The light absorption and scattering of underwater impurities lead to poor underwater imaging quality. The existing data-driven based underwater image enhancement (UIE) techniques suffer from the lack of a large-scale dataset containing various underwater scenes and high-fidelity reference images. Besides, the inconsistent attenuation in different color channels and space areas is not fully considered for boosted enhancement. In this work, we built a large scale underwater image (LSUI) dataset, which covers more abundant underwater scenes and better visual quality reference images than existing underwater datasets. The dataset contains 4279 real-world underwater image groups, in which each raw image’s clear reference images, semantic segmentation map and medium transmission map are paired correspondingly. We also reported an U-shape Transformer network where the transformer model is for the first time introduced to the UIE task. The U-shape Transformer is integrated with a channel-wise multi-scale feature fusion transformer (CMSFFT) module and a spatial-wise global feature modeling transformer (SGFMT) module specially designed for UIE task, which reinforce the network’s attention to the color channels and space areas with more serious attenuation. Meanwhile, in order to further improve the contrast and saturation, a novel loss function combining RGB, LAB and LCH color spaces is designed following the human vision principle. The extensive experiments on available datasets validate the state-of-the-art performance of the reported technique with more than 2dB superiority. The dataset and demo code are available on https://bianlab.github.io/.

To ensure the thriving development of low-altitude economy, countering unauthorized uncrewed aerial vehicles (UAVs) is an essential task. The existing widely deployed base stations hold great potential for joint communication and jamming (JCJ). In the light of this, this article investigates the joint design of beamforming to simultaneously support communication with legitimate users and countermeasure against unauthorized UAVs based on dual-functional multiple-input-multiple-output (MIMO) cellular systems. We first formulate a JCJ problem, relaxing it through semi-definite relaxation (SDR) to obtain a tractable semi-definite programming (SDP) problem, with SDR providing an essential step toward simplifying the complex JCJ design. Although the solution to the relaxed SDP problem cannot directly solve the original problem, it offers valuable insights for further refinement. Based on these insights, we design a novel constraint specifically tailored to the structure of the SDP problem, ensuring that the solution adheres to the rank-1 constraint of the original problem. Finally, we validate effectiveness of the proposed JCJ scheme through extensive simulations. The results confirm that the proposed JCJ scheme can operate effectively when the total number of legitimate users and unauthorized UAVs exceeds the number of antennas. Simulation codes are provided to reproduce the results in this article: https://github.com/LiZhuoRan0.

With the development of autonomous driving technologies, robo-taxis (shared autonomous vehicles) are being tested on real roads. In China, in particular, people in some cities such as Beijing and Shanghai can book a robo-taxi online and experience the service. To examine the influential factors on user acceptance of robo-taxi services, this study proposes and employs an extended technology acceptance model (TAM) with four external factors: perceived trust, government support, social influence, and perceived enjoyment. Data were collected through an online questionnaire in China, and responses from 403 respondents were analyzed using structural equation modeling. Both typical TAM factors—including perceived ease of use, perceived usefulness, and attitude—and external factors were found to play significant roles in predicting users’ intention to use robo-taxis. The four external factors influenced the user acceptance indirectly via typical TAM factors. Improving users’ perceived trust is important for increasing public adoption. A greater emphasis by manufacturers on safety concerns, wider dissemination of information on data protection and safety systems, and government support through incentives for manufacturers and users can help improve public adoption of robo-taxi services.

Recently, learning-based hyperspectral anomaly detection (HAD) methods have demonstrated outstanding performance, dominating mainstream research. However, existing learning-based approaches still have two issues: 1) They rarely consider both the spatial sparsity and inter-spectral similarity of hyperspectral imagery (HSI) simultaneously; 2) They treat all regions equally, often overlooking the importance of high-frequency information in HSI, which is key to distinguish background and anomalies. To address these challenges, we propose a novel HAD method based on spatial-spectral adaptive dual-domain learning, termed SSHAD. Specifically, we first introduce the spatial-wise Selected State Space Module (SSSM) with linear complexity and the spectral-wise Frequency Division Self-Attention Module (FDSM), which are combined in parallel to form a Spatial-Spectral Block (SS-Block). The SSSM captures the global receptive field by scanning the HSI spatial dimension through a multi-directional scanning mechanism. The FDSM extracts high-frequency and low-frequency information from the HSI via the discrete wavelet transform and applies multi-scale convolution and self-similarity attention respectively, ensuring the suppression of anomalies during the reconstruction process. This parallel structure enables the network to model cross-window connections, expanding its receptive field while maintaining linear complexity. We use the SS-Block as the main component of our adaptive dual-domain learning network, forming SSHAD. Furthermore, we introduce a frequency-wise loss function to inhibit the reconstruction of high-frequency anomalies during background reconstruction. Comprehensive experiments conducted on four public datasets and two unmanned aerial vehicle-borne datasets validate the superiority and effectiveness of SSHAD. The code will be publicly available at https://github.com/CZhu0066/SSHAD.