This work presents a novel technique for the detection of oil aging in electrical transformers using a single mode optical fiber sensor based on surface plasmon resonance (SPR). The aging of insulating oil is a critical issue in the maintenance and performance of electrical transformers, as it can lead to reduce insulation properties, increase risk of electrical breakdown, and decrease operational lifespan. Many parameters are calculated in this study in order to examine the efficiency of this sensor like sensitivity (S), signal to noise ratio (SNR), resolution (refractive index unit) and figure of merit (FOM) and the values are for figure of merit is 11.05, the signal to noise ratio is 20.3, the sensitivity is 6.63, and the resolution is 3

Researchers employ behavior based malware detection models that depend on API tracking and analyzing features to identify suspected PE applications. Those malware behavior models become more efficient than the signature based malware detection systems for detecting unknown malwares. This is because a simple polymorphic or metamorphic malware can defeat signature based detection systems easily. The growing number of computer malwares and the detection of malware have been the concern for security researchers for a large period of time. The use of logic formulae to model the malware behaviors is one of the most encouraging recent developments in malware research, which provides alternatives to classic virus detection methods. To address the l

This project sought to fabricate a flexible gas sensor based on a short functionalized multi-walled carbon nanotubes (f-MWCNTs) network for nitrogen dioxide gas detection. The network was prepared by filtration from the suspension (FFS) method and modified by coating with a layer of polypyrrole conductive polymer (PPy) prepared by the oxidative chemical polymerization to improve the properties of the network. The structural, optical, and morphological properties of the f-MWCNTs and f-MWCNTs/PPy network were studied using X-ray diffraction (XRD), Fourie-transform infrared (FTIR), with an AFM (atomic force microscopy). XRD proved that the structure of f-MWCNTs is unaffected by the synthesis procedure. The FTIR spectra verified the existence o

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

Moment invariants have wide applications in image recognition since they were proposed.

Most recent studies have focused on using modern intelligent techniques spatially, such as those
developed in the Intruder Detection Module (IDS). Such techniques have been built based on modern
artificial intelligence-based modules. Those modules act like a human brain. Thus, they should have had the
ability to learn and recognize what they had learned. The importance of developing such systems came after
the requests of customers and establishments to preserve their properties and avoid intruders’ damage. This
would be provided by an intelligent module that ensures the correct alarm. Thus, an interior visual intruder
detection module depending on Multi-Connect Architecture Associative Memory (MCA)