Guoqiang Mao

Electrical and Electronic Engineering · 2007–2019

A researcher in the Science corpus: 6 papers; trusts 78 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

Whom Guoqiang Mao trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Neal Patwari
  2. Paramvir Victor Bahl
  3. Yi Shang
  4. Lance Doherty
  5. Mark J. Coates
  6. Shotaro Akaho
  7. V. Padmanabhan
  8. Parameshwaran Krishnan
  9. Ralph E. Hudson
  10. Xiang Ji

Who trusts Guoqiang Mao overall

  1. Henk Wymeersch
  2. Melike Erol-Kantarci
  3. C. Laoudias
  4. Jaime Lien
  5. Hussein T. Mouftah
  6. Adriano J. C. Moreira
  7. Raghavendra V. Kulkarni
  8. Isaac Amundson
  9. Guangjie Han

Papers from Guoqiang Mao's perspective

Ordered by how strongly the researchers Guoqiang Mao trusts endorse each paper. Same corpus, a different order for every researcher.

  1. RADAR: an in-building RF-based user location and tracking system (Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064))
  2. Convex position estimation in wireless sensor networks (Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213))
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. ImageNet classification with deep convolutional neural networks
  5. Dynamic fine-grained localization in Ad-Hoc networks of sensors (ACM/IEEE International Conference on Mobile Computing and Networking)
  6. GPS-less low-cost outdoor localization for very small devices (IEEE wireless communications)
  7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  8. Gradient-based learning applied to document recognition
  9. ImageNet: A large-scale hierarchical image database
  10. Long Short-Term Memory

Their papers