Search Results for author: Nan Zhang

Found 8 papers, 2 papers with code

Super Reinforcement Bros: Playing Super Mario Bros with Reinforcement Learning

1 code implementation CUHK Course IERG5350 2020 Nan Zhang, Zixing Song

We plan to apply and adjust some well-known reinforcement learning (RL) algorithms to train an automatic agent to play the 1985 Nintendo game Super Mario Bros under a speedrun rule.

To Achieve Security and High Spectrum Efficiency: A New Transmission System Based on Faster-than-Nyquist and Deep Learning

no code implementations1 Aug 2020 Peiyang Song, Nan Zhang, Fengkui Gong, Qiang Li, Haiyang Ding

In this paper, we try to apply FTN into secure communications and propose a secure and high-spectrum-efficiency transmission system based on FTN and deep learning (DL).

Leveraging History for Faster Sampling of Online Social Networks

1 code implementation ‏‏‎ ‎ 2020 Zhuojie Zhou, Nan Zhang, Gautam Das

Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i. e., no access to the full graph topology).

Real Time 3D Indoor Human Image Capturing Based on FMCW Radar

no code implementations 2019 IEEE International Conference on Multimedia and Expo (ICME) 2019 Hangqing Guo, Nan Zhang, Wenjun Shi, Saeed ALI-AlQarni, Shaoen Wu, Honggang Wang

Compared to traditional camera-based computer vision and imaging, radio imaging based on wireless sensing does not require lighting and is friendly to privacy.

RF-based Pose Estimation

Understanding and Mitigating the Security Risks of Voice-Controlled Third-Party Skills on Amazon Alexa and Google Home

no code implementations3 May 2018 Nan Zhang, Xianghang Mi, Xuan Feng, Xiao-Feng Wang, Yuan Tian, Feng Qian

The significance of our findings have already been acknowledged by Amazon and Google, and further evidenced by the risky skills discovered on Alexa and Google markets by the new detection systems we built.

Cryptography and Security

Fine-Grained Change Detection of Misaligned Scenes With Varied Illuminations

no code implementations ICCV 2015 Wei Feng, Fei-Peng Tian, Qian Zhang, Nan Zhang, Liang Wan, Jizhou Sun

To guarantee detection sensitivity and accuracy of minute changes, in an observation, we capture a group of images under multiple illuminations, which need only to be roughly aligned to the last time lighting conditions.

Change Detection

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