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Found 5 papers, 0 papers with code
3D Spectrum Mapping and Reconstruction under Multi-Radiation Source Scenarios
Considering the variation of electromagnetic environment, we self-learn the path loss (PL) model based on the sampling data.
Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing
The radio environment map (REM) visually displays the spectrum information over the geographical map and plays a significant role in monitoring, management, and security of spectrum resources. In this paper, we present an efficient 3D REM construction scheme based on the sparse Bayesian learning (SBL), which aims to recovery the accurate REM with limited and optimized sampling data. In order to reduce the number of sampling sensors, an efficient sparse sampling method for unknown scenarios is proposed.
A Large-Scale Car Parts (LSCP) Dataset for Lightweight Fine-Grained Detection
Automotive related datasets have previously been used for training autonomous driving systems or vehicle classification tasks.
Machine-learning based methodologies for 3d x-ray measurement, characterization and optimization for buried structures in advanced ic packages
The extracted digital data was used to characterize and optimize the design and production of the interconnects and demonstrates a superior alternative to destructive physical analysis.
Cribriform pattern detection in prostate histopathological images using deep learning models
In this paper, we present an annotated cribriform dataset along with analysis of deep learning models and hand-crafted features for cribriform pattern detection in prostate histopathological images.
