Enhancing Indoor User Localization:An Adaptive Bayesian Approach for Multi-Floor Environments

Indoor localization systems are crucial in addressing the limitations of traditional global positioning system(GPS)in indoor environments due to signal attenuation issues.As complex indoor spaces become more sophisticated,indoor localization systems become essential for improving user experience,safety,and operational efficiency.Indoor localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database,but this can increase the computational burden in the online phase.Bayesian networks,which integrate prior knowledge or domain expertise,are an effective solution for accurately determining indoor user locations.These networks use probabilistic reasoning to model relationships among various localization parameters for indoor environments that are challenging to navigate.This article proposes an adaptive Bayesian model for multi-floor environments based on fingerprinting techniques to minimize errors in estimating user location.The proposed system is an off-the-shelf solution that uses existing Wi-Fi infrastructures to estimate user’s location.It operates in both online and offline phases.In the offline phase,a mobile device with Wi-Fi capability collects radio signals,while in the online phase,generating samples using Gibbs sampling based on the proposed Bayesian model and radio map to predict user’s location.Experimental results unequivocally showcase the superior performance of the proposed model when compared to other existing models and methods.The proposed model achieved an impressive lower average localization error,surpassing the accuracy of competing approaches.Notably,this noteworthy achievement was attained with minimal reliance on reference points,underscoring the efficiency and efficacy of the proposed model in accurately estimating user locations in indoor environments.

Fast measurement and prediction method for electromagnetic susceptibility of receiver

Aiming at evaluating and predicting rapidly and accurately a high sensitivity receiver’s adaptability in complex electromagnetic environments,a novel testing and prediction method based on dual-channel multi-frequency is proposed to improve the traditional two-tone test.Firstly,two signal generators are used to generate signals at the radio frequency(RF)by frequency scanning,and then a rapid measurement at the intermediate frequency(IF)output port is carried out to obtain a huge amount of sample data for the subsequent analysis.Secondly,the IF output response data are modeled and analyzed to construct the linear and nonlinear response constraint equations in the frequency domain and prediction models in the power domain,which provide the theoretical criteria for interpreting and predicting electromagnetic susceptibility(EMS)of the receiver.An experiment performed on a radar receiver confirms the reliability of the method proposed in this paper.It shows that the interference of each harmonic frequency and each order to the receiver can be identified and predicted with the sensitivity model.Based on this,fast and comprehensive evaluation and prediction of the receiver’s EMS in complex environment can be efficiently realized.

Dependence of Rydberg-atom-based sensor performance on different Rydberg atom populations in one atomic-vapor cell

The atomic-vapor cell is a vital component for Rydberg atomic microwave sensors,and impacts on overall capability of Rydberg sensors.However,the conventional analysis approach on effect of vapor-cell length contains two implicit assumptions,that is,the same atomic population density and buffer gas pressure,which make it unable to accurately capture actual response about effect of Rydberg-atom-based sensor performance on different Rydberg atom populations.Here,utilizing a stepped cesium atomic-vapor cell with five different dimensions at the same atomic population density and buffer gas pressure,the height and full width at half maximum of electromagnetically induced transparency(EIT)signal,and the sensitivity of the atomic superheterodyne sensor are comprehensively investigated under conditions of the same Rabi frequencies(saturated laser power).It is identified that EIT signal height is proportional to the cell length,full width at half maximum and sensitivity grow with the increment of cell length to a certain extent.Employing the coherent integration signal theory and atomic linear expansion coefficient method,theoretical analysis of the EIT height and sensitivity are further investigated.The results could shed new light on understanding and design of ultrahigh-sensitivity Rydberg atomic microwave sensors and find promising applications in quantum measurement,communication,and imaging.

Wideband spectrum sensing using step-sampling based on the multipath nyquist folding receiver

Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing,which is subject to the complexity of processing the modulated outputs.In this case,a multipath NYFR architecture with a step-sampling rate for the different paths is proposed.The different numbers of digital channels for each path are designed based on the Chinese remainder theorem(CRT).Then,the detectable frequency range is divided into multiple frequency grids,and the Nyquist zone(NZ) of the input can be obtained by sensing these grids.Thus,high-precision parameter estimation is performed by utilizing the NYFR characteristics.Compared with the existing methods,the scheme proposed in this paper overcomes the challenge of NZ estimation,information damage,many computations,low accuracy,and high false alarm probability.Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.

Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm

X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.

Crustal and uppermost mantle structure of the northeastern Qinghai-Xizang Plateau from joint inversion of surface wave dispersions and receiver functions with P velocity constraints

Lithospheric structure beneath the northeastern Qinghai-Xizang Plateau is of vital significance for studying the geodynamic processes of crustal thickening and expansion of the Qinghai-Xizang Plateau. We conducted a joint inversion of receiver functions and surface wave dispersions with P-wave velocity constraints using data from the Chin Array Ⅱ temporary stations deployed across the Qinghai-Xizang Plateau. Prior to joint inversion, we applied the H-κ-c method(Li JT et al., 2019) to the receiver function data in order to correct for the back-azimuthal variations in the arrival times of Ps phases and crustal multiples caused by crustal anisotropy and dipping interfaces. High-resolution images of vS, crustal thickness, and vP/vSstructures in the Qinghai-Xizang Plateau were simultaneously derived from the joint inversion. The seismic images reveal that crustal thickness decreases outward from the Qinghai-Xizang Plateau. The stable interiors of the Ordos and Alxa blocks exhibited higher velocities and lower crustal vP/vSratios. While, lower velocities and higher vP/vSratios were observed beneath the Qilian Orogen and Songpan-Ganzi terrane(SPGZ), which are geologically active and mechanically weak, especially in the mid-lower crust.Delamination or thermal erosion of the lithosphere triggered by hot asthenospheric flow contributes to the observed uppermost mantle low-velocity zones(LVZs) in the SPGZ. The crustal thickness, vS, and vP/vSratios suggest that whole lithospheric shortening is a plausible mechanism for crustal thickening in the Qinghai-Xizang Plateau, supporting the idea of coupled lithospheric-scale deformation in this region.

Crustal structure in the Anyuan Coal Mine and its adjacent areas of Jiangxi Province by P-wave receiver functions

We collected high-quality teleseismic events recorded by 12 broadband seismographs deployed in the Anyuan Coal Mine and its adjacent areas in Pingxiang City,Jiangxi Province for nearly two years.The H-κ-c stacking method was employed to obtain the crustal thickness and Poisson's ratio distribution,then the characteristics of crustal structure below the stations were obtained by using the time-domain linear inversion method.The crustal thickness in the Anyuan Coal Mine and its adjacent areas ranges from approximately 32~35 km,with an average thickness of 33 km,which is consistent with the crustal thickness results in South China from previous studies using the receiver function method.The average Poisson's ratio of the crustal bulk composition in the study area varies between 0.22 and 0.25,which is lower than the global value with a 0.27 average,indicating a predominantly intermediate-acidic or felsic crustal composition.There is a weak negative correlation between Poisson's ratio and crustal thickness estimates in the Anyuan Coal Mine and its adjacent areas,suggesting that the absence of mafic-ultramafic materials in the lower crust is associated with the process of crustal delamination.The velocity inversion results indicate that the crustal structure including three velocity discontinuity interfaces,with the first at a depth of approximately 1.5 km,the second at about 10~15 km,and the third being the Moho.The study also indicates that the results obtained by the H-κ-c stacking method are significantly better than those obtained by the H-κmethod,effectively reducing the standard deviation and dispersion of crustal thickness and vP/vSratio.

The Effectiveness of Peer Feedback on Second Language Writing

Peer feedback is a widely utilized practice in writing classrooms for second languages(L2)that has drawn a lot of attention.While some support the practice because of the clear advantages that peer feedback offers students,others doubt its efficacy.Comparing various forms of feedback is therefore necessary,as is determining the effects that peer feedback has on students and the variables that might affect how successful peer feedback is.This paper focuses on the usefulness of peer feedback for L2 writing,based on a review of studies on the subject.The findings imply that the functions of peer and teacher feedback are distinct,and that peer feedback is advantageous to both parties,particularly to the supplier.The attitudes and cultural backgrounds of the students are among the other factors that affect how effective peer feedback is.Lastly,some implications for future research and education are presented.

High Sensitivity Digital Instantaneous Frequency Measurement Receiver for Precise Frequency Analysis

There are numerous applications, such as Radar, that leverage wideband technology. However, the presence of noise introduces certain limitations and challenges. It is crucial to harness wideband technology for applications demanding the rapid and precise transmission of diverse information from one point to another within a short timeframe. The ability to report a signal without tuning within the input bandwidth stands out as one of the advantages of employing a digital wideband receiver. As indicated, a digital wideband receiver plays a pivotal role in achieving high precision and accuracy. The primary distinction between Analog and Digital Instantaneous Frequency Measurement lies in the fact that analog Instantaneous Frequency Measurement (IFM) receivers have traditionally covered extensive input bandwidths, reporting one accurate frequency per short pulse. In the contemporary landscape, digital IFM systems utilize high-sampling-rate Analog-to-Digital Converters (ADC) along with Hilbert transforms to generate two output channels featuring a 90-degree phase shift. This paper explores the improvement of sensitivity in current digital IFM receivers. The optimization efforts target the Hilbert transform and autocorrelations architectures, aiming to refine the system’s ability to report fine frequencies within a noisy wide bandwidth environment, thereby elevating its overall sensitivity.

Constraining the crustal structure under the central and western Tian Shan based on teleseismic receiver functions and gravity anomalies

The Tian Shan is a vast range that spans several countries in Asia.Understanding its evolutionary history may provide valuable insights into intracontinental orogenic dynamics.In this study,we explored the crustal characteristics of the Tian Shan and their relationships to the tectonic evolution of the region.A new H-stacking method that combines the P receiver function and gravity anomalies was used to estimate the thickness and ratio of P-to S-wave velocities(Vp/Vs)for 91 broadband seismic stations in the central and western Tian Shan.Our results revealed significant lateral variations in crustal thickness and Vp/Vs.A—45-km-thick crust and an intermediate-high Vp/Vs(-1.74-1.84)were found in the Kazakh Shield and Tarim Basin,which we interpreted to indicate a mafic crystalline basement and lower crust.The central Tian Shan varied greatly in crustal thickness(40-64 km)and Vp/Vs ratio(1.65-2.00).which may be due to crustal shortening,mafic underplating,and crustal melting.In contrast,we observed a relatively thin crust(42-50 km)with an intermediate Vp/Vs ratio(-1.78)in the western Tian Shan.The differences in the crustal structures between the western and central Tian Shan imply that the Talas-Fergana Fault may be trans-lithospheric.

Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network:A Case Study of Vietnam

Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated with landslides and erosion of roads within a short time.Most of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management.In this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,Vietnam.In the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors.The performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.

Deep learning-based LPI radar signals analysis and identification using a Nyquist Folding Receiver architecture

Nyquist Folding Receiver(NYFR)is a perceptron structure that realizes a low probability of intercept(LPI)signal analog to information.Aiming at the problem of LPI radar signal receiving,the time domain,frequency domain,and time-frequency domain problems of signals intercepted by NYFR structure are studied.Combined with the time-frequency analysis(TFA)method,a radar recognition scheme based on deep learning(DL)is introduced,which can reliably classify common LPI radar signals.First,the structure of NYFR and its characteristics in the time domain,frequency domain,and time and frequency domain are analyzed.Then,the received signal is then converted into a time-frequency image(TFI).Finally,four kinds of DL algorithms are used to classify LPI radar signals.Simulation results demonstrate the correctness of the NYFR structure,and the effectiveness of the proposed recognition method is verified by comparison experiments.

Waste Heat Recovery from a Drier Receiver of an A/C Unit Using Thermoelectric Generators

Thermoelectric generators(TEGs)are considered promising devices for waste heat recovery from various systems.The Seebeck effect can be utilized to generate power using the residual heat emitted by the filter dryer receiver(FDR)of an air conditioning(A/C)system,which would otherwise go to waste.The study aims to build a set of thermoelectric generators(TEG)to collect the waste heat of the FDR and generate low-power electricity.A novel electrical circuit with two transformers is designed and fabricated to produce a more stable voltage for operation and charging.The thermoelectric generator(TEGs)was installed on the FDR of the A/C unit.The test showed that climate conditions have a significant impact on the output power generated from the system.The results showed that the peak voltage recorded in the current study is 5.2 V per day(wet,cold,and wind weather)with an output power of 0.2 W.These values are acceptable for powering the load and charging a single battery with 3.5 V as the voltage increases battery 0.1 V/20 min charge.A case study of operating the emergency signs in a building was considered.The current heat recovery system is deemed to be easily installed and can be connected to a network of TEGs to produce more power.

Long-term and short-term stability characteristics of receiver inter system bias for BDS3/BDS2

The study of inter-system bias(ISB)is important for multi-system fusion and the performance of different signal compatibility.In this paper,the stability of ISB at the BDS3/BDS2 receiver end is calculated and analyzed for different time spans(DOY 060~090 in 2021)from a total of 31 MGEX and iGMAS stations.We adopted two estimation strategies,random walk and constant approach,using the precision products of orbit and clock bias provided by WUM,the influence of which on ISB was also analyzed.Our results showed that the ISB value varied little within a day,and the mean of daily ISB standard deviation was only 0.037 m when the observation condition was good.The signal reception was continuous,indicating a high ISB stability for one day.If extending the time series to one month,however,the ISB standard deviation calculated by constant approach,in which a constant ISB is estimated on a daily basis was about 0.1 m,and the results of adjacent days were not continuous,with no apparent pattern.Concerning the random walk approach,the obtained ISB time series also had a jump,and the conclusion was the same as that of the constant strategy.Besides,receiver types showed a strong regularity in ISB numerical situation,and the distribution of ISB values corresponding to the same receiver type was relatively close.Therefore,we conclude that the ISB parameters remain stable in the short term(one day)and less stable in the long-term period.It is recommended that the ISB term should be set as a constant estimate every day in BDS3/BDS2 solutions,regardless of receiver type consistency.

Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to climate.Due to a lack of time frame in existing works,conser-vation adoption affects the performance of existing works.The proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its extinction.The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species.However,the frame-work demonstrates the huge difference in the estimated significance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate change.The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.

Optimizing Optical Attocells Positioning of Indoor Visible Light Communication System

Visible light communication(VLC),which is a prominent emerging solution that complements the radio frequency(RF)technology,exhibits the potential to meet the demands of fifth-generation(5G)and beyond technologies.The random movement of mobile terminals in the indoor environment is a challenge in the VLC system.The model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio(SNR).As such,the optical attocells positions were optimized in this study with a developed try and error(TE)algorithm.The optimized optical attocells were examined and compared with previous models.This novel approach had successfully increased minimum received power from−1.29 to−0.225 dBm,along with enhanced SNR performance by 2.06 dB.The bit error rate(BER)was reduced to 4.42×10−8 and 6.63×10−14 by utilizing OOK-NRZ and BPSK modulation techniques,respectively.The optimized attocells positions displayed better uniform distribution,as both received power and SNR performances improved by 0.45 and 0.026,respectively.As the results of the proposed model are optimal,it is suitable for standard office and room model applications.

基于BP神经网络和泰勒级数的室内定位算法研究

在研究分析室内无线信号传播特性和传统的室内定位算法的基础上,提出了用BP神经网络来拟合室内无线信号传播模型,避免了对无线信号传播模型中参数A和n的不精确估计.在训练完成的BP神经网络的输入层输入接收信号强度值RSSI(Received Signal Strength Indicator),在输出层即可得到对应的距离值,再利用泰勒级数展开法确定盲节点的坐标位置.最终通过Matlab仿真和ZigBee平台实验验证了算法的可行性和有效性.

基于改进的RSA公钥密码算法的电子公文加密方案

随着信息技术的发展以及电子政务的深入实施,带来一系列电子公文安全性问题。RSA加密技术是一种用于信息安全传输的重要技术。提出了一种改进的RSA公钥密码算法,并应用于电子公文加密。在该算法中,找到RSA公钥密码算法的一类弱密钥,指出如果RSA数的两个素因子的比值能够被一个小分数有效逼近,则RSA数是容易分解的。

ROC曲线在临床医学诊断实验中的应用

随着先进医学技术的迅猛发展，各种新诊断试验层出不穷，但并不是每一种新诊断试验肯定比旧试验好。如果医学工作者，特别是临床医生，能够掌握一些评价与解释诊断试验的有关知识，及时取舍或灵活运用新的诊断试验，可以更好地为患者服务。为了进一步推广receiver operating characteristic（ROC）曲线分析方法的应用，本文首先介绍诊断实验的相关慨念，ROC曲线分析的定义、原理及分类，ROC曲线曲线下面积（AUC）及其最佳工作点的确定，然后用SPSS软件具体说明其应用。

基于RSSI变化序列的MAC地址与目标匹配方法

获得真实人物与所持通信设备MAC (Media Access Control)地址的联系对公共和网络安全有重大价值。针对现有方法的不足提出了更好的解决方法。方法布置多个Wi-Fi探针获取通信设备的MAC和实时RSSI (Received Signal Strength Indication)状态变化,构建RSSI状态变化序列;利用目标跟踪获取目标相对多个Wi-Fi探针的距离,构建距离状态变化序列;对2类状态变化序列进行匹配,选取最优匹配对作为目标与所携通信设备MAC地址匹配的结果。实验表明,该方法相比其他方法通用性更强,准确率更高。