One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.

No abstract

Brain tumour is a serious disease, and the number of people who are dying due to brain tumours is increasing. Manual tumour diagnosis from magnetic resonance images (MRIs) is a time consuming process and is insufficient for accurately detecting, localizing, and classifying the tumour type. This research proposes a novel two-phase multi-model automatic diagnosis system for brain tumour detection and localization. In the first phase, the system structure consists of preprocessing, feature extraction using a convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) approach. The purpose of the first system phase is to detect brain tumour by classifying the MRIs into normal and abnormal images. The aim of the second system phase is to localize the tumour within the abnormal MRIs using a fully designed five-layer region-based convolutional neural network (R-CNN). The performance of the first phase was assessed using three CNN models, namely, AlexNet, Visual Geometry Group (VGG)-16, and VGG-19, and a maximum detection accuracy of 99.55% was achieved with AlexNet using 349 images extracted from the standard Reference Image Database to Evaluate Response (RIDER) Neuro MRI database. The brain tumour localization phase was evaluated using 804 3D MRIs from the Brain Tumor Segmentation (BraTS) 2013 database, and a DICE score of 0.87 was achieved. The empirical work proved the outstanding performance of the proposed deep learning-based system in tumour detection compared to other non-deep-learning approaches in the literature. The obtained results also demonstrate the superiority of the proposed system concerning both tumour detection and localization.

No abstract

A design of low mutual coupling between two microstrip patch antennas for multi input multi output antenna is presented. The two antenna elements operate at 5.8 GHz for wireless applications. The reduction of mutual coupling between the antenna elements is achieved by using a defected ground structure (DGS). The DGS is inserted between the microstrip patch antenna elements to limit the surface waves between them. The separation between the edges of the two elements has been achieved to be only 0.058λ0. The analysis of the correlation coefficient, diversity gain and total active reflection coefficient is presented to validate the performance of the multiple-input–multiple-output (MIMO) antenna. The isolation of the proposed MIMO antenna is 28 dB at 5.8 GHz and the envelope correlation equals 0.003. Owing to these good performances each antenna can operate almost independently. A good agreement is achieved between the simulated and the measured results.