Muhammad Imran Ahmad
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Preferred name
Muhammad Imran Ahmad
Official Name
Muhammad Imran, Ahmad
Alternative Name
Ahmad, Muhammad Imran
Ahmad, M. I.
Imran Ahmad, Muhammad
Ahmad, Muhamad Imran
Main Affiliation
Scopus Author ID
57214845678
Researcher ID
GBE-1471-2022

Research Output

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  • Publication
    Biological sequence alignments: A review of hardware accelerators and a new PE computing strategy
    ( 2014)
    Mohd Nazrin Md Isa
    ;
    Muhammad Imran Ahmad
    ;
    Sohiful Anuar Zainol Murad
    ;
    Rizalafande Che Ismail
    ;
    Khaled Benkrid
    One of the most challenging tasks in sequence alignment is its repetitive and time-consuming alignment matrix computations. In addition, performing sequence alignment in hardware, i.e. FPGA requires more hardware resources as the number of processing elements is replicated to increase performance throughput. This paper first reviews the existing FPGA-based biological sequence alignment core architectures and then proposed an efficient scheduling strategy, the so-called overlap computation and configuration (OCC) towards realizing optimized biological sequence alignment core architecture targeting for pairwise sequence alignment. In this research work, double buffering-based core architecture have been proposed and implemented on Xilinx Virtex-5 FPGA. Results have shown that this approach gained more than 10K times speed-up as compared to the GPP solution.
      3  20
  • Publication
    A review paper on face recognition techniques
    (IEEE, 2023)
    Ibrahim Mahmood Rashid Al-Bakri
    ;
    Muhammad Imran Ahmad
    ;
    Mustafa Zuhaer Nayef Al-Dabagh
    ;
    Mohd Nazrin Md Isa
    The study of computer vision and pattern recognition is growing because of the various commercial and practical applications of these disciplines. Identification of individuals in a multitude, access control, forensics, and human-computer interactions are only among the topics studied by these areas. However, analyzing unconstrained face recognition poses ethical issues and privacy concerns. Many recent proposals employ Holistic Methods, Geometric Approach and Local-Texture Approach, methods and databases like ORL, FERET and AR Dataset to study constrained face recognition. At least some understanding of 2D perspective was achieved. This occurred in highly controlled environments where parameters such as camera angles, lighting and distance were strictly regulated. However, significant degradation in recognition performance occurred if the environment changed or the subject smiled or frowned. This critique discusses the technology utilized in face recognition, as well as the databases of methods that utilize this technology. To help guide future research, this article discusses current knowledge and suggests future directions for study in the field of facial recognition.