Mid- and near-infrared spectroscopy of polymers: time-resolved studies and remote sensing applications

1997 ◽  
Author(s):  
S. Okretic ◽  
N. Voelkl ◽  
Heinz W. Siesler
Keyword(s):  
Remote Sensing ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Time Resolved ◽  
Sensing Applications ◽  
Remote Sensing Applications

scholarly journals A Versatile Setup for Time-Resolved Functional Near Infrared Spectroscopy Based on Fast-Gated Single-Photon Avalanche Diode and on Four-Wave Mixing Laser

2019 ◽  
Vol 9 (11) ◽  
pp. 2366 ◽  
Author(s):  
Laura Di Sieno ◽  
Alberto Dalla Mora ◽  
Alessandro Torricelli ◽  
Lorenzo Spinelli ◽  
Rebecca Re ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Single Photon ◽  
Wave Mixing ◽  
Functional Near Infrared Spectroscopy ◽  
Time Resolved ◽  
Diffusive Medium ◽  
Spectroscopy System

In this paper, a time-domain fast gated near-infrared spectroscopy system is presented. The system is composed of a fiber-based laser providing two pulsed sources and two fast gated detectors. The system is characterized on phantoms and was tested in vivo, showing how the gating approach can improve the contrast and contrast-to-noise-ratio for detection of absorption perturbation inside a diffusive medium, regardless of source-detector separation.

ERRATA

PEDIATRICS ◽  
1993 ◽  
Vol 92 (1) ◽  
pp. 190-190
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Neurological Disorders ◽  
Near Infrared ◽  
Time Resolved

In the article, "A Report of the National Institute of Neurological Disorders and Stroke Workshop on Near Infrared Spectroscopy" by Hirtz (Pediatrics. 1993;91:414-417), on page 416, middle of the second paragraph, "The accuracy of time-resolved methods is 30% of saturation..." should read "The accuracy of time resolved methods is 3% of saturation ..."

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals Monitoring brain temperature by time-resolved near-infrared spectroscopy: pilot study

2014 ◽  
Vol 19 (5) ◽  
pp. 057005 ◽  
Author(s):  
Mohammad Fazel Bakhsheshi ◽  
Mamadou Diop ◽  
Keith St. Lawrence ◽  
Ting-Yim Lee
Keyword(s):  
Infrared Spectroscopy ◽  
Pilot Study ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Brain Temperature ◽  
Time Resolved

scholarly journals Optical properties of drying wood studied by time-resolved near-infrared spectroscopy

2016 ◽  
Vol 24 (9) ◽  
pp. 9561 ◽  
Author(s):  
Keiji Konagaya ◽  
Tetsuya Inagaki ◽  
Ryunosuke Kitamura ◽  
Satoru Tsuchikawa
Keyword(s):  
Optical Properties ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Time Resolved
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