A Ka-band rectangular TEio mode (TE10 square) to circular TEN mode (TE01o) converter with high efficiency, high mode purity, compactness and easy fabrication is proposed. It consists of a TE10 square- TE20 square, mode-converting section and a TE20 square-TE10 square, mode-converting section. This improved sidewall-type TE10 square-mode converter with built-in pins based on the stepped waveguide technique is easier to fabricate and performs well. The optimized simulation results show that the converter achieves a mode purity greater than 98% with a conversion efficiency of nearly 96% within the frequency band of 32.28 GHz to 37.54 GHz and that it is less than 6 wavelengths (to the center frequency 34.5 GHz) in length. A back-to-back measurement of such converters and a near-field test of the output of the converter indicate a very good agreement with the corresponding simulation results.

Given the context of the challenge of exponentially increasing data traffic on communication networks brought by the 5G era, this paper focuses on how to apply deep reinforcement learning (DRL) techniques to solve the problem of optimizing the energy efficiency of D2D communications in heterogeneous cellular network environments. We propose a joint resource allocation scheme based on multi-intelligent deep reinforcement learning, which enables D2D devices to intelligently switch between license-free and optimal license bands and adjust the transmit power in real time to maximize the energy efficiency improvement. In this work, a multi-intelligent deep reinforcement learning framework is designed to enable D2D users in heterogeneous networks to make collaborative decisions and dynamically adjust their communication strategies according to real-time network status and environmental changes. In this paper, a deep Q-network model with a graph attention network (GAT) as the core structure is constructed; this model can cope with the complexity and diversity of network states and learn and execute optimal resource allocation strategies. In this paper, we propose a targeted loss function design that balances the optimization goal of D2D communication energy efficiency with network stability and long-term gains. Through rigorous simulation experiments, this paper verifies that a DRL-based approach can significantly improve the energy efficiency of D2D communications in heterogeneous cellular networks in real-world scenarios while ensuring the stability of the quality of service (in terms of, e.g., rate, delay, and resource utilization).

Metallic nanoparticles are used in every stage of science and are still charming the researchers to explore the localized surface plasmon resonance (LSPR) properties for the promising application of sensors and catalysts. In this review, recent progresses on silver-based and gold-based single nanoparticle for resonance light scattering imaging analysis and applications are presented. Firstly, fundamental principles and technique of dark-field microscope (DFM) coupled the LSPR spectroscopy are described. Then, the species of silver-based and gold-based single nanoparticle are outlined, with an emphasis on the resonance light scattering imaging analysis of silver-based and gold-based single nanoparticle. Finally, recent achievements of the plasmonic single-particle imaging analysis technique in analytical determinations and reaction monitoring are also comprehensively introduced. We hope to shed some light on the forthcoming study by showing these advances and some future challenges of resonance light scattering imaging analysis.

Some sprinklers in underground mines spray water mist to lessen the amount of dust in the air. But water mist causes LiDAR to provide inaccurate point measurements, which are referred to as water mist noise that undermines the effectiveness of LiDAR-based localization and object recognition. Therefore, in order to reduce water mist noise, we have developed a new noise segmentation network that can operate on a CPU in real time-differential stability noise removal network (DSNRNet). This network consists of two sub-networks. The first sub-network is aimed at extracting differential stability features, so we named it the differential stability feature extraction network (sub-network 1). The second sub-network: a fully connected neural network (sub-network 2), is used to segment noise. To evaluate the DSNRNet's performance, we built a LiDAR semantic segmentation dataset of underground mines and run the DSNRNet in an Intel i7-11800H CPU. The experimental results demonstrate that this method is able to strike a better balance between speed (26.3 milliseconds) and accuracy (97.8 % ) compared with the other two most possible methods-DSOR (24.2 milliseconds, 3.2 % ) and WeatherNet (436.8ms, 98.5 % ).

This article summarises the controllability robustness of various directed complex networks under different attack strategies. Based on the theory of node-degree, edge-degree, node-betweenness and edge-betweenness, the controllability robustness evaluation criterion for complex networks is proposed. Considering directed complex networks, we describe the construction of seven network models (RGN, SFN, OLN, MCN, QSN, RTN and RRN). Based on six attack strategies (NABR, NABB, NABD, EABR, EABB and EABD), we conduct simulated attacks on the above seven network models and analyse the results. All the attacks are classified into node-based and edge-based. Through simulation experiments, we can see that under the same network environment, the damage caused by the betweenness-based attack to the network is greater than that of the degree-based attack. The controllability robustness of scale-free network and onion-like network is almost the same regardless of the attack. Compared with other networks, random rectangle network has the best controllability robustness. Therefore, the simulation results can also draw the conclusion that the multi-ring structure is helpful to improve the controllability robustness.

In this letter, the authors propose a high-gain and low-profile tapered slot antenna (TSA) with a composite patch made of ellipse and spoof surface plasmon polaritons (sSPPs) structures. The authors first demonstrate the construction details and associated dimensions of the antenna fabrication. Then the authors discuss the role of the patch, which is placed at the antenna's radiating opening to improve the directivity of the antenna. Measurements of the fabricated antenna show a wide operating bandwidth of 82.3% (5–12 GHz) is achieved. The gain of the antenna varies from 5.2 to 13.4 dBi, in which radiation patterns show good directivity and end-fire radiation performance. The measured results are in good agreement with the simulation results, indicating that the antenna performance has been improved as expected by adopting this design.

Aiming at the resource allocation problem of a non-orthogonal multiple access (NOMA) system, a fairness index based on sample variance of users’ transmission rates is proposed, which has a fixed range and high sensitivity. Based on the proposed fairness index, the fairness-constrained power allocation problem in NOMA system is studied; the problem is decoupled into the intra cluster power allocation problem and the inter cluster power allocation problem. The nonconvex optimization problem is solved by the continuous convex approximation (SCA) method, and an intra and inter cluster power iterative allocation algorithm with fairness constrained is proposed to maximize the total throughput. Simulation results show that the proposed algorithm can take into account intra cluster, inter cluster, and system fairness, and maximize the system throughput on the premise of fairness.

Maps with different representations play an essential role in the development of automotive intelligence. In order to enhance the capacity of autonomous trucks in surface mine, an extensible terrain mapping system based on LiDAR is proposed in this paper. Point cloud map, 2.5D grid map, and mesh map are integrated into a unified and extensible map-building framework. In order to adapt to the unique characteristics of surface mine, terrain mapping methods are proposed based on existing approaches. Each map-building method builds a local robot-centered map for time-sensitive tasks. Local maps are fused into global maps in the cloud for non-time-sensitive tasks. The construction method of the point cloud map can avoid the loss of information when updating the map by computing convex hulls. The 2.5D grid map can model the unstructured and rugged terrain of mines. The mesh map is built based on Poisson reconstruction, which is conducive to human-truck interaction. In addition, the map maintenance method in the framework is proposed. Experiments are conducted with datasets collected in real-world scenes.

X-ray excitation photodynamic therapy is a new photodynamic therapy mode that induces tumor cell apoptosis by indirectly stimulating photosensitizer on nanoparticles with X-ray as excitation light source and generating singlet oxygen. It solves the problem of light penetration in traditional photodynamic therapy and realizes the treatment of deep tumors. At present, the weak X-ray-induced luminescence restricts further development of X-ray photodynamic therapy. NaLuF4:Li+/Gd3+/Tb3+ rare-earth nanoparticles with uniform size and regular morphology were synthesized by solvothermal method. The X-ray-induced luminescence intensity of NaLuF4:Li+/Gd3+/Tb3+ NPs was improved by Li+ and Gd3+ ions co-doping. The light yield of NaLuF4:Li+/Gd3+/Tb3+ NPs with 5% Li+ ions doping was 1.4 times than that of reference sample due to the change of crystal field symmetry. Furthermore, the effective lifetime of persistent luminescence increased from 221.5 s to 251.9 s after 15% Li+ ions doping. The thermoluminescence curves confirmed that the trap concentration was increased after Li+ ions doping, which was responsible for enhancing persistent luminescence lifetime. Long persistent luminescence emission laid a foundation for further application in photodynamic therapy.

Lead-free double-perovskite Cs2SnX6 (X = Cl, Br, I) containing Sn4+ is a more stable lead-free photovoltaic material than its homologue CsSnX3. However, its stability in air still affects its application. In this Letter, lead-free inorganic Cs2SnX6 (X = Cl, Br, I) nanocrystals (NCs) were synthesized by a hot injection method and in-situ loaded into mesoporous SiO2. With the change of halogen content, the PL emission peak of pristine Cs2SnX6 NCs were tuned from 430 nm to 780 nm covering the visible and near infrared spectrum region. Taking Cs2SnI6/SiO2 NCs as an example, the optical property stability in solution is obviously improved, and it can be stored in air for over 2 h. Especially, the more stable NIR emission, non-toxicity and easy hydrolysis characteristics of Cs2SnX6/SiO2 composite nanoparticles will have potential medical applications like photothermal and photodynamic therapies for cancers.

Wireless cellular networks are usually modeled and analysed in two-dimensional (2-D) space. The 2-D model is suitable for the analysis of cellular networks in suburb area but not for the dense millimeter cellular networks in the urban environments. In this work, a three-dimensional (3-D) model based on stochastic geometry is proposed, in which the distribution of base stations (BSs) are modelled as a 3-D Poisson point process (PPP), the blockage is modelled as line of sight (LOS) ball, the shadowing of wireless channel is modelled as Nakagami-m fading, and both the transmitters and receivers obtain maximum gain of beamforming by a large array of antennas. Based on the model, the distribution of the distance between the target user and the nearest the BS is given, and then the average coverage probability and transmission rate of the networks are derived. We analyse the impact of parameters such as path loss, cell radius on average coverage and the relationship between BS density and average rate through Monte Carlo simulation. The simulation results show that in the dense urban environment, the performance of 3-D PPP model of the millimeter wave cellular network analysis is more precise.

The shortage of water resources has become a notable bottleneck, restricting the economic development of many countries and areas around the word, especially that of North-west China. The Inner Mongolia Autonomous Region and Shaanxi Province are important energy bases and food production areas in North-west China. However, the region is suffering from perennial drought and water shortage, which has become the most significant shortcoming for energy and food production. Guiding the decoupling between regional economic development and water consumption is a critical way to achieve sustainable development. Based on the analysis of the food and energy production value and their water consumption in North-west China from 2009 to 2019, this paper uses the Tapio model to analyze the decoupling relationship between food, energy production, and water consumption, and uses the Logarithmic Mean Divisional Index (LMDI) model to analyze the driving factors affecting decoupling. The results show that most water consumption for food and energy production in North-west China is out of the ideal strong decoupling, the decoupling status is unstable, and re-coupling occurs frequently. The increase in water intensity and the change in industrial structure are the promoting factors of decoupling between production value and water consumption in food and energy in North-west China, while the increase in production value and population size are the main restraining factors. Therefore, in pursuit of strong decoupling, the government should guide the food and energy industry to move toward implementing in water saving measures through policies and promote the enthusiasm and efficiency of the labor force through financial support and other ways. Moreover, ecological protective measures are needed to be strengthened, such as water source protection, and sewage treatment.

在无线传感网中进行数据通信的过程中,首要任务是定位出目标点的位置.然而,近几年随着5G时代的到来,大容量设备的接入势必会对定位精度有更高的要求.在传统的定位算法中一般只需要关注长度和宽度这两个维度就足以满足定位需求,但是在一些特定的场景下不仅需要长度和宽度这两个维度,还要关注高度,如紧急防洪;防震预警服务不仅要关注高度,还要关注时延误差等,这些给在三维空间下的定位算法带来了挑战.基于以上需求衍生出了一些比较好的定位算法,如对最小二乘算法进行加权,利于抑制定位算法中所累积的时延误差;采用粒子群约束将最小二乘法进行优化求解,利于提升定位精度.尽管如此,这些算法的共同特点均是二维空间下进行的,当运用在三维空间下会随着算法复杂度的提高,使得计算量过大,从而无法计算.文章提出一种可以不依赖于环境因素,且能满足定位精度和定位误差的一种基于种群的杂草优化竞争生存算法,仿真结果表明,本算法具有一定的适用性.

电力线与下方物体的距离是输电线路安全运行的重要指标,需要对其巡检以保证其距离满足安全距离条件.提出一种测距方案,使用无人机基于三维场景重建测量电力线与下面的地物间的距离.通过将激光点云数据、影像数据和位置信息在时空维度配准,实时生成电力线路附近区域彩色图像与激光雷达的融合信息,利用深度学习算法多模态神经网络的RGB-Depth语义分割提取输电线路特征,基于体素、大规模语义场景重建以及导线建模,通过拟合线模型铅垂线方法对下方地物安全距离进行实时检测,实现了判断电力线下方地物距离是否符合安全生产条件.实验结果证明该测距方案具有实用性和准确性,有助于提高输电线路运维水平.

To ensure an autonomous truck can operate safely in a dump area, it is crucial to detect a berm accurately in advance. However, there are two challenges. First, the berm is not a static terrain but a movable one because of soil dumping. Second, berms are often irregular in shape-they are neither straight lines nor smooth curves. We considered two types of possible existing methods, but only to find they are not accurate and can't provide height information. Therefore, this paper proposes a berm detection algorithm, which includes three steps. First, extract berm candidate 3D LiDAR points based on a 2D height difference grid map. Second, use a binary Bayes filter to build and update 3D dynamic probability grid maps. Last, use a fitting rectangle technique to recognize the berm. We call this algorithm a Probability Grid Berm Detection (PGBD) algorithm. Off-line experimental evaluations on PGBD carried on datasets show good performance, compared with two curb detection algorithms, which are Hough Transformation and Haar Wavelet Transformation. And the good performance of the PGBD algorithm is further verified in the real-time experiment.

针对煤炭价格具有周期性、影响因素多样性等特点,文章首先从各行业中挑选出最具代表性的煤炭价格影响指标,将熵权法与变异系数法进行博弈论综合赋权得到影响指标的综合权重,并将综合权重与基于专家打分的层次分析法结果进行比对,发现结果相似度较好,从理论上说明博弈论综合赋权在煤价影响指标选取中的可行性.相比于专家打分的层次分析法,博弈论综合赋权能在保证精度的条件下减少确权工作的耗时与复杂程度,具有一定的参考价值.

在无线传感网络研究中,实时定位是一个重要的应用.受限于硬件与能耗问题,文章采用一种基于信号接收强度(RSSI)值的定位研究.当两个锚节点以特定轨迹运动时,可作出数组垂直于轨道并通过信标节点的垂线,得到数个交点.对每个交点分别求算数、几何、调和平均,以获取中心节点位置,并计算RSSI值.仿真结果表明,在使用调和平均的情况下可有效提高特定轨迹上可移动多锚点情况下的中心定位精准度.

Understanding plasmon-driven photocatalytic reaction (PPR) is crucial for photoelectric or light-to-energy conversion. By using a local surface plasmon resonance (LSPR) spectroscopy coupled dark-field microscopy to visually monitor the LSPR scattering properties of a single silver nanoparticle (AgNP) and microscopically count the electrons gain and loss during the PPR of 4-nitrothiophenol (4-NTP) dimerizing into 4,4’-dimercaptoazobenzene (DMAB), herein we found that the PPR process is proton coupled electron transfer (PCET) dependent, during which alternation at first and then simultaneity of electrons gain and loss occur. This finding of both PCET and time-dependent alternation-simultaneity of electrons gain and loss in the PPR can furnish exciting new opportunities for improving efficient light-to-energy, photoelectric conversions and gaining an insight into plasmon-driven photocatalytic formation of the azo compound. Therefore, this visually monitoring strategy can help in implementing environmental remediation by reducing emissions of toxic substance such as azo compound and its derivatives at the source.

During the study of wireless green communication, energy efficiency (EE) optimisation of heterogeneous cellular networks (HCNs) is always the hot topic. This paper studied the EE of HCNs in homogenous Poisson point process (HPPP) and proposed a new EE optimisation method based on small cell transmitting power allocation. First, it modelled the HCNs by using HPPP. Second, it derived the expression coverage probability and achievable rata in Rayleigh channel situations, and deduced the expression of EE in closed form. Finally, it optimised the small cell transmitting power via convex-optimum method in order to maximise EE for HCNs. The simulation results show that the small cell's transmitting power has significant effects on the HCNs' EE. Its essence is through reasonable setting of small cell's transmitting power to available help improve its efficiency.