Thursday, 2023/03/30

  • CS/AI
  • C
  • TITECH
  • Switch Language
    • ja日本語 (Japanese)
    • enEnglish

Shimosaka Research Group

pursuing MIUBIQ (machine intelligence in UbiComp Research)

  • Home
    • Members
    • Location
  • News
  • Projects
  • Publications
  • Awards
  • Archives
    • Codes
    • Datasets
Navigation
Codes

Codes

npb4go: NonParametric Bayes for Going-Out behavior

Download

npb4go, a nonparametric Bayesian software for modeling going-out behavior written by Matlab, detects one’s own rhythm or patterns of going out behavior from home / away log. As well as rhythm extraction, predicting one’s future presence using the extracted models is also performed. Technical details are in our paper at Pervasive 2012 [1]. This software should be used freely. To encourage free use, the library is licensed under the very liberal open-source MIT License.

References

[1] S. Tominaga, M. Shimosaka, R. Fukui, and T. Sato: A Unified Framework for Modeling and Predicting Going-Out Behavior. In: J. Kay, P. Lukowicz, H. Tokuda, P. Olivier, A. Kruger, (eds.) Pervasive 2012. LNCS, vol. 7319, pp. 73 — 90, Springer, Heidelberg (2012)

Social Networks

  • twitter
  • rss

Recent News

  • Device-Free Multi-Person Indoor Localization Using the Change of ToF 2023/03/03
  • Presenting our paper on Device-Free Multi-Person Indoor Localization Using the Change of ToF at PerCom2023 2023/02/28
  • Our paper on Efficient Adaptive Beacon Deployment Optimization for Indoor Crowd Monitoring has been published in IMWUT. 2023/01/24
  • Presenting our paper on Robust Continuous MaxEnt IRL with RRT at IV2022 2022/06/09
  • Presenting our paper on Efficient Indoor Localization Model Construction by Sequential Recommendation of Data Gathering Position based on Bayesian Optimization at IPIN2021 2021/11/29
  • Adaptive incremental beacon placement optimization for crowd density monitoring applications 2021/11/01
  • Presenting 2 papers at ACM SIGSPATIAL 2021 2021/11/01
  • Fine-grained Urban Dynamics Prediction using Large-Scale Mobile Phone Location Data 2021/10/05
  • Robustifying Wi-Fi localization by Between-Location data augmentation 2021/09/28
  • Our paper on robustifying Wi-Fi localization by “Between-Location” data augmentation has been published in IEEE Sensors Journal 2021/09/17

Search

Copyright 2015 · Shimosaka Research Group at TITECH