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Artykuły w czasopismach na temat „Impedance reconstruction”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Basak, Rinku, and Khan A. Wahid. "A Rapid, Low-Cost, and High-Precision Multifrequency Electrical Impedance Tomography Data Acquisition System for Plant Phenotyping." Remote Sensing 14, no. 13 (2022): 3214. http://dx.doi.org/10.3390/rs14133214.

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Plant phenotyping plays an important role for the thorough assessment of plant traits such as growth, development, and physiological processes with the target of achieving higher crop yields by the proper crop management. The assessment can be done by utilizing two- and three-dimensional image reconstructions of the inhomogeneities. The quality of the reconstructed image is required to maintain a high accuracy and a good resolution, and it is desirable to reconstruct the images with the lowest possible noise. In this work, an electrical impedance tomography (EIT) data acquisition system is dev

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Tchórzewski, Paweł, Małgorzata Lalak - Dybała, Bartosz Przysucha, and Paweł Olszewski. "Use of electrical impedance tomography for lung volume reconstruction." Journal of Modern Science 57, no. 3 (2024): 622–36. http://dx.doi.org/10.13166/jms/191357.

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The article presents a study of the application of electro-impedance tomography (EIT) in diagnosing lung capacity using the Tikhonov regularization method. The possibility of reconstructing the lungs to monitor the degree of air filling was investigated. The experiment included a series of tests using a torso phantom designed to simulate different states of the lungs - from fully inflated to fully deflated. Lung-filling states were manipulated in controlled scenarios to test nine main experimental conditions reflecting different lung-filling states. In addition, the quality of reconstruction w

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3

Wang, Qi, Pengcheng Zhang, Jianming Wang, et al. "Patch-based sparse reconstruction for electrical impedance tomography." Sensor Review 37, no. 3 (2017): 257–69. http://dx.doi.org/10.1108/sr-07-2016-0126.

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Purpose Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction for EIT is a nonlinear problem. A generalized inverse operator is usually ill-posed and ill-conditioned. Therefore, the solutions for EIT are not unique and highly sensitive to the measurement noise. Design/methodology/approach This paper develops a novel image reconstruction algorithm for EIT based on patch-based sparse representation. The sparsifying dictionary opti

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4

Zheng, Hai-Ying, Yang Li, Nan Wang, et al. "A novel framework for three-dimensional electrical impedance tomography reconstruction of maize ear via feature reconfiguration and residual networks." PeerJ Computer Science 10 (April 11, 2024): e1944. http://dx.doi.org/10.7717/peerj-cs.1944.

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Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob. Therefore, an effective method for 3D conductivity reconstruction is necessary. In practical applications, fluctuations in the contact impedance of the maize ear occur, particularly with the increase

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5

Nguyen Diep, Quoc Tuan, Hoang Nhut Huynh, Thanh Ven Huynh, Minh Quan Cao Dinh, Anh Tu Tran, and Trung Nghia Tran. "Impact of ISTA and FISTA iterative optimization algorithms on electrical impedance tomography image reconstruction." Journal of Electrical Bioimpedance 16, no. 1 (2025): 11–22. https://doi.org/10.2478/joeb-2025-0003.

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Abstract Electrical Impedance Tomography (EIT) is a non-invasive method for imaging conductivity distributions within a target area. The inverse problem associated with EIT is nonlinear and ill-posed, leading to low spatial resolution reconstructions. Iterative algorithms are widely employed to address complex inverse problems. However, current iterative methods have notable limitations, such as the arbitrary and subjective selection of initial parameters, lengthy computational times due to numerous iterations, and the generation of reconstructions that suffer from shape blurring and a lack of

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6

Azzouz, Mustapha, Martin Hanke, Chantal Oesterlein, and Karl Schilcher. "The Factorization Method for Electrical Impedance Tomography Data from a New Planar Device." International Journal of Biomedical Imaging 2007 (2007): 1–7. http://dx.doi.org/10.1155/2007/83016.

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We present numerical results for two reconstruction methods for a new planar electrical impedance tomography device. This prototype allows noninvasive medical imaging techniques if only one side of a patient is accessible for electric measurements. The two reconstruction methods have different properties: one is a linearization-type method that allows quantitative reconstructions; the other one, that is, the factorization method, is a qualitative one, and is designed to detect anomalies within the body.

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7

Cieplak, Tomasz, Tomasz Rymarczyk, and Grzegorz Kłosowski. "USING MICROSERVICES ARCHITECTURE AS ANALYTICAL SYSTEM FOR ELECTRICAL IMPEDANCE TOMOGRAPHY IMAGING." Informatics Control Measurement in Economy and Environment Protection 8, no. 1 (2018): 52–55. http://dx.doi.org/10.5604/01.3001.0010.8652.

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An image reconstruction with use of EIT method has been found useful in many areas of medical, industrial and environmental applications. Papers show that computational systems used for image reconstructions are utilizing parallel and distributed computations and multi-tier architecture, as well as monolithic architecture. The aim of our research is to define an analytical system architecture that will be able to combine a variety of image reconstruction algorithms with their representations in different programming languages. Based on examples described in different proceedings and research p

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8

Chen, Rongqing, Sabine Krueger-Ziolek, Alberto Battistel, Stefan J. Rupitsch, and Knut Moeller. "Effect of a Patient-Specific Structural Prior Mask on Electrical Impedance Tomography Image Reconstructions." Sensors 23, no. 9 (2023): 4551. http://dx.doi.org/10.3390/s23094551.

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Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within

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9

Padilha Leitzke, Juliana, and Hubert Zangl. "Low-power electrical impedance tomography spectroscopy." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 5 (2019): 1480–92. http://dx.doi.org/10.1108/compel-12-2018-0530.

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Purpose This paper aims to present an approach based on electrical impedance tomography spectroscopy (EITS) for the determination of water and ice fraction in low-power applications such as autarkic wireless sensors, which require a low computational complexity reconstruction approach and a low number of electrodes. This paper also investigates how the electrode design can affect the reconstruction results in tomography. Design/methodology/approach EITS is performed by using a non-iterative method called optimal first order approximation. In addition to that, a planar electrode geometry is use

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10

Grayr, Aleksanyan, Katsupeev Andrey, Sulyz Andrey, Pyatnitsin Stanislav, and Peregorodiev Danil. "DEVELOPMENT OF THE WEB PORTAL FOR RESEARCH SUPPORT IN THE AREA OF ELECTRICAL IMPEDANCE TOMOGRAPHY." Eastern-European Journal of Enterprise Technologies 6, no. 2 (102) (2019): 6–15. https://doi.org/10.15587/1729-4061.2019.184318.

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The concept of electrical impedance tomography is considered. Modern software solutions implementing the methods and algorithms of electrical impedance tomography are studied. It is concluded that existing solutions for applied research and development in the field of electrical impedance tomography either do not implement differential reconstruction methods, or do not provide multi-user access. This imposes a number of limitations when conducting research and creates barriers to obtaining new results in the field of electrical impedance tomography. Given the current state of development of sc

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11

Yang, Fei, Jie Zhang, and Robert Patterson. "Development of an Anatomically Realistic Forward Solver for Thoracic Electrical Impedance Tomography." Journal of Medical Engineering 2013 (March 24, 2013): 1–7. http://dx.doi.org/10.1155/2013/983938.

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Electrical impedance tomography (EIT) has the potential to provide a low cost and safe imaging modality for clinically monitoring patients being treated with mechanical ventilation. Variations in reconstruction algorithms at different clinical settings, however, make interpretation of regional ventilation across institutions difficult, presenting the need for a unified algorithm for thoracic EIT reconstruction. Development of such a consensual reconstruction algorithm necessitates a forward model capable of predicting surface impedance measurements as well as electric fields in the interior of

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12

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 (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 partic

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13

Stefańczak, Daria, Marcin Dziadosz, Katarzyna Iskra, and Piotr Bednarczuk. "3D modeling of the urinary bladder using electrical impedance tomography: advanced reconstruction algorithms and medical applications." Journal of Modern Science 57, no. 3 (2024): 713–22. http://dx.doi.org/10.13166/jms/191398.

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Purpose: The research presented in this paper was conducted to obtain a detailed 3D model of the urinary bladder using electrical impedance tomography, a noninvasive tomographic technique. Methods: Electrical impedance tomography (EIT) is an imaging technique that measures electrical impedance inside the human body. Many methods, including those based on physical models and machine learning, are used to reconstruct the considered 3D object using EIT. The work focuses on the Gauss-Newton algorithm in its generalized form. Results: Three-dimensional reconstructions of the urinary bladder were ob

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14

Battistel, Alberto, Jack Wilkie, Rongqing Chen, and Knut Möller. "Multifrequency image reconstruction for electrical impedance tomography." Current Directions in Biomedical Engineering 10, no. 4 (2024): 61–65. https://doi.org/10.1515/cdbme-2024-2015.

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Abstract Electrical Impedance Tomography (EIT) is a medical imaging technique that is primarily used to monitor the respiration of a patient. Because EIT is based on electrical measurements, it is a safe, non-invasive, and cost-effective imaging technique. However, the EIT image reconstruction is a severely ill-posed problem that gives low spatial resolution where only large variations in tissue conductivity can be visualized. Furthermore, widely used time difference EIT relies on a single frequency alternating current measurement which does not allow for discrimination of different tissues on

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15

Theertham, Ganesh Teja, Santhosh Kumar Varanasi, and Phanindra Jampana. "Sparsity Constrained Reconstruction for Electrical Impedance Tomography." IFAC-PapersOnLine 53, no. 2 (2020): 355–60. http://dx.doi.org/10.1016/j.ifacol.2020.12.185.

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Adler, Andy, Tao Dai, and William R. B. Lionheart. "Temporal image reconstruction in electrical impedance tomography." Physiological Measurement 28, no. 7 (2007): S1—S11. http://dx.doi.org/10.1088/0967-3334/28/7/s01.

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Barber, D. C. "Image reconstruction problems in electrical impedance tomography." Clinical Physics and Physiological Measurement 11, no. 2 (1990): 181–82. http://dx.doi.org/10.1088/0143-0815/11/2/112.

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Qin, Hai-Hua, and Ji-Chuan Liu. "Reconstruction for cavities with impedance boundary condition." Journal of Integral Equations and Applications 25, no. 3 (2013): 431–54. http://dx.doi.org/10.1216/jie-2013-25-3-431.

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Li, Taoran, David Isaacson, Jonathan C. Newell, and Gary J. Saulnier. "Adaptive techniques in electrical impedance tomography reconstruction." Physiological Measurement 35, no. 6 (2014): 1111–24. http://dx.doi.org/10.1088/0967-3334/35/6/1111.

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Yorkey, Thomas J., John G. Webster, and Willis J. Tompkins. "Comparing Reconstruction Algorithms for Electrical Impedance Tomography." IEEE Transactions on Biomedical Engineering BME-34, no. 11 (1987): 843–52. http://dx.doi.org/10.1109/tbme.1987.326032.

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Yu, Fang-Ming, Chen-Ning Huang, Fang-Wei Chang, and Hung-Yuan Chung. "A Rotative Electrical Impedance Tomography Reconstruction System." Journal of Physics: Conference Series 48 (October 1, 2006): 542–49. http://dx.doi.org/10.1088/1742-6596/48/1/102.

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Ts, Munkh-Erdene, Eunjung Lee, Jin Keun Seo, Bastian Harrach, and Sungwhan Kim. "Projective Electrical Impedance Reconstruction with Two Measurements." SIAM Journal on Applied Mathematics 73, no. 4 (2013): 1659–75. http://dx.doi.org/10.1137/120879671.

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Bourgeois, Laurent, Nicolas Chaulet, and Houssem Haddar. "Stable reconstruction of generalized impedance boundary conditions." Inverse Problems 27, no. 9 (2011): 095002. http://dx.doi.org/10.1088/0266-5611/27/9/095002.

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Dong, G. Y., H. Endo, S. Hayano, S. K. Gao, and Y. Saito. "GVSPM for reconstruction in electrical impedance tomography." IEEE Transactions on Magnetics 39, no. 3 (2003): 1630–33. http://dx.doi.org/10.1109/tmag.2003.810336.

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Yorkey, T. J., and J. G. Webster. "A comparison of impedance tomographic reconstruction algorithms." Clinical Physics and Physiological Measurement 8, no. 4A (1987): 55–62. http://dx.doi.org/10.1088/0143-0815/8/4a/007.

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Liu, W. P., P. Hua, and J. G. Webster. "Three-dimensional reconstruction in electrical impedance tomography." Clinical Physics and Physiological Measurement 9, no. 4A (1988): 131–35. http://dx.doi.org/10.1088/0143-0815/9/4a/022.

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Hua, P., J. G. Webster, and W. J. Tompkins. "A regularised electrical impedance tomography reconstruction algorithm." Clinical Physics and Physiological Measurement 9, no. 4A (1988): 137–41. http://dx.doi.org/10.1088/0143-0815/9/4a/023.

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Yorkey, Thomas J. "Comparing reconstruction methods for electrical impedance tomography." Annals of Biomedical Engineering 15, no. 3-4 (1987): 406–7. http://dx.doi.org/10.1007/bf02584294.

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Liu, Xiayi, Jiafeng Yao, Tong Zhao, Hiromichi Obara, Yahui Cui, and Masahiro Takei. "Image Reconstruction Under Contact Impedance Effect in Micro Electrical Impedance Tomography Sensors." IEEE Transactions on Biomedical Circuits and Systems 12, no. 3 (2018): 623–31. http://dx.doi.org/10.1109/tbcas.2018.2816946.

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Agnelli, Juan P., Ville Kolehmainen, Matti J. Lassas, Petri Ola, and Samuli Siltanen. "Simultaneous Reconstruction of Conductivity, Boundary Shape, and Contact Impedances in Electrical Impedance Tomography." SIAM Journal on Imaging Sciences 14, no. 4 (2021): 1407–38. http://dx.doi.org/10.1137/21m1407975.

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Schullcke, Benjamin, Bo Gong, Sabine Krueger-Ziolek, and Knut Moeller. "Reconstruction of conductivity change in lung lobes utilizing electrical impedance tomography." Current Directions in Biomedical Engineering 3, no. 2 (2017): 513–16. http://dx.doi.org/10.1515/cdbme-2017-0108.

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AbstractElectrical Impedance Tomography (EIT) is a novel medical imaging technology which is expected to give valuable information for the treatment of mechanically ventilated patients as well as for patients with obstructive lung diseases. In lung-EIT electrodes are attached around the thorax to inject small alternating currents and to measure resulting voltages. These voltages depend on the internal conductivity distribution and thus on the amount of air in the lungs. Based on the measured voltages, image reconstruction algorithms are employed to generate tomographic images reflecting the re

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Rixen, Jöran, Steffen Leonhardt, Jochen Moll, Duy Hai Nguyen, and Chuong Ngo. "The D-Bar Algorithm Fusing Electrical Impedance Tomography with A Priori Radar Data: A Hands-On Analysis." Algorithms 16, no. 1 (2023): 43. http://dx.doi.org/10.3390/a16010043.

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Electrical impedance tomography (EIT) is an imaging modality that can estimate a visualization of the conductivity distribution inside the human body. However, the spatial resolution of EIT is limited because measurements are sensitive to noise. We investigate a technique to incorporate a priori information into the EIT reconstructions of the D-Bar algorithm. Our paper aims to help engineers understand the behavior of the D-Bar algorithm and its implementation. The a priori information is provided by a radar setup and a one-dimensional reconstruction of the radar data. The EIT reconstruction i

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Chen, Rongqing, and Knut Moeller. "Detection of Outdated Structural Priors in the Discrete Cosine Transformation-based Electrical Impedance Tomography Algorithm." Current Directions in Biomedical Engineering 7, no. 2 (2021): 676–79. http://dx.doi.org/10.1515/cdbme-2021-2172.

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Abstract Morphological prior information incorporated with the discrete cosine transformation (DCT) based electrical impedance tomography (EIT) algorithm can improve the interpretability of EIT reconstructions in clinical applications. However, an outdated structural prior can yield a misleading reconstruction compromising the accuracy of the clinical diagnosis and the appropriate treatment decision. In this contribution, we propose a redistribution index scaled between 0 and 1 to quantify the possible error in a DCT-based EIT reconstruction influenced by structural prior information. Two simu

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Lalak-Dybała, Malgorzata, Barbara Stefaniak, and Paweł Olszewski. "Analysis of the effectiveness of two different loss functions in training a neural network in lung image reconstruction using impedance tomography." Journal of Modern Science 57, no. 3 (2024): 594–608. http://dx.doi.org/10.13166/jms/191304.

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The article presents research findings on developing a medical diagnostic system based on electrical impedance tomography technology. One of the key components of this project is developing a method for reconstructing the structure of human lungs using this technology. The authors of the article compared the effectiveness of two different loss functions in training a neural network, which is tasked with accurately replicating the lung structure based on electrical impedance tomography data. The researchers analyzed various approaches to calculating loss functions, including cosine embedding lo

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Padilha Leitzke, Juliana, and Hubert Zangl. "A Review on Electrical Impedance Tomography Spectroscopy." Sensors 20, no. 18 (2020): 5160. http://dx.doi.org/10.3390/s20185160.

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Electrical Impedance Tomography Spectroscopy (EITS) enables the reconstruction of material distributions inside an object based on the frequency-dependent characteristics of different substances. In this paper, we present a review of EITS focusing on physical principles of the technology, sensor geometries, existing measurement systems, reconstruction algorithms, and image representation methods. In addition, a novel imaging method is proposed which could fill some of the gaps found in the literature. As an example of an application, EITS of ice and water mixtures is used.

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Ghosh, Santi Kumar, and Animesh Mandal. "Feasibility of approximate broadband estimation of acoustic impedance profile from first principles and band-limited reflection data." GEOPHYSICS 81, no. 3 (2016): R57—R74. http://dx.doi.org/10.1190/geo2015-0056.1.

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Because seismic reflection data are band limited, acoustic impedance profiles derived from them are nonunique. The conventional inversion methods counter the nonuniqueness either by stabilizing the answer with respect to an initial model or by imposing mathematical constraints such as sparsity of the reflection coefficients. By making a nominal assumption of an earth model locally consisting of a stack of homogeneous and horizontal layers, we have formulated a set of linear equations in which the reflection coefficients are the unknowns and the recursively integrated seismic trace constitute t

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Kursin, Serhii, and Oleh Velychko. "Study of autotransformer bridges for measurements of the impedance parameters." Ukrainian Metrological Journal, no. 4 (December 27, 2024): 3–10. https://doi.org/10.24027/2306-7039.4.2024.318844.

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The analysis of the existing impedance measurement methods showed that for the establishment of precision comparators that operate in a wide range of values in the audio frequency range, it is best to use transformer and autotransformer bridges. Autotransformer bridges are used for measurements in a wide range of the impedance values. The use of autotransformer bridges allows reducing the measurement error to 10–7–10–9. High metrological characteristics of transformer bridge circuits make it possible to use them in commercial devices and precision measuring equipment. Simple autotransformer br

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Morucci, Jean-Pierre, and Pierre-Marie Marsili. "Bioelectrical Impedance Techniques in Medicine Part III: Impedance Imaging Second Section: Reconstruction Algorithms." Critical Reviews in Biomedical Engineering 24, no. 4-6 (1996): 599–654. http://dx.doi.org/10.1615/critrevbiomedeng.v24.i4-6.50.

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Babikir Adam, Edriss Eisa, and Sathesh. "Survey on Medical Imaging of Electrical Impedance Tomography (EIT) by Variable Current Pattern Methods." June 2021 2, no. 2 (2021): 82–95. http://dx.doi.org/10.36548/jismac.2021.2.002.

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Recently, the image reconstruction study on EIT plays a vital role in the medical application field for validation and calibration purpose. This research article analyzes the different types of reconstruction algorithms of EIT in medical imaging applications. Besides, it reviews many methods involved in constructing the electrical impedance tomography. The spatial distribution and resolution with different sensitivity has been discussed here. The electrode arrangement of various methods involved in the EIT system is discussed here. This research article comprises of adjacent drive method, cros

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Dimas, Christos, Nikolaos Uzunoglu, and Paul Peter Sotiriadis. "A Parametric EIT System Spice Simulation with Phantom Equivalent Circuits." Technologies 8, no. 1 (2020): 13. http://dx.doi.org/10.3390/technologies8010013.

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In this paper a number of LT Spice simulations have been carried out on an Electrical Impedance Tomography (EIT) system, which includes the whole analog and digital circuitry as well as the subject to be examined (phantom model). The aim of this study is to show how the analog and digital parts, the electrodes and the subject’s physical properties may impact the measurements and the quality of the reconstructed image. This could provide a useful tool for designing an EIT system. Special attention has been given to the current source’s output impedance and swing, to the noise produced by the ci

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Öz, İbrahim. "Quantitative Assessment of Image Reconstruction Algorithms in Electrical Impedance Tomography." Kocaeli Journal of Science and Engineering 8, no. 1 (2025): 38–51. https://doi.org/10.34088/kojose.1556617.

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Electrical Impedance Tomography (EIT) is a noninvasive imaging technique used to estimate the internal conductivity distribution of a region that is either unknown or inaccessible. This is achieved by applying electrical currents to the region and measuring the resulting boundary voltages. The forward problem in EIT is typically solved using the Finite Element Method (FEM), and regularization techniques are employed to stabilize the ill-posed inverse problem during image reconstruction. This study evaluated the performance of two widely used image reconstruction algorithms: the delta conductiv

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HANKE, MARTIN, BASTIAN HARRACH, and NUUTTI HYVÖNEN. "JUSTIFICATION OF POINT ELECTRODE MODELS IN ELECTRICAL IMPEDANCE TOMOGRAPHY." Mathematical Models and Methods in Applied Sciences 21, no. 06 (2011): 1395–413. http://dx.doi.org/10.1142/s0218202511005362.

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The most accurate model for real-life electrical impedance tomography is the complete electrode model, which takes into account electrode shapes and (usually unknown) contact impedances at electrode-object interfaces. When the electrodes are small, however, it is tempting to formally replace them by point sources. This simplifies the model considerably and completely eliminates the effect of contact impedance. In this work we rigorously justify such a point electrode model for the important case of having difference measurements ("relative data") as data for the reconstruction problem. We do t

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Li, Xiuyan, Yong Zhou, Jianming Wang, et al. "A novel deep neural network method for electrical impedance tomography." Transactions of the Institute of Measurement and Control 41, no. 14 (2019): 4035–49. http://dx.doi.org/10.1177/0142331219845037.

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Image reconstruction for Electrical Impedance Tomography (EIT) is a highly nonlinear and ill-posed inverse problem. It requires the design and employment of feasible reconstruction methods capable to guarantee trustworthy image generation. Deep Neural Networks (DNN) have a powerful ability to express complex nonlinear functions. This research paper introduces a novel framework based on DNN aiming to achieve EIT image reconstruction. The proposed DNN model, comprises of the following two layers, namely: The Stacked Autoencoder (SAE) and the Logistic Regression (LR). It is trained using the larg

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Lifshitz, Ilan, Peder C. Pedersen, and Peter A. Lewin. "Reconstruction of the Acoustical Impedance Profile of a Multilayer Medium." Ultrasonic Imaging 14, no. 1 (1992): 40–68. http://dx.doi.org/10.1177/016173469201400104.

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The recovery of the acoustical reflectivity function and impedance profile of a layered medium from bandlimited and noisy pulse-echo ultrasonic data is considered. The effects of the transducer and the noise are reduced using Wiener filtering. With further signal processing and a priori knowledge of the attenuation and the velocity profiles, the compensated coefficients of the reflectivity function and the impedance profile are recovered. The reconstruction techniques are presented analytically, and are also evaluated in an experimental setup composed of a conventional pulse echo system, a dat

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Hintzen, E., T. Vennemann, and W. Mathis. "Systematic design of output filters for audio class-D amplifiers via Simplified Real Frequency Technique." Advances in Radio Science 12 (November 10, 2014): 49–52. http://dx.doi.org/10.5194/ars-12-49-2014.

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Abstract. In this paper a new filter design concept is proposed and implemented which takes into account the complex loudspeaker impedance. By means of techniques of broadband matching, that has been successfully applied in radio technology, we are able to optimize the reconstruction filter to achieve an overall linear frequency response. Here, a passive filter network is inserted between source and load that matches the complex load impedance to the complex source impedance within a desired frequency range. The design and calculation of the filter is usually done using numerical approximation

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Kumar, Munish. "Image Reconstruction Using Impedance Measurement as EIT Technique." IOSR Journal of Electrical and Electronics Engineering 7, no. 1 (2013): 84–87. http://dx.doi.org/10.9790/1676-0718487.

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Kim, Ji Hoon, Bong Yeol Choi, Umer Zeeshan Ijaz, Bong Seok Kim, Sin Kim, and Kyung Youn Kim. "Directional Algebraic Reconstruction Technique for Electrical Impedance Tomography." Journal of the Korean Physical Society 54, no. 4 (2009): 1439–47. http://dx.doi.org/10.3938/jkps.54.1439.

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Kleinermann, F., N. J. Avis, S. K. Judah, and D. C. Barber. "Three-dimensional image reconstruction for electrical impedance tomography." Physiological Measurement 17, no. 4A (1996): A77—A83. http://dx.doi.org/10.1088/0967-3334/17/4a/011.

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Hongze Liu, A. R. Hawkins, S. M. Schultz, and T. E. Oliphant. "Fast Nonlinear Image Reconstruction for Scanning Impedance Imaging." IEEE Transactions on Biomedical Engineering 55, no. 3 (2008): 970–77. http://dx.doi.org/10.1109/tbme.2007.905485.

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凤, 天宏. "Cavity Reconstruction Algorithm Based on Electrical Impedance Tomography." Advances in Applied Mathematics 04, no. 02 (2015): 189–96. http://dx.doi.org/10.12677/aam.2015.42024.

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