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| 1 | Surgical reconstruction of auricular relapsing polychondritis显示文摘 | Yiqun Z Ping Y Tianyi L | 2012 | Ann Plast Surg2012，68，3： | 1 |
| 2 | Blepharoptosis correction by excision of levator muscle and tarsus in Asians 显示文摘 | Tianyi L Haiyan S Fei L | 2010 | J Craniofac Surg2010，21，3： | 1 |
| 3 | Blepharoptosis Correction by Excision of Levator Muscle and Tarsus in Asians 显示文摘 | Tianyi L Haiyan S Fei L | 2010 | J Craniofac Surg2010，21，3： | 1 |
| 4 | Lateral Load Tests on a Two-Story Unreinforced Masonry Building显示文摘 | Tianyi Y Franklin L M Roberto T L | 2006 | Journal of Structural Engineering2006，132，5： | 1 |
| 5 | Analysis of a two-story unreinforced masonry building 显示文摘 | Yi Tianyi Moon F L Leon R T | 2005 | ACI Journal2005，，1： | 1 |
| 6 | Lateral load tests on a two-story unreinforced masonry building显示文摘 | Yi Tianyi Moon Franklin L Leon Roberto T | 2006 | Journal of Structural Engineering ASCE2006，132，5： | 1 |
| 7 | Blepharoptosis correction by excision of levator muscle and tarsus in Asians 显示文摘 | Tianyi L Haiyan S Fei L | 2010 | J Craniofac Surg2010，21，3： | 1 |
| 8 | Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding显示文摘Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning,our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, whereλ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media. | Yuhang Li Tianyi Gan Bijie Bai Cagatay Isıl Mona Jarrahi Aydogan Ozcan | 2023 | Advanced Photonics2023，5，4： | 0 |
| 9 | All-optical image denoising using a diffractive visual processor显示文摘Image denoising,one of the essential inverse problems,targets to remove noise/artifacts from input images.In general,digital image denoising algorithms,executed on computers,present latency due to several iterations implemented in,e.g.,graphics processing units(GPUs).While deep learning-enabled methods can operate non-iteratively,they also introduce latency and impose a significant computational burden,leading to increased power consumption.Here,we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images–implemented at the speed of light propagation within a thin diffractive visual processor that axially spans<250×λ,whereλis the wavelength of light.This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features,causing them to miss the output image Field-of-View(FoV)while retaining the object features of interest.Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of~30–40%.We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum.Owing to their speed,power-efficiency,and minimal computational overhead,all-optical diffractive denoisers can be transformative for various image display and projection systems,including,e.g.,holographic displays. | Çağatay Işıl Tianyi Gan Fazil Onuralp Ardic Koray Mentesoglu Jagrit Digani Huseyin Karaca Hanlong Chen Jingxi Li Deniz Mengu Mona Jarrahi Kaan Akşit Aydogan Ozcan | 2024 | Light(Science & Applications)2024，13，3： | 0 |
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