Low-dose computed tomography (LDCT) reduces radiation exposure but suffers from high noise, impacting image quality and diagnostic accuracy. Supervised learning has helped address this challenge but ...

Despite the impressive performance of current deep learning models in the field of medical imaging, transferring the lung segmentation task in X-ray images to clinical practice is still a pending task ...

Generative artificial intelligence (GAI) has emerged as a rapidly burgeoning field demonstrating significant potential in creating diverse content intelligently and automatically. To support such ...

In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on training combined with ...

A circular polarization (CP) folded reflectarray antenna is proposed at the 30 GHz band. A five-layered circular polarization selective metasurface (CPSM) is introduced to realize the path folding for ...

With the expansion of social robots’ working environments, developing strategies to mitigate their mistakes has become crucial, especially given the difficulty of entirely avoiding errors. Previous ...

As the demand for electric vehicles (EVs) continues to surge, improvements to energy management systems (EMS) prove essential for improving their efficiency, performance, and sustainability. This ...

Hyperspectral images (HSIs) with high spatial resolution are challenging to obtain directly due to sensor limitations. Deep learning is able to provide an end-to-end reconstruction solution from low ...

In this letter, we present SemGuarder, a novel deep learning-based semantic communication (DLSC) system that simultaneously incorporates physical-layer semantic encryption and adversarial ...

Human gaze provides valuable information on human focus and intentions, making it a crucial area of research. Recently, deep learning has revolutionized appearance-based gaze estimation. However, due ...

This paper introduces a novel optimized hybrid model combining Long Short-Term Memory (LSTM) and Transformer deep learning architectures designed for power load forecasting. It leverages the strengths ...

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, ...