Browsing by Author "Liu, Shing-Jiuan"

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Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization
(2019-01-01)
Liu, Shing-Jiuan
;
Chang, Ronald Y.
;
Chien, Feng-Tsun
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
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, especially in the indoor localization application. In this paper, we provide quantitative and visual explanations for the DNN learning process as well as the critical features that the DNN has learned during the process. Toward this end, we propose to use several visualization techniques, including 1) dimensionality reduction visualization, to project the high-dimensional feature space to the 2D space to facilitate visualization and interpretation, and 2) visual analytics and information visualization, to quantify relative contributions of each feature with the proposed feature manipulation procedures. The results provide insightful views and plausible explanations of the DNN in device-free Wi-Fi indoor localization using the channel state information (CSI) fingerprints.
Visual Analysis of Deep Neural Networks for Device-Free Wireless Localization
(2019-01-01)
Liu, Shing-Jiuan
;
Chang, Ronald Y.
;
Chien, Feng-Tsun
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
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 DNN-based location estimators trained with WiFi channel state information (CSI) fingerprints in a real-world experiment. We address such questions as 1) how well has the DNN learned and been trained, and 2) what critical features has the DNN learned to distinguish different classes, via visualization techniques. The results provide plausible explanations and allow for a better understanding of the mechanism of DNN-based wireless indoor localization.