Following the traditional total variational denoising model in removing medical image noise with blurred image texture details, among other problems, an adaptive medical image fractional-order total variational denoising model with an improved sparrow search algorithm is proposed in this study. This algorithm combines the characteristics of fractional-order differential operators and total variational models. The model preserves the weak texture region of the image improvement based on the unique amplitude-frequency characteristics of the fractional-order differential operator. The order of the fractional-order differential operator is adaptively determined by the improved sparrow search algorithm using both the sine search strategy and the diversity variation processing strategy, which can greatly improve the denoising ability of the fractional-order differential operator. The experimental results reveal that the model not only achieves the adaptivity of fractional-order total variable differential order, but also can effectively remove noise, preserve the texture structure of the image to the maximum extent, and improve the peak signal-to-noise ratio of the image; it also displays favorable prospects for applications in medical image denoising.

Comprehensive integration of 5G, artificial intelligence and other technologies into education has fully demonstrated openness and flexibility of distance education, which attracts a large number of learners to study. Good teacher-student interaction in distance teaching plays a significant role in improving the quality of course teaching and teachers’ career development. Based on the Classroom Assessment Scoring System (CLASS) questionnaire, the influence of emotional support, classroom organization and teaching support of teacher-student interaction on course teaching effect in distance education was analyzed, the difference of learning effect of whether students like distance education were analyzed. Results show that the reliability coefficient of the questionnaire is 0.938, and the KMO value is 0.892. The reliability and validity of the questionnaire are very good. Two aspects of teacher-student interaction (classroom organization and teaching support) can improve the learning effect. Whether samples like distance education have a significant effect on learning effect (p<0.05). The learning effect of learners who like distance education is significantly higher than those who do not. Conclusions have important reference value in clarifying the main types and contents of teacher-student interaction, improving students’ subjective feeling ability of curriculum education, promoting learners’ active participation in classroom teaching activities, and maintaining smooth interaction with teachers.

The mode mixing and mode dispersion in the multimode fiber (MMF) will produce complex speckle patterns in the distal end of the fiber as an object passes through the MMF, rendering image reconstruction to be a challenging task. In recent years, convolutional neural networks have been successfully applied to image reconstruction from speckles. However, the imaging spectra of these studies are mostly in the visible spectrum range and require complete speckle information for reconstruction. In this paper, we build an optical imaging system and use up-conversion imaging technology to collect speckle patterns formed by infrared light transmitting through a multimode fiber and a frequency-doubling crystal. We propose a GAN-based speckle restoration network (SRNet) to reconstruct speckle images. The generator of GAN uses ResNest and atrous spatial pyramid pooling(ASPP) to extract multi-level features and multi-scale context information, respectively. The discriminator of GAN can greatly improve the quality of the reconstructed image of the generator. In addition, we adopt a special training method named pre-training generator to avoid gradient disappearance or gradient explosion in the training process. With the designed network, we successfully reconstruct high-quality images even with only a portion of the speckle information.

With the rapid advancement of deep learning (DL) technology, data-driven fault diagnosis methods, particularly those that leverage convolutional neural networks (CNNs) for feature extraction, have demonstrated the potential to achieve high accuracy through end-to-end learning. However, most deep learning-based models cannot estimate the uncertainty of diagnostic results, which is crucial in practical applications, especially when encountering out-of-distribution (OOD) data. To address this issue, this paper proposes an Evidence One-Dimensional Convolutional Model (EODCM), which integrates evidence theory with multilayer one-dimensional convolution (ODC) for fault diagnosis. By introducing additional uncertainty estimation capabilities into the convolutional model, EODCM not only achieves high fault diagnosis accuracy but also provides uncertainty quantification for its predictions. Specifically, the proposed method treats the predictions of the ODC as subjective evidence and imposes a Dirichlet distribution on class probabilities, enabling effective evidence collection from data during training. Additionally, a specific loss function based on evidence theory is employed to enhance the uncertainty estimation capability of the model. To further improve generalization performance, a feature fusion strategy is adopted to integrate multi-view information, enriching the feature representation of one-dimensional data. Experimental validation on a gearbox fault diagnosis case from Southeast University demonstrates that EODCM effectively estimates predictive uncertainty and reduces overconfidence by 16.90% when facing OOD data. Furthermore, the proposed method maintains robust performance under varying noise conditions, highlighting its great potential for practical applications.

In recent years, deep learning has been effectively used to reconstruct speckled images created by the scattering media. However, when a network with previous data training is used to test new data, it usually exhibits poor performance due to significant differences in new data that differ in scattered media and data types. Retraining the network and collecting a big amount of new data are usually required to enhance performance, but this comes with a high cost and time expense. This article proposes a transfer learning approach to address this issue. We constructed an experimental setup for capturing infrared speckled images, which includes a laser module with a 4f resonant cavity structure capable of adjusting the aperture size of spatial filters. Speckled images are collected and preprocessed at different aperture sizes, then input into a convolutional neural network for training. Transfer learning is employed to study the reconstruction of speckled images between different aperture sizes. Subsequently, to recreate speckle patterns, a speckle reconstruction network named TCGAN (Transformer_CNN_GAN) is proposed. This network is based on pre-training TCGAN with an enormous quantity of data and fine-tuning its model with new data so that it can be adapted quickly with a small sample size. The experimental findings show that TCGAN may achieve good reconstruction without too much new data by means of transfer learning. Moreover, pretraining the network with more complex datasets helps enhance the network’s transfer learning capability. Therefore, conducting research on optical field imaging and restoration methods based on transfer learning holds significant theoretical and practical significance.