In this article, a novel 3-D image reconstruction method was proposed for planar array ECT by combining depth estimation and sparse representation. The central depth of target in the region of interest is first estimated from an image initially reconstructed by the Tikhonov regularization. Second, sparse representation is employed to depict the permittivity distribution using the Gaussian radial basis functions defined at voxels over the plane in the estimated depth. Then, the 3-D image is reconstructed by solving a dedicated optimization problem using the Gauss–Newton method. The proposed method reduces the dimension of the unknowns, improves the condition number of the inverse problem, and enhances the image quality greatly. Numerical simulations and experiments were carried out to validate the proposed method. Results show that the proposed method can provide more accurate 3-D images of single or multiple inclusions at varying depths than conventional Tikhonov, total variation (TV), and Landweber methods while relieving the adverse effect of the nonuniform sensitivity of planar array ECT with varying depths on image reconstruction. The proposed method is of great potential in detecting and imaging subsurface abnormities.

Electrical capacitance tomography has been widely used in flame monitoring. In this paper, a dual-modality electrical tomography system was developed for imaging of flame admittivity distribution, including conductivity and permittivity distributions. An admittance measuring circuit was developed to obtain both resistance and capacitance of the flame impedance, and a Calderon method-based reconstruction algorithm was developed, which could be used to directly reconstruct the cross-sectional images of both conductivity and permittivity in the flame. 2D and 3D phantoms were modeled in simulation to validate the feasibility of the developed system and algorithm. Experiments were carried out to monitor a Bunsen burner flame with varied air flow rates. Both simulation and experimental results confirmed that the developed technique not only reconstructed the cross-sectional distribution of admittivity in the flame, but also revealed variations in flame admittivity with height. In addition, it was discovered that the conductivity and permittivity distributions in the flame were affected by the air flow rates. Therefore, the system can be used to monitor the conductivity and permittivity distributions in the flame, and hence can further help study the combustion mechanism.

This paper proposes a low-cost, wearable gesture recognition system based on the two-terminal electrical impedance tomography (EIT) technique. The system includes a wearable EIT sensor of eight electrodes, a hardware device, and gesture recognition software running on a PC. Nine different gestures can be stably identified from the measured impedance changes through machine learning algorithms. Experimental results show that the Quadric Discriminator algorithm has the highest recognition rate of 98.49% for the filtered validation set. Besides, the recognition results in the two-terminal mode and transformed four-terminal mode are compared by applying a two-to-four-terminal mapping to the two-terminal EIT system, and the recognition rate decreases with the most classification models in the latter mode. Thus, it is supposed that contact impedance plays an important role in gesture recognition. By analyzing the data characteristics with variance inflation factor (VIF) test and principal component analysis (PCA), the supposition is explained and verified, proving the merit of a two-terminal EIT system in gesture recognition.

This article presents a method for real-time 3-D imaging and velocity measurement of two-phase flows using a twin-plane electrical capacitance tomography (ECT) sensor with 24 electrodes. A dedicated digital ECT system with a data acquisition rate of 241 frames/s is used for capacitance measurement. In this article, the sensitivity distribution of the ECT sensor is analyzed, and the two most commonly used algorithms, linear back projection (LBP) and Tikhonov, are used to achieve high-speed 3-D image reconstruction. The cross correlation technique is used to calculate the velocity of a two-phase flow. Experiments were carried out to evaluate the performance of the proposed method. Several typical distributions were constructed and used to analyze the axial imaging resolution. A free-falling sphere was used to test the ability of the system in velocity measurement. Experimental results show that the proposed method can provide high axial resolution in the central zone of the 3-D sensor and high temporal resolution in velocity measurement.