375 citations
- University of Central FloridaUS6 papers
- New York UniversityUS5 papers
- Nokia (Finland)FI5 papers
- Columbia UniversityUS4 papers
- Massachusetts Institute of TechnologyUS4 papers
- Nokia (Germany)DE4 papers
- Providence CollegeUS4 papers
- University of Maryland, College ParkUS4 papers
- University of Southern CaliforniaUS4 papers
- Imperial College LondonGB3 papers
- The University of QueenslandAU3 papers
- University of TorontoCA3 papers
9 papers · 1 filter
Roadmap on spatiotemporal light fields
Yijie Shen, Qiwen Zhan, Logan G. Wright +42
Spatiotemporal sculpturing of light pulse with ultimately sophisticated structures represents the holy grail of the human everlasting pursue of ultrafast information transmission a…
Deep Learning for Multi-User Proactive Beam Handoff: A 6G Application
Faris B. Mismar, Alperen Gundogan, Aliye Ozge Kaya +1
This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enabl…
Single chip photonic deep neural network with accelerated training
Saumil Bandyopadhyay, Alexander Sludds, Stefan Krastanov +6
As deep neural networks (DNNs) revolutionize machine learning, energy consumption and throughput are emerging as fundamental limitations of CMOS electronics. This has motivated a s…
A multi-scale approach to microstructure-sensitive thermal fatigue in solder joints
Yilun Xu, Jingwei Xian, Stoyan Stoyanov +4
This paper presents a multi-scale modelling approach to investigate the underpinning mechanisms of microstructure-sensitive damage of single crystal Sn-3Ag-0.5Cu (wt%, SAC305) sold…
Energy Efficient HARQ for Ultrareliability via Novel Outage Probability Bound and Geometric Programming
Kaiming Shen, Wei Yu, Xihan Chen +1
Hybrid automatic repeat 1 request (HARQ) is a key enabler for ultrareliable communications. This paper optimizes transmit power for the initial transmission and the subsequent retr…
Delocalized Photonic Deep Learning on the Internet's Edge
Alexander Sludds, Saumil Bandyopadhyay, Zaijun Chen +13
Advances in deep neural networks (DNNs) are transforming science and technology. However, the increasing computational demands of the most powerful DNNs limit deployment on low-pow…