Real-time waveform modulator based on dispersion engineering of magnetic surface plasmons

2018 ◽  
Vol 123 (24) ◽  
pp. 245106
Author(s):  
Zhuoluo Wang ◽  
Jiafu Wang ◽  
Xin Wang ◽  
Jie Yang ◽  
Yaodong Zhao ◽  
...  
Author(s):  
Gabriela Tognini Saba ◽  
Vinicius Caldeira Quintão ◽  
Suely Pereira Zeferino ◽  
Claudia Marquez Simões ◽  
Rafael Ferreira Coelho ◽  
...  

2022 ◽  
Vol 25 (3) ◽  
pp. 28-33
Author(s):  
Francesco Restuccia ◽  
Tommaso Melodia

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.


1996 ◽  
Vol 19 (4) ◽  
pp. 418-430 ◽  
Author(s):  
PENG-WIE E. HSIA ◽  
SYLVIA FRERK ◽  
CYNTHIA A. ALLEN ◽  
ROBERT M. WISE ◽  
NERI M. COHEN ◽  
...  

Author(s):  
Martin Danneberg ◽  
Zhongju Li ◽  
Paul Kuhne ◽  
Ahmad Nimr ◽  
Shahab Ehsanfar ◽  
...  
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