Hailong Zhang

Orcid: 0009-0000-0304-981X

Affiliations:
  • China Electric Power Research Institute, Beijing, China
  • Tianjin University, Tianjin, China


According to our database1, Hailong Zhang authored at least 11 papers between 2013 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2023
A fast adaptive acquisition method for burst spread spectrum communication system.
Proceedings of the IEEE Sustainable Power and Energy Conference, 2023

Research on throughput maximization of mass device access based on 5G big connection.
Proceedings of the 2023 International Conference on Communication Network and Machine Learning, 2023

2022
Intelligent IoT Scheduling Mechanism Based on Data Traffic Prediction.
Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition, 2022

Multi-Objective Optimization of Dynamic Resource Scheduling in IoT Cloud Platform.
Proceedings of the 5th International Conference on Algorithms, 2022

2019
Subcarrier and Power Allocations for Dimmable Enhanced ADO-OFDM With Iterative Interference Cancellation.
IEEE Access, 2019

Test Methods for EM Emission for Broadband Carrier Communication Systems over Low-voltage Power Lines.
Proceedings of the 2019 IEEE International Conference on Communications, 2019

The Coexistence Between Broadband Power Line Communication and Shortwave Radio Systems with an Optimized Power Allocation Strategy.
Proceedings of the 2019 IEEE International Conference on Communications, 2019

CSMA-and-NOMA-based Random Massive Access in Power Line Communication for Smart Gird Applications.
Proceedings of the 2019 IEEE International Conference on Communications, 2019

2018
Navigation Data Bit Decoding of Weak GNSS Signal.
Proceedings of the 2018 IEEE International Conference on Signal Processing, 2018

2015
Motion and Structure Information Based Adaptive Weighted Depth Video Estimation.
IEEE Trans. Broadcast., 2015

2013
Evaluation and modeling of depth feature incorporated visual attention for salient object segmentation.
Neurocomputing, 2013


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