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The aim of this study was to evaluate a perovskite catalyst for use as a carbon-resistant catalyst in methane dry reforming. The oxygen-rich ABO3+δ (CeCoxNi1-xO3+δ) perovskite was selected for this investigation due to its crystalline nature and ability to accommodate a wide range of cations; perovskites notably possess catalytically advantageous oxidation and reduction properties. The perovskite catalysts were prepared as multicomponent oxides by sol-gel synthesis, which is considered a most effective technique for preparing these compounds. The properties of fresh catalysts were evaluated through X-ray diffraction, Brunauer-Emmett-Teller surface area analysis, particle size distribution analysis, X-ray photoelectron spectroscopy, and scanning electron microscopy. The tested catalysts exhibited superior catalytic performance over previously-studied ABO3 catalysts, such as LaCoO3 and LaNiO3 systems. This high performance is attributed to electronic interactions between the Co and Ni sites, which results from the atomic-level mixing of ingredients in the sol-gel method. Analysis results revealed that when used as a catalyst in methane dry reforming, the presence of ceria in the perovskite system can improve coking resilience and confer stability even under prolonged usage in a carbon-rich environment.

Over the past few decades, hyperspectral image (HSI) classification has garnered increasing attention from the remote sensing research community. The largest challenge faced by HSI classification is the high feature dimensions represented by the different HSI bands given the limited number of labeled samples. Deep learning and convolutional neural networks (CNNs), in particular, have been shown to be highly effective in several computer vision problems such as object detection and image classification. In terms of accuracy and computational cost, one of the best CNN architectures is the Inception model i.e., the winner of the ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2014 challenge. Another architecture that has significantly improved image recognition performance is the Residual Network (ResNet) architecture i.e., the winner of the ILSVRC 2015 challenge. Inspired by the incredible performance introduced by the Inception and ResNet architectures, we investigate the possibility of combining the core ideas of these two models into a hybrid architecture to improve the HSI classification performance. We tested this combined model on four standard HSI datasets, and it shows competitive results compared with other existing HSI classification methods. Our hybrid deep ResNet-Inception architecture obtained accuracies of 95.31% on the Pavia University dataset, 99.02% on the Pavia Centre scenes dataset, 95.33% on the Salinas dataset and 90.57% on the Indian Pines dataset.

Phishing is one of the most frequently occurring forms of cybercrime that Internet users face and represents a violation of cybersecurity principles. Phishing is a fraudulent attack that is performed over the Internet with the purpose of obtaining and using without authorization the sensitive information of Internet users, such as usernames, passwords, credit card details, and bank account information. Some widely used phishing attempts involve using email spoofing or instant messaging, aiming to convince a victim to visit the spoofed websites, which will result in obtaining the victim’s information. In this work, we identify and analyze the most important features needed to detect the spoofed websites in virtue of two new feature selection techniques. The first proposed feature selection technique uses underlying feature selection methods that vote on each feature, and if such methods agree on a specific feature, that feature is selected. The second feature selection technique also uses underlying feature selection methods that vote on each feature, and if the majority vote on a specific feature, the feature is selected. We also propose a phishing detection technique based on both AdaBoost and LightGBM ensemble methods to detect the spoofed websites. The proposed method achieves a very high accuracy compared to that of the existing methods.

One of the most challenging topics in the field of intelligent transportation systems is the automatic interpretation of the driver’s behavior. This research investigates distracted driver posture recognition as a part of the human action recognition framework. Numerous car accidents have been reported that were caused by distracted drivers. Our aim was to improve the performance of detecting drivers’ distracted actions. The developed system involves a dashboard camera capable of detecting distracted drivers through 2D camera images. We use a combination of three of the most advanced techniques in deep learning, namely the inception module with a residual block and a hierarchical recurrent neural network to enhance the performance of detecting the distracted behaviors of drivers. The proposed method yields very good results. The distracted driver behaviors include texting, talking on the phone, operating the radio, drinking, reaching behind, fixing hair and makeup, and talking to the passenger.

Wireless Local Area Networks (WLANs) are increasingly integrated into our daily lives. Access Points (APs) are an integral part of the WLAN infrastructure, as they are responsible for coordinating wireless users and connecting them to the wired side of the network and, eventually, to the Internet. APs are deployed everywhere, from airports and shopping malls to coffee shops and hospitals, to provide Internet connectivity. One of the most serious security problems encountered by WLAN users is the existence of Rogue Access Points (RAPs). This article classifies existing solutions, identifies vulnerabilities, and suggests future directions for research into these RAPs. The ultimate objective is to classify existing detection techniques and find new RAP types that have not been classified by the research community. The literature typically categorizes Evil-twin, Unauthorized, Compromised, and Improperly Configured RAPs. Two other types have largely been abandoned by researchers, but can be classified as Denial of Service RAP attacks. These are deauthentication/disassociation attacks targeting wireless users, and the forging of the first message in a four-way handshake.

Solar water splitting for hydrogen generation can be a potential source of renewable energy for the future. Here we show that efficient and stable stoichiometric dissociation of water into hydrogen and oxygen can be achieved under visible light by eradicating the potential barrier on nonpolar surfaces of indium gallium nitride nanowires through controlled p-type dopant incorporation. An apparent quantum efficiency of ∼12.3% is achieved for overall neutral (pH∼7.0) water splitting under visible light illumination (400-475 nm). Moreover, using a double-band p-type gallium nitride/indium gallium nitride nanowire heterostructure, we show a solar-to-hydrogen conversion efficiency of ∼1.8% under concentrated sunlight. The dominant effect of near-surface band structure in transforming the photocatalytic performance is elucidated. The stability and efficiency of this recyclable, wafer-level nanoscale metal-nitride photocatalyst in neutral water demonstrates their potential use for large-scale solar-fuel conversion.