Sparse signals are encountered in many modern technologies. Compressed sensing methods create the opportunity of sampling sparse signals significantly lower than the Nyquist rate. However, sparse signal recovery still remains a challenge. In this work, we show that detecting the support of the sparse signal can be viewed as a classification problem, and hence efficiently be solved using neural networks (NN). We find the best NN configuration in support detection for random data sets, where an accuracy of 98% is achieved in highly sparse signals. After detecting the support of the signal, we show that signal recovery can be done efficiently. In this work, we develop two recovery methods. The simulation studies confirm that our proposed methods outperform counterpart algorithms in both accuracy and convergence speed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    NN-based Support Detection of Sparse Signals




    Publication date :

    2020-11-01


    Size :

    979573 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    QSR: Q-Learning Based Algorithm of Support Recovery for Sparse Signals

    Zhou, Tianzhi / Wang, Yibo / Wu, Zhangyan et al. | IEEE | 2024


    Pedestrian Detection Using Sparse Gabor Filter and Support Vector Machine

    Cheng, H. / Zheng, N. / Qin, J. et al. | British Library Conference Proceedings | 2005


    A Robust Reconstruction Algorithm for Sparse Signals

    Hao, Wenjie / Qi, Chun | British Library Online Contents | 2015


    Pedestrian detection using sparse Gabor filter and support vector machine

    Hong Cheng, / Nanning Zheng, / Junjie Qin, | IEEE | 2005


    The Research of The Liveness Detection Method Based on Sparse Support Vector Machines

    Yu, Lijun / Ding, Ying / Wang, Hui et al. | British Library Conference Proceedings | 2018