Aqeel Mahmood Jawad
@ruc.edu.iq
Department of Medical instrumentation Techniques Engineering Faculty Member / Quality assurance unit
Al Rafidain University College
Aqeel Mahmood Jawad received his B.Sc. in Computer and Communication Eng. from Al-Rafidain University College, Iraq in 2009, his MSc. in Electrical Eng. Universiti Tenaga nasional (UNITEN), Malaysia., 2014. He is received his Ph.D. degree with the Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environments, Universiti Kebangsaan Malaysia., 2021. He is with the Department of Computer Communication Engineering, Al-Rafidain University College, Baghdad-Iraq, as a Lecturer, and a head of communication lab. His research interests include, wireless communications, transmutation line and digital communication, satellite communications theory, energy-efficient wireless power transfer, WPT charging applications based on Unmanned aerial vehicle (UAV) techniques, advanced mathematics and wireless sensor networks applications.
EDUCATION
Bachelor: Computer an Communication Engineer
Master: Electrical Engineering
PhD: Electrical, Electronic and Systems Engineering
RESEARCH INTERESTS
Wireless: WPT, WSN, UAV Applications
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Nameer Hashim Qasim and Aqeel Mahmood Jawad
Elsevier BV
Abdulqader Faris Abdulqader, Ali Qutaiba Abdulrazzaq, Maha Zuhair Ahmed Alchalabi, Aqeel Mahmood Jawad, Alaa Saad Hamid, Yurii Khlaponin, and Enas Abdalhussein
IEEE
Background: The convergence of 5G technology with the Internet of Things (IoT) presents a potential paradigm shift in enhancing accessibility opportunities for those with visual impairments. The emergence of intelligent eyewear driven by these technologies is leading the way in this transformative era.Objective: The primary objective of this paper is to investigate the possible synergies between 5G and IoT technologies in developing improved smart glasses for those with visual impairments. The focus is on elucidating how the cooperation between these technologies might substantially enhance the overall quality of life for this particular demographic.Methodology: The current study examines 5G's promise for a fast, reliable connection. IoT-enabled smart glasses are tested for their capacity to process and respond to real-time data. This study analyzes how 5G's decreased latency affects smart glass-device connection. Tactile feedback gloves and intelligent city infrastructure may be included as assistive technology.Results: The results of the study suggest that the quick data transmission capabilities of 5G technology enable various functionalities such as real-time object identification, text-to-speech translation, and navigation help, hence augmenting the level of independence experienced by users. The glasses' interoperability with other devices and infrastructure enhances their usability, providing users with enhanced spatial awareness and a more integrated experience.Conclusion: Given the anticipated rise in the population of persons with visual impairments, it is imperative to prioritize allocating resources towards the development and implementation of 5G-IoT-enabled devices such as smart glasses. The article's conclusion highlights the clear advantages of using such technology in promoting inclusivity and enhancing the liberty of visually impaired individuals despite the hurdles related to data privacy and ethical concerns.
Wael Abdulateef Jasim, Bassam H. Habib, Mustafa Yaseen Abed, Aqeel Mahmood Jawad, Kamal Hameed Gati, Olena Dolya, and Harith Muthanna Noori
IEEE
The increasing of complexity of urban traffic call for innovative approaches in monitoring and management. While legacy, ground-based methods struggle with problems like a lack of complete sensing as well as latency, preventing real-time insights and response ability.This article investigate how cooperative Device-to-Device (D2D) communications with aid of Unmanned Aerial Vehicle (UAV) could potentially disrupt the conventional drone-assisted traffic monitoring in 5G networks. In this work, we introduce a new system to utilize the data collection possibility of drones in a distributed way, which can facilitate more effective urban data acquisition and assimilation.The article conducted extensive simulations in various traffic conditions to demonstrate the effectiveness of our system. The results were compared with traditional ground-based monitoring metrics, while against a drone-based system that without cooperative D2D communications.The proposed approach was evaluated through a number of wide-range simulations in diverse traffic scenarios. Simulation results indicate significant improvements in data collection and distribution over conventional strategies. Our proposed system on average improved the throughput by 35%, and latency by 45% over traditional ground-based monitoring methods. The energy efficiency of the system was also investigated, considering the UAVs' short flight durations. Results also showed the D2D communication technique, which increased through boosting by using drones, helps reduce energy consumption by 25% relative to traditional methods, maximizing drone uptime.This study indicates that cooperative D2D communications of drones can be useful for traffic monitoring. This technique helps to reduce the latency and atrocity with which data are collected for intelligent transport systems, thereby enhances the traffic management and road safety.
Salam Omar Alo, Mehdi Muhemed Mool, Noor Mohammed Mohammed Zakri Al-Dabooni, Aqeel Mahmood Jawad, Sura Hamed Mousa, Olha Tkachenko, and Enas Abdalhussein
IEEE
From design to use, smart textiles have a link between fashion and technology on the new field and is major progress by having an aesthetic appearance during using them. This progression is made possible due to the use of the LilyPad Arduino platform, which is known for its wearables' endurance as well as new potential 5G capabilities. Our next-gen textiles have existing applications, but the incredible speed and low latency 5G delivers, making instant data transfer and real-time applications possible between items big or small, opens up an array of options to further elevate smart textile functionality.The article discusses merging LilyPad Arduino with 5G technology for intelligent textiles at a significant level. We also explore use-cases: from health monitoring garments to media interfaces, identifying needs and solution domains where such a sensor is applicable for cost-effective real world deployment.The study methodology involved a comprehensive investigation of the technical and logistics challenges related to the integration of electrical components with flexible fabrics when under a 5G connection. The article examines several scenarios which could help offer uninterrupted low-latency connections and enable seamless integration to the clothing fabrics.The findings demonstrate that this integration substantially enhances the efficiency of smart textiles in terms of response, and flexibility. We have therefore effectively illustrated this through the study; while tackling some significant technological hurdles we showed the work on real-time health monitoring and interactive interfaces that are novel.From this research, was envisioned a promising future for the smart textile sector based on LilyPad Arduino and 5G technology combination with their abundant opportunities. The article offers these solutions, industrializing the alternatives, paving the path for better materials and novel-functional textiles to be employed within new configurations in a very practical way, representing a turning point toward normalizing consumer-friendly wearable technologies.
Nameer Hashim Qasim, Doaa Ali Jumaa, Fakher Rahim, Aqeel Mahmood Jawad, Ali Mohammed Khaleefah, Genadiy Zhyrov, and Haider Ali
Learning Gate
The IP Multimedia Subsystem (IMS) is essential to providing multimedia services over IP networks. However, maintaining its scalability in the face of increasing demand and changing next-generation networks is still a significant concern. In light of growing loads and service diversity, scalable solutions are required, as this article explores the shortcomings of the IMS designs in use today. To present an innovative, scalable architecture for IMS that integrates cutting-edge computer science techniques, enabling effective service delivery in next-generation networks. By bridging the gap between the static nature of previous IMS designs and the dynamic demands of contemporary telecommunications, this article hopes to deliver results. A mixed-method approach was utilized, integrating evaluations of architectural flexibility with network loads and service needs analysis. In order to evaluate the suggested designs' performance to current benchmarks, the study used simulations to predict the architectures' performance under various scenarios. Compared to conventional IMS frameworks, the suggested design showed outstanding scalability, enabling a tenfold increase in simultaneous connections and services. It notably improved fault tolerance and service delay and increased flexibility for network modifications and service diversification. Next-generation network demands are met by IMS designs that use modern computer science methodologies to improve scalability and flexibility significantly. More practical, scalable, and reliable service delivery methods will be possible thanks to the foundation our work creates for future research into dynamic, resilient telecommunications infrastructures.
Fakher Rahim, Nada Abdulkareem Hameed, Saja Abdulfattah Salih, Aqeel Mahmood Jawad, Hayder Mahmood Salman, and Dmytro Chornomordenko
Learning Gate
Natural Language Processing (NLP) is causing a significant change in the healthcare industry. This state-of-the-art technology is transforming the processing, analysis, and utilisation of healthcare data by improving patient care and clinical decision-making. This article aims to analyse the current and prospective uses of natural language processing (NLP) in the healthcare industry. We examine the tangible advancements that have been made and shed light on the bright prospects that lie ahead. The approach for early sickness diagnosis involves the use of advanced predictive analytics based on natural language processing (NLP). By analysing unstructured patient narratives, models may accurately identify potential health issues with exceptional precision, sometimes exceeding 90% accuracy. This article discusses the benefits of using voice recognition and chatbots, such as improved patient-provider communication and decreased administrative tasks. Systems powered by natural language processing have made significant advancements, as shown by statistical data indicating that they attain an accuracy rate of 80% or more on tasks like clinical text classification. Consequently, medical record review and data extraction have been automated, hence alleviating the burden on healthcare personnel. Hospitals may use sentiment analysis on online reviews to assess patient satisfaction levels and implement targeted improvements, therefore enhancing the overall patient experience. Undoubtedly, natural language processing (NLP) is revolutionizing the healthcare industry by enabling data-driven insights to optimise operations, personalize patient therapy, and enhance overall patient care quality. In order to fully harness the capabilities of natural language processing, we must address existing challenges and promote continuous research in areas such as deep learning and explainability. Only by doing so can we establish a healthcare system that is both more health-oriented and reliant on data.
, Subhi Hammadi Hamdoun, Salah Yehia Hussain, , Saadi Mohamed Dhahir Nuzal, , Aqeel Mahmood Jawad, , Ibraheem Nadher, and
Editorial Board of Journal Radioelectronics, Nanosystems, Information Technology RENSIT
Background: Finding effective energy storage technologies is crucial for transitioning to sustainable energy systems. Magnetic nanoparticles have emerged as options due to their distinctive magnetic characteristics, which can considerably improve the performance of energy storage systems. Objective: This research aims to investigate innovative magnetic nanoparticles and assess to increase the efficiency and capacity of energy storage systems. The emphasis is on developing materials with optimal magnetic characteristics and incorporating them into current energy storage methods. Methods: Co-precipitation and thermal breakdown were used to create a range of new magnetic nanomaterials. Vibrational sample magnetometry, X-ray diffraction, and cyclic voltammetry were used to determine these materials' magnetic characteristics, structural integrity, and electrochemical performance. Subsequent integration into supercapacitors and lithium-ion batteries was carried out to evaluate energy storage capacity. Results: The synthesized magnetic nanoparticles showed improved magnetic saturation and charge-discharge characteristics compared to standard materials. When used in supercapacitors, they increased capacitance by 20% and improved cycle stability by 25%. Similarly, these nanomaterials improved the energy density of lithium-ion batteries by 15% and increased their longevity by 30%. Conclusion: The unique magnetic nanoparticles created in this work significantly improve the performance of energy storage devices. The increased capacitance, energy density, and operational stability indicate that these materials for future energy storage applications. Further study is required to optimize these materials for commercial application and investigate their scalability and environmental effects.
, Mohammed Yaseen Abdullah, Laith Baqir Salman, , Ahmed Sadoon Obaed, , Aqeel Mahmood Jawad, , Halal Azam Sbhui, and
Editorial Board of Journal Radioelectronics, Nanosystems, Information Technology RENSIT
Background: As the world shifts towards renewable energy, there is a growing demand for efficient energy storage systems. Because of their unique features, nanostructured materials have emerged as key to improving the performance and efficiency of these storage systems. Objective: This research aims to assess the most recent discoveries and uses of nanostructured materials in energy storage technologies, emphasizing its disruption in the industry. Methods: A detailed assessment of recent advances was undertaken, focusing on diverse nanostructured materials utilized in batteries, supercapacitors, and other energy storage systems. Synthesis and characterization techniques for these materials and their incorporation into energy storage devices were investigated. Results: The nanostructured materials considerably increase energy density, charge/discharge speeds, and durability of energy storage devices. Graphene, carbon nanotubes, and metal-oxide nanostructures have significantly improved performance metrics across various applications. Conclusion: Nanostructured materials have to revolutionize energy storage systems. Continued research and development in this sector are critical for developing high-performance, long-term energy storage technologies capable of meeting the growing demands of contemporary technology and renewable energy systems. Further investigation into these materials' scalability and environmental effects is necessary to ensure their practicality and sustainability.
Aqeel Mahmood Jawad, Loai Alamro, Lway Faisal Abdulrazak, Akram AbdelBaqi AbdelRahman, and Iryna Bezklubenko
IEEE
Background: As imaging technology advances, cinema, photography, computer vision, and image processing need high-quality visual data. The image quality depends on camera efficiency and resolution.Objective: This study examines the trade-offs between camera efficiency and image quality concerning camera performance measures (e.g., pixel count, sensor size, dynamic range) and picture resolution. Image clarity, detail, and performance are greatly affected by camera resolutions, notably in digital and mobile phone cameras.Methods: The study tests digital and mobile phone cameras with different resolutions to determine image quality and feature extraction. Additionally, the effects of torch (or flash) use on feature extraction and picture quality are examined to determine how illumination affects camera performance.Results: The importance of camera efficiency in creating high-quality pictures for various applications, including photography and scientific imaging, is highlighted by preliminary findings. The article illuminates the complex link between camera specs and image quality, giving valuable insights to photographers and manufacturers.Conclusion: This study provides insights on enhancing camera efficiency and picture resolution, which may influence imaging equipment, computational image processing, and photographic design. The insights gained should develop camera technology to meet the needs of varied applications that need better picture capture and processing.
Aymen Dheyaa Khaleel, Osman Ghazali, Aqeel Mahmood Jawad, Ayman Mohammed Ibrahim, Massudi Mahmuddin, Ahmed Jamal Abdullah Al-Gburi, and Mohammed Najah Mahdi Al-Niamey
Institute of Advanced Engineering and Science
This research study presents a cube dielectric resonator antenna (DRA) with four different radiation patterns for internet of things (IoT) applications. The various radiation patterns are determined by the grounded capacitor loading to reduce interference. The DRA is constructed of ceramic material with a dielectric constant of 30 and is fed via a coaxial probe located in the antenna’s center. Capacitors are used to load the four parasitic microstrip feed lines. Each pattern of radiation is adjustable by adjusting the capacitors loading on the feed line. The proposed antenna works at 3.5 GHz with -10 narrow impedance bandwidth of 74 MHz.
Aqeel Mahmood Jawad, Rosdiadee Nordin, Haider Mahmood Jawad, Sadik Kamel Gharghan, Asma’ Abu-Samah, Mahmood Jawad Abu-Alshaeer, and Nor Fadzilah Abdullah
MDPI AG
Recent major advancements in drone charging station design are related to the differences in coil design between the material (copper or aluminum) and inner thickness (diameter design) to address power transfer optimization and increased efficiency. The designs are normally challenged with reduced weight on the drone’s side, which can lead to reduced payload or misalignment position issues between receiver and transmitter, limiting the performance of wireless charging. In this work, the coil combination was tested in vertical alignment from 2 cm to 50 cm, and in lateral misalignment positions that were stretched across 2, 5, 8, 10, and 15 cm ranges. Simulated and experimental results demonstrated improved transfer distances when the drone battery load was 100 Ω. With the proposed design, the vertical transfer power that was achieved was 21.12 W, 0.460 A, with 81.5% transfer efficiency, while the maximum lateral misalignment air gap that was achieved was 2 cm with 19.22 W and 74.15% efficiency. This study provides evidence that the developed circuit that is based on magnetic resonant coupling (MRC) is an effective technique towards improving power transfer efficiency across different remote and unmanned Internet of Things (IoT) applications, including drones for radiation monitoring and smart agriculture.
Aqeel Mahmood Jawad, Nameer Hashim Qasim, and Volodymyr Pyliavskyi
IEEE
The paper deals with the issues of metamerism in multimedia and telecommunication paths. Attention is focused on the end devices of the end-to-end path. A comparison is made in the assessments using traditional coordinate systems for representing colors, and in the coordinates of modern color models CAM16, CAM20u, ZCAM. Numerical differences in the obtained data are shown under the same input conditions. Statistical data of metamer estimates using Mac Adam ellipses are presented. A study was conducted in the work, showing that the spectral compositions of colors can be perceived as one color (based on McAdam's data), but when processed, their number can be different. Based on research, we can say that the differences can reach to 15%. When processing ZCAM models, the number of colors taken into account during transmission will be greater, which makes it possible to reduce the degree of influence of metamerism on color reproduction.
Nameer Hashim Qasim, Aqeel Mahmood Jawad Abu-Alshaeer, Haidar Mahmood Jawad, Yurii Khlaponin, and Oleksandr Nikitchyn
Private Company Technology Center
UAVs or drones as an alternative solution to providing high-quality Internet service in difficult terrain are environmentally friendly and do not consume electricity during the day as is the case with communication towers. But the developers of the network face difficulties in the drone communication system associated with the need to take into consideration unpredictable weather conditions and terrain, as well as the short life of the drone's batteries. Therefore, the object of this study is the process of managing UAV traffic through the use of gNB-IoT in 5G. The possibility of using a mobile UAV repeater during traffic management using radio resources (RR), radio access network (RAN), the infrastructure with broadcasting tools and dynamic connection using MU-MIMO modulation is shown. The use of these tools makes it possible to connect the drone to the wired base network from the provider and then restore the radio frequency signal and broadcast to another coverage area where this subscriber does not have network coverage, use the channel quality indicator (CQI) representation as a QoE function. Undoubtedly, traffic management is the process of obtaining information about traffic control from one endpoint to another, which confirms the reliability and management of data transmission. Meanwhile, drone traffic management can be used to reduce time delays and remove network interference by relying on Internet of Things programs that use NB-5G technology. The UAV's traffic management improvement process uses a proposed algorithm to generate dynamic flow data management to enhance traffic processing of flow control in the IoT
Barbaros Preveze, Ahmed Alkhayyat, Firas Abedi, Aqeel Mahmood Jawad, and Ali S. Abosinnee
Hindawi Limited
In the last decent, the number of Internet of Things (IoT) health-based paradigm reached to a huge number of users, services, and applications across different disciplines. Thus, hundreds of wireless devices seem to be distrusted over a limited or small area. To provide a more efficient network, the software-defined network (SDN) thought to be a good candidate to deal with these huge number of wireless users. In this work, after a novel SDN algorithm is proposed for the hospital environment, it is also designed and integrated into an Internet of Health Things (IoHT) paradigm. The novel algorithm called adaptive switching (AS) is proposed as a novel adaptive access strategy based on adaptively hoping among existing Go-Back-N and Selective Repeat techniques. Finally, the throughput performance of the proposed AS method is compared with the performances of traditional Go-Back-N and Selective Repeat ARQ methods using the developed MATLAB simulation. For this, an optimal P error rate that the network should prefer to switch either from Go-Back-N to Selective Repeat or from Selective Repeat to Go-Back-N method to maximize the network throughput performance is determined. The evaluated results are also confirmed by theoretical calculation results using well-known Mathis throughput formula. It is observed from the simulation results that the best throughput performance can be evaluated, when AS switches to Go-Back-N if the P error is less than 3.5% and it switches back to Selective Repeat when the P error is greater than 3.5%. By this way, it is also observed that the throughput always has its best possible results for all P error rates and up to 37.52% throughput improvement is provided by the use of novel proposed adaptive switching (AS) algorithm.
Amer T. Abed, Aqeel M. Jawad, Haider M. Jawad, and Mahmood J. Abu-Alshaeer
IEEE
This research presented a novel of (F-J) antenna, where the tapered feeding strip line fed a frame that loops back to the ground. Within this frame, branches forming the structures F and J. The antenna has measured wide impedance bandwidth of 3.4-30 GHz that can be used for many wireless communications such as WiMAX (3.4GHz–3.6GHz /5GHz– 6GHz), Wi-Fi (5GHz–5.8GHz), 5G (5-6 and 27-28 GHz), ultra wideband (UWB) (3.1–10.6 GHz), and multichannel video and data distribution service (MVDDS) (12.2–12.7 GHz). . The effect of varying the dimensions of some parameters and the surface current distribution had been investigated carefully in this research to optimize the dimensions of the proposed antenna. A good matching was observed between the measured and the simulated data for the reflection coefficient, the radiation patterns and the gain.
Amer T. Abed, Mandeep S. J. Singh, and Aqeel M. Jawad
Cambridge University Press (CUP)
AbstractThis paper describes and analyzes a new technique used inQ-slot antenna to generate circular polarization (CP). The CP characteristics were investigated carefully by studying the surface current distribution, the phase difference between the left hand circular polarization (LHCP) and right hand circular polarization (RHCP) at some resonant frequencies, and the measured values of the axial ratio bandwidth (ARBW). Normal arms (E1andE2) were cut in the upper elliptical feeding strip line to form an open-mouth structure. The armsE1andE2were made equal in length and set perpendicular to each other to have normal electric fields, leading to the generation of CP radiation. A formula was modified for the dual resonant frequenciesf1,f2of the modesTM010andTM001. The measured values of the ARBW indicated that the antenna has a wide ARBW of 4.8–5.93 GHz, which is approximately 52% of the 3rd operating band of 4.7–6.8 GHz. The wide ARBW in a small size indicated that the design of theQ-slot antenna overcame the limits of designing antennas with wide ARBW in small size and low profile. A formula for normalized field was driven according to the complementary of theQ-slot antenna.
Haider Mahmood Jawad, Aqeel Mahmood Jawad, Rosdiadee Nordin, Sadik Kamel Gharghan, Nor Fadzilah Abdullah, Mahamod Ismail, and Mahmood Jawad Abu-AlShaeer
Institute of Electrical and Electronics Engineers (IEEE)
Wireless sensor networks (WSNs) have received significant attention in the last few years in the agriculture field. Among the major challenges for sensor nodes’ deployment in agriculture is the path loss in the presence of dense grass or the height of trees. This results in degradation of communication link quality due to absorption, scattering, and attenuation through the crop’s foliage or trees. In this study, two new path-loss models were formulated based on the MATLAB curve-fitting tool for ZigBee WSN in a farm field. The path loss between the router node (mounted on a drone) and the coordinator node was modeled and derived based on the received signal strength indicator (RSSI) measurements with the particle swarm optimization (PSO) algorithm in the farm field. Two path-loss models were formulated based on exponential (EXP) and polynomial (POLY) functions. Both functions were combined with PSO, namely, the hybrid EXP-PSO and POLY-PSO algorithms, to find the optimal coefficients of functions that would result in accurate path-loss models. The results show that the hybrid EXP-PSO and POLY-PSO models noticeably improved the coefficient of determination (R2) of the regression line, with the mean absolute error (MAE) found to be 1.6 and 2.7 dBm for EXP-PSO and POLY-PSO algorithms. The achieved R2 in this study outperformed the previous state-of-the-art models. An accurate path-loss model is essential for smart agriculture application to determine the behavior of the propagated signals and to deploy the nodes in the WSN in a position that ensures data communication without unnecessary packets’ loss between nodes.
Aqeel Mahmood Jawad, Haider Mahmood Jawad, Rosdiadee Nordin, Sadik Kamel Gharghan, Nor Fadzilah Abdullah, and Mahmood Jawad Abu-Alshaeer
Institute of Electrical and Electronics Engineers (IEEE)
Drones can be used in agriculture applications to monitor crop yield and climate conditions and to extend the communication range of wireless sensor networks in monitoring areas. However, monitoring the climate conditions in agriculture applications faces challenges and limitations, such as drone flight time, power consumption, and communication distance, which are addressed in this study. Wireless power transfer (WPT) can be used to charge drone batteries. WPT using a magnetic resonant coupling (MRC) technique was considered in this study because it allows high transfer power and efficiency with tens of centimeters, power transfers can be achieved in misalignment situations, charging several devices simultaneously, and unaffected by weather conditions. WPT was practically implemented based on a solar cell using a proposed flat spiral coil (FSC) in the transmitter circuit and multiturn coil (MTC) in a receiver circuit (drone) for the alignment and misalignment of two coils at different distances. FSC and MTC improved power transfer and efficiency to 20.46 W and 85.25%, respectively, at 0 cm with the loaded system under alignment condition. In addition, the two coils achieved appropriate transfer efficiencies and power for charging the drone battery under misaligned conditions. The maximum power transfer and efficiency were 17.1 W and 71% for the misalignment condition, at an air gap of 1 cm between two coils when the system was loaded with the drone battery. Moreover, the battery life of the drone was extended to 851 minutes based on the proposed sleep/active strategy relative to the traditional operation (i.e., 25.84 minutes). Consequently, a 96.9% battery power saving was achieved based on this strategy. Comparison results showed that the proposed system outperformed some present techniques in terms of the transfer power, transfer efficiency, and drone battery life. The proposed WPT technique developed in this study has been proven to solve the misalignment issue. Thus it offers a great opportunity as a key deployment component for the automation of farming practices toward the Internet of Farming applications.
Amer Tawfeeq Abed and Aqeel Mahmood Jawad
Institute of Electrical and Electronics Engineers (IEEE)
In this study, an Amer fractal slot antenna is proposed as a multiple input, multiple output (MIMO) antenna with four ports. The antenna is excited by CPW (coplanar waveguide) to control the leakage of electromagnetic energy, which leads to a high match between the antenna and input impedance, thus achieving dual operating bands of 1.5–19.2 GHz and 25–37.2 GHz for port 1, dual operating bands of 1.4–19 GHz and 20–35.5 GHz for port 2, a wide operating band of 1.4–29 GHz for port 3, and dual operating bands of 1.6–21 GHz and 22–37 GHz for port 4. Therefore, the proposed antenna meets all the market needs of wireless communication technologies such as 3G, Long Term Evolution (LTE, 2.6 GHz /3.5 GHz), Wireless Local Area Network (WLAN, 2.4 GHz/5 GHz), Worldwide Interoperability for Microwave Access (WiMAX, 2.5 GHz/3.5 GHz/5 GHz), Industrial, Scientific and Medical (ISM, 2.4 GHz/ 5 GHz), and 5G (5–6 GHz and 27–28 GHz). The proposed antenna can be used as dual opposite ports for the frequency range 1.5–15 GHz and as four-element MIMO arrays for frequencies of 15–30 GHz. The MIMO fractal antenna has circular polarization characteristics with axial ratio bandwidths (ARBWs) of 4.7–5.8 GHz for port 1, 2.5–2.6 GHz and 5.4–6.5 GHz for the port 2, 4–5.9 GHz for port 3, and 5.5–10 GHz for port 4. Due to its compact size (33 mm $\\times33$ mm $\\times0.8$ mm), low profile, and acceptable values of gain and efficiency, the proposed antenna is suitable for many portable wireless communication devices.
Haider Jawad, Rosdiadee Nordin, Sadik Gharghan, Aqeel Jawad, Mahamod Ismail, and Mahmood Abu-AlShaeer
MDPI AG
The use of wireless sensor networks (WSNs) in modern precision agriculture to monitor climate conditions and to provide agriculturalists with a considerable amount of useful information is currently being widely considered. However, WSNs exhibit several limitations when deployed in real-world applications. One of the challenges faced by WSNs is prolonging the life of sensor nodes. This challenge is the primary motivation for this work, in which we aim to further minimize the energy consumption of a wireless agriculture system (WAS), which includes air temperature, air humidity, and soil moisture. Two power reduction schemes are proposed to decrease the power consumption of the sensor and router nodes. First, a sleep/wake scheme based on duty cycling is presented. Second, the sleep/wake scheme is merged with redundant data about soil moisture, thereby resulting in a new algorithm called sleep/wake on redundant data (SWORD). SWORD can minimize the power consumption and data communication of the sensor node. A 12 V/5 W solar cell is embedded into the WAS to sustain its operation. Results show that the power consumption of the sensor and router nodes is minimized and power savings are improved by the sleep/wake scheme. The power consumption of the sensor and router nodes is improved by 99.48% relative to that in traditional operation when the SWORD algorithm is applied. In addition, data communication in the SWORD algorithm is minimized by 86.45% relative to that in the sleep/wake scheme. The comparison results indicate that the proposed algorithms outperform power reduction techniques proposed in other studies. The average current consumptions of the sensor nodes in the sleep/wake scheme and the SWORD algorithm are 0.731 mA and 0.1 mA, respectively.
Sadik Kamel Gharghan, Rosdiadee Nordin, Aqeel Mahmood Jawad, Haider Mahmood Jawad, and Mahamod Ismail
Institute of Electrical and Electronics Engineers (IEEE)
When localizing wireless sensor networks, estimating the distances of sensor nodes according to the known locations of the anchor nodes remains a challenge. As nodes may transfer from one place to another, a localization technique that can measure or determine the location of a mobile node is necessary. In this paper, the distance between a bicycle when moves on the cycling track and a coordinator node (i.e., coach), which positioned on the middle of the cycling field was estimated for the indoor and outdoor velodromes. The distance was determined based on two methods. First, the raw estimate is done by using the log-normal shadowing model (LNSM) and later, the intelligence technique, based on adaptive neural fuzzy inference system (ANFIS) is applied to improve the distance estimation accuracy, especially in an indoor environment, which the signal is severely dominated by the effect of wireless multipath impairments. The received signal strength indicator from anchor nodes based on ZigBee wireless protocol are employed as inputs to the ANFIS and LNSM. In addition, the parameters of the propagation channel, such as standard deviation and path loss exponent were measured. The results shown that the distance estimation accuracy was improved by 84% and 99% for indoor and outdoor velodromes, respectively, after applying the ANFIS optimization, relative to the rough estimate by the LNSM method. Moreover, the proposed ANFIS technique outperforms the previous studies in terms of errors of estimated distance with minimal mean absolute error of 0.023 m (outdoor velodrome) and 0.283 m (indoor velodrome).