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Found 9 papers, 1 papers with code
Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning
A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation.
Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty.
RIDERS: Radar-Infrared Depth Estimation for Robust Sensing
Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning.
Multi-User Matching and Resource Allocation in Vision Aided Communications
Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system.
Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection
In this letter, we propose a novel mmWave beam selection method based on the environment semantics extracted from user-side camera images.
Vision-Aided Blockage Avoidance in UAV-assisted V2X Communications
The blockage is a key challenge for millimeter wave communication systems, since these systems mainly work on line-of-sight (LOS) links, and the blockage can degrade the system performance significantly.
Computer Vision Aided mmWave Beam Alignment in V2X Communications
Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status.
Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images
Channel covariance matrix (CCM) is one critical parameter for designing the communications systems.
3D Scene Based Beam Selection for mmWave Communications
In this paper, we present a novel framework of 3D scene based beam selection for mmWave communications that relies only on the environmental data and deep learning techniques.
