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Directive and Steerable Radiation Pattern using SASPA Array
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This work examines the ability of a special type of smart antenna array known as Switched Active Switched Parasitic Antenna (SASPA) to produce a directive and electronically steerable radiation pattern. The SASPA array consists of antenna elements that are switchable between active and parasitic states by using P-Intrinsic-N (PIN) diodes. The active element is the element that is supplied by the radio frequency while short-circuiting the terminals of an element in the array results in a parasitic element. Due to the strong mutual coupling between the elements, a directional radiation pattern with high gain and a small beamwidth can be produced with only one active element operating at a time. By changing the parasitic state to the active state sequentially for all elements, the directed radiation pattern can be easily rotated. The antenna array structure used in this work is the uniform circular array (UCA) to achieve symmetrical radiation patterns and full coverage of the entire azimuth plane. Also, a novel method for reducing the mutual coupling effect in SASPA arrays is proposed in this work. By using this method, some parameters of the generated SASPA’s radiation pattern can be controlled. The simulated results obtained from this work depict that an N-element SASPA-UCA produces N-symmetrical, switchable, and steerable radiation patterns with high gain, small beamwidth, and a high Front-to-Back (F/B) ratio. Also, the results show that further improvements in these parameters can be achieved by increasing the number of elements in the array. Additional simulations demonstrate that by including decaying function weight in each element's circuitry, the mutual coupling between the components of the SASPA-UCA array can be minimized. The aforementioned parameters can then be efficiently modified using this mutual coupling reduction.

 

 

 

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Publication Date
Fri Feb 28 2020
Journal Name
Iraqi Journal Of Agricultural Sciences (ijas)
PHYTOTOXICITY TEST OF KEROSENE-CONTAMINATED SOIL USING BARLEY
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This study was aimed to determine a phytotoxicity experiment with kerosene as a model of a total petroleum hydrocarbon (TPHs) as Kerosene pollutant at different concentrations (1% and 6%) with aeration rate (0 and 1 L/min) and retention time (7, 14, 21, 28 and 42 days), was carried out in a subsurface flow system (SSF) on the Barley wetland. It was noted that greatest elimination 95.7% recorded at 1% kerosene levels and aeration rate 1L / min after a period of 42 days of exposure; whereas it was 47% in the control test without plants. Furthermore, the percent of elimination efficiencies of hydrocarbons from the soil was ranged between 34.155%-95.7% for all TPHs (Kerosene) concentrations at aeration rate (0 and 1 L/min). The Barley c

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Engineering
Risk Assessment in BOT Contracts using AHP Technique
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The process of risk assessment in the build-operate transfer (BOT) project is very important to identify and analyze the risks in order to make the appropriate decision to respond to them. In this paper, AHP Technique was used to make the appropriate decision regarding response to the most prominent risks that were generated in BOT projects, which includes a comparison between the criteria for each risk as well as the available alternatives and by mathematical methods using matrices to reach an appropriate decision to respond to each risk.Ten common risks in BOT contracts are adopted for analysis in this paper, which is grouped into six main risk headings.The procedures followed in this paper are the questionnaire method

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Arabic Sentiment Analysis (ASA) Using Deep Learning Approach
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Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

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Publication Date
Fri Jan 01 2016
Journal Name
Bio-inspired Computing – Theories And Applications
Image Segmentation Using Membrane Computing: A Literature Survey
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Publication Date
Thu Dec 01 2022
Journal Name
Al-khwarizmi Engineering Journal
BCI-Based Smart Room Control using EEG Signals
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In this paper, we implement and examine a Simulink model with electroencephalography (EEG) to control many actuators based on brain waves. This will be in great demand since it will be useful for certain individuals who are unable to access some control units that need direct contact with humans. In the beginning, ten volunteers of a wide range of (20-66) participated in this study, and the statistical measurements were first calculated for all eight channels. Then the number of channels was reduced by half according to the activation of brain regions within the utilized protocol and the processing time also decreased. Consequently, four of the participants (three males and one female) were chosen to examine the Simulink model duri

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Publication Date
Sun Mar 30 2025
Journal Name
Iraqi Journal Of Science
Segmentation of Aerial Images Using Different Clustering Techniques
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The segmentation of aerial images using different clustering techniques offers valuable insights into interpreting and analyzing such images. By partitioning the images into meaningful regions, clustering techniques help identify and differentiate various objects and areas of interest, facilitating various applications, including urban planning, environmental monitoring, and disaster management. This paper aims to segment color aerial images to provide a means of organizing and understanding the visual information contained within the image for various applications and research purposes. It is also important to look into and compare the basic workings of three popular clustering algorithms: K-Medoids, Fuzzy C-Mean (FCM), and Gaussia

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Publication Date
Sun Nov 01 2020
Journal Name
Iraqi Journal Of Laser
Urinary Tract Lithotripsy Using Holmium: YAG (2100nm) Laser
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Background: Laser urinary stone lithotripsy is an established endourological modality. Ho:YAG(2100nm) laser have broadened  the indications for ureteroscopic stone managements  to include larger stone sizes throughout the whole urinary tract.

Purpose: To evaluate the effectiveness and safety of Holmium: YAG(2100nm) laser lithotripsy with a semirigid uretero scope for urinary stone calculi in a prospective cohort of 17 patients.

Patients and Methods: Holmium: YAG(2100nm) laser lithotripsy was performed with a semirigid ureteroscope in 17 patients from September 2016 to December  2016. Calculi were located in the lower ureter in 9 patients (52.9%), the midure

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Publication Date
Tue Dec 05 2017
Journal Name
Asian Journal Of Biological And Life Sciences
Bioethanol Production from Banana Peels using Different Pretreatments
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Publication Date
Tue Dec 23 2025
Journal Name
Journal Of Engineering
Image Compression Using 3-D Two-Level Techniques
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In this paper three techniques for image compression are implemented. The proposed techniques consist of three dimension (3-D) two level discrete wavelet transform (DWT), 3-D two level discrete multi-wavelet transform (DMWT) and 3-D two level hybrid (wavelet-multiwavelet transform) technique. Daubechies and Haar are used in discrete wavelet transform and Critically Sampled preprocessing is used in discrete multi-wavelet transform. The aim is to maintain to increase the compression ratio (CR) with respect to increase the level of the transformation in case of 3-D transformation, so, the compression ratio is measured for each level. To get a good compression, the image data properties, were measured, such as, image entropy (He), percent root-

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Publication Date
Sun Oct 01 2023
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Intelligence framework dust forecasting using regression algorithms models
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<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

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