A novel target recognition scheme for WSNs

Mohammed Al-Naeem, Asad I. Khan

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    Abstract

    Many existing pattern recognition schemes in wireless sensor networks suffer from pattern displacement, pattern scaling, and pattern rotation issues. We propose a novel one-shot learning associative memory method for target recognition in wireless sensor networks. This method, known as Macroscopic Object Heuristics Algorithm (MOHA), is able to address all of the above issues. Our proposed scheme is also capable of reducing the power and memory consumptions of wireless sensor networks. The experimental results show that the proposed scheme can effectively and efficiently handle pattern displaced, pattern scaling, and pattern rotation issues.

    Original languageEnglish
    Title of host publicationThe 2012 International Joint Conference on Neural Networks (IJCNN 2012)
    Subtitle of host publication10-15 June 2012, Brisbane, QLD, Australia [proceedings]
    EditorsDaryl Essam, Ruhul Sarker
    Place of PublicationPiscataway, NJ [USA]
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages2413-2418
    Number of pages6
    ISBN (Print)9781467314909, 9781467314886
    DOIs
    Publication statusPublished - 2012
    EventIEEE International Joint Conference on Neural Networks 2012 - Brisbane Convention & Exhibition Centre, Brisbane, Australia
    Duration: 10 Jun 201215 Jun 2012
    https://ieeexplore.ieee.org/xpl/conhome/6241467/proceeding (Proceedings)

    Conference

    ConferenceIEEE International Joint Conference on Neural Networks 2012
    Abbreviated titleIJCNN 2012
    CountryAustralia
    CityBrisbane
    Period10/06/1215/06/12
    Internet address

    Keywords

    • Wireless sensor networks
    • Graph Neuron
    • Macroscopic Object Heuristics Algorithm
    • Object recognition
    • Pattern recognition
    • Associative memory
    • Sensor field

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