Difference between revisions of "Resource:Seminar"

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{{Latest_seminar
{{Latest_seminar
|abstract = LoRa technology promises to enable Internet of Things applications over large geographical areas. However, its performance is often hampered by poor channel quality in urban environments, where blockage and multipath effects are prevalent. Our study uncovers that a slight shift in the position or attitude of the receiving antenna can substantially improve the received signal quality. This phenomenon can be attributed to the rich multipath characteristics of wireless signal propagation in urban environments, wherein even small antenna movement can alter the dominant signal path or reduce the polarization angular difference between transceivers. Leveraging these key observations, we propose and implement MoLoRa, an intelligent mobile antenna system designed to enhance LoRa packet reception. At its core, MoLoRa represents the position and attitude of an antenna as a state and employs a statistical optimization method to search for states that offer optimal signal quality efficiently. Through extensive evaluation, we demonstrate that MoLoRa achieves a maximum Signal-to-Noise Ratio (SNR) gain of 13 dB in a few attempts, enabling formerly problematic blind spots to reconnect and strengthening links for other nodes.
|abstract = The recent proliferation of spacecraft in Earth's orbits has ushered in the rise of large-scale satellite constellations. However, this unprecedented growth of constellations has introduced a previously unforeseen challenge: ground track congestion. Specifically, the increasing density of orbital slots forces satellites to share similar orbit planes, causing their nadir-point projections on Earth's surface (i.e., ground tracks) to overlap or remain in close proximity within short time intervals. Such orbit-endowed ground track congestion can degrade constellation performance in remote sensing operations, specified by limited constellation coverage, redundant satellite count, and delayed data delivery. To address this issue, we propose SpaceSched, a hierarchical scheduling framework designed to resolve ground track congestion in satellite constellations. SpaceSched consists of two key components: an on-ground constellation scheduling pipeline, comprising a coverage distributor and a satellite selector, and an in-space downlink scheduling pipeline, featuring a queue regulator. The coverage distributor assigns attitude profiles over time to satellites, ensuring non-overlapping imagery capture regions. The satellite selector optimizes the constellation by strategically selecting a subset of satellites while maintaining coverage efficiency. During in-space downlink scheduling, the queue regulator manages the downlink traffic queue to minimize the delay of high-value data transmission. We evaluate SpaceSched on two operational modes (i.e., stripmap and spotlight) across three well-established satellite constellation systems: SKYSAT, LEMUR, and FLOCK, with 17, 50, and 126 evaluated satellites, respectively. Experimental results demonstrate that SpaceSched improves coverage by up to 1.84×, reduces satellite counts by up to 2.38×, and decreases downlink queue load by up to 36.46×, compared to the plain satellite constellation systems. Furthermore, our case study highlights SpaceSched's potential to meet diverse task demands.
|confname =SenSys'25
|confname =Mobicom'25
|link = https://dl.acm.org/doi/10.1145/3715014.3722075
|link = https://dl.acm.org/doi/10.1145/3680207.3765249
|title= MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments
|title= SpaceSched: A Constellation-Wide Scheduling System for Resolving Ground Track Congestion in Remote Sensing
|speaker=Kai Chen
|speaker=Yifei
|date=2026-1-30
|date=2026-3-13
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract =Large language models (LLMs) achieve superior performance in generative tasks. However, due to the natural gap between language model generation and structured information extraction in three dimensions: task type, output format, and modeling granularity, they often fall short in structured information extraction, a crucial capability for effective data utilization on the web. In this paper, we define the generation process of the language model as the controllable state transition, aligning the generation and extraction processes to ensure the integrity of the output structure and adapt to the goals of the information extraction task. Furthermore, we propose the Structure2Text decider to help the language model understand the fine-grained extraction information, which converts the structured output into natural language and makes state decisions, thereby focusing on the task-specific information kernels, and alleviating language model hallucinations and incorrect content generation. We conduct extensive experiments and detailed analyses on myriad information extraction tasks, including named entity recognition, relation extraction, and event argument extraction. Our method not only achieves significant performance improvements but also considerably enhances the model's capability to generate precise and relevant content, making the extracted content easy to parse.
|abstract =Offering high-quality immersive content is the ultimate goal of volumetric video streaming. Although point clouds and meshes are dominant volumetric representations, their limitations in depicting photo-realistic content often undermine user experience. The recent advent of neural radiance fields (NeRF) offers a promising alternative content representation with superior photo-realism. However, streaming NeRF-based volumetric videos over wireless networks to mobile headsets faces significant challenges, including substantial bandwidth usage because of the large frame size, degraded visual quality due to even a low packet loss rate, and content artifacts caused by performance optimizations (e.g., remote rendering at the network edge). To address these challenges, in this paper, we introduce NeVo, a next-generation volumetric video streaming system for efficient delivery of neural content such as NeRF. NeVo incorporates the following innovations into a holistic system: (1) a novel method to model visibility of implicitly encoded neural content, thereby avoiding non-essential transmission to drastically reduce network data usage, (2) a lightweight, learning-based model for real-time content reconstruction after packet loss with carefully chosen data, and (3) judicious identification and selective delivery of intermediate data in edge-based NeRF rendering to effectively mitigate artifacts. Our extensive experiments indicate that compared with the state-of-the-art, NeVo saves up to 68.3% of bandwidth usage, maintains high visual quality despite packet loss, and enhances user experience by reducing artifacts.
|confname =WWW'25
|confname =Mobicom'25
|link = https://dl.acm.org/doi/abs/10.1145/3696410.3714571
|link = https://dl.acm.org/doi/10.1145/3680207.3723473
|title= Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition
|title= NeVo: Advancing Volumetric Video Streaming with Neural Content Representation
|speaker=Daobin
|speaker=Mengfan
|date=2026-1-30
|date=2026-3-13
}}
}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 01:47, 13 March 2026

Time: 2026-01-30 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [Mobicom'25] SpaceSched: A Constellation-Wide Scheduling System for Resolving Ground Track Congestion in Remote Sensing, Yifei
    Abstract: The recent proliferation of spacecraft in Earth's orbits has ushered in the rise of large-scale satellite constellations. However, this unprecedented growth of constellations has introduced a previously unforeseen challenge: ground track congestion. Specifically, the increasing density of orbital slots forces satellites to share similar orbit planes, causing their nadir-point projections on Earth's surface (i.e., ground tracks) to overlap or remain in close proximity within short time intervals. Such orbit-endowed ground track congestion can degrade constellation performance in remote sensing operations, specified by limited constellation coverage, redundant satellite count, and delayed data delivery. To address this issue, we propose SpaceSched, a hierarchical scheduling framework designed to resolve ground track congestion in satellite constellations. SpaceSched consists of two key components: an on-ground constellation scheduling pipeline, comprising a coverage distributor and a satellite selector, and an in-space downlink scheduling pipeline, featuring a queue regulator. The coverage distributor assigns attitude profiles over time to satellites, ensuring non-overlapping imagery capture regions. The satellite selector optimizes the constellation by strategically selecting a subset of satellites while maintaining coverage efficiency. During in-space downlink scheduling, the queue regulator manages the downlink traffic queue to minimize the delay of high-value data transmission. We evaluate SpaceSched on two operational modes (i.e., stripmap and spotlight) across three well-established satellite constellation systems: SKYSAT, LEMUR, and FLOCK, with 17, 50, and 126 evaluated satellites, respectively. Experimental results demonstrate that SpaceSched improves coverage by up to 1.84×, reduces satellite counts by up to 2.38×, and decreases downlink queue load by up to 36.46×, compared to the plain satellite constellation systems. Furthermore, our case study highlights SpaceSched's potential to meet diverse task demands.
  2. [Mobicom'25] NeVo: Advancing Volumetric Video Streaming with Neural Content Representation, Mengfan
    Abstract: Offering high-quality immersive content is the ultimate goal of volumetric video streaming. Although point clouds and meshes are dominant volumetric representations, their limitations in depicting photo-realistic content often undermine user experience. The recent advent of neural radiance fields (NeRF) offers a promising alternative content representation with superior photo-realism. However, streaming NeRF-based volumetric videos over wireless networks to mobile headsets faces significant challenges, including substantial bandwidth usage because of the large frame size, degraded visual quality due to even a low packet loss rate, and content artifacts caused by performance optimizations (e.g., remote rendering at the network edge). To address these challenges, in this paper, we introduce NeVo, a next-generation volumetric video streaming system for efficient delivery of neural content such as NeRF. NeVo incorporates the following innovations into a holistic system: (1) a novel method to model visibility of implicitly encoded neural content, thereby avoiding non-essential transmission to drastically reduce network data usage, (2) a lightweight, learning-based model for real-time content reconstruction after packet loss with carefully chosen data, and (3) judicious identification and selective delivery of intermediate data in edge-based NeRF rendering to effectively mitigate artifacts. Our extensive experiments indicate that compared with the state-of-the-art, NeVo saves up to 68.3% of bandwidth usage, maintains high visual quality despite packet loss, and enhances user experience by reducing artifacts.

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

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