Hallen's integral equation for cylindrical antennas is modified to deal with finite gap excitation. Because it is based on more realistic modeling, the solution is more accurate, and the convergence is guaranteed. The new equation is written with a new excitation function dependent on the gap width. The moment method analysis is presented where the piecewise sinusoidal surface current functions are used in Galerkin's procedure. Total, external and internal current distributions can be determined. Numerical results for cylindrical antennas with wide variety of gap width and radius are shown, and are compared with the numerical results by the Pocklington type integral equation and those by measurement.

In this paper, we propose an inconsistency resolution method based on a new concept, insecure backtracking role mapping. By analyzing the role graph, we prove that the root cause of security inconsistency in distributed interoperation is the existence of insecure backtracking role mapping. We propose a novel and efficient algorithm to detect the inconsistency via finding all of the insecure backtracking role mappings. Our detection algorithm will not only report the existence of inconsistency, but also generate the inconsistency information for the resolution. We reduce the inconsistency resolution problem to the known Minimum-Cut problem, and based on the results generated by our detection algorithm we propose an inconsistency resolution algorithm which could guarantee the security of distributed interoperation. We demonstrate the effectiveness of our approach through simulated tests and a case study.

This paper proposes a security violation detection method for RBAC based interoperation to meet the requirements of secure interoperation among distributed systems. We use role mappings between RBAC systems to implement trans-system access control, analyze security violation of interoperation with role mappings, and formalize definitions of secure interoperation. A minimum detection method according to the feature of RBAC system in distributed environment is introduced in detail. This method reduces complexity by decreasing the amount of roles involved in detection. Finally, we analyze security violation further based on the minimum detection method to help administrators eliminate security violation.

Collective motion stems from the coordinated behaviors among individuals of crowds, and has attracted growing interest from the physics and computer vision communities. Collectiveness is a metric of the degree to which the state of crowd motion is ordered or synchronized. In this letter, we present a scheme to measure collectiveness via link prediction. Toward this aim, we propose a similarity index called superposed random walk with restarts (SRWR) and construct a novel collectiveness descriptor using the SRWR index and the Laplacian spectrum of a network. Experiments show that our approach gives promising results in real-world crowd scenes, and performs better than the state-of-the-art methods.

Automatic modulation recognition (AMR) plays a critical role in modern communication systems. Owing to the recent advancements of deep learning (DL) techniques, the application of DL has been widely studied in AMR, and a large number of DL-AMR algorithms with high recognition rates have been developed. Most DL-AMR algorithm models have high recognition accuracy but have numerous parameters and are huge, complex models, which make them hard to deploy on resource-constrained platforms, such as satellite platforms. Some lightweight and low-complexity DL-AMR algorithm models also struggle to meet the accuracy requirements. Based on this, this paper proposes a lightweight and high-recognition-rate DL-AMR algorithm model called Lightweight Densely Connected Convolutional Network (DenseNet) Long Short-Term Memory network (LDLSTM). The model cascade of DenseNet and LSTM can achieve the same recognition accuracy as other advanced DL-AMR algorithms, but the parameter volume is only 1/12 that of these algorithms. Thus, it is advantageous to deploy LDLSTM in resource-constrained systems.

A new numerical technique, termed the method of matrix-order reduction (MMOR), is developed for handling electromagnetic problems in this paper, in which the matrix equation resulted from a method-of-moments analysis is converted either to an eigenvalue equation or to another matrix equation with the matrix order in both cases being much reduced, and also, the accuracy of solution obtained by solving either of above equations is improved by means of a newly proposed generalized Jacobian iteration. As a result, this technique enjoys the advantages of less computational expenses and a relatively good solution accuracy as well. To testify this new technique, a number of wire antennas are examined and the calculated results are compared with those obtained by using the method of moments.

In this paper, we propose a jointly optimized predictive-adaptive partitioned block transform to exploit the spatial characteristics of intra residuals and improve video coding performance. Under the assumptions of traditional Markov representations, the asymmetric discrete sine transform (ADST) can be combined with a discrete cosine transform (DCT) for video coding. In comparison, the interpolative Markov representation has a lower mean-square error for images or regions that have relatively high contrast, and is insensitive to changes in image statistics. Hence, we derive an even discrete sine transform (EDST) from the interpolative Markov model, and use a coding scheme to switch between EDST and DCT, depending on the prediction direction and boundary information. To obtain an implementation independent of multipliers, we also propose an orthogonal 4-point integer EDST, which consists solely of adds and bit-shifts. We implement our hybrid transform coding scheme within the H.264/AVC intra-mode framework. Experimental results show that the proposed scheme significantly outperforms standard DCT and ADST. It also greatly reduces the blocking artifacts typically observed around block edges, because the new transform is more adaptable to the characteristics of intra-prediction residuals.