8,117 research outputs found
Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting
In this paper, we propose hybrid building/floor classification and
floor-level two-dimensional location coordinates regression using a
single-input and multi-output (SIMO) deep neural network (DNN) for large-scale
indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits
the different nature of the estimation of building/floor and floor-level
location coordinates and uses a different estimation framework for each task
with a dedicated output and hidden layers enabled by SIMO DNN architecture. We
carry out preliminary evaluation of the performance of the hybrid floor
classification and floor-level two-dimensional location coordinates regression
using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere
University of Technology (TUT), Finland, covering a single building with five
floors. Experimental results demonstrate that the proposed SIMO-DNN-based
hybrid classification/regression scheme outperforms existing schemes in terms
of both floor detection rate and mean positioning errors.Comment: 6 pages, 4 figures, 3rd International Workshop on GPU Computing and
AI (GCA'18
A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting
One of the key technologies for future large-scale location-aware services
covering a complex of multi-story buildings --- e.g., a big shopping mall and a
university campus --- is a scalable indoor localization technique. In this
paper, we report the current status of our investigation on the use of deep
neural networks (DNNs) for scalable building/floor classification and
floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the
hierarchical nature of the building/floor estimation and floor-level
coordinates estimation of a location, we propose a new DNN architecture
consisting of a stacked autoencoder for the reduction of feature space
dimension and a feed-forward classifier for multi-label classification of
building/floor/location, on which the multi-building and multi-floor indoor
localization system based on Wi-Fi fingerprinting is built. Experimental
results for the performance of building/floor estimation and floor-level
coordinates estimation of a given location demonstrate the feasibility of the
proposed DNN-based indoor localization system, which can provide near
state-of-the-art performance using a single DNN, for the implementation with
lower complexity and energy consumption at mobile devices.Comment: 9 pages, 6 figure
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
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