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Iterative Hard Thresholding Algorithm Using Norm Exponent

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Artificial Intelligence for Sustainable Energy (GENCITY 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1142))

  • 26 Accesses

Abstract

Due to numerous data that is required to transfer in the information age, there is an increasing demand of computation and memory usage. Compressed sensing (CS) appears to be a promising technique in order to save both the computation and data storage. Iterative hard thresholding (IHT) is one of the signal recovery methods in the CS. Despite its fast computation, the IHT often delivers poor performance in the signal reconstruction accuracy. To solve this issue, we present an improved IHT in this work by using the fractional norm. Numerical simulation is demonstrated to illustrate better accuracy of the signal reconstruction than former approaches in various scenarios.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Srinakharinwirot University, 63 Mu 7, Rangsit-Nakhonnayok Road, Canal 16, Ongkharak, Nakhonnayok, 26120, Thailand

    Bamrung Tausiesakul

  2. Department of Electrical Engineering, Faculty of Engineering, Mahidol University, 999, Phutthamonthon Sai 4 Road, Salaya, Phutthamonthon, Nakhon Pathom, 73170, Thailand

    Krissada Asavaskulkiet

Authors
  1. Bamrung Tausiesakul
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  2. Krissada Asavaskulkiet
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Corresponding author

Correspondence to Krissada Asavaskulkiet .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, Bihar, India

    Jimson Mathew

  2. Department of Electrical and Electronic Engineering, University of Southampton Malaysia, Iskandar Puteri, Johor, Malaysia

    Lenin Gopal

  3. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China

    Filbert H. Juwono

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Tausiesakul, B., Asavaskulkiet, K. (2024). Iterative Hard Thresholding Algorithm Using Norm Exponent. In: Mathew, J., Gopal, L., Juwono, F.H. (eds) Artificial Intelligence for Sustainable Energy. GENCITY 2023. Lecture Notes in Electrical Engineering, vol 1142. Springer, Singapore. https://doi.org/10.1007/978-981-99-9833-3_6

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  • DOI: https://doi.org/10.1007/978-981-99-9833-3_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9832-6

  • Online ISBN: 978-981-99-9833-3

  • eBook Packages: EnergyEnergy (R0)

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