No abstract

With the increasingly extensive application of networking technology, security of network becomes significant than ever before. Encryption algorithm plays a key role in construction of a secure network system. However, the encryption algorithm implemented on resource-constrained device is difficult to achieve ideal performance. The issue of power consumption becomes essential to performance of data encryption algorithm. Many methods are proposed to evaluate the power consumption of encryption algorithms yet the authors do not ensure which one is effective. In this study, they give a comprehensive review for the methods of power evaluation. They then design a series of experiments to evaluate the effectiveness of three main types of methods by implementing several traditional symmetric encryption algorithms on a workstation. The experimental results show that external measurement and software profiling are more accurate than that of uninterruptible power system battery. The improvement of power consumption is 27.44 and 33.53% which implies the method of external measurement and software profiling is more effective in power consumption evaluation.

No abstract

Due to their ability to offer more comprehensive information than data from a single view, multi-view (e.g., multi-source, multi-modal, multi-perspective) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality is becoming more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN)-based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexibly in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness.