MIMO-DCSK communication scheme and its performance analysis over multipath fading channels

The differential chaotic shift keying （DCSK） communication in multiple input multiple output （MIMO） multipath fading chan- nels is considered. A simple MIMO-DCSK communication scheme based on orthogonal multi-codes （OMCs） and equal gain combination （EGC） is proposed, in which OMCs are used to spread the same information bit at each transmitting antenna and the infor- mation bit is detected by EGC at receiving antenna. The OMCs are constructed from one chaotic sequence by means of othogo- nal space-time block coding （OSTBC）. The output signal-to-noise ratio （SNR） after EGC is given based on central limit theory （CLT）, and it can effectively exploit the spatial diversity of the underlying MIMO system. Simulation results show that the full spatial diversity gain is achieved without channel estimation in the MIMO-DCSK communication scheme and it performs better than MC-EGC for a large number of transmitting antennas.

Investigation on the effects of fluid intensification based preconditioning process on the decarburization enhancement of fly ash

Fly ash(FA)is a complex and abundant solid waste created by humans,and has caused environmental issues,for which flotation is an effective technique employed before its comprehensive utilization.However,the complex and hydrophilic characteristics of FA particles cannot naturally fulfill the selective separation by common flotation.Therefore,this study aims to provide an insight into fluid intensification effects on flotation to achieve the enhancement of FA surface property and decarburization.The relevant effects and mechanisms are investigated,based on the measurements of zeta potential,infrared spectroscopy,contact/wrap angle,induction time,size distribution and scanning electron microscopy–energy dispersive spectrometry.Experimental results manifested that the maximum unburned carbon recovery(73.25%)and flotation rate(0.2037 s^(-1)) were achieved with preconditioning energy inputs of 14.23 and6.57 W·kg^(-1) respectively.With increasing preconditioning energy inputs,fluid intensification effects could promote the inter-particle collision/attrition,detachment of hydrophilic existence and collector adsorption on particles.Correspondingly,absorbance of some hydrophobic and hydrophilic functional groups was strengthened and weakened respectively,which accounted for the improved interfacial properties,reflected as the increased contact and wrap angles,together with declined induction time.Overall,this article revealed the positive influences of fluid intensification based preconditioning process on rendering particle surface hydrophobic and improving separation performance.

Investigation of artifacts by mapping SAR in thermoacoustic imaging

Microwave-induced thermoacoustic imaging(MI-TAI)remains one of the focus of attention among biomedical imaging modalities over the last decade.However,the transmission and dis-tribution of microwave inside bio-tissues are complicated,thus result in severe artifacts.In this study,to reveal the underlying mechanisms of artifacts,we deeply investigate the distribution of specific absorption rate(SAR)inside tissue-mimicking phantoms with varied morphological features using both mathematical simulations and corresponding experiments.Our simulated results,which are confirmed by the associated experimental results,show that the SAR distri-bution highly depends on the geometries of the imaging targets and the polarizing features of the microwave.In addition,we propose the potential mechanisms including Mie-scattering,Fabry-Perot-feature,small curvature effect to interpret the diffraction effect in different scenarios,which may provide basic guidance to predict and distinguish the artifacts for TAI in both fundamental and clinical studies.

A NEW METHOD OF BAD POINTS ELIMINATION BASED ON HOUGH TRANSFORM

In experimental tests, besides data in range of allowable error, the experimenters usually get some unexpected wrong data called bad points. In usual experimental data processing, the method of bad points exclusion based on automatic programming is seldom taken into consideration by researchers. This paper presents a new method to reject bad points based on Hough transform, which is modified to save computational and memory consumptions. It is fit for linear data processing and can be extended to process data that is possible to be transformed into and from linear form; curved lines, which can be effectively detected by Hough transform. In this paper, the premise is the distribution of data, such as linear distribution and exponential distribution, is predetermined. Steps of the algorithm start from searching for an approximate curve line that minimizes the sum of parameters of data points. The data points, whose parameters are above a self-adapting threshold, will be deleted. Simulation experiments have manifested that the method proposed in this paper performs efficiently and robustly.

Autonomous exploration using UWB and LiDAR

In autonomous exploration,a robot navigates itself in an unknown environment while building a 2D map of the environment.This is typically done using a LiDAR sensor,which however is susceptible to error accumulation.To handle this issue,a UWB/LiDAR fusion SLAM is proposed,which can be decoupled into a localization problem and a mapping problem.For localization problem,we firstly apply extended Kalman filter(EKF)to localize all UWB beacons and then use particle filter(PF)to estimate the robot’s state based on the two on-board UWB nodes’estimated locations.For mapping problem,we firstly fine-tune the robot’s state using a recursive adaptive-trust-region scan matcher,which is termed as RASM,and then construct the map based on the refined robot’s state.We also propose a method to correct UWB beacons’locations using the robot’s refined location.Furthermore,the information obtained from the proposed fusion SLAM is utilized to sketch the region where the robot is going to explore next.That is,a where-to-explore strategy is proposed to guide the robot to the less-explored areas.Overall,the proposed exploration system is infrastructure-less and avoid mapping error to accumulate over time.Extensive experiments with comparisons to the state-of-the-art methods are conducted in two different environments:a cluttered workshop and a spacious garden in order to verify the effectiveness of our proposed strategy.The experimental tests are filmed and the video is available in the supplementary materials.

铁电阴极相对论行波管初步研究

研究了铁电材料的电子发射特性,根据实验和数值模拟结果给出了铁电发射特性的定性解释,并指出目前的几种铁电发射理论都不足以完备地解释各种实验现象。设计了Pierce型铁电阴极电子枪,实现了束流的二级压缩,其总束流大于100 A。在此基础上,分析和设计了具有盘荷波导慢波结构的相对论行波管,得到了带宽500 MHz,增益约25 dB的X波段行波放大输出。

Adaptive Backstepping Sliding Mode Control of Uncertain Semi-Strict Nonlinear Systems and Application to Permanent Magnet Synchronous Motor

A disturbance observer(DOB)based-backstepping sliding mode control scheme is discussed for a class of semi-strict nonlinear system with unknown parameters and mismatched uncertainty.Firstly,adaptive technique and DOB are respectively applied to tackle the unknown parameters and mismatched uncertainty,where the DOB can effectively alleviate the chattering problem of sliding mode control(SMC).Then,exponential sliding mode surface is proposed to improve the convergence rate of the sliding mode state.The‘explosion of complexity’problem inherent in conventional backstepping control is overcome by designing the novel first-order filter.The stability of the closed-loop system is analyzed in the framework of Lyapunov stability theory,in which the tracking error converges to an arbitrarily small neighborhood around zero(ASNZ).At last,two examples are given to illustrate the effectiveness of the proposed control strategy.

Coordinated Demand Response of Rail Transit Load and Energy Storage System Considering Driving Comfort

Electric trains typically travel across the railway networks in an inter-provincial,inter-city and intra-city manner.The electric train generally serves as a load/source in tractive/brake mode,through which power networks and railway networks are closely coupled and mutually influenced.Based on the operational mode of rail trains and the characteristics of their load power,this paper proposes a coordinated optimal decisionmaking method of demand response for controllable load of rail trains and energy storage systems.First,a coordinated approach of dynamically adjusting the load of the controllable rail train in considering the driving comfort and energy storage battery is designed.Secondly,under the time conditions that satisfy the train’s operational diagram,the functional relationship between the train speed and the load power is presented.Based on this,in considering the constraints of the train’s arrival time,driving speed,motor power,and driving comfort,the capacity of energy storage batteries and other constraints,an optimization model for demand response in managing the traction power supply system under a two-part price and time-of-use(TOU)price is proposed.The objective is to minimize the energy consumption costs of rail transit trains,and optimize the speed trajectory of rail trains,the load power of traction system,and the output of energy storage batteries.

Biomedical microwave-induced thermoacoustic imaging

Microwave induced thermoacoustic imaging(MTAI)has emerged as a potential biomedical imaging modality with over 20-year growth.MTAI typically employs pulsed microwave as the pumping source,and detects the microwave-induced ultrasound wave via acoustic transducers.Therefore,it features high acoustic resolution,rich elect romagnetic contrast,and large imaging depth.Benefiting from these unique advantages,MTAI has been extensively applied to various fields including pathology,biology,material and medicine.Till now,MTAI has been deployed for a wide range of biomedical applications,including cancer diagnosis,joint evaluation,brain in-vestigation and endoscopy.This paper provides a comprehensive review on(1)essential physics(endogenous/exogenous contrast mechanisms,penetration depth and resolution),(2)hardware configurations and software implementations(excit ation source,antenna,ultrasound detector and image recovery algorithm),(3)animal studies and clinical applications,and(4)future directions.

CFSA-Net:Efficient Large-Scale Point Cloud Semantic Segmentation Based on Cross-Fusion Self-Attention

Traditional models for semantic segmentation in point clouds primarily focus on smaller scales.However,in real-world applications,point clouds often exhibit larger scales,leading to heavy computational and memory requirements.The key to handling large-scale point clouds lies in leveraging random sampling,which offers higher computational efficiency and lower memory consumption compared to other sampling methods.Nevertheless,the use of random sampling can potentially result in the loss of crucial points during the encoding stage.To address these issues,this paper proposes cross-fusion self-attention network(CFSA-Net),a lightweight and efficient network architecture specifically designed for directly processing large-scale point clouds.At the core of this network is the incorporation of random sampling alongside a local feature extraction module based on cross-fusion self-attention(CFSA).This module effectively integrates long-range contextual dependencies between points by employing hierarchical position encoding(HPC).Furthermore,it enhances the interaction between each point’s coordinates and feature information through cross-fusion self-attention pooling,enabling the acquisition of more comprehensive geometric information.Finally,a residual optimization(RO)structure is introduced to extend the receptive field of individual points by stacking hierarchical position encoding and cross-fusion self-attention pooling,thereby reducing the impact of information loss caused by random sampling.Experimental results on the Stanford Large-Scale 3D Indoor Spaces(S3DIS),Semantic3D,and SemanticKITTI datasets demonstrate the superiority of this algorithm over advanced approaches such as RandLA-Net and KPConv.These findings underscore the excellent performance of CFSA-Net in large-scale 3D semantic segmentation.

Nonparametric TOA estimators for low-resolution IR-UWB digital receiver

Nonparametric time-of-arrival（TOA） estimators for impulse radio ultra-wideband（IR-UWB） signals are proposed. Nonparametric detection is obviously useful in situations where detailed information about the statistics of the noise is unavailable or not accurate. Such TOA estimators are obtained based on conditional statistical tests with only a symmetry distribution assumption on the noise probability density function. The nonparametric estimators are attractive choices for low-resolution IR-UWB digital receivers which can be implemented by fast comparators or high sampling rate low resolution analog-to-digital converters（ADCs）,in place of high sampling rate high resolution ADCs which may not be available in practice. Simulation results demonstrate that nonparametric TOA estimators provide more effective and robust performance than typical energy detection（ED） based estimators.

Distributed Optimal Power Flow of DC Microgrids:A Penalty Based ADMM Approach

The popularity of direct current(DC)networks have made their optimal power flow(OPF)problem a hot topic.With the proliferation of distributed generation,the many problems of centralized optimization methods,such as single point failure and slow response speed,have led to utilization of measures such as distributed OPF methods.The OPF problem is non-convex,which makes it difficult to obtain an optimal solution.The second-order cone programming(SOCP)relaxation method is widely utilized to make the OPF problem convex.It is difficult to guarantee its exactness,especially when line constraints are considered.This paper proposes a penalty based ADMM approach using difference-of-convex programming(DCP)to solve the non-convex OPF problem in a distributed manner.The algorithm is composed of distributed x iteration,z iteration and A,/i iteration.Specifically,in the distributed z iteration,the active power flow injection equation of each line is formulated as a difference of two convex functions,and then the SOCP relaxation is given in a different form.If the SOCP relaxation is inexact,a penalty item is added to drive the solution to be feasible.Then,an optimal solution can be obtained using a local nonlinear programming method.Finally,simulations on a 14-bus system and the IEEE 123-bus system validate the effectiveness of the proposed approach.

Electrospun Cu-doped In_(2)O_(3) hollow nanofibers with enhanced H2S gas sensing performance

One-dimensional nanofibers can be transformed into hollow structures with larger specific surface area, which contributes to the enhancement of gas adsorption. We firstly fabricated Cu-doped In_(2)O_(3) (Cu-In_(2)O_(3)) hollow nanofibers by electrospinning and calcination for detecting H2S. The experimental results show that the Cu doping concentration besides the operating temperature, gas concentration, and relative humidity can greatly affect the H2S sensing performance of the In_(2)O_(3)-based sensors. In particular, the responses of 6%Cu-In_(2)O_(3) hollow nanofibers are 350.7 and 4201.5 to 50 and 100 ppm H2S at 250 ℃, which are over 20 and 140 times higher than those of pristine In_(2)O_(3) hollow nanofibers, respectively. Moreover, the corresponding sensor exhibits excellent selectivity and good reproducibility towards H2S, and the response of 6%Cu-In_(2)O_(3) is still 1.5 to 1 ppm H2S. Finally, the gas sensing mechanism of Cu-In_(2)O_(3) hollow nanofibers is thoroughly discussed, along with the assistance of first-principles calculations. Both the formation of hollow structure and Cu doping contribute to provide more active sites, and meanwhile a little CuO can form p–n heterojunctions with In_(2)O_(3) and react with H2S, resulting in significant improvement of gas sensing performance. The Cu-In_(2)O_(3) hollow nanofibers can be tailored for practical application to selectively detect H2S at lower concentrations.

Multivariate Two-stage Adaptive-stacking Prediction of Regional Integrated Energy System

To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system(RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables stakeholders to make effective decisions for carbon peaking and carbon neutrality goals. To this end, this paper proposes a multivariate two-stage adaptive-stacking prediction(M2ASP) framework. First, a preprocessing module based on ensemble learning is proposed. The input data are preprocessed to provide a reliable database for M2ASP, and highly correlated input variables of multi-energy load prediction are determined. Then, the load prediction results of four predictors are adaptively combined in the first stage of M2ASP to enhance generalization ability. Predictor hyper-parameters and intermediate data sets of M2ASP are trained with a metaheuristic method named collaborative atomic chaotic search(CACS) to achieve the adaptive staking of M2ASP. Finally, a prediction correction of the peak load consumption period is conducted in the second stage of M2ASP. The case studies indicate that the proposed framework has higher prediction accuracy, generalization ability, and stability than other benchmark prediction models.

Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data

To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system with colored noise is transformed into the system with white noise. In order to improve estimates, the estimated noise variance is employed as a weighting factor in the algorithm. Meanwhile, a modified covariance resetting method is also integrated in the proposed algorithm to increase the convergence rate. A numerical example and an industrial example validate the proposed algorithm.

A strategy for enhancing luminescence thermometry via Ta^(5+)-triggered K_(0.5)Na_(0.5)NbO_(3):Er^(3+)transparent ceramics

Luminescence thermometry is a reliable approach for remote thermal sensing,and extensive studies have been devoted to designing a luminescence thermometer with heightened thermal sensitivity.Herein,we report a promising luminescence thermometric material,Ta^(5+)-substituted K_(0.5)Na_(0.5)NbO_(3):0.003Er^(3+)transparent ferroelectric ceramics.The temperature sensing sensitivity is significantly improved by adjusting the concentration of Ta^(5+)in the material.Specifically,utilizing the fluorescence intensity ratio from the 2H_(11/2) and 4S_(3/2) thermally coupled states of Er^(3+)as a detecting signal within the temperature range of 273–543 K,an optimal maximum absolute sensitivity of 0.0058 K–1 and relative sensitivity of 0.0158 K–1 are achieved for K_(0.5)Na_(0.5)NbO_(3):0.65Ta^(5+)/0.003Er^(3+).Simultaneously,as the concentration of Ta5+increase,a unique evolution of structural phase transitions is observed from orthorhombic to tetragonal and then to cubic.This is accompanied by an improvement in luminescence temperature sensing properties,and the best sensitivity is demonstrated in the cubic-phase region.Intriguingly,a huge change in infrared luminescence properties as a function of temperature is found around the structure transition temperature of the samples.These results indicate a promising potential for achieving highly sensitive thermometry or monitoring phase structure transitions through luminescence thermometry behavior in the K_(0.5)Na_(0.5)NbO_(3) host.

Efficient photocatalytic hydrogen evolution coupled with benzaldehyde production over 0D Cd_(0.5)Zn_(0.5)S/2D Ti_(3)C2 Schottky heterojunction

Converting water into hydrogen fuel and oxidizing benzyl alcohol to benzaldehyde simultaneously under visible light illumination is of great significance,but the fast recombination of photogenerated carriers in photocatalysts seriously decreases the conversion efficiency.Herein,a novel dual-functional 0D Cd_(0.5)Zn_(0.5)S/2D Ti_(3)C2 hybrid was fabricated by a solvothermally in-situ generated assembling method.The Cd_(0.5)Zn_(0.5)S nano-spheres with a fluffy surface completely and uniformly covered the ultrathin Ti_(3)C2 nanosheets,leading to the increased Schottky barrier(SB)sites due to a large contact area,which could accelerate the electron–hole separation and improve the light utilization.The optimized Cd_(0.5)Zn_(0.5)S/Ti_(3)C2 hybrid simultaneously presents a hydrogen evolution rate of 5.3 mmol/(g·h)and a benzaldehyde production rate of 29.3 mmol/(g·h),which are~3.2 and 2 times higher than those of pristine Cd_(0.5)Zn_(0.5)S,respectively.Both the multiple experimental measurements and the density functional theory(DFT)calculations further demonstrate the tight connection between Cd_(0.5)Zn_(0.5)S and Ti_(3)C2,formation of Schottky junction,and efficient photogenerated electron–hole separation.This paper suggests a dual-functional composite catalyst for photocatalytic hydrogen evolution and benzaldehyde production,and provides a new strategy for preventing the photogenerated electrons and holes from recombining by constructing a 0D/2D heterojunction with increased SB sites.