The dissemination of countermeasures is widely recognized as one of the most effective strategies of inhibiting malware propagation, and the study of general countermeasure and infection has an important and practical significance. On this point, a dynamical model incorporating generic nonlinear countermeasure and infection probabilities is proposed. Theoretical analysis shows that the model has a unique equilibrium which is globally asymptotically stable. Accordingly, a real network based on the model assumptions is constructed, and some numerical simulations are conducted on it. Simulations not only illustrate theoretical results but also demonstrate the reasonability of general countermeasure and infection.

In this correspondence, we introduce a new imaging method to obtain high-resolution (HR) images. The image acquisition is performed in two stages, compressive measurement and optimization reconstruction. In order to reconstruct HR images by a small number of sensors, compressive measurements are made. Specifically, compressive measurements are made by a low-resolution (LR) camera with randomly fluttering shutter, which can be viewed as a moving random exposure pattern. In the optimization reconstruction stage, the HR image is computed by different models according to the prior knowledge of scenes. The proposed imaging method offers a new way of acquiring HR images of essentially static scenes when the camera resolution is limited by severe constraints such as cost, battery capacity, memory space, transmission bandwidth, etc. and when the prior knowledge of scenes is available. The simulation results demonstrate the effectiveness of the proposed imaging method.