To address the challenges posed by early weak fault signals hindering fault feature extraction
and leading to diminished recognition accuracy in deep learning models, we present a …

Automatic modulation recognition (AMR) plays an important role in various communications
systems. It has the ability of adaptive modulation and can adapt to various complex …

As computer vision advances, synthetic aperture radar (SAR) ship detection increasingly
incorporates deep learning techniques based on convolutional neural networks (CNNs). …

Wireless multimedia sensor networks (WMSNs) are widely used in various fields where
coverage control is a critical difficulty because multiple requirements need to be considered such …

Safety surveillance is considered one of the most important factors in many constructing
industries for green internet of things (IoT) applications. However, traditional safety monitoring …

In recent years, object detection in computer vision has developed rapidly. However, crowded
pedestrian detection in object detection remains a challenging problem, especially in one-…

With the rapid advancements in computer vision object detection networks, several detection
networks based on remote sensing data have been proposed for oriented object detection. …

Remote sensing (RS) evapotranspiration (ET) models are commonly used to estimate ET at
a large scale. These models often include spatially varying parameters that need to be …

Since the rolling bearing fault signal captured by a vibration sensor contains a large amount
of background noise, fault features cannot be accurately extracted. To address this problem, …

To strengthen the toughness of thermosetting resins under low temperatures, tetrafunctional
epoxy-modified silicone resins (TESR-1/4/9) with different lengths of flexible chains were …