P‐130: A Full Screen Biometric Identification Approach for OLED Displays by Using Near‐Infrared OLED

2020 ◽  
Vol 51 (1) ◽  
pp. 1855-1858
Author(s):  
Chi-Hao Lin ◽  
Karim S. Karim ◽  
Ya-Hsiang Tai
Sensors ◽  
2012 ◽  
Vol 12 (1) ◽  
pp. 987-1001 ◽  
Author(s):  
Carlos M. Travieso ◽  
Juan Carlos Briceño ◽  
Jesús B. Alonso

2020 ◽  
Vol 68 (4) ◽  
pp. 265-276 ◽  
Author(s):  
Giuseppe Bonifazi ◽  
Riccardo Gasbarrone ◽  
Roberta Palmieri ◽  
Silvia Serranti

AbstractThe technological innovation and the relentless marketing of new electronic products with improved performance generate increasing quantities of Waste from Electrical and Electronic Equipment (WEEE). In this scenario, End-Of-Life (EOL) flat monitors and screens represent a category generating, as a consequence of the rapid change in technology, an important amount of waste. Considering future estimations, the implementation of an adequate recycling infrastructure is necessary. An efficient, reliable and low-cost analytical tool is thus needed to perform detection/control actions in order to assess: i) waste composition and ii) physical-chemical attributes of the resulting materials. The knowledge of these information is a requirement to set-up and to implement correct recycling actions.In this study, a cascade identification approach, based on Near InfraRed (NIR) – HyperSpectral Imaging (HSI), was carried out. More in detail, a four-steps classification was designed, implemented and set-up in order to recognize different materials occurring in a specific WEEE stream: EOL milled monitors and flat screens. Adopting the proposed approach, different material categories are correctly recognized and classified. Obtained results can be useful not only to set-up a quality control system, but also to improve sorting actions in this specific recycling sector.


2014 ◽  
Vol 573 ◽  
pp. 495-500
Author(s):  
M. Thenmozhi ◽  
P. Gnanaskanda Parthiban

A biometric identification system may be a pc application for mechanically distinctive or confirmative of an individual from a digital image or a video frame from a video supply. One in all the ways that to try and do this can be by examination designated face expression from the image and a facial information. This paper planned dynamic face recognition from near-infrared images by exploitation sparse representation classifier. Most of the prevailing datasets for facial expressions are captured in a very visible light spectrum. However, the visible light (VIS) will modify with time and placement, causing important variations in look and texture. This new framework was designed to attain strength to pose variation and occlusion and to resolve uncontrolled environmental illumination for reliable biometric identification. This paper gift a unique analysis on a dynamic facial features recognition, exploitation near-infrared (NIR) datasets and LBP(Local binary patterns) feature descriptors. It shows sensible and strong results against illumination variations by exploitation infrared imaging system.


Nanoscale ◽  
2020 ◽  
Vol 12 (14) ◽  
pp. 7875-7887 ◽  
Author(s):  
Ying Lan ◽  
Xiaohui Zhu ◽  
Ming Tang ◽  
Yihan Wu ◽  
Jing Zhang ◽  
...  

A near-infrared (NIR) activated theranostic nanoplatform based on upconversion nanoparticles (UCNPs) is developed in order to overcome the hypoxia-associated resistance in photodynamic therapy by photo-release of NO upon NIR illumination.


2020 ◽  
Vol 56 (43) ◽  
pp. 5819-5822
Author(s):  
Jing Zheng ◽  
Yongzhuo Liu ◽  
Fengling Song ◽  
Long Jiao ◽  
Yingnan Wu ◽  
...  

In this study, a near-infrared (NIR) theranostic photosensitizer was developed based on a heptamethine aminocyanine dye with a long-lived triplet state.


2020 ◽  
Vol 48 (6) ◽  
pp. 2657-2667
Author(s):  
Felipe Montecinos-Franjola ◽  
John Y. Lin ◽  
Erik A. Rodriguez

Noninvasive fluorescent imaging requires far-red and near-infrared fluorescent proteins for deeper imaging. Near-infrared light penetrates biological tissue with blood vessels due to low absorbance, scattering, and reflection of light and has a greater signal-to-noise due to less autofluorescence. Far-red and near-infrared fluorescent proteins absorb light >600 nm to expand the color palette for imaging multiple biosensors and noninvasive in vivo imaging. The ideal fluorescent proteins are bright, photobleach minimally, express well in the desired cells, do not oligomerize, and generate or incorporate exogenous fluorophores efficiently. Coral-derived red fluorescent proteins require oxygen for fluorophore formation and release two hydrogen peroxide molecules. New fluorescent proteins based on phytochrome and phycobiliproteins use biliverdin IXα as fluorophores, do not require oxygen for maturation to image anaerobic organisms and tumor core, and do not generate hydrogen peroxide. The small Ultra-Red Fluorescent Protein (smURFP) was evolved from a cyanobacterial phycobiliprotein to covalently attach biliverdin as an exogenous fluorophore. The small Ultra-Red Fluorescent Protein is biophysically as bright as the enhanced green fluorescent protein, is exceptionally photostable, used for biosensor development, and visible in living mice. Novel applications of smURFP include in vitro protein diagnostics with attomolar (10−18 M) sensitivity, encapsulation in viral particles, and fluorescent protein nanoparticles. However, the availability of biliverdin limits the fluorescence of biliverdin-attaching fluorescent proteins; hence, extra biliverdin is needed to enhance brightness. New methods for improved biliverdin bioavailability are necessary to develop improved bright far-red and near-infrared fluorescent proteins for noninvasive imaging in vivo.


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