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Статті в журналах з теми "Permittivity reconstruction"

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Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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1

Khoshdel, Vahab, Ahmed Ashraf, and Joe LoVetri. "Enhancement of Multimodal Microwave-Ultrasound Breast Imaging Using a Deep-Learning Technique." Sensors 19, no. 18 (September 19, 2019): 4050. http://dx.doi.org/10.3390/s19184050.

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We present a deep learning method used in conjunction with dual-modal microwave-ultrasound imaging to produce tomographic reconstructions of the complex-valued permittivity of numerical breast phantoms. We also assess tumor segmentation performance using the reconstructed permittivity as a feature. The contrast source inversion (CSI) technique is used to create the complex-permittivity images of the breast with ultrasound-derived tissue regions utilized as prior information. However, imaging artifacts make the detection of tumors difficult. To overcome this issue we train a convolutional neural network (CNN) that takes in, as input, the dual-modal CSI reconstruction and attempts to produce the true image of the complex tissue permittivity. The neural network consists of successive convolutional and downsampling layers, followed by successive deconvolutional and upsampling layers based on the U-Net architecture. To train the neural network, the input-output pairs consist of CSI’s dual-modal reconstructions, along with the true numerical phantom images from which the microwave scattered field was synthetically generated. The reconstructed permittivity images produced by the CNN show that the network is not only able to remove the artifacts that are typical of CSI reconstructions, but can also improve the detectability of tumors. The performance of the CNN is assessed using a four-fold cross-validation on our dataset that shows improvement over CSI both in terms of reconstruction error and tumor segmentation performance.

2

Al Hosani, E., and M. Soleimani. "Multiphase permittivity imaging using absolute value electrical capacitance tomography data and a level set algorithm." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2070 (June 28, 2016): 20150332. http://dx.doi.org/10.1098/rsta.2015.0332.

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Multiphase flow imaging is a very challenging and critical topic in industrial process tomography. In this article, simulation and experimental results of reconstructing the permittivity profile of multiphase material from data collected in electrical capacitance tomography (ECT) are presented. A multiphase narrowband level set algorithm is developed to reconstruct the interfaces between three- or four-phase permittivity values. The level set algorithm is capable of imaging multiphase permittivity by using one set of ECT measurement data, so-called absolute value ECT reconstruction, and this is tested with high-contrast and low-contrast multiphase data. Simulation and experimental results showed the superiority of this algorithm over classical pixel-based image reconstruction methods. The multiphase level set algorithm and absolute ECT reconstruction are presented for the first time, to the best of our knowledge, in this paper and critically evaluated. This article is part of the themed issue ‘Supersensing through industrial process tomography’.

3

KIDERA, Shouhei. "Complex Permittivity Reconstruction for Microwave Imaging." Journal of the Visualization Society of Japan 40, no. 159 (2020): 22–25. http://dx.doi.org/10.3154/jvs.40.159_22.

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4

Beilina, Larisa, and Eric Lindström. "An Adaptive Finite Element/Finite Difference Domain Decomposition Method for Applications in Microwave Imaging." Electronics 11, no. 9 (April 24, 2022): 1359. http://dx.doi.org/10.3390/electronics11091359.

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A new domain decomposition method for Maxwell’s equations in conductive media is presented. Using this method, reconstruction algorithms are developed for the determination of the dielectric permittivity function using time-dependent scattered data of an electric field. All reconstruction algorithms are based on an optimization approach to find the stationary point of the Lagrangian. Adaptive reconstruction algorithms and space-mesh refinement indicators are also presented. Our computational tests show the qualitative reconstruction of the dielectric permittivity function using an anatomically realistic breast phantom.

5

Sena, Arcangelo G., and M. Nafi Toksöz. "Simultaneous reconstruction of permittivity and conductivity for crosshole geometries." GEOPHYSICS 55, no. 10 (October 1990): 1302–11. http://dx.doi.org/10.1190/1.1442777.

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We develop the theory and algorithm for the simultaneous inversion of permittivity and conductivity maps in the region between two boreholes in the earth. The three‐dimensional region is modeled as an inhomogeneous annulus of known thickness and height with radial and vertical variations of the electrical properties. The medium of interest is probed by a vertical magnetic dipole located on the axis of the cylindrical geometry and the receivers can be placed anywhere outside the inhomogeneous annulus. The inversion procedure is formulated in terms of a source‐type integral equation using monochromatic data. The integral equation is solved using an iterative approach where a Born approximation is applied at each iteration step. The nonuniqueness of the problem is overcome by imposing additional constraints on the solution using the method of regularization of Tikhonov. The distribution of electrical properties is obtained directly by this method. Numerical simulations, including multisource inversions, show that good results can be obtained for smoothly varying electrical properties, even for large contrast cases. In the case of rapid local variations of such properties, convergence can still be reached but at a slower rate and the reconstructions are smoothed versions of the original properties. In the presence of noise, the permittivity reconstruction is more robust than the conductivity reconstruction for the model considered here.

6

Yakovlev, Vadim V., Ethan K. Murphy, and E. Eugene Eves. "Neural networks for FDTD‐backed permittivity reconstruction." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 24, no. 1 (March 2005): 291–304. http://dx.doi.org/10.1108/03321640510571318.

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7

Moll, Jochen, Thomas N. Kelly, Dallan Byrne, Mantalena Sarafianou, Viktor Krozer, and Ian J. Craddock. "Microwave Radar Imaging of Heterogeneous Breast Tissue Integrating A Priori Information." International Journal of Biomedical Imaging 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/943549.

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Conventional radar-based image reconstruction techniques fail when they are applied to heterogeneous breast tissue, since the underlying in-breast relative permittivity is unknown or assumed to be constant. This results in a systematic error during the process of image formation. A recent trend in microwave biomedical imaging is to extract the relative permittivity from the object under test to improve the image reconstruction quality and thereby to enhance the diagnostic assessment. In this paper, we present a novel radar-based methodology for microwave breast cancer detection in heterogeneous breast tissue integrating a 3D map of relative permittivity as a priori information. This leads to a novel image reconstruction formulation where the delay-and-sum focusing takes place in time rather than range domain. Results are shown for a heterogeneous dense (class-4) and a scattered fibroglandular (class-2) numerical breast phantom using Bristol’s 31-element array configuration.

8

Ren, Shangjie, and Feng Dong. "Interface and permittivity simultaneous reconstruction in electrical capacitance tomography based on boundary and finite-elements coupling method." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2070 (June 28, 2016): 20150333. http://dx.doi.org/10.1098/rsta.2015.0333.

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Electrical capacitance tomography (ECT) is a non-destructive detection technique for imaging the permittivity distributions inside an observed domain from the capacitances measurements on its boundary. Owing to its advantages of non-contact, non-radiation, high speed and low cost, ECT is promising in the measurements of many industrial or biological processes. However, in the practical industrial or biological systems, a deposit is normally seen in the inner wall of its pipe or vessel. As the actual region of interest (ROI) of ECT is surrounded by the deposit layer, the capacitance measurements become weakly sensitive to the permittivity perturbation occurring at the ROI. When there is a major permittivity difference between the deposit and the ROI, this kind of shielding effect is significant, and the permittivity reconstruction becomes challenging. To deal with the issue, an interface and permittivity simultaneous reconstruction approach is proposed. Both the permittivity at the ROI and the geometry of the deposit layer are recovered using the block coordinate descent method. The boundary and finite-elements coupling method is employed to improve the computational efficiency. The performance of the proposed method is evaluated with the simulation tests. This article is part of the themed issue ‘Supersensing through industrial process tomography’.

9

Garnero, L., A. Franchois, J. P. Hugonin, C. Pichot, and N. Joachimowicz. "Microwave imaging-complex permittivity reconstruction-by simulated annealing." IEEE Transactions on Microwave Theory and Techniques 39, no. 11 (1991): 1801–7. http://dx.doi.org/10.1109/22.97480.

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10

Fang, Weifu. "Reconstruction of permittivity profile from boundary capacitance data." Applied Mathematics and Computation 177, no. 1 (June 2006): 178–88. http://dx.doi.org/10.1016/j.amc.2005.10.046.

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11

Magdum, A. D., M. Erramshetty, and R. P. K. Jagannath. "Fractional Regularized Distorted Born Iterative Method for Permittivity Reconstruction." Radioengineering 31, no. 1 (April 14, 2022): 62–68. http://dx.doi.org/10.13164/re.2022.0062.

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12

Habashy, Tarek M., Michael L. Oristaglio, and Adrianus T. de Hoop. "Simultaneous nonlinear reconstruction of two-dimensional permittivity and conductivity." Radio Science 29, no. 4 (July 1994): 1101–18. http://dx.doi.org/10.1029/93rs03448.

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13

Lee, J. M., S. Y. Kim, and J. W. Ra. "Spectral inverse technique for reconstruction of complex permittivity profiles." Electronics Letters 24, no. 9 (1988): 556. http://dx.doi.org/10.1049/el:19880378.

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14

Baganas, K., A. Kehagias, and A. Charalambopoulos. "Inhomogeneous Dielectric Media: Wave Propagation and Dielectric Permittivity Reconstruction." Journal of Electromagnetic Waves and Applications 15, no. 10 (January 2001): 1373–99. http://dx.doi.org/10.1163/156939301x01282.

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15

Lee, Kyeong-Soo, and Jung-Woong Ra. "Angular spectral inversion for reconstruction of complex permittivity profiles." Microwave and Optical Technology Letters 5, no. 8 (July 1992): 359–61. http://dx.doi.org/10.1002/mop.4650050805.

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16

Chen, Qian, Zhuo Long, Naoki Shinohara, and Changjun Liu. "A Substrate Integrated Waveguide Resonator Sensor for Dual-Band Complex Permittivity Measurement." Processes 10, no. 4 (April 5, 2022): 708. http://dx.doi.org/10.3390/pr10040708.

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This paper presents a novel dual-band substrate integrated waveguide (SIW) sensor that is designed to measure the complex permittivities of liquids or solid powders at two industrial, scientific, and medical (ISM) frequencies simultaneously. Resonant frequencies and quality factors are obtained from S-parameter measurements with the proposed SIW sensor, and applied to reconstructing the permittivities of materials under test through an artificial neural network. The water–ethanol mixed liquids were measured with the proposed sensor. The maximum deviations of the measured permittivities at 2.45 and 5.8 GHz are within 3% of literature results. The measurement by the proposed SIW sensor with artificial neural network reconstruction is accurate and efficient.

17

Handayani, Nita, Kharisma Fajar H, Freddy Haryanto, Siti Nurul K, Marlin R. Baidillah, and Warsito P. Taruno. "Simulasi Rekonstruksi Citra Pada Sensor Brain ECVT (Electrical Capacitance Volume Tomography) dengan Metode ILBP (Iterative Linear Back Projection)." INDONESIAN JOURNAL OF APPLIED PHYSICS 6, no. 02 (February 28, 2017): 107. http://dx.doi.org/10.13057/ijap.v6i02.1480.

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<p>The purpose of this study is to simulate the sensor 32-channel Brain ECVT image reconstruction using ILBP (Iterative Linear Back Projection) methods. ECVT is a dynamic volume imaging technique that utilizes non-linear difference of electric field distribution to determine the distribution of permittivity in the sensing area. ECVT has measured the capacitance of data as a result of changes in the permittivity distribution between the electrode pairs. ECVT device consists of three main parts: helmet-shaped sensors, DAS (Data Acquisition System), PC for display and image reconstruction process. Simulation of sensor design using COMSOL Multiphysics 3.5 software, while the process of image reconstruction and analysis of the results using Matlab software 2009a. The principle of ECVT includes two stages of data collection capacitance of electrodes (forward problem) and image reconstruction from the measured capacitance (inverse problem). In the study, the simulation of image reconstruction was done by varying the object position, the number of objects and charge density of the object. From the simulation results showed that the reconstructed image with ILBP method is influenced by several parameters: the object's position in the sensor,charge density value of the object, an alpha value and the number of iterations was selected.</p>

18

O’Loughlin, Declan, Bárbara L. Oliveira, Martin Glavin, Edward Jones, and Martin O’Halloran. "Comparing Radar-Based Breast Imaging Algorithm Performance with Realistic Patient-Specific Permittivity Estimation." Journal of Imaging 5, no. 11 (November 19, 2019): 87. http://dx.doi.org/10.3390/jimaging5110087.

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Radar-based breast imaging has shown promise as an imaging modality for early-stage cancer detection, and clinical investigations of two commercial imaging systems are ongoing. Many imaging algorithms have been proposed, which seek to improve the quality of the reconstructed microwave image to enhance the potential clinical decision. However, in many cases, the radar-based imaging algorithms have only been tested in limited numerical or experimental test cases or with simplifying assumptions such as using one estimate of permittivity for all patient test cases. In this work, the potential impact of patient-specific permittivity estimation on algorithm comparison is highlighted using representative experimental breast phantoms. In particular, the case studies presented help show that the permittivity estimate can impact the conclusions of the algorithm comparison. Overall, this work suggests that it is important that imaging algorithm comparisons use realistic test cases with and without breast abnormalities and with reconstruction permittivity estimation.

19

Wang, Hui, Shan Ouyang, Qinghua Liu, Kefei Liao, and Lijun Zhou. "Deep-Learning-Based Method for Estimating Permittivity of Ground-Penetrating Radar Targets." Remote Sensing 14, no. 17 (August 31, 2022): 4293. http://dx.doi.org/10.3390/rs14174293.

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Correctly estimating the relative permittivity of buried targets is crucial for accurately determining the target type, geometric size, and reconstruction of shallow surface geological structures. In order to effectively identify the dielectric properties of buried targets, on the basis of extracting the feature information of B-SCAN images, we propose an inversion method based on a deep neural network (DNN) to estimate the relative permittivity of targets. We first take the physical mechanism of ground-penetrating radar (GPR), working in the reflection measurement mode as the constrain condition, and then design a convolutional neural network (CNN) to extract the feature hyperbola of the underground target, which is used to calculate the buried depth of the target and the relative permittivity of the background medium. We further build a regression network and train the network model with the labeled sample set to estimate the relative permittivity of the target. Tests were carried out on the GPR simulation dataset and the field dataset of underground rainwater pipelines, respectively. The results show that the inversion method has high accuracy in estimating the relative permittivity of the target.

20

Rahman, M., and R. Marklein. "Time-Domain Techniques for Computation and Reconstruction of One-Dimensional Profiles." Advances in Radio Science 3 (May 12, 2005): 219–25. http://dx.doi.org/10.5194/ars-3-219-2005.

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Abstract. This paper presents a time-domain technique to compute the electromagnetic fields and to reconstruct the permittivity profile within a one-dimensional medium of finite length. The medium is characterized by a permittivity as well as conductivity profile which vary only with depth. The discussed scattering problem is thus one-dimensional. The modeling tool is divided into two different schemes which are named as the forward solver and the inverse solver. The task of the forward solver is to compute the internal fields of the specimen which is performed by Green’s function approach. When a known electromagnetic wave is incident normally on the media, the resulting electromagnetic field within the media can be calculated by constructing a Green’s operator. This operator maps the incident field on either side of the medium to the field at an arbitrary observation point. It is nothing but a matrix of integral operators with kernels satisfying known partial differential equations. The reflection and transmission behavior of the medium is also determined from the boundary values of the Green's operator. The inverse solver is responsible for solving an inverse scattering problem by reconstructing the permittivity profile of the medium. Though it is possible to use several algorithms to solve this problem, the invariant embedding method, also known as the layer-stripping method, has been implemented here due to the advantage that it requires a finite time trace of reflection data. Here only one round trip of reflection data is used, where one round trip is defined by the time required by the pulse to propagate through the medium and back again. The inversion process begins by retrieving the reflection kernel from the reflected wave data by simply using a deconvolution technique. The rest of the task can easily be performed by applying a numerical approach to determine different profile parameters. Both the solvers have been found to have the ability to deal with different types of slabs and incident electromagnetic pulses. Slabs having continuous and discontinuous relative permittivity have already been tested successfully. The tested electromagnetic pulses are a Dirac, Gaussian and sinusoidal pulse. Due to sampling, the resolution of the system also plays a significant role in obtaining better outputs from this scheme.

21

Huang, C. H., Y. F. Chen, and C. C. Chiu. "Permittivity Distribution Reconstruction of Dielectric Objects by a Cascaded Method." Journal of Electromagnetic Waves and Applications 21, no. 2 (January 1, 2007): 145–59. http://dx.doi.org/10.1163/156939307779378790.

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22

Franchois, A., and C. Pichot. "Microwave imaging-complex permittivity reconstruction with a Levenberg-Marquardt method." IEEE Transactions on Antennas and Propagation 45, no. 2 (1997): 203–15. http://dx.doi.org/10.1109/8.560338.

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23

Magdum, Amit, Mallikarjun Erramshetty, and Ravi Prasad K. Jagannath. "Regularized minimal residual method for permittivity reconstruction in microwave imaging." Microwave and Optical Technology Letters 62, no. 12 (June 9, 2020): 3682–94. http://dx.doi.org/10.1002/mop.32487.

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24

Wei Bing and Ge De-Biao. "Reconstruction of transverse permittivity and conductivity for a lossy anisotropic plate." Acta Physica Sinica 54, no. 2 (2005): 648. http://dx.doi.org/10.7498/aps.54.648.

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25

Gorriti, A. G., and E. C. Slob. "A new tool for accurate S-parameters measurements and permittivity reconstruction." IEEE Transactions on Geoscience and Remote Sensing 43, no. 8 (August 2005): 1727–35. http://dx.doi.org/10.1109/tgrs.2005.851163.

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26

Gorriti, A. G., and E. C. Slob. "Comparison of the different reconstruction techniques of permittivity from S-parameters." IEEE Transactions on Geoscience and Remote Sensing 43, no. 9 (September 2005): 2051–57. http://dx.doi.org/10.1109/tgrs.2005.854312.

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27

Zhang, Wenji, and Ahmad Hoorfar. "Reconstruction of Two-Dimensional Permittivity Distribution With Distorted Rytov Iterative Method." IEEE Antennas and Wireless Propagation Letters 10 (2011): 1072–75. http://dx.doi.org/10.1109/lawp.2011.2169643.

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28

Mikhnev, V. A., E. Nyfors, and P. Vainkainen. "Reconstruction of the permittivity profile using a nonlinear guided wave technique." IEEE Transactions on Antennas and Propagation 45, no. 9 (1997): 1405–10. http://dx.doi.org/10.1109/8.623130.

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29

Zaytsev, Kirill I., Nikita V. Chernomyrdin, and Valentin I. Alekhnovich. "Novel technique for medium permittivity profile reconstruction using THz pulsed spectroscopy." Journal of Physics: Conference Series 486 (March 18, 2014): 012010. http://dx.doi.org/10.1088/1742-6596/486/1/012010.

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30

Song, Yizhuang, and Jin Keun Seo. "Conductivity and Permittivity Image Reconstruction at the Larmor Frequency Using MRI." SIAM Journal on Applied Mathematics 73, no. 6 (January 2013): 2262–80. http://dx.doi.org/10.1137/130906842.

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31

Karchevsky, A. L., and V. A. Dedok. "Reconstruction of Permittivity from the Modulus of a Scattered Electric Field." Journal of Applied and Industrial Mathematics 12, no. 3 (July 2018): 470–78. http://dx.doi.org/10.1134/s1990478918030079.

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32

Mirjahanmardi, Seyed Hossein, Ali M. Albishi, and Omar M. Ramahi. "Permittivity Reconstruction of Nondispersive Materials Using Transmitted Power at Microwave Frequencies." IEEE Transactions on Instrumentation and Measurement 69, no. 10 (October 2020): 8270–78. http://dx.doi.org/10.1109/tim.2020.2988329.

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33

Yang, C. L., A. Mohammed, Y. Mohamadou, T. I. Oh, and M. Soleimani. "Complex conductivity reconstruction in multiple frequency electrical impedance tomography for fabric-based pressure sensor." Sensor Review 35, no. 1 (January 19, 2015): 85–97. http://dx.doi.org/10.1108/sr-03-2014-626.

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Purpose – The aim of this paper is to introduce and to evaluate the performance of a multiple frequency complex impedance reconstruction for fabric-based EIT pressure sensor. Pressure mapping is an important and challenging area of modern sensing technology. It has many applications in areas such as artificial skins in Robotics and pressure monitoring on soft tissue in biomechanics. Fabric-based sensors are being developed in conjunction with electrical impedance tomography (EIT) for pressure mapping imaging. This is potentially a very cost-effective pressure mapping imaging solution in particular for imaging large areas. Fabric-based EIT pressure sensors aim to provide a pressure mapping image using current carrying and voltage sensing electrodes attached on the boundary of the fabric patch. Design/methodology/approach – Recently, promising results are being achieved in conductivity imaging for these sensors. However, the fabric structure presents capacitive behaviour that could also be exploited for pressure mapping imaging. Complex impedance reconstructions with multiple frequencies are implemented to observe both conductivity and permittivity changes due to the pressure applied to the fabric sensor. Findings – Experimental studies on detecting changes of complex impedance on fabric-based sensor are performed. First, electrical impedance spectroscopy on a fabric-based sensor is performed. Secondly, the complex impedance tomography is carried out on fabric and compared with traditional EIT tank phantoms. Quantitative image quality measures are used to evaluate the performance of a fabric-based sensor at various frequencies and against the tank phantom. Originality/value – The paper demonstrates for the first time the useful information on pressure mapping imaging from the permittivity component of fabric EIT. Multiple frequency EIT reconstruction reveals spectral behaviour of the fabric-based EIT, which opens up new opportunities in exploration of these sensors.

34

Ijaz, U. Z., J. H. Kim, M. C. Kim, Sin Kim, J. W. Park, and K. Y. Kim. "Nondestructive Dynamic Process Monitoring Using Electrical Capacitance Tomography." Key Engineering Materials 321-323 (October 2006): 1671–74. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.1671.

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In this paper, we propose a dynamic Electrical Capacitance Tomography (ECT) image reconstruction algorithm based on the extended Kalman filter (EKF) to estimate the rapidly time-varying changes in the permittivity within the time taken to acquire a full set of independent measurement data. The ECT inverse problem is formulated as a state estimation problem in which the system is modeled with the state equation and the observation equation. Computer simulation with synthetic data is provided and comparison is done with existing modified Newton Raphson (mNR) method to illustrate the reconstruction performance of the proposed algorithm.

35

Wei, Bing, Fei Wang, and De-Biao Ge. "RECONSTRUCTION PERMITTIVITY TENSOR AND PRINCIPAL AXIS FOR UNIAXIAL MEDIUM IN MICROWAVE BAND." Progress In Electromagnetics Research M 6 (2009): 107–22. http://dx.doi.org/10.2528/pierm09021306.

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36

Fang, Weifu. "Multi-phase permittivity reconstruction in electrical capacitance tomography by level-set methods." Inverse Problems in Science and Engineering 15, no. 3 (April 2007): 213–47. http://dx.doi.org/10.1080/17415970600725078.

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37

Brovko, A. V., E. K. Murphy, and V. V. Yakovlev. "Waveguide Microwave Imaging: Neural Network Reconstruction of Functional 2-D Permittivity Profiles." IEEE Transactions on Microwave Theory and Techniques 57, no. 2 (February 2009): 406–14. http://dx.doi.org/10.1109/tmtt.2008.2011203.

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38

Golubkov, A. A., and V. A. Makarov. "Reconstruction of dielectric permittivity profile of a plate with strong frequency dispersion." Moscow University Physics Bulletin 64, no. 6 (December 2009): 658–60. http://dx.doi.org/10.3103/s0027134909060204.

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39

Chew, W. C., and Y. M. Wang. "Reconstruction of two-dimensional permittivity distribution using the distorted Born iterative method." IEEE Transactions on Medical Imaging 9, no. 2 (June 1990): 218–25. http://dx.doi.org/10.1109/42.56334.

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40

Jianglei Ma, Weng Cho Chew, Cai-Cheng Lu, and Jiming Song. "Image reconstruction from TE scattering data using equation of strong permittivity fluctuation." IEEE Transactions on Antennas and Propagation 48, no. 6 (June 2000): 860–67. http://dx.doi.org/10.1109/8.865217.

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41

Habashy, T. M., W. C. Chew, and E. Y. Chow. "Simultaneous reconstruction of permittivity and conductivity profiles in a radially inhomogeneous slab." Radio Science 21, no. 4 (July 1986): 635–45. http://dx.doi.org/10.1029/rs021i004p00635.

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42

Zheng, Jin, Jinku Li, Yi Li, and Lihui Peng. "A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography." Sensors 18, no. 11 (October 31, 2018): 3701. http://dx.doi.org/10.3390/s18113701.

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Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

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Ding, Ming-Hui, Hongyu Liu, and Guang-Hui Zheng. "Shape reconstructions by using plasmon resonances." ESAIM: Mathematical Modelling and Numerical Analysis 56, no. 2 (March 2022): 705–26. http://dx.doi.org/10.1051/m2an/2022021.

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We study the shape reconstruction of an inclusion from the faraway measurement of the associated electric field. This is an inverse problem of practical importance in biomedical imaging and is known to be notoriously ill-posed. By incorporating Drude’s model of the permittivity parameter, we propose a novel reconstruction scheme by using the plasmon resonance with a significantly enhanced resonant field. We conduct a delicate sensitivity analysis to establish a sharp relationship between the sensitivity of the reconstruction and the plasmon resonance. It is shown that when plasmon resonance occurs, the sensitivity functional blows up and hence ensures a more robust and effective construction. Then we combine the Tikhonov regularization with the Laplace approximation to solve the inverse problem, which is an organic hybridization of the deterministic and stochastic methods and can quickly calculate the minimizer while capture the uncertainty of the solution. We conduct extensive numerical experiments to illustrate the promising features of the proposed reconstruction scheme.

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Kazmin, Aleksandr I., and Pavel A. Fedjunin. "Evaluation of the accuracy of reconstruction of the electrophysical and geometric parameters of multilayer dielectric coatings by the multi-frequency radio wave method of a slow surface electromagnetic waves." Izmeritel`naya Tekhnika, no. 8 (2020): 51–58. http://dx.doi.org/10.32446/0368-1025it.2020-8-51-58.

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One of the most important diagnostic problems multilayer dielectric materials and coatings is the development of methods for quantitative interpretation of the checkout results their electrophysical and geometric parameters. The results of a study of the potential informativeness of the multi-frequency radio wave method of surface electromagnetic waves during reconstruction of the electrophysical and geometric parameters of multilayer dielectric coatings are presented. The simulation model is presented that makes it possible to evaluate of the accuracy of reconstruction of the electrophysical and geometric parameters of multilayer dielectric coatings. The model takes into account the values of the electrophysical and geometric parameters of the coating, the noise level in the measurement data and the measurement bandwidth. The results of simulation and experimental investigations of reconstruction of the structure of relative permittivitties and thicknesses of single-layer and double-layer dielectric coatings with different thicknesses, with different values of the standard deviation (RMS) of the noise level in the measured attenuation coefficients of the surface slow electromagnetic wave are presented. Coatings based on the following materials were investigated: polymethyl methacrylate, F-4D PTFE, RO3010. The accuracy of reconstruction of the electrophysical parameters of the layers decreases with an increase in the number of evaluated parameters and an increase in the noise level. The accuracy of the estimates of the electrophysical parameters of the layers also decreases with a decrease in their relative permittivity and thickness. The results of experimental studies confirm the adequacy of the developed simulation model. The presented model allows for a specific measuring complex that implements the multi-frequency radio wave method of surface electromagnetic waves, to quantify the potential possibilities for the accuracy of reconstruction of the electrophysical and geometric parameters of multilayer dielectric materials and coatings. Experimental investigations and simulation results of a multilayer dielectric coating demonstrated the theoretical capabilities gained relative error permittivity and thickness of the individual layers with relative error not greater than 10 %, with a measurement bandwidth of 1 GHz and RMS of noise level 0,003–0,004.

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Kryszyn, Jacek, and Waldemar Smolik. "TOOLBOX FOR 3D MODELLING AND IMAGE RECONSTRUCTION IN ELECTRICAL CAPACITANCE TOMOGRAPHY." Informatics Control Measurement in Economy and Environment Protection 7, no. 1 (March 30, 2017): 0. http://dx.doi.org/10.5604/01.3001.0010.4603.

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Electrical Capacitance Tomography is used to visualize a spatial distribution of electric permittivity in a tomographic sensor. ECT is able to create even thousands of frames per second which is suitable for application in the industry, e.g. monitoring of multiphase flows or material mixing. A tool for sensor modelling and image reconstruction is needed in order to develop improved solutions and to better understand phenomena in ECT. A software for 2D and 2D modelling is developed in the Division of Nuclear and Medical Electronics. In this paper a Matlab toolbox called ECTsim for 3D modelling is presented.

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Kandlbinder-Paret, Christoph, Alice Fischerauer, and Gerhard Fischerauer. "Dynamic water fill level measurement using a phantom-dependent adaptive electrical capacitance tomography (ECT) method." tm - Technisches Messen 88, no. 9 (April 17, 2021): 519–30. http://dx.doi.org/10.1515/teme-2021-0006.

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Abstract In electrical capacitance tomography (ECT), the resolution of the reconstructed permittivity distribution improves with the number of electrodes used whereas the number of capacitance measurements and the measurement time increases with the number of electrodes. To cope with this tradeoff, we present a phantom-dependent adaptation scheme in which coarse measurements are performed with terminal electrodes interconnected to form a synthetic electrode ring with fewer but larger electrodes. The concept was tested by observing the sloshing of water inside a pipe. We compare the reconstructed results based on eight synthetic electrodes, on 16 elementary electrodes, and on the adaptation scheme involving both the eight synthetic electrodes and some of the elementary capacitances. The reconstruction used the projected Landweber algorithm for capacitances determined by a finite-element simulation and for measured capacitances. The results contain artefacts attributed to the influence of the high permittivity of water compared to the low permittivity of the pipe wall. The adaptation scheme leads to nearly the same information as a full measurement of all 120 elementary capacitances but only requires the measurement of 30 % fewer capacitances. By detecting the fill level using a tomometric method, it can be determined within an uncertainty of 5 % FS.

47

Drobakhin, O. O., and S. G. Alexin. "RECONSTRUCTION OF PERMITTIVITY PROFILE OF STRATIFIED LOSSY DIELECTRIC USING NEWTON-KANTOROVICH ITERATIVE SCHEME." Telecommunications and Radio Engineering 69, no. 9 (2010): 815–37. http://dx.doi.org/10.1615/telecomradeng.v69.i9.60.

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48

Zaytsev, Kirill I., Valeriy E. Karasik, Irina N. Fokina, and Valentin I. Alekhnovich. "Invariant embedding technique for medium permittivity profile reconstruction using terahertz time-domain spectroscopy." Optical Engineering 52, no. 6 (June 18, 2013): 068203. http://dx.doi.org/10.1117/1.oe.52.6.068203.

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49

Chang, Liuyong, Boxuan Cui, Chenglin Zhang, Zheng Xu, Guangze Li, and Longfei Chen. "Monitoring and Characterizing the Flame State of a Bluff-Body Stabilized Burner by Electrical Capacitance Tomography." Processes 11, no. 8 (August 10, 2023): 2403. http://dx.doi.org/10.3390/pr11082403.

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Unstable combustion phenomena such as flame flashback, flame liftoff, extinction and blowout frequently take place during the operation of the bluff-body stabilized burner. Therefore, flame state monitoring is necessary for the safe operation of the bluff-body stabilized burner. In the present study, an electrical capacitance tomography (ECT) system was deployed to detect the permittivity distribution in the premixing channel and further characterize the flame states of stabilization, flashback, liftoff, extinction and blowout. A calderon-based reconstruction method was modified to reconstruct the permittivity distribution in the annular premixing channel. The detection results indicate that the permittivity in the premixing channel increases steeply when the flame flashback takes place and decreases obviously when the flame lifts off from the combustor rim. Based on the varied permittivity distribution at different flame states, a flame state index was proposed to characterize the flame state in quantification. The flame state index is 0, positive, in the range of −0.64–0, and lower than −0.64 when the flame is at the state of stable, flashback, liftoff and blowout, respectively. The flame state index at the flame state of extinction is the same as that at the flame state of liftoff. The extinction state and the blowout state can be distinguished by judging whether the flame flashback takes place before the flame is extinguished. These results reveal that the ECT system is capable of monitoring the flame state, and that the proposed flame state index can be used to characterize the flame state.

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Tong, Guowei, Shi Liu, and Sha Liu. "Computationally efficient image reconstruction algorithm for electrical capacitance tomography." Transactions of the Institute of Measurement and Control 41, no. 3 (May 9, 2018): 631–46. http://dx.doi.org/10.1177/0142331218763013.

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The electrical capacitance tomography (ECT) is a visualization measurement method and can reconstruct the spatial permittivity distribution information in a measurement domain based on given capacitance values, in which the effectiveness of the image reconstruction algorithm plays a vital role in real-world engineering applications. Unlike common imaging methods, within the framework of the Tikhonov regularization methodology and the transform-domain sparsity method, a new cost function encapsulating the wavelet-based sparsity constraint is proposed to model the ECT imaging problem. An iteration scheme that integrates the superiorities of the alternating direction method of multipliers algorithm and splits a complicated optimization problem into several simpler sub-problems is developed to seek for the optimal solution of the proposed cost function. Numerical experiments validate that the proposed imaging algorithm is practicable and effective, and can improve the reconstruction accuracy and robustness.