Miniaturized, wireless multi-channel potentiostat platform for wearable sensing and monitoring applications

2021 ◽  
Author(s):  
Fahmida Alam ◽  
Muhammad M. Hasan ◽  
Masudur R. Siddiquee ◽  
Shahrzad Forouzanfar ◽  
Ahmed H. Jalal ◽  
...  
Keyword(s):  
Wearable Sensing ◽  
Monitoring Applications

Real-Time Monitoring Applications for the Power Grid under Geomagnetic Disturbances

2020 ◽  
Author(s):  
Cecilia Klauber ◽  
Komal S. Shetye ◽  
Zeyu Mao ◽  
Thomas J. Overbye ◽  
Jennifer Gannon ◽  
...  
Keyword(s):  
Real Time ◽  
Power Grid ◽  
Real Time Monitoring ◽  
Geomagnetic Disturbances ◽  
Monitoring Applications

scholarly journals Fatigue Testing of Wearable Sensing Technologies: Issues and Opportunities

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4070
Author(s):  
Andrea Karen Persons ◽  
John E. Ball ◽  
Charles Freeman ◽  
David M. Macias ◽  
Chartrisa LaShan Simpson ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Prediction Models ◽  
Fatigue Testing ◽  
Low Cycle Fatigue ◽  
Wearable Sensors ◽  
Fatigue Tests ◽  
Proof Of Concept ◽  
Cycle Fatigue ◽  
Wearable Sensing ◽  
Failure Data

Standards for the fatigue testing of wearable sensing technologies are lacking. The majority of published fatigue tests for wearable sensors are performed on proof-of-concept stretch sensors fabricated from a variety of materials. Due to their flexibility and stretchability, polymers are often used in the fabrication of wearable sensors. Other materials, including textiles, carbon nanotubes, graphene, and conductive metals or inks, may be used in conjunction with polymers to fabricate wearable sensors. Depending on the combination of the materials used, the fatigue behaviors of wearable sensors can vary. Additionally, fatigue testing methodologies for the sensors also vary, with most tests focusing only on the low-cycle fatigue (LCF) regime, and few sensors are cycled until failure or runout are achieved. Fatigue life predictions of wearable sensors are also lacking. These issues make direct comparisons of wearable sensors difficult. To facilitate direct comparisons of wearable sensors and to move proof-of-concept sensors from “bench to bedside,” fatigue testing standards should be established. Further, both high-cycle fatigue (HCF) and failure data are needed to determine the appropriateness in the use, modification, development, and validation of fatigue life prediction models and to further the understanding of how cracks initiate and propagate in wearable sensing technologies.

scholarly journals A High-Accuracy and Power-Efficient Self-Optimizing Wireless Water Level Monitoring IoT Device for Smart City

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1936
Author(s):  
Tsun-Kuang Chi ◽  
Hsiao-Chi Chen ◽  
Shih-Lun Chen ◽  
Patricia Angela R. Abu
Keyword(s):  
Power Consumption ◽  
Water Level ◽  
Smart City ◽  
High Accuracy ◽  
Control Device ◽  
Power Efficient ◽  
Sensor Module ◽  
Monitoring Applications ◽  
Level Monitoring ◽  
Water Level Monitoring

In this paper, a novel self-optimizing water level monitoring methodology is proposed for smart city applications. Considering system maintenance, the efficiency of power consumption and accuracy will be important for Internet of Things (IoT) devices and systems. A multi-step measurement mechanism and power self-charging process are proposed in this study for improving the efficiency of a device for water level monitoring applications. The proposed methodology improved accuracy by 0.16–0.39% by moving the sensor to estimate the distance relative to different locations. Additional power is generated by executing a multi-step measurement while the power self-optimizing process used dynamically adjusts the settings to balance the current of charging and discharging. The battery level can efficiently go over 50% in a stable charging simulation. These methodologies were successfully implemented using an embedded control device, an ultrasonic sensor module, a LORA transmission module, and a stepper motor. According to the experimental results, the proposed multi-step methodology has the benefits of high accuracy and efficient power consumption for water level monitoring applications.

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