Hui Xia

Magnetic induction tomography (MIT) is a non-contact, low-cost electromagnetic imaging technique with broad applications in industrial monitoring, biomedical imaging, and non-destructive inspection. However, existing MIT imaging algorithms suffer from slow reconstruction speed, limited imaging accuracy, and weak robustness to noise, which hinder their practical deployment. To overcome these limitations, this paper proposes an adaptive truncated singular value decomposition method based on the discrete Picard criterion, termed Picard-Driven TSVD (PTSVD). The method quantitatively analyzes ill-conditioned matrices via Picard curves, establishes an adaptive regularization parameter selection strategy, and integrates Tikhonov-Gaussian filtering to enhance noise suppression. Compared to traditional Tikhonov and TSVD methods, PTSVD reduces reliance on empirical parameters, avoids excessive image smoothing, and improves noise robustness. Simulation results show that under noise-free conditions, the image correlation coefficient increases by 28.8% and 75.9%, respectively. Even at a signal-to-noise ratio of 5 dB, the correlation coefficient remains at 0.63. Experimental validation on a self-built MIT platform further confirms the algorithm’s effectiveness and practicality. This study provides a high-accuracy, noise-resistant solution for real-time MIT detection, showing strong potential in applications such as molten metal flow monitoring, multiphase flow analysis, and subsurface target detection.

Transcranial focused magnetoacoustic electrical stimulation (TFMAES) is an emerging composite neurostimulation technique, which can achieve precise electrical modulation of brain neural tissues with the help of low-intensity focused ultrasound. The aim of this study was to investigate the ameliorative effect of TFMAES on Alzheimer's disease (AD) symptoms. Through finite element simulation calculations, we determined the parameters of the TFMAES experiment. And 6 transgenic AD rats were used to perform continuous TFMAES experiments for 7 days at 2 different symptom stages in the progression of AD symptoms to observe the changes of electroencephalography (EEG) signals before and after the stimulation. The results showed that in the first stage, the amyloid beta (Aβ) deposition period, TFMAES led to a decrease in the delta-band energy percentage and an increase in the gamma-band energy percentage of EEG in AD rats. In the second stage, the onset of dementia symptoms, the effect of TFMAES on the delta-band power percentage of AD was significant compared to the gamma-band power percentage, but the effect on the overall EEG activity remained significant. These preliminary results suggest that TFMAES has a significant effect on the delta-band of EEG in AD rats, which may improve the activity of the cerebral cortex and thus improve AD symptoms. Moreover, this ameliorative effect is more significant in the early AD stage, which provides a basis for subsequent research on the mechanism of TFMAES quantitative parameters in AD improvement.

Magneto-acousto-electrical tomography (MAET) is a powerful tool for numerous applications, including medical imaging and process tomography. In these applications, and to enable precise reconstruction of images of a given process using MAET, high-quality measurements must be obtained from the target of interest. It is thus obvious that the magnetic field excitation used in these measurements plays an important role in this task. However, the existing reports in the literature to date have usually assumed that the static magnetic field is a uniform field, which is challenging to achieve in practice. In this article, we circumvent this challenge and present a comprehensive mathematical model for MAET in the presence of nonuniform magnetic fields. In the proposed model, a relationship is established between the measurement signal and the reciprocal current density. The conductivity distribution is obtained via a simulation model and compared with the second norm error of the images and the various uniformities of the nonuniform magnetic fields obtained from the imaging results. The model is then validated for different test cases, which range from a simple phantom with three layers to a phantom with a cylindrical geometry. It is concluded that the use of a nonuniform magnetic field can lead to significant changes and improvements in the reconstruction of the conductivity, thus making it possible to use this technique in bedside detection applications.

Aero-engine blade is one of the core components of aero-engine and its safe service is crucial to the normal operation of aero-engine. Thus, it is very important to localize and quantify cracks of aero-engine blades to prevent safety accidents. To guarantee the structural health of aero-engine blades, the air-flow thermography (AFT) method which has advantages of non-contact, high performance, and sensitivity, was proposed for detection of the blade cracks. Firstly, the theoretical thermal response model was established and the crack simulation models with different depths were constructed. Then a detection system was established to detect the artificial cracks with different depths and orientations. Results showed that the proposed method can effectively quantify the cracks with different depths from 0.2 mm to1.0 mm and characterize the crack orientations. In addition, the natural crack on the blade was detected and the principal component analysis (PCA) method was used to enhance the crack contrast with the background.

In oil mining system, stress cracks on thread usually lead to drill pipe failure. To maintain normal operation of drill pipes, the thread needs to be nondestructively tested. However, due to complicated structure, it is challenging to identify and quantify the cracks the using existing nondestructive testing (NDT) methods. In this article, a scanning induction thermography (SIT) system for automatic detection of drill pipe thread (DPT) is presented. Specifically, a profiling heating method (PHM) is proposed to produce uniform heating on the root part of thread tooth where most of the stress cracks occur. A weighted least squares fitting (WLSF)-based difference algorithm is used to eliminate the background temperature of scanning data. The algorithm can extract crack thermal response with the influence of lift-off distance change and marginal effect of electric field in SIT. Experiments show that the proposed system can obtain 3k thermal contrast between crack and defective-free area when testing the crack with only 0.3 mm depth. Three SIT systems with other heating methods are also used for comparison. Better heating uniformity, signal intensity, robustness of structural change, linear property with depth information, and signal consistency when testing similar cracks in different areas of the root part can be demonstrated in the proposed system.

Wire ropes (WRs) are widely used in various areas, such as tourist cables, mining, and elevators. Thus, it is very important to detect broken wires (BWs) to prevent safety issues. To guarantee the structural health of WRs, this paper proposes the use of induction thermography (IT) to detect the BWs. An image segmentation algorithm based on scale morphology is proposed to eliminate the background influence of non-uniform heating and visualize the BWs. Further, the area of the BW region, after the image binarization, subtraction, and filtering, is extracted as a feature to quantify the number of BWs. Results show that the proposed methods can effectively visualize and quantify the broken region within 1 BWs.

Nowadays, scanning induction thermography (SIT) could be efficient and practical for the inspection of elongated ferromagnetic specimens in the manufacturing process. Owing to the relative movement between the specimen and excitation coil, defect visualization and depth quantification in SIT remain challenging. Focusing on these two challenges, surface-breaking notches with different depths in a steel plate at different scanning speeds have been studied in this paper. A visualization method, including image reconstruction and Wiener filter, is proposed to realize defect visualization within 600 mm/s. Then, thermal features are extracted to realize depth quantification for notches within 3.0 mm. In addition, the effect of scanning speed on the SIT detectability is investigated and it is found that the signal-to-noise ratio (SNR) decreases significantly with increasing scanning speed.