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All Journal International Journal of Electrical and Computer Engineering Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science
Qais Al-Gayem
University of Babylon
Computer Science & IT Electrical & Electronics Engineering

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Four dimensional hyperchaotic communication system based on dynamic feedback synchronization technique for image encryption systems Hayder Mazin Makki Alibraheemi; Qais Al-Gayem; Ehab AbdulRazzaq Hussein
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp957-965

Abstract

This paper presents the design and simulation of a hyperchaotic communication system based on four dimensions (4D) Lorenz generator. The synchronization technique that used between the master/transmitter and the slave/receiver is based on dynamic feedback modulation technique (DFM). The mismatch error between the master dynamics and slave dynamics are calculated continuously to maintain the sync process. The information signal (binary image) is masked (encrypted) by the hyperchaotic sample x of Lorenz generator. The design and simulation of the overall system are carried out using MATLAB Simulink software. The simulation results prove that the system is suitable for securing the plain-data, in particular the image data with a size of 128×128 pixels within 0.1 second required for encryption, and decryption in the presence of the channel noise. The decryption results for gray and colored images show that the system can accurately decipher the ciphered image, but with low level distortion in the image pixels due to the channel noise. These results make the proposed cryptosystem suitable for real time secure communications.
Self-diagnostic approach for cell counting biosensor Qais Al-Gayem; Hussain F. Jaafar; Saad S. Hreshee
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp688-698

Abstract

In this research, a test monitoring strategy for an array of biosensors is proposed. The principle idea of this diagnostic technique is to measure and compare the impedance of each sensor in the array to achieve fully controlled online health monitoring technique at the system level. The work includes implementation of the diagnostic system, system architecture for analogue part, and SNR analysis. The technique has been applied on a cell coulter counting biochip where the design and fabrication of this sensing chip with electrodes make the coulter counter be an effective mean to count and analyses the cells in a blood sample. The experimental results show that the indication factor of the sensing electrodes has increased from 1 to 1.8 gradually depending on the fault level.
Hybrid Temporal–Spatial Electroencephalographic Classification Using Principal Component Analysis and Ensemble Learning: Epilepsy, Attention-Deficit/Hyperactivity Disorder, and Schizophrenia Dawood S. Hasan; Qais Al-Gayem; Hilal Al-Libawy
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 5 (2026): October
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i5.16803

Abstract

Electroencephalography (EEG) allows noninvasive access to temporal brain activity; however, many EEG classifiers are tailored to a single dataset or disorder and are not easily compared across heterogeneous recording settings. A gap that this study fills is the evaluation of a fixed hybrid temporal–spatial EEG representation pipeline with an identical processing flow on public datasets for epilepsy, attention-deficit/hyperactivity disorder (ADHD), and schizophrenia. Each window passes through a one-dimensional temporal branch and a two-dimensional spatial branch. Their outputs are projected and concatenated into a 256-dimensional fused vector, then standardized and reduced to 64 components by principal component analysis (PCA) fitted only on the training data. The six post-fusion classifiers were tested under dataset-specific validation protocols, with experiment-specific blending applied where applicable, without using held-out test data for scaling, PCA fitting, model selection, threshold adjustment, or blending. Subject-wise ADHD classification achieved around 98.62% accuracy and approximately 98.60% macro-F1. Under 28-fold leave-one-subject-out evaluation, schizophrenia classification yielded an aggregated segment-level accuracy of 96.28% and a fold-averaged macro-F1 score of 73.92%. The selected intra-subject CHB-MIT chb02 experiment attained a seizure-class F1 of 92.31%. The internal four-class experiment was considered exploratory feature-space analysis rather than evidence of dataset-independent clinical diagnosis, as the classes were drawn from disparate datasets and acquisition conditions. The results provide a consistent engineering baseline across the investigated protocols and may inform future EEG decision-support studies, although broader clinical interpretation requires harmonized cohorts, broader multi-subject evaluation, and external validation.
Co-Authors Dawood S. Hasan Ehab AbdulRazzaq Hussein Hayder Mazin Makki Alibraheemi Hilal Al-Libawy Hussain F. Jaafar Saad S. Hreshee