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Shing-Jiuan Liu
Academia Sinica
Information visualizationVisualizationComputer scienceArtificial neural networkVisual analytics
4Publications
2H-index
5Citations
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Publications 4
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Dec 1, 2019 in GLOBECOM (Global Communications Conference)
#1Shing-Jiuan Liu (AS: Academia Sinica)
#1Shing-Jiuan Liu (AS: Academia Sinica)H-Index: 2
Last. Feng-Tsun Chien (NCTU: National Chiao Tung University)H-Index: 8
view all 3 authors...
Device-free indoor localization is a key enabling technology for many Internet of Things (IoT) applications. Deep neural network (DNN)-based location estimators achieve high-precision localization performance by automatically learning discriminative features from noisy wireless signals without much human intervention. However, the inner workings of DNN are not transparent and not adequately understood especially in wireless localization applications. In this paper, we conduct visual analyses of ...
Source
#1Kevin M. Chen (CIT: Center for Information Technology)H-Index: 1
#2Ronald Y. Chang (CIT: Center for Information Technology)H-Index: 12
Last. Shing-Jiuan Liu (UC Davis: University of California, Davis)H-Index: 2
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In this paper, we propose a convolutional neural network (CNN) model for device-free fingerprinting indoor localization based on Wi-Fi channel state information (CSI). Besides, we develop an interpretation framework to understand the representations learned by the model. By quantifying and visualizing CNN in comparison with the fully-connected feedforward deep neural network (DNN) (or multilayer perceptron), we observe that each model can automatically identify location-specific patterns, which ...
1 CitationsSource
#1Shing-Jiuan Liu (CIT: Center for Information Technology)H-Index: 2
#2Ronald Y. Chang (CIT: Center for Information Technology)H-Index: 12
Last. Feng-Tsun Chien (NCTU: National Chiao Tung University)H-Index: 8
view all 3 authors...
Device-free Wi-Fi indoor localization has received significant attention as a key enabling technology for many Internet of Things (IoT) applications. Machine learning-based location estimators, such as the deep neural network (DNN), carry proven potential in achieving high-precision localization performance by automatically learning discriminative features from the noisy wireless signal measurements. However, the inner workings of the DNNs are not transparent and not adequately understood, espec...
Source
Dec 1, 2018 in GLOBECOM (Global Communications Conference)
#1Ronald Y. Chang (CIT: Center for Information Technology)H-Index: 12
#2Shing-Jiuan Liu (CIT: Center for Information Technology)H-Index: 2
Last. Yen-Kai Cheng (CIT: Center for Information Technology)H-Index: 2
view all 3 authors...
This paper proposes an economical, nonintrusive, and high-precision indoor localization scheme based on Wi-Fi fingerprinting that requires only a single Wi- Fi access point and a single fixed-location receiver. A deep neural network (DNN) based classification model is trained with Wi-Fi channel state information (CSI) fingerprints for localizing the target without any device attached (i.e., device-free). CSI provides finer-grained information than received signal strength (RSS). CSI pre- process...
2 CitationsSource
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