Electrical impedance tomography (EIT) is used to determine the spatial conductivity distribution of a measurement environment that involves several applications in the areas of process engineering and medical diagnostics. In common EIT systems, the measurements are performed by means of current patterns which are injected into the measurement environment using a serial time-division-multiplexing (TDM) scheme for the excitation of different electrodes. The measurement rate can be increased by performing parallel excitation using orthogonal signals. In this paper, a code-divisionmultiplexing (CDM) measurement procedure is presented. To optimize the separation between different measurement channels and the dynamic range, orthogonal Walsh-Hadamard codes are used. The measurements are conducted with a fast EIT system with nine parallel excitation sources and 18 parallel measurement channels. The measured crosstalk rejection between different channels is larger than 98 dB. The maximum absolute deviation between different measurement sets for repeated measurements is less than 24 µV with a mean standard deviation of less than 8.2 µV. The dynamic range for impedance measurements based on different excitation procedures (TDM, frequencydivision-multiplexing, and CDM) has been determined. Furthermore, reconstructed conductivity distributions based on measurements with the different excitation procedures have been compared with each other for different measurement scenarios (a root mean square difference of less than 1.2%). Finally, the influences of frequency-dependent measurement objects on the excitation procedures have been discussed.
In the process industry, measurement systems are required for process development and optimization, as well as for monitoring and control. The processes often involve multiphase mixtures or flows that can be analyzed using tomography systems, which visualize the spatial material distribution within a certain measurement domain, e.g., a process pipe. In recent years, we studied the applicability of soft-field electromagnetic tomography methods for multiphase flow imaging, focusing on concepts for high-speed data acquisition and image reconstruction. Different non-intrusive electrical impedance and microwave tomography systems were developed at our institute, which are sensitive to the local contrasts of the electrical properties of the materials. These systems offer a very high measurement and image reconstruction rate of up to 1000 frames per second in conjunction with a dynamic range of up to 120 dB. This paper provides an overview of the underlying concepts and recent improvements in terms of sensor design, data acquisition and signal processing. We introduce a generalized description for modeling the electromagnetic behavior of the different sensors based on the finite element method (FEM) and for the reconstruction of the electrical property distribution using the Gauss-Newton method and Newton's one-step error reconstructor (NOSER) algorithm. Finally, we exemplify the applicability of the systems for different measurement scenarios. They are suitable for the analysis of rapidly-changing inhomogeneous scenarios, where a relatively low spatial resolution is sufficient.
Electrical impedance tomography (EIT) is used to determine the spatial conductivity distribution of a measurement environment, what involves several applications in the area of process engineering or medical science. In common systems a measurement environment is usually stimulated by defined current patterns in a serial time-multiplexing manner. Higher measurement rates can be achieved with code-multiplexed (CDM) excitations for parallel measurements. In order to optimize channel separation and dynamic range, orthogonal Walsh-Hadamard codes have been implemented on a fast EIT system. The crosstalk between different channels has been analyzed for 9 parallel coded excitation signals. Furthermore reconstructed conductivity distributions have been compared to reconstruction results obtained from frequency-multiplexed (FDM) measurements (root mean square difference of 0.02%).
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