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Rapid advancements in Internet of Things (IoT) technology have promoted numerous novel applications that are sensitive to information timeliness. With different applications coexisting in one wireless network, each of which has heterogeneous traffic characteristics, it is of paramount importance while challenging to address how to characterize and optimize the information freshness performance network-wide, particularly in the massive access scenario. This paper casts attention on the heterogeneous slotted Aloha network and uses the Peak Age of Information (PAoI) metric to quantify information freshness. By assuming that the sensors in each group are equipped with unit-sized buffers and Bernoulli packet arrival, we derive the network steady-state point and PAoI. Depending on whether one group or all groups are age-sensitive, we focus on single-group PAoI and global PAoI optimization respectively. When only one single group is age-sensitive, we derive its optimal transmission probability. When all groups are age-sensitive, we propose a heuristic algorithm based on the particle swarm optimization method, where the bi-stability of Aloha network is considered for avoiding the risk of rapid performance deterioration. Extensive simulation results are presented to verify our analysis and the effectiveness of the proposed algorithm.

Microwave is widely used in present navigation system. It works well, yet it faces precision limit and other challenges. Quantum entanglement has been applied in various fields and resulted in quite remarkable improvements, especially in precision performance. Hence, we attempt to apply quantum entanglement to enhance merits of navigation system performed in microwave regime. This paper introduces principles of microwave-optical entanglement prepared by electro-opto-mechanical converters in detail. Meanwhile, a navigation ranging scheme exploiting this type of entanglement is proposed. The performance of the scheme is analyzed, and factors influencing entanglement feature and ranging feature are discussed. It shows that the ranging scheme has achieved quite precise results with help of microwave-optical entanglement, and it may be regarded as a promising candidate of new navigation ranging scheme.

Grid cells are important neurons related to spatial cognition and navigation in the animal brain and can respond to both external information and self-motion information. The existing models simulated these two types of responses separately, but how to simultaneously develop the two types of connection to respond to the different information has not been simulated. In this paper, first, we develop the connections from place cells to grid cells through improved nonnegative principal component analysis. Then, we construct a continuous attractor network model and an autoencoder network model of grid cell module and convert the parameter learning in the continuous attractor network into weight learning in the autoencoder network. Through parameter learning, the continuous attractor network model can spontaneously generate a hexagonal firing pattern. Ultimately, the grid cell firing fields driven by place cell inputs and self-motion information have the same spacing and direction, which means that the grid cell module can respond the same to these two types of information. This model can provide a reference for the construction of an unmanned agent brain-inspired navigation system.

Short-term passenger flow prediction of urban rail transit is very important to traffic management. The prediction result is a key input for the crowd early warning system. However, a reliable approach of short-term passenger flow prediction is still greatly desired. At present, most researches focus on single-site prediction (SSP), and its technique cannot be improved due to its maturity, and any further breakthrough is difficult to make. In this paper, a multi-sites prediction method (MSP) of passenger flow in subway station is proposed. Real time passenger flow data collected from multi-sites in a subway station is used as inputs, and delay parameter is introduced to identify the correlation between measurements at multiple sites in this paper. In order to achieve a stable predictive effect, wavelet decomposition and reconstruction are used to process the data. Dynamic weights combination of Support Vector Machines (SVM) and Radial Basis Function Neural Network (RBF) are applied to obtain the prediction of each frequency component. A case study of a station in Beijing subway shows that the proposed MSP method reduces the average absolute prediction error by 15.8% compared with the SSP. It is concluded that the proposed method has good performance in the case.

In order to simplify the design procedure of traditional neural network backstepping and improve the robustness and precision of control, an improved scheme is studied for a class of nonlinear systems. To avoid reconstructing virtual control inputs in each recursive step, RBF neural networks are utilized as approximators to estimate the desired feedback control of the whole system only. Meanwhile, the integral action of tracking error is introduced into the backstepping design procedure, which not only participates in updating the neural network weight, but also serves as a component part of the control input. This design may benefit the parameter tuning and make controller perform better sometimes. Based on the Lyapunov synthesis approach, theoretical analysis and simulation results are provided to show the feasibility of the improved scheme.

Passenger assignment of rail transit has recently attracted increasing research interest due to its potential applications in large-scale intelligent transportation systems. In the rail transit system, the foundation of passenger assignment is passengers' origin and destination demand (OD matrix). However, due to the nature of stochastic of the short-term dynamic OD matrix, how to accurately predict the distribution of passenger travel spatio-temporally is still an open challenge. In this paper, combined multisource data with deep learning method is proposed to improve prediction of dynamic OD matrix accuracy. Firstly, multisource data such as smart card data, weather data and mobile phone data are introduced. And after quantitative analysis of the influencing factors, choosing 31 features as model inputs. Secondly, considering the superiority of Long Short-term Memory Network in time series, we improve the structure of LSTM by redesigning the hidden layer and neuron, in view of the spatio-temporal characteristics of spatio-temporal Long Short-term Memory Network (STLSTM) of rail transit passenger flow. Finally, using the Beijing subway network which had 54,056OD for verification. Extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of STLSTM over commonly used prediction models and standard LSTM model for short-term prediction of dynamic OD matrix. In addition, the application method of multisource data in OD prediction in this paper can deal with more data from other sources to further improve the information exploit effect on passenger flow law.

A novel design method, which can transform the nonlinear circuits design to the impedance matching network (IMN) design, is proposed for wideband analog predistoter (APD) with the characteristics of frequency-dependent gain and phase conversions in this paper. First, the design method for the desired gain and phase conversions is introduced. The ABCD parameters of the IMN can be computed. To simplify the design, the obtained parameters are transformed to the IMN terminal impedance. Then we can only need to design the IMN terminal impedance to obtain the desired transfer characteristics. In the meanwhile, an APD with 4.5 dB gain conversion and 50° phase conversion is designed and simulated to validate the design method for specify gain and phase conversions. The simulated results show good effectiveness of the design method. Second, for wideband application, the complex gain and phase conversions surface design is transformed to the IMN terminal impendence curve versus frequency design. This gives the designers a well-defined strategy for wideband APD design focusing on frequency-dependent phase conversion as well as gain conversion. To validate the proposed method, a wideband APD operating at K-band (17-20 GHz) is designed, fabricated and measured. A linearized 100W K-band TWTA is shown to achieve a noise power ratio (NPR) >20 dB at 3 dB output power back-off, and phase change

The goal of video super-resolution technique is to address the problem of effectively restoring high-resolution (HR) videos from low-resolution (LR) ones. Previous methods commonly used optical flow to perform frame alignment and designed a framework from the perspective of space and time. However, inaccurate optical flow estimation may occur easily which leads to inferior restoration effects. In addition, how to effectively fuse the features of various video frames remains a challenging problem. In this paper, we propose a Local-Global Fusion Network (LGFN) to solve the above issues from a novel viewpoint. As an alternative to optical flow, deformable convolutions (DCs) with decreased multi-dilation convolution units (DMDCUs) are applied for efficient implicit alignment. Moreover, a structure with two branches, consisting of a Local Fusion Module (LFM) and a Global Fusion Module (GFM), is proposed to combine information from two different aspects. Specifically, LFM focuses on the relationship between adjacent frames and maintains the temporal consistency while GFM attempts to take advantage of all related features globally with a video shuffle strategy. Benefiting from our advanced network, experimental results on several datasets demonstrate that our LGFN can not only achieve comparative performance with state-of-the-art methods but also possess reliable ability on restoring a variety of video frames. The results on benchmark datasets of our LGFN are presented on https://github.com/BIOINSu/LGFN and the source code will be released as soon as the paper is accepted.

Increasingly, blockchain technology is attracting significant attentions in various agricultural applications. These applications could satisfy the diverse needs in the ecosystem of agricultural products, e.g., increasing transparency of food safety and IoT based food quality control, provenance traceability, improvement of contract exchanges, and transactions efficiency. As multiple untrusted parties, including small-scale farmers, food processors, logistic companies, distributors and retailers, are involved into the complex farm-to-fork pipeline, it becomes vital to achieve optimal trade-off between efficiency and integrity of the agricultural management systems as required in contexts. In this paper, we provide a survey to study both techniques and applications of blockchain technology used in the agricultural sector. First, the technical elements, including data structure, cryptographic methods, and consensus mechanisms are explained in detail. Secondly, the existing agricultural blockchain applications are categorized and reviewed to demonstrate the use of the blockchain techniques. In addition, the popular platforms and smart contract are provided to show how practitioners use them to develop these agricultural applications. Thirdly, we identify the key challenges in many prospective agricultural systems, and discuss the efforts and potential solutions to tackle these problems. Further, we conduct an improved food supply chain in the post COVID-19 pandemic economy as an illustration to demonstrate an effective use of blockchain technology.

A bolt-clamped type piezoelectric actuator, which could output rotary motions by using longitudinal-torsional hybrid vibration, was proposed and tested in this paper. The proposed actuator is mainly composed of a piezoelectric stack, a leftward bending longitudinal-torsional convertor (LBLTC), and a rightward bending longitudinal-torsional convertor (RBLTC). The piezoelectric stack, which is clamped between the LBLTC and RBLTC, is utilized to generate longitudinal vibration under exciting of a sinusoidal signal with a certain frequency. The two convertors transform part of the longitudinal vibration generated by the piezoelectric stack into the torsional vibration and generate elliptical motions at the ends of the driving tips, which can push the rotors to rotate step by step. The structure of the proposed actuator was designed, and the working principle for the rotary driving was analyzed. Then, modal, harmonic, and transient analyses were carried out by the finite element method to determine the structural dimensions of the convertors and verify the movement trajectories of the driving tips. Finally, a prototype was fabricated, and its vibration and output characteristics were tested. The experimental results indicated that the maximum no-load speed and maximum torque of the actuator were 342 r/min and 0.072 N·m under the preload of 11 N and the voltage of 300 VP-P. The simulation and experimental results verified the feasibility of the proposed actuator.

In this paper, based on the advantages of Newtonian mechanics method, mixed coordinate method, and analytical mechanics method, the dynamic model of rigid-aeroelasticity-sloshing coupling is established for longitudinal vibration and liquid fuel sloshing, as well as the coupling between elastic deformation and liquid sloshing and their influence on the liquid rocket motion are analyzed. Considering the coupling effect between aeroelasticity and liquid sloshing, a new flutter analysis model is proposed, and the corresponding flutter analysis is carried out. First, the simplified model of the body and the liquid of the tank are established. By using Newtonian mechanics and analytical mechanics method, the nonlinear structural dynamic model considering the 3-D vibration and liquid sloshing is obtained. Second, influence of rigid-aeroelasticity, rigid-sloshing, aeroelasticity-sloshing, and rigid-aeroelasticity-sloshing coupling is simulated and analyzed. Finally, a new simplified model for the analysis of the flutter of the body is built by taking into account the impact of the liquid fuel sloshing. The results demonstrate that the elastic deformation and liquid sloshing change the motion characteristics of the rocket, and there is a strong coupling between the aeroelasticity and sloshing. Meanwhile, liquid sloshing has different effects on the flutter in all directions.

The foot-terrain interaction mechanics of human is important in understanding the biped locomotion mechanism. This paper aims to investigate the effect of walking speed and the hardness of the terrain on foot-terrain interaction mechanics. In the experiments, mobile portable plantar mechanical measuring insoles with 107 sensor elements were used to gauge the plantar force and contact area in real-time. Seven subjects with healthy feet have participated in typical experiments. The statistical methods including correlation analysis, univariance analysis, and systematic identification are primarily used to obtain several main results. Bimodal functions in describing the total pressure-time relations were performed at slow and regular speeds but only one hump is left if walking at fast paces; they can be unified and summed by three subsections' unimodal functions. The forefoot produces a large peak impact force in slow and regular quasi-static walking on both hard ground and deformable terrains, while the heel absorbs more dynamic impact shocks in fast walking on hard ground. The torques generated by the ankle joints are calculated based on plantar force or its derived information such as zero moment point. Some implications are drawn, for example, the dynamically changed positions of zero moment point (ZMP) for humans are similar to that generated by reptiles wiggling through the flowable terrains; increasing the stride length on the hard ground produces more impact vibrations than quickening paces but more effective in accelerating walks on deformable terrains.

The video super-resolution (VSR) task aims to restore a high-resolution (HR) video frame by using its corresponding low-resolution (LR) frame and multiple neighboring frames. At present, many deep learning-based VSR methods rely on optical flow to perform frame alignment. The final recovery results will be greatly affected by the accuracy of optical flow. However, optical flow estimation cannot be completely accurate, and there are always some errors. In this paper, we propose a novel deformable non-local network (DNLN) which is a non-optical-flow-based method. Specifically, we apply the deformable convolution and improve its ability of adaptive alignment at the feature level. Furthermore, we utilize a non-local structure to capture the global correlation between the reference frame and the aligned neighboring frames, and simultaneously enhance desired fine details in the aligned frames. To reconstruct the final high-quality HR video frames, we use residual in residual dense blocks to take full advantage of the hierarchical features. Experimental results on benchmark datasets demonstrate that the proposed DNLN can achieve state-of-the-art performance on VSR task. The code is available at https://github.com/wh1h/DNLN.

Icing on airport runway is an important issue that affecting the operation of the airport. The research on runway icing is of great significance for ensuring the operation and safety of the airport. Firstly, in this paper, the runway temperature and ice thickness were studied by considering the underground temperature. Then combined with industrial configuration software and ActiveX controls, the runway icing prediction system was designed and verified. Through the analysis of the temperature field inside and outside of the runway as well as the studies of the meteorological factors around the surface and the runway heat exchange, the prediction equation of the runway temperature was obtained. Based on the equation, by using the predicted values of temperature and other factors, the BP neural network was trained to predict the ice thickness of the road in future. Furthermore, this paper designed an airport runway icing prediction system which installed with ActiveX controls where embedded with temperature prediction algorithm and neural network training algorithm. The system used multisensor to collect data, and wireless data transmission via GPRS DTU device. The virtual interface software and the configuration software embedded with algorithm were installed on the industrial PC to realize the prediction. By comparing the prediction of ice thickness with or without underground temperature, the average prediction accuracy of ice thickness by considering underground temperature was 13% higher than that without considering underground temperature, which verified the accuracy and feasibility of the ice prediction system.

Traditional soft margin support vector machine usually uses hinge loss to build a classifier with the “maximum-margin” principle. However, C-SVM depends on support vectors causing the loss of data information. Then, least square support vector machine is proposed with square loss ( $l_{2}$ -loss). It establishes equality constraints instead of inequalities and considering all the instances. However, the square loss is still not the perfect one, since it gives equivalent punishment to the instances at both sides of the center plane. It does not match the reality considering the instances between two center planes deserve heavier penalty than the others. To this end, we propose a novel SVM method with the adoption of the asymmetry LINEX (linear-exponential) loss, which we called it LINEX-SVM. The LINEX loss gives different treatments to instances based on the importance of each point. It gives a heavier penalty to the points between two center planes while drawing light penalty to the points outside of the corresponding center planes. The comprehensive experiments have been implemented to validate the effectiveness of the LINEX-SVM.

Focusing on the problem of vulnerable anti-deception and weak anti-jamming ability in classical pseudorandom code correlation ranging, we present a navigation ranging scheme with true random entangled microwave signals. One path of the entangled microwave signals uses for transmitting signal and another path of the signals preserves at locality. Firstly, the target that possesses the function of quantum repeater receives transmitting signal and forwards it to the locality. Then, correlation detection is implemented between the forwarded signal and the local delayed signal. Finally, the target distance can be calculated by delay measurement. We analyze the ranging precision and anti-deception ability. Results indicate that the scheme not only is superior to classical code correlation ranging precision, but also has very strong anti-multipath ability and anti-multipath interference ability, which will enhance the security of the navigation ranging system. Our investigation has significant application prospects.

The modern and future communication systems demand higher data rate exceeds current capabilities. Faster-than-Nyquist (FTN) signaling is a promising technique for the reason it can improve the spectral efficiency. This paper deals with the receiver design for FTN signaling equalization in nonlinear channels. To handle the correlated noise samples induced by FTN signaling, we use the auto-regressive process to model the colored noise. Moreover, due to the nonlinearities, the complexity of receiver becomes prohibitively high. To reduce the complexity, we linearize the Volterra model and formulate the considered system as linear system. Then, based on the factor graph and message passing framework, the FTN symbols are detected with low complexity. Simulation results show that the proposed low complexity receiver can achieve similar performance of Nyquist signaling while increase 25% data rate.

In this paper, an aircraft ground centralized deicing monitoring system of multi-information integration is proposed. The multi-information integration system consists of system architecture, database, and monitoring terminal. Among them, the prediction module of ice accumulation calculates ice thickness of the aircraft based on the meteorological data that are collected by the data acquisition module and saved in the database. At the same time, the system uses the methods that GIS is used for making map to locate deicing vehicle precisely, data are transmitted through GPRS, and computer scheduling is adopted, which can ensure the smooth aircraft deicing process and improve the efficiency of the aircraft ground deicing. The practical results show that the proposed monitoring system of aircraft ground centralized deicing increases the efficiency of deicing by 26%. Furthermore, the feasibility and effectiveness of the proposed system are verified.

The study of direct push sampling characteristics forms the theoretical basis for deep sampling tool design. In this paper, a ground test of the direct push sampling was carried out to analyze three influencing factors: tube diameter; friction coefficient; and soil plug length. Using the discrete element method software, EDEM, a lunar subsurface direct push sampling model was established. We propose a vortex structure of the particle displacement field in the sampling tube and establish a connection between the three influencing factors and the structure disturbance of the soil plug. The EDEM simulation revealed the microscopic mechanism of penetration sampling disturbance. The results show that the soil plug in the compressive state had a small disturbance and a high degree of sequence retention and that the internal stress was changed compared to that in the in-situ state. The structure disturbance of the soil plug in the stick-slip state was large, while the parts of the soil plug that are away from the center line of the tube were in disorder. In the center line of the tube, the in-situ sequence state was gradually restored with the increase in tube diameter or decrease in soil plug length. Increasing the friction coefficient would change the soil plug from the compression state to the stick-slip or adhesion state. In case of the stick-slip state or adhesion, reducing the diameter of the tube or increasing the length of the soil plug would change the soil plug from the stick-slip state or adhesion state to the compression state.

A rotary driving component is the power output unit of a lunar sampler. A planetary gear system is used as the transmission system. The lunar sampler is subjected to an extremely rigorous mechanical environment during transportation. In this paper, the time-varying meshing stiffness curves of planetary gears are calculated by the Shi Chuan method. A translational torsional dynamic model of a two-stage planetary gear system is established. Based on the Runge-Kutta method, a solution program is used to obtain the inherent characteristics of the transmission, and corresponding verification tests are performed. With respect to the transportation process (i.e., the stage between launch and landing), the dynamic response of the transmission is examined. With respect to adverse geological conditions, the load sharing characteristics of the transmission are calculated. The aforementioned investigations provide a theoretical basis for parameter setting and structural design of a lunar exploration project.