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Yen-Kai Cheng
Center for Information Technology
Support vector machineChannel state informationComputer scienceArtificial neural networkRobustness (computer science)
5Publications
2H-index
12Citations
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Publications 5
Newest
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: 1
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
Dec 1, 2017 in GLOBECOM (Global Communications Conference)
#1Yen-Kai Cheng (CIT: Center for Information Technology)H-Index: 2
#2Ronald Y. Chang (CIT: Center for Information Technology)H-Index: 12
People/crowd counting is a critical technique in many people-centric Internet of Things (IoT) applications, e.g., security monitoring and energy management for smart homes. Device-free people counting systems can in general be categorized as image-based and non-image-based. Non-image-based methods have the advantages of being economical and nonintrusive, as only ambient wireless signals from off-the-shelf wireless devices such as Wi-Fi are used. In this paper, we propose a non-image- based peopl...
9 CitationsSource
#1Yen-Kai Cheng (CIT: Center for Information Technology)H-Index: 2
#2Ronald Y. Chang (CIT: Center for Information Technology)H-Index: 12
This paper considers autonomous environment discovery for energy harvesting Internet of things (IoT) applications. A self-sustainable mobile micro-robot explores an unknown environment to collect data under energy constraints. The data are fed into a machine- learning model to construct an energy harvesting map of the environment, which will then be used by IoT devices for self-sustainable operations in the same environment. The objective is to develop an efficient robotic exploration algorithm ...
Source
#1Yen-Kai Cheng (CIT: Center for Information Technology)H-Index: 2
#2Ronald Y. Chang (CIT: Center for Information Technology)H-Index: 12
Last. Ling-Jyh Chen (AS: Academia Sinica)H-Index: 23
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Wireless indoor localization is a key technology for the future Internet of things (IoT) paradigm. In this paper, we perform an experimental comparative study of machine learning-based localization schemes, such as k-nearest neighbor (k-NN) and variants of support vector machine (SVM), based on the received signal strength (RSS) measurements of the ambient frequency modulation (FM) and digital video broadcasting- terrestrial (DVB-T) signals in three real testbed environments. The consideration o...
1 CitationsSource
May 15, 2016 in VTC (Vehicular Technology Conference)
#1Yen-Kai Cheng (CIT: Center for Information Technology)H-Index: 2
#2Hsin-Jui Chou (CIT: Center for Information Technology)H-Index: 4
Last. Ronald Y. Chang (CIT: Center for Information Technology)H-Index: 12
view all 3 authors...
Indoor localization technique is a key enabling technology for the future Internet of things (IoT) paradigm. Improving the precision of indoor localization will expand the horizon of indoor IoT applications. In this paper, we propose an enhanced machine-learning indoor localization scheme which incorporates access point (AP) selection and the proposed signal strength reconstruction to enhance robustness in noisy environments. The proposed signal strength reconstruction scheme estimates/reconstru...
8 CitationsSource
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