Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.

This paper presents an optimal method for optimizing network reconfiguration problems in a power distribution system in order to enhance reliability and reduce power losses. Network reconfiguration can be viewed as an optimization problem involving a set of criteria that must be reduced when adhering to various constraints. The energy not supplied (ENS) during permanent network faults and active power losses are the objective functions that are optimized in this study during the reconfiguration phase. These objectives are expressed mathematically and will be integrated into various optimization algorithms used throughout the study. To begin, a mathematical formulation of the objectives to be optimized, as well as all the constraints that must be met, is proposed. Then, to solve this difficult combinatorial problem, we use the exhaustive approach, genetic algorithm (GA), and particle swarm optimization (PSO) on an IEEE 33-bus electrical distribution network. Finally, a performance evaluation of the proposed approaches is developed. The results show that optimizing the distribution network topology using the PSO approach contributed significantly to improving the reliability, node voltage, line currents, and calculation time.

In the Internet of Things (IoT), a lot of constrained devices are interconnected. The data collected from those devices can be the target of cyberattacks. In this paper, a lightweight cryptosystem that can be efficiently implemented in highly constrained IOT devices is proposed. The algorithm is mainly based on Advanced Encryption Standard (AES) and a new chaotic S-box. Since its adoption by the IEEE 802.15.4 protocol, AES in embedded platforms have been increasingly used. The main cryptographic properties of the generated S-box have been validated. The randomness of the generated S-box has been confirmed by the NIST tests. Experimental results and security analysis demonstrated that the cryptosystem can, on the one hand, reach good encryption results and respects the limitation of the sensor’s resources, on the other hand. So the proposed solution could be reliably applied in image encryption and secure communication between networked smart objects.

By bringing smart and advanced solutions, the Internet of Things (IoT) has opened up new dimensions of innovative services and processing power for contemporary living standards. IoT has a wide range of devices and communication entities as a result of the widespread applications of these services, making network management a challenging task. Therefore, it is critical to redesign the IoT network’s management. The inherent programmability and centralized capabilities of software-defined networking (SDN) make network management simpler, enable network abstraction, make network evolution easier, and have the potential to handle the IoT network. However, security issues still present the IoT dilemma. Distributed Denial of Service (DDoS) attacks are among the most significant security threats in IoT systems. This paper studies in-depth DDoS attacks in IoT and in SDN. A review of different detection and mitigation techniques based on SDN, blockchain and machine learning models is conducted. A holistic, secure IoT system is proposed on the basis of SDN with multicontrollers. Blockchain is considered to guarantee security in distributed SDN-IoT networks and ANN to improve the detection and mitigation process.

Metamaterial absorbers are very attractive due to their significant absorption behavior at optical wavelengths, which can be implemented for energy harvesting, plasmonic sensors, imaging, optical modulators, photovoltaic detectors, etc. This paper presents a numerical study of an ultra-wide-band double square ring (DSR) metamaterial absorber (MMA) for the complete visible optical wavelength region, which is designed with a three-layer (tungsten-silicon dioxide-tungsten) substrate material. Due to the symmetricity, a polarization-insensitive absorption is obtained for both transverse electric (TE) and transverse magnetic (TM) modes by simulation. An absorption above 92.2% and an average absorption of 97% are achieved in the visible optical wavelength region. A peak absorption of 99.99% is achieved at 521.83 nm. A wide range of oblique incident angle stabilities is found for stable absorption properties. A similar absorption is found for different banding angles, which may occur due to external forces during the installation of the absorber. The absorption is calculated by the interference theory (IT) model, and the polarization conversion ratio (PCR) is also validated to verify the perfect MMA. The electric field and magnetic field of the structure analysis are performed to understand the absorption property of the MMA. The presented MMA may be used in various applications such as solar cells, light detection, the biomedical field, sensors, and imaging.

This paper presents a novel compact 2 × 2 planar MIMO antenna system with ultra‐wide band capability. Antenna system is specifically designed to target lower 5th generation operating bands ranging from 2 GHz to 12 GHz. This band also covers the IEEE 802.11 a/b/g/n/ac. The antenna array geometry has been simulated using CST MWS. The design is extremely miniaturized with total structure size of 13 × 25 × 0.254 mm³. The simulated and measured results have been presented. Measured and simulated return loss values for designed antenna are less than −10 dB over the operating band and lowest values of −35 dB and −32.5 dB can been seen at 5.2 GHz and 9.2 GHz, respectively, whereas at the center frequency the return loss is −25.2 dB. The mutual coupling between both elements is less than −20 dB over the transmission bandwidth. Simulated and measured radiation patterns in E and H planes at center frequency show nearly isotropic far fields. The maximum gain is measured as 4.8 dB. Promising results of Envelope Correlation Coefficient and gain diversity of the design have been achieved. Simulated and measured results are found in good agreement. The fractional bandwidth of antenna is measured as 143.2% which satisfies its ultra‐wide band response.