A study on solar blind UV ICCD detection performance

Novel green synthesis and improved solar-blind detection performance of hierarchical γ-Ga2O3 nanospheres

Journal of Materials Chemistry C ◽

10.1039/c7tc02615c ◽

2017 ◽

Vol 5 (29) ◽

pp. 7161-7166 ◽

Cited By ~ 11

Author(s):

Mao Mao Ruan ◽

Le Xin Song ◽

Zun Yang ◽

Yue Teng ◽

Qing Shan Wang ◽

...

Keyword(s):

Green Synthesis ◽

Direct Conversion ◽

Detection Performance ◽

Blind Detection ◽

High Quality ◽

Precursor Complex ◽

Solar Blind

High-quality γ-Ga2O3 nanospheres (diameter, 130 nm) were successfully synthesized by direct conversion of a precursor complex of Ga3+ ions and tartrate ions (L2−) in water.

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Understanding Target Detection Performance When Receiving Visual or Auditory Cues

PsycEXTRA Dataset ◽

10.1037/e618602012-013 ◽

2002 ◽

Author(s):

David C. Cibik ◽

Erich W. Meyerhoff

Keyword(s):

Target Detection ◽

Detection Performance ◽

Auditory Cues

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CNN performance dependence on linear image processing

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-182 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 310-1-310-7

Author(s):

Khalid Omer ◽

Luca Caucci ◽

Meredith Kupinski

Keyword(s):

Image Processing ◽

Texture Classification ◽

Full Rank ◽

Detection Performance ◽

Ideal Observer ◽

Training Data ◽

Image Texture ◽

Training Images ◽

Analytic Expressions ◽

Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

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