Hasliza A Rahim @ Samsuddin
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Preferred name
Hasliza A Rahim @ Samsuddin
Official Name
A Rahim @ Samsuddin, Hasliza
Alternative Name
A. Rahim, Hasliza
Rahim, Hasliza A.
Rahim Samsuddin, H. A.
Rahim, Hasliza Abdul
Rahim, Hazliza A.
Rahim, H. A.
Rahim, H.
Hasliza,
Rahim H, Abd
RahimAtSamusuddin, Hasliza A.
A Rahim, Hasliza
Rahim At Samsuddin, Hasliza A.
ARahim, H.
Rahim Samsuddin, A. Hasliza
Rahim At Samsuddin, H. A.
Rahim, Hasliza Abd
Rahim, Hasliza
Rahim At Shamsuddin, H. A.
Main Affiliation
Scopus Author ID
57202496362
Researcher ID
ABE-3328-2020

Research Output
Now showing 1 - 1 of 1
  • Publication
    A hybrid modified sine cosine algorithm using inverse filtering and clipping methods for low autocorrelation binary sequences
    ( 2022-01-01)
    Rosli S.J.
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Khairul Najmy Abdul Rani
    ;
    Ruzelita Ngadiran
    ;
    Wan Azani Wan Mustafa
    ;
    Abdulmalek M.
    ;
    Mohd Najib Mohd Yasin
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Wan Nur Suryani Firuz Wan Ariffin
    ;
    Alkhayyat A.
    The essential purpose of radar is to detect a target of interest and provide information concerning the target's location, motion, size, and other parameters. The knowledge about the pulse trains' properties shows that a class of signals is mainlywell suited to digital processing of increasing practical importance. A low autocorrelation binary sequence (LABS) is a complex combinatorial problem. The main problems of LABS are low Merit Factor (MF) and shorter length sequences. Besides, the maximumpossibleMF equals 12.3248 as infinity length is unable to be achieved. Therefore, this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA) using Inverse Filtering (IF) and clipping method to achieve better results. The proposed algorithms, LABS-IF and HMSCACSA-IF, achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237, respectively, where the optimal solutions belong to the skew-symmetric sequences. TheMFoutperformed up to 24.335% and 2.708% against the state-of-the-art LABS heuristic algorithm, xLastovka, and Golay, respectively. These results indicated that the proposed algorithm's simulation had quality solutions in terms of fast convergence curve with better optimal means, and standard deviation.
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