While many techniques have been developed for the design of different types of antennas, such as wire antenna, patch antenna, lenses, and reflectors, these cannot be said general-purpose strategies for the synthesis and design of antennas to achieve the performance characteristics specified by users. Recently, there has been an increasing need for the development of antenna design techniques because of the advent of 5G and a variety of space, defense, biological, and similar applications, for which a robust and general-purpose design tool is not to be developed. The main objective of this study is to take a look at antenna design from the field manipulation point of view, which has the potential to partially fulfill this need. We review the existing field manipulation techniques, including field transformation methods based on Maxwell’s and wave equations, point out some limitations of these techniques, and then present ways to improve the performance of these methods. Next, we introduce an alternative approach for field manipulation based on two-dimensional (2D) metasurfaces, and present laws of the generalized reflection and refraction that are based on 2D surface electromagnetics. Then, we explore how to overcome the limitations of conventional reflection and refraction processes that are strictly bounded by the critical angle. Finally, we provide some application examples of field manipulation methods in the antenna design, with a view on developing a general-purpose strategy for antenna design for future communication.
A multi-band multi-antenna system has become an important trend in the development of mobile communication systems. However, strong mutual coupling tends to occur between antenna elements with a small space, distorting array antennas’ performance. Therefore, in the multiple-input multiple-output (MIMO) antenna system, high isolation based on miniaturization of the antenna array has been pursued. We study in depth the methods of decoupling between antenna elements. Reasons for the existence of mutual coupling and advantages of mutual coupling reduction are analyzed. Then the decoupling methods proposed in recent works are compared and analyzed. Finally, we propose a metasurface consisting of double-layer short wires, which can be applied to improve the port isolation of antennas arranged along the H-plane and E-plane. Results show that the proposed metasurface has good decoupling effect on a closely placed antenna array.
Future communications will provide higher transmission rates and higher operating frequencies. In addition, agile beam tracking will be an inevitable trend in technology development. The terahertz retrodirective antenna array proposed and discussed in this paper can be a better solution for agile beam tracking. The array receives a 40-GHz navigation signal and accurately retransmits a 120-GHz beam in the direction of the arrival wave. Simulation results indicate that the proposed array with a stacked sandwich structure has realized the tracking of the received wave. The scanning radiation pattern shows that the array gain is 23.87 dB at 19.9° when the incident angle is 20° with a relative error of only 0.5%, meaning that there is a lateral error of only 8.7 m at a transmission distance of 5 km.
Graph is a significant data structure that describes the relationship between entries. Many application domains in the real world are heavily dependent on graph data. However, graph applications are vastly different from traditional applications. It is inefficient to use general-purpose platforms for graph applications, thus contributing to the research of specific graph processing platforms. In this survey, we systematically categorize the graph workloads and applications, and provide a detailed review of existing graph processing platforms by dividing them into generalpurpose and specialized systems. We thoroughly analyze the implementation technologies including programming models, partitioning strategies, communication models, execution models, and fault tolerance strategies. Finally, we analyze recent advances and present four open problems for future research.
In the past several years, various visual object tracking benchmarks have been proposed, and some of them have been used widely in numerous recently proposed trackers. However, most of the discussions focus on the overall performance, and cannot describe the strengths and weaknesses of the trackers in detail. Meanwhile, several benchmark measures that are often used in tests lack convincing interpretation. In this paper, 12 frame-wise visual attributes that reflect different aspects of the characteristics of image sequences are collated, and a normalized quantitative formulaic definition has been given to each of them for the first time. Based on these definitions, we propose two novel test methodologies, a correlation-based test and a weight-based test, which can provide a more intuitive and easier demonstration of the trackers’ performance for each aspect. Then these methods have been applied to the raw results from one of the most famous tracking challenges, the Video Object Tracking (VOT) Challenge 2017. From the tests, most trackers did not perform well when the size of the target changed rapidly or intensely, and even the advanced deep learning based trackers did not perfectly solve the problem. The scale of the targets was not considered in the calculation of the center location error; however, in a practical test, the center location error is still sensitive to the targets’ changes in size.
Potential behavior prediction involves understanding the latent human behavior of specific groups, andcan assist organizations in making strategic decisions. Progress in information technology has made it possible to acquire more and more data about human behavior. In this paper, we examine behavior data obtained in realworld scenarios as an information network composed of two types of objects (humans and actions) associated with various attributes and three types of relationships (human-human, human-action, and action-action), which we call the heterogeneous behavior network (HBN). To exploit the abundance and heterogeneity of the HBN, we propose a novel network embedding method, human-action-attribute-aware heterogeneous network embedding (a4HNE), which jointly considers structural proximity, attribute resemblance, and heterogeneity fusion. Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.
Online encyclopedias such as Wikipedia provide a large and growing number of articles on many topics. However, the content of many articles is still far from complete. In this paper, we propose EncyCatalogRec, a system to help generate a more comprehensive article by recommending catalogs. First, we represent articles and catalog items as embedding vectors, and obtain similar articles via the locality sensitive hashing technology, where the items of these articles are considered as the candidate items. Then a relation graph is built from the articles and the candidate items. This is further transformed into a product graph. So, the recommendation problem is changed to a transductive learning problem in the product graph. Finally, the recommended items are sorted by the learning-to-rank technology. Experimental results demonstrate that our approach achieves state-of-the-art performance on catalog recommendation in both warm- and cold-start scenarios. We have validated our approach by a case study.
Keyword search is an alternative for structured languages in querying graph-structured data. A result to a keyword query is a connected structure covering all or part of the queried keywords. The textual coverage and structural compactness have been known as the two main properties of a relevant result to a keyword query. Many previous works examined these properties after retrieving all of the candidate results using a ranking function in a comparative manner. However, this needs a time-consuming search process, which is not appropriate for an interactive system in which the user expects results in the least possible time. This problem has been addressed in recent works by confining the shape of results to examine their coverage and compactness during the search. However, these methods still suffer from the existence of redundant nodes in the retrieved results. In this paper, we introduce the semantic of minimal covered r-clique (MCCr) for the results of a keyword query as an extended model of existing definitions. We propose some efficient algorithms to detect the MCCrs of a given query. These algorithms can retrieve a comprehensive set of non-duplicate MCCrs in response to a keyword query. In addition, these algorithms can be executed in a distributive manner, which makes them outstanding in the field of keyword search. We also propose the approximate versions of these algorithms to retrieve the top-k approximate MCCrs in a polynomial delay. It is proved that the approximate algorithms can retrieve results in two-approximation. Extensive experiments on two real-world datasets confirm the efficiency and effectiveness of the proposed algorithms.
A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code (STBC) based multiple-input multiple-output (MIMO) systems. We exploit the zero-forcing equalization technique to modify the typical average likelihood ratio test (ALRT) function. The proposed ALRT function has a low computational complexity compared to existing ALRT functions for MIMO systems classification. The proposed approach is analyzed for blind channel scenarios when the receiver has imperfect channel state information (CSI). Performance analysis is carried out for scenarios with different numbers of antennas. Alamouti-STBC systems with 2 ×2 and 2 ×1 and space-time transmit diversity with a 4 ×4 transmit and receive antenna configuration are considered to verify the proposed approach. Some popular modulation schemes are used as the modulation test pool. Monte-Carlo simulations are performed to evaluate the proposed methodology, using the probability of correct classification as the criterion. Simulation results show that the proposed approach has high classification accuracy at low signal-to-noise ratios and exhibits robust behavior against high CSI estimation error variance.
Parameter estimation of the 2R-1C model is usually performed using iterative methods that require high-performance processing units. Consequently, there is a strong motivation to develop less time-consuming and more power-efficient parameter estimation methods. Such low-complexity algorithms would be suitable for implementation in portable microcontroller-based devices. In this study, we propose the quadratic interpolation non-iterative parameter estimation (QINIPE) method, based on quadratic interpolation of the imaginary part of the measured impedance, which enables more accurate estimation of the characteristic frequency. The 2R-1C model parameters are subsequently calculated from the real and imaginary parts of the measured impedance using a set of closed-form expressions. Comparative analysis conducted on the impedance data of the 2R-1C model obtained in both simulation and measurements shows that the proposed QINIPE method reduces the number of required measurement points by 80% in comparison with our previously reported non-iterative parameter estimation (NIPE) method, while keeping the relative estimation error to less than 1% for all estimated parameters. Both non-iterative methods are implemented on a microcontroller-based device; the estimation accuracy, RAM, flash memory usage, and execution time are monitored. Experiments show that the QINIPE method slightly increases the execution time by 0.576 ms (about 6.7%), and requires 24% (1.2 KB) more flash memory and just 2.4% (32 bytes) more RAM in comparison to the NIPE method. However, the impedance root mean square errors (RMSEs) of the QINIPE method are decreased to 42.8% (for the real part) and 64.5% (for the imaginary part) of the corresponding RMSEs obtained using the NIPE method. Moreover, we compared the QINIPE and the complex nonlinear least squares (CNLS) estimation of the 2R-1C model parameters. The results obtained show that although the estimation accuracy of the QINIPE is somewhat lower than the estimation accuracy of the CNLS, it is still satisfactory for many practical purposes and its execution time reduces to