Matches in SemOpenAlex for { <https://semopenalex.org/work/W2761932327> ?p ?o ?g. }
Showing items 1 to 69 of
69
with 100 items per page.
- W2761932327 abstract "Passive Presence Detection is the task of detecting when a person is present or not, without the need for them to carry any wireless device. For that, a wireless sensor network consisting of Bluetooth Low Energy (BLE) devices, often called nodes or beacons must be employed in an area of interest in which their positions are not considered known. Beacons transmit and receive BLE signals and they report the Received Signal Strength (RSS) of them, counted in decibels. The RSS data can be explored in search of patterns that can classify presence versus non-presence of people near a wireless sensor network. Machine Learning and Signal Processing techniques are used to tackle the classification problem of Passive Presence Detection. Specifically, Random Forests and two versions of a Sparse Representationbased classification model are employed. Random Forests create an ensemble of classification trees, based on random features. Each of the trees votes for a class. The Sparse representation-based classifier exploits the theory of compressed sensing to find sparse representations of the RSS data, i.e new data matrices which contain only a few non-zero elements. Compressed sensing states that if a data set has a sparse representation in some overcomplete basis, then a high quality reconstruction is possible with much fewer measurements than we would normally need. This method finds an overcomplete basis for each class and classifies the input data based on the most accurate reconstruction. Two data sets were used under various experimental settings to verify the efficacy of the proposed methods. Both Random Forests and the Sparse Representation-based classifier report 100% classification accuracy." @default.
- W2761932327 created "2017-10-20" @default.
- W2761932327 creator A5022315976 @default.
- W2761932327 date "2017-10-05" @default.
- W2761932327 modified "2023-09-27" @default.
- W2761932327 title "Evaluation of Machine Learning techniques for Passive Presence Detection" @default.
- W2761932327 hasPublicationYear "2017" @default.
- W2761932327 type Work @default.
- W2761932327 sameAs 2761932327 @default.
- W2761932327 citedByCount "0" @default.
- W2761932327 crossrefType "journal-article" @default.
- W2761932327 hasAuthorship W2761932327A5022315976 @default.
- W2761932327 hasConcept C102168758 @default.
- W2761932327 hasConcept C111919701 @default.
- W2761932327 hasConcept C119857082 @default.
- W2761932327 hasConcept C124066611 @default.
- W2761932327 hasConcept C124101348 @default.
- W2761932327 hasConcept C124851039 @default.
- W2761932327 hasConcept C153180895 @default.
- W2761932327 hasConcept C154945302 @default.
- W2761932327 hasConcept C169258074 @default.
- W2761932327 hasConcept C2385561 @default.
- W2761932327 hasConcept C24590314 @default.
- W2761932327 hasConcept C31258907 @default.
- W2761932327 hasConcept C41008148 @default.
- W2761932327 hasConcept C79403827 @default.
- W2761932327 hasConcept C95623464 @default.
- W2761932327 hasConceptScore W2761932327C102168758 @default.
- W2761932327 hasConceptScore W2761932327C111919701 @default.
- W2761932327 hasConceptScore W2761932327C119857082 @default.
- W2761932327 hasConceptScore W2761932327C124066611 @default.
- W2761932327 hasConceptScore W2761932327C124101348 @default.
- W2761932327 hasConceptScore W2761932327C124851039 @default.
- W2761932327 hasConceptScore W2761932327C153180895 @default.
- W2761932327 hasConceptScore W2761932327C154945302 @default.
- W2761932327 hasConceptScore W2761932327C169258074 @default.
- W2761932327 hasConceptScore W2761932327C2385561 @default.
- W2761932327 hasConceptScore W2761932327C24590314 @default.
- W2761932327 hasConceptScore W2761932327C31258907 @default.
- W2761932327 hasConceptScore W2761932327C41008148 @default.
- W2761932327 hasConceptScore W2761932327C79403827 @default.
- W2761932327 hasConceptScore W2761932327C95623464 @default.
- W2761932327 hasLocation W27619323271 @default.
- W2761932327 hasOpenAccess W2761932327 @default.
- W2761932327 hasPrimaryLocation W27619323271 @default.
- W2761932327 hasRelatedWork W1547922449 @default.
- W2761932327 hasRelatedWork W1605703418 @default.
- W2761932327 hasRelatedWork W1775703606 @default.
- W2761932327 hasRelatedWork W194309744 @default.
- W2761932327 hasRelatedWork W1989418060 @default.
- W2761932327 hasRelatedWork W1993344450 @default.
- W2761932327 hasRelatedWork W2057968826 @default.
- W2761932327 hasRelatedWork W2130394964 @default.
- W2761932327 hasRelatedWork W2295701977 @default.
- W2761932327 hasRelatedWork W2592941527 @default.
- W2761932327 hasRelatedWork W2739299403 @default.
- W2761932327 hasRelatedWork W2956108894 @default.
- W2761932327 hasRelatedWork W3129902359 @default.
- W2761932327 hasRelatedWork W626785281 @default.
- W2761932327 hasRelatedWork W71778187 @default.
- W2761932327 hasRelatedWork W8597964 @default.
- W2761932327 hasRelatedWork W2603634308 @default.
- W2761932327 hasRelatedWork W2836550259 @default.
- W2761932327 hasRelatedWork W3089372596 @default.
- W2761932327 hasRelatedWork W3119279940 @default.
- W2761932327 isParatext "false" @default.
- W2761932327 isRetracted "false" @default.
- W2761932327 magId "2761932327" @default.
- W2761932327 workType "article" @default.