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A Preliminary Study and Implementing Algorithm Using Finite State Automaton for Remote Identification of Drones
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Electronic remote identification (ER-ID) is a new radio frequency (RF) technology that is initiated by the Federal Aviation Authorities (FAA). For security reasons, traffic control, and so on, ER-ID has been applied for drones by the FAA to enable them to transmit their unique identification and location so that unauthorized drones can be identified. The current limitation of the existing ER-ID algorithms is that the application is limited to the Wi-Fi and Bluetooth wireless controllers, which results in a maximum range of 10–20 m for Bluetooth and 50–100 m for Wi-Fi. In this study, a mathematical computing technique based on finite state automaton (FSA) is introduced to expand the range of the ER-ID RF system and reduce the energy required by the drone to use the technology. A finite number of states have been designed to include a larger range of wireless network techniques, enabling the drones to be recognized while they are further away and in remote areas. This is achieved by including other means of RF channels, such as 4G/5G, Automatic Dependent Surveillance-Broadcast (ADS-B), long range Internet of things (IoT), and satellite communications, in the suggested ER-ID algorithm of this study. The introduced algorithm is tested via a case study. The results showed the ability to detect drones using all types of available radio frequency communication systems (RF-CS) while also minimizing the consumed energy. Hence, the authorities can control the licensed drones by using available RF-CS devices, such as Bluetooth and Wi-Fi, which are already widely used for mobile phones, as an example.

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Publication Date
Sat Sep 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
Testing the Assumption of Linearity for Sales of State Company for Electrical Industries
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In this study, the four tests employed for non-linear dependence which is Engle (1982), McLeod &Li (1983), Tsay (1986), and Hinich & Patterson (1995). To test the null hypothesis that the time series is a serially independent and identical distribution process .The linear structure is removed from the data which is represent the sales of State Company for Electrical Industries, through a pre-whitening model, AR (p) model .From The results for tests to the data is not so clear.

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Publication Date
Wed Nov 28 2018
Journal Name
International Journal Of Engineering & Technology
Finite Element Analysis for The Response of URM Walls Supporting RC Slab
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The aim for this research is to investigate the effect of inclusion of crack incidence into the 2D numerical model of the masonry units and bonding mortar on the behavior of unreinforced masonry walls supporting a loaded reinforced concrete slab. The finite element method was implemented for the modeling and analysis of unreinforced masonry walls. In this paper, ABAQUS, FE software with implicit solver was used to model and analyze unreinforced masonry walls which are subjected to a vertical load. Detailed Micro Modeling technique was used to model the masonry units, mortar and unit-mortar interface separately. It was found that considering potential pure tensional cracks located vertically in the middle of the mortar and units show

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Publication Date
Fri Jul 17 2026
Journal Name
Journal Of Revenue And Pricing Management
Economic sustainability in state-owned extractive industries: a dynamic pricing framework for the Iraqi mining sector
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This study provides an empirical and strategic contribution to the revenue management literature by investigating the transition from static cost-plus pricing to a dynamic, elasticity-based framework within state-owned extractive industries (SOEs). While dynamic pricing is extensively studied in flexible, data-rich private markets, its application in structurally rigid SOEs within transitional economies remains a critical research gap. Drawing on primary fiscal and production data from the 2024 fiscal year of the General Company for Mining Industries (SCMI), we developed a mathematical model to determine optimal pricing ( ) based on marginal cost principles and demand elasticity. The findings demonstrate that replacing rigid accounting rout

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Publication Date
Sun Oct 19 2025
Journal Name
Lecture Notes In Networks And Systems
Challenges of Using Literary Translation and Artificial Intelligence for Postgraduate Students: A Case Study of Baghdad University
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Publication Date
Wed Aug 18 2021
Journal Name
Chemical Papers
Analytical methods for the identification of micro/nano metals in e-cigarette emission samples: a review
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Publication Date
Wed Aug 18 2021
Journal Name
Chemical Papers
Analytical methods for the identification of micro/nano metals in e-cigarette emission samples: a review
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Publication Date
Thu Jun 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
A missing data imputation method based on salp swarm algorithm for diabetes disease
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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

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Publication Date
Wed Jan 01 2020
Journal Name
Ieee Access
Modified Elman Spike Neural Network for Identification and Control of Dynamic System
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Publication Date
Sat Apr 01 2023
Journal Name
Fluid Phase Equilibria
Prediction of solubility of vitamins in the mixed solvents using equation of state
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Publication Date
Sun Sep 22 2024
Journal Name
Journal Of Petroleum Research And Studies
Optimizing Gas Lift for Improved Oil Recovery in a Middle East Field: A Genetic Algorithm Approach
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This paper presents a study of the application of gas lift (GL) to improve oil production in a Middle East field. The field has been experiencing a rapid decline in production due to a drop in reservoir pressure. GL is a widely used artificial lift technique that can be used to increase oil production by reducing the hydrostatic pressure in the wellbore. The study used a full field model to simulate the effects of GL on production. The model was run under different production scenarios, including different water cut and reservoir pressure values. The results showed that GL can significantly increase oil production under all scenarios. The study also found that most wells in the field will soon be closed due to high water cuts. Howev

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