SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based algorithm.Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

面向通信SNR增强的RIS三维空间位置部署研究

针对可重构智能表面(Reconfigurable Intelligent Surface,RIS)辅助无线通信系统存在“双衰落效应”问题,提出了一种RIS位置部署优化方法。建立了符合实际应用场景的RIS辅助无线通信系统模型,该模型考虑RIS可部署在三维空间中任意位置。以最大化用户接收信噪比(Signal to Noise Ratio,SNR)为目标,通过数值分析三维空间中基站、RIS和用户之间的相对位置关系,理论推导出RIS最优部署位置的闭式解。仿真实验结果表明,将RIS部署在通过数值分析方法获取的最优位置处,提高了实际应用场景下RIS辅助无线通信系统中的用户接收SNR。

Post-processing Free Quantum Random Number Generator Based on Avalanche Photodiode Array

Quantum random number generators adopting single negligible dead time of avalanche photodiodes （APDs） photon detection have been restricted due to the non- We propose a new approach based on an APD array to improve the generation rate of random numbers significantly. This method compares the detectors＇ responses to consecutive optical pulses and generates the random sequence. We implement a demonstration experiment to show its simplicity, compactness and scalability. The generated numbers are proved to be unbiased, post-processing free, ready to use, and their randomness is verified by using the national institute of standard technology statistical test suite. The random bit generation efficiency is as high as 32.8% and the potential generation rate adopting the 32× 32 APD array is up to tens of Gbits/s.

Pre-processing and post-processing method for geostress simulation using seismic interpretation results

In order to carry out numerical simulation using geologic structural data obtained from Landmark(seismic interpretation system), underground geological structures are abstracted into mechanical models which can reflect actual situations and facilitate their computation and analyses.Given the importance of model building, further processing methods about traditional seismic interpretation results from Landmark should be studied and the processed result can then be directly used in numerical simulation computations.Through this data conversion procedure, Landmark and FLAC(the international general stress software) are seamlessly connected.Thus, the format conversion between the two systems and the pre-and post-processing in simulation computation is realized.A practical application indicates that this method has many advantages such as simple operation, high accuracy of the element subdivision and high speed, which may definitely satisfy the actual needs of floor grid cutting.

Development of a Post-Processing Automation Procedure for the GPS-Based Travel Time Data Collection Technique

The travel time data collection method is used to assist the congestion management. The use of traditional sensors (e.g. inductive loops, AVI sensors) or more recent Bluetooth sensors installed on major roads for collecting data is not sufficient because of their limited coverage and expensive costs for installation and maintenance. Application of the Global Positioning Systems (GPS) in travel time and delay data collections is proven to be efficient in terms of accuracy, level of details for the data and required data collection of man-power. While data collection automation is improved by the GPS technique, human errors can easily find their way through the post-processing phase, and therefore data post-processing remains a challenge especially in case of big projects with high amount of data. This paper introduces a stand-alone post-processing tool called GPS Calculator, which provides an easy-to-use environment to carry out data post-processing. This is a Visual Basic application that processes the data files obtained in the field and integrates them into Geographic Information Systems (GIS) for analysis and representation. The results show that this tool obtains similar results to the currently used data post-processing method, reduces the post-processing effort, and also eliminates the need for the second person during the data collection.

A study on fast post-processing massive data of casting numerical simulation on personal computers

When castings become complicated and the demands for precision of numerical simulation become higher,the numerical data of casting numerical simulation become more massive.On a general personal computer,these massive numerical data may probably exceed the capacity of available memory,resulting in failure of rendering.Based on the out-of-core technique,this paper proposes a method to effectively utilize external storage and reduce memory usage dramatically,so as to solve the problem of insufficient memory for massive data rendering on general personal computers.Based on this method,a new postprocessor is developed.It is capable to illustrate filling and solidification processes of casting,as well as thermal stess.The new post-processor also provides fast interaction to simulation results.Theoretical analysis as well as several practical examples prove that the memory usage and loading time of the post-processor are independent of the size of the relevant files,but the proportion of the number of cells on surface.Meanwhile,the speed of rendering and fetching of value from the mouse is appreciable,and the demands of real-time and interaction are satisfied.

A method of line spectrum extraction based on target radiated spectrum feature and its post-processing

To improve the ability of detecting underwater targets under strong wideband interference environment,an efficient method of line spectrum extraction is proposed,which fully utilizes the feature of the target spectrum that the high intense and stable line spectrum is superimposed on the wide continuous spectrum.This method modifies the traditional beam forming algorithm by calculating and fusing the beam forming results at multi-frequency band and multi-azimuth interval,showing an excellent way to extract the line spectrum when the interference and the target are not in the same azimuth interval simultaneously.Statistical efficiency of the estimated azimuth variance and corresponding power of the line spectrum band depends on the line spectra ratio(LSR)of the line spectrum.The change laws of the output signal to noise ratio(SNR)with the LSR,the input SNR,the integration time and the filtering bandwidth of different algorithms bring the selection principle of the critical LSR.As the basis,the detection gain of wideband energy integration and the narrowband line spectrum algorithm are theoretically analyzed.The simulation detection gain demonstrates a good match with the theoretical model.The application conditions of all methods are verified by the receiver operating characteristic(ROC)curve and experimental data from Qiandao Lake.In fact,combining the two methods for target detection reduces the missed detection rate.The proposed post-processing method in2-dimension with the Kalman filter in the time dimension and the background equalization algorithm in the azimuth dimension makes use of the strong correlation between adjacent frames,could further remove background fluctuation and improve the display effect.

Improved Sinusoid Analysis and Post-Processing in Parametric Audio Coding

This paper proposed improvements to the low bit rate parametric audio coder with sinusoid model as its kernel. Firstly, we propose a new method to effectively order and select the perceptually most important sinusoids. The sinusoid which contributes most to the reduction of overall NMR is chosen. Combined with our improved parametric psychoacoustic model and advanced peak riddling techniques, the number of sinusoids required can be greatly reduced and the coding efficiency can be greatly enhanced. A lightweight version is also given to reduce the amount of computation with only little sacrifice of performance. Secondly, we propose two enhancement techniques for sinusoid synthesis: bandwidth enhancement and line enhancement. With little overhead, the effective bandwidth can be extended one more octave; the timbre tends to sound much brighter, thicker and more beautiful.

A Post-Processing Algorithm for Boosting Contrast of MRI Images

Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images.Drastic changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings.To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well.This method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression.After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are computed.Enhanced grey level values are computed from the resultant cumulative histograms.The performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.

Hybrid Post-Processing Procedure for Displacement-Based Plane Elements

In the analysis of high-rise building, traditional displacement-based plane elements are often used to get the in-plane internal forces of the shear walls by stress integration. Limited by the singular problem produced by wall holes and the loss of precision induced by using differential method to derive strains, the displacement-based elements cannot always present accuracy enough for design. In this paper, the hybrid post-processing procedure based on the Hellinger-Reissner variational principle is used for improving the stress precision of two quadrilateral plane elements. In order to find the best stress field, three different forms are assumed for the displacement-based plane elements and with drilling DOF. Numerical results show that by using the proposed method, the accuracy of stress solutions of these two displacement-based plane elements can be improved.

散射通信系统中SNR自适应预测技术

对流层散射通信的快衰落特性导致接收信号信噪比(Signal-to-Noise Ratio,SNR)不断变化,根据时变SNR进行自适应编码调制,可以使业务速率实时跟随SNR的变化而改变,在保证可靠传输的前提下有效提升通信吞吐量。针对散射通信系统自适应编码调制的需求,在归一化最小均方(Normalization Least Mean Square,NLMS)算法和递归最小二乘(Recursive Least Square,RLS)算法的基础上,提出了改进递归最小二乘(Modified Recursive Least Square,MRLS)SNR预测算法。SNR预测算法可以解决接收端估计的SNR反馈到发送端的延迟问题。经过仿真和外场实验测试,相比NLMS算法和RLS算法,所提出的MRLS算法具有更小的SNR预测误差。采用所提出的MRLS算法进行自适应编码调制流程后,相比NLMS算法和RLS算法,在外场实验中分别可获得约0.4、2 Mb/s的平均业务速率提升,证明了所提出算法的性能优势。

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

The main components of Cognitive Radio networks are Primary Users(PU)and Secondary Users(SU).The most essential method used in Cognitive networks is Spectrum Sensing,which detects the spectrum band and opportunistically accesses the free white areas for different users.Exploiting the free spaces helps to increase the spectrum efficiency.But the existing spectrum sensing techniques such as energy detectors,cyclo-stationary detectors suffer from various problems such as complexity,non-responsive behaviors under low Signal to Noise Ratio(SNR)and computational overhead,which affects the performance of the sensing accuracy.Many algorithms such as Long-Short Term Memory(LSTM),Convolutional Neural Networks(CNN),and Recurrent Neural Networks(RNN)play an important role in designing intelligent spectrum sensing techniques due to the excellent learning ability of deep learning frameworks,but still require improvisation in terms of sensing accuracy under dynamic environmental conditions.This paper,we propose the novel and hybrid CNN-Cuttle-Fish Optimized Long Short Term Memory(COLSTM),an improved version of LSTM that is well suited for the dynamic changes of environmental SNR with less computational overhead and complexity.The proposed COLSTM based spectrum sensing technique exploits the various statistical features from spectrum data of PU to improve the sensing efficiency.Furthermore,the addition of shuttle-fish optimization in LSTM has reduced the computational overhead and complexity which in turn enhanced the sensing performances.The proposed methodology is validated on spectrum data acquired using RaspberryPi-RTLSDR experimental test-beds.The proposed spectrum sensing technique and the existing classical spectrum sensing techniques are compared.Experimental results show that the proposed scheme has shown the brighter enhancement of performance under different SNR environments.Further,the improvised performance has been achieved at low complexity and low computational overhead when compared with the other existing LSTM networks.

室内超宽带测距定位及NLOS抑制方法

针对UWB定位过程中易受NLOS等误差影响导致定位精度和稳定性下降,并且现有的NLOS识别剔除方法模型复杂、环境依赖性强等问题,本文提出一种信道特征二元假设和基于滑窗改进的UWB自主完好性监测相结合的误差识别剔除方法。该方法首先通过信道特征二元假设对粗差、部分NLOS误差识别剔除,在提升数据质量的同时能够最大限度地降低UWB定位自主完好性监测的单测距值故障假设的局限性;然后针对量级较小的NLOS误差,仍然存在识别不敏感问题,因此,结合滑窗改进的UWB定位自主完好性监测方法;最后在UWB定位自主完好性监测方法的基础上引入测距精度提升监测性能,并结合滑窗理论降低漏检情况,能够针对信道特征二元假设方法未识别的NLOS测距值进行有效识别剔除,实现对NLOS误差的进一步识别剔除。试验结果表明,本文方法能够对NLOS等误差进行有效识别剔除,提高UWB定位精度和可靠性。

红外遥感相机成像参数优化研究

为了更好地优化红外遥感相机成像参数,文章首次提出信噪比-动态范围积相机性能综合评价指标。建立了红外遥感相机的噪声电压模型并深入分析噪声影响,当恒定噪声电压项远大于时变噪声电压项时噪声电压模型可以近似简化为线性模型。进而建模分析了积分电容和积分时间对信噪比和动态范围的影响,并首次提出信噪比-动态范围积作为相机性能评价的指标。信噪比-动态范围积随着积分时间同步增大,积分时间较小时增速较快,积分时间较大时增速迅速下降;信噪比-动态范围积随着积分电容的增大先增加后减小,存在极值。研究结果对红外遥感相机的参数优化设计具有重要作用。

低轨星座红外探测器对临近空间高超声速目标的可探测性分析

以STSS LEO红外探测平台为例,分析其对AGM-183A类空射式高超声速目标的可探测性。为了直观比较不同条件下目标的光电探测特性,将其量化为焦平面上输出信噪比超过阈值的像元数量。首先计算目标气动温度及光谱辐射强度,再利用探测器模型,计算在不同探测方向、距离和角度下,焦平面的信噪比峰值以及超过信噪比阈值的像元数量。分析结果表明,星下点模式下信噪比峰值最高(335),且超过信噪比阈值(6)的像元数最多(54×54),此像元数代表LEO星座对AGM-183A目标的最大可探测性。在临边探测模式下,给出目标可探测性随探测角度和目标温度的变化规律。结果表明,当目标温度接近800 K且探测方位角ψ小于10°(或大于170°)时,焦平面上超过信噪比阈值的像元数量为4×4,说明此时目标可探测性已经接近探测器的理论极限(3×3)。对比来看,温度的改变对目标可探测性的影响更为显著。在临边探测模式下,目标在飞行中段采用主动冷却手段降低表面气动温度以逃脱LEO探测的概率更高。从提高预警能力角度,要提高探测器在最不利角度对温度接近800 K目标的信噪比阈值(方位角ψ小于10°或大于170°时焦平面像元数量不低于8×8)。

智能手机GNSS数据质量分析与随机模型建立

为提高移动智能终端定位精度,利用小米8和华为Mate20手机采集多个卫星系统的观测数据并进行质量分析,通过不同卫星系统的伪距残差分别对信噪比模型参数进行估计,最后进行静态伪距单点定位实验。结果表明,手机端观测的BDS和GPS伪距精度接近,且高于GLONASS;相较于高度角,伪距残差与信噪比的相关性更强;在信噪比模型参数估计中,小米8拟合效果优于华为Mate20,BDS拟合效果差于其他卫星系统;参数拟合后的信噪比模型在水平和高程方向的定位精度分别为2.37 m和8.38 m,相较于高度角模型,定位精度提升约10%。

自适应动态滤波网络地震随机噪声压制方法

由于地质及环境条件复杂,导致地震勘探采集资料信噪比相对较低,对后续的研究带来不利影响,因此地震勘探数据处理中对随机噪声的压制一直备受关注。现有算法无法较好实现对噪声的有效压制和对有效信号的极大保留,为此,将传统方法和深度学习相结合,提出了基于自适应动态滤波网络(Adaptive Dynamic Filtering Net,ADFNet)的方法压制地震资料中的随机噪声。该网络以编码器—解码器为架构,首先引入通道注意力机制(Attention Mechanism,AM)的思想,通过通道AM对空洞卷积多尺度数据特征集成,为网络提供了精准且丰富的特征表示;然后,引入动态卷积,以较低的计算复杂度实现对地震资料高频特征的学习,从而保留更丰富的细节信息。合成数据和实际数据的实验结果均表明,ADFNet可有效压制地震资料中的随机噪声,同时保留更丰富的地震数据细节,处理后的地震数据信噪比得到显著提升。

基于多维小波特征的有源雷达欺骗干扰识别

针对跟踪工作模式下常见雷达有源欺骗干扰识别问题,通过分析雷达信号和干扰模型,提出一种基于多维小波特征的识别算法。对目标回波多个相干处理间隔中的首个脉冲重复间隔内信号采样累积,构成一维离散序列,利用Mallat算法提取多尺度小波系数,计算不同尺度下小波系数的移位相关系数,并组成特征向量用于分类识别。构建不同参数的信号样本仿真,在信噪比为-5 dB时仍有高于80%的识别正确率。该算法能将距离假目标干扰和距离拖引干扰有效识别,证明了算法的准确性、通用性和严谨性。此外,针对实际情况,可以调整特征向量维度,选择更适合的母小波函数,进一步优化干扰识别效果。

GNSS-R水位监测研究进展与其在我国水利行业应用展望

水位监测在水文学、水利工程、灾害防治等领域都具有非常重要的意义.传统的水位监测方法存在成本高、覆盖范围小等缺点,GNSS不仅具有导航、定位及授时的功能,还可以利用其反射信号获取反射面的特性信息并将其称为全球卫星导航系统反射测量(Global Navigation Satellite System-Reflectometry,GNSS-R).近年来,GNSS-R技术以低成本、全天候、高时空分辨率等优势为水位监测提供了一种新方法.本文对GNSS-R水位监测的研究现状、不同方法的影响因素以及当前应用过程中存在的问题进行了总结归纳提出了下一步的发展趋势,最后对其在我国水利行业的应用进行了展望.

简易烈度计记录的噪声水平评价:以泸定M_(S)6.8级地震为例

在我国地震预警和烈度速报系统中,烈度计被广泛使用。烈度计的频谱特征主要是通过检定实验室进行标定,但在破坏性地震中,烈度计响应特点缺少必要的研究分析。该文以2022年四川泸定M_(S)6.8级地震为例,对比分析了烈度计记录和强震仪记录在噪声水平、信噪比(signal-to-noise ratio,SNR)、高通滤波截止频率(f_(HP))取值以及有效周期范围(T_(max))方面的特点,综合评价了烈度计记录的频谱特征,分析了噪声影响。得到以下结论:在整个频域范围内,烈度计记录的SNR要比强震仪记录平均意义上低一个数量级,可以估计烈度计动态范围较强震仪大致低20 dB;烈度计记录的最佳f_(HP)取值相较于强震仪记录存在更大的不确定性,尤其是在竖直方向上;烈度计记录的最佳f_(HP)取值更大,反应谱T_(max)较传统强震仪记录在水平方向小1~2 s,在竖直方向小3 s左右。该研究结果有助于合理应用烈度计记录,服务地震学和地震工程学相关研究。