A frequency domain estimation and compensation method for system synchronization parameters of distributed-HFSWR

To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on the target detection performance,firstly,a spatial detection model of distributed high-frequency surface wave radar(distributed-HFSWR)is established in this paper.In this model,a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio(SNR),and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error.The direct wave component is extracted from the spectrum,the range estimation error and Doppler estimation error are reduced by the method of curve fitting,and the fitting accuracy of the parameters is improved.Then,the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed.The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given.Finally,the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting,the experimental data is compensated to correct the shift of the target,and finally the correct target parameter information is obtained.Simulations and experimental results demonstrate the superiority and correctness of the proposed method,theoretical derivation and detection model proposed in this paper.

Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.

Effects of Mg-doping temperature on the structural and electrical properties of nonpolar a-plane p-type GaN films

Nonpolar(11–20) a-plane p-type GaN films were successfully grown on r-plane sapphire substrate with the metal–organic chemical vapor deposition(MOCVD) system. The effects of Mg-doping temperature on the structural and electrical properties of nonpolar p-type GaN films were investigated in detail. It is found that all the surface morphology, crystalline quality, strains, and electrical properties of nonpolar a-plane p-type GaN films are interconnected, and are closely related to the Mg-doping temperature. This means that a proper performance of nonpolar p-type GaN can be expected by optimizing the Mg-doping temperature. In fact, a hole concentration of 1.3×10^(18)cm^(-3), a high Mg activation efficiency of 6.5%,an activation energy of 114 me V for Mg acceptor, and a low anisotropy of 8.3% in crystalline quality were achieved with a growth temperature of 990℃. This approach to optimizing the Mg-doping temperature of the nonpolar a-plane p-type GaN film provides an effective way to fabricate high-efficiency optoelectronic devices in the future.

Emission and capture characteristics of deep hole trap in n-GaN by optical deep level transient spectroscopy

Emission and capture characteristics of a deep hole trap(H1)in n-GaN Schottky barrier diodes(SBDs)have been investigated by optical deep level transient spectroscopy(ODLTS).Activation energy(Eemi)and capture cross-section(σ_(p))of H1 are determined to be 0.75 eV and 4.67×10^(−15)cm^(2),respectively.Distribution of apparent trap concentration in space charge region is demonstrated.Temperature-enhanced emission process is revealed by decrease of emission time constant.Electricfield-boosted trap emission kinetics are analyzed by the Poole−Frenkel emission(PFE)model.In addition,H1 shows point defect capture properties and temperature-enhanced capture kinetics.Taking both hole capture and emission processes into account during laser beam incidence,H1 features a trap concentration of 2.67×10^(15)cm^(−3).The method and obtained results may facilitate understanding of minority carrier trap properties in wide bandgap semiconductor material and can be applied for device reliability assessment.

Microarrow sensor array with enhanced skin adhesion for transdermal continuous monitoring of glucose and reactive oxygen species

Conventional blood sampling for glucose detection is prone to cause pain and fails to continuously record glucose fluctuations in vivo.Continuous glucose monitoring based on implantable electrodes could induce pain and potential tissue inflammation,and the presence of reactive oxygen species(ROS)due to inflammationmay affect glucose detection.Microneedle technology is less invasive,yet microneedle adhesion with skin tissue is limited.In this work,we developed a microarrow sensor array(MASA),which provided enhanced skin surface adhesion and enabled simultaneous detection of glucose and H_(2)O_(2)(representative of ROS)in interstitial fluid in vivo.The microarrows fabricated via laser micromachining were modified with functional coating and integrated into a patch of a three-dimensional(3D)microneedle array.Due to the arrow tip mechanically interlocking with the tissue,the microarrow array could better adhere to the skin surface after penetration into skin.The MASA was demonstrated to provide continuous in vivo monitoring of glucose and H_(2)O_(2) concentrations,with the detection of H_(2)O_(2) providing a valuable reference for assessing the inflammation state.Finally,the MASA was integrated into a monitoring system using custom circuitry.This work provides a promising tool for the stable and reliable monitoring of blood glucose in diabetic patients.

Alkyl chain modulation of asymmetric hexacyclic fused acceptor synergistically with wide bandgap third component for high efficiency ternary organic solar cells

Herein,two asymmetric hexacyclic fused small molecule acceptors(SMAs),namely BP4F-HU and BP4F-UU,were synthesized.The elongated outside chains in the BP4F-UU molecule played a crucial role in optimizing the morphology of blend film,thereby improving charge mobility and reducing energy loss within the corresponding film.Notably,the PM6:BP4F-UU device exhibited a higher open-circuit voltage(V_(oc))of 0.878 V compared to the PM6:BP4F-HU device with a V_(oc)of 0.863 V.Further,a new wide bandgap SMA named BTP-TA was designed and synthesized as the third component to the PM6:BP4F-UU host binary devices,which showed an ideal complementary absorption spectrum in PM6:BP4F-UU system.In addition,BTP-TA can achieve efficient intermolecular energy transfer to BP4F-UU by fluorescence resonance energy transfer(FRET)pathway,due to the good overlap between the photoluminescence(PL)spectrum of BTP-TA and the absorption region of BP4F-UU.Consequently,ternary devices with 15wt%BTP-TA exhibits broader photon utilization,optimal blend morphology,and reduced charge recombination compared to the corresponding binary devices.Consequently,PM6:BP4F-UU:BTP-TA ternary device achieved an optimal power conversion efficiency(PCE)of 17.83%with simultaneously increased V_(oc)of 0.905 V,short-circuit current density(J_(sc))of 26.14 mA/cm^(2),and fill factor(FF)of 75.38%.

Two Monte Carlo-based simulators for imaging-system modeling and projection simulation of flat-panel X-ray source

The advantages of a flat-panel X-ray source(FPXS)make it a promising candidate for imaging applications.Accurate imaging-system modeling and projection simulation are critical for analyzing imaging performance and resolving overlapping projection issues in FPXS.The conventional analytical ray-tracing approach is limited by the number of patterns and is not applicable to FPXS-projection calculations.However,the computation time of Monte Carlo(MC)simulation is independent of the size of the patterned arrays in FPXS.This study proposes two high-efficiency MC projection simulators for FPXS:a graphics processing unit(GPU)-based phase-space sampling MC(gPSMC)simulator and GPU-based fluence sampling MC(gFSMC)simulator.The two simulators comprise three components:imaging-system modeling,photon initialization,and physical-interaction simulations in the phantom.Imaging-system modeling was performed by modeling the FPXS,imaging geometry,and detector.The gPSMC simulator samples the initial photons from the phase space,whereas the gFSMC simulator performs photon initialization from the calculated energy spectrum and fluence map.The entire process of photon interaction with the geometry and arrival at the detector was simulated in parallel using multiple GPU kernels,and projections based on the two simulators were calculated.The accuracies of the two simulators were evaluated by comparing them with the conventional analytical ray-tracing approach and acquired projections,and the efficiencies were evaluated by comparing the computation time.The results of simulated and realistic experiments illustrate the accuracy and efficiency of the proposed gPSMC and gFSMC simulators in the projection calculation of various phantoms.

Revealing the Role of Defect in 3D Graphene-Based Photocatalytic Composite for Efficient Elimination of Antibiotic and Heavy Metal Combined Pollution

Defect engineering can give birth to novel properties for adsorption and photocatalysis in the control of antibiotics and heavy metal combined pollution with photocatalytic composites.However,the role of defects and the process mechanism are complicated and indefinable.Herein,TiO_(2)/CN/3DC was fabricated and defects were introduced into the tripartite structure with separate O_(2)plasma treatment for the single component.We find that defect engineering can improve the photocatalytic activity,attributing to the increase of the contribution from h^(+)and OH.In contrast to TiO_(2)/CN/3DC with a photocatalytic tetracycline removal rate of 75.2%,the removal rate of TC with D-TiO_(2)/CN/3DC has increased to 88.5%.Moreover,the reactive sites of tetracycline can be increased by adsorbing on the defective composites.The defect construction on TiO_(2)shows the advantages in tetracycline degradation and Cu^(2+)adsorption,but also suffers significant inhibition for the tetracycline degradation in a tetracycline/Cu^(2+)combined system.In contrast,the defect construction on graphene can achieve the cooperative removal of tetracycline and Cu^(2+).These findings can provide new insights into water treatment strategies with defect engineering.

Intelligent Internet of Things with Reliable Communication and Collaboration Technologies

The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.

Design of AI-Enhanced and Hardware-Supported Multimodal E-Skin for Environmental Object Recognition and Wireless Toxic Gas Alarm

Post-earthquake rescue missions are full of challenges due to the unstable structure of ruins and successive aftershocks.Most of the current rescue robots lack the ability to interact with environments,leading to low rescue efficiency.The multimodal electronic skin(e-skin)proposed not only reproduces the pressure,temperature,and humidity sensing capabilities of natural skin but also develops sensing functions beyond it—perceiving object proximity and NO2 gas.Its multilayer stacked structure based on Ecoflex and organohydrogel endows the e-skin with mechanical properties similar to natural skin.Rescue robots integrated with multimodal e-skin and artificial intelligence(AI)algorithms show strong environmental perception capabilities and can accurately distinguish objects and identify human limbs through grasping,laying the foundation for automated post-earthquake rescue.Besides,the combination of e-skin and NO2 wireless alarm circuits allows robots to sense toxic gases in the environment in real time,thereby adopting appropriate measures to protect trapped people from the toxic environment.Multimodal e-skin powered by AI algorithms and hardware circuits exhibits powerful environmental perception and information processing capabilities,which,as an interface for interaction with the physical world,dramatically expands intelligent robots’application scenarios.

Terahertz Spectroscopic Study of Standard Substances for Bituminous Coal and Anthracite

This study underscores the significance of online monitoring of standard substances for bituminous coal and anthracite,two commonly used fossil fuels.Terahertz technology emerges as a powerful non-destructive detection method capable of revealing the physical and chemical properties of measured objects.In this research,terahertz time-domain spectroscopy technology was employed to investigate the spectral characteristics of four distinct types of bituminous coal and anthracite samples.The refractive index and absorption coefficient spectra of these samples were calculated across a frequency range of 0.5 THz to 2.5 THz.Furthermore,principal component analysis was conducted using all refractive index and absorption coefficient data within this frequency band.Through the analysis and comparison with known parameters of coal standard materials,it was established that carbon content primarily influences the refractive index of bituminous coal and anthracite,while ash content predominantly affects the absorption effect.These findings underscore the potential of terahertz spectroscopy in conjunction with principal component analysis to qualitatively assess the similarities and differences between coal samples,thus offering novel insights for the online monitoring of diverse coal types and qualities.

Object detection based on combination of local and spatial information

A method of object detection based on combination of local and spatial information is proposed. Firstly, the categorygiven representative images are chosen through clustering to be templates, and the local and spatial information of template are ex- tracted and generalized as the template feature. At the same time, the codebook dictionary of local contour is also built up. Secondly, based on the codebook dictionary, sliding-window mechanism and the vote algorithm are used to select initial candidate object win- dows. Lastly, the final object windows are got from initial candidate windows based on local and spatial structure feature matching. Experimental results demonstrate that the proposed approach is able to consistently identify and accurately detect the objects with better performance than the existing methods.

Design and Implementation of Agricultural Information Data-Delivery System Based on Web

Data-delivery of agricultural information is a very tedious work, traditional data-delivery patterns and methods can not meet the requirements of the practical work. This paper provided the design idea and implement method for data-delivery system of agricultural information based on Web. Report and data will be separated in this system, and the person can change template and data at any time on demand. The problem that report template and data fixed together would be solved. The agricultural information resources sharing would be also implemented.

A Survey of Web Information Technology and Application

0 Introduction The surprising growth of the Internet, coupled with the rapid development of Web technique and more and more emergence of web information system and application, is bring great opportunities and big challenges to us. Since the Web provides cross-platform universal access to resources for the massive user population, even greater demand is requested to manage data and services effectively.

Assessment and sequencing of air target threat based on intuitionistic fuzzy entropy and dynamic VIKOR

In view of the fact that traditional air target threat assessment methods are difficult to reflect the combat characteristics of uncertain, dynamic and hybrid formation, an algorithm is proposed to solve the multi-target threat assessment problems. The target attribute weight is calculated by the intuitionistic fuzzy entropy(IFE) algorithm and the time series weight is gained by the Poisson distribution method based on multi-times data. Finally,assessment and sequencing of the air multi-target threat model based on IFE and dynamic Vlse Kriterijumska Optimizacija I Kompromisno Resenje(VIKOR) is established with an example which indicates that the method is reasonable and effective.

Accuracy improvement of GPS/MEMS-INS integrated navigation system during GPS signal outage for land vehicle navigation

To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of taskdecomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.

Electromagnetism and Absorptivity of the Modified Micro-coiled Chiral Carbon Fibers

Micro-coiled chiral carbon fibers are modified by nano-Ni. X-ray diffraction （XRD） and scanning electron microscopy （SEM） are used to compare the composition and morphology of the unmodified and the modified fibers. The results show that electromagnetism parameters of the modified are different from those of the unmodified. After modification by nano-Ni, the micro-coiled chiral carbon fibers have decreased permittivity and electrical loss. The permeability and magnetic loss of the modified carbon fibers become larger than those of the unmodified ones. Moreover, the modification of unmodified chiral carbon fibers into the modified is much like changing hollow electric windings into those with magnetic cores inside. The modifier intensifies the cross polarization of the chiral carbon fibers and makes the permittivity and the permeability get closer to each other which improves the matching performance and enhances absorbability of coatings. In the range of 6-18 GHz, the reflectivity of the coating is 6-8dB and the bandwidth is 12 GHz. The area density of the coating is below 3 kg/m^2.

On the Probability Density Function of the Real and Imaginary Parts in WFRFT Signals

Recently a Hybrid Carrier （HC） scheme based on Weighted-type Fractional Fourier Transform （WFRFT） was proposed and developed, which contains Single Carrier （SC） and Multi-Carrier （MC） synergetie transmission. The wide interest is primarily due to its appealing characteristics, such as the robust performances in different types of selective fading channels and a great deal of potential for secure communications. According to the literatures, the HC signal and SC or MC signal probability distributions are different. In particular, some benefits of this HC scheme are brought by the quasi-Gaussian distribution of WFRFT signals. However, until now researchers have only presented statistic properties through computer simulations, and the accurate expressions of signals are not derived yet. In this paper, we derive the accu- rate and rigorously established closed-form expressions of Probability Density Function （PDF） of WFRFT signal real and imaginary parts with a large number of QPSK subcarriers, and this PDF can describe the behavior of data modulated by WFRFT, avoiding the complex computation for extensive computer simulations. Furthermore, the components of PDF expression are described and analyzed, and it is revealed that the tendency of signal quasi-Gaussian changes with the increasing of the parameter a （a in （0,1]）. To validate the analytical results, extensive simulations have been conducted, showing a very good match between the analytical results and the real situations. The contribution of this paper may be useful to deduce the closed form expressions of Bit Error Ratio （BER）, the Complementary Cumulative Distribution Function （CCDF） of Peak to Average Power Ratio （PAPR）, and other analytical studies which adopt the PDF.

Humidity Sensing of Stretchable and Transparent Hydrogel Films for Wireless Respiration Monitoring

Respiratory monitoring plays a pivotal role in health assessment and provides an important application prospect for flexible humidity sensors.However,traditional humidity sensors suffer from a trade-off between deformability,sensitivity,and transparency,and thus the development of high-performance,stretchable,and low-cost humidity sensors is urgently needed as wearable electronics.Here,ultrasensitive,highly deformable,and transparent humidity sensors are fabricated based on cost-effective polyacrylamide-based double network hydrogels.Concomitantly,a general method for preparing hydrogel films with controllable thickness is proposed to boost the sensitivity of hydrogel-based sensors due to the extensively increased specific surface area,which can be applied to different polymer networks and facilitate the development of flexible integrated electronics.In addition,sustainable tapioca rich in hydrophilic polar groups is introduced for the first time as a second cross-linked network,exhibiting excellent water adsorption capacity.Through the synergistic optimization of structure and composition,the obtained hydrogel film exhibits an ultrahigh sensitivity of 13,462.1%/%RH,which is unprecedented.Moreover,the hydrogel film-based sensor exhibits excellent repeatability and the ability to work normally under stretching with even enhanced sensitivity.As a proof of concept,we integrate the stretchable sensor with a specially designed wireless circuit and mask to fabricate a wireless respiratory interruption detection system with Bluetooth transmission,enabling real-time monitoring of human health status.This work provides a general strategy to construct high-performance,stretchable,and miniaturized hydrogel-based sensors as next-generation wearable devices for real-time monitoring of various physiological signals.