Dhamyaa Husam AlNuaimi

@gu.edu.iq

Lecturer at the Department of Electronics & Communications, Faculty of Engineering, Gilgamesh University
Department of Electronics & Communications, Gilgamesh University

Dhamyaa Husam AlNuaimi


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https://researchid.co/dhamyaahusamalnuaimi

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Artificial Intelligence, Computer Networks and Communications, Signal Processing
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Scopus Publications

Scopus Publications

  • Utilize the Complex Sadik Transform to Solution Volterra Integro-Differential Equations of Second Type
    Nada Sabeeh Mohammed, Emad A. Kuffi, Dhamyaa Husam Al-Nuaimi
    Springer Proceedings in Mathematics and Statistics, 2024
  • Investigation of dualband fan-shaped microstrip bandpass filter
    Seevan F. Abdulkareem, Zainab Faydh, Dhamyaa H. Al-Nuaimi
    Telkomnika Telecommunication Computing Electronics and Control, 2021
    In this study, design and simulation of microtrip bandpass filter is presented using RT/Duroid 6010.2 lm substrate. This filter has fan-shaped topology with small dimensions of 12x12 mm2, designed for dual band frequencies at 3.41 and 6.14 GHz. The insertion loss and return loss of initial band at 3.41 GHz are -0.7 and -38.224 dB respectively and its bandwidth ranged from 3.3561 to 3.48 GHz. On the other hand, for 2nd band at 6.14 GHz, the insertion loss and return loss have been -1.377 and -14 dB respectively with bandwidth ranged from 6.0951 to 6.1782 GHz.\n
  • AMC2-Pyramid: Intelligent Pyramidal Feature Engineering and Multi-Distance Decision Making for Automatic Multi-Carrier Modulation Classification
    Dhamyaa Husam Al-Nuaimi, Nor Ashidi Mat Isa, Muhammad Firdaus Akbar, Intan Sorfina Zainal Abidin
    IEEE Access, 2021
    Automatic modulation classification (AMC) is a method that supported different wireless communication systems for modulation type classification. Currently, orthogonal frequency division multiplexing, multiple-input, multiple-output systems are widely using this technique. Recent AMC methods are designed for a single-carrier system identifying a few modulation types. To motivate the AMC for the current communication systems, we present an intelligent pyramid model for automatic multi-carrier modulation classification (AMC2-pyramid) with three significant operations, namely, signal fortification, feature engineering and modulation classification. First, signal quality is estimated to reduce the complexity in classification because some signals are affected by noise and other environmental or channel artefacts. Hence, before preprocessing the signal, the quality is assessed according to the channel state information, signal to inference plus noise ratio, received signal strength indicator and spectral efficiency. For low quality, quality augmentation is applied. Then, quality augmentation is implemented with noise elimination, equalisation, quantisation and channel frequency offset compensation. In the feature engineering step, feature extraction and clustering are presented using the gated feature response pyramid network, and a twin-functioned human mental search algorithm is used. The modulation classification is implemented using amulti-distance-based nearest centroid classifier, and improved Q-learning is used to identify signals as any of 16QAM, 32QAM, 64QAM, 128QAM, QPSK, BPSK, DPSK, ASK and FSK. The performance of the proposed AMC2-pyramid is implemented using MatlabR2017b, where accuracy, precision, recall, F-score, error rate and computational time is computed for the proposed work including previous well-known methods. The proposed work proves that this method outperforms the previous ones.
  • Amc2n: Automatic modulation classification using feature clustering-based two-lane capsule networks
    Dhamyaa H. Al-Nuaimi, Muhammad F. Akbar, Laith B. Salman, Intan S. Zainal Abidin, Nor Ashidi Mat Isa
    Electronics Switzerland, 2021
    The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.
  • Performance of feature-based techniques for automatic digital modulation recognition and classification—a review
    Dhamyaa H. Al-Nuaimi, Ivan A. Hashim, Intan S. Zainal Abidin, Laith B. Salman, Nor Ashidi Mat Isa
    Electronics Switzerland, 2019
    The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.