Abstract: Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering (DC), ...

Abstract: Advanced deep-learning models have shown excellent performance in the task of fault-bearing diagnosis over traditional machine learning and signal-processing techniques. Few-shot learning ...

Abstract: The infrared small and dim (S&D) target detection is one of the key techniques in the infrared search and tracking system. Since the local regions similar to infrared S&D targets spread over ...

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a prominent technique with the potential to become a mainstream method for 3D representations. It can effectively transform multi-view images into ...

Abstract: The hyperspectral image (HSI) encompasses abundant spatial and spectral details, while light detection and ranging (LiDAR) delivers precise elevation data. The amalgamation of HSI and LiDAR ...

Abstract: This study presents a multimodal human-exoskeleton cooperative control method to realize different control modes smoothly switching each other with satisfactory stable performance.

Abstract: In this paper, we investigate the integration of integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RIS) for providing wide-coverage and ultra-reliable ...

Abstract: Denoising diffusion probabilistic models (DDPMs) have achieved unprecedented success in computer vision. However, they remain underutilized in medical imaging, a field crucial for disease ...

Abstract: Direct RF sampling relieves the analog front-end design and delivers high system flexibility. In $\gt10 \mathrm{GS} / \mathrm{s}\gt10 \mathrm{~b}$ ADCs, time-interleaving (TI) is inescapable ...

Abstract: Generative Adversarial Networks are a class of artificial intelligence algorithms that consist of a generator and a discriminator trained simultaneously through adversarial training. GANs ...

Abstract: Imbalanced data constitute a significant challenge in intelligent fault diagnosis cases because they can result in degraded diagnosis accuracy, which can in turn jeopardize the safety and ...

Abstract: This research addresses the imperative need for advanced detection mechanisms for the identification of phishing websites. For this purpose, we explore state-of-the-art machine learning, ...