English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose a scheme to employ backpropagation neural networks (BPNNs) forboth stages of fingerprinting-based indoor positioning using WLAN/WiFi signalstrengths (FWIPS): radio map construction during the offline stage, andlocalization during the online stage. Given a training radio map (TRM), i.e., aset of coordinate vectors and associated WLAN/WiFi signal strengths of theavailable access points, a BPNN can be trained to output the expected signalstrengths for any input position within the region of interest (BPNN-RM). Thiscan be used to provide a continuous representation of the radio map and tofilter, densify or decimate a discrete radio map. Correspondingly, the TRM canalso be used to train another BPNN to output the expected position within theregion of interest for any input vector of recorded signal strengths and thuscarry out localization (BPNN-LA).Key aspects of the design of such artificialneural networks for a specific application are the selection of designparameters like the number of hidden layers and nodes within the network, andthe training procedure. Summarizing extensive numerical simulations, based onreal measurements in a testbed, we analyze the impact of these design choiceson the performance of the BPNN and compare the results in particular to thoseobtained using the $k$ nearest neighbors ($k$NN) and weighted $k$ nearestneighbors approaches to FWIPS.

Application of backpropagation neural networks to both stages of fingerprinting based WIPS