The December issue is now online! For electronic browsing click here |
| IJEEE welcomes scientifically and technically valid articles from all areas of electrical, electronic engineering, and computer science.
With a broad scope, the journal is meant to provide a unified and reputable outlet for rigorously peer-reviewed and well-conducted scientific research. See the full Aims & Scope here. As well as original articles, IJEEE publishes comprehensive review articles and short articles. The Iraqi Journal of Electrical and Electronic Engineering (IJEEE) is a peer-reviewed open access journal that undergoes a rigorous evaluation process and is freely accessible to the public. As of January 1, 2024, the publishing processing fee is set at 300,000 IQD (200 $). More details can be found here. |
Most Recent Articles
| Open Access | |
| Design a Stable an Intelligent Controller for an Eight-legged Robot | |
| Fatima H. Khayoun, and Ammar A. Aldair | |
| Version of record online: 10 February 2025 | DOI: 10.37917/ijeee.21.2.20 | Full Text (PDF) | |
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At recent days, the robot performs many tasks on behalf of humans or in support of humans. Among the most prominent benefits of robots for humans are removing the risk factor from humans, completing routine tasks for humans, saving a lot of time and effort, and mastering work. This paper presents a model of an eight-legged robot equipped with an intelligent controller that was simulated using MATLAB. The designed structure contains 24 controllers, three for each leg, to provide flexibility in movement and rotation. Proportional Integral Derivative (PID) controller has been used in this work, each leg contains three PIDs. A particle swarm optimization algorithm (PSO) was used to adjust the parameters of the PID controller (Kp, Ki and Kd). The structure of eight legs robot with controller is implemented using Simscape Multibody in the MATLAB program, where the movement of the eight-legged robot is visualized and analyzed without the need for complex analysis associated with a mathematical model. The simulation results were conducted in a three-dimensional environment and were presented in two scenarios. The first was implementing and simulating the robot without using a controller, which leads to the robot falling at the starting point. The second was when a PID controllers are used with the system, where better movement was obtained. Finally, the robustness of the controller was verified by changing the load that the robot bears. | Open Access | |
| Design and Implementation of a Climbing Robot Limb for Clinging to Rough Walls | |
| Mohammed K. Jodah, Mofeed Turky Rashid, and Raed S. Batbooti | |
| Version of record online: 10 February 2025 | DOI: 10.37917/ijeee.21.2.19 | Full Text (PDF) | |
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In recent years, the urgent need for robotics applications in various sensitive work areas and high buildings has led to a significant development in the design of robots intended for climbing rough surfaces. Where, attention became focused on the ideal clinging mechanism. In this paper, a gripper of the climbing robot has been designed to achieve clinging on rough walls. The objective of this design is to be lightweight with high performance of clinging, therefore, a robot gripper has been designed based on a model of a limb inspired by the hand and claws of a cat, in which the robot claws were implemented by fishing hooks. These hooks are arranged in an arc so that each hook can move independently on the wall’s surface to increase the force of clinging to the rough wall. SolidWorks platform has been used to design the clinging limb and implemented using a 3D printer. In addition, the proposed design has been validated by performing several simulations using the SolidWorks platform. Experimental work has conducted to test the proposed design, and the results proved the success of the design.| Open Access | |
| A Novel Deep Learning Object Detection Based on PCA Features for Self Driving Cars | |
| Namareq Odey, and Ali Marhoon | |
| Version of record online: 8 February 2025 | DOI: 10.37917/ijeee.21.2.18 | Full Text (PDF) | |
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In recent years, self-driving cars and reducing the number of accident casualties have drawn a lot of attention. Although it is crucial to increase driver awareness on the road, autonomous vehicles can emulate human driving and guarantee improved levels of road safety. Artificial intelligence (AI) technologies are often employed for this purpose. However, deep learning, a subset of AI, is prone to numerous errors, a wide range of threats, and needs to handle vast amounts of data, which imposes high-performance hardware requirements. This study suggests a deep learning model for object recognition that employs characteristics to describe data rather than images. Our model employs the COCO dataset as the training foundation, and it was suggested that the features be retrieved using the principal component analysis PCA extraction method. The current results demonstrate the efficacy and precision of our model, with an accuracy of 99.96 %. Furthermore, the performance indices, i.e., recall, precision, and F1-score, achieved about 1 for most of the COCO classes in training phase and promising results in testing phase.|
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