Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-06-09 16:00
|time='''2025-06-13 10:30-12:00'''
|addr=Main Building B1-612
|addr=4th Research Building A518
|note=The reading list could be found [[Resource:Reading_List|here]]. Schedules are [[Resource:Seminar_schedules|here]]. Previous seminars can be found [[Resource:Previous_Seminars|here]].
|note=Useful links: [[Resource:Reading_List|📚 Readling list]]; [[Resource:Seminar_schedules|📆 Schedules]]; [[Resource:Previous_Seminars|🧐 Previous seminars]].
}}
}}


===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|confname=Topic
|abstract = In the metaverse era, point cloud video (PCV) streaming on mobile XR devices is pivotal. While most current methods focus on PCV compression from traditional 3-DoF video services, emerging AI techniques extract vital semantic information, producing content resembling the original. However, these are early-stage and computationally intensive. To enhance the inference efficacy of AI-based approaches, accommodate dynamic environments, and facilitate applicability to metaverse XR devices, we present ISCom, an interest-aware semantic communication scheme for lightweight PCV streaming. ISCom is featured with a region-of-interest (ROI) selection module, a lightweight encoder-decoder training module, and a learning-based scheduler to achieve real-time PCV decoding and rendering on resource-constrained devices. ISCom’s dual-stage ROI selection provides significantly reduces data volume according to real-time interest. The lightweight PCV encoder-decoder training is tailored to resource-constrained devices and adapts to the heterogeneous computing capabilities of devices. Furthermore, We provide a deep reinforcement learning (DRL)-based scheduler to select optimal encoder-decoder model for various devices adaptivelly, considering the dynamic network environments and device computing capabilities. Our extensive experiments demonstrate that ISCom outperforms baselines on mobile devices, achieving a minimum rendering frame rate improvement of 10 FPS and up to 22 FPS. Furthermore, our method significantly reduces memory usage by 41.7% compared to the state-of-the-art AITransfer method. These results highlight the effectiveness of ISCom in enabling lightweight PCV streaming and its potential to improve immersive experiences for emerging metaverse application.
|link=https://mobinets.org/index.php?title=Resource:Seminar
|confname =JSAC'24
|title= Path Reconstruction in Wireless Network
|link = https://dl.acm.org/doi/10.1109/JSAC.2023.3345430
|speaker=Luwei Fu
|title= ISCom: Interest-Aware Semantic Communication Scheme for Point Cloud Video Streaming on Metaverse XR Devices
|date=2021-06-08
|speaker=Jiyi
|abstract=This talk is about to expand the recent advances in path reconstruction in wireless networks and my thoughts on dynamic wireless networks with uncertain topologies.
|date=2025-06-13
}}
}}
{{Latest_seminar
{{Latest_seminar
|confname=INFOCOM'2021
|abstract = Scientific Illustration Tutorial
|link=https://www.jianguoyun.com/p/DfMXogcQ_LXjBxiz6PkD
|confname = TUTORIAL
|title= Mobility- and Load-Adaptive Controller Placement and Assignment in LEO Satellite Networks
|link = https://mobinets.cn/Resource:Seminar
|speaker=Linyuanqi Zhang
|title= Idea share
|date=2021-06-08
|speaker=OldBee
|abstract=Software-defined networking (SDN) based LEO satellite networks can make full use of satellite resources through flexible function configuration and efficient resource management of controllers. Consequently, controllers have to be carefully deployed based on dynamical topology and time-varying workload. However, existing work on controller placement and assignment is not applicable to LEO satellite networks with highly dynamic topology and randomly fluctuating load. In this paper, we first formulate the adaptive controller placement and assignment (ACPA) problem and prove its NP-hardness. Then, we propose the control relation graph (CRG) to quantitatively capture the control overhead in LEO satellite networks. Next, we propose the CRG-based controller placement and assignment (CCPA) algorithm with a bounded approximation ratio. Finally, using the predicted topology and estimated traffic load, a lookahead-based improvement algorithm is designed to further decrease the overall management costs. Extensive emulation results demonstrate that the CCPA algorithm outperforms related schemes in terms of response time and load balancing.
|date=2025-06-13
}}
}}


=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 08:34, 16 June 2025

Time: 2025-06-13 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [JSAC'24] ISCom: Interest-Aware Semantic Communication Scheme for Point Cloud Video Streaming on Metaverse XR Devices, Jiyi
    Abstract: In the metaverse era, point cloud video (PCV) streaming on mobile XR devices is pivotal. While most current methods focus on PCV compression from traditional 3-DoF video services, emerging AI techniques extract vital semantic information, producing content resembling the original. However, these are early-stage and computationally intensive. To enhance the inference efficacy of AI-based approaches, accommodate dynamic environments, and facilitate applicability to metaverse XR devices, we present ISCom, an interest-aware semantic communication scheme for lightweight PCV streaming. ISCom is featured with a region-of-interest (ROI) selection module, a lightweight encoder-decoder training module, and a learning-based scheduler to achieve real-time PCV decoding and rendering on resource-constrained devices. ISCom’s dual-stage ROI selection provides significantly reduces data volume according to real-time interest. The lightweight PCV encoder-decoder training is tailored to resource-constrained devices and adapts to the heterogeneous computing capabilities of devices. Furthermore, We provide a deep reinforcement learning (DRL)-based scheduler to select optimal encoder-decoder model for various devices adaptivelly, considering the dynamic network environments and device computing capabilities. Our extensive experiments demonstrate that ISCom outperforms baselines on mobile devices, achieving a minimum rendering frame rate improvement of 10 FPS and up to 22 FPS. Furthermore, our method significantly reduces memory usage by 41.7% compared to the state-of-the-art AITransfer method. These results highlight the effectiveness of ISCom in enabling lightweight PCV streaming and its potential to improve immersive experiences for emerging metaverse application.
  2. [TUTORIAL] Idea share, OldBee
    Abstract: Scientific Illustration Tutorial

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

Instructions

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