Feature-Sharing in Cascade Detection Systems with Multiple Applications
Long Le, Douglas L. Jones

Citation
Long Le, Douglas L. Jones. "Feature-Sharing in Cascade Detection Systems with Multiple Applications". Journal of Selected Topics in Signal Processing, May 2017.

Abstract
Traditional distributed detection systems are often designed for a single target application. However, with the emergence of the Internet of Things (IoT) paradigm, next-generation systems are expected to be a shared infrastructure for multiple applications. To this end, we propose a modular, cascade design for resource-efficient, multi-task detection systems. Two (classes of) applications are considered in the system, a primary and a secondary one. The primary application has universal features that can be shared with other applications, to reduce the overall feature extraction cost, while the secondary application does not. In this setting, the two applications can collaborate via feature sharing. We provide a method to optimize the operation of the multi-application cascade system based on an accurate resource consumption model. In addition, the inherent uncertainties in feature models are articulated and taken into account. For evaluation, the twin-comparison argument is invoked, and it is shown that, with the optimal feature sharing strategy, a system can achieve 9X resource saving and 1.43X improvement in detection performance.

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Citation formats

• HTML

  Long Le, Douglas L. Jones. <a
  href="http://www.terraswarm.org/pubs/918.html"
  >Feature-Sharing in Cascade Detection Systems with
  Multiple Applications</a>, <i>Journal of
  Selected Topics in Signal Processing</i>, May 2017.

• Plain text

  Long Le, Douglas L. Jones. "Feature-Sharing in Cascade
  Detection Systems with Multiple Applications".
  <i>Journal of Selected Topics in Signal
  Processing</i>, May 2017.

• BibTeX

  @article{LeJones17_FeatureSharingInCascadeDetectionSystemsWithMultiple,
      author = {Long Le and Douglas L. Jones},
      title = {Feature-Sharing in Cascade Detection Systems with
                Multiple Applications},
      journal = {Journal of Selected Topics in Signal Processing},
      month = {May},
      year = {2017},
      abstract = {Traditional distributed detection systems are
                often designed for a single target application.
                However, with the emergence of the Internet of
                Things (IoT) paradigm, next-generation systems are
                expected to be a shared infrastructure for
                multiple applications. To this end, we propose a
                modular, cascade design for resource-efficient,
                multi-task detection systems. Two (classes of)
                applications are considered in the system, a
                primary and a secondary one. The primary
                application has universal features that can be
                shared with other applications, to reduce the
                overall feature extraction cost, while the
                secondary application does not. In this setting,
                the two applications can collaborate via feature
                sharing. We provide a method to optimize the
                operation of the multi-application cascade system
                based on an accurate resource consumption model.
                In addition, the inherent uncertainties in feature
                models are articulated and taken into account. For
                evaluation, the twin-comparison argument is
                invoked, and it is shown that, with the optimal
                feature sharing strategy, a system can achieve 9X
                resource saving and 1.43X improvement in detection
                performance.},
      URL = {http://terraswarm.org/pubs/918.html}
  }
  

Posted by Mary Stewart on 6 Mar 2017.
Groups: pw