Abstract: Multivariate time series (MTS) forecasting presents significant challenges due to the diverse noise distributions and complex periodic patterns across different channels. Existing ...

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

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: Change detection (CD) is an essential aspect of urban planning and resource management. Deep learning (DL) has the potential to detect complex changes from massive data more automatically ...

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

Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=41 ...