A method for reduction of eye fatigue by optimizing the ambient light conditions in radiology reading rooms

2006 ◽  
Author(s):  
Amarpreet S. Chawla ◽  
Ehsan Samei
Keyword(s):  
Ambient Light ◽  
Light Conditions ◽  
Eye Fatigue

scholarly journals Improved CoChR Variants Restore Visual Acuity and Contrast Sensitivity in a Mouse Model of Blindness under Ambient Light Conditions

2019 ◽  
Vol 27 (6) ◽  
pp. 1195-1205 ◽  
Author(s):  
Tushar H. Ganjawala ◽  
Qi Lu ◽  
Mitchell D. Fenner ◽  
Gary W. Abrams ◽  
Zhuo-Hua Pan
Keyword(s):  
Visual Acuity ◽  
Mouse Model ◽  
Contrast Sensitivity ◽  
Ambient Light ◽  
Light Conditions

scholarly journals The diel pattern in harbour porpoise clicking behaviour is not a response to prey activity

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Anna N. Osiecka ◽  
Owen Jones ◽  
Magnus Wahlberg
Keyword(s):  
Behaviour Pattern ◽  
Ambient Light ◽  
Light Conditions ◽  
Phocoena Phocoena ◽  
Diel Pattern ◽  
Acoustic Activity ◽  
Clear Peak ◽  
Similar Temperature ◽  
Free Ranging ◽  
Diel Behaviour

Abstract Wild harbour porpoises (Phocoena phocoena) mainly forage during the night and, because they rely on echolocation to detect their prey, this is also when they are most acoustically active. It has been hypothesised that this activity pattern is a response to the diel behaviour of their major prey species. To test this hypothesis, we monitored the acoustic activity of two captive harbour porpoises held in a net pen continuously during a full year and fed by their human keepers during daylight hours, thus removing the influence of prey activity. The porpoises were exposed to similar temperature and ambient light conditions as free-ranging animals living in the same region. Throughout the year, there was a pronounced diel pattern in acoustic activity of the porpoises, with significantly greater activity at night, and a clear peak around sunrise and sunset throughout the year. Clicking activity was not dependent on lunar illumination or water level. Because the porpoises in the pen are fed and trained during daylight hours, the results indicate that factors other than fish behaviour are strongly influencing the diel clicking behaviour pattern of the species.

scholarly journals Real-Time Detection of Concealed Chemical Hazards Under Ambient Light Conditions Using Raman Spectroscopy

2013 ◽  
Vol 58 (4) ◽  
pp. 1008-1014 ◽  
Author(s):  
Biju Cletus ◽  
William Olds ◽  
Peter M. Fredericks ◽  
Esa Jaatinen ◽  
Emad L. Izake
Keyword(s):  
Raman Spectroscopy ◽  
Real Time ◽  
Ambient Light ◽  
Light Conditions ◽  
Chemical Hazards ◽  
Real Time Detection

Bright ambient light conditions reduce the effect of tryptophan depletion in healthy females

2013 ◽  
Vol 210 (1) ◽  
pp. 109-114 ◽  
Author(s):  
Michaela Defrancesco ◽  
Harald Niederstätter ◽  
Walther Parson ◽  
Georg Kemmler ◽  
Hartmann Hinterhuber ◽  
...  
Keyword(s):  
Tryptophan Depletion ◽  
Ambient Light ◽  
Light Conditions ◽  
Healthy Females

scholarly journals Classifying Reflectance Targets under Ambient Light Conditions Using Passive Spectral Measurements

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5375
Author(s):  
Ali Hamidisepehr ◽  
Michael P. Sama ◽  
Joseph S. Dvorak ◽  
Ole O. Wendroth ◽  
Michael D. Montross
Keyword(s):  
Spectral Data ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Machine Learning Algorithms ◽  
Spectral Signature ◽  
Integration Time ◽  
Ambient Light ◽  
Light Conditions ◽  
Light Spectrum ◽  
Compensation Methods

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near−infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient−light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two−step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%.

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