Search Results for author: Zan Li

Found 7 papers, 2 papers with code

STAR-RIS Aided Integrated Sensing and Communication over High Mobility Scenario

no code implementations18 Mar 2024 Muye Li, Shun Zhang, Yao Ge, Zan Li, Feifei Gao, Pingzhi Fan

With the help of sensing results, the phase shifts of the STAR-RIS are delicately designed, which can significantly improve the received signal strength for both the RSUs and the in-vehicle UE, and can finally enhance the sensing and communication performance.

RepBNN: towards a precise Binary Neural Network with Enhanced Feature Map via Repeating

1 code implementation19 Jul 2022 Xulong Shi, Zhi Qi, Jiaxuan Cai, Keqi Fu, Yaru Zhao, Zan Li, Xuanyu Liu, Hao liu

Binary neural network (BNN) is an extreme quantization version of convolutional neural networks (CNNs) with all features and weights mapped to just 1-bit.

Binarization Quantization

Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System

no code implementations4 Nov 2021 Kyongmin Yeo, Zan Li, Wesley M. Gifford

We present a deep learning model for data-driven simulations of random dynamical systems without a distributional assumption.

Generative Adversarial Network

MCM-aware Twin-least-square GAN for Hyperspectral Anomaly Detection

no code implementations1 Jan 2021 Jiaping Zhong, Weiying Xie, Jie Lei, Yunsong Li, Zan Li

Hyperspectral anomaly detection under high-dimensional data and interference of deteriorated bands without any prior information has been challenging and attracted close attention in the exploration of the unknown in real scenarios.

Anomaly Detection Generative Adversarial Network

Sparse Coding-inspired GAN for Weakly Supervised Hyperspectral Anomaly Detection

no code implementations1 Jan 2021 Tao Jiang, Weiying Xie, Jie Lei, Yunsong Li, Zan Li

For solving these problems, this paper proposes a sparse coding-inspired generative adversarial network (GAN) for weakly supervised HAD, named sparseHAD.

Anomaly Detection Generative Adversarial Network +1

Multi-ary Pulse Amplitude Modulated Signal Processing Using Bistable Stochastic Resonance

no code implementations International Conference on Noise and Fluctuations (ICNF) 2015 Jin Liu, Zan Li, Rui Gao, Jun Bai, Linlin Liang

On this basis, the mechanism of the BSR system response to MPAM signal inputs is elucidated, and a corresponding decoding scheme is proposed.

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