A5016216832- Digital Holography and Microscopy
- Image Processing Techniques and Applications
- Cell Image Analysis Techniques
- Microfluidic and Bio-sensing Technologies
- Orbital Angular Momentum in Optics
- Advanced Sensor and Control Systems
- Advanced Algorithms and Applications
- Advanced Optical Imaging Technologies
- Optical Polarization and Ellipsometry
- Cytomegalovirus and herpesvirus research
- Cosmology and Gravitation Theories
- Plasmonic and Surface Plasmon Research
- Advanced Vision and Imaging
- Viral Infections and Outbreaks Research
- Cold Atom Physics and Bose-Einstein Condensates
- Wireless Sensor Networks and IoT
- Astrophysical Phenomena and Observations
- Metamaterials and Metasurfaces Applications
- Black Holes and Theoretical Physics
- Adaptive optics and wavefront sensing
- Biosensors and Analytical Detection
Minzu University of China
2024
Zhejiang University
2022-2024
Zhejiang University of Technology
2022
Hunan Normal University
2021
University of California, Los Angeles
2018-2020
Aggregation-based assays, using micro- and nano-particles have been widely accepted as an efficient cost-effective bio-sensing tool, particularly in microbiology, where particle clustering events are used a metric to infer the presence of specific target analyte quantify its concentration. Here, we present sensitive automated readout method for aggregation-based assays wide-field lens-free on-chip microscope, with ability rapidly analyze microscopic aggregation 3D, deep learning-based...
Holographic imaging poses significant challenges when facing real-time disturbances introduced by dynamic environments. The existing deep-learning methods for holographic often depend solely on the specific condition based given data distributions, thus hindering their generalization across multiple scenes. One critical problem is how to guarantee alignment between any downstream tasks and pretrained models. We analyze physical mechanism of image degradation caused turbulence innovatively...
The low‐spatial‐coherence imaging capability of computer‐generated holography (CGH) is a key to high‐resolution displays, virtual reality, augmented and holographic microscopy. low spatial coherence caused by complex disturbances can damage the image quality irreversibly. optical field with has large fluctuations, making it difficult be quantified modeled directly. To tackle these challenges, deep neural network‐based model U‐residual dense network (U‐RDN) proposed, which obtains optimal...
We study the Maxwell quasinormal spectrum on asymptotically anti--de Sitter black holes with a set of two Robin type boundary conditions, by requiring energy flux to vanish at asymptotic infinity. Focusing, for illustrative purposes, Schwarzschild--anti--de both without and global monopole, we unveil that, one hand, bifurcates as hole radius increases which is termed mode split effect; while other an appropriate fixed but increasing monopole parameter, first (second) condition may trigger...
We propose a controllable exponential-Cosine Gaussian vortex (ECGV) beam, which can evolve into the different beam profiles with three parameters: distance modulation factor (DMF), split (SMF) and rotation (RMF). When SMF is 0, ECGV appears as perfect single-ring ring radius be adjusted by DMF. deduce from mathematics give reason for characteristics. not splits symmetrically. DMF, RMF control number, angle of split, respectively. Our experiments verify correctness theory.
Deep learning-based lensless holographic microscopy enables a high-throughput and rapid readout for particle-aggregation-based virus sensor, achieving high sensitivity of ~5 viral copies per μL. © 2019 The Author(s)
Get PDF Email Share with Facebook Tweet This Post on reddit LinkedIn Add to CiteULike Mendeley BibSonomy Citation Copy Text Y. Wu, A. Ray, Q. Wei, Feizi, X. Tong, E. Chen, Luo, and Ozcan, "Particle-Aggregation Based Virus Sensor Using Deep Learning Lensless Digital Holography," in Conference Lasers Electro-Optics, OSA Technical Digest (Optica Publishing Group, 2019), paper ATu4K.3. Export BibTex Endnote (RIS) HTML Plain alert Save article
We demonstrate an automatic, high-throughput and high-sensitivity particle aggregation-based sensor that uses wide-field, compact cost-effective lens-less microscopy, powered by deep neural networks. In this method, the post-reaction assay is imaged a snapshot hologram over wide field-of-view (20mm²). Using learning-based holographic reconstruction, all clusters are simultaneously reconstructed in ~30s. we demonstrated accurate rapid readout of immunoassay to detect herpes simplex virus,...