Magnetic resonance imaging (MRI) provides diverse perspectives on anatomical structures, enabling multi-contrast super-resolution (SR) techniques that leverage complementary information across modalities to significantly enhance image quality. However, most existing multi-contrast SR methods are computationally intensive and lack lightweight solutions. In this study, we propose a novel lightweight progressive aggregation network (PAN) architecture for multi-contrast MRI SR. Our approach introduces multi-perception and residual feature aggregation mechanisms, which effectively capture and integrate anatomical details from low-resolution and reference images. Extensive experiments demonstrate that our lightweight method achieves superior efficiency, significantly outperforming other multi-contrast MRI SR methods in experiments, offering a promising solution for resource-constrained multi-contrast MRI super-resolution scenarios where computational efficiency is critical. The code can be found at https://github.com/Huaibei-normal-university-cv-laboratory/PAN .

ABSTRACT Depression, a prevalent mental disorder in modern society, significantly impacts people's daily lives. Recently, there have been advancements in developing automated diagnosis models for detecting depression. However, data scarcity, primarily due to privacy concerns, has posed a challenge. Traditional speech features have limitations in representing knowledge for depression diagnosis, and the complexity of deep learning algorithms necessitates substantial data support. Furthermore, existing multimodal methods based on neural networks overlook the heterogeneity gap between different modalities, potentially resulting in redundant information. To address these issues, we propose a multimodal depression detection model based on the Enhanced Cross‐Attention (ECA) Mechanism. This model effectively explores text‐speech interactions while considering modality heterogeneity. Data scarcity has been mitigated by fine‐tuning pre‐trained models. Additionally, we design a modal fusion module based on ECA, which emphasizes similarity responses and updates the weight of each modal feature based on the similarity information between modal features. Furthermore, for speech feature extraction, we have reduced the computational complexity of the model by integrating a multi‐window self‐attention mechanism with the Fourier transform. The proposed model is evaluated on the public dataset, DAIC‐WOZ, achieving an accuracy of 80.0% and an average F 1 value improvement of 4.3% compared with relevant methods.

Summary For improving the speed of character recognition, this paper applies data mining technology to character recognition under the condition of big data architecture. As an effective method of data mining, artificial neural network is a computational model established by simplifying, abstracting, and simulating the biological neural mechanism of human brain. Artificial neural network ensemble method, by training several weak learning devices to form the final strong learning device, can better improve the learning generalization ability of the model, and it has a broad application prospect. The main difficulties in the research of license plate recognition are the low quality of vehicle license plate image, the small proportion of license plate area, the distortion of characters to be recognized, and the large interference noise. In this paper, wavelet transform, edge detection, and line scanning methods are used to realize multi‐size and multi‐type license plate location, which overcomes the influence of illumination, color, size, and position. In the character recognition module, the improved BP neural network and the neural network integrated classifier based on weight clipping are used to identify. Experimental results show that the proposed data mining algorithm can further improve the learning efficiency and improve the performance of the time.