Mobility of polypropylene microplastics in stormwater biofilters under freeze-thaw cycles
V.S. Koutnik, A. Borthakur, J. Leonard, S. Alkidim, H. Ceylan Koydemir, D. Tseng, A. Ozcan, S. Ravi, S.K. Mohanty, “Mobility of polypropylene microplastics in stormwater biofilters under freeze-thaw cycles” Journal of Hazardous Materials Letters (2022) DOI:...
Computational Imaging Without a Computer: Seeing Through Random Diffusers at the Speed of Light
Y. Luo, Y. Zhao, J. Li, E. Cetintas, Y. Rivenson, M. Jarrahi, A. Ozcan, “Computational Imaging Without a Computer: Seeing Through Random Diffusers at the Speed of Light,” eLight (2022) DOI: 10.1186/s43593-022-00012-4 – PDF
Characterization of exhaled e-cigarette aerosols in a vape shop using a field-portable holographic on-chip microscope
E. Cetintas, Y. Luo, C. Nguyen, Y. Guo, L. Li, Y. Zhu, A. Ozcan, “Characterization of exhaled e-cigarette aerosols in a vape shop using a field-portable holographic on-chip microscope,” Scientific Reports (2022) DOI: 10.1038/s41598-022-07150-2
Sub-picomolar lateral flow antigen detection with two-wavelength imaging of composite nanoparticles
B.S. Miller, M.R. Thomas, M. Banner, J. Kim, Y. Chen, Q.Wei, D.K. Tseng, Z.S. Göröcs, A. Ozcan, M.M. Stevens, R.A. McKendry “Sub-picomolar lateral flow antigen detection with two-wavelength imaging of composite nanoparticles,” Biosensors and Bioelectronics (2022) DOI:...
Smartphone-enabled rapid quantification of microplastics
J. Leonard, H.C. Koydemir, V.S. Koutnik, D. Tseng, A. Ozcan, and S. Mohanty, “Smartphone-enabled rapid quantification of microplastics” Journal of Hazardous Materials Letters (2022) DOI: 10.1016/j.hazl.2022.100052
Cascadable all-optical NAND gates using diffractive networks
Y. Luo, D. Mengu and A. Ozcan, “Cascadable all-optical NAND gates using diffractive networks” Scientific Reports (2022) DOI: 10.1038/s41598-022-11331-4
Classification and reconstruction of spatially overlapping phase images using diffractive optical networks
D. Mengu, M. Veli, Y. Rivenson and A. Ozcan, “Classification and reconstruction of spatially overlapping phase images using diffractive optical networks” Scientific Reports (2022) DOI: 10.1038/s41598-022-12020-y
At the intersection of optics and deep learning: statistical inference, computing, and inverse design
D. Mengu, S.S. Rahman, Y. Luo, J. Li and A. Ozcan, “At the intersection of optics and deep learning: statistical inference, computing, and inverse design” Advances in Optics and Photonics (2022) DOI: 10.1364/AOP.450345– PDF
All-optical phase recovery: diffractive computing for quantitative phase imaging
D. Mengu and A. Ozcan, “All-optical phase recovery: diffractive computing for quantitative phase imaging” Advanced Optical Materials (2022) DOI: 10.1002/adom.202200281– PDF
Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
J. Li, Y. Hung, O. Kulce, D. Mengu and A. Ozcan, “Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network,” Light: Science & Applications (Nature Publishing...
Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network
L. Huang, X. Yang, T. Liu, and A. Ozcan, “Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network” APL Photonics (2022) DOI: 10.1063/5.0090582
Deep Learning-enabled Detection and Classification of Bacterial Colonies Using a Thin Film Transistor (TFT) Image Sensor
Y. Li, T. Liu, H.C. Koydemir, H. Wang, K. O’Riordan, B. Bai, Y. Haga, J. Kobashi, H. Tanaka, T. Tamaru, K. Yamaguchi, and A. Ozcan, “Deep Learning-enabled Detection and Classification of Bacterial Colonies Using a Thin Film Transistor (TFT) Image Sensor” ACS Photonics...
Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers
Y. Li, Y. Luo, B. Bai, and A. Ozcan, “Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers,” IEEE Journal of Selected Topics in Quantum Electronics (IEEE JSTQE) (2022) DOI: 10.1109/JSTQE.2022.3194574– PDF
Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
H. Chen, L. Huang, T. Liu and A. Ozcan, “Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization,” Light: Science & Applications (2022) DOI: 10.1038/s41377-022-00949-8– PDF
Virtual stain transfer in histology via cascaded deep neural networks
X. Yang, B. Bai, Y. Li, Y. Zhang, T. Liu, K. de Haan, and A. Ozcan, “Virtual stain transfer in histology via cascaded deep neural networks” ACS Photonics (2022) DOI: 10.1021/acsphotonics.2c00932
Deep learning accelerates whole slide imaging for next generation digital pathology applications
Y. Rivenson and A. Ozcan, “Deep learning accelerates whole slide imaging for next generation digital pathology applications,” Light: Science & Applications (Nature Publishing Group) (2022) DOI:10.1038/s41377-022-00999-y
Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning
B. Bai, H. Wang, Y. Li, K. de Haan, F. Colonnese, Y. Wan, J. Zuo, N.B. Doan, X. Zhang, Y. Zhang, J. Li, X. Yang, W. Dong, M. Angus Darrow, E. Kamangar, H. Sung Lee, Y. Rivenson, A. Ozcan, “Label-free virtual HER2 immunohistochemical staining of breast tissue using...
Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks
Y. Zhang, L. Huang, T. Liu, K. Cheng, K. de Haan, Y. Li, B. Bai, and A. Ozcan, “Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks,” Intelligent Computing (AAAS) (2022) DOI: 10.34133/2022/9818965
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