一种应用于毫米波车流量检测雷达的背景功率谱识别方法

背景功率谱的准确识别是确保毫米波车流量检测雷达测量精度的首要问题.本文研究了毫米波交通雷达的工作原理和回波功率性质,并提出了一种新颖实用的背景功率谱识别算法,该算法基于相干平均和有序统计量.文中给出了不同实验环境下的测试结果,结果证明了算法的有效性,背景功率谱的识别精度有了明显提高.     

Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.     

一种基于连续波雷达的手势识别方法

为了解决依靠光学传感器进行手势识别对外部环境依赖较大的问题,提出了一种基于连续波(Continuous Wave,CW)雷达的手势识别方法,并建立了4种手势动作的回波数据库.首先,对CW雷达回波进行短时傅里叶变换(Short-Time Fourier Transform,STFT)获取手势动作的时频谱;然后,通过设立阈...     

A Review of the Dawn of Benchtop EPR Spectrometers—Innovation That Shaped the Future of This Technology

By the early 1980s, unique devices appeared in the USSR: a series of benchtop specialized EPR spectrometers. This equipment was quickly accepted not only in science but also in medicine and in many technical and economic areas including chemical industries and geologic exploration. The appearance of these devices was perceived as a salvation for the Soviet magnetic resonance (MR) scientific instrumentation by those who worked in the field of EPR spectroscopy in the USSR. (However, the program of MR scientific instrumentation ceased to exist along with the USSR a few years later). The Belarusian State University in Minsk was the center of these developments. At that moment and for many years afterwards, these devices were unique with no analogues in the worldwide EPR industry. They remained the only mass-produced MR spectrometers on the territory of the former USSR after its collapse. For the first time, based on archival materials, patents, and our personal memoirs, we describe the development of these EPR spectrometers and discuss the most original technical solutions and the scientific tasks solved with this equipment We also remember the participants of the work, showing the historical context of these events.     

复合双频光栅轮廓术研究

对于存在突变的不连续物体的测量,使用低频光栅时物体面形能够恢复,但测量精度不高;使用高频光栅时不连续部分的轮廓细节能够清晰的恢复,但存在相位包裹现象而出现截断相位.据此,提出将2种光栅的优点融和在一起的方法:首先设计适合的低频光栅在物体的突变部分产生一根条纹,使低频光栅产生的相位不需进行相位展开;然后由2种光栅的频率比值及高频的截断相位来确定高频光栅所产生的条纹的整数级数n2(x,y);最后求出高频的展开相位,从而高精度地恢复不连续物体的高度信息.计算机仿真结果表明了理论分析的正确性.     

干涉直条纹周期测量及其在位相检测中的应用

干涉条纹周期是干涉精密测量中的一个较为重要的参数.为了可以简单快速而不失准确性地测量条纹周期,采用了图像处理实现干涉条纹周期测量的较为简单实用的算法.通过MATLAB平台设计相应的图像数据处理软件,分析影响条纹周期测量精度的因素,给出处理过程对条纹倾斜度的要求,并应用于相板的位相的定量检测,获得了相板的位相大小.结果表明,测量相位精度达λ/20～λ/50或π/10～π/25.这一结果对位相物体的厚度和折射率的测量有很大的帮助.     

求解多尺度目标电磁散射的积分方程快速算法

建立了大尺度分层粗糙面散射物理模型, 基于Kirchhoff近似、几何光学和射线追踪方法, 推导给出粗糙面散射场的计算公式, 提出了一种快速模拟机载正下视合成孔径雷达(Synthetic Aperture Radar, SAR)探测冰川回波的数值仿真方法, 以帮助分析实测数据.对SAR系统设计、雷达回波信号处理算法研究都有很大帮助, 可以应用到星体地下结构探测回波的模拟.理论公式及数值仿真结果验证了此数值仿真方法的正确性.在不同的仿真场景和雷达系统参数下, 数值仿真模拟了冰川散射回波, 定量分析了冰层表面粗糙度、次表面粗糙度、山体倾斜度、雷达系统参数等对天底区域和非天底区域散射回波的影响.本文的仿真方法可以快速计算任意大尺度特定地形所对应的雷达探测仪回波数据.     

基于极化散射特性的空间锥体目标宽带回波对齐

包络对齐是宽带回波预处理中重要的一部分,然而对于空间锥体这类只有较少散射中心且目标存在微动的情况,现有的包络对齐方法并不能得到较好的对齐结果。对于一些利用宽带回波进行微动目标参数估计的算法,包络对齐是其算法的关键一步。该文提出一种基于极化散射特性进行包络对齐的算法,算法首先利用极化谱估计与幅相联合估计(P-CAPES)超分辨方法估计宽带极化回波中的散射中心的数目、位置和极化散射矩阵,然后计算前后时刻回波散射中心的极化相似性参数,最后利用得到相似性参数进行包络对齐。基于电磁计算数据的实验结果验证了算法的有效性。     

一种基于动目标聚焦的SAR-GMTI方法

由于输入信杂噪比(Signal to Clutter Noise Ratio, SCNR)较低,杂波抑制后超高频(Ultra-High Frequency, UHF)波段合成孔径雷达(Synthetic Aperture Radar, SAR)图像中剩余静止目标杂波导致系统虚警概率较高。该文提出一种动目标筛选方法,能够判断恒虚警概率(Constant False Alarm Rate, CFAR)检测器检测到的目标是否为动目标。提出一种动目原始数据恢复方法,能够从整幅SAR图像中恢复任意孤立点的多普勒相位历史。采用距离多普勒处理对恢复的数据成像,然后采用方位自聚焦处理对所成子图像进行重新聚焦。如果子图像中目标为静止目标,则聚焦前后子图像不变,否则图像被重新聚集。通过检测图像的变化可以排除虚假动目标。仿真及实测数据处理结果说明了该方法的有效性。     

基于知识辅助的MIMO雷达波形设计方法

该文针对雷达系统受到天线主瓣和副瓣杂波以及强干扰影响时性能下降问题,提出基于距离扩展目标和杂波先验信息的MIMO雷达波形设计方法。首先建立了目标函数,综合考虑了波束主瓣增益、旁瓣杂波抑制能力以及目标输出SCNR的改善性能;然后在优化问题求解中对约束条件进行松弛,使得波形矩阵空域和时域2维解耦合,从而实现空域波束形成和时域波形设计独立优化求解;其次利用L-BFGS算法设计恒模的发射波形矩阵,形成低副瓣的波束方向图和较深的强杂波抑制凹口,并基于目标输出SCNR最大化准则,利用迭代算法分步求解优化的主瓣发射波形和接收滤波器;最后通过电磁仿真的距离扩展目标数据验证所提算法的有效性。