Semantic segmentation of high-resolution remote sensing images is vital in downstream applications such as land-cover mapping, urban planning, and disaster assessment. Existing Transformer-based ...

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often have different scales of topology and highly coupled spatiotemporal features. However, ...

Autonomous Underwater Vehicles (AUVs) epitomize a revolutionary stride in underwater exploration, seamlessly assuming tasks once exclusive to manned vehicles. Their collaborative prowess within joint ...

Single image dehazing is a challenging ill-posed problem which estimates latent haze-free images from observed hazy images. Some existing deep learning based methods are devoted to improving the model ...

The transport sector has experienced a boom in electric mobility over the past decade as it moves towards a more sustainable future associated with the Sustainable Development Goals (SDGs). This paper ...

Underwater imaging is often affected by light attenuation and scattering in water, leading to degraded visual quality, such as color distortion, reduced contrast, and noise. Existing underwater image ...

Contemporary multi-modal trackers achieve strong performance by leveraging complex backbones and fusion strategies, but this comes at the cost of computational efficiency, limiting their deployment in ...

Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging ...

Motorimagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the performance of classification is affected by the non-stationarity and ...

The rapid development of the large language model (LLM) presents huge opportunities for 6G communications – for example, network optimization and management – by allowing users to input task ...

The detection of airborne small targets amidst cluttered environments poses significant challenges. Factors such as the susceptibility of a single RGB image to interference from the environment in ...

Recently, Transformer networks have demonstrated outstanding performance in the field of image restoration due to the global receptive field and adaptability to input. However, the quadratic ...