Beam Training and Tracking in mmWave Communication:A Survey

Communicating on millimeter wave(mmWave)bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission.However,mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles.To overcome these challenges,highly directional beams are exploited to achieve robust links by hybrid beamforming.Accurately aligning the transmitter and receiver beams,i.e.beam training,is vitally important to high data rate transmission.However,it may cause huge overhead which has negative effects on initial access,handover,and tracking.Besides,the mobility patterns of users are complicated and dynamic,which may cause tracking error and large tracking latency.An efficient beam tracking method has a positive effect on sustaining robust links.This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation(6G)mobile network.Especially,some open research problems are proposed to realize fast,accurate,and robust beam training and tracking.We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

Sulfhydryl-functionalized COF-based electrolyte strengthens chemical affinity toward polysulfides in quasi-solid-state Li-S batteries

For lithium-sulfur batteries(Li-S batteries),a high-content electrolyte typically can exacerbate the shuttle effect,while a lean electrolyte may lead to decreased Li-ion conductivity and reduced catalytic conversion efficiency,so achieving an appropriate electrolyte-to-sulfur ratio(E/S ratio)is essential for improving the battery cycling efficiency.A quasi-solid electrolyte(COF-SH@PVDF-HFP)with strong adsorption and high catalytic conversion was constructed for in situ covalent organic framework(COF)growth on highly polarized polyvinylidene fluoride-hexafluoropropylene(PVDF-HFP)fibers.COF-SH@PVDF-HFP enables efficient Li-ion conductivity with low-content liquid electrolyte and effectively suppresses the shuttle effect.The results based on in situ Fourier-transform infrared,in situ Raman,UV–Vis,X-ray photoelectron,and density functional theory calculations confirmed the high catalytic conversion of COF-SH layer containing sulfhydryl and imine groups for the lithium polysulfides.Lithium plating/stripping tests based on Li/COF-SH@PVDF-HFP/Li show excellent lithium compatibility(5 mAh cm^(-2) for 1400 h).The assembled Li-S battery exhibits excellent rate(2 C 688.7 mAh g^(-1))and cycle performance(at 2 C of 568.8 mAh g^(-1) with a capacity retention of 77.3%after 800 cycles).This is the first report to improve the cycling stability of quasi-solid-state Li-S batteries by reducing both the E/S ratio and the designing strategy of sulfhydryl-functionalized COF for quasi-solid electro-lytes.This process opens up the possibility of the high performance of solid-state Li-S batteries.

Face animation based on multiple sources and perspective alignment

Background Face image animation generates a synthetic human face video that harmoniously integrates the identity derived from the source image and facial motion obtained from the driving video.This technology could be beneficial in multiple medical fields,such as diagnosis and privacy protection.Previous studies on face animation often relied on a single source image to generate an output video.With a significant pose difference between the source image and the driving frame,the quality of the generated video is likely to be suboptimal because the source image may not provide sufficient features for the warped feature map.Methods In this study,we propose a novel face-animation scheme based on multiple sources and perspective alignment to address these issues.We first introduce a multiple-source sampling and selection module to screen the optimal source image set from the provided driving video.We then propose an inter-frame interpolation and alignment module to further eliminate the misalignment between the selected source image and the driving frame.Conclusions The proposed method exhibits superior performance in terms of objective metrics and visual quality in large-angle animation scenes compared to other state-of-the-art face animation methods.It indicates the effectiveness of the proposed method in addressing the distortion issues in large-angle animation.

Reinforced Lewis covalent bond by twinborn nitride heterostructure for lithium-sulfur batteries

The practical application of lithium-sulfur(Li-S)batteries,as promising next-generation batteries,is hindered by their shuttle effect and the slow redox kinetics.Herein,a tungsten and molybdenum nitride heterostructure functionalized with hollow metal-organic framework-derived carbon(W_(2)N/Mo_(2)N)was proposed as the sulfur host.The hollow spherical structure provides storage space for sulfur,enhances electrical conductivity,and inhibits volume expansion.The metal atoms in the nitrides bonded with lithium polysulfides(Li PSs)through Lewis covalent bonds,enhancing the high catalytic activity of the nitrides and effectively reducing the energy barrier of Li PSs redox conversion.Moreover,the high intrinsic conductivity of nitrides and the ability of the heterostructure interface to accelerate electron/ion transport improved the Li+transmission.By leveraging the combined properties of strong adsorption and high catalytic activity,the sulfur host effectively inhibited the shuttle effect and accelerated the redox kinetics of Li PSs.High-efficiency Li+transmission,strong adsorption,and the efficient catalytic conversion activities of Li PSs in the heterostructure were experimentally and theoretically verified.The results indicate that the W_(2)N/Mo_(2)N cathode provides stable,and long-term cycling(over 2000 cycles)at 3 C with a low attenuation rate of 0.0196%per cycle.The design strategy of a twinborn nitride heterostructure thus provides a functionalized solution for advanced Li-S batteries.

UAV-Aided Data and Energy Integrated Network: System Design and Prototype Development

Terminal devices deployed in outdoor environments are facing a thorny problem of power supply.Data and energy integrated network(DEIN)is a promising technology to solve the problem,which simultaneously transfers data and energy through radio frequency signals.State-of-the-art researches mostly focus on theoretical aspects.By contrast,we provide a complete design and implementation of a fully functioning DEIN system with the support of an unmanned aerial vehicle(UAV).The UAV can be dispatched to areas of interest to remotely recharge batteryless terminals,while collecting essential information from them.Then,the UAV uploads the information to remote base stations.Our system verifies the feasibility of the DEIN in practical applications.

An Unambiguity and Anti-Range Eclipse Method for PD Radar Using Biphase Coded Signals

Target detection is an important research content in the radar field.At present,efforts are being made to optimize the precision of detection information.In this paper,we use the high pulse repetition frequency(HPRF)transmission method and orthogonal biphase coded signals in each pulse to avoid velocity ambiguity and range ambiguity of radar detection.In addition,We also apply Walsh matrix and genetic algorithm(GA)to generate satisfying orthogonal biphase coded signals with low auto-correlation sidelobe peak and cross-correlation peak,which make the results more accurate.In a radar receiver,data rearrangement of echo signals is performed,and then pulse compression and moving target detection(MTD)are utilized to get the final velocity and range information of a target without velocity ambiguity and range ambiguity.Besides,a small transmitting pulse time width is adopted to reduce the working blind area,and two different high pulse repetition frequencies(HPRFs)are adopted to solve the problem of range eclipse.Simulation results finally prove the effectiveness and feasibility of the proposed method.

A Polarization Programmable Antenna Array

Reconfigurable antennas are becoming a major antenna technology for future wireless communications and sensing systems.It is known that,with a single linear polarization(LP)reconfigurable antenna element,a preferred polarization can be produced from a set of multiple polarization states,thus improving the quality of the communication link.This paper presents a new concept of a polarization programmable reconfigurable antenna array that consists of a number of polarization reconfigurable antenna elements with a finite number of possible polarization states.By employing a new optimization strategy and programming the polarization states of all the array elements,we demonstrate that it is possible to realize any desired LP in the vectorial array radiation pattern with accurate control of sidelobe and crosspolarization levels(XPLs),thereby achieving the desired polarization to perfectly match that of the required communications signal.Both numerical and experimental results are provided to prove the concept,and they agree well with each other.

A review of deep learningg methods for ligand based drug virtual screening

Drug discovery is costly and time consuming,and modern drug discovery endeavors are progressively reliant on computational methodologies,aiming to mitigate temporal and financial expenditures associated with the process.In particular,the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic.Recently,the performance of deep learning methods in drug virtual screening has been particularly prominent.It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening,select different models for different drug screening problems,exploit the advantages of deep learning models,and further improve the capability of deep learning in drug virtual screening.This review first introduces the basic concepts of drug virtual screening,common datasets,and data representation methods.Then,large numbers of common deep learning methods for drug virtual screening are compared and analyzed.In addition,a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening.Finally,the existing challenges and future directions in the field of virtual screening are presented.

Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been conducted.We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model effect.We constructed three kernels in miRNA and disease space and conducted kernel fusion using HSIC-MKL.Link propagation uses matrix factorization and matrix approximation to effectively reduce computation and time costs.The results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.

Anti-swarm UAV radar system based on detection data fusion

There is a growing body of research on the swarm unmanned aerial vehicle(UAV)in recent years,which has the characteristics of small,low speed,and low height as radar target.To confront the swarm UAV,the design of anti-UAV radar system based on multiple input multiple output(MIMO)is put forward,which can elevate the performance of resolution,angle accuracy,high data rate,and tracking flexibility for swarm UAV detection.Target resolution and detection are the core problem in detecting the swarm UAV.The distinct advantage of MIMO system in angular accuracy measurement is demonstrated by comparing MIMO radar with phased array radar.Since MIMO radar has better performance in resolution,swarm UAV detection still has difficulty in target detection.This paper proposes a multi-mode data fusion algorithm based on deep neural networks to improve the detection effect.Subsequently,signal processing and data processing based on the detection fusion algorithm above are designed,forming a high resolution detection loop.Several simulations are designed to illustrate the feasibility of the designed system and the proposed algorithm.

A highly ionic transference number eutectogel hybrid electrolytes based on spontaneous coupling inhibitor for solid-state lithium metal batteries

Polymer-based solid electrolytes have been extensively studied for solid-state lithium metal batteries to achieve high energy density and reliable security.But,its practical application is severely limited by low ionic conductivity and slow Li+transference.Herein,based on the“binary electrolytes”of poly(vinylidene fluoride-chlorotrifluoroethylene)(P(VDF-CTFE))and lithium salt(LiTFSI),a kind of eutectogel hybrid electrolytes(EHEs)with high Li+transference number was developed via tuning the spontaneous coupling of charge and vacated space generated by Li-cation diffusion utilizing the Li6.4La3Zr1.4Ta0.6O12(LLZTO)dopant.LLZTO doping promotes the dissociation of lithium salt,increases Li+carrier density,and boosts ion jumping and the coordination/decoupling reactions of Li+.As a result,the optimized EHEs-10%possess a high Li-transference number of 0.86 and a high Li+conductivity of 3.2×10–4 S·cm–1 at room temperature.Moreover,the prepared EHEs-10%composite solid electrolyte presents excellent lithiumphilic and compatibility,and can be tested stably for 1,200 h at 0.3 mA·cm–2 with assembled lithium symmetric batteries.Likewise,the EHEs-10%films match well with high-loading LiFePO4 and LiCoO2 cathodes(>10 mg·cm–2)and exhibit remarkable interface stability.Particularly,the LiFePO4//EHEs-10%//Li and LiCoO2//EHEs-10%//Li cells deliver high rate performance of 118 mAh·g–1 at 1 C and 93.7 mAh·g–1 at 2 C with coulombic efficiency of 99.3%and 98.1%,respectively.This work provides an in-depth understanding and new insights into our design for polymer electrolytes with fast Li+diffusion.

MXene derivative Ta_(4)C_(3)-Ta_(2)O_(5) heterostructure as bi-functional barrier for Li-S batteries

The shuttle effect of polysulfides during the charging and discharging of lithium-sulfur(Li-S)batteries and the growth of Li dendrites are crucial obstacles to hinder the commercialization of Li-S batteries.Heterostructure engineering is an effective strategy to accelerate catalytic conversion and suppress the dissolution of polysulfides.Herein,we report a Ta_(4)C_(3)-Ta_(2)O_(5) heterostructure composite as a bi-functional modified separator that not only achieves effective protection for lithium metal but also accelerates the polysulfides redox kinetics process.This heterostructure possesses efficient chemical anchoring and abundant active sites to immobilize polysulfides by synergistic effect,which endows a stable long cycling performance for Li-S batteries.This corresponds to an initial high capacity of 801.9 mAh g^(–1) at 1 C with a decay rate of 0.086%for 500 cycles.Due to its high Young’s modulus(up to 384 GPa),Ta_(4)C_(3) contributes to forming a protective layer on the Li metal surface to inhibit the growth of Li dendrites.Accordingly,the symmetrical cell has a stable overpotential for 700 cycles at 20 mA cm^(–2)/20 mAh cm^(–2).So,this“one stone two birds”design affords a novel perspective for high-energy Li-S battery storage system design and Li metal protection.

Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level

Cancer treatments always face challenging problems,particularly drug resistance due to tumor cell heterogeneity.The existing datasets include the relationship between gene expression and drug sensitivities;however,the majority are based on tissue-level studies.Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy.Fortunately,machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression.Good modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments.In this paper,we review machine learning and deep learning approaches in drug research.By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis,we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level.We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.

Biological Sequence Classification:A Review on Data and General Methods

With the rapid development of biotechnology,the number of biological sequences has grown exponentially.The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information.There are many branches of biological sequence classification research.In this review,we mainly focus on the function and modification classification of biological sequences based on machine learning.Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA,RNA,proteins,and peptides.However,there are hundreds of classification models developed for biological sequences,and the quite varied specific methods seem dizzying at first glance.Here,we aim to establish a long-term support website(http://lab.malab.cn/~acy/BioseqData/home.html),which provides readers with detailed information on the classification method and download links to relevant datasets.We briefly introduce the steps to build an effective model framework for biological sequence data.In addition,a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included.Finally,we discuss the current challenges and future perspectives of biological sequence classification research.

Sandwich-type composite multilayer films of strontium titanate and barium strontium titanate and their controllable dielectric properties

It is a challenge to reduce the dielectric loss and increase the tunability of pure barium strontium titanate(BST)films for microwave tunable application because these two properties change simultaneously.Herein,a novel composite of strontium titanate(ST)and potassium-doped BST(KBST)has been designed as ST/KBST/ST sandwich-type film with various ST and KBST layers.X-ray diffraction patterns show that the film exhibits cubic perovskite polycrystalline structure composed of BST and ST phase,mainly grow along(110)crystal plane with average grain size of less than 20 nm and decreasing BST phase/ST phase ratio with increasing film thickness.Scanning electron microscope shows that no interfacial layer can be observed,indicating that ST and KBST are fully compounded.Low dielectric loss and high tunability at-10-10 V and stable and excellent dielectric properties at 1 GHz are achieved,meeting the needs of microwave tunable application at high frequency.The surface structures are also studied by other analysis methods,and ST/MgBST/ST sandwich-type film is compared.