Search Results - (( java implication based algorithm ) OR ( wave classification approach algorithm ))

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  1. 1

    Classification Analysis Of High Frequency Stress Wave For Autonomous Detection Of Defect In Steel Tubes by Abd Halim, Zakiah, Jamaludin, Nordin, Junaidi, Syarif, Syed Yahya, Syed Yusaini

    Published 2014
    “…Interpretation of propagated high frequency stress wave signals in steel tubes is noteworthy for defect identification.This paper demonstrated a successful new approach for autonomous defect detection in steel tubes using classification analysis of high frequency stress waves.Classification analysis using Principal Component Analysis (PCA) algorithm involved feature extraction to reduce the dimensionality of the complex stress waves propagation path.Two defective tubes containing a slot defect of different orientation and a reference tube are inspected using Vibration Impact Acoustic Emission (VIAE) technique.The tubes are externally excited using impact hammer.The variation of stress wave transmission path are captured by high frequency Acoustic Emission sensor.The propagated stress waves in the steel tubes are classified using PCA algorithm.Classification results are graphically illustrated using a dendrogram that demonstrated the arrangement of the natural clusters of the stress wave signals.The inspection of steel tubes showed good recognition of defect in circumferential and longitudinal orientation.This approach successfully classified stress wave signals from VIAE testing and provide fast and accurate defect identification of defective steel tubes from non-defective tubes. …”
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    A deterministic approach for finding the T onset parameter of flatten T wave in ECG by Iqbal, Uzair, Teh, Ying Wah, Rehman, Muhammad Habib Ur, Mastoi, Qurat-Ul-Ain

    Published 2019
    “…Identification of the exact nature of flatten T wave in ECG signal is Classification of normal and abnormal T wave episodes especially, regarding Flatten T wave in electrocardiography (ECG) signal is still a complex phenomenon for cardiologists. …”
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  4. 4

    Empirical Analysis of Intra vs. Inter-Subject Variability in VR EEG-Based Emotion Modelling by N.S. Suhaimi, J. Teo, J. Mountstephens

    Published 2018
    “…The highest subject-dependent classification accuracy achieved was 97.9% while the highest subject-independent classification accuracy obtained was 91.4% throughout the brain wave spectrum (α, β, γ, δ, θ). …”
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  5. 5

    Detection of tube defect using the autoregressive algorithm by Abd Halim, Zakiah, Jamaludin, Nordin, Junaidi, Syarif, Syed Yusainee, Syed Yahya

    Published 2015
    “…This study is aimed to automate defect detection using the pattern recognition approach based on the classification of high frequency stress wave signals. …”
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  6. 6

    Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval by Naffouti, S.E., Fougerolle, Y., Aouissaoui, I., Sakly, A., Mériaudeau, F.

    Published 2018
    “…This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. …”
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  7. 7

    Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval by Naffouti, S.E., Fougerolle, Y., Aouissaoui, I., Sakly, A., Mériaudeau, F.

    Published 2018
    “…This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. …”
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  8. 8

    Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification by Al-Sharhan, Salah, Bimba, Andrew

    Published 2019
    “…EEG signal analysis involves multi-frequency non-stationary brain waves from multiple channels. Segmenting these signals, extracting features to obtain the important properties of the signal and classification are key aspects of detecting epileptic seizures. …”
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    Forward scattering radar for real-time detection of human activities and fall classification by Abdulhameed, Ali Ahmed

    Published 2019
    “…The frequency spectrum signatures extracted from the experimentally collected data were used as input features to the classification unit. Support Vector Machine (SVM) algorithm modified by kernel linear function was used for classifying the fall event from the other activities. …”
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    Objective quantification of selective attention in schizophrenia a hybrid TMS – EEG approach by W Azlan, Wan Amirah

    Published 2017
    “…In a further investigation, the C4.5 decision tree algorithm was implemented to classify the N1-P2 wave of control and schizophrenia subjects elicited by sTMS and rTMS. …”
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  14. 14

    Development of an automated detector and counter for bagworm census by Ahmad, Mohd Najib

    Published 2020
    “…The development of an image processing algorithm for detection and counting of Metisa plana Walker, a species of Malaysia’s local bagworm using image segmentation was proposed as it was found to be better than the thermal approach after some preliminary field tests. …”
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