• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Technology Innovation and Engineering Education Unit
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Technology Innovation and Engineering Education Unit
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Multiple-view time-frequency distribution based on the empirical mode decomposition

    No Thumbnail [120x130]
    Date
    2010-08
    Author
    Stevenson, N.J
    Mesbah, M
    Boashash, B
    Metadata
    Show full item record
    Abstract
    This study proposes a novel, composite time-frequency distribution (TFD) constructed using a multiple-view approach. This composite TFD utilises the intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD) to generate each view that are then combined using the arithmetic mean. This process has the potential to eliminate the inter-component interference generated by a quadratic TFD (QTFD), as the IMFs of the EMD are, in general, monocomponent signals. The formulation of the multiple-view TFD in the ambiguity domain results in faster computation, compared to a convolutive implementation in the time-frequency domain, and a more robust TFD in the presence of noise. The composite TFD, referred to as the EMD-TFD, was shown to generate a heuristically more accurate representation of the distribution of time-frequency energy in a signal. It was also shown to have performance comparable to the Wigner-Ville distribution when estimating the instantaneous frequency of multiple signal components in the presence of noise.
    DOI/handle
    http://hdl.handle.net/10576/10760
    http://dx.doi.org/10.1049/iet-spr.2009.0084
    Collections
    • Technology Innovation and Engineering Education Unit [‎63‎ items ]

    entitlement

    Related items

    Showing items related by title, author, creator and subject.

    • No Thumbnail [110x130]

      A Deep Learning Model for LoRa Signals Classification Using Cyclostationay Features 

      Almohamad A.; Hasna , Mazen; Althunibat S.; Tekbiyik K.; Qaraqe K. ( IEEE Computer Society , 2021 , Conference)
      With the witnessed exponential growth of Internet of Things (IoT) nodes deployment following the emerging applications, multiple variants of technologies have been proposed to handle the IoT requirements. Among the proposed ...
    • No Thumbnail [110x130]

      Time-frequency features for pattern recognition using high-resolution TFDs: A tutorial review 

      Boashash B.; Khan N.A.; Ben-Jabeur T. ( Elsevier Inc. , 2015 , Article)
      This paper presents a tutorial review of recent advances in the field of time-frequency (t, f) signal processing with focus on exploiting (t, f) image feature information using pattern recognition techniques for detection ...
    • No Thumbnail [110x130]

      Time-frequency detection of slowly varying periodic signals with harmonics: Methods and performance evaluation 

      O'Toole J.M.; Boashash B. (2011 , Article)
      We consider the problem of detecting an unknown signal from an unknown noise type. We restrict the signal type to a class of slowly varying periodic signals with harmonic components, a class which includes real signals ...

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission

    Help

    Item Submission Publisher policiesUser guides FAQs

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video

    NoThumbnail