### Convolution Patterns in EMT: A Projection-Based Approach  
In 1998, Yann LeCun et al. developed LeNet-5, the first successful convolutional neural network designed for handwritten digit recognition on the MNIST dataset [27]. In recent years, CNNs have transcended their initial confinement to image processing and have been extensively applied in various domains, such as natural language processing, speech recognition, and medical image analysis. Innovative CNN architectures have been employed in the field of deep learning for Electrical Tomography, in addition to those mentioned in [24; 25]. Tan et al. introduced a CNN-based method for image reconstruction in Electrical Resistance Tomography, achieving an impressive Image Correlation Coefficient of 0.95. Moreover, the study enhanced the model's generalization capability and training speed by exploring adjustments in network architecture, such as the addition of dropout layers and moving averages [28]. Zheng et al. constructed a deep convolutional neural network based on the physical principles of Electrical Capacitance Tomography, which can address both the forward and inverse problems. The network comprises two sub-networks: an encoder consisting of convolutional and pooling layers for estimating capacitance values and a decoder composed of fully connected layers for reconstructing images of the dielectric constant distribution [29]. Chen et al. introduced a CNN-based algorithm for the non-invasive and high-resolution detection of breast tumors through Magnetic Detection Electrical Impedance Tomography. The results indicated that the relative reconstruction error with the CNN algorithm could be reduced to 10% compared to the Truncated Singular Value Decomposition algorithm and the Backpropagation algorithm [30]. Li et al. proposed a Densely Connected Convolutional Neural Network to improve image reconstruction in Electrical Resistance Tomography, effectively mitigating the issues of information and gradient vanishing and significantly enhancing the accuracy and visual quality of the reconstructed images [31]. Wu et al. improved the convolutional neural network approach based on the Visual Geometry Group model by incorporating Batch Normalization layers, ELU activation functions, Global Average Pooling layers, and radial basis function neural networks to accelerate network convergence and improve reconstruction precision and robustness [32]. Klosowski et al. explored the use of Electrical Impedance Tomography in conjunction with deep learning techniques, specifically convolutional neural networks and Long Short-Term Memory networks, as well as their hybrid models, for detecting moisture distribution within building walls [33]. However, there is scant specialized research concerning the convolution methods applied to the input detection signals, particularly those integrating ET's unique characteristics. In [28; 32], convolution was performed on a 16 \(\times\) 13 matrix. In [29], convolution was applied to a flattened 1 \(\times\) 28 vector. Li et al. conducted detailed research on the characteristics of voltage data in Electrical Impedance Tomography. They proposed an image reconstruction method based on one-dimensional convolutional neural networks (1D-CNNs). The experimental results indicated that this network significantly outperforms traditional DNNs and 2D-CNNs in terms of image reconstruction quality [34]. The authors also elucidated the reasons why 1D-CNNs are superior to 2D-CNNs. An excerpt from their discussion states, 'It is time-consuming for converting 1D samples to 2D. Furthermore, the structure of the original measurement signal may be destroyed so that incorrect features may be extracted from 2D signals.' Building upon this work, we wish to further explore the physical significance behind the fact that the correlation of signals is destroyed after converting 1D samples to 2D. To this end, it is necessary to trace back to the origins and fundamental concepts of the two technologies, CNN and tomography.