A Pressure Sensing System with ±0.75 mmHg (3σ) Inaccuracy for Battery-Powered Low Power IoT Applications

2020 ◽  
Author(s):  
Seokhyeon Jeong ◽  
Yejoong Kim ◽  
Gyouho Kim ◽  
David Blaauw
Keyword(s):  
Low Power ◽  
Pressure Sensing ◽  
Iot Applications ◽  
Sensing System

Micro - GAGE: A Low-power Compact GAGE Hash Function Processor for IoT Applications

2020 ◽  
Author(s):  
Mohamed El-Hadedy ◽  
Martin Margala ◽  
Sergiu Mosanu ◽  
Danilo Gligoroski ◽  
Jinjun Xiong ◽  
...  
Keyword(s):  
Low Power ◽  
Hash Function ◽  
Iot Applications

PVT ‐compensated low‐voltage and low‐power CMOS LNA for IoT applications

2020 ◽  
Vol 30 (11) ◽  
Author(s):  
Sajad Nejadhasan ◽  
Fatemeh Zaheri ◽  
Ebrahim Abiri ◽  
Mohammad Reza Salehi
Keyword(s):  
Low Power ◽  
Low Voltage ◽  
Iot Applications ◽  
Low Power Cmos ◽  
Cmos Lna

scholarly journals Novel Application of a Multiscale Entropy Index as a Sensitive Tool for Detecting Subtle Vascular Abnormalities in the Aged and Diabetic

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Hsien-Tsai Wu ◽  
Men-Tzung Lo ◽  
Guan-Hong Chen ◽  
Cheuk-Kwan Sun ◽  
Jian-Jung Chen
Keyword(s):  
Glycosylated Hemoglobin ◽  
Reactive Hyperemia ◽  
Multiscale Entropy ◽  
Arterial Pulse ◽  
Vascular Changes ◽  
Entropy Index ◽  
Pressure Sensing ◽  
Vascular Abnormalities ◽  
Sensing System ◽  
Sensitive Tool

Although previous studies have shown the successful use of pressure-induced reactive hyperemia as a tool for the assessment of endothelial function, its sensitivity remains questionable. This study aims to investigate the feasibility and sensitivity of a novel multiscale entropy index (MEI) in detecting subtle vascular abnormalities in healthy and diabetic subjects. Basic anthropometric and hemodynamic parameters, serum lipid profiles, and glycosylated hemoglobin levels were recorded. Arterial pulse wave signals were acquired from the wrist with an air pressure sensing system (APSS), followed by MEI and dilatation index (DI) analyses. MEI succeeded in detecting significant differences among the four groups of subjects: healthy young individuals, healthy middle-aged or elderly individuals, well-controlled diabetic individuals, and poorly controlled diabetic individuals. A reduction in multiscale entropy reflected age- and diabetes-related vascular changes and may serve as a more sensitive indicator of subtle vascular abnormalities compared with DI in the setting of diabetes.

A Residual Phase Noise Compensation Method for IEEE 802.15.4 Compliant Dual-Mode Receiver for Diverse Low Power IoT Applications

2019 ◽  
Vol 6 (2) ◽  
pp. 3437-3447 ◽  
Author(s):  
Abdullah Zubair Mohammed ◽  
Ajay Kumar Nain ◽  
Jagadish Bandaru ◽  
Ajay Kumar ◽  
D. Santhosh Reddy ◽  
...  
Keyword(s):  
Low Power ◽  
Phase Noise ◽  
Ieee 802.15.4 ◽  
Compensation Method ◽  
Dual Mode ◽  
Noise Compensation ◽  
Iot Applications ◽  
Residual Phase

scholarly journals LifeChair: A Conductive Fabric Sensor-Based Smart Cushion for Actively Shaping Sitting Posture

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2261 ◽  
Author(s):  
Karlos Ishac ◽  
Kenji Suzuki
Keyword(s):  
Detection Accuracy ◽  
Upright Posture ◽  
Performance Study ◽  
Vibrotactile Feedback ◽  
Pressure Sensing ◽  
Sitting Posture ◽  
Conductive Fabric ◽  
The Past ◽  
Sensing System ◽  
Average Accuracy

The LifeChair is a smart cushion that provides vibrotactile feedback by actively sensing and classifying sitting postures to encourage upright posture and reduce slouching. The key component of the LifeChair is our novel conductive fabric pressure sensing array. Fabric sensors have been explored in the past, but a full sensing solution for embedded real world use has not been proposed. We have designed our system with commercial use in mind, and as a result, it has a high focus on manufacturability, cost-effectiveness and adaptiveness. We demonstrate the performance of our fabric sensing system by installing it into the LifeChair and comparing its posture detection accuracy with our previous study that implemented a conventional flexible printed PCB-sensing system. In this study, it is shown that the LifeChair can detect all 11 postures across 20 participants with an improved average accuracy of 98.1%, and it demonstrates significantly lower variance when interfacing with different users. We also conduct a performance study with 10 participants to evaluate the effectiveness of the LifeChair device in improving upright posture and reducing slouching. Our performance study demonstrates that the LifeChair is effective in encouraging users to sit upright with an increase of 68.1% in time spent seated upright when vibrotactile feedback is activated.

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