Online Time-Resolved Reconstruction Method for Acoustic Tomography System

Bao, Yong; Jia, Jiabin

doi:10.1109/tim.2019.2947949

Cited by 17 publications

(5 citation statements)

References 94 publications

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“…die Simultaneous Iterative Reconstruction Technique (SIRT) in verschiedenen Bereichen der Laufzeittomographie verbreitet [7][8][9] und wird für die GHATT adaptiert. Die verschiedenen Implementierungen der SIRT (wie z.B.…”

Section: 𝑠𝑠 ≔ 𝑐𝑐 −1unclassified

6.4 - Validierung der tomographischen Rekonstruktion eines neuartigen akustischen Sensorsystems zur Bestimmung von Gasgehaltsverteilungen in Mehrphasensystemen

Elfering¹,

Annas²,

Czajka³

et al. 2022

Vorträge

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Section: 𝑠𝑠 ≔ 𝑐𝑐 −1unclassified

6.4 - Validierung der tomographischen Rekonstruktion eines neuartigen akustischen Sensorsystems zur Bestimmung von Gasgehaltsverteilungen in Mehrphasensystemen

Elfering¹,

Annas²,

Czajka³

et al. 2022

Vorträge

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“…Bao et al also developed an online time-resolved reconstruction method, which can resolve the dynamic changes with a reduced number of TOFs. As a result, the measuring time per frame was reduced from 320 ms to 60 ms. 17 The existing algorithms usually only consider the measurement information. Li et al 18 built an objective function considering both the measurement information and dynamic evolution information of the temperature distribution, extended the function with robust estimation, and solved the function by using a method combining a tunneling algorithm and a local minimization technique.…”

Section: Introductionmentioning

confidence: 99%

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

et al. 2022

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Acoustic tomography (AT) is one of a few non-contact measurement techniques that can present information about the temperature distribution. Its successful application greatly depends on the performance of the reconstruction algorithm. In this paper, a temperature distribution reconstruction method based on compressed sensing (CS) is proposed. Firstly, a measurement matrix of an AT system in a CS framework is established. Secondly, a sparse basis is selected based on the mutual coherence between the measurement matrix and sparse basis. Thirdly, an improvement of the orthogonal matching pursuit (OMP) algorithm, called the IMOMP algorithm, is proposed for pursuing efficiency in recovering sparse signals. Reconstruction experiments of Gaussian sparse signals showed that IMOMP was better than OMP in both success ratio and running time, and the selection method of sparse basis was effective. Finally, a temperature distribution reconstruction algorithm based on compressed sensing, that is, the CS-IMOMP algorithm, is proposed. Simulation and experiment results show that, compared with the least square algorithm and the Simultaneous Iterative Reconstruction Technique algorithm, the CS-IMOMP algorithm has smaller reconstruction errors and provides more accurate information about the temperature distribution.

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“…Time-of-flight (ToF) tomography is a well known noninvasive imaging technique used in several industrial [1]- [3], medical [4]- [6], manufacturing [7] and testing [8], [9] applications [10]. In general, the materials/features in a regionof-interest (RoI) can alter the speed of electromagnetic or mechanical signals traveling through the RoI.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the established ToF tomography methods use least squares or maximum likelihood based approaches to estimate the tomographic parameters (features) in the RoI [11], [12]. Depending on the tomographic application, a wide verity of signals such as radar [7]- [9], [13], ultrasound [2], acoustic [1], [3], [6], [14], and so on can be used for tomographic reconstruction of features within a RoI.…”

Section: Introductionmentioning

confidence: 99%

Joint Calibration and Tomographic Reconstruction for Time-of-Flight Sensor Systems

bhuvana¹,

Och²,

Schuster³

2022

Preprint

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<p>A typical time-of-flight (ToF) tomography method requires accurate information about the system’s calibration parameters in order to reconstruct the features of interest within a region of interest (RoI). This paper proposes a constrained joint calibration and tomography (CJCT) method based on a separable least squares (SLS) approach for ToF tomographic systems. Contrary to the existing offline and online calibration methods, the proposed method utilizes prior information about the calibration parameters in the form of constraints. The imposed constraints allow for an efficient search for the minimum of the inherent SLS cost function by reducing the system and measurement ambiguities. Thus, the proposed CJCT method can find the optimum values for both tomography and calibration parameters more efficiently compared to unconstrained methods. The general applicability of the proposed method is demonstrated by evaluating it on two applications: tomographic reconstruction of the permittivity distribution in a RoI using ToF measurements from a high resolution millimeter-wave radar system, and the temperature distribution inside an industrial blast furnace using measured and simulated acoustic ToF values. The simulation and experimental results show that the proposed method performs significantly better in terms of tomographic reconstruction accuracy compared to unconstrained methods.</p>

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Online Time-Resolved Reconstruction Method for Acoustic Tomography System

Cited by 17 publications

References 94 publications

6.4 - Validierung der tomographischen Rekonstruktion eines neuartigen akustischen Sensorsystems zur Bestimmung von Gasgehaltsverteilungen in Mehrphasensystemen

6.4 - Validierung der tomographischen Rekonstruktion eines neuartigen akustischen Sensorsystems zur Bestimmung von Gasgehaltsverteilungen in Mehrphasensystemen

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

Joint Calibration and Tomographic Reconstruction for Time-of-Flight Sensor Systems

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