Scopus Research — Ahmed Hasan Kadhim Janabi

UOWC (Underwater Optical Wireless Communication) provides an effective solution in aquatic environments, with high data rates, reasonable costs, and rapid response times. However, this system faces challenges such as light scattering and water absorption. Research efforts have focused on boosting performance and lengthening the communication range, where OOK-NRZ and OOK-CSRZ modulation schemes have been proposed. SISO and MIMO configurations have also been used to simulate optical transmission networks. The system effectiveness has been evaluated in different types of water and distances, including turbid, coastal, and clear water. The UOWC system was implemented in 4 × 4 MIMO configuration, and it showed its superiority over other systems in terms of link range and error rate. In turbid water conditions, the system achieved a communication range of about 23.3 m at a transmission speed of 10 Gbps and a bit error rate of 4.99 × 10− 8. While in clear water, the link range was about 239 m featuring a bit error rate of 4.23 × 10− 8. This system provides lower bit error rates and better transmission distances compared to previous technologies. © The Author(s), under exclusive licence to The Optical Society of India 2025.

This research introduces an innovative thermal energy system that combines solar and wind energy to produce electricity, generate hydrogen, and facilitate liquefaction. This system includes a parabolic trough solar collector (PTSC) that heats nitrate salts, transferring the thermal energy to a supercritical carbon dioxide Brayton cycle (SCO2-BC). Furthermore, thermoelectric generators (TEG) are integrated to capture energy from waste heat sources. Additionally, this study breaks new ground by incorporating solar and wind power with a supercritical CO2 cycle alongside hydrogen liquefaction, a field that is still relatively uncharted. A detailed techno-economic and environmental model is utilized to assess the system's performance, concentrating on critical indicators such as second law efficiency, total cost rate, hydrogen production rate, net power output, levelized costs, and the rate of CO2 emission reduction. Following this, an optimization process is carried out using a genetic algorithm to investigate two different scenarios. Finally, the LINMAP method is applied to identify optimal solutions for each scenario. The study reveals that the system generated a grid power output of 461.2 kW and produced 8.3 kg of liquid hydrogen per hour. The overall cost of operation was established at 103.8 $/h with an exergy efficiency of 16.2 %. Further refinements resulted in values of 19.33 % for second-law efficiency, 124.80 $/h for cost rate, and 1021.64 kW for grid power. © 2025 The Authors.

This study involves a comparison of the experimental findings obtained from testing conducted in the Mode Stirred Reverberation Chamber (MSRC) and the Anechoic Chamber (AC). Directly comparing the reactions of different items under test proved challenging due to variations in the electromagnetic surroundings for both procedures. The tests conducted in both rooms have exhibited varying responses based on the equipment's directivity. Furthermore, the outcomes derived from this examination exhibit variability contingent upon the conditions under which the test is conducted. Hence, the test results obtained from the two chambers exhibit similar error biases. The error bias refers to the proportion of a measured response obtained under specified test conditions compared to the maximum possible reaction. The paper examines the coupling uncertainty and anticipated error bias for both test procedures, analyzing how they vary with apparent directivity. The measured AC data is utilized to ascertain the magnitude and configuration of the apparent directivity of equipment responses. © 2025, Semarak Ilmu Publishing. All rights reserved.

Dynamic load balancing is a critical challenge in cloud computing environments, where efficient workload distribution is essential for maintaining system performance and cost-effectiveness. This research addresses a significant gap in conventional load balancing methods by integrating machine learning techniques to enable adaptive, real-time resource allocation. Our proposed framework combines predictive algorithms with traditional load balancing strategies, allowing the system to anticipate workload fluctuations and dynamically adjust resource distribution accordingly. Through a series of rigorous experiments in a hybrid cloud environment, we evaluated the performance of our approach using both synthetic and real-world datasets. The results demonstrate a marked reduction in response time, enhanced throughput, and a substantial decrease in error rates compared to traditional techniques. Moreover, the predictive capabilities of the machine learning component improved overall system resilience under varying load conditions. By systematically analyzing key performance metrics, our study confirms that machine learning-driven dynamic load balancing not only meets but surpasses the operational efficiency of conventional methods. In summary, this work contributes a novel adaptive load balancing framework that bridges the gap between static approaches and the dynamic requirements of modern cloud systems, offering significant potential for improving cloud resource management and inspiring future innovations in the field. © 2025 IEEE.

It is clear that the security of voice data transmitted over digital interfaces has never been more crucial. In this paper, a new method of speaker identification through encryption of the speech signals is suggested which uses frequency analysis and amplitude modulation and wavelet transformation. Firstly, non-stationary continuous speeches are shifted to their continuous frequency signals. Then, AM modulation is adopted and after that the data is encrypted by using wavelet transform. The signal is divided into two parts: on approximation and detail and Ã' coefficients obtained from these portions are changed with a key from the user before reconstructing the signal. The following statistical tests were used when the results of the inquiry and the performance of the suggested algorithm were being reviewed. Thus, the estimated correlation of the original and encrypted signals was carried out to show the extent of resemblance between the two collected signals. Also, for signal distribution analysis there are provided comparisons between the first and the last histogram and power spectrum analysis of the speaker analyzing the difference in frequency content of the speaker after intervention. Besides, evaluating the signal quality of the decrypted signal, signal-to-noise ratio (SNR) and mean squared error (MSE) were conducted in order to compare the decrypted signal and the original one. In conclusion, the examined encryption technique offers a strong resistance to illegal access and, at the same time, minimises the signal quality loss. As many techniques go into an encryption, such encryption is applied with a central view of protecting the algorithm from unauthorized access and interceptions. Based on the findings of this study, it is possible to make use of the new technique to enhance security in voice communication in telecommunication networks, VoIP, and military service communications. © 2025 Author(s).

Iraqi diesel is characterized by its high sulfur content, which causes the emission of high concentrations of particulate matter. This work focuses on the evaluation of these particle size distribution when the engine is fuelled by pure diesel fuel blended with biodiesel. The study focused on particulates matters mass concentration. The first goal consists of monitoring the percentage of particulate matter substances emitted by the diesel engine powered with pure diesel fuel and biodiesel-diesel blends. The emissions of particles of all sizes decreased from biodiesel blends with a significant effect on particles measured in nano and fine particles. Under constant engine speed and variable load and, PM2.5 was reduced by 7.2%, 16.7%, 32.2% and 42.8% for DB20, DB35, DB50 and B100 compared to diesel, respectively. For the same testing conditions, the TSP reduced by 4.98%, 12.07%, 21.54% and 26.53%, respectively. The use of biodiesel blends also resulted in a significant reduction in particulate matter compared to diesel when the engine run at variable speed and fixed load. The reduction rate for PM1 was 12.13%, 36.65%, 60.92% and 81.06% for DB20, DB35, DB50 and B100, respectively. The PM10 reduced by 9%, 25.98%, 43% and 61.3%, respectively. © 2025, Semarak Ilmu Publishing. All rights reserved.

In the rapidly evolving field of network architecture, Software-Defined Networking (SDN) has emerged as a transformative approach, providing unprecedented flexibility and control over network resources. While SDN enhances efficiency and programmability, it also introduces various security vulnerabilities, primarily due to its architecture, which distinctly separates the control plane from the data plane. This division enables dynamic and adaptable network management but also exposes networks to sophisticated cyber threats, including Distributed Denial of Service (DDoS) attacks, SQL injections, and other forms of intrusion targeting the centralised SDN controllers and open interfaces of its switches. This paper explores the complex security landscape of SDN, identifying critical vulnerabilities within this modern networking model. By analysing prevalent network attacks such as DDoS, DoS, Probe, and SQL Injection, we underscore the pressing need for resilient intrusion detection systems (IDS) that are specifically designed to meet the unique security challenges of SDN environments. Our investigation highlights significant gaps in current research, particularly in the development of real-time traffic processing and system overload mitigation strategies, both of which are vital for establishing durable and resilient SDN architectures. This study contributes to the discourse on SDN security by proposing a strategic framework for developing sophisticated IDS solutions that can adapt to the evolving dynamics of network threats. Our findings emphasise the importance of continuous innovation and a focus on sustainable, secure infrastructure within Software-Defined Networking, supporting its role as a safe and efficient foundation for future network developments. © 2013 IEEE.

The study focuses on using remote sensing to gather data about the Earth’s surface, particularly in urban environments, using satellites and aircraft-mounted sensors. It aims to develop a classification framework for road targets using multi-spectral imagery. By integrating Convolutional Neural Networks (CNNs) with XGBoost, the study seeks to enhance the accuracy and efficiency of road target identification, aiding urban infrastructure management and transportation planning. A novel aspect of the research is the incorporation of quantum sensors, which improve the resolution and sensitivity of the data. The model achieved high predictive accuracy with an MSE of 0.025, R-squared of 0.85, RMSE of 0.158, and MAE of 0.12. The CNN model showed excellent performance in road detection with 92% accuracy, 88% precision, 90% recall, and an f1-score of 89%. These results demonstrate the model’s robustness and applicability in real-world urban planning scenarios, further enhanced by data augmentation and early stopping techniques. © 2024 Techno-Press, Ltd.

Software-Defined Networking is an innovative architecture approach in the networking field. This technology allows networks to be centrally and intelligently managed by unified applications such as traffic classification and security management. Traditional networks' static nature has a minimal capacity to meet organisations business requirements. Software-Defined Networks (SDNs) are the emerging architectures that address a range of networking challenges with new solutions. Nevertheless, these centralised and programmable techniques face various challenges and issues that require contemporary security solutions such as Intrusion Detection Systems. Recently, the majority of this type of security solution has been developed using Machine Learning techniques. Deep Learning algorithms have recently been used to provide more accuracy and efficiency. This paper presents a new detection approach based on Convolutional Neural Network (CNN). The experiments proved that the proposed model could be successfully implemented in a Software-Defined Network controller to detect various attacks with 100% accuracy, achieved a low degradation rate of 2.3% throughput and 1.8% latency when executed in a large-scale network. © 2013 IEEE.

In Software-Defined Networks, the Intrusion Detection System is receiving growing attention, due to the expansion of the internet and cloud storage. This system is vital for institutions that use cloud services and have many users. Although the Intrusion Detection System offers several security features, its performance is lagging behind in large enterprise networks. Existing approaches are based on centralised processing and use many features to implement a protection system. Therefore, system overload and poor performance occur on the controller and OpenFlow switches. As a result, the current solutions create issues that must be considered, especially when they are implemented on large networks. Furthermore, enhancements in security applications improve the reliability of networks. Following a literature review of the existing Intrusion Detection Systems, this paper presents a new model that offers decentralised processing and exchanges data over an independent channel, in order to solve issues relating to system overload and poor performance. Our model utilises an appropriate feature selection method to reduce the number of extracted features and minimise the data transmitted over the channels. Additionally, the Naive Bayes algorithm has been employed for flow classification purposes, since it is a fast classifier. We successfully implemented our framework, using the Mininet emulator, which provides a suitable networking environment. Evaluations indicate that our proposed system can detect various attacks with an accuracy of 98.46% and nominal decreasing rates of 1.5% in throughput and 0.7% in latency analyses, when the model is implemented in wide range networks. © 2013 IEEE.

Due to the growing number of Chip-Multiprocessors, the researchers have proposed new designs and architectures. So, there is a constant need to know the most accurate simulators used in this scope, which should be used to identify their outcomes. Computer System Architecture (CSA) simulators are usually used to validate the designs, architectures that new discovered and developments. This paper is to provide an overview and insight into the most critical simulations used in Computer System Architecture and possible standards that distinguish simulators from the other. The essential aspects and parameters that determine these simulators are accuracy, speed, performance, flexibility, and functionality as well. Cycle-Accurate, Event-Driven and Full Systems Simulators of CSA, this taxonomy of simulators that w discusses in this paper. © 2019 International Association of Computer Science and Information Technology.