<rdf:RDF
 xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
 xmlns="http://purl.org/rss/1.0/"
 xmlns:dc="http://purl.org/dc/elements/1.1/"
 xmlns:cc="http://web.resource.org/cc/"
 >
<!--
	This ontology document is licensed under the Creative Commons
	Attribution License. To view a copy of this license, visit
	http://creativecommons.org/licenses/by/2.0/ or send a letter to
	Creative Commons, 559 Nathan Abbott Way, Stanford, California
	94305, USA.
-->
 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=compressive+sensing">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=compressive+sensing]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for compressive sensing]]></description>
  <items>
    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/907/An-Ensemble-Approach-for-Compressive-Sensing-with-Quantum-Annealers"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/891/Compressive-Geospatial-Analytics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/904/A-Survey-on-Compressive-Sensing-Classical-Results-and-Recent-Advancements"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/850/SAT-based-Compressive-Sensing"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/845/Quantum-Annealing-Based-Binary-Compressive-Sensing-with-Matrix-Uncertainty"/>
    </rdf:Seq>
  </items>
 </channel>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/907/An-Ensemble-Approach-for-Compressive-Sensing-with-Quantum-Annealers">
  <title><![CDATA[An Ensemble Approach for Compressive Sensing with Quantum Annealers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/907/An-Ensemble-Approach-for-Compressive-Sensing-with-Quantum-Annealers</link>
  <description><![CDATA[We leverage the idea of a statistical ensemble to improve the quality of quantum annealing based binary compressive sensing.  Since executing quantum machine instructions on a quantum annealer can result in an excited state, rather than the ground state of the given Hamiltonian, we use different penalty parameters to generate multiple distinct quadratic unconstrained binary optimization (QUBO) functions whose ground state(s) represent a potential solution of the original problem.  We then emp...]]></description>
  <dc:date>2020-06-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers">
  <title><![CDATA[Leveraging Artificial Intelligence to Advance Problem-Solving with Quantum Annealers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers</link>
  <description><![CDATA[We show how to advance quantum information processing, specifically problem-solving with quantum annealers, in the realm of artificial intelligence.  We introduce SAT++, as a novel quantum programming paradigm, that can compile classical algorithms (implemented in classical programming languages) and execute them on quantum annealers.   Moreover, we introduce a post-quantum error correction method that can find samples with significantly lower energy values, compared to the state-of-the-art t...]]></description>
  <dc:date>2020-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/891/Compressive-Geospatial-Analytics">
  <title><![CDATA[Compressive Geospatial Analytics]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/891/Compressive-Geospatial-Analytics</link>
  <description><![CDATA[Compressive sensing is a randomized data acquisition method that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling theorem, and outperforms traditional signal processing techniques through performing both sensing and size reduction tasks simultaneously. Edge-computing is a decentralization approach that provides several properties (specifically reducing the need for moving a large volume of data) via pushing the computation towards the edge of t...]]></description>
  <dc:date>2019-12-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/904/A-Survey-on-Compressive-Sensing-Classical-Results-and-Recent-Advancements">
  <title><![CDATA[A Survey on Compressive Sensing: Classical Results and Recent Advancements]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/904/A-Survey-on-Compressive-Sensing-Classical-Results-and-Recent-Advancements</link>
  <description><![CDATA[Recovering sparse signals from linear measurements has demonstrated outstanding utility in a vast variety of real-world applications. Compressive sensing is the topic that studies the associated raised questions for the possibility of a successful recovery. This topic is well-nourished and numerous results are available in the literature. However, their dispersity makes it challenging and time-consuming for readers and practitioners to quickly grasp its main ideas and classical algorithms, an...]]></description>
  <dc:date>2019-08-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/850/SAT-based-Compressive-Sensing">
  <title><![CDATA[SAT-based Compressive Sensing]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/850/SAT-based-Compressive-Sensing</link>
  <description><![CDATA[We propose to reduce the original well-posed problem of compressive sensing to weighted-MAX-SAT. Compressive sensing is a novel randomized data acquisition approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate. The original problem of compressive sensing in sparse recovery is NP-hard; therefore, in addition to restrictions for the uniqueness of the sparse solution, the coding matrix has also to satisfy additional stringent constra...]]></description>
  <dc:date>2019-03-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/845/Quantum-Annealing-Based-Binary-Compressive-Sensing-with-Matrix-Uncertainty">
  <title><![CDATA[Quantum Annealing Based Binary Compressive Sensing  with Matrix Uncertainty]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/845/Quantum-Annealing-Based-Binary-Compressive-Sensing-with-Matrix-Uncertainty</link>
  <description><![CDATA[Compressive sensing is a novel approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate and outperforms traditional signal processing techniques in acquiring and reconstructing such signals. Compressive sensing with matrix uncertainty is an extension of the standard compressive sensing problem that appears in various applications, including but not limited to cognitive radio sensing, calibration of the antenna, and deconvolution. The...]]></description>
  <dc:date>2019-01-01</dc:date>
 </item>
</rdf:RDF>
