Flight parameter calculation method of multi-projectiles using temporal and spatial information constraint

The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.

Matching while Learning: Wireless Scheduling for Age of Information Optimization at the Edge

In this paper,we investigate the minimization of age of information(AoI),a metric that measures the information freshness,at the network edge with unreliable wireless communications.Particularly,we consider a set of users transmitting status updates,which are collected by the user randomly over time,to an edge server through unreliable orthogonal channels.It begs a natural question:with random status update arrivals and obscure channel conditions,can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI?To give an adequate answer,we define a bipartite graph and formulate a dynamic edge activation problem with stability constraints.Then,we propose an online matching while learning algorithm(MatL)and discuss its implementation for wireless scheduling.Finally,simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users’buffers for fresher information at the edge.

The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degra certain limitations in sustaining the advancement of Moore’s law.Two-dimensional(2D)materials have emerged as highly promising candidates for the post-Moore era,offering significant potential in domains such as integrated circuits and next-generation computing.Here,in this review,the progress of 2D semiconductors in process engineering and various electronic applications are summarized.A careful introduction of material synthesis,transistor engineering focused on device configuration,dielectric engineering,contact engineering,and material integration are given first.Then 2D transistors for certain electronic applications including digital and analog circuits,heterogeneous integration chips,and sensing circuits are discussed.Moreover,several promising applications(artificial intelligence chips and quantum chips)based on specific mechanism devices are introduced.Finally,the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed,and potential development pathways or roadmaps are further speculated and outlooked.

A review of artificial intelligence applications in high-speed railway systems

In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.

Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the interleaver.We experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

Targeted regeneration and upcycling of spent graphite by defect‐driven tin nucleation

The recycling of spent batteries has become increasingly important owing to their wide applications,abundant raw material supply,and sustainable development.Compared with the degraded cathode,spent anode graphite often has a relatively intact structure with few defects after long cycling.Yet,most spent graphite is simply burned or discarded due to its limited value and inferior performance on using conventional recycling methods that are complex,have low efficiency,and fail in performance restoration.Herein,we propose a fast,efficient,and“intelligent”strategy to regenerate and upcycle spent graphite based on defect‐driven targeted remediation.Using Sn as a nanoscale healant,we used rapid heating(~50 ms)to enable dynamic Sn droplets to automatically nucleate around the surface defects on the graphite upon cooling owing to strong binding to the defects(~5.84 eV/atom),thus simultaneously achieving Sn dispersion and graphite remediation.As a result,the regenerated graphite showed enhanced capacity and cycle stability(458.9 mAh g^(−1) at 0.2 A g^(−1) after 100 cycles),superior to those of commercial graphite.Benefiting from the self‐adaption of Sn dispersion,spent graphite with different degrees of defects can be regenerated to similar structures and performance.EverBatt analysis indicates that targeted regeneration and upcycling have significantly lower energy consumption(~99%reduction)and near‐zero CO_(2) emission,and yield much higher profit than hydrometallurgy,which opens a new avenue for direct upcycling of spend graphite in an efficient,green,and profitable manner for sustainable battery manufacture.

A Probing Model of Secret Key Generation Based on Channel Autocorrelation Function

Secret key generation(SKG)is a promising solution to the problem of wireless communications security.As the first step of SKG,channel probing affects it significantly.Although there have been some probing schemes,there is a lack of research on the optimization of the probing process.This study investigates how to optimize correlated parameters to maximize the SKG rate(SKGR)in the time-division duplex(TDD)mode.First,we build a probing model which includes the effects of transmitting power,the probing period,and the dimension of sample vectors.Based on the model,the analytical expression of the SKGR is given.Next,we formulate an optimization problem for maximizing the SKGR and give an algorithm to solve it.We conclude the SKGR monotonically increases as the transmitting power increases.Relevant mathematical proofs are given in this study.From the simulation results,increasing appropriately the probing period and the dimension of the sample vector could increase the SKGR dramatically compared to a yardstick,which indicates the importance of optimizing the parameters related to the channel probing phase.

Task Offloading and Resource Allocation in NOMA-VEC:A Multi-Agent Deep Graph Reinforcement Learning Algorithm

Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility.

Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.

Flight Time Minimization of UAV for Cooperative Data Collection in Probabilistic LoS Channel

This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabilistic line-of-sight(LoS) channel. Especially, access points(APs) are introduced to collect data from some sensors in the unlicensed band to improve data collection efficiency. We formulate a mixed-integer non-convex optimization problem to minimize the UAV flight time by jointly designing the UAV 3D trajectory and sensors’ scheduling, while ensuring the required amount of data can be collected under the limited UAV energy. To solve this nonconvex problem, we recast the objective problem into a tractable form. Then, the problem is further divided into several sub-problems to solve iteratively, and the successive convex approximation(SCA) scheme is applied to solve each non-convex subproblem. Finally,the bisection search is adopted to speed up the searching for the minimum UAV flight time. Simulation results verify that the UAV flight time can be shortened by the proposed method effectively.

Morphological Classification of Infrared Galaxies Based on WISE

This study introduces a novel convolutional neural network,the WISE Galaxy Classification Network(WGC),for classifying spiral and elliptical galaxies using Wide-field Infrared Survey Explorer(WISE)images.WGC attains an accuracy of 89.03%,surpassing the combined use of K-means or SVM with the Color-Color method in more accurately identifying galaxy morphologies.The enhanced variant,WGC_mag,integrates magnitude parameters with image features,further boosting the accuracy to 89.89%.The research also delves into the criteria for galaxy classification,discovering that WGC primarily categorizes dust-rich images as elliptical galaxies,corresponding to their lower star formation rates,and classifies less dusty images as spiral galaxies.The paper explores the consistency and complementarity of WISE infrared images with SDSS optical images in galaxy morphology classification.The SDSS Galaxy Classification Network(SGC),trained on SDSS images,achieved an accuracy of 94.64%.The accuracy reached 99.30% when predictions from SGC and WGC were consistent.Leveraging the complementarity of features in WISE and SDSS images,a novel variant of a classifier,namely the Multi-band Galaxy Morphology Integrated Classifier,has been developed.This classifier elevates the overall prediction accuracy to 95.39%.Lastly,the versatility of WGC was validated in other data sets.On the HyperLEDA data set,the distinction between elliptical galaxies and Sc,Scd and Sd spiral galaxies was most pronounced,achieving an accuracy of 90%,surpassing the classification results of the Galaxy Zoo 2 labeled WISE data set.This research not only demonstrates the effectiveness of WISE images in galaxy morphology classification but also represents an attempt to integrate multi-band astronomical data to enhance understanding of galaxy structures and evolution.

Long-term variation of Arctic Sudden Stratospheric Warmings(SSW)and potential causes

Utilizing the European Centre for Medium-Range Weather Forecast(ECMWF)Reanalysis v5(ERA5),for the first time,we have confirmed close links among Sudden Stratospheric Warmings(SSWs)in the Northern Hemisphere(NH),the polar vortices,and stratospheric Planetary Waves(PWs)by analyzing and comparing their trends.Interestingly,within overall increasing trends,the duration and strength of SSWs exhibit increasing and decreasing trends before and after the winter of 2002,respectively.To reveal possible physical mechanisms driving these trends,we analyzed the long-term trends of the winter(from December to February)polar vortices and of stratospheric PWs with zonal wave number 1.Notably,our results show that in all three time periods(the entire period of 41winters,1980 to 2020,and the two subperiods—1980-2002 and 2002-2020)enhancing SSWs were always accompanied by weakening winter polar vortices and strengthening polar PWs like Stationary Planetary Waves(SPWs)and 16-day waves,and vice versa.This is the first proof,based on ERA5 long-term trend data,that weakening polar vortices and enhancing stratospheric PWs(especially SPWs)could cause an increase in SSWs.

A Review of NILM Applications with Machine Learning Approaches

In recent years,Non-Intrusive LoadMonitoring (NILM) has become an emerging approach that provides affordableenergy management solutions using aggregated load obtained from a single smart meter in the power grid.Furthermore, by integrating Machine Learning (ML), NILM can efficiently use electrical energy and offer less ofa burden for the energy monitoring process. However, conducted research works have limitations for real-timeimplementation due to the practical issues. This paper aims to identify the contribution of ML approaches todeveloping a reliable Energy Management (EM) solution with NILM. Firstly, phases of the NILM are discussed,along with the research works that have been conducted in the domain. Secondly, the contribution of machinelearning approaches in three aspects is discussed: Supervised learning, unsupervised learning, and hybridmodeling.It highlights the limitations in the applicability of ML approaches in the field. Then, the challenges in the realtimeimplementation are concerned with six use cases: Difficulty in recognizing multiple loads at a given time,cost of running the NILM system, lack of universal framework for appliance detection, anomaly detection andnew appliance identification, and complexity of the electricity loads and real-time demand side management.Furthermore, options for selecting an approach for an efficientNILMframework are suggested. Finally, suggestionsare provided for future research directions.

Deployment of Edge Computing Nodes in IoT:Effective Implementation of Simulated Annealing Method Based on User Location

Edge computing paradigm for 5G architecture has been considered as one of the most effective ways to realize low latency and highly reliable communication,which brings computing tasks and network resources to the edge of network.The deployment of edge computing nodes is a key factor affecting the service performance of edge computing systems.In this paper,we propose a method for deploying edge computing nodes based on user location.Through the combination of Simulation of Urban Mobility(SUMO)and Network Simulator-3(NS-3),a simulation platform is built to generate data of hotspot areas in Io T scenario.By effectively using the data generated by the communication between users in Io T scenario,the location area of the user terminal can be obtained.On this basis,the deployment problem is expressed as a mixed integer linear problem,which can be solved by Simulated Annealing(SA)method.The analysis of the results shows that,compared with the traditional method,the proposed method has faster convergence speed and better performance.

Hybrid bonding of GaAs and Si wafers at low temperature by Ar plasma activation

High-quality bonding of 4-inch GaAs and Si is achieved using plasma-activated bonding technology.The influence of Ar plasma activation on surface morphology is discussed.When the annealing temperature is 300℃,the bonding strength reaches a maximum of 6.2 MPa.In addition,a thermal stress model for GaAs/Si wafers is established based on finite element analysis to obtain the distribution of equivalent stress and deformation variables at different temperatures.The shape varia-tion of the wafer is directly proportional to the annealing temperature.At an annealing temperature of 400℃,the maximum protrusion of 4 inches GaAs/Si wafers is 3.6 mm.The interface of GaAs/Si wafers is observed to be dense and defect-free using a transmission electron microscope.The characterization of interface elements by X-ray energy dispersion spectroscopy indi-cates that the elements at the interface undergo mutual diffusion,which is beneficial for improving the bonding strength of the interface.There is an amorphous transition layer with a thickness of about 5 nm at the bonding interface.The preparation of Si-based GaAs heterojunctions can enrich the types of materials required for the development of integrated circuits,improve the performance of materials and devices,and promote the development of microelectronics technology.

How do the non-linear I-V curves of ZnO-based adaptive composites behave with electrodes placed on the opposite sides with a series of horizontal distances?

Recently,ZnO-based composites have been widely applied in the field of electric power.To meet the diverse application requirements,it is necessary to figure out the I-V characteristics of ZnO composites whose high-voltage and ground-voltage electrodes are arranged on the opposite sides with a certain horizontal distance.30 vol%,40 vol%and 50 vol%ZnO-based silicone rubber composites were prepared.The horizontal distance between their electrodes was set as 50,100,500μm,1 and 2 mm,respectively.Results showed that with the increase of ZnO fillers volume fraction under a fixed horizontal distance of 100μm,from 30 vol%to 50 vol%,the I-V curves shifted left,the leakage current increased and the switching voltage decreased.When the horizontal distance between electrodes increased from 50μm to 1 mm under a fixed doping concentration of 40%,the I-V curves shifted to the right,the leakage current dropped and the switching voltage rose.The mathematical and physical models were established to explain the results.This work provides a referential significance for the practical application of ZnO composites,such as 5G folding mobile phones and power electronic modules.

Wireless Information and Power Transfer in Underwater Acoustic Sensor Networks

Wireless information and power transfer(WIPT) enables simultaneously communications and sustainable power supplement without the erection of power supply lines and the replacement operation of the batteries for the terminals. The application of WIPT to the underwater acoustic sensor networks(UWASNs) not only retains the long range communication capabilities, but also provides an auxiliary and convenient energy supplement way for the terminal sensors, and thus is a promising scheme to solve the energy-limited problem for the UWASNs. In this paper, we propose the integration of WIPT into the UWASNs and provide an overview on various enabling techniques for the WIPT based UWASNs(WIPT-UWASNs) as well as pointing out future research challenges and opportunities for WIPT-UWASNs.

Low-Complexity Reconstruction of Covariance Matrix in Hybrid Uniform Circular Array

Spatial covariance matrix(SCM) is essential in many multi-antenna systems such as massive multiple-input multiple-output(MIMO). For multi-antenna systems operating at millimeter-wave bands, hybrid analog-digital structure has been widely adopted to reduce the cost of radio frequency chains.In this situation, signals received at the antennas are unavailable to the digital receiver, and as a consequence, traditional sample average approach cannot be used for SCM reconstruction in hybrid multi-antenna systems. To address this issue, beam sweeping algorithm(BSA) which can reconstruct the SCM effectively for a hybrid uniform linear array, has been proposed in our previous works. However, direct extension of BSA to a hybrid uniform circular array(UCA)will result in a huge computational burden. To this end, a low-complexity approach is proposed in this paper. By exploiting the symmetry features of SCM for the UCA, the number of unknowns can be reduced significantly and thus the complexity of reconstruction can be saved accordingly. Furthermore, an insightful analysis is also presented in this paper, showing that the reduction of the number of unknowns can also improve the accuracy of the reconstructed SCM. Simulation results are also shown to demonstrate the proposed approach.

Mitigating Straggler Effect in Federated Learning Based on Reconfigurable Intelligent Surface over Internet of Vehicles

To protect vehicular privacy and speed up the execution of tasks,federated learning is introduced in the Internet of Vehicles(IoV)where users execute model training locally and upload local models to the base station without massive raw data exchange.However,heterogeneous computing and communication resources of vehicles cause straggler effect which weakens the reliability of federated learning.Dropping out vehicles with limited resources confines the training data.As a result,the accuracy and applicability of federated learning models will be reduced.To mitigate the straggler effect and improve performance of federated learning,we propose a reconfigurable intelligent surface(RIS)-assisted federated learning framework to enhance the communication reliability for parameter transmission in the IoV.Furthermore,we optimize the phase shift of RIS to achieve a more reliable communication environment.In addition,we define vehicular competence to measure both vehicular trustworthiness and resources.Based on the vehicular competence,the straggler effect is mitigated where training tasks of computing stragglers are offloaded to surrounding vehicles with high competence.The experiment results verify that our proposed framework can improve the reliability of federated learning in terms of computing and communication in the IoV.

OFDM with Generalized Cascade Index Modulation for Secure Communications

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error performance.In CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital algorithm.Then, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. Computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.