Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2023-06-01 9:30'''
|time='''2025-04-11 10:30-12:00'''
|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
|abstract = While existing strategies to execute deep learning-based classification on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i.e. narrowing down a classification task to the current deployment context consisting of only recent inference queries can substantially enhance performance in resource-constrained environments. We propose a new paradigm, CACTUS, for scalable and efficient context-aware classification where a micro-classifier recognizes a small set of classes relevant to the current context and, when context change happens (e.g., a new class comes into the scene), rapidly switches to another suitable micro-classifier. CACTUS features several innovations, including optimizing the training cost of context-aware classifiers, enabling on-the-fly context-aware switching between classifiers, and balancing context switching costs and performance gains via simple yet effective switching policies. We show that CACTUS achieves significant benefits in accuracy, latency, and compute budget across a range of datasets and IoT platforms.
evaluate the performance of CHLoRa. The results show that ...
|confname = Mobisys'24
|confname=INFOCOM 2024
|link = https://dl.acm.org/doi/abs/10.1145/3643832.3661888
|link=https://mobinets.org/index.php?title=Resource:Seminar
|title= CACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference
|title=CHLoRa: Pushing the Limits of LoRa Concurrent Transmissions with Channel Hopping Subchirps
|speaker= Zhenhua
|speaker=Wenliang}}
|date=2025-04-18
}}
{{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 = Nowadays, volumetric videos have emerged as an attractive multimedia application providing highly immersive watching experiences since viewers could adjust their viewports at 6 degrees-of-freedom. However, the point cloud frames composing the video are prohibitively large, and effective compression techniques should be developed. There are two classes of compression methods. One suggests exploiting the conventional video codecs (2D-based methods) and the other proposes to compress the points in 3D space directly (3D-based methods). Though the 3D-based methods feature fast coding speeds, their compression ratios are low since the failure of leveraging inter-frame redundancy. To resolve this problem, we design a patch-wise compression framework working in the 3D space. Specifically, we search rigid moves of patches via the iterative closest point algorithm and construct a common geometric structure, which is followed by color compensation. We implement our decoder on a GPU platform so that real-time decoding and rendering are realized. We compare our method with GROOT, the state-of-the-art 3D-based compression method, and it reduces the bitrate by up to 5.98×. Moreover, by trimming invisible content, our scheme achieves comparable bandwidth demand of V-PCC, the representative 2D-based method, in FoV-adaptive streaming.
|confname=SEC 2023
|confname = TC'24
|link=https://mobinets.org/index.php?title=Resource:Seminar
|link = https://ieeexplore.ieee.org/document/10360355
|title=EdgeLight: Smart Traffic Lights with Ambient Edge Intelligence
|title= A GPU-Enabled Real-Time Framework for Compressing and Rendering Volumetric Videos
|speaker=Xianyang}}
|speaker=Mengfan
 
|date=2025-04-18
 
}}
 
=== History ===


{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 10:54, 18 April 2025

Time: 2025-04-11 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [Mobisys'24] CACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference, Zhenhua
    Abstract: While existing strategies to execute deep learning-based classification on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i.e. narrowing down a classification task to the current deployment context consisting of only recent inference queries can substantially enhance performance in resource-constrained environments. We propose a new paradigm, CACTUS, for scalable and efficient context-aware classification where a micro-classifier recognizes a small set of classes relevant to the current context and, when context change happens (e.g., a new class comes into the scene), rapidly switches to another suitable micro-classifier. CACTUS features several innovations, including optimizing the training cost of context-aware classifiers, enabling on-the-fly context-aware switching between classifiers, and balancing context switching costs and performance gains via simple yet effective switching policies. We show that CACTUS achieves significant benefits in accuracy, latency, and compute budget across a range of datasets and IoT platforms.
  2. [TC'24] A GPU-Enabled Real-Time Framework for Compressing and Rendering Volumetric Videos, Mengfan
    Abstract: Nowadays, volumetric videos have emerged as an attractive multimedia application providing highly immersive watching experiences since viewers could adjust their viewports at 6 degrees-of-freedom. However, the point cloud frames composing the video are prohibitively large, and effective compression techniques should be developed. There are two classes of compression methods. One suggests exploiting the conventional video codecs (2D-based methods) and the other proposes to compress the points in 3D space directly (3D-based methods). Though the 3D-based methods feature fast coding speeds, their compression ratios are low since the failure of leveraging inter-frame redundancy. To resolve this problem, we design a patch-wise compression framework working in the 3D space. Specifically, we search rigid moves of patches via the iterative closest point algorithm and construct a common geometric structure, which is followed by color compensation. We implement our decoder on a GPU platform so that real-time decoding and rendering are realized. We compare our method with GROOT, the state-of-the-art 3D-based compression method, and it reduces the bitrate by up to 5.98×. Moreover, by trimming invisible content, our scheme achieves comparable bandwidth demand of V-PCC, the representative 2D-based method, in FoV-adaptive streaming.

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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