High-Accuracy Complex Permittivity Characterization of Solid Materials Using Parallel Interdigital Capacitor-Based Planar Microwave Sensor

2020 ◽  
pp. 1-1
Author(s):  
Cong Wang ◽  
Luqman Ali ◽  
Fan-Yi Meng ◽  
Kishor Kumar Adhikari ◽  
Zhong-Liang Zhou ◽  
...  
Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3385
Author(s):  
Jialu Ma ◽  
Jingchao Tang ◽  
Kaicheng Wang ◽  
Lianghao Guo ◽  
Yubin Gong ◽  
...  

A complex permittivity characterization method for liquid samples has been proposed. The measurement is carried out based on a self-designed microwave sensor with a split ring resonator (SRR), the unload resonant frequency of which is 5.05 GHz. The liquid samples in capillary are placed in the resonant zone of the fabricated senor for high sensitivity measurement. The frequency shift of 58.7 MHz is achieved when the capillary is filled with ethanol, corresponding a sensitivity of 97.46 MHz/μL. The complex permittivity of methanol, ethanol, isopropanol (IPA) and deionized water at the resonant frequency are measured and calibrated by the first order Debye model. Then, the complex permittivity of different concentrations of aqueous solutions of these materials are measured by using the calibrated sensor system. The results show that the proposed sensor has high sensitivity and accuracy in measuring the complex permittivity of liquid samples with volumes as small as 0.13 μL. It provides a useful reference for the complex permittivity characterization of small amount of liquid chemical samples. In addition, the characterization of an important biological sample (inositol) is carried out by using the proposed sensor.


Author(s):  
Luqman Ali ◽  
Cong Wang ◽  
Fan-Yi Meng ◽  
Kishor Kumar Adhikari ◽  
Yu-Chen Wei ◽  
...  

2021 ◽  
pp. 1-1
Author(s):  
Luqman Ali ◽  
Cong Wang ◽  
Fan-Yi Meng ◽  
Kishor Kumar Adhikari ◽  
Yu-Chen Wei ◽  
...  

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 62779-62787
Author(s):  
Ammar Armghan ◽  
Turki M. Alanazi ◽  
Ahsan Altaf ◽  
Tanveerul Haq

2021 ◽  
pp. 1-1
Author(s):  
Yukang Chen ◽  
Jie Huang ◽  
Yuhan Xiang ◽  
Linglong Fu ◽  
Wenwen Gu ◽  
...  

2006 ◽  
Vol 38 (7) ◽  
pp. 575-582
Author(s):  
O. M. Diaz ◽  
J. Prat ◽  
I. Tafur Monroy ◽  
H. de Waardt
Keyword(s):  

2019 ◽  
Vol 3 (2) ◽  
pp. 363-383 ◽  
Author(s):  
Lisa Byrge ◽  
Daniel P. Kennedy

Connectome fingerprinting—a method that uses many thousands of functional connections in aggregate to identify individuals—holds promise for individualized neuroimaging. A better characterization of the features underlying successful fingerprinting performance—how many and which functional connections are necessary and/or sufficient for high accuracy—will further inform our understanding of uniqueness in brain functioning. Thus, here we examine the limits of high-accuracy individual identification from functional connectomes. Using ∼3,300 scans from the Human Connectome Project in a split-half design and an independent replication sample, we find that a remarkably small “thin slice” of the connectome—as few as 40 out of 64,620 functional connections—was sufficient to uniquely identify individuals. Yet, we find that no specific connections or even specific networks were necessary for identification, as even small random samples of the connectome were sufficient. These results have important conceptual and practical implications for the manifestation and detection of uniqueness in the brain.


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