Firas Fadhil Abedi
@atu.edu.iq
Al-Furat Al-Awsat Technical University
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering, Computer Vision and Pattern Recognition
Scopus Publications
Scopus Publications
Firas Abedi
Springer Science and Business Media LLC
Firas Abedi
AIP Publishing
Firas Abedi
Springer Science and Business Media LLC
Mohannad A. M. Al-Ja'afari, Laith F. M. H. Al-Rammahi, Firas Abedi, Hussein M. H. Al-Rikabi, and Gurjot Singh Gaba
AIP Publishing
Firas Abedi, Mohammad Essa, Ali S. Abosinnee, Adeil Abbas Alwan, Mohanned Adnan, and Hassan M. Al-Jawahry
IEEE
In machine vision for industrial growth, camera calibration accuracy is essential. In this paper, we propose an algorithm for camera calibration that combines an enhanced version of the two-step approach with Gray Wolf Optimization algorithm (GWO). First, the two-step process is enhanced to lessen distortion's impact on camera calibration. After employing the principle of slope invariance to determine the camera's distortion parameters, we employ Zhang's calibration technique to acquire the camera's internal and exterior parameters. The above-mentioned camera parameters are then optimized using a GWO approach that combines a dynamic inertia weight and a variation idea. Finally, the proposed algorithm is subjected to a series of comparative experiments, the results of which demonstrate that it achieves higher accuracy, overcomes the problem of slipping into local optimal more effectively, and achieves faster convergence in parameter optimization.
Firas Abedi, Hayder M. A. Ghanimi, Abeer D. Algarni, Naglaa F. Soliman, Walid El-Shafai, Ali Hashim Abbas, Zahraa H. Kareem, Hussein Muhi Hariz, and Ahmed Alkhayyat
Computers, Materials and Continua (Tech Science Press)
Firas Abedi, Hayder M. A. Ghanimi, Abeer D. Algarni, Naglaa F. Soliman, Walid El-Shafai, Ali Hashim Abbas, Zahraa H. Kareem, Hussein Muhi Hariz, and Ahmed Alkhayyat
Computers, Materials and Continua (Tech Science Press)
Ali Alsalamy, Firas Abedi, Fatima Hashim Abbas, Mohammed S. Noori, Mohamed Ayad Alkhafaji, Ahmed Alkhayyat, Sameer Alani, Muhammet Tahir Guneser, and Sarmad Nozad Mahmood
IEEE
Vehicular Adhoc Networks (VANETs) have widespread applications in intelligent transportation systems, serving diverse purposes. These networks are characterized by their dynamic nature, which unfortunately leads to communication instability, causing increased energy consumption, delays, and routing overhead. To overcome obstacles at ground level, Unmanned Aerial Vehicles (UAVs) are introduced, enabling data transmission through an aerial medium, free from ground-level obstructions. However, for effective communication in UAV-assisted VANETs, a reliable routing protocol is essential. This paper proposes an improved routing model for UAV-assisted VANETs, called IUAVA-RP, which employs parameter-based routing and optimal path selection. The optimal path selection is performed using a decision-making process, resulting in highly effective and optimal routing for efficient data transmission. The proposed IUAVA-RP protocol is simulated using NS2 and SUMO, and its performance analysis includes parameters such as: energy efficiency, packet delivery ratio, end-to-end delay, and routing overhead. Comparative analysis is conducted with two existing protocols, HGFA-RP and AOMDV-RP. The results demonstrate that the proposed IUAVA-RP protocol achieves higher energy efficiency and packet delivery ratio, as well as lower end-to-end delay and routing overhead compared to the earlier protocols.
Firas Abedi, Hayder M. A. Ghanimi, Mohammed A. M. Sadeeq, Ahmed Alkhayyat, Zahraa H. Kareem, Sarmad Nozad Mahmood, Ali Hashim Abbas, Ali S. Abosinnee, Waleed Khaild Al-Azzawi, Mustafa Musa Jaber,et al.
Computers, Materials and Continua (Tech Science Press)
Firas Abedi, Subhi R.M. Zeebaree, Zainab Salih Ageed, Hayder M.A. Ghanimi, Ahmed Alkhayyat, Mohammed A.M. Sadeeq, Sarmad Nozad Mahmood, Ali S. Abosinnee, Zahraa H. Kareem, Ali Hashim Abbas,et al.
Computers, Materials and Continua (Tech Science Press)
Ahmed Mohammed Abdulkarem, Firas Abedi, Hayder M. A. Ghanimi, Sachin Kumar, Waleed Khalid Al-Azzawi, Ali Hashim Abbas, Ali S. Abosinnee, Ihab Mahdi Almaameri, and Ahmed Alkhayyat
MDPI AG
This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals’ time-frequency information was first extracted using CWT as a data source. The convolutional neural network was fed input from 2D pictures. The second step included feeding the proposed algorithm the 2D time-frequency information it had obtained in order to classify the different kinds of modulations. Six different types of modulations, including amplitude-shift keying (ASK), phase-shift keying (PSK), frequency-shift keying (FSK), quadrature amplitude-shift keying (QASK), quadrature phase-shift keying (QPSK), and quadrature frequency-shift keying (QFSK), are automatically recognized using a new digital modulation classification model between 0 and 25 dB SNRs. Modulation types are used in satellite communication, underwater communication, and military communication. In comparison with earlier research, the recommended convolutional neural network learning model performs better in the presence of varying noise levels.
Nejood Faisal Abdulsattar, Firas Abedi, Hayder M. A. Ghanimi, Sachin Kumar, Ali Hashim Abbas, Ali S. Abosinnee, Ahmed Alkhayyat, Mustafa Hamid Hassan, and Fatima Hashim Abbas
MDPI AG
Flying ad hoc networks (FANETs) or drone technologies have attracted great focus recently because of their crucial implementations. Hence, diverse research has been performed on establishing FANET implementations in disparate disciplines. Indeed, civil airspaces have progressively embraced FANET technology in their systems. Nevertheless, the FANETs’ distinct characteristics can be tuned and reinforced for evolving security threats (STs), specifically for intrusion detection (ID). In this study, we introduce a deep learning approach to detect botnet threats in FANET. The proposed approach uses a hybrid shark and bear smell optimization algorithm (HSBSOA) to extract the essential features. This hybrid algorithm allows for searching different feature solutions within the search space regions to guarantee a superior solution. Then, a dilated convolutional autoencoder classifier is used to detect and classify the security threats. Some of the most common botnet attacks use the N-BaIoT dataset, which automatically learns features from raw data to capture a malicious file. The proposed framework is named the hybrid shark and bear smell optimized dilated convolutional autoencoder (HSBSOpt_DCA). The experiments show that the proposed approach outperforms existing models such as CNN-SSDI, BI-LSTM, ODNN, and RPCO-BCNN. The proposed HSBSOpt_DCA can achieve improvements of 97% accuracy, 89% precision, 98% recall, and 98% F1-score as compared with those existing models.
Ahmed Alkhayyat, Firas Abedi, Ashish Bagwari, Pooja Joshi, Haider Mahmood Jawad, Sarmad Nozad Mahmood, and Yousif K Yousif
SAGE Publications
Cognitive radios are expected to play an important role in capturing the constantly growing traffic interest on remote networks. To improve the usage of the radio range, a cognitive radio hub detects the weather, evaluates the open-air qualities, and then makes certain decisions and distributes the executives’ space assets. The cognitive radio works in tandem with artificial intelligence and artificial intelligence methodologies to provide a flexible and intelligent allocation for continuous production cycles. The purpose is to provide a single source of information in the form of a survey research to enable academics better understand how artificial intelligence methodologies, such as fuzzy logics, genetic algorithms, and artificial neural networks, are used to various cognitive radio systems. The various artificial intelligence approaches used in cognitive radio engines to improve cognition capabilities in cognitive radio networks are examined in this study. Computerized reasoning approaches, such as fuzzy logic, evolutionary algorithms, and artificial neural networks, are used in the writing audit. This topic also covers cognitive radio network implementation and the typical learning challenges that arise in cognitive radio systems.
Azher M. Abed, Zryan Najat Rashid, Firas Abedi, Subhi R. M. Zeebaree, Mouayad A. Sahib, Anwar Ja'afar Mohamad Jawad, Ghusn Abdul Redha Ibraheem, Rami A. Maher, Ahmed Ibraheem Abdulkareem, Ibraheem Kasim Ibraheem,et al.
Measurement and Control (United Kingdom) SAGE Publications
This work proposes a new kind of trajectory tracking controller for the differential drive mobile robot (DDMR), namely, the nonlinear neural network fractional-order proportional integral derivative (NNFOPID) controller. The suggested controller’s coefficients comprise integral, proportional, and derivative gains as well as derivative and integral powers. The adjustment of these coefficients turns the design of the proposed NNFOPID control further problematic than the conventional proportional-integral-derivative control. To handle this issue, an Enhanced Fruit Fly Swarm Optimization algorithm has been developed and proposed in this work to tune the NNFOPID’s parameters. The enhancement achieved on the standard fruit fly optimization technique lies in the increased uncertainty in the values of the initialized coefficients to convey a broader search space. subsequently, the search range is varied throughout the updating stage by beginning with a big radius and declines gradually during the course of the searching stage. The proposed NNFOPID controller has been validated its ability to track specific three types of continuous trajectories (circle, line, and lemniscate) while minimizing the mean square error and the control energy. Demonstrations have been run under MATLAB environment and revealed the practicality of the designed NNFOPID motion controller, where its performance has been compared with that of a nonlinear Neural Network Proportional Integral Derivative controller on the tracking of one of the aforementioned trajectories of the DDMR.
Abbas M. Ali Al-muqarm, Firas Abedi, and Ali S. Abosinnee
Korea Institute of Information and Communication Engineering
Firas Abedi, Hassoon Salman Fahama, Ayad Hamzah, and Firas Mohammed
IEEE
In a Broadband System, a High-Power Amplifier (HPA) is widely used for coverage expansion and availability due to its high efficiency. However, distortion has appeared on the input signal when the operation point exists almost in the saturation zone, which generates unwanted frequencies in adjacent channels and Inter-symbol interference (ISI). This channel interference is tightly controlled by the communications standard being used, and it must be reduced as much as possible. For this reason, several development techniques have been presented to enhance the broadband system performance one of them is Digital Pre-distorter (DPD). In this paper, a DPD has been investigated and evaluated to mitigate the Amplitude/Amplitude converter (AM/AM), Amplitude/Phase converter (AM/PM) and Memory using an adaptive filter with Least Mean Square (LMS) algorithm. This DPD is investigated on the Orthogonal Frequency Division Multiplexing (OFDM) signal with 16-QAM and 64-QAM to evaluate the performance using Matlab. The result shows high elimination to ISI effect. In addition, the AM/AM and the AM/PM effects have been eliminated worthy.
Yaser Norouzi, Ali N Jamaluddin, Firas Abedi, and Aboothar Mahmood Shakir
IEEE
Tank gauge measurement sensors have wide application in petrochemical industries. There are different technologies used for this application, including mechanical, acoustic based and microwave-based sensors. The microwave-based sensors (i.e., radar) are more accurate since the microwave propagation speed is not affected by temperature and pressure. In this paper a dual frequency FMCW radar is proposed for tank gauge measurement. This radar system operates on two different frequencies. But the band width around any frequency is limited to 100MHz. it is shown that while the band-width is many times less than that of the conventional tank gauging radars, the accuracy (at the same SNR value) is many times better than the conventional ones.
Kifah T. Khudhair, Ola N. Kadhim, Fallah H. Najjar, Firas Abedi, Ali N. Jamaluddin, and Ibrahim H. Al-Kharsan
IEEE
One of the most common image operation analyses is the edge detection technique. Edge detection is used for shaping the edge of an image. Also, it is used for enhancing images. This paper presents a new approach to detecting the edge of color image using the Mamdani fuzzy inference classifier based on the Fuzzy Set Membership Function (FSMF). Here, Gaussian Curve Membership Function (GCMF) is used as a FSMF. GCMF is used for each class to assign that class to each pixel. In this approach, two windows/filters are used in size (1x2) and (2x1). Several standard color images are used to test our proposed algorithm (City, Jelly_cc11, Baboon, Lena, and Peppers). In order to parametric evaluation of selected images, Peak Signal Noise Ratio (PSNR) and Mean Square Error (MSE) are considered. However, the performance of our proposed algorithm compared with other well-known approaches (Canny, Prewitt, and Sobel) is somehow very similar but significantly faster.
Anwer Sabah Ahmed, Yousif Jawad Kadhim, Firas Abedi, and Ihab Mahdi Almaameri
IEEE
This paper examines the construction of a rectangular patch antenna with circular polarization and strip line feed with the goal of reducing the microstrip patch antenna’s size. With the help of a genetic algorithm, we were able to cut a rectangular slot in the corner of the patch and keep cutting it until we got the ideal design at the operating frequency (5 GHz) with a reduced size of (68.6%) based on the current distribution on the patch (GA). With the proposed design, there is a noticeable increase in bandwidth. COMSOL simulation software was used for the simulation. The first antenna design’s operating frequency is set to 6 GHz, and it is then forced to run at that frequency (5 GHz).
Fallah H. Najjar, Kifah T. Khudhair, Ali Hussein Abdul Khaleq, Ola N. Kadhim, Firas Abedi, and Ibrahim H. Al-Kharsan
IEEE
An edge is where an image’s intensity values rapidly change from low to high-intensity values or vice versa. The edge itself is at the midpoint of this change. Edge detection remains a challenge in computer vision despite recent advances. It cannot be applied to an image with excessive brightness and contrast. This paper produces a new method based on the standard deviation histogram feature to reduce the onerousness. The proposed method aims to prepare the input image for the edge detection approaches by performing a histogram feature extraction. The main characteristics of the proposed approach are simplicity and functionality. The authors utilize twenty MATLAB standard images as well as ADNI brain images. The authors use the Canny edge detection method to defect edges from the proposed method. The authors use edge detection evaluation metrics such as Figure of Merit (FOM), Structural Similarity Index Metric (SSIM), Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE) measures for evaluating and justifying edge quality. The experimental results show that the proposed method performs better in visual and statistical edge quality than both classical and fractional-order edge detection methods.
Mohammed Najeh Nemah, Ghufran Mahdi Hatem, Firas Abedi, Ali N Jamaluddin, and Ali S. Abosinnee
IEEE
Upper-limb amputation imposes a significant burden on amputees thereby restricting them from fully exploring their environments during activities of daily living. Thus, the quality of hand prostheses for patients with upper limb amputation has improved dramatically in recent years. Driving the prosthetic arm automatically by the users is the main challenge that faced the researchers in this field of study. Electromyography (EMG) signals produced by muscle contractions are one important typical control approach. In this paper, a sensor was developed to capture the electrical signals in the muscle of a patient using electromyography sensors. The proposed system has the ability for wirelessly transferring the captured data from the sensory system to computer, which represents the controlling and processing zone. The measured signals are then used to control the prosthesis arms. To achieve this, first the hardware and firmware of the sensor are developed and documented. Two Matlab scripts were developed to display the measured signals in the time and frequency domain. Finally, the characteristics of the developed sensors were verified by means of measurements. These measurements have shown that it is possible to distinguish whether a muscle is flexed or relaxed by means of the measured values captured with the sensor.
Sadeq Al-Murshedi, Firas Abedi, Ali Mohammed Ali, Rasha Alali, and Ihab Mahdi Almaameri
IEEE
In medical imaging, differences in patient size can cause some difficulties, particularly when it comes to choosing the optimal imaging protocol and this can influence image quality and radiation dose given to the patient. The purpose of this study is to investigate the influence of patient thickness on radiation dose received when undertaking abdomen x-ray examination. An adult anthropomorphic abdomen phantom was used for simulating the abdomen area of a real patient. In addition, three different layers of fat added above the phantom were utilised for simulating the different patient sizes. The phantom was imaged using different acquisition parameters and the radiation dose values were recorded. Radiation dose values increased with increasing phantom size and decreased with higher kVp values for all the different phantom sizes.
Ali Mohammed Ali, Sadeq Al-Murshedi, Batool Luay Aziz, Firas Abedi, and Ali S. Abosinnee
IEEE
The advanced digital imaging systems are producing much less noise associated radiographs. Physical image quality are becoming recognizable in replacing visual human evaluating of image quality. When radiation does shows strong correlation with image quality, one can directly control image quality using dose. Thus, this study aim to explore relationship between physical image quality (taken from real patient with lung infection) and radiation quantity (or doses) and to investigate possible reduction in radiation doses. The study used public data source of plain radiographs, that contains around 160,000 x-ray images. Then filtered them according the study aim and practicality to be used in measuring signal to noise ratio (SNR) and contrast to noise ratio (CNR). Strong relation between radiation dose and SNR was found, while CNR showed weak relationship with radiation dose. Very low SNR value was found to be suitable to produce visible image quality. The study concluded the possibility of utilizing physical image quality with possibility in reducing dose to the patients.
Ahmed Jasim Mohammed Younis, Ahmed Ghanim Wadday, Mohanned A. Aljaafari, and Firas Abedi
IEEE
Because of the improved overall system throughput, spectrum efficiency, and user fairness, non-orthogonal multiple access (NOMA) is the next stage in telecommunication systems. NOMA breaks the orthogonality of traditional orthogonal multiple access systems by allowing several users to use the same radio resource at the same time. This paper improves the selectivity of resource allocation methods for NOMA by using the Genetic Algorithm (GA) and Grey Wolf optimization (GWO). The performance of the algorithms in simulated scenarios is evaluated using a scale based on the users’ sum rate. Furthermore, the performance of algorithms was compared in the presence and absence of interference from neighboring cells, as this influenced each algorithm’s total sum rate performance. Another comparison is made to show the override of the proposed method on orthogonal multiple access and NOMA with GA. The results show that the proposed method (NOMA using GWO) is superior to orthogonal multiple access and NOMA using GA in terms of total sum rate.