scholarly journals A Data Compression Hardware Accelerator Enabling Long-Term Biosignal Monitoring Based on Ultra-Low Power IoT Platforms

Electronics ◽  
2017 ◽  
Vol 6 (3) ◽  
pp. 54 ◽  
Author(s):  
Christos P. Antonopoulos ◽  
Nikolaos S. Voros
Chemosensors ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 41
Author(s):  
Hao Zeng ◽  
Guozhu Zhang ◽  
Kazuki Nagashima ◽  
Tsunaki Takahashi ◽  
Takuro Hosomi ◽  
...  

During the past two decades, one–dimensional (1D) metal–oxide nanowire (NW)-based molecular sensors have been witnessed as promising candidates to electrically detect volatile organic compounds (VOCs) due to their high surface to volume ratio, single crystallinity, and well-defined crystal orientations. Furthermore, these unique physical/chemical features allow the integrated sensor electronics to work with a long-term stability, ultra-low power consumption, and miniature device size, which promote the fast development of “trillion sensor electronics” for Internet of things (IoT) applications. This review gives a comprehensive overview of the recent studies and achievements in 1D metal–oxide nanowire synthesis, sensor device fabrication, sensing material functionalization, and sensing mechanisms. In addition, some critical issues that impede the practical application of the 1D metal–oxide nanowire-based sensor electronics, including selectivity, long-term stability, and low power consumption, will be highlighted. Finally, we give a prospective account of the remaining issues toward the laboratory-to-market transformation of the 1D nanostructure-based sensor electronics.


Author(s):  
Sebastian Marzetti ◽  
Valentin Gies ◽  
Valentin Barchasz ◽  
Paul Best ◽  
Sebastien Paris ◽  
...  
Keyword(s):  

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2211
Author(s):  
Suleman Khan ◽  
Jongbin Won ◽  
Junsik Shin ◽  
Junyoung Park ◽  
Jong-Woong Park ◽  
...  

Structural health monitoring (SHM) is crucial for quantitative behavioral analysis of structural members such as fatigue, buckling, and crack propagation identification. However, formerly developed approaches cannot be implemented effectively for long-term infrastructure monitoring, owing to power inefficiency and data management challenges. This study presents the development of a high-fidelity and ultra-low-power strain sensing and visualization module (SSVM), along with an effective data management technique. Deployment of 24-bit resolution analog to a digital converter and precise half-bridge circuit for strain sensing are two significant factors for efficient strain measurement and power management circuit incorporating a low-power microcontroller unit (MCU), and electronic-paper display (EPD) enabled long-term operation. A prototype for SSVM was developed that performs strain sensing and encodes the strain response in a QR code for visualization on the EPD. For efficient power management, SSVM only activated when the trigger-signal was generated and stayed in power-saving mode consuming 18 mA and 337.9 μA, respectively. The trigger-signal was designed to be generated either periodically by a timer or intentionally by a push-button. A smartphone application and cloud database were developed for efficient data acquisition and management. A lab-scale experiment was carried out to validate the proposed system with a reference strain sensing system. A cantilever beam was deflected by increasing load at its free end, and the resultant strain response of SSVM was compared with the reference. The proposed system was successfully validated to use for long-term static strain measurement.


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