An effective operation mode and a space-time synchronization technique for the spaceborne/airborne hybrid bistatic synthetic aperture radar (SA-BSAR) using sources of opportunity are presented. Our motivation lies in the fact that the existing approaches in the literature, where the transmitter antenna must be steered, can only be used in the hybrid bistatic SAR systems with cooperative transmitter. The presented mode is to widen the receiving beam for the purpose to increase the scene extension in azimuth. The inspiration comes from the much shorter receiving distance as compared to the one in mono-static spaceborne SAR. This means that the receiving gain can be significantly reduced to provide the same signal-to-noise ratio (SNR) with respect to the mono-static case. The feasibility of the wide-beam mode is first preliminarily verified by a quantitative analysis of SNR and a demonstration that the pulse repetition frequency (PRF) used in the spaceborne illuminator can easily satisfy the PRF constraints of the SA-BSAR. The influence on the azimuth ambiguity to signal ratio (AASR) is also discussed and the corresponding broadening factor of the maximum allowable for receiver beamwidth is subsequently derived. Afterwards, the formulae for calculating the overlap time, the scene extension and the azimuth resolution are deduced. As there are no grating lobes in satellite antenna pattern since the non-cooperative illuminator normally operates in the side-looking mode, an existing technique for the space-time synchronization in cooperative hybrid systems can not be directly applied. The modification performed and its underlying principle are presented in detail. The simulation results demonstrate the effectiveness of the wide-beam mode, and show that in most cases a useful scene extension (on the order of at least 1 km) can be achieved with a roughly equivalent azimuth resolution as compared to the one in mono-static spaceborne SAR. In some cases, explicit measures to suppress the azimuth ambiguity must be taken to achieve the expected scene extension.

Recently, channel-aware steganography has been presented for high security. The corresponding selection-channel-aware (SCA) detecting algorithms have also been proposed for improving the detection performance. In this paper, we propose a novel detecting algorithm of JPEG steganography, where the embedding probability and block evaluation are integrated into the new probability. This probability can embody the change due to data embedding. We choose the same high-pass filters as maximum diversity cascade filter residual (MD-CFR) to obtain different image residuals and a weighted histogram method is used to extract detection features. Experimental results on detecting two typical steganographic methods show that the proposed method can improve the performance compared with the state-of-art methods.

Metal particulate tape is one of the most advanced tape media to offer excellent performance at high recording densities. An accurate micromagnetic model of the metal particulate tape has been developed to analyze the magnetic properties of MP tapes. Both particle size distributions and orientation distribution are included in the model, and the magnetostatic interactions among particles are accurately calculated with the shape of ellipsoids. A partial mean field approximation applied in the calculation is proved to be effective by M-H loop analysis.

The paper employed an Alexnet, which is a deep learning framework, to automatically diagnose the damages of wind power generator blade surfaces. The original images of wind power generator blade surfaces were captured by machine visions of a 4-rotor UAV (unmanned aerial vehicle). Firstly, an 8-layer Alexnet, totally including 21 functional sub-layers, is constructed and parameterized. Secondly, the Alexnet was trained with 10000 images and then was tested by 6-turn 350 images. Finally, the statistic of network tests shows that the average accuracy of damage diagnosis by Alexnet is about 99.001%. We also trained and tested a traditional BP (Back Propagation) neural network, which have 20-neuron input layer, 5-neuron hidden layer, and 1-neuron output layer, with the same image data. The average accuracy of damage diagnosis of BP neural network is 19.424% lower than that of Alexnet. The point shows that it is feasible to apply the UAV image acquisition and the deep learning classifier to diagnose the damages of wind turbine blades in service automatically.

Cloud computing has attracted a growing number of enterprises to move their business to the cloud because of the associated operational and cost benefits. Improving availability is one of the major concerns of cloud application owners because modern applications generally comprise a large number of components and failures are common at scale. Fault tolerance enables an application to continue operating properly when failure occurs, but fault tolerance strategy is typically employed for the most important components because of financial concerns. Therefore, identifying important components has become a critical research issue. To address this problem, we propose a failure-sensitive structure-based component ranking approach (FSCRank), which integrates component failure impact and application structure information into component importance evaluation. An iterative ranking algorithm is developed according to the structural characteristics of cloud applications. The experimental results show that FSCRank outperforms the other two structure-based ranking algorithms for cloud applications. In addition, factors that affect application availability optimization are analyzed and summarized. The experimental results suggest that the availability of cloud applications can be greatly improved by implementing fault tolerance strategy for the important components identified by FSCRank.