Recently, the weakness of existing compressive sensing process from the perspective of the chosen-plaintext attack is discovered. Some algorithms directly use Gaussian matrix as the measurement matrix to do linear dimension reduction projection, which will fail to resist chosen-plaintext attack. To enhance the security and performance of compressive sensing process, double random phase encoding based block compressive sensing is designed, which is chaos-based random phase encoding in fractional Fourier domain for each image block. Moreover, image encryption method using DRPE-based block compressive sensing-combined random phase encoding is proposed. The experimental results demonstrate that the proposed encryption method not only achieves high security level but also has better reconstruction quality compared with other existing encryption methods. Keywords: Securely compressive sensing, fractional Fourier transform, random phase encoding
The most weakness link in credible monitoring is that how to process multidimensional dynamic behavior data effectively. System behavior monitoring often needs to deal with different kinds of behavior data and those data can adopt status snapshot in multi-dimensional vector form to express. Obviously, data has strong useful knowledge information, which is regarded as a kind of classification ability. So we need to finish the mapping and classification between a variety of network behavior snapshot and dependable level. This paper introduces on network state snapshot owning the characteristics of high dimension, heterogeneous and dynamic and uses the theory of interval intuitionistic fuzzy to judge credible degree in the system and generate behavior quality trust level of nodes.
As a promising way to the certain area surveillance, the application of wireless sensor network on the unknown subsea observation is becoming more significant. After placing the sensor nodes, each sensor node will generate a common communication frame and choose the next hop neighbor node randomly, which could results in redundant energy consumption. In this paper, a data transmission strategy is proposed including routing scheme, frame format and the corresponding topology structure. By considering the transmitting distance, energy consumption and transmission probability, the introduced strategy can reduce the redundant energy consumption, balance the network payload and prolong the subsea observation network lifetime. The simulation results show that the strategy is more efficient than the common one.
Compressed sensing (CS) is a new developed theoretical framework for information acquisition and processing that breaks through the conventional Nyquist sampling limit. This paper proposes a sparse representation schemes based on principal component analysis (PCA) for CS that will be used for hyperspectral images compressed sampling. This scheme employs the prediction transform matrix to remove the correlations among successive hyperspectral measurement vectors. Experiment processes using the hyperspectral image from Earth Observing One (EO-1), and it shows a desired result both at reconstruction and denoising.
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