Shihong Yue

Electrical impedance tomography (EIT) is widely used in industrial and biomedical applications, but weak central sensitivity can obscure internal targets. This study proposes a joint enhancement (JE) method combining center-residual feedback with regional sensitivity-based measurement enhancement. Reconstructed peripheral structures are used to approximate unavailable target-free measurements and iteratively extract central-target residual information. Regional sensitivity decomposition enhances the central measurement component, which is reconstructed using Tikhonov regularization and integrated with the peripheral structure. Tests on five simulated thoracic models and five saline-tank configurations show that JE achieves the highest correlation coefficient (CC) and lowest relative error (RE) among the four compared methods. Relative to the initial linear back-projection reconstruction, JE increases mean CC by approximately 15.48% and reduces mean RE by 25.87% in simulation; the corresponding experimental improvements are 21.68% and 12.82%, respectively. These results support improved central-target recovery for the tested configurations. The proposed method as a whole has the potential to be applied to general EIT processes.

As an advanced visualization technique, electrical impedance tomography (EIT) can reconstruct the distribution of electrical parameters and thereby visually show object distribution within a detection field. The quality of EIT reconstruction greatly depends on a selected sensitivity matrix, while various matrices can lead to very different EIT reconstruction results. A large number of efforts to improve the sensitivity matrix have been made to enhance EIT reconstruction quality, but how to construct and select a better matrix in a general and feasible way remains unsolved to date. In this paper, we use the matrix transformation method to address the issue, and two types of symmetric and diagonally dominant matrices are optimally selected to multiply the sensitivity matrix on the left and right, respectively. Therefore, EIT reconstruction quality can generally be improved. The feasibility and robustness of the proposed method have been theoretically demonstrated for both Gaussian kernel-based and power function-based matrices. Experiments validate the proposed method by EIT reconstruction quality.

This study presents and evaluates a system-level optimization of an electrical resistance tomography (ERT) measurement platform based on a ZYNQ-7020 all-programmable system-on-chip (APS-SoC). The design combines deterministic programmable-logic (PL) acquisition, processing-system (PS) configuration and communication scheduling, AXI/DMA data movement, Gigabit Ethernet transmission, and a seventh-order Butterworth excitation filter. FFT-based amplitude extraction and Tikhonov reconstruction remain on the host computer so that the reconstruction algorithm and regularization settings remain identical for the baseline and proposed systems; the present prototype is therefore not claimed as a fully standalone smart sensor. Under the same 16-electrode tap-water testing configuration, the average frame rate increased from 58.23 ± 1.88 FPS to 123.02 ± 1.62 FPS (mean ± sample standard deviation, n = 10), end-to-end latency decreased from 5.2 ms to 2.1 ms, SFDR increased from 68 dB to 95 dB, and SSIM increased from 0.72 to 0.94. In one representative static hardware record at 160 kHz, the calculated amplitude-stability SNR values were 65 dB and 100 dB for the baseline and proposed excitation paths, respectively, while total harmonic distortion decreased from 15.2% to 7.5%. These single-condition signal quality values are descriptive rather than uncertainty-bounded performance specifications. The image-quality differences are attributed primarily to cleaner boundary-voltage measurements with an unchanged reconstruction method, whereas the frame-rate gain reflects the combined PL/PS data path and Gigabit Ethernet upgrade.

The convolutional neural network (CNN) has been used in almost all research and application fields. With ever-increasing actual requirements and the explosive growth of available big data, the topology structure of CNN has become more complex, particularly in terms of the number of layers and nodes. The complex structures decrease the generalization capability and efficiency of CNN. To solve the problems, we propose a CNN-induced network. By merely replacing the fully connected layer with n nodes using a Choquet integral (CI) block, a typical CNN can be extended to n! CNNs without increasing the number of layers and nodes. As an example, in the solid-liquid two-phase flow of dredging engineering, the proposed network is evaluated by the computation accuracy of solid-phase fraction (SPF). A large number of samples are collected due to complex flow patterns and fast-changing measurement conditions, but a typical CNN has poor generalization and learning capability on complex datasets. Inversely, the new network solves the above problem easily and practicably. Experiments show that the new network not only provides a robust approach for learning from big dataset but also provides an effective and stable method for SPF computation.

As an advanced visualization technique, the electrical impendence tomography (EIT) can reconstruct the distribution of electrical parameter and thereby visually show the object distribution within a detection field. The EIT reconstruction quality greatly depends on a selected sensitivity matrix, while various matrixes can lead to very different EIT reconstruction results. A large number of efforts to improve the sensitivity matrix have been made to enhance EIT reconstruction quality, but how to construct and select the best matrix in a generally and feasibly way remains unsolved to date. In this paper, we use the matrix transformation method to address the issue, and two types of symmetric and diagonally dominant matrixes are optimally selected to multiply the sensitivity matrix on left and right, respectively. Therefore, the EIT reconstruction quality can generally be improved. The optimality and generalization of the proposed method have been theoretically demonstrated when specially using either Gaussian kernel-based or power function-based matrices, respectively. Experiments validate the proposed method by the EIT reconstruction quality.

Solid-phase fraction (SPF) is a critical parameter in solid-liquid two-phase flow, and can be measured using the convolutional neural networks (CNNs) with electrical resistance tomography (ERT) measurements. But CNNs can estimate SPF from the time series of ERT measurements under stable conditions rather than under rapidly changing flow patterns and complex measuring conditions. To address this issue, this study proposes a novel multi-scale hybrid Mamba-CNN model, termed State Space CNN (SSC). Compared with CNNs, the SSC has linear scalability and synergistic integration between global state-space patterns from the Mamba module and local spatiotemporal features from CNNs. Moreover, SSC presents a robust modeling of the complex multi-scale temporal dynamics and conductivity-related ambiguities present in the ERT measurements. Experiments shown that the SSC significantly outperforms existing CNNs, and can achieve a 34.2% re-duction in MAPE (12.3% vs. 18.7%) and a 19.7% reduction in MAE (2.41 vs. 3.05). Consequently, the SSC provides a robust solution for accurate, real-time SPF estimation and enhanced accuracy in dynamic complex measuring conditions.

As a visual detection technique, Electrical Impedance Tomography (EIT) can reconstruct the distribution of electrical parameters within a detection field. EIT reconstruction greatly depends on a physical equation that includes a sensitivity matrix and measurements, but the sensitivity matrix fails to be optimized for various reconstruction tasks. This issue decreases the applicable range of the physical equation and EIT reconstruction quality. To address this issue, this paper optimizes both the residual error for measurements and the sensitivity matrix in the equation, which leads to higher EIT reconstruction quality. The optimization solution is theoretically and experimentally verified. Results indicate that the proposed methods can reduce the relative error of EIT reconstruction quality by about 12.0%.

The regularization parameter plays an important role in regularization-based electrical tomography (ET) algorithms, but the existing methods generally cannot determine the parameter. Moreover, these methods are not real-time since a thorough search must be performed for the best parameter. To address the issue, a reproducing kernel-based interpolation approximation method is proposed to efficiently estimate the best regularization parameter from a group of representative samples. The optimization and generation of the new method have been verified by theoretical analysis and experimental demonstration. The theoretical evaluation is conducted in a Hilbert space with a known reproducing kernel, and its symmetry ensures the uniqueness of the interpolation. And experimental validation is carried out using both simulated and actual models, each with a range of distinct features. Results indicate that the new method can approximately find the best regularization parameter. Consequently, when using the regularization parameter, the new method can effectively improve both the spatial resolution and steadiness of ET imaging process.