Search

The subarray-level multibeam structure is advantageous for minimizing system complexity and enhancing multi-target detection and estimation performance. Furthermore, the subarray configuration plays a major role in determining the multibeam antenna pattern parameters. Therefore, we propose a subarray partition technique based on the tournament algorithm to enable spatial multibeam synthesis. In this paper, subarray configuration and excitation are alternately optimized through multiple rounds of tournament iterations. Each iteration calculates subarray excitations using convex optimization methods to fulfill requirements for the main radiation direction and low sidelobes, among other performance criteria. At the same time, excitations for each unassigned array element are calculated within each subarray separately. Simulation results for linear arrays demonstrate the proposed algorithm is not only suitable for linear arrays but also applicable to other array configurations, such as circular ring arrays and cylindrical arrays. And the proposed algorithm is effective in subarray periodicity, suppressing sidelobe levels, and allocating resources based on priority.

Future broadband satellite communication (SatCom) systems require a high throughput of data transmission, which calls for operation at higher frequency bands. Adaptive coding and modulation (ACM) technology has been considered as a means to obtain improved performance further at these frequencies. However, the ACM protocols in current DVB-S2 and DVB-S2(X) standards do not take the effects that arise from the non-linear link with memory into account, which is generated by the cascade of a high-power amplifier (HPA) and digital root raised cosine (RRC) filters. This paper first reveals the non-linear characteristics of the SatCom link, and transfers this into an equivalent full-linear link with an additive noise source thanks to the pre-distortion algorithm. Using this equivalent modeling, an approach of selecting/adapting the optimum coding, modulation scheme and associated system configurations for a given channel fading level is proposed. Both simulations and experimental test-bed performance are presented to validate the proposed method.

In this paper, the concept and recent development of exploiting frequency diverse array (FDA) and its variants for the physical-layer wireless security have been revisited and carefully examined. Following rigorous analytical derivation and illustrative simulations, the authors realize that the investigations performed in some recent works overlooked one critical issue facing the real-world applications, and system models established and used before were based on a limited assumption, i.e. that the legitimate and eavesdropping users at different ranges sample the signal waveforms at the same time instant. The limitation of this assumption results in their studies inconclusive. The authors aim to take the first step to divert research efforts and rectify the previous incomplete analyses. The authors argue that in the current technology base the FDA cannot secure a free-space wireless transmission in range domain, because the previously claimed `secure reception region' propagates in range domain as time elapses.

Remote sensing images are primary data sources for land use classification. High spatial resolution images enable more accurate analysis and identification of land cover types. However, a higher spatial resolution also brings new challenges to the existing classification methods. In the low-level feature spaces of remote sensing images, it is difficult to improve classification performance by modifying classifiers. Probabilistic topic models can connect low-level features and high-level semantics of remote sensing images. Latent Dirichlet allocation (LDA) models are representatives of probabilistic topic models. However, at present, probabilistic topic models are mainly adopted for scene classification and image retrieval in remote sensing image analysis only. In this study, multiscale segmentation was employed to construct bag-of-words (BoW) representations of high-resolution images. The segmented patches were then utilized as “image documents.” A structural topic model was used with an LDA model to import spatial information from the image documents at two levels: topical prevalence and topical content in the form of covariates. In this way, latent topic features in image documents can be more accurately deduced. The proposed method showed more satisfactory classification performance than standard LDA models and demonstrated a certain degree of robustness against the changes in the segmentation scale. Acknowledgement for the data support from “Yangtze River Delta Science Data Center, National Earth System Science Data Center, National Science & Technology Infrastructure of China (http://nnu.geodata.cn:8008)”.

Topologies are crucial for the cadastral data model. Considering the development of 3-D cadaster and temporal cadaster, 0-D–4-D multidimensional cadastral objects should be considered in the cadastral data model. There are few data models to support 0-D–4-D multidimensional cadastral objects and their topological relations, and some existing models represent the topological relations using complicated methods. This paper presents a multidimensional cadastral topological data model (MDCTDM). This model represents the topological relations of all cadastral objects simply and efficiently using a single basic element to associate the cadastral objects of each dimension in the same manner. Based on the generalized map, we extend the cell-tuple structure and propose an object-tuple structure for the MDCTDM. We design the data structure for the MDCTDM and implement it in a relational database. In this database, the topological relations of cadastral objects can be easily queried by using simple structured query language (SQL) statements. To verify the operations of the MDCTDM in the database, we describe a case of the insert operation. The case demonstrates that the operation can be implemented easily by SQL statements.

In this paper, we investigate the reliability and security performance of cooperative multi-relay systems, where both source and relay nodes are energy-constrained nonlinear energy harvesters, scavenging energy from a power beacon nearby. Our analysis is based on practical model since residual hardware impairments (RHIs) and channel estimation errors (CEEs) are considered. Aiming at improving the system efficiency, three representative relay selection strategies are considered: 1) random relay selection (RRS); 2) suboptimal relay selection (SRS); and 3) optimal relay selection (ORS). To characterize the security performance of the considered strategies, we derive closed-form analytical expressions of the reliability and security in terms of outage probability (OP) and intercept probability (IP). We further discuss the asymptotic expressions and scaling laws of OP with the number of relays. The IP is analyzed for non-colluding and colluding scenarios. The numerical results illustrate that: 1) There is a tradeoff between reliability and security, that is when the outage constraint is relaxed, the IP can be enhanced, and vice versa; ii) The outage performance of the ORS and SRS schemes outperform RRS, indicating that relay selection can enhance reliability performance; iii) There are error floors for the OP due to the CEEs; iv) Colluding eavesdroppers can enhance eavesdropping attacks by sharing their intercepted information; and v) Although RHIs and CEEs have deleterious effects on the OP, they can protect the information transmission against eavesdropping attacks.

This paper addresses the constrained multi-objective optimization problem of sparse conformal arrays designing. The objective of array synthesis is to find an optimal element arrangement on a conformal surface and its associated excitation strategy, which generate the main radiation beam along a pre-selected spatial direction with maximum gain and, simultaneously, suppress sidelobe levels elsewhere. A hybrid algorithm particle swarm optimization (PSO)-second-order cone programming (SOCP), comprising of PSO and SOCP, each for a dedicated purpose, is proposed to fulfill this task in this paper. More specifically, the PSO algorithm is introduced to optimize sparse conformal array element positions, whereas the SOCP is applied to seek optimal excitation coefficients for each array layout obtained. After extensive simulation with the examples of sparse circular array and sparse conical arrays, we can find that our proposed method can synthesize better radiation patterns with regard to peak sidelobe levels, compared with those obtained through other traditional algorithms.

This paper presents a thorough experimental study on key generation principles, i.e., temporal variation, channel reciprocity, and spatial decorrelation, through a testbed constructed by using wireless open-access research platform. It is the first comprehensive study through: 1) carrying out a number of experiments in different multipath environments, including an anechoic chamber, a reverberation chamber, and an indoor office environment, which represents little, rich, and moderate multipath, respectively; 2) considering static, object moving, and mobile scenarios in these environments, which represents different levels of channel dynamicity; and 3) studying two most popular channel parameters, i.e., channel state information and received signal strength. Through results collected from over a hundred tests, this paper offers insights to the design of a secure and efficient key generation system. We show that multipath is essential and beneficial to key generation as it increases the channel randomness. We also find that the movement of users/objects can help introduce temporal variation/randomness and help users reach an agreement on the keys. This paper complements existing research by experiments constructed by a new hardware platform.

Understanding the actions of other people is a key component of social interaction. This paper used an electroencephalography and functional near infrared spectroscopy (EEG-fNIRS) bimodal system to investigate the temporal-spatial features of action intention understanding. We measured brain activation while participants observed three actions: 1) grasping a cup for drinking; 2) grasping a cup for moving; and 3) no meaningful intention. Analysis of EEG maximum standardized current density revealed that brain activation transitioned from the left to the right hemisphere. EEG-fNIRS source analysis results revealed that both the mirror neuron system and theory of mind network are involved in action intention understanding, and the extent to which these two systems are engaged appears to be determined by the clarity of the observed intention. These findings indicate that action intention understanding is a complex and dynamic process.