Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2023-06-01 9:30'''
|time='''2026-01-30 10:30'''
|addr=4th Research Building A518
|addr=4th Research Building A518
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|📚 Readling list]]; [[Resource:Seminar_schedules|📆 Schedules]]; [[Resource:Previous_Seminars|🧐 Previous seminars]].
}}
}}


===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=In the last decade, LoRa has emerged and prevailed as a promising technology to offer the long range and low power communication service. The packet collisions caused by concurrent transmissions(CTs) severely limit the LoRa network capacity, which becomes the key obstacle to releasing the potential of LoRa. The existing collision-resolution researches need frequency domain features to separate different packets in the collision. When there exists multiple packets in the collision, these features are more likely to overlap with each other and cannot be distinguished, which leads to performance degradation of these studies. To address this issue, in this paper, we propose channel hopping LoRa (CHLoRa) as a physical approach that utilize the multi-channel diversity to against multi-packet collisions. In CHLoRa, the LoRa chirp is divided into several subchirps and spread into different channels. As all the subchirp-pieces of the original chirp are likely to be collided with the subchirps with different bins, CHLoRa can recover the original chirp’s bin through merging the same bins of its subchirps. However, it is hard to obtain precise demodulation results of subchirps especially in collision, as using shorter time-span subchirps decreases the frequency resolution. We propose a subchirp merging scheme to group and merge subchirps’ bins according to their collision-free confidence. We conduct simulation experiments to evaluate the performance of CHLoRa. The results show that ...
|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.
|confname=INFOCOM 2024
|confname =SenSys'25
|link=https://mobinets.org/index.php?title=Resource:Seminar
|link = https://dl.acm.org/doi/10.1145/3715014.3722075
|title=CHLoRa: Pushing the Limits of LoRa Concurrent Transmissions with Channel Hopping Subchirps
|title= MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments
|speaker=Wenliang}}
|speaker=Kai Chen
|date=2026-1-30
}}
{{Latest_seminar
{{Latest_seminar
|abstract = Accurate, real-time object detection on resource-constrained devices enables autonomous mobile vision applications such as traffic surveillance. However, analyzing real-time video poses severe challenges to today’s network and computation systems. Rather than either pure local processing or offloading, we merge large objects across the boundary locally and objects from the edge. To balance accuracy, latency, payment and reliability, we present EdgeLight, a crowd-assisted real-time video analytics framework, which coordinates computationally weak cameras with more powerful edge servers to enable video analytics under the accuracy, latency and payment requirements of applications. Furthermore, we design a connectionless service discovery protocol to reduce invalid wifi connections.
|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.
|confname=SEC 2023
|confname =WWW'25
|link=https://mobinets.org/index.php?title=Resource:Seminar
|link = https://dl.acm.org/doi/abs/10.1145/3696410.3714571
|title=EdgeLight: Smart Traffic Lights with Ambient Edge Intelligence
|title= Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition
|speaker=Xianyang}}
|speaker=Daobin
 
|date=2026-1-30
 
}}
 
=== History ===
 
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 10:51, 30 January 2026

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

Latest

  1. [SenSys'25] MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments, Kai Chen
    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.
  2. [WWW'25] Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition, Daobin
    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.

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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