Huihua Kong

Investigating the pore structure and understanding the relationship between pore characteristics and mechanical properties are crucial to research in the study of cement mortar. At present, the segmentation of large-scale concrete pores is mainly conducted using traditional algorithms or software, which are time-consuming and operate in a semi-automated manner. However, the application of these methods faces challenges when analyzing large-scale rock pores due to factors such as a lack of data, artifacts, and inconsistent contrast. In this study, six series of cement mortars were subjected to real-time CT scanning under uniaxial loading (RT-CT) to collect real-time three-dimensional data on the evolution of pore structures during loading. To address issues such as artifacts and inconsistent contrast, a new augmentation method was proposed to overcome artifacts and enhance contrast consistency. Finally, the augmented dataset was utilized for training, and the Fast R-CNN algorithm served as the framework for developing the pore recognition model. The results indicate that the improved algorithm demonstrates enhanced convergence and greater accuracy in pore segmentation. A mathematical model is developed to relate uniaxial compressive strength (UCS) to pore fractal dimension and porosity, based on pore segmentation analysis. The fractal dimensions evolution of each specimen is consistent with the progressive failure indicated by the strain-stress curve. Under uniaxial loading, specimens with a 4:1 cement–sand ratio exhibited peak strength. The incorporation of fractals improved particle contact, thereby facilitating the formation of the skeletal structure. These efforts contribute to improving the identification of the deformation of cement mortars.

Electromagnetic Tomography (EMT) is a non-destructive testing technique that reconstructs images of conductivity distributions within a sensitive field based on boundary electromagnetic measurement data. This technology offers the advantages of a simple structure, low cost, non-contact operation, non-invasiveness, and rapid imaging, making it widely applicable in industrial and medical fields. Currently, methods for detecting the diameter of rebar in concrete suffer from high costs, operational complexity, and poor imaging quality, with a lack of low-cost and effective detection solutions. EMT technology can effectively address these challenges. In this work, an EMT system with a 9-coil planar array sensor is employed to detect rebars of varying diameters, and its effectiveness is validated through finite element simulation software. Furthermore, an EMT image reconstruction algorithm is proposed, which utilizes a primal-dual acceleration algorithm to solve a multi-objective feature function incorporating L 1 − norm and Total Variation (TV) regularization. The numerical simulation results demonstrate that, compared to traditional algorithms, the proposed method achieves reconstructed images closer to the true distribution with minimal error, enabling more accurate reconstruction of detection targets. Moreover, it exhibits superior robustness, and real experiments validate its high efficacy in practical detection. This highlights its potential for practical applications in concrete reinforcement inspection.

Material analysis in sandstone is essential for oil and gas extraction. Energy spectrum Computed Tomography (CT) can acquire various spectrally distinct datasets and reconstruct energy-selective images. Additionally, deep learning significantly improves the accuracy of material decomposition by establishing a nonlinear mapping relationship between multi-energy channel reconstructed images and their corresponding multi-material reconstructed images. However, traditional convolutional neural networks (CNNs) demonstrate limited effectiveness in capturing non-local features. In this paper, we present a multi-encoder single-decoder network architecture named I-MultiEncFusion-Net, designed for material decomposition. In this framework, multiple encoders concentrate on the distinctive features of reconstructed images from different energy spectrum channels, while a single decoder enables feature fusion. The encoder incorporates Inception_B modules that utilize three parallel branches to comprehensively capture image features, while integrating a Local-Nonlocal Feature Aggregation (LNFA) module to fuse both local and global characteristics. The non-local feature extraction module constructs non-local neighborhood relationships and employs Euclidean distance metrics to extract global contextual features from images, thereby enhancing the material decomposition process. To further enhance model accuracy, the decoder computes Huber loss between each output and its corresponding label, while simultaneously incorporating correlations of base material images extracted by a High-Resolution Network (HRNet) as an auxiliary loss constraint for material decomposition. Validation experiments using spectral CT data of sandstone demonstrate the method’s efficacy. Both simulated and practical results indicate that I-MultiEncFusion-Net exhibits superior generalization capability, preserves internal image details, and produces decomposed images with sharper edges.

The MEMS inertial sensors based on the pedestrian localization system assisted by the zero-velocity update (ZUPT) algorithm has gained widespread attention, due to its effective independent localization in indoor environments. However, in the realistic pedestrian localization test, the system often appears to drift because of the long-term error accumulation of inertial sensors and the limitation of the error suppression of traditional pedestrian localization algorithms. In this article, based on full analysis of existing constraint-based methods, a multi-condition constrained pedestrian localization algorithm is proposed, which integrates zero velocity detection based on phase threshold constraint, single and dual feet fusion constraint algorithms, to suppress drift and improve localization accuracy. The experimental results demonstrate that the multi-condition constraint algorithm can reduce localization errors by 59% compared to the unconstrained approach, and by 42% and 26% compared to algorithms using only single-foot or dual-feet constraints, respectively. The trajectory generated from the experiments further shows that the proposed algorithm produces a trajectory that more closely aligns with the actual walking path.

Reconstruction of computed tomography (CT) images under sparse data conditions often leads to compromised quality, which can result in diagnostic inaccuracies. This study introduces a novel iterative reconstruction algorithm that combines a second-order differential Laplacian operator with a bilateral weighted relative total variation model to enhance the CT image quality from sparse datasets. The approach is designed to efficiently capture sharp edges and fine textures while reducing noise and maintaining critical edge features. Numerical simulations and preliminary clinical testing demonstrate that the algorithm significantly reduces streak artifacts and improves edge clarity, outperforming traditional methods in both qualitative and quantitative analyses. In summary, the developed iterative reconstruction algorithm substantially enhances the reconstruction quality of CT images with sparse data, showing significant advantages over conventional techniques, and promises to significantly improve the accuracy and reliability of clinical imaging diagnostics.

Multi-focal image and multi-modal image fusion technology can fully take advantage of different sensors or different times, retaining the image feature information and improving the image quality. A multi-source image fusion algorithm based on bidimensional empirical mode decomposition (BEMD) and a region sharpness-guided region overlapping algorithm are studied in this article. Firstly, source images are decomposed into multi-layer bidimensional intrinsic mode functions (BIMFs) and residuals from high-frequency layer to low-frequency layer by BEMD. Gaussian bidimensional intrinsic mode functions (GBIMFs) are obtained by applying Gaussian filtering operated on BIMF and calculating the sharpness value of segmented regions using an improved weighted operator based on the Tenengrad function, which is the key to comparison selection and fusion. Then, the GBIMFs and residuals selected by sharpness comparison strategy are fused by the region overlapping method, and the stacked layers are weighted to construct the final fusion image. Finally, based on qualitative evaluation and quantitative evaluation indicators, the proposed algorithm is compared with six typical image fusion algorithms. The comparison results show that the proposed algorithm can effectively capture the feature information of images in different states and reduce the redundant information.

The non-local means (NLM) is a classical image denoising algorithm. However, the denoising effect of the NLM algorithm is easily affected by the noise level of neighboring pixel and image edge information, which leads to poor denoising effect for high noise level image. In this paper, an improved NLM (I-NLM) denoising algorithm is proposed, which can extract the gradient information of the image more accurately by fusing the Laplacian of Gaussian operator. At the same time, the algorithm combines the real domain information and the gradient information of the image to calculate the weight of the similarity between the image blocks. Experimental results show that compared with the traditional NLM algorithm, the proposed I-NLM algorithm can effectively preserve the edge of the image while suppressing the noise, and recover the CT images with high Peak signal-to-noise ratio (PSNR) and SSIM values.

Aiming at the problem of the failure of the global positioning system in the satellite denied environment, and facing the needs of the navigation and positioning in indoor space, the technology of indoor magnetic field vector positioning and navigation have been studied. Based on the geomagnetic matching technology, the wavelet analysis method is used to estimate the noise components and statistical characteristics of the magnetic field three-dimensional signal; combined with the Kalman filtering algorithm, through estimating and optimizing the magnetic field vector signal, an indoor magnetic positioning signal processing algorithm with wavelet analysis and Kalman fusion is established, realizing the optimization of the measurement accuracy of the geomagnetic signal and the accuracy of the fingerprint map. The experimental results indicate that the signal-to-noise ratio of the test sequenceis increasedby 3db, the rootmean square error is reducedby about30%, and themaximumpositioning error reaches 1.34m, which can meet the requirements of indoor positioning.

Photon-counting detector based spectral computed tomography (CT) can obtain energy-discriminative attenuation map of an object in different energy channels, extending the conventional volumetric image along a spectral dimension. However, compared with the full spectrum data, the noise in a narrower energy channel is significantly increased. In order to improve image quality of spectral CT images, this paper proposes an iterative reconstruction algorithm based on the prior image constrained compressed sensing (PICCS) and dictionary learning (DL) theories, which is called PICCS-DL. The PICCS-DL utilizes the correlation of the images reconstructed from different energy channels by taking the broad spectrum image as a prior constraint, and it utilizes the sparse of the images by taking the total variation (TV) and DL as prior constraints. The alternating minimization, Split-Bregman and the steepest descent (SD) methods are used to solve the objective function. The effectiveness of the proposed method is validated with numerical simulations and preclinical applications. The results demonstrate that the proposed algorithm generally produces superior image quality, especially for noisy and sparse projection data.