Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-10-25 16:30'''
|time='''2022-11-01 16:30'''
|addr=4th Research Building A527-B
|addr=4th Research Building A527-B
|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]].

Revision as of 11:30, 31 October 2022

Time: 2022-11-01 16:30
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [MobiCom 2022] Real-time neural network inference on extremely weak devices: agile offloading with explainable AI, Crong
    Abstract: With the wide adoption of AI applications, there is a pressing need of enabling real-time neural network (NN) inference on small embedded devices, but deploying NNs and achieving high performance of NN inference on these small devices is challenging due to their extremely weak capabilities. Although NN partitioning and offloading can contribute to such deployment, they are incapable of minimizing the local costs at embedded devices. Instead, we suggest to address this challenge via agile NN offloading, which migrates the required computations in NN offloading from online inference to offline learning. In this paper, we present AgileNN, a new NN offloading technique that achieves real-time NN inference on weak embedded devices by leveraging eXplainable AI techniques, so as to explicitly enforce feature sparsity during the training phase and minimize the online computation and communication costs. Experiment results show that AgileNN's inference latency is >6X lower than the existing schemes, ensuring that sensory data on embedded devices can be timely consumed. It also reduces the local device's resource consumption by >8X, without impairing the inference accuracy.
  2. [INFOCOM 2022] An RFID and Computer Vision Fusion System for Book Inventory using Mobile Robot, Zhuoliu
    Abstract: Mobile robot-assisted book inventory such as book identification and book order detection has become increasingly popular in smart library, replacing the manual book inventory which is time-consuming and error-prone. The existing systems are either computer vision (CV)-based or RFID-based, however several limitations are inevitable. CV-based systems may not be able to identify books effectively due to low accuracy of detecting texts on book spine. RFID tags attached to books can be used to identify a book uniquely. However, in high tag density scenarios such as library, tag coupling effects of adjacent tags may seriously affect the accuracy of tag reading. To overcome these limitations, this paper presents a novel RFID and CV fusion system for Book Inventory using mobile robot (RC-BI). RFID and CV are first used individually to obtain book order, then the information will be fused by the sequence based matching algorithm to remove ambiguity and improve overall accuracy. Specifically, we address three technical challenges. We design a deep neural network (DNN) model with multiple inputs and mixed data to filter out interference of RFID tags on other tiers, and propose a video information extracting schema to extract book spine information accurately, and use strong link to align and match RFID- and CV-based timestamp vs. book-name sequences to avoid errors during fusion. Extensive experiments indicate that our system achieves an average accuracy of 98.4% for tier filtering and an average accuracy of 98.9% for book order, significantly outperforming the state-of-the-arts.


History

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

Template loop detected: Resource:Previous Seminars

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