Abstract: Imbalanced datasets pose significant challenges to classification tasks, as traditional classifiers often favor majority classes. Although numerous methods have been proposed to balance data ...

Abstract: Driven by the increasing demand for accurate and efficient representation of 3D data in various domains, point cloud sampling has emerged as a pivotal research topic in 3D computer vision.

Abstract: Health care technology is advancing rapidly, transforming diagnostics through compact, non-invasive, and user-friendly biomedical devices. For instance, diabetes diagnosis that once required ...

Abstract: Active learning (AL) has achieved great success in remotely sensed hyperspectral image (HSI) classification due to its ability to select highly informative training samples. An appropriate ...

Abstract: Class imbalance is a pervasive challenge in real-world machine learning (ML) applications, where the minority class, often the class of interest, is significantly underrepresented. This ...

Abstract: A DRFM-based interrupted sampling repeater jammer (ISRJ) poses a serious threat to radar by achieving signal processing gain and maintaining main-lobe jamming. A class of ...

Abstract: We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with ...

Abstract: Interrupted sampling repeater jamming (ISRJ) features sound coherence, flexible modulation and fast response capability, thus producing both suppression and deception effects at the radar ...

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Abstract: This article presents a closed-loop type burst-mode clock and data recovery (BM-CDR) circuit with fast phase offset detection using 8/3x-fractional oversampling in the periodic preamble. The ...