Abstract: Deep learning has garnered extensive attention in hyperspectral image (HSI) processing. However, its application in HSI semantic segmentation tasks has been relatively limited. Although ...

Abstract: In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models ...

Abstract: The adoption of voluntary environmental standards has emerged as a promising approach to coping with climate change and achieving sustainable development. While prior research has ...

Abstract: Building the next-generation wireless systems that could support services such as the metaverse, digital twins (DTs), and holographic teleportation is challenging to achieve exclusively ...

Abstract: The traditional Rapidly-exploring Random Tree Star (RRT*) suffers from the low path generation efficiency, numerous invalid exploration points, and unsuitability for navigation in unknown ...

Abstract: This brief presents a 12-bit low-power successive-approximation-register (SAR) capacitance-to-digital converter (CDC) for capacitive pressure sensors. It adopts a capacitance-to-voltage ...

Abstract: Recent studies have shown that the deep domain adaptation (DA) technique has achieved remarkable results in cross-domain hyperspectral image (HSI) classification task. However, these DA ...

Abstract: With the continuous development of intelligent UAV technology, efficient and accurate sensing of surrounding objects through onboard sensors has become an important research direction. Among ...

Abstract: This article simultaneously addresses the dual-rate and view constraints issues for the image-based visual servoing (IBVS) system of robot manipulators. Considering the low sampling ...

Abstract: Considerable interindividual variability exists in electroencephalogram (EEG) signals, resulting in challenges for subject-independent emotion recognition tasks. Current research in ...

Abstract: Deep learning models have been widely investigated for computing and analyzing brain images across various downstream tasks such as disease diagnosis and age regression. Most existing models ...

Abstract: Dynamic constrained multi-objective optimization problems (DCMOPs) involve complex changes in objective functions and constraints over time. These changes challenge most existing algorithms ...