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EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier
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This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it is obvious that the number of moments selected by the SP should exceed 30% of the overall EEG samples for accuracy to be over 90%.

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Publication Date
Sun May 01 2022
Journal Name
Expert Systems With Applications
Novel large scale brain network models for EEG epileptic pattern generations
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Background: Unlike normal EEG patterns, the epileptiform abnormal pattern is characterized by different mor phologies such as the high-frequency oscillations (HFOs) of ripples on spikes, spikes and waves, continuous and sporadic spikes, and ploy2 spikes. Several studies have reported that HFOs can be novel biomarkers in human epilepsy study. S) Method: To regenerate and investigate these patterns, we have proposed three large scale brain network models (BNM by linking the neural mass model (NMM) of Stefanescu-Jirsa 2D (S-J 2D) with our own structural con nectivity derived from the realistic biological data, so called, large-scale connectivity connectome. These models include multiple network connectivity of brain regions at different

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Publication Date
Sun Mar 01 2020
Journal Name
Journal Of Engineering
Performance Evaluation of Trickling Filter and Extended Aeration of Wastewater Treatment Plants
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In recent decades, there has been increasing interest in wastewater treatment because of its direct impact on the environment and public health. Over time, other forms of treatment have been developed and modified, including extended aeration. This process is included in the suspended growth system. In this paper, a comparative study was conducted between the efficiency of the extended aeration plant and that of the trickling filter plant in removal of BOD and COD.  The method of comparison was done by knowing the value of the pollutant before and after the treatment and then extract the removal ratio of each pollutant within each plant. The results showed that the percentage of removal of BOD in the trickling filte

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Publication Date
Mon Dec 30 2002
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Aerobic Filter Treatment of Pharmaceutical Waste
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Publication Date
Fri May 01 2020
Journal Name
Journal Of Engineering
Semi-Analytical Prediction of Flank Tool Wear in Orthogonal Cutting of Aluminum
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This study aims to model the flank wear prediction equation in metal cutting, depending on the workpiece material properties and almost cutting conditions. A new method of energy transferred solution between the cutting tool and workpiece was introduced through the flow stress of chip formation by using the Johnson-Cook model. To investigate this model, an orthogonal cutting test coupled with finite element analysis was carried out to solve this model and finding a wear coefficient of cutting 6061-T6 aluminum and the given carbide tool.

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Publication Date
Sun Mar 02 2008
Journal Name
Baghdad Science Journal
Orthogonal Functions Solving Linear functional Differential EquationsUsing Chebyshev Polynomial
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A method for Approximated evaluation of linear functional differential equations is described. where a function approximation as a linear combination of a set of orthogonal basis functions which are chebyshev functions .The coefficients of the approximation are determined by (least square and Galerkin’s) methods. The property of chebyshev polynomials leads to good results , which are demonstrated with examples.

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Publication Date
Sun Sep 11 2022
Journal Name
Concurrency And Computation: Practice And Experience
Fast and accurate computation of high‐order Tchebichef polynomials
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Publication Date
Wed Nov 06 2024
Journal Name
2024 17th International Conference On Development In Esystem Engineering (dese)
Speech Enhancement Algorithm using Deep Learning and Hahn Polynomials
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Publication Date
Wed Dec 27 2017
Journal Name
Al-khwarizmi Engineering Journal
Human Face Recognition Using GABOR Filter And Different Self Organizing Maps Neural Networks
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This work implements the face recognition system based on two stages, the first stage is feature extraction stage and the second stage is the classification stage. The feature extraction stage consists of Self-Organizing Maps (SOM) in a hierarchical format in conjunction with Gabor Filters and local image sampling. Different types of SOM’s were used and a comparison between the results from these SOM’s was given.

The next stage is the classification stage, and consists of self-organizing map neural network; the goal of this stage is to find the similar image to the input image. The proposal method algorithm implemented by using C++ packages, this work is successful classifier for a face database consist of 20

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Publication Date
Mon Jun 01 2009
Journal Name
2009 Etp International Conference On Future Computer And Communication
Signal Processing Techniques for Robust Spectrum Sensing
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Cognitive radios have the potential to greatly improve spectral efficiency in wireless networks. Cognitive radios are considered lower priority or secondary users of spectrum allocated to a primary user. Their fundamental requirement is to avoid interference to potential primary users in their vicinity. Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. In addition, reliable sensing creates spectrum opportunities for capacity increase of cognitive networks. One of the key challenges in spectrum sensing is the robust detection of primary signals in highly negative signal-to-noise regimes (SNR).In this paper ,

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Publication Date
Fri Jul 21 2023
Journal Name
Journal Of Engineering
GENERATION OF MPSK SIGNAL USING LOGIC CIRCUITS
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The traditional technique of generating MPSK signals is basically to use IQ modulator that involves analog processing like multiplication and addition where inaccuracies may exist and would lead to imbalance problems that affects the output modulated signal and hence the overall performance of the system. In this paper, a simple method is presented for generating the MPSK using logic circuits that basically generated M-carrier signals each carrier of different equally spaced phase shift. Then these carriers are time multiplexed, according to the data symbols, into the output modulated signal.

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