scholarly journals Nanoscale optical pulse limiter enabled by refractory metallic quantum wells

2020 ◽  
Vol 6 (20) ◽  
pp. eaay3456 ◽  
Author(s):  
Haoliang Qian ◽  
Shilong Li ◽  
Yingmin Li ◽  
Ching-Fu Chen ◽  
Wenfan Chen ◽  
...  

The past several decades have witnessed rapid development of high-intensity, ultrashort pulse lasers, enabling deeper laboratory investigation of nonlinear optics, plasma physics, and quantum science and technology than previously possible. Naturally, with their increasing use, the risk of accidental damage to optical detection systems rises commensurately. Thus, various optical limiting mechanisms and devices have been proposed. However, restricted by the weak optical nonlinearity of natural materials, state-of-the-art optical limiters rely on bulk liquid or solid media, operating in the transmission mode. Device miniaturization becomes complicated with these designs while maintaining superior integrability and controllability. Here, we demonstrate a reflection-mode pulse limiter (sub–100 nm) using nanoscale refractory films made of Al2O3/TiN/Al2O3 metallic quantum wells (MQWs), which provide large and ultrafast Kerr-type optical nonlinearities due to the quantum size effect of the MQW. Functional multilayers consisting of these MQWs could find important applications in nanophotonics, nonlinear optics, and meta-optics.

Author(s):  
Min Li ◽  
Cong Wang ◽  
Lude Wang ◽  
Han Zhang

The rapid development of photonic devices requires the exploration of novel materials with superior nonlinear optical (NLO) properties. Colloidal semiconductor nanocrystals (NCs) exhibit size-tunable exciton resonances and excellent NLO properties....


1993 ◽  
Author(s):  
Zhiwei Xu ◽  
Li X. Zheng ◽  
Jury V. Vandyshev ◽  
Gary W. Wicks ◽  
Philippe M. Fauchet ◽  
...  

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Haoliang Qian ◽  
Shilong Li ◽  
Ching-Fu Chen ◽  
Su-Wen Hsu ◽  
Steven Edward Bopp ◽  
...  

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4372 ◽  
Author(s):  
Yan Naung Soe ◽  
Yaokai Feng ◽  
Paulus Insap Santosa ◽  
Rudy Hartanto ◽  
Kouichi Sakurai

With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.


2003 ◽  
Vol 17 ◽  
pp. 313-315 ◽  
Author(s):  
E.I Rogacheva ◽  
O.N Nashchekina ◽  
T.V Tavrina ◽  
M Us ◽  
M.S Dresselhaus ◽  
...  

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