The novel coronavirus 2019 first appeared in Wuhan province of China and spread quickly around the globe and became a pandemic. The gold standard for confirming COVID-19 infection is through Reverse Transcription-Polymerase Chain Reaction (RT-PCR) assay. The lack of sufficient RT-PCR testing capacity, false negative results of RT-PCR, time to get back the results and other logistical constraints enabled the epidemic to continue to spread albeit interventions like regional or complete country lockdowns. Therefore, chest radiographs such as CT and X-ray can be used to supplement PCR in combating the virus from spreading. In this work, we focus on proposing a deep learning tool that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images. The result of the experiments shows that the utilized models can provide accuracy up to 98% via pre-trained network and 94.1% accuracy by using the modified CNN.

The demand for more sophisticated Location Based Services (LBS) in terms of applications variety and accuracy is tripling every year since the emergence of the smartphone few years ago. Equally, smartphone manufacturers are mounting several wireless communication and localization technologies, inertial sensors as well as powerful processing capability to cater for such LBS applications. Hybrid of some wireless technologies is needed to provide seamless localization solutions and to improve accuracy, to reduce time to fix, and to reduce power consumption. The review of localization techniques/technologies of this emerging field is therefore important. This paper reviews the recent research-oriented and commercial localization solutions on smartphones. The focus of this paper is on the implementation challenges associated with utilizing these positioning solutions on Android-based smartphones. Furthermore, taxonomy of smartphone-location techniques is highlighted with a special focus on the detail of each technique and their hybridization. The comparative study of the paper compares the indoor localization techniques based on the accuracy, the utilized wireless technology, overhead and the used localization technique. The pursuit for achieving ubiquitous localization outdoors and indoors for critical LBS applications such as security and safety shall dominate future research efforts.

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Radio access technologies, such as Cellular vehicleto-everything (C-V2X) and dedicated short range communications (DSRC), have been used to support robust communication in connected vehicles' scenarios. However, existing studies mostly dealt with homogenous vehicular networks, where coexistence of different radio access technologies in vehicles are not considered. More precisely, such multi radio interface environments burden communications among vehicles. In this paper, we first review DSRC and C-V2X radio access technologies and existing packet relaying mechanisms that are specifically designed for homogenous or heterogeneous vehicular environments. We then present quality of service aware relaying algorithm (QR) that incorporates multi metric to prioritize dual interface vehicles (DVs) and provide robust communications among vehicles that are equipped with different radio access technologies. The simulation results confirm the superiority of the proposed QR in terms of message reception and relaying count as compared to the standard protocols.

In indoor/outdoor environments, special cares need to be given to locate smartphones which are used by most of the people. Locating or tracking is valuable for those people who are in dangerous falling-situations or they are used for shopping and billing services, inside the buildings. This tracking system needs a new positioning mechanism to offer very accurate services to the special needy people. To this end, this paper presents a hybrid mechanism to locate indoors smartphones; specifically Wi-Fi access-points signals are available. The proposed mechanism incorporates onboard Wi-Fi and sensor devices including gyroscopes and accelerometers to provide accurate indoor smart phone positioning. This paper proposes an integrated approach to offer indoor smartphone positioning. The purpose of the integrated approach is to fuse multi-technologies measurements on smartphones. The mechanism uses proximity-level (based on received-signal-strength 'RSS' measurement) technique between the smartphone and Wi-Fi access-points which they are exist in the vicinity. Then it combines this proximity measurement with uncertainty calculations from onboard dead-reckoning measurements using Extended-Kalman filter, which can provide seamless, low cost, and improve location accuracy significantly, especially when deep indoor. This means, in deep indoor, the approach can utilize only a single Wi-Fi access-points signals as well as using prior-estimate positions based on artificial conditions. The results from different trial experiments (using Android-based smartphones) show that around 2.5-m positioning accuracy can be obtained.