Air Force Communication NCO Academy

In this letter, an artificial magnetic conductor (AMC) structure based on circular-loop patch, and a grounded substrate, are designed to realize 180° reflection phase difference in an ultrawide frequency band. This letter applies phase property of this AMC structure and grounded substrate to redirect the scattering fields of a radar target to reduce its radar cross section (RCS). This method of RCS reduction can be realized by covering with a chessboard surface composed of the AMC structure and grounded substrate, so the RCS reduction in a wide frequency band can be achieved as well. Compared with the same-sized metal surface, this proposed chessboard surface can reduce RCS drastically from 8 to 20 GHz. Meanwhile, this surface also can be used as an antenna. By precisely designing feed network, the feed of two parts of antenna can achieve 90° phase shift, so the circular polarization (CP) antenna can be designed as well. The CP antenna has a low profile. The simulated impedance-matching frequency band is from 9.3 to 10.9 GHz, and the simulated axial-ratio bandwidth is from 9.25 to 10.02 GHz. Excellent agreement is obtained between simulation and measurement for CP antenna and chessboard surface. Such method will give a way for integrated design of antenna and metasurface, so the RCS can be reduced, and the radiation properties can be maintained at the same time.

In this letter, a shared aperture 1 bit metasurface for orbital angular momentum (OAM) multiplexing is studied. A phased array antenna fed by the phase shift network is embedded on the reflective aperture of the 1 bit metasurface. The proposed device can work on reflectarray mode and phased array mode from 7.1 to 7.5 GHz and 7 to 7.5 GHz, respectively. Correspondingly, OAM vortex beam with topological charge l = -1 and 1 can be generated. The transmission coefficient between the two working modes is below -30 dB, and the envelope correlation coefficient is quite close to 0, which reveals the excellent isolation and diversity performance. A prototype of the proposed device is fabricated and tested. The measured S-parameters and radiation patterns agree well with the simulation results.

Cognitive radio (CR) has become a tempting technology that achieves significant improvement in spectrum utilization. To resolve the hidden terminal problem, collaborative spectrum sensing (CSS), which profits from spatial diversity, has been studied intensively in recent years. As CSS is vulnerable to the attacks launched by malicious secondary users (SUs), certain CSS security schemes based on the Dempster–Shafer theory of evidence have been proposed. Nevertheless, the available works only focus on the real-time difference of SUs, like the difference in similarity degree or SNR, to evaluate the credibility of each SU. Since the real-time difference is unilateral and sometimes inexact, the statistical information comprised in SUs’ historical behaviors should not be ignored. In this paper, we propose a robust CSS method based on evidence theory and credibility calculation. It is executed in four consecutive procedures, which are basic probability assignment (BPA), holistic credibility calculation, option and amelioration of BPA and evidence combination via the Dempster–Shafer rule, respectively. Our scheme evaluates the holistic credibility of SUs from both the real-time difference and statistical sensing behavior of SUs. Moreover, considering that the transmitted data increase with the number of SUs increasing, we introduce the projection approximation approach to adjust the evidence theory to the binary hypothesis test in CSS; on this account, both the data volume to be transmitted and the workload at the data fusion center have been reduced. Malicious SUs can be distinguished from genuine ones based on their historical sensing behaviors, and SUs’ real-time difference can be reserved to acquire a superior current performance. Abounding simulation results have proven that the proposed method outperforms the existing ones under the effect of different attack modes and different numbers of malicious SUs.

Realization of complete magnetization reversal is a major challenge for electrical control of magnetism. This paper focuses on voltage pulse induced magnetization reversal which is mediated by localized strain generated in a two-phase magnetostrictive/piezoelectric multiferroic heterostructure. This paradigm takes advantage of the damping and precessional property of magnetization dynamics, of which the magnetization reversal needs precise control of the voltage pulse. We have performed a micromagnetic study of varied-size elliptical single-domain Ni and Terfenol-D nanomagnets at T = 300 K. By using the actual ground states of the nanomagnets at T = 300 K as the initial states, we obtained the key parameters of the voltage pulse which can induce repeated magnetization reversal. Then we uncovered the mechanism of the size-dependent incoherent magnetization switching of Terfenol-D nanomagnets by a quantitative analysis of the interaction between the exchange energy, demagnetization energy and stress anisotropy energy. These results lay the foundation for the application of strain-mediated magnetization switching technology, and also provide a set of guidelines for signal design and the choice of nanomagnets with proper material and size in the design of specific straintronic circuits.

The micro-Doppler (m-D) effect of radar targets reflects the target motion attribute, which can provide important information for target recognition. Based on the m-D effect, the estimation of micromotion parameters of the cone is of great significance in the field of antimissile. However, the existing methods based on monostatic radar can only extract some micromotion parameters, and the research on m-D properties is not in-depth enough. In this article, the m-D properties of the precession cone with fins are studied. First, the motion model of the precession cone with fins is given. Subsequently, the frequency shift equation of the fin is studied, and a set of equations containing the relationship between the micromotion parameters is constructed. The micromotion parameters (precession angle, bottom radius, angle between the coning vector and the radar line of sight, etc.) are estimated by solving the set of equations. The effectiveness of the algorithm is verified by simulated data and measured data.

To flexibly meet users' demands in cloud computing, it is essential for providers to establish the efficient virtual mapping in datacenters. Accordingly, virtualization has become a key aspect of cloud computing. It is possible to consolidate resources based on the single objective of reducing energy consumption. However, it is challenging for the provider to consolidate resources efficiently based on a multiobjective optimization strategy. In this paper, we present a novel migration algorithm to consolidate resources adaptively using a two-level scheduling algorithm. First, we propose the grey relational analysis (GRA) and technique for order preference by similarity to the ideal solution (TOPSIS) policy to simultaneously determine the hotspots by the main selected factors, including the CPU and the memory. Second, a two-level hybrid heuristic algorithm is designed to consolidate resources in order to reduce costs and energy consumption, mainly depending on the PSO and ACO algorithms. The improved PSO can determine the migrating VMs quickly, and the proposed ACO can locate the positions. Extensive experiments demonstrate that the two-level scheduling algorithm performs the consolidation strategy efficiently during the dynamic allocation process.

Using micromagnetic simulations, we investigate the voltage control of magnetic vortex polarity based on a designed multiferroic heterostructure that contains two separate piezoelectric films beneath a magnetostrictive nanodisk. The results show that controllable switching of vortex polarity can be achieved by proper modulation of the phase difference between two sinusoidal voltage pulses V1 and V2, which are applied to the two separate piezoelectric films, respectively. The frequencies of V1 and V2 are set at the gyrotropic eigenfrequency fG of the nanodisk, and the vortex polarity switching is completed via the nucleation-annihilation process of the vortex-antivortex pair. Our findings provide an additional effective means for ultralow power switching of the magnetic vortex, which lays the foundation for voltage-controlled vortex random access memory.

Abstract In this paper, we propose a target detection and localization method on distributed monopulse arrays for tracking radar. An optimized mainlobe jamming (MLJ) cancellation filter was designed by maximizing the power ratio of the received siackgnal to the jamming-plus-noise. By exploiting the different correlation characteristics between the target echo and MLJ on distributed antennas, the designed filter is able to cancel MLJ and maintain the target echo. By applying the identical filter on sum-difference beams, MLJ can be cancelled, and the monopulse ratio can be maintained simultaneously. Hence, we simply detect and locate the target on the filtering output of sum-difference beams according to the monopulse principle. Monte Carlo simulations demonstrated that the proposed filter outperforms the conventional algorithms.

As we all know, YOLOv4 can achieve excellent detection performance in object detection and has been effectively applied in many fields. However, the inconsistency of scale features affects the prediction accuracy of the path aggregation network (PANet) in YOLOv4 for small objects, resulting in low detection accuracy. This paper presents YOLOv4, which uses an adaptive recursive path aggregation network (AR-PANet) to improve the detection accuracy of small objects. First, the output characteristics of the PANet are fed back into the backbone network by using a recursive structure to enrich the characteristic information of the object. Second, an adaptive approach is developed to eliminate conflicting information in multi-scale feature space, thereby enhancing scale invariance and promoting feature extraction accuracy for small objects. Finally, the CBAM is used to map the multi-scale features obtained from the AR-PANet to independent channels and spatial dimensions to achieve feature refinement, thus improving the detection accuracy of small objects. Experimental results show that our proposed method can effectively improve the accuracy of small object detection in multiple datasets, addressing this challenging problem with impressive results. Thus, our proposed approach has great potential and valuable applications in the fields of remote sensing and intelligent transportation.

Dipole coupled nanomagnets controlled by the static Zeeman field can form various magnetic logic interconnects. However, the corner wire interconnect is often unreliable and error-prone at room temperature. In this study, we address this problem by making it into a reliable type with trapezoid-shaped nanomagnets, the shape anisotropy of which helps to offer the robustness. The building method of the proposed corner wire interconnect is discussed, and both its static and dynamic magnetization properties are investigated. Static micromagnetic simulation demonstrates that it can work correctly and reliably. Dynamic response results are reached by imposing an ac microwave field on the proposed corner wire. It is found that strong ferromagnetic resonance absorption appears at a low frequency. With the help of a very small ac field with the peak resonance frequency, the required static Zeeman field to switch the corner wire is significantly decreased by ∼21 mT. This novel interconnect would pave the way for the realization of reliable and low power nanomagnetic logic circuits.

With the development of modern society, the demand for indoor positioning technology is higher and higher. The existing indoor positioning technology is difficult to really solve the problem of accuracy and achieve high-performance indoor positioning system design. Based on iBeacon equipment, this paper proposes a method to optimize received signal strength indication indoor positioning algorithm by using the Gaussian filtering method so as to reduce the adverse impact of multipath fading in indoor environment. In order to further improve the accuracy of indoor positioning algorithm, the stack automatic encoder in the deep neural network algorithm is introduced. Through the deep learning method, the high-dimensional information of the fingerprint database collected by the system can be extracted and the adverse impact of data noise on the database is also reduced. Through the simulation test of the system, it can be seen that the error of received signal strength indication indoor positioning algorithm based on extended Gaussian filter is small. Compared with the traditional iBeacon algorithm, the improved algorithm can achieve better data classification. The maximum error of the whole system is 1.02 M. Comprehensive analysis shows that the proposed indoor positioning system has a certain practical value and can be applied to the indoor positioning needs in a certain range of environment.

In some certain specific scenarios, path planning for UAVs is a hot topic of current research. This paper takes the low-altitude flight path planning of UAV in a single radar detection range as the research background. In the conventional SAS algorithm, the search area is greatly reduced. However, in the process of node expansion, it can only expand in a fixed direction, which is not flexible and has a certain chance to miss the best node. To solve this problem, this paper proposes an improved directional SAS algorithm. The algorithm first expands the search area by taking the line between two adjacent path points as the angular bisector, using the smallest step length as the search radius and adds the UAV's attitude limit to the expanded node to reduce the search area. The simulation is carried out and the results show that SAS algorithm proposed in this paper has the following advantages compared with the conventional SAS algorithm: 1) shorter path length 2) less calculation time 3) lower exposure ratio and lower mean high detection probability P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> .

We investigated the routing problem in the mega low earth orbit (LEO) satellite constellations. In Walker-Delta constellation, each satellite establishes 4 stable inter satellite links (ISLs) with neighboring satellites to forward data packets. We considered the factors such as traffic volume of the satellites, the length and the interference of the ISLs in routing. To minimize the delay from the source satellite to the destination satellite, we formulated it as an optimization problem and proposed a GRLR routing algorithm. The GRLR integrates the benefits of reinforcement learning (RL) and graph neural network (GNN), which builds the decision network based on Actor-Critic RL framework and builds feature extraction network based on GNN to realize distributed intelligent routing decision. The simulation results demonstrate that the proposed strategy exhibits accelerated convergence and reduced latency compared to the baseline strategies.

Elasticity is the key technique to provisioning resources dynamically in order to flexibly meet the users' demand. Namely, the elasticity is aimed at meeting the demand at any time. However, the aforementioned approaches usually provision virtual machines (VMs) in a coarse-grained manner just by the CPU utilization. Actually, two or more elements are needed for the performance metric, including the CPU and the memory. It is challenging to determine a suitable threshold to efficiently scale the resources up or down. In this paper we present an elastic scaling framework that is implemented by the cloud layer model. First we propose the elastic resource provisioning (ERP) approach on the performance threshold. The proposed threshold is based on the Grey relational analysis (GRA) policy, including the CPU and the memory. Secondly, according to the fixed threshold, we scale up the resources from different granularities, such as in the physical machine level (PM-level) or virtual machine level (VM-level). In contrast, we scale down the resources and shut down the spare machines. Finally, we evaluate the effectiveness of the proposed approach in real workloads. The extensive experiments show that the ERP algorithm performs the elastic strategy efficiently by reducing the overhead and response time.

Quantum-dot cellular automata (QCA) is a candidate paradigm for current CMOS logic circuits. Its inherent deep pipelining nature provides an expedience for bit-serial operations. However, many previous systems were implemented with bit- parallel circuits design because they can usually be achieved by direct equivalent conversion in lieu of conventional counterparts, which would cause problems of large area delay products and functional insufficiency. It is supposed to further exploit the aforementioned potentiality of QCA. In this work, we present a serial input serial output Hamming encoder/decoder communications system with QCA, which refers to both configurational and mathematical operations of bit string. The main contributions of this work are: first, a serial signal distribution network (SSDN) is proposed to achieve bit-serial data reshaping such as editing and alteration; second, we find the convolution computation may be a basic format for bit-serial operating circuits; third, the proposed system has a lower area delay product than previous work and employs an easily programmable structure.

With the development in the Cloud datacenters, the purpose of the efficient resource allocation is to meet the demand of the users instantly with the minimum rent cost. Thus, the elastic resource allocation strategy is usually combined with the prediction technology. This article proposes a novel predict method combination forecast technique, including both exponential smoothing (ES) and auto-regressive and polynomial fitting (PF) model. The aim of combination prediction is to achieve an efficient forecast technique according to the periodic and random feature of the workload and meet the application service level agreement (SLA) with the minimum cost. Moreover, the ES prediction with PSO algorithm gives a fine-grained scaling up and down the resources combining the heuristic algorithm in the future. APWP would solve the periodical or hybrid fluctuation of the workload in the cloud data centers. Finally, experiments improve that the combined prediction model meets the SLA with the better precision accuracy with the minimum renting cost.

The backtracking search optimization algorithm (BSA) is a recently proposed evolutionary algorithm with simple structure and well global exploration capability, which has been widely used to solve optimization problems. However, the exploitation capability of the BSA is poor. This paper proposes a deep‐mining backtracking search optimization algorithm guided by collective wisdom (MBSAgC) to improve its performance. The proposed algorithm develops two learning mechanisms, i.e., a novel topological opposition‐based learning operator and a linear combination strategy, by deeply mining the winner‐tendency of collective wisdom. The topological opposition‐based learning operator guides MBSAgC to search the vertices in a hypercube about the best individual. The linear combination strategy contains a difference vector guiding individuals learning from the best individual. In addition, in order to balance the overall performance, MBSAgC simulates the clusterity‐tendency strategy of collective wisdom to develop another difference vector in the above linear combination strategy. The vector guides individuals to learn from the mean value of the current generation. The performance of MBSAgC is tested on CEC2005 benchmark functions (including 10‐dimension and 30‐dimension), CEC2014 benchmark functions, and a test suite composed of five engineering design problems. The experimental results of MBSAgC are very competitive compared with those of the original BSA and state‐of‐the‐art algorithms.

Cloud computing is becoming an urgent technology in the enterprises. One key characteristic in the cloud computing is the elasticity. Then, it is urgent for the users how to rank the renting services reasonably. Considering the main features of the elasticity, this article gives classification on resource optimization. However, one of the major challenges is how to optimize resource allocation in an elastic manner. Due to the special pay-as-you-go manner, resource optimizing strategies are associated with the goal of minimizing the costs on the premise of service-level-agreement (SLA). Another challenge of resource optimizing strategies is to how to dynamically respond to the application demands. In this paper, the authors sketch the elastic definition more clearly. Secondly, different dimensions are described on elastic resource allocations. Thirdly, it is important to seek out the proper resource allocation strategy. Finally, the challenges and conclusions are discussed in this article.

Aiming at the low accuracy of crowd counting and density estimation algorithm in the environment of dense crowd with poor characteristics and complex background, a Crowd counting and density estimation method based on Multi-column CNN and adaptive projections onto convex sets was proposed. Firstly, a series of images translated from a single image by a specific value are put into the Multi-column CNN to obtain initial density estimation maps with information difference by sub-pixel displacement. Secondly, the crowd density recognition process is regarded as the sampling and mapping form the real space to density map, and the recognition ambiguity is simulated by the point spread function. In the training of convolutional neural network, the adaptive parameters of projections onto convex sets are trained synchronously to optimize the constraints of convex sets and the degree of image fusion correction. Finally, several density estimation maps with information difference by sub-pixel displacement are fused by adaptive projections onto convex sets to obtain the final density estimation map. The accuracy and robustness of the proposed model are proved by experiments

In many practical applications, dielectric electromagnetic (EM) windows are usually under large-angle incidence of EM waves rather than normal incidence. To guarantee normal operation of devices inside, high transmission must be maintained under large incident angles, especially for TE-polarized waves. In this work, we propose a method of achieving broadband transmission of TE-polarized waves under large incident angles by embedding meta-atoms within dielectric plates. To this end, long metallic wires and S-shaped structures are embedded in the original dielectric plate, the former of which will dilute the effective permittivity due to plasma oscillation and the latter will increase the effective permeability due to induced strong current loops under large incident angles. In this way, two consecutive transmission peaks can be generated, forming a broad transmission band under large incident angles. A proof-of-principle Ku-band prototype was designed, fabricated, and measured to verify this strategy. Both simulated and measured results show that the prototype can operate in the whole Ku-band under incident angle [60°, 85°] for TE-polarized waves, with significantly enhanced transmission. This work provides an effective method of enhancing large-angle transmission of EM waves and may find applications in radar, communications and others.