In the early named entity recognition models, most text processing focused only on the representation of individual words and character vectors, and paid little attention to the semantic relationships between the preceding and following text in an utterance, which led to the inability to handle the problem of multiple meanings of a word during recognition. To address this problem, most models introduce the attention mechanism of Transformer model to solve the problem of multiple meanings of a word in text. However, the traditional Transformer model leads to a high computational overhead due to its fully connected structure. Therefore, this paper proposes a new model, the BERT-Star-Transformer-CNN-BiLSTM-CRF model, to solve the problem of the computational efficiency of the traditional Transformer. First, the input text is dynamically generated into a character vector using the BERT model pre-trained in large-scale preconditioning to solve the problem of multiple meanings of words, and then the lightweight Star-Transformer model is used as the feature extraction module to perform local feature extraction on the word vector sequence, while the CNN-BiLSTM joint model is used to perform global feature extraction on the context in the text. The obtained feature sequences are fused. Finally, the fused feature vector sequences are input to CRF for prediction of the final results. After the experiments, it is shown that the model has a significant improvement in precision, recall and F1 value compared with the traditional model, and the computational efficiency is improved by nearly 40%.

Addressing challenges in infrared imaging, such as low contrast, blurriness, and detail scarcity due to environmental limitations and the target’s limited radiative capacity, this research introduces a novel infrared image enhancement approach using low‐light image guidance. Initially, the Cbc‐SwinIR model (coordinate‐based convolution‐ image restoration using Swin Transformer) is applied for super‐resolution reconstruction of both shimmer and infrared images, improving their resolution and clarity. Next, the MAXIM model (multiaxis MLP for image processing) enhances the visibility of low‐light images under low illumination. Finally, the AILI (adaptive infrared and low‐light)‐fusion algorithm fuses the processed low‐light image with the infrared image, achieving comprehensive visual enhancement. The enhanced infrared image exhibits significant improvements: a 0.08 increase in fractal dimension (FD), 0.094 rise in information entropy, 0.00512 elevation in mean square error (MSE), and a 12.206 reduction in peak signal‐to‐noise ratio (PSNR). These advancements in FD and information entropy highlight a substantial improvement in the complexity and diversity of the infrared image’s features. Despite a decrease in PSNR and an increase in MSE, this indicates that the newly introduced information enhances contrast and enriches texture details in the infrared images, resulting in pixel‐level variations. This methodology demonstrates considerable improvements in visual content and analytical value, proving relevant, innovative, and efficient in infrared image enhancement with broad application prospects.

The development of the Internet of Things has led to great development of data sharing and data interaction, which has made security and privacy more and more a concern for users. How to ensure the safe sharing of data, avoid the leakage of sensitive information, and protect the privacy of users is a serious challenge. Access control is an important issue to ensure the trust of the Internet of Things. This paper proposes an access control scheme based on ciphertext attribute authentication and threshold policy, which uses the identity authentication of hidden attributes and divides the user’s permission grade by setting the threshold function with the user’s attributes. Users obtain different permission grades according to attribute authentication and access data of different sensitivity grades to achieve fine-grained, flexible and secure access to data in the cloud server while protecting personal privacy issues. In addition, when the resource is acquired, the identity and permission joint authentication method is adopted to avoid the collusion attack of the illegal member, which makes the resource access control more secure.

Unmanned aerial vehicle (UAV) is an attractive application because of its flexibility and economy. It may use a digital signature scheme to protect commands sent to UAVs. Moreover, the digital signature scheme should guarantee the real-time performance of UAVs executing commands and protect the signer’s privacy. Therefore, we proposed an attribute-based proxy signature (ABPS) scheme supporting flexible threshold predicate for UAV networks and proved its security. It has existential unforgeability under selective-predicate and chosen message attacks (EUF-sP-CMA) and can protect the signer’s privacy. We analyzed its computation costs based on experimental data and communication costs. The analysis results indicate that our ABPS scheme has less computation costs than other ABPS schemes and is at the same level as other ABPS schemes on communication costs.