This paper presents a new approach by training and improving a convolutional neural network (CNN) based on You Only Look Once version 2 (YOLOv2) to efficiently detect fuel trucks from images in embedded systems. The proposed method considers the entire image area for strong object detection compared with existing methods that only focus on the image area where the class object exists to predict its probability to be in a class. The loss function for CNN is improved to enhance effective learning, especially when only a limited amount of data is available for training. The class probability can be learned by improving the loss function although the anchor boxes are not in the center of the target object. The learning process of the model can be in a limited range and achieve rapid convergence although the sizes of the initial anchor and target boundary boxes are different. Experimental results of various fuel truck images show the efficiency of the proposed approach under different detection scenarios of real fuel trucks. The detection rate of the proposed method is approximately 4% higher than the YOLOv2 object detection method. The proposed method is suitable to monitor long country borders using unmanned drones.

With the reliance of humans on mobile smart devices that have wireless communication, modules have significantly increased in recent years. Using these devices to communicate with the survivors during a disaster or its aftermath can significantly increase the chances of locating and saving them. Accordingly, a method is proposed in this study to extend the lifetime of the nodes in a Mobile Ad Hoc Network (MANET) while maintaining communications with the nearest base station (BS). Such a methodology allows the rapid establishment of temporary communications with these survivors, as restoring the complex infrastructure is a time-consuming process. The proposed method achieves the longer lifetime of the network by balancing the load throughout the nodes and avoids exhausting those with limited remaining energy. The proposed method has shown significant improvement in the lifetime of the MANET while maintaining similar Packet Delivery Rate (PDR) and route generation time, compared to existing methods.

A novel design of wearable antenna depending on metamaterials inspired-fractal Minkowski-shaped for industrial, scientific, and medical (ISM) applications is presented. The antenna consists of a conventional monopole and a Chebyshev transformer coupled with a unit cell of a fractal Minkowski curve to obtain three bands covering the ISM and Wireless Local Area Network (WLAN) applications. To enhance the antenna performance, the authors proposed the Electromagnetic Band Gap (EBG) layer of 3×4 array is introduced into the design structure. The authors used material FR4 dielectric as a substrate to design the antenna with dimensions of 51mm x 45mm x 1.6mm and fed by a 50 Ω port. The Antenna performance is analyzed numerically using CST Microwave Studio (CSTMWS) depending the Finite Integral Technique (FIT). Various investigation analyses have carried out to verify optimum antenna performance. The proposed antenna realizes reconfiguration by using the PIN diode. In both cases (switching= ON, OFF) the antenna achieves good bandwidth, |S₁₁|<-10dB, and excellent impedance matching.

Detecting malware is crucial for ensuring the security of computer systems. Traditional machine learning models face challenges in effectively detecting malware, mainly due to the class imbalance problem, where the number of malware samples is significantly smaller than that of non-malware samples. Additionally, malware’s dynamic and evolving nature, continuously altering its structure and tactics, presents a substantial challenge for conventional artificial intelligence algorithms, further complicating the detection task. In pursuing an optimized malware detection technique, researchers initially explored traditional machine learning algorithms, focusing on the features of portable executable file headers. However, the inherent issues, such as imbalanced datasets and the deceptive nature of malware, have raised concerns about the credibility of the attained results. This can result in misclassifying malware as non-malware, leading to security vulnerabilities. One-class Classification (OCC) methods have emerged as a promising approach to improve the detection of unknown malware. However, traditional OCC approaches face the challenge of the curse of dimensionality. This research proposes adapting subspace learning-based OCC methods to overcome the curse of dimensionality and effectively handle the class imbalance problem. We propose a pipeline for detecting malware using methods that jointly optimize a subspace and data description for OCC.We evaluate the performance of various one-class classifiers on three different datasets. We observed that the subspace-learning-based OCC is a promising approach. Evaluating various classifiers on three datasets reveals promising results, with a True Positive Rate (TPR) of 100 % for subspace-learning-based OCC. The proposed pipeline can serve as a valuable tool for improving the security of computer systems by accurately detecting malware and protecting against potential attacks.

The internet of things (IoT) implementation leads to an extended attack surface that requires continuous mitigation of security. Mission-critical situations (e.g., Smart Grid, Intelligent Transportation Systems, video surveillance, e-health) to business-oriented applications are all examples of IoT applications (e.g., banking, logistics, insurance, and contract law). IoT has made human existence considerably easier and more efficient. Because IoT devices are less expensive to build due to the use of various software, sensors, and the use of Artificial Intelligence, they violate several security regulations. That's why, in the IoT systems; extensive security support is required, particularly for mission-critical applications, but also for downstream commercial applications. There have been a variety of security strategies and approaches suggested and/or used. Blockchain is an amazing concept that manages a distributed ecosystem without the need for a third party. Researchers discovered that it has the ability to solve IoT software-related security problems. The biggest obstacle is combining blockchain with resource-constrained IoT computers. An experimental setup to incorporate blockchain on a smart laboratory IoT -based was completed, and then tested against Wireshark sniffing attacks, the system was more vulnerable to cyber-crimes attacks as we found out that when we employed blockchain technology as the communication medium of IoT devices, the no. of bytes needed for packet transmission was more than the bytes needed without applying Blockchain with about 959 bytes on average.

The high peak‐to‐average power ratio (PAPR) of the transmit signal is a major shortcoming of OFDM systems, which results in band radiation and distortion due to the nonlinearity of the high‐power amplifier (H.P). To resolve the traditional OFDM high‐PAPR issue, where the transmit sequence is designed to avoid similar data from being sent in the same order to reduce PAPR, there are numerous conventional ways for lowering the PAPR for OFDM system, such as selective mapping, tone reservation, block coding, filtering, clipping, and partial transmit sequence (PTS). This study proposes a new method called polygon number algorithm (PN) with conventional partial transmit sequence (C‐PTS). This method (PN‐PTS) processes the entered data before sending it, taking advantage of the number nonsimilarity according to the geometry of the number to prevent direct sending of similar data via PTS, and thus, this improved the level of PAPR rise in the proposed system. The amount of reduction that can be achieved in PAPR is up to 8 dB by different techniques. The best result obtained was the amount of reduction between the conventional method and the proposed method is 4.5683 where N = 64. Besides this, there is no transmission of side information (SI), which improves transmission efficiency. Finally, this method is easy in the calculation process and the ciphering and deciphering of data, which adds a few calculations.