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    Design and analysis of 50 channel by 40 Gbps DWDM RoF system for 5G communication based on Fronthaul scenario
    (American Institute of Physics Inc., 2023) Almetwali, Abdullah S.; Bayat, Oguz; Abdulwahid, Maan M.; Mohamadwasel, Noorulden Basil
    The establishment of 5G networks launched before few years and is projected to bring about significant changes in people's daily lives, which connect nodes using optical transceiver modules and optical fibers. The connection between the Central and Base Stations is the most intriguing part of the 5G network, and many academics have examined it extensively in order to improve and maximize network efficiency and achieving the highest data rate with less complexity, and cost-effectiveness. As a result, this paper has been used the Optisystem program and a Dense Wavelength Division Multiplexing (DWDM) Radio over Fiber (RoF) approach to demonstrate, plan, and execute in this article. For higher-speed transmission systems targeted toward Tbps connectivity, a 50 by 40 Gbps data transmission system is proposed. Channels 1, 4, 8, ., 48, and 50 were chosen as samples for the investigation. The output analysis was based on the eye-opening parameters, Quality Factor (QF), and Min Bit Error Rate (MBER) for the distances ranging between (60-180) Km. The results showed a higher data rate performance for the proposed system to reach 2 Tbps for future applications. Furthermore, QF parameters showed encourage results as the averaged obtained values were above the threshold by ranging between (0.22-13) dBm. © 2023 Author(s).
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    Enhancing IoT Security Through Hardware Security Modules (HSMs)
    (Institute of Electrical and Electronics Engineers Inc., 2024) Khan, Mansoor; Ilyas, Muhammad; Bayat, Oguz
    Strong security measures must be integrated in an era where data security is critical, particularly for sensitive data handled by IoT devices. In order to strengthen Internet of Things security, the use of Hardware Security Modules (HSMs) is investigated in this research. We examine the development and effectiveness of HSMs in boosting IoT security through a thorough study of the literature. Our results demonstrate the vital role that HSMs play in protecting cryptographic keys and thwarting any attacks. We explore the difficulties of incorporating HSMs into IoT environments and suggest practical approaches. This research concludes by highlighting the role that HSMs play in strengthening IoT security architecture. © 2024 IEEE.
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    Hybrid solution of challenges future problems in the new generation of the artificial intelligence industry used operations research industrial processes
    (Association for Computing Machinery, 2021) Mohammed, Tareq Abed; Qasim, Mohammed N.; Bayat, Oguz
    Key technologies such as a new generation of industrial systems highly depends on artificial intelligence, and electronic physical systems that can digitize the entire supply chain together with data mining, machine learning, and more. At present, uses of artificial intelligence-based solutions are very important to improve the accuracy and efficiency of production processes. Artificial intelligence (AI) is playing a key role in the fourth industrial revolution, and we see significant improvements in different methods of machine learning. Artificial intelligence is widely used by practitioner engineers to solve various problems. This journal provides an international forum for quick articles that describes the practical application of artificial intelligence in all areas of mechanical engineering. Many researchers cited the development of technology in industrial fields to reduce problems in industry. Both the Operations Research (OR) community and Artificial Intelligence (AI) show that these problems are still interesting. While AI focuses linearly on increasing production and mitigating industry difficulties that may be seen as a revolution in the future. AI techniques offer a richer and more flexible presentation of real problems. The article presents the architecture of the industrial laboratory and the challenges associated with the use of artificial intelligence in industrial processes. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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    IFT and Chebyshev-based planar array thinning for adaptive interference suppression
    (Springer, 2023) Agha, Maryam H.; AL-Adwany, Maan A. S.; Bayat, Oguz; Hamdoon, Hind Th.
    Smart antenna arrays with adaptive nulling capability are emerging as a promising solution to suppress the interference in radar applications and wireless communications in real time. Many adaptive nulling methods have been commonly used, such as controlling amplitude or/and phase excitations of the antenna elements and controlling the position of the elements. Generally, most adaptive nulling methods demand digital beamforming to create the correlation matrix from signals that arrived to array antennas. The digital beamforming is costly and needs frequent calibration; therefore, it does not appropriate for large antenna arrays. Among adaptive nulling methods, an array thinning does not require digital beamforming. It takes advantage of the adaptive algorithm to make the element active or inactive. In this paper, an IFT and Chebyshev techniques-based random thinning is presented to suppress the interference adaptively by lowering the SLL or place nulls toward the interference direction. The proposed method works with the antenna arrays that have transmit/receive modules (TRM) with RF switches. The results show the ability of both IFT and Chebyshev techniques to suppress the interference for the small array. In addition, the results indicate the superiority of IFT technique over Chebyshev in the large arrays; it is considerably faster and more efficient (in terms of lowering the sidelobe levels and nulls formation) than the Chebyshev technique. The advantage of the proposed method is no digital beamforming is needed. Consequently, a considerable reduction in complexity, power consumption, and cost can be attained.
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    Introduction to the special section on new trends in data mining, games engineering and database systems
    (Pergamon-Elsevier Science Ltd, 2018) Aljawarneh, Shadi A.; Bayat, Oguz; Essaaidi, Mohamed
    [No abstract available]
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    Motor-imagery BCI task classification using riemannian geometry and averaging with mean absolute deviation
    (Ieee, 2019) Miah, Abu Saleh Musa; Ahmed, Saadaldeen Rashid Ahmed; Ahmed, Mohammed Rashid; Bayat, Oguz; Duru, Adil Deniz; Molla, Md. Khademul Islam
    Brain Computer interface (BCI) is thought as a better way to link within brain and computer alternative machine. Many types of physiological signal will work BCI framework. Motor imagery (MI) has incontestable to be a excellent way to work a BCI system. Recent research concerning MI based mostly BCI framework, lower performance accuracy and intense of time have common issues. Main focuses of this paper is select the appropriate central point of tangent space in Tangent Space Linear Discriminant analysis-based Motor-Imagery Brain-Computer interfacing. Method name tangent space mapping LDA (TSMLDA) analysis takes its moves from the observations that normally, the EEG signal embodies outliers, so the centrality as a geometric mean of tangent space might not be the simplest alternative. We tend to propose the employment of strong estimators of variance matrices average. Specifically, Median Absolute Deviation(MAD) going to be planned and mentioned. Associate in Nursing experimental analysis can show the advance of Tangent house Linear Discriminant Analysis corresponding to the planned strong estimators. Experimental results show that our proposed method performs 3% better than the recently developed algorithms.
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    A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems (vol 8, pg 23169, 2020)
    (Ieee-Inst Electrical Electronics Engineers Inc, 2020) Moreb, Mohammed; Mohammed, Tareq Abed; Bayat, Oguz; Ata, Oguz
    [No abstract available]
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    The Statistical Learning Methods in image processing and Facial Recognition
    (Association for Computing Machinery, 2021) Mohammed, Tareq Abed; Rafeeq, Sarbaz Omar; Bayat, Oguz
    The aim of this paper is to develop a new approach for The Statistical Learning Methods in image processing and Facial Recognition using the deep learning techniques in python. In the recent years there have been significant advances in face recognition by using deep neural networks. One of the potential next steps is to develop optimized 3D facial recognition. Shifting from 2D to 3D increases complexity of the problem by adding an- other dimension to data, making possible solutions more resource hungry. We will investigate different depth camera based facial recognition techniques and test their performance by deploying them on an embedded processor. We focus on applications for embedded systems and use a small low-resolution time of flight (ToF) camera with our system to keep overall system portable and compact. All faces images are then projected on the feature space ("face space") to find the corresponding coordinators. The face space is composed of "Eigenfaces"or "Fisherfaces"which are actually eigenvectors found after doing a matrix composition - Eigen decomposition. At the heart of Eigenface method is the Principal Component Analysis (PCA) - one of the most popular unsupervised learning algorithms - while Fisherface is a better version of the previous one which makes use of both Principal Component Analysis and Linear Discrimination Analysis (LDA) to get more reliable results. The algorithms were realized by Python in Anaconda. Given initial images in the database, the program can detect and recognize the human faces in the provided pictures before saving them in the database to improve the calculation accuracy in the future. After evaluation, the recognition general results are exported on the screen with details included in the text files. © 2021 ACM.
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    YOLO-V3 based real-time drone detection algorithm
    (Multimedia Tools and Applications, 2022) Alsanad, Hamid R.; Sadik, Amin Z.; Bayat, Oguz
    Drones are currently being used in a wide range of useful tasks that are too dangerous or/and expensive to be performed by humans. However, this is increasingly developing security breaching issues due to the possibility of misuse of unmanned aircraft in illegal activities such as drug smuggling, terrorism, etc. Thus, the detection and tracking of drones are becoming a crucial topic. Unfortunately, due to the drone’s small size, its detection methods are generally unreliable: high false alarm rate, low accuracy rate, and low detection speed are well-known aspects of this detection. The new emerging real-time algorithm based on the improved “You Only Look Once” (YOLO-V3) algorithm is proposed here for drone detection. This newly designed algorithm comprises multiple phases and has shown the potential to outperform the traditional detection approaches. The proposed algorithm enhances the performance of YOLO-V3 by designing and building a CNN to solve the problem of a large number of YOLO-V3 parameters, using densely connected modules to enhance the interlayer connection of CNNs and further strengthen the connection between dense neural network blocks, and finally improving the YOLO-V3 multiple-scale detection by expanding the three-scale to four-scale detection to increase the accuracy of detecting small objects like drones. The evaluation results of our algorithm obtain 96% on average precision and 95.60% accuracy.