigation measures against infectious aerosols are desperately needed. We aimed to: 1) compare germicidal ultraviolet radiation (GUV) at 254 nm (254-GUV) and 222 nm (222-GUV) with portable high efficiency particulate air (HEPA) filters to inactivate/remove airborne bacteriophage ϕX174, 2) measure the effect of air mixing on the effectiveness of 254-GUV, and 3) determine the relative susceptibility of ϕX174, SARS-CoV-2, and Influenza A(H3N2) to GUV (254 nm, 222 nm). A nebulizer generated ϕX174 laden aerosols in an occupied clinical room (sealed-low flow).
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