Abstract: A dual-operation-mode ring oscillator that employs dual-delay paths is presented. The two operation modes, referred to as the differential and common modes, have different output waveform ...
Abstract: Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling bearings. However, these neural networks are lack of interpretability for fault diagnosis tasks.
Abstract: This article presents a hybrid trajectory optimization method designed to generate collision-free, smooth trajectories for autonomous mobile robots. By combining sampling-based model ...
Abstract: In this paper, we study the secrecy performance of mixed radio-frequency (RF) - free space optical (FSO) systems by considering both RF and FSO eavesdropper attacks. More precisely, we shed ...
Abstract: A K-/Ka-band shared-aperture endfire phased array antenna is presented in this article. The antenna has the properties of dual wide bandwidths, orthogonal circular polarizations (CPs), wide ...
Abstract: The operation of wireless power transfer systems with multiple transmitters (TXs) or receivers (RXs) is investigated. With multiple TXs or RXs in a limited space, couplings occur between TXs ...
Abstract: Prognostics and health management applications rely heavily on predicting industrial equipment’s remaining useful life (RUL). The traditional RUL prediction approaches mainly consider the ...
Abstract: We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles (AVs), lidar sensors collect 3D point clouds ...
Abstract: Classroom learning behavior recognition can provide effective technical support for teaching and learning. However, in natural classroom teaching scenarios, classroom learning behaviors are ...
Abstract: Field-programmable gate arrays (FPGAs) have been shown to provide high computational density and efficiency for many computing applications by allowing circuits to be customized to any ...
Hierarchical Graph Attention Based Multi-View Convolutional Neural Network for 3D Object Recognition
Abstract: For multi-view convolutional neural network based 3D object recognition, how to fuse the information of multiple views is a key factor affecting the recognition performance. Most traditional ...
Abstract: This paper presents an advanced deep ensemble learning framework for short-term load forecasting (STLF). The refined deep ensemble model (DEM), complemented ...
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