Search Results - (( motion estimation means algorithm ) OR ( level classification system algorithm ))

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  1. 1
    Detection and Classification of Moving Objects for an Automated Surveillance System

    Detection and Classification of Moving Objects for an Automated Surveillance System by Md. Tomari, Mohd Razali

    Published 2006
    “…A completely automated system means a computer will perform the entire task from low level detection to higher level motion analysis. …”
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  2. 2
    Detection and classification of moving objects for an automated surveillance system

    Detection and classification of moving objects for an automated surveillance system by Md Tomari, Mohd Razali

    Published 2006
    “…A completely automated system means a computer will perforin the entire task from low level detection to higher level motion analysis. …”
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  3. 3
    Detection and classification of moving objects for an automated surveillance system

    Detection and classification of moving objects for an automated surveillance system by Md Tomari, Mohd Razali

    Published 2006
    “…A completely automated system means a computer will perforin the entire task from low level detection to higher level motion analysis. …”
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  4. 4
    Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition

    Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition by Burhan, Nuradebah

    Published 2018
    “…Electromyography (EMG) signal is a biomedical signal which measures physical activity of human muscle.It has been acknowledged to be widely used in rehabilitation or recovery application system assisting physiotherapist to monitor a patient’s physical strength,function,motion and overall well-being by addressing the underlying physical issues.In application system associated with rehabilitation,a signal processing and classification techniques are implemented to classify EMG signal obtained.For real time application in the rehabilitation, the classification is crucial issue.The success of the signal classification depends on the selection of the features that represent a raw EMG signal in the signal processing.Therefore,a robust and resilient denoising method and spectral estimation technique have been acknowledged as necessary to distinguish and detect the EMG pattern.The present study was undertaken to determine the characteristic of EMG features using denoising method and spectral estimation technique for assessing the EMG pattern based on a supervised classification algorithm.In the study,the combination of time-frequency domain (TFD) and time domain (TD) were identified as the preferred denoising method and spectral estimation techniques.In the first part of study, the recorded EMG signal filtered the contaminated noise by using wavelet transform (WT) approach which implemented discrete wavelet transform (DWT) method of the wavelet-denoising signal. …”
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  5. 5
    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
    “…Moreover, we investigate the behavior of our algorithm under challenging conditions including the subject's motions and illumination variation, which shows that our algorithm can reduce the influences of illumination interference and rigid motions significantly. …”
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  6. 6
    A new Doppler Spread Estimation Algorithm Based on Zero Crossings of The Auto-correlation

    A new Doppler Spread Estimation Algorithm Based on Zero Crossings of The Auto-correlation by Altag Ali, Gassan, Jeoti , Varun, Manzoor, Shahid

    Published 2010
    “…A comparison between our proposed algorithm and the algorithm proposed by Tevfik Yucek in term of the Normalized Mean Square Error has been shown.…”
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  7. 7
    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab

    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab by Bassam Ali Qasem, Al-Qatab

    Published 2020
    “…Firstly, intra-severity classification intended to identify the level of severity of the dysarthric speakers. …”
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  8. 8
    Jogging activity recognition using k-NN algorithm

    Jogging activity recognition using k-NN algorithm by Afifah Ismail

    Published 2022
    “…Jogging activity recognition using the k-NN algorithm is a system that can help users collect information data of user speed movement using speed sensor and give the classification of jogging activity to the user. …”
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    Academic Exercise
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  9. 9
    Data filtering of 5-axis inertial measurement unit using kalman filter

    Data filtering of 5-axis inertial measurement unit using kalman filter by Nur Syazwani, Samsudin

    Published 2013
    “…The main contribution of these algorithms is the in-motion alignment approach with unknown initial conditions. …”
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    Undergraduates Project Papers
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  10. 10
    A Real time specific weed discrimination system using multi-Level wavelet decomposition

    A Real time specific weed discrimination system using multi-Level wavelet decomposition by Siddiqi, M.H., Sulaiman, S., Faye, Ibrahima, Ahmad, I.

    Published 2009
    “…The developed algorithm was used for the real time specific weed discrimination employing multi-level wavelet decomposition. …”
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  11. 11
    A Real time specific weed discrimination system using multi-Level wavelet decomposition

    A Real time specific weed discrimination system using multi-Level wavelet decomposition by Siddiqi, M.H., Sulaiman, S., Faye, Ibrahima, Ahmad, I.

    Published 2009
    “…The developed algorithm was used for the real time specific weed discrimination employing multi-level wavelet decomposition. …”
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  12. 12
    Modeling financial environments using geometric fractional Brownian motion model with long memory stochastic volatility

    Modeling financial environments using geometric fractional Brownian motion model with long memory stochastic volatility by Al Haqyan, Mohammed Kamel Mohammed

    Published 2018
    “…The results of simulation reveal that the proposed estimators are efficient based on the bias, variance, and mean square error. …”
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  13. 13
    Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms

    Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms by Rachmad, Iqbal, Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Tata, Sutabri

    Published 2024
    “…The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. …”
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  14. 14
    Text spam messages classification using Artificial Immune System (AIS) algorithms

    Text spam messages classification using Artificial Immune System (AIS) algorithms by Nurul Fadhilah Sulaiman

    Published 2024
    thesis::master thesis
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  15. 15
    Neural network diagnostic system for dengue patients risk classification

    Neural network diagnostic system for dengue patients risk classification by Faisal, T., Taib, M.N., Ibrahim, Fatimah

    Published 2012
    “…However, it has been a great challenge for the physicians to identify the level of risk in dengue patients due to overlapping of the medical classification criteria. …”
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  16. 16
    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim

    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim by Ibrahim, Shafaf

    Published 2015
    “…For this purpose, an algorithm which hybridized the Intensity Based Analysis (IBA), Grey Level Co-occurrence Matrices (GLCM), Adaptive Network-based Fuzzy Inference System (ANFIS) and £article ~warm Optimization (PSO) Clustering Algorithm (CAPSOCA) is proposed. …”
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  17. 17
    Classification System for Wood Recognition using K-Nearest Neighbor with Optimized Features from Binary Gravitational Algorithm

    Classification System for Wood Recognition using K-Nearest Neighbor with Optimized Features from Binary Gravitational Algorithm by Taman, Ishak, Md Rosid, Nur Atika, Karis, Mohd Safirin, Hasim, Saipol Hadi, Zainal Abidin, Amar Faiz, Nordin, Nur Anis, Omar, Norhaizat, Jaafar, Hazriq Izzuan, Ab Ghani, Zailani, Hassan, Jefery

    Published 2014
    “…The project proposes a classification system using Gray Level Co-Occurrence Matrix (GLCM) as feature extractor, K-Nearest Neighbor (K-NN) as classifier and Binary Gravitational Search Algorithm (BGSA) as the optimizer for GLCM’s feature selection and parameters. …”
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  18. 18
    Performance evaluation of direction of arrival (DOA) for linear array antenna design using multiple signal classification (MUSIC) algorithm in variation of displacement vectors and noise / Norhayati Hamzah

    Performance evaluation of direction of arrival (DOA) for linear array antenna design using multiple signal classification (MUSIC) algorithm in variation of displacement vectors and... by Hamzah, Norhayati

    Published 2007
    “…In simple terms, these array antennas can reduce the co channel interference and effectively utilize the bandwidth by steering a high gain in the direction of interest and low gains in the undesired directions. In Smart Antenna system, the intelligence of the system depends on the information collected, processed and implemented through an algorithm i.e. …”
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  19. 19
    Classification for the fruit maturity using Neural Network

    Classification for the fruit maturity using Neural Network by Mohamad Naeem Hussien

    Published 2008
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    Learning Object
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  20. 20
    Parameter estimation of stochastic differential equation

    Parameter estimation of stochastic differential equation by Haliza Abd. Rahman, Arifah Bahar, Norhayati Rosli, Madihah Md. Salleh

    Published 2012
    “…The results showed that the Mean Square Errors (MSE) for stochastic model with parameters estimated using optimal knot for 1,000, 5,000 and 10,000 runs of Brownian motions are smaller than the SDE models with estimated parameters using knot selected heuristically. …”
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