Zobrazeno 1 - 10
of 962
pro vyhledávání: '"LITTLE, BRENT"'
Autor:
Yang, Xinyu, Zhu, Xiaotian, Murray, Caitlin, Paryoonyong, Chawaphon, Xu, Xingyuan, Tan, Mengxi, Morandotti, Roberto, Little, Brent E., Moss, David J., Chu, Sai T., Corcoran, Bill, Su, Donglin
The deterministic generation of robust soliton comb has significant meaning for the optical frequency combs to be widely used in various applications. As a novel form of microcomb, Soliton crystal holds the advantages of easy generation, high convers
Externí odkaz:
http://arxiv.org/abs/2411.17069
Autor:
Sun, Yang, Wu, Jiayang, Li, Yang, Xu, Xingyuan, Ren, Guanghui, Tan, Mengxi, Chu, Sai Tak, Little, Brent E., Morandotti, Roberto, Mitchell, Arnan, Moss, David J.
Publikováno v:
Journal of Lightwave Technology Volume 41, (2023)
Microwave photonic (MWP) transversal signal processors offer a compelling solution for realizing versatile high-speed information processing by combining the advantages of reconfigurable electrical digital signal processing and high-bandwidth photoni
Externí odkaz:
http://arxiv.org/abs/2309.07155
Autor:
Mittal, Pardeep K., Little, Brent, Harri, Peter A., Miller, Frank H., Alexander, Lauren F., Kalb, Bobby, Camacho, Juan C., Master, Viraj, Hartman, Matthew, Moreno, Courtney C.
Infertility is defined herein as the inability to achieve pregnancy after frequently engaging in unprotected sexual intercourse for 1 year. Among infertile couples, the cause of infertility involves the male partner in approximately 50% of cases. Mal
Externí odkaz:
http://hdl.handle.net/10150/624637
http://arizona.openrepository.com/arizona/handle/10150/624637
http://arizona.openrepository.com/arizona/handle/10150/624637
Autor:
Li, Guangkuo, Li, Yuhua, Ye, Feng, Li, Qian, Wang, Shao Hao, Wetzel, Benjamin, Davidson, Roy, Little, Brent E., Chu, Sai Tak
We investigate the effect of a lower index oxide layer inclusion within a highly doped silica glass slot waveguide for optimized supercontinuum generation at telecom wavelengths. By controlling the thickness of the oxide slot, we demonstrate that one
Externí odkaz:
http://arxiv.org/abs/2304.00800
Autor:
Wang, Zhichuang, Shi, Lei, Hu, Xiaohong, Little, Brent E., Chu, Sai T., Wang, Weiqiang, Zhang, Wenfu
Publikováno v:
In Optics and Laser Technology January 2025 180
Autor:
Zippi, Zachary D., Cortopassi, Isabel O., Grage, Rolf A., Johnson, Elizabeth M., McCann, Matthew R., Mergo, Patricia J., Sonavane, Sushilkumar K., Stowell, Justin T., White, Richard D., Little, Brent P.
Publikováno v:
In Clinical Imaging September 2024 113
Autor:
Prayoonyong, Chawaphon, Boes, Andreas, Xu, Xingyuan, Tan, Mengxi, Chu, Sai T., Little, Brent E., Morandotti, Roberto, Mitchell, Arnan, Moss, David J., Corcoran, Bill
Publikováno v:
Journal of Lightwave Technology Volume 39 Early Access (2021)
Optical frequency combs can potentially provide an efficient light source for multi-terabit-per-second optical superchannels. However, as the bandwidth of these multi-wavelength light sources is increased, it can result in low per-line power. Optical
Externí odkaz:
http://arxiv.org/abs/2110.12848
Autor:
Li, Yuhua, Kang, Zhe, Zhu, Kun, Ai, Shiqi, Wang, Xiang, Davidson, Roy R., Wu, Yan, Morandotti, Roberto, Little, Brent E., Moss, David J., Chu, Sai Tak
Publikováno v:
Optics Letters Volume 46, Issue 7, pages 1574-1577 (2021)
We report an all-optical radio-frequency (RF) spectrum analyzer with a bandwidth greater than 5 terahertz (THz), based on a 50-cm long spiral waveguide in a CMOS-compatible high-index doped silica platform. By carefully mapping out the dispersion pro
Externí odkaz:
http://arxiv.org/abs/2103.10557
11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks
Autor:
Xu, Xingyuan, Tan, Mengxi, Corcoran, Bill, Wu, Jiayang, Boes, Andreas, Nguyen, Thach G., Chu, Sai T., Little, Brent E., Hicks, Damien G., Morandotti, Roberto, Mitchell, Arnan, Moss, David J.
Publikováno v:
Nature, Volume 589 Issue 7840. pages 44-51 (2021)
Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and e
Externí odkaz:
http://arxiv.org/abs/2011.07393