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  1. 1
    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence

    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence by S. S. S. , Ranjit

    Published 2011
    “…Block-matching algorithm is the most common technique applied in block-based motion estimation technique. …”
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  2. 2
    An adaptive block-based matching algorithm for crowd motion sequences

    An adaptive block-based matching algorithm for crowd motion sequences by Kajo, I., Kamel, N., Malik, A.S.

    Published 2018
    “…For crowd analytics and surveillance systems, motion estimation is an essential first step. Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. …”
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  3. 3
    An adaptive block-based matching algorithm for crowd motion sequences

    An adaptive block-based matching algorithm for crowd motion sequences by Kajo, I., Kamel, N., Malik, A.S.

    Published 2018
    “…For crowd analytics and surveillance systems, motion estimation is an essential first step. Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. …”
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  4. 4
    An Adaptive Motion Estimation Algorithm for Irregular Motion of Objects

    An Adaptive Motion Estimation Algorithm for Irregular Motion of Objects by Kajo, Ibrahim, Malik, Aamir Saeed, Kamel , Nidal

    Published 2014
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  5. 5
    SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS

    SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS by YAZID, EDWAR

    Published 2015
    “…Three steps of improvements had been made to increase the modeling capacity of input-output models. The first step is presenting the backward estimator and combined forward-backward estimator instead of the only forward estimator in the original input-output models; the second step is reformulating the input-output models into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the model coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony (ABC) to form the PSO-KS, GA-KS and ABC-KS as estimation methods.…”
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  6. 6
    An adaptive motion estimation algorithm for irregular motion of objects

    An adaptive motion estimation algorithm for irregular motion of objects by Kajo, I., Malik, A.S., Kamel, N.

    Published 2014
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  7. 7
    Multilevel optimization for dense motion estimation

    Multilevel optimization for dense motion estimation by Saaban, Azizan, Kalmoun, El Mostafa, Ibrahim, Haslinda, Ramli, Razamin, Omar, Zurni

    Published 2011
    “…Experimental results on three image sequences using four models of optical flow with different computational efforts show that the FMG/Opt algorithm outperforms significantly both the TN and MR/Opt algorithms in terms of the computational work and the quality of the optical flow estimation.…”
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  8. 8
    Non-pressurized magnetorheological damper controlled using fuzzy semi active damping force estimator (fSADE)

    Non-pressurized magnetorheological damper controlled using fuzzy semi active damping force estimator (fSADE) by Saiful Anuar, Abu Bakar, Hishamuddin, Jamaluddin, Roslan, Abd Rahman, Pakharuddin, Mohd Samin, Ryosuke, Masuda, Hiromu, Hashimoto, Takeshi, Inaba

    Published 2012
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  9. 9
    Robust remote heart rate estimation from multiple asynchronous noisy channels using autoregressive model with Kalman filter

    Robust remote heart rate estimation from multiple asynchronous noisy channels using autoregressive model with Kalman filter by Nooralishahi, Parham, Loo, Chu Kiong, Shiung, Liew Wei

    Published 2019
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  10. 10
    Investigation of block matching algorithm for video coding

    Investigation of block matching algorithm for video coding by Faizul Hadi Mohamad Jamil

    Published 2013
    “…The temporal model deals with motion estimation (ME) and motion compensation (MC) algorithm with the matching technique called “Block Matching Algorithm” (BMA) to produce the next encoded video frame with motion vector. …”
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  11. 11
    Estimation of geometric fractional Brownian motion perturbed by stochastic volatility model

    Estimation of geometric fractional Brownian motion perturbed by stochastic volatility model by Alhagyan, Mohammed, Misiran, Masnita, Omar, Zurni

    Published 2015
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  12. 12
    The impact of executive function and aerobic exercise recognition in obese children under deep learning

    The impact of executive function and aerobic exercise recognition in obese children under deep learning by JING, XIN, ABDULLAH, BORHANNUDIN, ABU SAAD, HAZIZI, YANG, XIANGKUN

    Published 2025
    “…Initially, a motion recognition model based on STN and Lucas–Kanade optical flow algorithm optimization was constructed. …”
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  13. 13
    Estimation of Response Transfer Functions of Offshore Structures Using the Time-Varying ARX Model

    Estimation of Response Transfer Functions of Offshore Structures Using the Time-Varying ARX Model by Yazid, Edwar, Liew, M. S., Parman, Setyamartana, Kurian, V.J.

    Published 2013
    “…KEY WORDS: Motion responses; Kalman smoother; Time-varying ARX model; Transfer function.…”
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  14. 14
    MARKERLESS ARTICULATED HUMAN MOTION TRACKING USING HIERARCHICAL MULTI-SWARM COOPERATIVE PARTICLE SWARM OPTIMIZATION

    MARKERLESS ARTICULATED HUMAN MOTION TRACKING USING HIERARCHICAL MULTI-SWARM COOPERATIVE PARTICLE SWARM OPTIMIZATION by SAINI, SANJA Y

    Published 2016
    “…The Particle Filtering (PF) algorithm is the most extensively used tor generative model based articulated human motion tracking. …”
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  15. 15
    Two level Differential Evolution algorithms for ARMA parameters estimatio

    Two level Differential Evolution algorithms for ARMA parameters estimatio by Salami, Momoh Jimoh Emiyoka, Tijani, Ismaila, Aibinu, Abiodun Musa

    Published 2013
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  16. 16
    A novel approach to human motion estimation with applications in human-robot safety

    A novel approach to human motion estimation with applications in human-robot safety by Motlagh, O., Tang, Sai Hong, Ismail, Napsiah, Ramli, Abdul Rahman, Samin, Razali

    Published 2008
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  17. 17
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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  18. 18
    A review on particle swarm optimization algorithm and its variants to human motion tracking

    A review on particle swarm optimization algorithm and its variants to human motion tracking by Saini, S., Rambli, D.R.B.A., Zakaria, M.N.B., Sulaiman, S.B.

    Published 2014
    “…The goal of human motion capture is to estimate the joints angles of human body at any time. …”
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  19. 19
    Performance comparison between skyhook and semi active damping force estimator (SADE) algorithms for semi active suspension system / S. A. Abu Bakar ... [et al.]

    Performance comparison between skyhook and semi active damping force estimator (SADE) algorithms for semi active suspension system / S. A. Abu Bakar ... [et al.] by Abu Bakar, S. A., Abdul Majid, M. M., Mansor, S., Abdul Hamid, M.K., Daud, Z.C.

    Published 2018
    “…The Skyhook and Semi Active Damping Force Estimator (SADE) algorithms are used to control the operation of the MR damper in quarter car model’s suspension system. …”
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  20. 20
    Maximum likelihood estimation with dynamic measurement errors and application to interest rate modeling

    Maximum likelihood estimation with dynamic measurement errors and application to interest rate modeling by Misiran, Masnita

    Published 2016
    “…A crucial problem in its application is how the unknown parameters in the model are to be estimated. We propose the innovation algorithm, and follow by the maximum likelihood estimation approach, which enables us to derive the estimators of parameters involved in this model.We will also present the simulation outcomes to illustrate the efficiency and reliability of the proposed method.…”
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