Study of removing the influence of microwave reflection in the time domain measurement data of the characteristic impedance of transmission line

Author(s):  
Zhu Jiangmiao ◽  
Li Chaoming ◽  
Miao Jingyuan
1982 ◽  
Vol 242 (2) ◽  
pp. H197-H202 ◽  
Author(s):  
J. P. Dujardin ◽  
D. N. Stone ◽  
C. D. Forcino ◽  
L. T. Paul ◽  
H. P. Pieper

Experiments were performed on eight anesthetized dogs to study the response of the characteristic impedance (Zc) of the main pulmonary artery to changes in circulating blood volume. Pressure and flow were measured in the proximal main pulmonary artery under control conditions, after hemorrhage (-15% of the estimated blood volume), again under control conditions, and finally after volume expansion (+30% of the estimated blood volume). Two different methods were used to determine Zc from these recordings. With the frequency-domain method values for Zc were obtained by averaging the input impedance moduli between 2 and 15 Hz. With the time-domain method Zc was derived as the slope of the early ejection pressure-flow relationship. The values for Zc obtained with the two methods were not statistically different. In the time-domain method the average increase in Zc with hemorrhage was 30.7 +/- 7.4 (SE) %, and the average decrease with volume expansion was -21.1 +/- 5.0 (SE) %. Because the time-domain method allowed the values of Zc during control conditions and after hemorrhage to be obtained in the same pressure range, it was concluded that the observed changes were caused by a change in the activity of the smooth muscle in the pulmonary arterial wall. Similarly, it was concluded that the decrease in Zc after volume expansion was active in nature.


IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 7241-7250 ◽  
Author(s):  
Yadong Liu ◽  
Gehao Sheng ◽  
Yue Hu ◽  
Yong Qian ◽  
Xiuchen Jiang ◽  
...  

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Wenji Zhang ◽  
Moeness G. Amin ◽  
Fauzia Ahmad ◽  
Ahmad Hoorfar ◽  
Graeme E. Smith

Compressive Sensing (CS) provides a new perspective for addressing radar applications requiring large amount of measurements and long data acquisition time; both issues are inherent in through-the-wall radar imaging (TWRI). Most CS techniques applied to TWRI consider stepped-frequency radar platforms. In this paper, the impulse radar two-dimensional (2D) TWRI problem is cast within the framework of CS and solved by the sparse constraint optimization performed on time-domain samples. Instead of the direct sampling of the time domain signal at the Nyquist rate, the Random Modulation Preintegration architecture is employed for the CS projection measurement, which significantly reduces the amount of measurement data for TWRI. Numerical results for point-like and spatially extended targets show that high-quality reliable TWRI based on the CS imaging approach can be achieved with a number of data points with an order of magnitude less than that required by conventional beamforming using the entire data volume.


1980 ◽  
Author(s):  
H. J. Price ◽  
R. H. St. John ◽  
D. E. Merewether

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. E161-E171 ◽  
Author(s):  
M. Zaslavsky ◽  
V. Druskin ◽  
A. Abubakar ◽  
T. Habashy ◽  
V. Simoncini

Transient data controlled-source electromagnetic measurements are usually interpreted via extracting few frequencies and solving the corresponding inverse frequency-domain problem. Coarse frequency sampling may result in loss of information and affect the quality of interpretation; however, refined sampling increases computational cost. Fitting data directly in the time domain has similar drawbacks, i.e., its large computational cost, in particular, when the Gauss-Newton (GN) algorithm is used for the misfit minimization. That cost is mainly comprised of the multiple solutions of the forward problem and linear algebraic operations using the Jacobian matrix for calculating the GN step. For large-scale 2.5D and 3D problems with multiple sources and receivers, the corresponding cost grows enormously for inversion algorithms using conventional finite-difference time-domain (FDTD) algorithms. A fast 3D forward solver based on the rational Krylov subspace (RKS) reduction algorithm using an optimal subspace selection was proposed earlier to partially mitigate this problem. We applied the same approach to reduce the size of the time-domain Jacobian matrix. The reduced-order model (ROM) is obtained by projecting a discretized large-scale Maxwell system onto an RKS with optimized poles. The RKS expansion replaces the time discretization for forward and inverse problems; however, for the same or better accuracy, its subspace dimension is much smaller than the number of time steps of the conventional FDTD. The crucial new development of this work is the space-time data compression of the ROM forward operator and decomposition of the ROM’s time-domain Jacobian matrix via chain rule, as a product of time- and space-dependent terms, thus effectively decoupling the discretizations in the time and parameter spaces. The developed technique can be equivalently applied to finely sampled frequency-domain data. We tested our approach using synthetic 2.5D examples of hydrocarbon reservoirs in the marine environment.


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