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Shen Wu

Publications

Citations

Publications by Year

20232025

Co-Authors:

Zhi CaiHuijie ChenXinyu DengJuan FangXin Jin

Show All Co-Authors (12)

Shen Wu

Affiliation

College of Computer Science

Beijing University of Technology

Beijing, China

Publication Topics

Edge Computing,
Edge Server,
Limited Resources,
Markov Decision Process,
Mobile Edge Computing,
Objective Function,
Service Requests,
Base Station,
Combinatorial Optimization Problem,
Computational Cost,
Computational Resources,
Deep Reinforcement Learning

Biography

Shen Wu received the BSc degree in computer science and technology from Yanshan University. He is currently working toward the MSc degree in electronic information with the College of Computer Science, Beijing University of Technology. He is a student member of the CCF.(Based on document published on 24 December 2024).

Publications

Citations

Publications by Year

20232025

Co-Authors:

Zhi Cai
Huijie Chen
Xinyu Deng
Juan Fang
Xin Jin

Show All Co-Authors (12)

Author's Published Works

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Showing 1-2 of 2 results

Conferences (2)

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Kazuki Naganuma(2)
Keitaro Yamashita(2)
Shunsuke Ono(2)
Tokyo Institute of Technology, Japan(1)
Dept. of Computer Science, Institute of Science Tokyo, Tokyo, Japan(1)
2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)(1)
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(1)
IEEE(2)
Hyderabad, India(1)
Macau, Macao(1)
Frobenius Norm(2)
General Gradient(2)
Graph Signal(2)
Nuclear Norm(2)
Original Signal(2)

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Controlling the Number of Sample-Contributive Vertices in Generalized Sampling of Graph Signals

Keitaro Yamashita;Kazuki Naganuma;Shunsuke Ono

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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This paper proposes a method for sampling graph signals by designing a flexible sampling operator via a difference-of-convex (DC) based algorithm. Departing from conventional methods limited to bandlimited signals, our method extend the generalized sampling theory to handle graph signals beyond bandlimitedness. Our method aims to design a flexible sampling operator that mixes vertex values, while ...Show More

Controlling the Number of Sample-Contributive Vertices in Generalized Sampling of Graph Signals

Keitaro Yamashita;Kazuki Naganuma;Shunsuke Ono

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

Generalized Graph Signal Sampling under Subspace Priors by Difference-of-Convex Minimization

Keitaro Yamashita;Kazuki Naganuma;Shunsuke Ono

2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2024 | Conference Paper |

Cited by: Papers (2)

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This paper proposes an effective approach for sampling graph signals under the subspace prior. Unlike conventional methods that assume bandlimited signals, our method, based on generalized sampling theory, designs a sampling operator suitable for general graph signals beyond bandlimitedness. We formulate a feasibility problem for designing the sampling operator for graph signals under the subspace...Show More

Generalized Graph Signal Sampling under Subspace Priors by Difference-of-Convex Minimization

Keitaro Yamashita;Kazuki Naganuma;Shunsuke Ono

2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

Year: 2024 | Conference Paper |

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