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  1. 1
    Development of automated neighborhood pattern sensitive faults syndrome generator for static random access memory

    Development of automated neighborhood pattern sensitive faults syndrome generator for static random access memory by Rusli, Julie Roslita

    Published 2011
    “…Moreover, most of the memory test algorithm does not have the capability to detect and diagnose NPSF. …”
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  2. 2
    Analysis of disturb coupling faults coverage by the March AZ2 algorithm in memory BIST

    Analysis of disturb coupling faults coverage by the March AZ2 algorithm in memory BIST by Md Zin, Ahmad Ridhwan, Jidin, Aiman Zakwan, Razaidi, Hussin, Mispan, Mohd Syafiq

    Published 2024
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  3. 3
    A fault syndromes simulator for random access memories

    A fault syndromes simulator for random access memories by Wan Hasan, Wan Zuha, Abdul Halim, Izhal, Mohd Sidek, Roslina, Othman, Masuri

    Published 2008
    “…Due to this, a systematic approach for developing and evaluating memory test algorithm is required. …”
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  4. 4
    March-based diagnosis algorithm for static random-access memory stuck-at faults and transition faults

    March-based diagnosis algorithm for static random-access memory stuck-at faults and transition faults by Mat Isa, Masnita

    Published 2012
    “…This factor contributes to challenges faced in the area of memory testing. Functional test is a standard testing procedure to determine the functionality of the memory by performing a sequence of read write operations to detect faults. …”
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  5. 5
    An efficient fault syndromes simulator for SRAM memories

    An efficient fault syndromes simulator for SRAM memories by Wan Hasan, Wan Zuha, Abdul Halin, Izhal, Mohd Sidek, Roslina, Othman, Masuri

    Published 2009
    “…Due to this, a systematic approach for developing and evaluating memory test algorithm is required. …”
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  6. 6
    Multiple and solid data background scheme for testing static single cell faults on SRAM memories

    Multiple and solid data background scheme for testing static single cell faults on SRAM memories by Zakaria, Nor Azura

    Published 2013
    “…Memory testing is a method that requires an algorithm capable of detecting faulty memory as comprehensively as possible to facilitate the efficient manufacture of fault free memory products. …”
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  7. 7
    Optimization of blood vessel detection in retina images using multithreading and native code for portable devices

    Optimization of blood vessel detection in retina images using multithreading and native code for portable devices by Tran, Duc Ngoc, Hussin, Fawnizu Azmadi, Yusoff, Mohd Zuki

    Published 2013
    “…The optimization of a computationally intensive algorithm such as this on a mobile platform is challenging due to the limited resources available. …”
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  8. 8
    Generation of new low-complexity March algorithms for optimum faults detection in SRAM

    Generation of new low-complexity March algorithms for optimum faults detection in SRAM by Jidin, Aiman Zakwan, Hussin, Razaidi, Lee, Weng Fook, Mispan, Mohd Syafiq, Zakaria, Nor Azura, Loh, Wan Ying, Zamin, Norshuhani

    Published 2023
    “…A high-complexity test algorithm like the March MSS (18N) can guarantee the detection of all unlinked static faults in SRAM. …”
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  9. 9
    Reduced Galloping Column Algorithm For Memory Testing

    Reduced Galloping Column Algorithm For Memory Testing by Ngieng , Siew Ching

    Published 2015
    “…There are lots of memory algorithms used for memory testing, including the galloping column algorithm (GalCol). …”
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  10. 10
    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
    “…Hence, the objective of this research is to propose suitable and optimize algorithm for ANPR system on Android mobile phone. …”
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  11. 11
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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  12. 12
    Testing static single cell faults using static and dynamic data background

    Testing static single cell faults using static and dynamic data background by Zakaria, Nor Azura, Wan Hasan, Wan Zuha, Abdul Halin, Izhal, Mohd Sidek, Roslina, Wen, Xiaoqing

    Published 2011
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  13. 13
    Optimizing Ram Testing Method For Test Time Saving Using Automatic Test Equipment

    Optimizing Ram Testing Method For Test Time Saving Using Automatic Test Equipment by Kesavan Prabagaran, Premkumar

    Published 2017
    “…A memory Build-in Self-test (BIST) design with capability of algorithm failing sequence capture have been developed to implement in the Automate Test Equipment (ATE) flow for production screen. 3 selected algorithm have been tested on the 8 detect units in ATE flow to prove the concept of this method. …”
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  14. 14
    Detecting JFIF header using FORHEADER

    Detecting JFIF header using FORHEADER by Kamaruddin Malik, Mohamad, Herawan, Tutut, Mustafa, Mat Deris

    Published 2011
    “…Header and footer are important in digital investigation for JPEG file detection as only 16% of files are fragmented. The use of efficient algorithm to detect them is vital to reduce time taken for analyzing ever increasing data in hard drive or physical memory. …”
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  15. 15
    Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things

    Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

    Published 2022
    “…The experimental results showed that the accuracy of the algorithm over the NSL-KDD dataset was 99.72%, with a memory reduction of 10%. …”
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  16. 16
    BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning

    BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning by Chimeleze C., Jamil N., Ismail R., Lam K.-Y., Teh J.S., Samual J., Akachukwu Okeke C.

    Published 2023
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  17. 17
    Ant colony optimization algorithm for load balancing in grid computing

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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  18. 18
    MBIST implementation and evaluation in FPGA based on low-complexity March algorithms

    MBIST implementation and evaluation in FPGA based on low-complexity March algorithms by Jidin, Aiman Zakwan, Mispan, Mohd Syafiq, Hussin, Razaidi, Weng, Fook Lee

    Published 2024
    “…March algorithms are widely used in Memory Built-In Self-Test (MBIST) on-chip memory testing, providing linear test complexities that reduce the test time and cost. …”
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  19. 19
    Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction

    Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction by Alkawsi G., Al-amri R., Baashar Y., Ghorashi S., Alabdulkreem E., Kiong Tiong S.

    Published 2024
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  20. 20
    Embedded vision system development using 32-bit Single Board Computer and GNU/Linux

    Embedded vision system development using 32-bit Single Board Computer and GNU/Linux by Nur Farhan, Kahar

    Published 2011
    “…The increasing affordability of powerful processors and memory chips, availability of real-time operating systems, low complexity intelligent algorithms and the coming-of-age of system development software are the key factor that makes this development possible. …”
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