E. De Poorter
Electrical and Electronic Engineering · 2017–2018
A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 69 citations into it
- Computer Networks and Communications — 32 citations into it
- Artificial Intelligence — 12 citations into it
- Biomedical Engineering — 9 citations into it
- Signal Processing — 9 citations into it
Whom E. De Poorter trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts E. De Poorter overall
Papers from E. De Poorter's perspective
Ordered by how strongly the researchers E. De Poorter trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- On the coverage of LPWANs: range evaluation and channel attenuation model for LoRa technology (International Conference on ITS Telecommunications)
- Dropout: a simple way to prevent neural networks from overfitting
- A Study of LoRa: Long Range & Low Power Networks for the Internet of Things (Italian National Conference on Sensors)
- Deep Learning
- Spatially Sparse Precoding in Millimeter Wave MIMO Systems (IEEE Transactions on Wireless Communications)
- Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems (IEEE Journal on Selected Topics in Signal Processing)
- U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
- Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results (IEEE Communications Magazine)
Their papers
- A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives (2018) — 738 citations
- A Survey of LoRaWAN for IoT: From Technology to Application (2018) — 540 citations
- End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications (2017) — 320 citations