An optoelectronic band-to-band tunnel transistor for near-infrared sensing applications: Device physics, modeling, and simulation

2016 ◽  
Vol 120 (8) ◽  
pp. 084510 ◽  
Author(s):  
Partha Sarathi Gupta ◽  
Hafizur Rahaman ◽  
Kunal Sinha ◽  
Sanatan Chattopadhyay
Keyword(s):  
Modeling And Simulation ◽  
Near Infrared ◽  
Device Physics ◽  
Infrared Sensing ◽  
Sensing Applications ◽  
Tunnel Transistor ◽  
Physics Modeling

A Novel Photosensitive Tunneling Transistor for Near-Infrared Sensing Applications: Design, Modeling, and Simulation

2015 ◽  
Vol 62 (5) ◽  
pp. 1516-1523 ◽  
Author(s):  
Partha Sarathi Gupta ◽  
Sanatan Chattopadhyay ◽  
Parthasarathi Dasgupta ◽  
Hafizur Rahaman
Keyword(s):  
Modeling And Simulation ◽  
Near Infrared ◽  
Infrared Sensing ◽  
Sensing Applications

Trilayer conducting polymer transduction: device physics, modeling, and simulation

2021 ◽  
Author(s):  
Sébastien Grondel ◽  
Sofiane Ghenna ◽  
Caroline Soyer ◽  
Eric Cattan ◽  
John D. W. Madden ◽  
...  
Keyword(s):  
Modeling And Simulation ◽  
Conducting Polymer ◽  
Device Physics ◽  
Physics Modeling

scholarly journals Mode Sensitivity Exploration of Silica–Titania Waveguide for Refractive Index Sensing Applications

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7452
Author(s):  
Muhammad A. Butt ◽  
Andrzej Kaźmierczak ◽  
Cuma Tyszkiewicz ◽  
Paweł Karasiński ◽  
Ryszard Piramidowicz
Keyword(s):  
Refractive Index ◽  
High Performance ◽  
Near Infrared ◽  
Sol Gel ◽  
Cost Effective ◽  
Dip Coating ◽  
Infrared Wavelength ◽  
Refractive Index Sensing ◽  
Sensing Applications ◽  
Coating Method

In this paper, a novel and cost-effective photonic platform based on silica–titania material is discussed. The silica–titania thin films were grown utilizing the sol–gel dip-coating method and characterized with the help of the prism-insertion technique. Afterwards, the mode sensitivity analysis of the silica–titania ridge waveguide is investigated via the finite element method. Silica–titania waveguide systems are highly attractive due to their ease of development, low fabrication cost, low propagation losses and operation in both visible and near-infrared wavelength ranges. Finally, a ring resonator (RR) sensor device was modelled for refractive index sensing applications, offering a sensitivity of 230 nm/RIU, a figure of merit (FOM) of 418.2 RIU−1, and Q-factor of 2247.5 at the improved geometric parameters. We believe that the abovementioned integrated photonics platform is highly suitable for high-performance and economically reasonable optical sensing devices.

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>

Near-infrared sensing utilizing the evanescent field

1993 ◽  
Vol 281 (2) ◽  
pp. 265-270 ◽  
Author(s):  
A.R. Lennie ◽  
F. Kvasnik
Keyword(s):  
Near Infrared ◽  
Evanescent Field ◽  
Infrared Sensing

Performance analysis of a new graphene based-phototransistor for ultra-sensitive infrared sensing applications

Optik ◽  
2019 ◽  
Vol 176 ◽  
pp. 24-31 ◽  
Author(s):  
A. Kadri ◽  
F. Djeffal ◽  
H. Ferhati ◽  
F. Menacer ◽  
Z. Dibi
Keyword(s):  
Performance Analysis ◽  
Infrared Sensing ◽  
Sensing Applications
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