Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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In this paper, a robotic system dedicated to remote wrist rehabilitation is proposed as an Internet of Things (IoT) application. The system offers patients home rehabilitation. Since the physiotherapist and the patient are on different sites, the system guarantees that the physiotherapist controls and supervises the rehabilitation process and that the patient repeats the same gestures made by the physiotherapist. A human-machine interface (HMI) has been developed to allow the physiotherapist to remotely control the robot and supervise the rehabilitation process. Based on a computer vision system, physiotherapist gestures are sent to the robot in the form of control instructions. Wrist range of motion (RoM), EMG signal, sensor current measurement, and streaming from the patient’s environment are returned to the control station. The various acquired data are displayed in the HMI and recorded in its database, which allows later monitoring of the patient’s progress. During the rehabilitation process, the developed system makes it possible to follow the muscle contraction thanks to an extraction of the Electromyography (EMG) signal as well as the patient’s resistance thanks to a feedback from a current sensor. Feature extraction algorithms are implemented to transform the EMG raw signal into a relevant data reflecting the muscle contraction. The solution incorporates a cascade fuzzy-based decision system to indicate the patient’s pain. As measurement safety, when the pain exceeds a certain threshold, the robot should stop the action even if the desired angle is not yet reached. Information on the patient, the evolution of his state of health and the activities followed, are all recorded, which makes it possible to provide an electronic health record. Experiments on 3 different subjects showed the effectiveness of the developed robotic solution.

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This paper proposes a new systolic array architecture to perform division operations over GF(2 m ) based on the modified Stein's algorithm. The systolic structure is extracted by applying a regular approach to the division algorithm. This approach starts by obtaining the dependency graph for the intended algorithm and assigning a time value to each node in the dependency graph using a scheduling function and ends by projecting several nodes of the dependency graph to a processing element to constitute the systolic array. The obtained design structure has the advantage of reducing the number of flip-flops required to store the intermediate variables of the algorithm and hence reduces the total gate counts to a large extent compared to the other related designs. The analytical results show that the proposed design outperforms the related designs in terms of area (at least 32% reduction in area) and speed (at least 60% reduction in the total computation time) and has the lowest AT complexity that ranges from 80% to 94%.

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