Abduljalil Mohamed

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Electroencephalogram (EEG) is one of the potential physiological signals used for detecting epileptic seizure. Discriminant features, representing different brain conditions, are often extracted for diagnosis purposes. On-line detection necessitates that these features are to be computed efficiently. In this work, an evidence theory-based approach for(More)
We propose a new distributed alarm correlation and fault identification in computer networks. The managed network is divided into a disjoint management domains and each management domain is assigned a dedicated intelligent agent. The intelligent agent is responsible for collecting, analyzing, and correlating alarms emitted form emitted from its constituent(More)
Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as representative signal carrying valuable information pertaining to the current brain state. For these techniques to be efficient and reliable, a set of discriminant, epileptic-related features has first to be obtained. Furthermore, depending on the classifier model used, a(More)
Wireless electroencephalogram (EEG) sensors have been successfully applied in many medical and computer brain interface classifications. A common characteristic of wireless EEG sensors is that they are low powered devices, and hence an efficient usage of sensor energy resources is critical for any practical application. One way of minimizing energy(More)
Experienced pipeline operators utilize Magnetic Flux Leakage (MFL) sensors to probe oil and gas pipelines for the purpose of localizing and sizing different defect types. A large number of sensors is usually used to cover the targeted pipelines. The sensors are equally distributed around the circumference of the pipeline, and every three millimeters the(More)
Magnetic Flux Leakage (MFL) sensors are commonly utilized to detect defects in oil and gas pipelines and determine their depths and sizes. As a preprocessing step, MFL data are often reduced into a representative feature set that is capable of accurately estimating pipeline defect depths. However, this estimation capability may vary depending on the(More)
To determine the severity of metal-loss defects in oil and gas pipelines, the depth of potential defects, along with their length, needs first to be estimated. For this purpose, pipeline engineers use intelligent Magnetic Flux Leakage (MFL) sensors that scan the metal pipelines and collect defect-related data. However, due to the huge amount of the(More)
—Experienced engineers utilize Magnetic Flux Leakage (MFL) sensors to scan oil and gas pipelines for the purpose of localizing and sizing different defect types. The huge amount of raw data obtained by these sensors, however, makes the inspection task error-prone and time-consuming. In this paper, we propose a defect depth estimation approach using(More)
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