Springer Science and Business Media LLC

This paper addresses an optimal, cooperative output regulation problem for multi-agent systems with distributed denial of service attacks and unknown system dynamics. Unlike existing studies, the proposed solution is essentially a learning-based control strategy such that one can obtain a distributed control policy with internal models through online data and analyze the resilience of closed-loop systems, both without the precise knowledge of system dynamics in the state-space model. The efficiency of the proposed methodology is validated using computer simulations.

In China’s first lunar exploration project, Chang-E 1 (CE-1), a multi-channel microwave radiometer was aboard the satellite, with the purpose of measuring microwave brightness temperature from lunar surface and surveying the global distribution of lunar regolith layer thickness. In this paper, the primary 621 tracks of swath data measured by Chang-E 1 microwave radiometer from November 2007 to February 2008 are collected and analyzed. Using nearest neighbor interpolation based on the sun incidence angle in observations, global distributions of microwave brightness temperature from lunar surface at lunar daytime and nighttime are constructed. Using the three-layer model (the top dust-soil, regolith and underlying rock media) for microwave thermal emission of lunar surface, the measurements of brightness temperature and dependence upon latitude, frequency and FeO+TiO2 content, etc. are discussed. On the basis of the ground measurements at Apollo landing sites, the observed brightness temperature at these locations are validated and calibrated by numerical three-layer modeling. Using the empirical dependence of physical temperature upon the latitude verified by the measurements at Apollo landing sites, the global distribution of regolith layer thickness is then inverted from the brightness temperature data of CE-1 at 3 GHz channel. Those inversions at Apollo landing sites are compared with the Apollo in situ measurements. Finally, the statistical property of regolith thickness distribution is analyzed and discussed.

Medical image segmentation is of pivotal importance in computer-aided clinical diagnosis. Many factors, including noises, bias field effect, local volume effect, as well as tissue movement may affect the medical image, thus causing blurring or uneven characteristics when forming a picture. Such quality defects will inevitably impair the gray-scale difference between adjacent tissues and lead to insufficient segmentation or even leakage during tissue or organ segmentation. In the present investigation, a local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics (LARFS) was proposed. By combining segmentation algorithm principles for traditional region growing (RG) and robust feature statistics (RFS), the location and neighborhood image information of input seed point can be comprehensively analyzed by LARFS. Results show that, for different segmentation objects, under controlling the input parameter of growing factor within certain range, LARFS segmentation algorithm can adapt well to the regional geometric shape. And because the robust feature statistics is applied in the contour evolution process, LARFS algorithm is not sensitive to noises and not easily influenced by image contrast and object topology. Hence, the leakage and excessive segmentation effects are ameliorated with a smooth edge, and the accuracy can be controlled within the effective error range.

A large sensor network needs multiple base stations (BSs) to collect data efficiently. The design effort often aims at maximizing the network lifetime. The lifetime of a network depends on, besides battery capacity, factors such as node density, sampling period and the partitioning of the network for the multiple base stations. In this paper, we propose a stochastic approach to finding the lifetime of large sensor networks with multiple BSs and randomly scattered nodes. We begin by partitioning the network into regions by the Voronoi diagram method, one for each base station. We then propose a method to find the minimum transmission range needed for guaranteeing network connectivity. With nodes connected, we derive the traffic rate of nodes at specific distance from the base station. Based on the traffic rate and energy consumption model, the lifetime distribution of a region is calculated. Combining the results of individual regions, the network lifetime distribution is obtained. Case studies are presented and verified by computer simulation.

Personally identifiable information (PII) is widely used for many aspects such as network privacy leak detection, network forensics, and user portraits. Internet service providers (ISPs) and administrators are usually concerned with whether PII has been extracted during the network transmission process. However, most studies have focused on the extractions occurring on the client side and server side. This study proposes a static tainting extraction approach that automatically extracts PII from large-scale network traffic without requiring any manual work and feedback on the ISP-level network traffic. The proposed approach does not deploy any additional applications on the client side. The information flow graph is drawn via a tainting process that involves two steps: inter-domain routing and intra-domain infection that contains a constraint function (CF) to limit the “over-tainting”. Compared with the existing semantic-based approach that uses network traffic from the ISP, the proposed approach performs better, with 92.37% precision and 94.04% recall. Furthermore, three methods that reduce the computing time and the memory overhead are presented herein. The number of rounds is reduced to 0.0883%, and the execution time overhead is reduced to 0.0153% of the original approach.

Electrical tomography (ET) imaging, developed in the 1980s, has attracted much industrial and research attentions owing to its low cost, quick response, lack of radiation exposure, and non-intrusiveness compared to other tomography modalities. However, to date applications thereof have been limited owing to its low imaging resolution. The issue with space resolution in existing ET imaging reconstruction methods is that they employ a mathematical approach based on an ill-posed equation with inconsistent solutions. In this paper, we propose a novel ET imaging method based on a data-driven approach. By recovering the cluster structures hidden in the ET imaging process followed by the application of a fuzzy clustering algorithm to identify the cluster structures, there is no need to study the ill-posed mathematical formulation. The proposed method has been tested by means of three experiments, including image reconstructions of a human lung image and plastic rode shape, as well as two simulations executed on the Comsol platform. The results show that the proposed method can reconstruct ET images with much higher space resolution more quickly than the existing algorithms.

This paper considers a multi-cell downlink transmission system, where a reconfigurable intelligent surface (RIS) is deployed to support the connectivity of users in blind areas (i.e., at the cell edges). In order to reduce the instantaneous channel state information (CSI) acquisition overhead at each base station (BS), only statistical CSI is considered at both the BSs and the RIS. Particularly, each BS has only statistical CSI within its own cell. Under these assumptions, we first obtain the approximated ergodic spectral efficiency (SE) for each user. Then, we investigate the BSs beamforming and RIS phase shift joint optimization under different criteria. For the approximated ergodic weighted sum SE maximization criteria, a sub-optimal algorithm is proposed, based on complex circle manifold, to design the RIS phase shift while the statistical maximum ratio transmission beamforming is employed at each BS. To ensure fairness, we adopt the projected subgradient-based algorithm in order to optimize the approximated minimum ergodic user SE. Simulation results demonstrate that the approximated ergodic SE expression matches well with the exact expression, whilst the superiority of the weighted sum SE and fairness performance of the proposed algorithms are verified.