Search Results - (( verification implementation evolution algorithm ) OR ( java segmentation using algorithm ))

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  1. 1
    An FPGA implementation of exp-bet scheduling algorithm in LTE networks / Yusmardiah Yusuf

    An FPGA implementation of exp-bet scheduling algorithm in LTE networks / Yusmardiah Yusuf by Yusuf, Yusmardiah

    Published 2017
    “…It is performed by the scheduling algorithm implemented in the Long Term Evolution (LTE) base station, Evolved Node B. …”
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  2. 2
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…Finally, the algorithm found, which would solve the image segmentation problem.…”
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  3. 3
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
    “…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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  4. 4
    Development of seven segment display recognition using TensorFlow on Raspberry Pi

    Development of seven segment display recognition using TensorFlow on Raspberry Pi by Gunawan, Teddy Surya, Mohammad Reduwan, Haque, Yaacob, Mashkuri, Mohamed Azmin, Nor Fadhillah, Ismail, Nanang, Ismail, Md Yusof, Kartiwi, Mira, A. Rahman, Azhar

    Published 2022
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  5. 5
    A modified group authentication scheme for machine type communication in LTE/LTE-a networks

    A modified group authentication scheme for machine type communication in LTE/LTE-a networks by Ismail, Haqi Khalid

    Published 2018
    “…Meeting the necessities of control usage of devices and mass implement transmission is a key issue in the implementation of MTC in the Long Term Evolution/Long Term Evolution Advanced systems. …”
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  6. 6
    Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System

    Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System by Zainal Abidin, Zaheera, -, M.Manaf, Shibghatullah, Abdul Samad, -, S.H.A.Mohd Yunos, -, S.Anawar, -, Z.Ayop

    Published 2012
    “…Here, iris segmentation has been implemented using Hough Transform and Integro-Differential Operator techniques. …”
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  7. 7
    Toward Quantum-Resilient PKI: A Systematic Literature Review of Post-Quantum Certificates Model

    Toward Quantum-Resilient PKI: A Systematic Literature Review of Post-Quantum Certificates Model by Nor Azeala Mohd Yusof, A H Azni, Farida Hazwani Mohd Ridzuan, Nur Hafiza Zakaria, Sakinah Ali Pitchay, Abdul Alif Zakaria, Tasnuva Ali

    Published 2026
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  8. 8
    Iris Segmentation Analysis Using Integro-Differential Operator And Hough Transform In Biometric System

    Iris Segmentation Analysis Using Integro-Differential Operator And Hough Transform In Biometric System by Zainal Abidin, Zaheera, Anawar, Syarulnaziah, Ayop, Zakiah, Manaf, Mazani, Shibghatullah, A.S., Mohd Yunos, S.H.A.

    Published 2012
    “…Iris segmentation is foremost part of iris recognition system.There are four steps in iris recognition: segmentation,normalization,encoding and matching.Here, iris segmentation has been implemented using Hough Transform and IntegroDifferential Operator techniques.The performance of iris recognition system depends on segmentation and normalization technique.Iris recognition systems capture an image from individual eye.Then the image captured is segmented and normalized for encoding process.The matching technique,Hamming Distance,is used to match the iris codes of iris in the database weather it is same with the newly enrolled for verification stage.These processes produce values of average circle pupil,average circle iris,error rate and edge points.The values provide acceptable measures of accuracy False Accept Rate (FAR) or False Reject Rate (FRR).Hough Transform algorithm,provide better performance,at the expense of higher computational complexity.It is used to evolve a contour that can fit to a non-circular iris boundary.However,edge information is required to control the evolution and stopping the contour.The performance of Hough Transform for CASIA database was 80.88% due to the lack of edge information.The GAR value using Hough Transform is 98.9% genuine while 98.6% through Integro-Differential Operator.…”
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