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  1. 1
    Evaluation of sparsifying algorithms for speech signals

    Evaluation of sparsifying algorithms for speech signals by Kassim, Liban A., Khalifa, Othman Omran, Gunawan, Teddy Surya

    Published 2012
    “…It has also played an important role in compressive sensing algorithms since it performs well in sparse signals. …”
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  2. 2
    Speech compression using compressive sensing on a multicore system

    Speech compression using compressive sensing on a multicore system by Gunawan, Teddy Surya, Khalifa, Othman Omran, Shafie, Amir Akramin, Ambikairajah, Eliathamby

    Published 2011
    “…Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals, i.e. speech signal. …”
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  3. 3
    Compressed Sensing Implementations For Sparse Channel Estimation In OFDM Systems

    Compressed Sensing Implementations For Sparse Channel Estimation In OFDM Systems by Uwaechia, Anthony Ngozichukwuka

    Published 2018
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  4. 4
    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach by Qayyum A., Saeed Malik A., Saad N.M., Iqbal M., Abdullah M.F., Rasheed W., Abdullah T.A.B.R., Bin Jafaar M.Y.

    Published 2023
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  5. 5
    Scene classification for aerial images based on CNN using sparse coding technique

    Scene classification for aerial images based on CNN using sparse coding technique by Qayyum, A., Malik, A.S., Saad, N.M., Iqbal, M., Faris Abdullah, M., Rasheed, W., Rashid Abdullah, T.A., Bin Jafaar, M.Y.

    Published 2017
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  6. 6
    Speech enhancement in non-stationary noise using compressive sensing

    Speech enhancement in non-stationary noise using compressive sensing by Sulong, Amart, Gunawan, Teddy Surya, Khalifa, Othman Omran, Kartiwi, Mira

    Published 2016
    “…Compressive sensing using GPSR technique is then incorporated by randomly selected the sparse signal of unconstrained problem with suitable basis and reconstruct the noiseless distortion to the enhanced speech. …”
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  7. 7
    Scene classification for aerial images based on CNN using sparse coding technique

    Scene classification for aerial images based on CNN using sparse coding technique by Qayyum A., Malik A.S., Saad N.M., Iqbal M., Faris Abdullah M., Rasheed W., Rashid Abdullah T.A., Bin Jafaar M.Y.

    Published 2023
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  8. 8
    Speech enhancement based on compressive sensing algorithm

    Speech enhancement based on compressive sensing algorithm by Sulong, Amart, Gunawan, Teddy Surya, Khalifa, Othman Omran, Chebil, Jalel

    Published 2013
    “…A novel speech enhancement by using compressive sensing (CS) is a new paradigm of acquiring signals, fundamentally different from uniform rate digitization followed by compression, often used for transmission or storage. …”
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  9. 9
    Speech enhancement based on compressive sensing algorithm

    Speech enhancement based on compressive sensing algorithm by Sulong, Amart, Gunawan, Teddy Surya, Khalifa, Othman Omran, Chebil , Jalel

    Published 2013
    “…A novel speech enhancement by using compressive sensing (CS) is a new paradigm of acquiring signals, fundamentally different from uniform rate digitization followed by compression, often used for transmission or storage. …”
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  10. 10
    Single channel speech enhancement using Wiener filter and compressive sensing

    Single channel speech enhancement using Wiener filter and compressive sensing by Sulong, Amart, Gunawan, Teddy Surya, Khalifa, Othman Omran, Kartiwi, Mira, Dao, Hassan

    Published 2017
    “…The challenges focus on corrupted speech solution between noise reduction and signal distortion. We used a modified Wiener filter and compressive sensing (CS) to investigate and evaluate the improvement of speech quality. …”
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  11. 11
    Reconstruction Algorithm In Ofdm System

    Reconstruction Algorithm In Ofdm System by Hwong, Sing Pui

    Published 2017
    “…Consequently, various widely used compressive sensing reconstruction algorithm such as: Orthogonal Matching Pursuit (OMP), Compressed Sensing Matching Pursuit (CoSaMP), and Subspace Pursuit (SP) will be evaluated to test their efficacy sparse estimation performances in OFDM system.…”
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  12. 12
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  13. 13
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  14. 14
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  15. 15
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  16. 16
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  17. 17
    Remote sensing technologies for unlocking new groundwater insights: a comprehensive review

    Remote sensing technologies for unlocking new groundwater insights: a comprehensive review by Ibrahim, Abba, Wayayok, Aimrun, Mohd Shafri, Helmi Zulhaidi, Toridi, Noorellimia Mat

    Published 2024
    “…The evolution of the techniques analyses spans from empirical reliance on sparse point data to the assimilation of multi-platform satellite measurements using sophisticated machine learning algorithms. …”
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  18. 18
    The implications for ahybrid detection technique against malicious sqlattacks on web applications

    The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

    Published 2025
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  19. 19
    Class binarization with self-adaptive algorithm to improve human activity recognition

    Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…These kind of activities highly sparsely distributed in the input space which is problematic to be distinguish using traditional classifier model. …”
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  20. 20
    Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method

    Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

    Published 2024
    thesis::doctoral thesis
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