scholarly journals Hyperspectral imaging sensor for optimization of small molecule formulations

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Lalit Mohan Kandpal ◽  
Jagdish Tewari ◽  
Kenny Tran ◽  
Ernie Quan ◽  
Nishanth Gopinathan ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Small Molecule ◽  
Imaging Sensor

scholarly journals Highly Multiplexed Label-Free Imaging Sensor for Accurate Quantification of Small-Molecule Binding Kinetics

ACS Omega ◽  
2020 ◽  
Vol 5 (39) ◽  
pp. 25358-25364
Author(s):  
Elisa Chiodi ◽  
Allison M. Marn ◽  
Matthew T. Geib ◽  
Fulya Ekiz Kanik ◽  
John Rejman ◽  
...  
Keyword(s):  
Small Molecule ◽  
Binding Kinetics ◽  
Label Free ◽  
Imaging Sensor ◽  
Accurate Quantification

scholarly journals Retrieval of NO2 Column Amounts from Ground-Based Hyperspectral Imaging Sensor Measurements

2019 ◽  
Vol 11 (24) ◽  
pp. 3005 ◽  
Author(s):  
Hyeon-Ju Park ◽  
Jin-Soo Park ◽  
Sang-Woo Kim ◽  
Heesung Chong ◽  
Hana Lee ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Zenith Angle ◽  
Vertical Profile ◽  
Azimuth Angle ◽  
Fitting Method ◽  
Intensive Observation ◽  
Imaging Sensor ◽  
Total Column ◽  
Observation Period ◽  
Good Agreement

Total column amounts of NO2 (TCN) were estimated from ground-based hyperspectral imaging sensor (HIS) measurements in a polluted urban area (Seoul, Korea) by applying the radiance ratio fitting method with five wavelength pairs from 400 to 460 nm. We quantified the uncertainty of the retrieved TCN based on several factors. The estimated TCN uncertainty was up to 0.09 Dobson unit (DU), equivalent to 2.687 × 1020 molecules m−2) given a 1° error for the observation geometries, including the solar zenith angle, viewing zenith angle, and relative azimuth angle. About 0.1 DU (6.8%) was estimated for an aerosol optical depth (AOD) uncertainty of 0.01. In addition, the uncertainty due to the NO2 vertical profile was 14% to 22%. Compared with the co-located Pandora spectrophotometer measurements, the HIS captured the temporal variation of the TCN during the intensive observation period. The correlation between the TCN from the HIS and Pandora also showed good agreement, with a slight positive bias (bias: 0.6 DU, root mean square error: 0.7 DU).

Miniature infrared hyperspectral imaging sensor for airborne applications

2017 ◽  
Author(s):  
Michele Hinnrichs ◽  
Bradford Hinnrichs ◽  
Earl McCutchen
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Sensor

scholarly journals Forgery Detection in a Questioned Hyperspectral Document Image using K-means Clustering

2020 ◽  
Author(s):  
Maria Yaseen ◽  
Rammal Aftab ◽  
Rimsha mahrukh
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Response ◽  
Document Image ◽  
Spectral Signature ◽  
Detection Techniques ◽  
Spectral Signatures ◽  
Spectral Bands ◽  
Mismatch Detection ◽  
The One ◽  
Imaging Sensor

Hyperspectral imaging allows for analysis of images in several hundred of spectral bands depending on the spectral resolution of the imaging sensor. Hyperspectral document image is the one which has been captured by a hyperspectral camera so that the document can be observed in the different bands on the basis of their unique spectral signatures. To detect the forgery in a document various Ink mismatch detection techniques based on hyperspectral imaging have presented vast potential in differentiating visually similar inks. Inks of different materials exhibit different spectral signature even if they have the same color. Hyperspectral analysis of document images allows identification and discrimination of visually similar inks. Based on this analysis forensic experts can identify the authenticity of the document. In this paper an extensive ink mismatch detection technique is presented which uses KMean Clustering to identify different inks on the basis of their unique spectral response and separates them into different clusters.

scholarly journals Forgery Detection in a Questioned Hyperspectral Document Image using K-means Clustering

2020 ◽  
Author(s):  
Maria Yaseen ◽  
Rammal Aftab ◽  
Rimsha mahrukh
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Response ◽  
Document Image ◽  
Spectral Signature ◽  
Detection Techniques ◽  
Spectral Signatures ◽  
Spectral Bands ◽  
Mismatch Detection ◽  
The One ◽  
Imaging Sensor

Hyperspectral imaging allows for analysis of images in several hundred of spectral bands depending on the spectral resolution of the imaging sensor. Hyperspectral document image is the one which has been captured by a hyperspectral camera so that the document can be observed in the different bands on the basis of their unique spectral signatures. To detect the forgery in a document various Ink mismatch detection techniques based on hyperspectral imaging have presented vast potential in differentiating visually similar inks. Inks of different materials exhibit different spectral signature even if they have the same color. Hyperspectral analysis of document images allows identification and discrimination of visually similar inks. Based on this analysis forensic experts can identify the authenticity of the document. In this paper an extensive ink mismatch detection technique is presented which uses KMean Clustering to identify different inks on the basis of their unique spectral response and separates them into different clusters.

Investigation of a compressive line sensing hyperspectral imaging sensor

2018 ◽  
Author(s):  
Bing Ouyang ◽  
Michael Twardowski ◽  
Yanjun Li ◽  
Fraser R. Dalgleish
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Sensor
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