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    Reduced-rank technique for joint channel estimation in TD-SCDMA systems. by Ismail, Alyani, Sali, Aduwati, Mohd Ali, Borhanuddin, Khatun, Sabira

    Published 2013
    “…The detectors: zero forcing block linear equalizer and minimum mean square error block linear equalizer algorithms are considered in this paper to recover the data. …”
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    Article
  3. 3

    Reduced rank technique for joint channel estimation and joint data detection in TD-SCDMA systems by Marzook, Ali Kamil, Ismail, Alyani, Mohd Ali, Borhanuddin, Sali, Aduwati, Khalaf, Mohannad H., Khatun, Sabira

    Published 2013
    “…The detectors: zero forcing block linear equalizer and minimum mean square error block linear equalizer algorithms are considered in this paper to recover the data. …”
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    Article
  4. 4

    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

    Published 2009
    “…The Ordinary Least Squares (OLS) method is often used to estimate the parameters of a linear model. Under certain assumptions, the OLS estimates are the best linear unbiased estimates. …”
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    Thesis
  5. 5

    Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems by Sabira, Khatun, Ali K., Marzook, Alyani, Ismail, Aduwati, Sali, Mohannad Hamed, Khalaf, Borhan, M. Ali

    Published 2012
    “…The detectors: zero forcing block linear equalizer and minimum mean square error block linear equalizer algorithms are considered in this paper to recover the data. …”
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    Article
  6. 6

    Missing value estimation methods for data in linear functional relationship model by Adilah Abdul Ghapor, Yong Zulina Zubairi, A.H.M. Rahmatullah Imon

    Published 2017
    “…The performance of EM and EMB are measured using mean absolute error, root-mean-square error and estimated bias. …”
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    Article
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    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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    Conference or Workshop Item
  9. 9

    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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    Final Year Project
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    Low-distortion MMSE speech enhancement estimator based on laplacian prior by Mahmmod, Basheera M., Ramli, Abd Rahman, Abdulhussain, Sadiq H., Syed Mohamed, Syed Abdul Rahman Al-Haddad, A. Jassim, Wissam

    Published 2017
    “…Reducing the effects of such issues require more robust linear and non-linear estimators. This paper proposes new optimum linear and non-linear Laplacian distribution-based estimators. …”
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    Article
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    SNR estimation using extended kalman filter technique for orthogonal frequency division multiplexing (OFDM) system by Ong, Sylvia Ai Ling

    Published 2012
    “…The performance of SNR estimation algorithm is contingent on channel estimates obtained through channel estimation schemes. …”
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    Thesis
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    Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control by Srazhidinov, Radik

    Published 2016
    “…Filtered-X least mean square (FXLMS) control algorithm is a conventional algorithm employed to cancel the noise in linear environment. …”
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    Thesis
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    Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network by Md Akib, Afif, Saad, Nordin, Asirvadam, Vijanth

    Published 2011
    “…A new learning algorithm called the ensemble dual algorithm for estimating the mass-flow rate of the flow after leakage is proposed. …”
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    Book Section
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    Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems by Kek, Sie Long

    Published 2011
    “…In the first sub-algorithm, the state mean propagation removes the Gaussian white noise to obtain the expected solution. …”
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    Thesis
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    Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane by Julakha, Jahan Jui, Mohd Ashraf, Ahmad, Muhammad Ikram, Mohd Rashid

    Published 2021
    “…The HMVOSCA algorithm is used to tune the linear and nonlinear parameters to reduce the gap between the estimated results and the actual results. …”
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    Conference or Workshop Item
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    Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models by Khairudin K., Ul-Saufie A.Z., Senin S.F., Zainudin Z., Rashid A.M., Abu Bakar N.F., Anas Abd Wahid M.Z., Azha S.F., Abd-Wahab F., Wang L., Sahar F.N., Osman M.S.

    Published 2025
    “…Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. …”
    Article
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    Estimating Marine CSEM Responses Using Gaussian Process Regression Based on Synthetic Models by Mohd Aris, M.N., Daud, H., Mohd Noh, K.A., Dass, S.C.

    Published 2022
    “…Current practice for processing marine CSEM responses utilizes meshes-based algorithms. The ad hoc algorithms require high computational time to solve the integrals and linear equations. …”
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    Article