Two-Mirror Telescope Optical Axis Alignment by Additive Color Mixing Method

2016 ◽  
pp. 731-741
Author(s):  
Feng-Ming Yeh ◽  
Der-Chin Chen ◽  
Shih-Chieh Lee ◽  
Ya-Hui Hsieh
Keyword(s):  
Optical Axis ◽  
Axis Alignment ◽  
Mixing Method ◽  
Color Mixing ◽  
Mirror Telescope

Experimental Study on Optical Axis Alignment of the Optical Antenna under the Spot Broken

2017 ◽  
Vol 46 (4) ◽  
pp. 406002
Author(s):  
柯熙政 KE Xi-zheng ◽  
李世艳 LI Shi-yan
Keyword(s):  
Experimental Study ◽  
Optical Axis ◽  
Optical Antenna ◽  
Axis Alignment

The Design of Gaussian Matching Model in the Optical Axis Alignment

2012 ◽  
Vol 271-272 ◽  
pp. 1164-1167
Author(s):  
Yi Feng Zhu ◽  
Lin Tao Yu ◽  
Lei Shi ◽  
Rui Rui Shang
Keyword(s):  
Information Transmission ◽  
Atmospheric Turbulence ◽  
Optical Axis ◽  
Alignment Accuracy ◽  
Matching Algorithm ◽  
Gaussian Fitting ◽  
Axis Alignment ◽  
Laser Communications ◽  
Spot Image ◽  
Outdoor Experiments

In the space laser communications, Information transmission by atmospheric turbulence and background light and other atmospheric interference of the random channel. Resulting in the optical axis alignment accuracy decreased. In order to improve the alignment accuracy of the optical axis, Proposing the Gaussian fitting matching algorithm. Outdoor experiments confirmed the Gaussian fitting matching algorithm to ensure a stable spot image.

Colorful LED backlight FM & diming and color mixing method

2015 ◽  
Vol 30 (5) ◽  
pp. 872-876
Author(s):  
杨宗阳 YANG Zong-yang ◽  
刘 波 LIU Bo ◽  
章小兵 ZHANG Xiao-bing ◽  
祁凌云 QI Ling-yun
Keyword(s):  
Mixing Method ◽  
Color Mixing ◽  
Led Backlight

scholarly journals Discrimination of Chinese yellow wine from different origins based on flavor fingerprint

2020 ◽  
Vol 32 (2) ◽  
pp. 139-144
Author(s):  
Tong Chen ◽  
Xingpu Qi ◽  
Mingjie Chen ◽  
Daoli Lu ◽  
Bin Chen
Keyword(s):  
Ion Mobility ◽  
Principal Component ◽  
Classification Model ◽  
Color Model ◽  
Quadratic Discriminant Analysis ◽  
Volatile Flavor ◽  
Volatile Organic ◽  
Mixing Method ◽  
Color Mixing ◽  
Different Origins

In this study, discrimination of Chinese yellow wines from Shaoxing, Shandong, and Hubei in China has been carried out according to volatile flavor components. A total of 122 yellow wine samples were characterized by gas chromatography–ion mobility spectrometry (GC–IMS). A simple color mixing method was visually used to select characteristic peaks based on the RGB color model. Then, the volatile organic compounds corresponding to the selected characteristic peaks were identified via library searching, and the height values of those peaks were arranged for further chemometric pretreatment. Principal component analysis was employed to reveal significant differences and potential patterns between samples. Finally, quadratic discriminant analysis was applied to develop a classification model and achieved a correct classified rate of 95.35% for the prediction set. The results prove that the aroma composition combined with chemometric tools can be used as a fingerprinting technique to protect the product of origin and enable the authenticity of Chinese yellow wine.

Analysis of 3-d molecular distribution with the digital imaging microscope

1988 ◽  
Vol 46 ◽  
pp. 40-41
Author(s):  
W.A. Carrington ◽  
F.S. Fay ◽  
K.E. Fogarty ◽  
L. Lifshitz
Keyword(s):  
Image Restoration ◽  
Digital Imaging ◽  
Fluorescent Dyes ◽  
Optical Axis ◽  
Computer Hardware ◽  
Computer Algorithms ◽  
Low Contrast ◽  
Specific Proteins ◽  
Effective Use ◽  
Single Living Cells

Advances in digital imaging microscopy and in the synthesis of fluorescent dyes allow the determination of 3D distribution of specific proteins, ions, GNA or DNA in single living cells. Effective use of this technology requires a combination of optical and computer hardware and software for image restoration, feature extraction and computer graphics.The digital imaging microscope consists of a conventional epifluorescence microscope with computer controlled focus, excitation and emission wavelength and duration of excitation. Images are recorded with a cooled (-80°C) CCD. 3D images are obtained as a series of optical sections at .25 - .5 μm intervals.A conventional microscope has substantial blurring along its optical axis. Out of focus contributions to a single optical section cause low contrast and flare; details are poorly resolved along the optical axis. We have developed new computer algorithms for reversing these distortions. These image restoration techniques and scanning confocal microscopes yield significantly better images; the results from the two are comparable.

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