The global stability analysis for the mathematical model of an infectious disease is discussed here. The endemic equilibrium is shown to be globally stable by using a modification of the Volterra–Lyapunov matrix method. The basis of the method is the combination of Lyapunov functions and the Volterra–Lyapunov matrices. By reducing the dimensions of the matrices and under some conditions, we can easily show the global stability of the endemic equilibrium. To prove the stability based on Volterra–Lyapunov matrices, we use matrices with the symmetry properties (symmetric positive definite). The results developed in this paper can be applied in more complex systems with nonlinear incidence rates. Numerical simulations are presented to illustrate the analytical results.

The COVID‐19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer‐aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID‐19. Here, a method based on fuzzy C‐ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X‐ray COVID‐19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state‐of‐the‐art methods, including FOMPA, MID, and 4S‐DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F1‐score rate gives the uppermost value compared to the others.

Aggregated Internet of Things (IoT) communication aims to use core network resources efficiently by providing cellular access to a group of IoT devices over the same subscriber identity. Leveraging the low data rates and long data sending intervals of IoT devices, several of the IoT devices in the same serving area of the core network are grouped together and take turns to send their data to their servers without causing overlaps in their communication. In this paper, we take this approach further and benefiting from the flexibility in data sending schedules, we aim to increase savings in cellular resources by shifting (delaying or performing earlier) the regular traffic patterns of IoT devices slightly. To this end, we consider two different traffic shifting models, namely, consistent and inconsistent shifting. We first solve the optimal aggregation of IoT devices under each model by using Integer Linear Programming (ILP). In order to avoid the high complexity of ILP solution, we then develop a heuristic based solution that runs in polynomial time. Through simulations, we show that heuristic based solution provides close to optimal results in various scenarios and shifting based aggregated communication offers more resource optimization (i.e., smaller number of bearers needed to connect all IoT devices) than the aggregated communication with no shifting.

Watermarking refers to a way of protecting copyright. From one perspective, watermarking algorithms are categorized as key-based and non-key-based. In the key-based watermarking, targeted blocks are selected by using a key to embed the watermark bits; in non-key-based watermarking on the other hand, watermark bits are embedded in a predefined position of the host image. In general, key-based schemes are expected to be more robust against targeted removal attacks. In this paper we propose two attacks against two key-based watermarking algorithm, which are based on the QR decomposition. Although the key based watermarking algorithms are supposed to be more robust, we show that they are vulnerable to our proposed attacks. Our experimental results indicate that the existence of a key is also not a guarantee for the safety of watermarking methods.

Cellular-connected Unmanned Aerial Vehicles (UAVs) need consistent cellular network connectivity to effectively accomplish their designated missions. However, when navigating through regions with partial coverage, such as rural areas, the task of planning the flight paths for these UAV missions becomes notably intricate. Algorithms designed to solve this issue require significant computational resources, making them infeasible for active deployment where an algorithm must run in real time using small compute power. Furthermore, these algorithms exponentially scale in run-time with respect to the number of UAVs being considered. To tackle this problem, we model the parameter space as a discrete grid-world, enable collaboration between drones, and gather supervised data from nonlinear programming and unsupervised data from a simulated version of the environment with associated rewards. We then train a Deep Neural Network (DNN) on this data and approximate optimal results by combining imitation and reinforcement learning methods. This DNN can successfully be deployed at fast speeds using relatively small computational power and can generalize to unseen maps where drone collaboration can be used to reduce mission time. By using the results of a network trained on supervised data as a guiding hand during training, our reinforcement learning approach achieves results better than either method in isolation.

International mobile subscriber identity (IMSI) sharing-based aggregated communication aims to connect multiple Internet of Things (IoT) devices to the mobile operator’s core network over the same subscriber line. IoT devices with low data rates and long data sending intervals are first grouped together and assigned the same subscriber identity. They then take turns to perform their data exchanges using the same cellular connection, yielding huge savings in resource (e.g., number of active bearers) usage. Current solutions however do not consider different device traffic characteristics, the flexibility in traffic patterns, and dynamic network environments where new IoT devices join and existing ones leave the network. In this article, we study the problem of the grouping of IoT devices that will share the same subscriber identity based on their traffic patterns which can also be slightly shifted. We also study the efficient regrouping of these devices as the set of devices in the network changes. We first solve the optimal grouping and traffic aggregation problem for the initial and updated network states using integer linear programming (ILP). Then, to avoid the high complexity of ILP solutions, we develop heuristic-based solutions. Through extensive simulations, we show that heuristic-based algorithms can provide close to optimal ILP-based results while running much faster. The results also show that shifting-based grouping provides more resource saving compared to no-shifting-based aggregation and the proposed solution for dynamic environments can maintain the resource saving with a much lower complexity.