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1.
Opt Lett ; 46(7): 1696-1699, 2021 Apr 01.
Artículo en Inglés | MEDLINE | ID: mdl-33793521

RESUMEN

In this paper, a flexible multi-band linearly frequency modulated (LFM) signal generator based on a dual-polarization binary phase-shift keying (DP-BPSK) modulator is proposed and demonstrated by experiment. Two dual-drive Mach-Zehnder modulators of the DP-BPSK modulator are driven by a local oscillator (LO) signal and an LFM signal, respectively. By appropriately changing the phase difference introduced by a polarization controller, flexible multi-band LFM signals can be obtained after photoelectric conversion. The LO signal is phase modulated to eliminate self-heterodyne, which will affect the quality of the resulting signal.

2.
ACS Nano ; 16(5): 8358-8369, 2022 05 24.
Artículo en Inglés | MEDLINE | ID: mdl-35485406

RESUMEN

Flexible pressure sensors with high sensitivity over a broad pressure range are highly desired, yet challenging to build to meet the requirements of practical applications in daily activities and more significant in some extreme environments. This work demonstrates a thin, lightweight, and high-performance pressure sensor based on flexible porous phenyl-silicone/functionalized carbon nanotube (PS/FCNT) film. The formed crack-across-pore endows the pressure sensor with high sensitivity of 19.77 kPa-1 and 1.6 kPa-1 in the linear range of 0-33 kPa and 0.2-2 MPa, respectively, as well as ultralow detection limit (∼1.3 Pa). Furthermore, the resulting pressure sensor possesses a low fatigue over 4000 loading/unloading cycles even under a high pressure of 2 MPa and excellent durability (>6000 cycles) after heating at high temperature (200 °C), attributed to the strong chemical bonding between PS and FCNT, excellent mechanical stability, and high temperature resistance of PS/FCNT film. These superior properties set a foundation for applying the single sensor device in detecting diverse stimuli from the very low to high pressure range, including weak airflow, sway, vibrations, biophysical signal monitoring, and even car pressure. Besides, a deep neural network based on transformer (TRM) has been engaged for human action recognition with an overall classification rate of 94.96% on six human actions, offering high accuracy in real-time practical scenarios.


Asunto(s)
Nanotubos de Carbono , Dispositivos Electrónicos Vestibles , Humanos , Presión , Reconocimiento de Normas Patrones Automatizadas , Nanotubos de Carbono/química , Redes Neurales de la Computación
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