Electrical Impedance Tomography (EIT) is an advanced visualization technique characterized by its cost-effectiveness, rapid response, non-ionizing radiation, and non-intrusiveness compared with other tomography modalities. However, the quality of EIT imaging is compromised by the inherent ill-posed solution and the soft-field effect, which hinder the effective reconstruction of target objects. Various methods have been employed to address these issues, but they can make limited progress in high-quality reconstruction. In this paper, efforts are made by applying the clustering technique to the EIT reconstruction. The key features hidden in all EIT measurements are extracted, and whereby each pixel can be represented by a feature vector. After clustering these vectors over all pixels, we proposed a novel EIT reconstruction algorithm. Experimental results show that the novel algorithm can reconstruct the EIT images more accurately and effectively than the existing algorithms. Consequently, the implementation of a clustering algorithm instead of the existing algorithm offers a practical and effective approach to enhance the EIT imaging quality.

Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data objects or that of the clusters. Numerous variants of FCM have been proposed to address these issues. However, most of them cannot effectively apply the available information on data objects or clusters. In this paper, a double-constraint fuzzy clustering algorithm is proposed to reflect the importance degrees of both individual data objects and clusters. By incorporating double constraints into each data object and cluster, the objective function of FCM is reformulated and its realization equation is mathematically conducted. Consequently, the clustering accuracy of FCM is improved by applying the available information on both data objects and clusters. Especially, the proposed algorithm effectively addresses the limitations inherent in the existing variants of FCM. The experimental results validate the effectiveness, implementation, and robustness of the new fuzzy clustering algorithm.

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.

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.

Electrical impedance tomography (EIT) is an advanced visualization modality. In recent years, high-speed devices are commonly employed in EIT systems to accomplish complex tasks and enhance the real-time capability. Signal integrity (SI) and power integrity (PI) are major concerns in the high-speed system design. However, SI/PI issues have received limited attention due to their negligible impact on early developed low-speed EIT systems. The hardware performance degradation introduced by SI/PI issues gradually emerges in the high-frequency range, which may degrade the SNR of the EIT system and aggravate the image distortion caused by the inherent ill-posed problem of the EIT technique. This study comprehensively analyzes the major causes of SI/PI issues in the high-speed EIT systems and proposes the corresponding hardware optimization methods. The proposed methods are applied to a practical EIT system to validate their effectiveness. Experimental results illustrate that the SI/PI issues are effectively mitigated. The average signal-to-noise ratio (SNR) of the EIT system is improved by 5.4dB and the imaging quality is generally enhanced. The methods proposed in this study offer a low-cost, supplementary approach to improve the hardware performance of the high-speed EIT systems.