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Leveraging unmanned aerial vehicles (UAVs) as relays, the space-air-ground integrated Internet of Things (SAG-IoT) networks can provide wireless access services to the connected devices with high data rate, high flexibility, and wide coverage. However, besides bringing lots of benefits, the SAG-IoT systems also face many challenges due to the high mobility, unreliable satellite links, and limited resources. Especially, when multiple UAVs act relays for cross-tier communication to transfer the collected data from ground network tier to the satellite tier, how to select an appropriate number of UAVs as gateways to improve system spectral efficiency (SE) is an important issue. To the best of our knowledge, it is an entirely new problem since most existing works utilize all UAVs as relay nodes and neglect the spectrum allocation. Toward this end, we present this paper to discuss the joint optimization problem of gateway selection, bandwidth allocation, and UAV deployment with the objective of maximizing the system SE. A Dinkelbach method based iterative algorithm is proposed as our solution by alternately adopting simulated annealing and successively convex programming technologies. Experimental results show that our designed joint optimization schemes are able to significantly improve the system SE and can make optimal decisions of gateway selection, bandwidth allocation, as well as UAV deployment.

This article unveils the importance of statistical quality of service (QoS) for resource allocation in a two-hop network. Particularly, an access point (AP) serves multiple IoT devices for information transfer with the assistance of the energy harvesting (EH) relaying. To explore the maximum constant data arrival metric, we aim to maximize the effective capacity (EC) under specified QoS requirements. Also, the statistical QoS inspired resource allocation policies are investigated for half/full duplex (HD/FD) modes, respectively, to jointly optimize power allocation and power splitting (PS) ratio. To solve the formulated problem, we first derive the closed-form solution of the optimal power allocation at the AP and the PS ratio. To gain more insights, we further derive the boundary conditions of optimal power allocation and PS ratio. Finally, numerical results are demonstrated to validate the theoretical derivations, which highlights the proposed scheme in terms of EC performance in comparison to the benchmark scheme.

The accuracy of the Bluetooth position, which has a high position speed, is not good. Visual information positioning takes more time, but its positioning accuracy is better. Since single positioning means cannot meet the needs of indoor positioning, we designed an indoor Location system based on fusion of Bluetooth signal and visual information in combination with their characteristics: According to the result of the rough positioning of the Bluetooth signal and the range of the Bluetooth positioning error, we can narrow the scope of the image database and use the visual information to locate the position with a high speed.; Then, according to the posterior probability distribution of the two kinds of positioning, we will integrate the two positioning results into the final positioning results. The simulation results show that the positioning performance of the system is better than that of pure Bluetooth positioning and visual information positioning.

The ground-air collaborative cluster system can fully improve the efficiency and ability of the robot system to perform investigation and combat tasks in unknown fields by combining the advantages of ground and aerial robots. Task allocation is a key technology and a complex decision and optimization problem for the ground-air collaborative cluster system. In this paper, based on the background that the ground-air collaborative cluster performs the ground targets attack task on the battlefield, a combinatorial optimization model of the task allocation problem is established. Then, to solve the problem that the standard firefly algorithm is easy to fall into local optimum, two improved firefly algorithms based on simulated annealing and adaptive mutation are used to solve the task allocation problem respectively. The final simulation results show that the two improved algorithms can produce uniform and reasonable task allocation schemes, with the better optimization ability and higher solution accuracy, which verifies the effectiveness of the two proposed algorithms.

Building extraction based on high-resolution remote sensing imagery has been widely used in automatic surveying and mapping. Recently, the instance segmentation algorithm has been introduced to the building extraction, which can calculate the number and area of buildings simultaneously. However, there are some challenges: 1) multi-scale buildings; 2) occlusion by other adjacent buildings. In this paper, to solve these problems, we propose a multi-scale building instance extraction framework based on feature pyramid object-aware convolution neural network (CNN). In order to solve the multi-scale problem, a feature pyramid CNN is proposed, which combines features from both the bottom-up and top-down architectures. In order to solve the occlusion problem, a multi-scale object-aware instance proposal network is proposed, which introduces the multiscale attention mechanism to aware objects. The experiments conducted on two public datasets and a self-constructed dataset of Changzhou show that the proposed method can achieve an excellent performance.

With the development of remote sensing, a large amount of high-spatial-resolution (HSR) images is available, which makes refined land-cover mapping possible. However, the details of ground objects in HSR images are complex, especially in edges, therefore brings new challenges in land-cover classification. Existing deep learning method views it as a semantic segmentation task based on the fully convolutional networks (FCN), taking no account of complex details recognition. In this paper, we tackle this problem by proposing a point representation network (PointNet) for HSR land-cover classification Specifically, the uncertain point selection is designed for finding the most uncertain details at the end of ResNet encoder. According to these points, the coarse and fine features in the encoder are fused, followed by a multilayer perceptron (MLP). Different from convolutional sampling, the MLP focuses on the recognition of uncertain points, which modifies the network optimization during training. During the prediction, the coarse features are successively upsampled with the points refining, improving the performance on land-cover details recognition. Experimental results on a Nanjing land-cover dataset demonstrate that the PointNet outperforms the state-of-the-art methods.

Fault localization can facilitate software debugging and thus is a key technology in software maintenance. Spectrum-based fault localization (SBFL) has been known as an effective and lightweight approach. Nevertheless, the existence of multiple faults within the same system is still the bottleneck of SBFL. Inspired by feature selection in data mining, this paper improves the Relief algorithm and proposes fault-relevant statistics (FRS) to calculate the suspiciousness of program elements. Specifically, it takes test cases as samples, execution results as labels, and spectrum information as features, so that the problem of multi-fault localization can be viewed as a feature selection problem. Unlike the clustering method, this paper only takes into account the closest failing and successful case for each sampled test case when computing FRS. Experiments on open source software systems show that FRS improves the efficiency of fault localization with low computational cost.

This paper proposes a spacecraft dynamics full-physical simulator based on air bearing technology, which can effectively simulate the micro-damping dynamic environment of spacecraft in outer space. This paper gives the kinematics model and dynamic equations of the simulator, and analyzes the similarity of the attitude dynamics between the simulator and the real spacecraft. Referring to the real on-orbit spacecraft, the simulator selects the reaction flywheel and the gas injection system as the actuators, designs a control switching algorithm, and uses data fusion technology to switch the mode of the attitude measurement system. Finally, an attitude control simulation test is carried out. The test results verify the correctness and feasibility of the simulator. Using the full- physical simulator proposed in this paper, we not only can carry out spacecraft dynamics experiments on the ground, but also can verify various algorithms, which has important engineering application value.

Energy scarcity becomes a key factor restricting the development of the wireless communications network. Wireless power transfer, as it can provide a sustainable and reliable energy supply for wireless communications devices, has drawn a lot of interests in recent a few years. Thus, combining wireless power transfer to the wireless communications network can potentially extend the lifetime of wireless devices and thus prolong the operating-time of networks. In this paper, we consider the two-hop wireless networks, where the source node transmits information to the destination assisted by the relay node and the relay simultaneously harvests energy and receive information from the source node. In order to maximize the effective capacity (EC), which is the maximum constant arrival rate under specified quality of service (QoS) requirements, we investigate the statistical QoS supported resource allocation policies under full duplex transmission mode for wireless powered relay networks. Finally, numerical results are demonstrated to validate the theoretical derivations, which highlights the proposed scheme in terms of EC performance in comparison to the benchmark scheme.

As the latest evolving architecture of space networks, Internet of Satellites (IoSat) is regarded as a promising paradigm in the future beyond 5G and 6G wireless systems. However, due to the extremely large number of satellites and open links, it is challenging to ensure communication security in IoSat, especially for wiretap resisting. To the best of our knowledge, it is an entirely new problem to study the security issue in IoSat, since existing works concerning physical layer security (PLS) in satellite networks mainly focused on the space-to-terrestrial links. It is also noted that, we are the first to investigate PLS problem in IoSat. In light of this, we present in this paper an analytical model of PLS in IoSat where a terrestrial transmitter delivers its information to multi-satellite in the presence of eavesdroppers. By adopting the key parameters such as satellites' deployment density, minimum elevation angle, and orbit height, two major secrecy metric including average secrecy capacity and probability are derived and analyzed. As demonstrated by extensive numerical results, the presented theoretical framework can be utilized to efficiently evaluate the secrecy performance of IoSat, and guide the design and optimization for communication security in such systems.

The paper gives a new moving window random adaptively weighting adaptively estimation method. This new method adopts the concept of weighting estimation with moving window to control predicted residuals and the observational residuals for resisting the disturbances on observations and the system model errors. The theories and methods of moving window adaptively weighting estimation can estimated the covariance matrix of the observational residuals and the innovation vectors. From the experimental results and its comparisons that can demonstrate this proposed moving window random adaptively weighting adaptively estimation method cannot only control the observational error vector and predicted residual vector of the covariance matrix, but also it can effectively resist the disturbances of system error. By the calculation it can be obtained this method is much better than the traditional Kalman filtering method and adaptive filtering method.

As a new remote sensing monitoring information, sun-induced chlorophyll fluorescence (SIF) has been widely used in the detection of changes in vegetation state at global and regional scales in recent years. SIF study in agricultural field has quickly become a research hotspot. TanSat is a global scientific experiment satellite for carbon dioxide monitoring launched in December 2016 with SIF monitoring capability. This paper aims to retrieve SIF based on a simplified physical model using TanSat data. The KI Fraunhofer Line at 770.1 nm was selected, which is less affected by the atmosphere. L1B-level data of TanSat was used to extract SIF of southeast China in July 2017. We used cloud mask products from MODIS data at the same period to eliminate cloud-affected areas and obtain SIF under clear sky conditions. The SIF value of our retrieval result is between −2 mW.m−2. sr−1. nm−1 and 5 mW. m−2. sr−1. nm−1. Comparing the result with SIF products showed that there is an encouraging consistency between them. This indicated that the inversion algorithm based on the simplified physical model can be used to retrieve SIF from TanSat data.

There is alternating electromagnetic field around 10 kV transformer when it is live. When the worker enters the electric field during live maintenance, the distribution of electric field will change unevenly. When the electric field of human body reaches a certain intensity, it will bring personal injury to the worker. In order to avoid this situation, the finite element method is used to establish the transformer and human body model for numerical calculation of the working area. The distribution of electromagnetic field around the transformer and the change of electromagnetic field after joining the human body model are obtained. The safe distance is obtained by measuring the distance, so as to determine whether the corresponding protection needs to be taken. Protection measures. This paper provides technical and theoretical basis for the safe operation of live working of 10 kV high voltage transformer.

Indium-zinc-oxide electric-double-layer (EDL) thin-film-transistors (TFTs) were fabricated on ITO glass substrates gated by agar electrolyte dielectrics. The humidity-dependent device performance was tested such as specific capacitance of the agar electrolyte, transfer characteristics, and gate leakage current. The humidity-dependent characteristics indicate that the EDL TFTs have potential applications in humidity sensing.

In this paper, a frequency domain identification-based power prediction system is developed for the photovoltaic (PV) power plant. A first-order plus dead time (FOPDT) model of the PV power plant is first identified using the daily PV power plant operating data. Given the identified FOPDT model, we do discretization to obtain an FOPDT-based iterative calculation formula for the PV power prediction. Finally, a portable Power Prediction software of Python is developed using the resulting iterative calculation formula. In numerical experiments, we showcase the effectiveness of the Power Prediction software by applying it to real data.

To resolve issues such as the degraded particle performance and difficulty in selecting the importance density function of particle filtering, this paper introduces an adaptive Unscented particle filter (UPF) based interacting multiple model algorithm. The method benefits from both the interacting multiple model filter and Unscented particle filter. It first uses the Unscented kalman filter to get the latest measurement information at k time for each model. And then, it takes interaction of the corresponding particle as input of each model, after matching the model and updating the probability of model and using residual re-sampling. Finally, it outputs interaction of the corresponding particle for each model. The proposed algorithm has been used in the GPS/DR integrated navigation system. Simulation results and their analysis demonstrate that the position and velocity error calculation of the proposed algorithm is much better than the Unscented particle filter, and the algorithm certainly improves the calculation accuracy of the GPS/DR integrated navigation system.

Ahstract- With the rapid development of network equipment, single-port capacity becomes larger. The traditional Quality of Service applications based on single level queue and single level port become the bottleneck of the development of Software Defined Network switching equipment. Therefore, a five-level queue scheduling model based on Software Defined Network is proposed. Through five-level pipeline scheduling, this model can reduce the number of queue which every level scheduler maintenance. By choosing the appropriate scheduling algorithm and flow control algorithm, the multiple users, various flows and different types of network traffic can be uniformly managed and hierarchically scheduled. The simulation result shows that the five-level queue scheduling model reduces the weighted packet loss rate on the premise of guaranteeing the output bandwidth. It can support multiple types of traffic scheduling and flow control, and can ensure the Quality of Service of different business to some extent.

Burst traffic is a common traffic pattern in modern communication networks, and this traffic is the main cause of the unfairness problem in shared memory switch. In this paper, a shared-private buffer management (SPBM) scheme is proposed, which can efficiently utilize the switch buffer and reduce overall packet loss rate under burst traffic. The key idea of SPBM is to partition the total memory space into shared area and private area. Each output port has a private memory area that cannot be used by other ports. The shared area is completely shared among all output ports. A theoretical queuing model of the proposed scheme is formulated and closed-form formula for the overall packet loss rate is derived. By analyzing the numerical results, the optimal partition of the switch buffer is obtained. Besides, simulation result shows that the proposed scheme can reduce the overall packet loss rate by about 20% on average compared with existing buffer management schemes.

The OpenFlow protocol specifies a variety of match fields, including exact fields, prefix fields, and range fields. Existing hardware-implemented packet classification causes prefix expansion when searching for a range field, resulting in large memory requirement. In order to reduce the cost and energy consumption, the high speed packet classification supporting multi-field for flow tables in OpenFlow is proposed. The solution is based on bit-vector method. Firstly, divide fields into two groups according to the matching type of fields. Then, select suitable matching methods for each match field to get intermediate results. Finally, a bit-wise logical AND operation among all intermediate results is performed to get the final matching result. The hardware implementation structure is a two-dimensional pipeline structure. According to theoretical analysis and simulation results, the solution can sustain a throughput of 100 Gigabit Per Second (Gbps).

In this paper, a feedforward-feedback coordinated control scheme is proposed for the hybrid wind farm and combined cycle power plant (CCPP) system. In this system, we first identify the power control loop model of CPCC and the process model of the wind turbine generator (WTG), according to the automatic generation control and daily wind farm operating data. Specifically, the process frequency response is estimated using the measured input and output data of the process loop; their transfer functions are obtained by solving least squares with the variables amplitude and phase. Based on above CCPP and WTG models, the improved Internal Model Control (IMC) and optimal feedforward controller methods are then used to design the feedback and feedforward controllers. In comparative study we demonstrate that, the proposed scheme achieves significant improvement in reducing power deviation in the grid caused by the intermittent wind power.

In this paper, we develop statistical quality of service (QoS)-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless networks. In particular, first, we analyze the long-term available energy constraints and formulate the EEE maximization problem. Then, we derive the closed-form solutions of optimal power control policies to the EEE maximization problem under the battery capacity dominated constraint, the average harvested energy constraint, and both the battery capacity and average harvested energy constraints, respectively. Furthermore, we derive the threshold to judge whether the optimal power control policy is limited by the battery capacity. The obtained numerical results validate our analyses and show that our developed optimal power control policies can optimize the EEE with statistical QoS provisioning over energy harvesting based wireless networks.

In this paper, we develop the resource allocation scheme with heterogeneous statistical QoS provisioning for wireless powered sensor networks (WPSNs). In particular, we build up the downlink energy transfer and heterogeneous statistical QoS provisioning uplink data transmission models, where the aggregate effective capacity (AEC) is defined as the aggregate throughput under the statistical QoS constraints, for WPSNs. Based on the models, we formulate the AEC maximization problem to jointly optimize the downlink energy assignment and uplink power control scheme. To efficiently solve this problem, we divide it into the hybrid access point determined downlink energy assignment problem and the sensor node determined uplink power control problem, solving which yields the joint downlink energy assignment and uplink power control scheme. Extensive simulations are conducted to evaluate the performance of our proposed heterogeneous QoS-driven resource allocation scheme. The obtained results show that the heterogeneous QoSdriven resource allocation scheme can significantly increase the AEC as compared with the homogeneous QoS-driven resource allocation scheme.

We fabricate and demonstrate on-chip two-mode (de)multiplexing using a tapered asymmetrical grating-assisted contradirectional coupler-based $\text{TE}_{0}$ and $\text{TE}_{2}$ mode multiplexer and demultiplexer on the silicon-on-insulator platform. The designed silicon photonic device is composed of apodized grating couplers, a narrow waveguide, a bus waveguide, and an asymmetrical multimode grating-assisted contradirectional coupler. The operation principle of such a mode (de)multiplexer is analyzed according to the coupled mode theory. System experiments of orthogonal-frequency-division-multiplexing-based offset quadrature amplitude modulation (OFDM/OQAM) 256-ary QAM data transmission have been carried out for two-mode (de)multiplexing application at 23.89 Gbit/s. The bit error rate (BER) performance for the signal transmission through the fabricated two-mode (de)multiplexer is evaluated. The optical signal-to-noise ratio (OSNR) penalties of data transmission through the fabricated two-mode (de)multiplexer are less than 2 dB at a BER of $2\mathrm{e}^{-2}$ (20% forward error correction threshold).

Fishery industry has been developed rapidly in recent years. To establish an accurate and automatic fishery forecast system becomes concern of industrialists and even researchers. Existing fisheries forecast approaches mainly rely on typical common-used classification models. However, there still not exists systematical evaluation and empirical research for these approaches, which leads to application confusion in fisheries forecast field. To address this problem, three typical classification forecast models, logistic regression. Naive Bayes and J48 decision tree, are studied through sulflcient experiments and comparison in this paper. In addition, a synthetical evaluation approach is proposed and implemented. The empirical research results show that the size of sample dataset heavily impacts the accuracy and efficiency of each classification model. When the scale of training dataset is larger, the Naive Bayes model fits more; when the attributes of training dataset have strong correlation, correspondingly, J48 decision tree model has more advantages.

Hard x-ray research taking advantage of both the coherence and timing properties of next generation low-emittance storage rings will thrive in exploring the spatial and temporal correlations at the nanometer and nanosecond scales. Current gating technology with fast detectors and mechanical choppers will not be able to handle the high repetition-rates of these sources and requires the development of a fast gating device that can fill the needs of the timing community. Here, we introduce a MEMS device that has the potential to meet the future requirements of x-ray timing science. We demonstrate a device that is frequency matched to the operating sub-frequency of a high rep-rate bunch mode at the APS (Advanced Photon Source) to select single pulses from the pulse train with separations of ∼153 ns.

Virtual MIMO, also known as Multiuser-MIMO, has been widely investigated to improve data transmission rate of various wireless uplink systems. However, virtual MIMO transmission leads to performance degradation due to the multiuser interference. To obtain good trade-off between the system throughput and transmission performance, we investigate joint user grouping and resource allocation under the consideration of system throughput and average MSE performance in uplink SC-FDMA systems. Based on linear MIMO detection, we first develop MSE-oriented user grouping criterion for evaluation of transmission performance, then propose a dynamic user grouping and optimal resource allocation algorithm with elastic average MSE constraint. We evaluate the proposed joint resource allocation algorithms in LTE uplink scenarios and the results show that it achieves alterable trade-off between system throughput and average MSE.

Broadcast authentication, a fundamental feature in wireless sensor networks (WSNs), enables users to broadcast to multiple sensor nodes in an authenticated way. Symmetric-key-based μTESLA-like schemes are a popular solution due to their energy efficiency, but most schemes are not scalable (e.g. in terms of the number of senders). On the other hand, several schemes based on public key cryptography (PKC) are proposed to secure the multi-user broadcast in WSNs. However, the computation and communication overheads in the underlying PKC infrastructure restrict its implementation in resource-constrained WSNs. This paper presents an efficient scheme, which supports multi-sender broadcast authentication and allows dynamic addition of new senders. We delay disclosing the keys of the master key chain generated by the base station to authenticate the commitment of each sender's key chain. Considering that senders may be captured and compromised by adversaries in a hostile environment, our scheme also provides a mechanism to revoke the compromised senders' broadcast authentication capability. Refereed/Peer-reviewed

Wireless Mesh Network is one of the most important wireless communication technologies. A wireless resource allocation with QoS and scheduling procedure is proposed based on TDMA - TDMSH. The TDMSH framework contains service category manager, scheduling queue selector, bandwidth allocator and bandwidth statistics. A Two-Round scheduling is proposed to support QoS. Simulation shows that the TDMSH can effectively assure the QoS of different service flow, and also has better performance.