Abstract: In this issue, “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used ...

Abstract: Accurate semantic segmentation of remote sensing data plays a crucial role in the success of geoscience research and applications. Recently, multimodal fusion-based segmentation models have ...

Abstract: There are still various challenges in remote sensing semantic segmentation due to objects diversity and complexity. Transformer-based models have significant advantages in capturing global ...

Abstract: In low-light conditions, the detection scene can be harsh, some fundamental image features of the target to be lost, which can result in the disappearance of essential visual characteristics ...

Abstract: Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) is promising for future sixth-generation mobile communication systems. For OFDM-based ISAC ...

Abstract: Oriented object detection in aerial images has made significant advancements propelled by well-developed detection frameworks and diverse representation approaches to oriented bounding boxes ...

Abstract: The definition of the language syntax and semantics for SystemVerilog, which is a unified hardware design, specification, and verification language, is provided. This standard includes ...

Abstract: Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread ...

Abstract: Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization, has demonstrated remarkable performance ...

Abstract: Recent studies have integrated convolutions into transformers to introduce inductive bias and improve generalization performance. However, the static nature of conventional convolution ...

Abstract: Multilabel feature selection solves the dimension distress of high-dimensional multilabel data by selecting the optimal subset of features. Noisy and incomplete labels of raw multilabel data ...

Abstract: To better characterize the differences in category features in Facial Expression Recognition (FER) tasks, and improve inter-class separability and intra-class compactness, we propose a ...