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

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, ...

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 ...

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 ...

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 ...

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 ...

Methods based on 3D Gaussian Splatting (3DGS) for surface reconstruction face challenges when applied to large-scale scenes captured by UAV. Because the number of 3D Gaussians increases dramatically, ...

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 ...

Quantum computing, a transformative field that emerged from quantum mechanics and computer science, has gained immense attention for its potential to revolutionize computation. This paper aims to ...

In today’s digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and ...