Zobrazeno 1 - 10
of 3 222
pro vyhledávání: '"Beling, A."'
Autor:
Hu, Yaowen, Song, Yunxiang, Zhu, Xinrui, Guo, Xiangwen, Lu, Shengyuan, Zhang, Qihang, He, Lingyan, Franken, C. A. A., Powell, Keith, Warner, Hana, Assumpcao, Daniel, Renaud, Dylan, Wang, Ying, Magalhães, Letícia, Rosborough, Victoria, Shams-Ansari, Amirhassan, Li, Xudong, Cheng, Rebecca, Luke, Kevin, Yang, Kiyoul, Barbastathis, George, Zhang, Mian, Zhu, Di, Johansson, Leif, Beling, Andreas, Sinclair, Neil, Loncar, Marko
Here we show a photonic computing accelerator utilizing a system-level thin-film lithium niobate circuit which overcomes this limitation. Leveraging the strong electro-optic (Pockels) effect and the scalability of this platform, we demonstrate photon
Externí odkaz:
http://arxiv.org/abs/2411.02734
We demonstrate ultra-broadband optoelectronic mixing of frequency combs that provides phase-coherent detection of a repetition frequency up to 500 GHz, using a high-speed modified uni-traveling carrier (MUTC) photodiode. Nonlinear photo-electron effe
Externí odkaz:
http://arxiv.org/abs/2410.21768
Autor:
Abdeen, Zain ul, Roy, Padmaksha, Al-Tawaha, Ahmad, Jia, Rouxi, Freeman, Laura, Beling, Peter, Liu, Chen-Ching, Sangiovanni-Vincentelli, Alberto, Jin, Ming
There is an upward trend of deploying distributed energy resource management systems (DERMS) to control modern power grids. However, DERMS controller communication lines are vulnerable to cyberattacks that could potentially impact operational reliabi
Externí odkaz:
http://arxiv.org/abs/2405.02989
The machine learning formulation of online learning is incomplete from a systems theoretic perspective. Typically, machine learning research emphasizes domains and tasks, and a problem solving worldview. It focuses on algorithm parameters, features,
Externí odkaz:
http://arxiv.org/abs/2404.03775
This paper addresses the urgent need for messaging standards in the operational test and evaluation (T&E) of machine learning (ML) applications, particularly in edge ML applications embedded in systems like robots, satellites, and unmanned vehicles.
Externí odkaz:
http://arxiv.org/abs/2404.03769
Kerr optical frequency division with integrated photonics for stable microwave and mmWave generation
Autor:
Sun, Shuman, Harrington, Mark W., Tabatabaei, Fatemehsadat, Hanifi, Samin, Liu, Kaikai, Wang, Jiawei, Wang, Beichen, Yang, Zijiao, Liu, Ruxuan, Morgan, Jesse S., Bowers, Steven M., Morton, Paul A., Nelson, Karl D., Beling, Andreas, Blumenthal, Daniel J., Yi, Xu
Optical frequency division (OFD) has revolutionized microwave and mmWave generation and set spectral purity records owing to its unique capability to transfer high fractional stability from optical to electronic frequencies. Recently, rapid developme
Externí odkaz:
http://arxiv.org/abs/2402.11772
There is a lack of formalism for some key foundational concepts in systems engineering. One of the most recently acknowledged deficits is the inadequacy of systems engineering practices for engineering intelligent systems. In our previous works, we p
Externí odkaz:
http://arxiv.org/abs/2311.10786
Autor:
Sun, Shuman, Wang, Beichen, Liu, Kaikai, Harrington, Mark, Tabatabaei, Fatemehsadat, Liu, Ruxuan, Wang, Jiawei, Hanifi, Samin, Morgan, Jesse S., Jahanbozorgi, Mandana, Yang, Zijiao, Bowers, Steven, Morton, Paul, Nelson, Karl, Beling, Andreas, Blumenthal, Daniel, Yi, Xu
The generation of ultra-low noise microwave and mmWave in miniaturized, chip-based platforms can transform communication, radar, and sensing systems. Optical frequency division that leverages optical references and optical frequency combs has emerged
Externí odkaz:
http://arxiv.org/abs/2305.13575
Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data labeling but are heavily model-reliant. This has led to the inability o
Externí odkaz:
http://arxiv.org/abs/2302.14567
Autor:
Huang, Lanxiao, Cody, Tyler, Redino, Christopher, Rahman, Abdul, Kakkar, Akshay, Kushwaha, Deepak, Wang, Cheng, Clark, Ryan, Radke, Daniel, Beling, Peter, Bowen, Edward
Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detection routes (SDR). This work extends previous efforts on developing RL
Externí odkaz:
http://arxiv.org/abs/2211.03027