The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.

Recently, image forensics community has paid attention to the research on the design of effective algorithms based on deep learning technique. And facts proved that combining the domain knowledge of image forensics and deep learning would achieve more robust and better performance than the traditional schemes. Instead of improving algorithm performance, in this paper, the safety of deep learning based methods in the field of image forensics is taken into account. To the best of our knowledge, this is the first work focusing on this topic. Specifically, we experimentally find that the method using deep learning would fail when adding the slight noise into the images (adversarial images). Furthermore, two kinds of strategies are proposed to enforce security of deep learning-based methods. Firstly, a penalty term to the loss function is added, which is the 2-norm of the gradient of the loss with respect to the input images, and then an novel training method is adopt to train the model by fusing the normal and adversarial images. Experimental results show that the proposed algorithm can achieve good performance even in the case of adversarial images and provide a security consideration for deep learning-based image forensics.

In this paper, a Many Integrated Core Architecture (MIC) accelerated parallel method of moment (MoM) algorithm is proposed to solve electromagnetic problems in practical applications, where MIC means a kind of coprocessor or accelerator in computer systems which is used to accelerate the computation performed by Central Processing Unit (CPU). Three critical points are introduced in this paper in detail. The first one is the design of the parallel framework, which ensures that the algorithm can run on distributed memory platform with multiple nodes. The hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) programming model is designed to achieve the purposes. The second one is the out-of-core algorithm, which greatly breaks the restriction of MIC memory. The third one is the pipeline algorithm which overlaps the data movement with MIC computation. The pipeline algorithm successfully hides the communication and thus greatly enhances the performance of hybrid MIC/CPU MoM. Numerical result indicates that the proposed algorithm has good parallel efficiency and scalability, and twice faster performance when compared with the corresponding CPU algorithm.

A new fuzzy level set method (FLSM) based on the global search capability of quantum particle swarm optimization (QPSO) is proposed to improve the stability and precision of image segmentation, and reduce the sensitivity of initialization. The new combination of QPSO-FLSM algorithm iteratively optimizes initial contours using the QPSO method and fuzzy c-means clustering, and then utilizes level set method (LSM) to segment images. The new algorithm exploits the global search capability of QPSO to obtain a stable cluster center and a pre-segmentation contour closer to the region of interest during the iteration. In the implementation of the new method in segmenting liver tumors, brain tissues, and lightning images, the fitness function of the objective function of QPSO-FLSM algorithm is optimized by 10% in comparison to the original FLSM algorithm. The achieved initial contours from the QPSO-FLSM algorithm are also more stable than that from the FLSM. The QPSO-FLSM resulted in improved final image segmentation.

A numerical hybrid method for analyzing the wireless channel of Multiple-Input Multiple-Output (MIMO) communication system is proposed by combining of the method of moments (MoM) and the finite difference time domain (FDTD) method. The proposed method is capable of investigating a more practical MIMO wireless channel than the conventional methods, and CPU time is much less than that of the FDTD method in analysis of spatial statistical characteristics of received signals. Based on the channel transfer matrix obtained by the proposed method, the wall effect on indoor MIMO channel capacity are investigated with consideration of received power, Ricean K-factor and effective degrees of freedom (EDOF) of multipaths by changing the wall locations and material.

Phase synchronization is a crucial problem in Bistatic Synthetic Aperture Radar (BiSAR). As phase synchronization error and Doppler phase have nearly the same form, Doppler Centroid (DC) cannot be estimated with traditional method in BiSAR. A DC estimation method is proposed through phase-interferometry of Dual-channel direct signal. Through phase interferometry, phase synchronization error can be counteracted while Doppler phase is reserved and DC can be estimated from the reserved phase.

The range-dependence of clutter spectrum for forward-looking airborne radar strongly affects the accuracy of the estimation of clutter covariance matrix at the range under test, which results in poor clutter suppression performance if the conventional space-time adaptive processing (STAP) algorithms were applied, especially in the short range cells. Therefore, a new STAP algorithm with clutter spectrum compensation by utilizing knowledge-aided subspace projection is proposed to suppress clutter for forward-looking airborne radar in this paper. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then by decomposing the corresponding space-time covariance matrix to calculate the clutter subspace projection matrix which is applied to transform the secondary range samples so that the compensation of clutter spectrum for forward-looking airborne radar is accomplished. After that the conventional STAP algorithm can be applied to suppress clutter in the range under test. The proposed method is compared with the sample matrix inversion (SMI) and the Doppler Warping (DW) methods. The simulation results show that the proposed STAP method can effectively compensate the clutter spectrum and mitigate the range-dependence significantly.

Aiming at the problems of traditional algorithms that require high prior knowledge and weak timeliness, this paper proposes an emergency communication network topology planning method based on deep reinforcement learning. Based on the characteristics of the emergency communication network, and drawing on chess, we map the node layout and topology planning problems in the network planning to chess game problems; The two factors of network coverage and connectivity are considered to construct the evaluation criteria for network planning; The method of combining Monte Carlo tree search and self-game is used to realize network planning sample data generation, and the network planning strategy network and value network structure based on residual network are designed. On this basis, the model was constructed and trained based on Tensorflow library. Simulation results show that the proposed planning method can effectively implement intelligent planning of network topology, and has excellent timeliness and feasibility.

Effects of wall material on the channel capacity of an indoor multiple input multiple output (MIMO) system are investigated using a hybrid technique of the method of moments (MoM) and the finite difference time domain (FDTD) method with consideration of the Ricean K factor and the effective degrees of freedom (EDOF) of multiple paths.

The effects of wall reflection on indoor MIMO channel capacity are statistically investigated with consideration of the average received power, the effective degrees of freedom (EDOF) of multipaths and the eigenvalues of transfer channel covariance matrix. It is found that the stronger wall reflection can lead to higher MIMO channel capacity.

This paper creates a new hybrid Space-Time Adaptive Processing (STAP) algorithm that combines Direct Data Domain (D3) method and Space-Time Multiple-Beam (STMB) algorithm, which can effectively suppress discrete interference in the nonhomogeneous clutter environment. In the proposed hybrid algorithm, the D3 method is applied to process the discrete interference in the primary range cell, and the residual clutter is suppressed by the STMB algorithm. The performance of the proposed hybrid STAP algorithm is demonstrated in a simulation.

A sanitizable signature scheme is a signature scheme which, after the signer generates a valid signature of a message, allows a specific entity (sanitizer) to modify the message for hiding several parts. Existing sanitizable signature schemes require the message to be divided into pre-defined blocks before signing so that each block can be sanitized independently. However, there are cases where the parts of the message which are needed to be sanitized can not be determined in the time of signing. Thus, it is difficult to decide the partition of the blocks in such cases. Since the length of the signature is usually proportional to the number of blocks, signing every bit independently will make the signature too long. In this paper, we propose a solution by introducing a new concept called sequential bitwise sanitizable signature schemes, where any sequence of bits of the signed document can be made sanitizable without pre-defining them, and without increasing the length of signature. We also show that a one-way permutation suffices to get a secure construction, which is theoretically interesting in its own right, since all the other existing schemes are constructed using stronger assumptions.

The double threshold detecting strategy is widely used for spatially distributed target detection in practical systems. However, the detector is limited in terms of robustness and effectiveness. In this paper, an improved double threshold detector is proposed that avoids these shortcomings. In the proposed detector, the energy in range cells that exceed the first threshold is accumulated and then the output of the accumulator is compared with the second threshold for detection. The threshold selection strategy is derived to guarantee the constant false-alarm rate (CFAR) property. A simulation shows that the proposed detector is superior to the conventional approach in terms of both robustness and effectiveness.

The effect of wall and indoor scatterers on the indoor multiple input multiple output (MIMO) communication system is investigated by using the hybrid technique of finite difference time domain (FDTD) method and method of moments (MoM). MIMO channel capacity with the wall reflection is investigated with consideration of the eigenvalue of channel covariance matrix, the received power and the effective multipaths of MIMO system. It is found that the stronger side wall reflection can lead to the higher MIMO channel capacity. MIMO system with indoor scatterers is also analyzed and compared with the line of sight (LOS) indoor MIMO system. It is found that the scatterer material has different effect on the received power and the effective multipaths of MIMO system.