Xuancheng Jin
A5102999088- Molecular Communication and Nanonetworks
- Astrophysics and Cosmic Phenomena
- Dark Matter and Cosmic Phenomena
- Advanced biosensing and bioanalysis techniques
- Particle Detector Development and Performance
- Energy Harvesting in Wireless Networks
- Data Stream Mining Techniques
- Neutrino Physics Research
- Machine Learning and Data Classification
- Gene Regulatory Network Analysis
- Anomaly Detection Techniques and Applications
- Wireless Body Area Networks
Zhejiang University of Technology
2023-2024
Tongji University
2023
University of Science and Technology of China
2017-2019
The precise measurement of the spectrum protons, most abundant component cosmic radiation, is necessary to understand source and acceleration rays in Milky Way. This work reports ray proton fluxes with kinetic energies from 40 GeV 100 TeV, two a half years data recorded by DArk Matter Particle Explorer (DAMPE). first time an experiment directly measures protons up ~100 TeV high statistics. measured confirms spectral hardening found previous experiments reveals softening at ~13.6 index...
The mobile molecular communication (MMC) system has promising prospects in the field of biomedical drug delivery. signal detection plays significant roles improving performance MMC with time-varying channel. However, when channel state information is unknown, traditional strategies are not effective and detectors using deep learning can exhibit better ability. This letter proposes a sequence detector for based on Informer model which one learning-based detectors. computation autocorrelation...
In this paper, the optimizations of multi-hop cooperative molecular communication (CMC) system in cylindrical anomalous-diffusive channel three-dimensional enviroment are investigated.First, we derive performance bit error probability (BEP) CMC under decode-and-forward relay strategy.Then for achieving minimum average BEP, optimization variables detection thresholds at nodes and destination node, corresponding problem is formulated.Furthermore, use conjugate gradient (CG) algorithm to solve...
Evolving data streams containing concept drift breaks the assumption that are independent and identically distributed (IID) in traditional machine learning models. A series of ensemble-based models have been adapted to stream tasks due abilities dynamic partially update cooperate with detection methods. Adaptive Random Forests (ARF), one state-of-the-art setting, encounters high resource requirements both efficiency memory like all ensemble We committed reducing running cost without...