activity
20192026
most citedDeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction

11 citations · 13 across the 7 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

eess.SP2023

A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform

Yuxiao Li, Santiago Mazuelas, Yuan Shen

Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. L…

eess.SP2023

Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification

Yuxiao Li, Santiago Mazuelas, Yuan Shen

Received waveforms contain rich information for both range information and environment semantics. However, its full potential is hard to exploit under multipath and non-line-of-sig…

cs.LG2023

A Deep Learning Approach for Generating Soft Range Information from RF Data

Yuxiao Li, Santiago Mazuelas, Yuan Shen

Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range informati…

cs.LG2023

Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation

Yuxiao Li, Santiago Mazuelas, Yuan Shen

Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments. The emerging…

cs.CV2023★ 1 cited

Generalized Expectation Maximization Framework for Blind Image Super Resolution

Yuxiao Li, Zhiming Wang, Yuan Shen

Learning-based methods for blind single image super resolution (SISR) conduct the restoration by a learned mapping between high-resolution (HR) images and their low-resolution (LR)…

cs.CV2023

Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables

Yuxiao Li, Santiago Mazuelas, Yuan Shen

Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains chall…