A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine
- Submitting institution
-
London South Bank University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 288641
- Type
- D - Journal article
- DOI
-
10.1016/j.sigpro.2020.107915
- Title of journal
- Signal Processing
- Article number
- 107915
- First page
- 107915
- Volume
- 181
- Issue
- -
- ISSN
- 0165-1684
- Open access status
- Access exception
- Month of publication
- November
- Year of publication
- 2020
- URL
-
https://www.sciencedirect.com/science/article/pii/S016516842030459X
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
A - The BioEngineering Research Centre
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Domestic implementation of an intelligent wireless sensor network that learns user location and movement within its environment and relays information to a central system has undergone many investigations. GPS and GNSS technologies are not applicable indoor, because of the unpredictability of the radio propagation, very weak GPS signals sent from satellites to devices while penetrating through buildings and the low visibility of satellite in indoor areas. This paper is related to the funded EPSRC grant on wireless sensors for the reduction of energy use in indoor environments (EP/K002473/1, £911k, “Digital Agent Networking for Customer Energy Reduction (DANCER)”, 2012-2017).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
