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pro vyhledávání: '"Yu, Zhenming"'
To effectively mitigate the influence of atmospheric turbulence, a novel discrete-time analog transmission free-space optical (DTAT-FSO) communication scheme is proposed. It directly maps information sources to discrete-time analog symbols via joint
Externí odkaz:
http://arxiv.org/abs/2409.11928
Publikováno v:
56th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2022, pp. 1142-1146
Simulation frameworks such MemTorch, DNN+NeuroSim, and aihwkit are commonly used to facilitate the end-to-end co-design of memristive machine learning (ML) accelerators. These simulators can take device nonidealities into account and are integrated w
Externí odkaz:
http://arxiv.org/abs/2403.06746
Autor:
Yu, Zhenming, Yang, Ming-Jay, Finkbeiner, Jan, Siegel, Sebastian, Strachan, John Paul, Neftci, Emre
Publikováno v:
2024 AICAS, Abu Dhabi, United Arab Emirates, 2024, pp. 398-402
Memristive devices hold promise to improve the scale and efficiency of machine learning and neuromorphic hardware, thanks to their compact size, low power consumption, and the ability to perform matrix multiplications in constant time. However, on-ch
Externí odkaz:
http://arxiv.org/abs/2403.06712
Autor:
Bégon-Lours, Laura, Halter, Mattia, Popoff, Youri, Yu, Zhenming, Falcone, Donato Francesco, Offrein, Bert Jan
The persistent and switchable polarization of ferroelectric materials based on HfO$_2$-based ferroelectric compounds, compatible with large-scale integration, are attractive synaptic elements for neuromorphic computing. To achieve a record current de
Externí odkaz:
http://arxiv.org/abs/2309.12070
The current optical communication systems minimize bit or symbol errors without considering the semantic meaning behind digital bits, thus transmitting a lot of unnecessary information. We propose and experimentally demonstrate a semantic optical fib
Externí odkaz:
http://arxiv.org/abs/2212.14739
We consider using {\bf\em untrained neural networks} to solve the reconstruction problem of snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to capture a high-dimensional (usually 3D) data-cube in a compressed manner. Va
Externí odkaz:
http://arxiv.org/abs/2108.12654
Autor:
Zang, Yubin, Yu, Zhenming, Xu, Kun, Lan, Xingzeng, Chen, Minghua, Yang, Sigang, Chen, Hongwei
In this paper, a novel principle-driven fiber transmission model based on physical induced neural network (PINN) is proposed. Unlike data-driven models which regard fiber transmission problem as data regression tasks, this model views it as an equati
Externí odkaz:
http://arxiv.org/abs/2108.10734
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An intelligent optical performance monitor using multi-task learning based artificial neural network (MTL-ANN) is designed for simultaneous OSNR monitoring and modulation format identification (MFI). Signals' amplitude histograms (AHs) after constant
Externí odkaz:
http://arxiv.org/abs/1812.03792
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