Flexible and Anisotropic Strain Sensors with the Asymmetrical Cross-Conducting Network for Versatile Bio-Mechanical Signal Recognition

2021 ◽  
Vol 13 (37) ◽  
pp. 44925-44934
Author(s):  
Guanjun Zhu ◽  
Penggang Ren ◽  
Jie Hu ◽  
Junjun Yang ◽  
Yangpeng Jia ◽  
...  
Keyword(s):  
Strain Sensors ◽  
Signal Recognition ◽  
Anisotropic Strain ◽  
Mechanical Signal

Near‐Field Electrospinning Enabled Highly Sensitive and Anisotropic Strain Sensors

2020 ◽  
Vol 5 (11) ◽  
pp. 2000550
Author(s):  
Yan Huang ◽  
Xiangyu You ◽  
Xiangyu Fan ◽  
Ching Ping Wong ◽  
Ping Guo ◽  
...  
Keyword(s):  
Near Field ◽  
Strain Sensors ◽  
Anisotropic Strain ◽  
Highly Sensitive

Methodology of ECG Interpretation in the AVA Program

1990 ◽  
Vol 29 (04) ◽  
pp. 337-340 ◽  
Author(s):  
H. A. Pipberger ◽  
H. V. Pipberger ◽  
C. D. McManus
Keyword(s):  
Right Atrial ◽  
Signal Recognition ◽  
Multivariate Statistical ◽  
Reference Base ◽  
T Wave ◽  
Electrolyte Disturbances ◽  
Ecg Criteria ◽  
Wall Injury ◽  
Ecg Leads ◽  
Diagnostic Module

AbstractThe AVA program combines a thirty-year history with an approach that remains innovative; namely: multivariate statistical analysis on orthogonal ECG leads. Its diagnostic reference base includes only diagnoses independently verified by non-ECG criteria. The diagnostic module assesses probabilities of nine alternative disease categories, based on QRS-T parameters; or four other categories in case of conduction defects. Probabilities of left or right atrial overload are also computed. The program also recognizes wall injury, T-wave abnormalities, electrolyte disturbances, myocardial ischemia, and makes differential diagnoses between strain and digitalis effects. An arrhythmia classification module can generate any of 40 rhythm statements. Signal recognition is based on the spatial velocity function. The program has been translated to a microcomputer version.

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

Modeling of Contact Patch in Dual-Chamber Pneumatic Tires

2018 ◽  
Vol 46 (2) ◽  
pp. 78-92 ◽  
Author(s):  
A. I. Kubba ◽  
G. J. Hall ◽  
S. Varghese ◽  
O. A. Olatunbosun ◽  
C. J. Anthony
Keyword(s):  
Strain Sensors ◽  
Surface Strain ◽  
Wireless Data ◽  
Data Logger ◽  
Dual Chamber ◽  
Piezoelectric Polymer ◽  
Pure Rolling ◽  
Piezoelectric Elements ◽  
Strain Data ◽  
Deformation Patterns

ABSTRACT This study presents an investigation of the inner tire surface strain measurement by using piezoelectric polymer transducers adhered on the inner liner of the tire, acting as strain sensors in both conventional and dual-chamber tires. The piezoelectric elements generate electrical charges when strain is applied. The inner liner tire strain can be found from the generated charge. A wireless data logger was employed to measure and transmit the measured signals from the piezoelectric elements to a PC to store and display the readout signals in real time. The strain data can be used as a monitoring system to recognize tire-loading conditions (e.g., traction, braking, and cornering) in smart tire technology. Finite element simulations, using ABAQUS, were employed to estimate tire deformation patterns in both conventional and dual-chamber tires for pure rolling and steady-state cornering conditions for different inflation pressures to simulate on-road and off-road riding tire performances and to compare with the experimental results obtained from both the piezoelectric transducers and tire test rig.

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