Yao Feng

ETH Zurich

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Co-Authors

Anjaneyulu Putta

Boise State University

Shankar Gairhe

University of South Dakota

Haoran Sun

University of California, San Diego

Lingchen Yang

ETH Zurich

Li Huang

Xi'an University of Architecture and Technology

Kun Zhou

Zhejiang University

Youyi Zheng

Zhejiang University

Keyu Wu

Institute for Infocomm Research

Yuefan Shen

Zhejiang University

Hongbo Fu

Hong Kong University of Science and Technology

Cooperative Institutions

Universidad del Noreste

Zhejiang University

Northeastern University

Chinese Academy of Sciences

Fudan University

University of California, San Diego

Beijing University of Posts and Telecommunications

University of Hong Kong

Nanjing Normal University

University of Chinese Academy of Sciences

Author Statistics

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CCDC 2053443: Experimental Crystal Structure Determination

The Cambridge Structural Database (2022)

Anjaneyulu Putta Shankar Gairhe Yao Feng Haoran Sun

10.5517/ccdc.csd.cc26xs31

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CCDC 2053444: Experimental Crystal Structure Determination

The Cambridge Structural Database (2022)

Anjaneyulu Putta Shankar Gairhe Yao Feng Haoran Sun

10.5517/ccdc.csd.cc26xs42

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Chaotic-Sequence-Based Channel Estimation Algorithm for Secure Optical OFDM Systems

Applied Mechanics and Materials (2014)

Han Dong Wang Li Huang Yao Feng

In this paper, in order to improve the security of optical OFDM system, we propose a new method to generate the pilot sequence of channel estimation using chaotic sequence. Firstly, we assign initial values to chaotic system, and calculate a chaotic sequence through an iteration method, then converting it into a binary sequence as the pilot of a DFT-based channel estimation algorithm. When the receiving end doesn’t know the chaotic-sequence-based pilot, the system will have very high BER, and can not restore the original data, which indicate that the new method provides a certain degree of security for optical OFDM systems without increasing the system complexity.

Sequence (biology)

Pseudorandom binary sequence

10.4028/www.scientific.net/amm.602-605.3165

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Citations (1)

MonoHair: High-Fidelity Hair Modeling from a Monocular Video

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

Keyu Wu Lingchen Yang Zhiyi Kuang Yao Feng Xutao Han

Monocular

High fidelity

10.1109/cvpr52733.2024.02281

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Citations (1)

Research on O-OFDM System Based on Hyper Chaos Scrambling Encryption Algorithm

Applied Mechanics and Materials (2014)

Yao Feng Li Huang

A new scrambling method in the O-OFDM [ [ signal is proposed based on the hyper-chaotic sequences. By scrambling the frequency signal, it can reach the role of encryption. First, a hyper-chaotic sequence is produced by a hyper-chaotic system; then scrambling the O-OFDM signal on the frequency and time domain by using the pseudo-randomness of the hyper-chaotic sequence. In this paper a 256×256lena image is as the original information to prove the effect of this new method. The experiment that conducted in the simulation O-OFDM system of the Optisystem software indicates: the algorithm has high security; and because of the character of the hyper-chaotic system, the secret key space has largely improved. It can ensure the safety of the information transmission effectively.

Scrambling

Key space

SIGNAL (programming language)

Sequence (biology)

10.4028/www.scientific.net/amm.513-517.1903

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Fast Iterative Graph Computing with Updated Neighbor States

2022 IEEE 38th International Conference on Data Engineering (ICDE) (2024)

Yijie Zhou Shufeng Gong Yao Feng Hanzhang Chen Song Yu

10.1109/icde60146.2024.00193

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MonoHair: High-Fidelity Hair Modeling from a Monocular Video

arXiv (Cornell University) (2024)

Keyu Wu Lingchen Yang Zhiyi Kuang Yao Feng Xutao Han

Undoubtedly, high-fidelity 3D hair is crucial for achieving realism, artistic expression, and immersion in computer graphics. While existing 3D hair modeling methods have achieved impressive performance, the challenge of achieving high-quality hair reconstruction persists: they either require strict capture conditions, making practical applications difficult, or heavily rely on learned prior data, obscuring fine-grained details in images. To address these challenges, we propose MonoHair,a generic framework to achieve high-fidelity hair reconstruction from a monocular video, without specific requirements for environments. Our approach bifurcates the hair modeling process into two main stages: precise exterior reconstruction and interior structure inference. The exterior is meticulously crafted using our Patch-based Multi-View Optimization (PMVO). This method strategically collects and integrates hair information from multiple views, independent of prior data, to produce a high-fidelity exterior 3D line map. This map not only captures intricate details but also facilitates the inference of the hair's inner structure. For the interior, we employ a data-driven, multi-view 3D hair reconstruction method. This method utilizes 2D structural renderings derived from the reconstructed exterior, mirroring the synthetic 2D inputs used during training. This alignment effectively bridges the domain gap between our training data and real-world data, thereby enhancing the accuracy and reliability of our interior structure inference. Lastly, we generate a strand model and resolve the directional ambiguity by our hair growth algorithm. Our experiments demonstrate that our method exhibits robustness across diverse hairstyles and achieves state-of-the-art performance. For more results, please refer to our project page https://keyuwu-cs.github.io/MonoHair/.

Monocular

High fidelity

10.48550/arxiv.2403.18356

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CCDC 2053442: Experimental Crystal Structure Determination

The Cambridge Structural Database (2022)

Anjaneyulu Putta Shankar Gairhe Yao Feng Haoran Sun

10.5517/ccdc.csd.cc26xs20

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Citations (0)