一种 QAM星座点优化方法及性能分析

正交幅度调制(Quadrature Amplitude Modulation,QAM)是目前无线通信中大量应用的调制技术,其位于4个角的星座点易受链路的影响而导致较高误码率。提出了一种QAM星座点整形优化方法,并对整形优化后的QAM星座点性能进行了计算机仿真分析。以标准星座点为初始值,通过遗传算法逐步迭代,搜索出抗削峰的星座点,从而降低解调误码。仿真结果表明,相比标准QAM,整形优化后的星座点峰均比降低1 dB@1E-6,且在OBO=3.5 dB时有大约0.3 dB@SER=1E-3的解调增益提升。

相位噪声对 16QAM 通信系统性能影响及其均衡算法研究

在分析相位噪声对16QAM通信性能影响的基础上,提出了一种基于误差判决的多模均衡算法(Multiple Modulus Algorithm Based on Error Judgment,MMA-EJ),该算法不仅利用信号的相位信息消除了卫星通信中载波频偏引入的相位失真,还考虑了多电平幅度调制中不同半径星座点引入的幅度误差影响,因此相对于恒模算法(Constant Modulus Algorithm,CMA)来说,提高了均衡算法的精度。仿真结果表明,与传统的恒模均衡算法相比,提出的基于误差判决的MMA算法不仅可以消除高速信号传输时的码间串扰(Inter Symbol Interference,ISI),还可以消除传输系统中的相位噪声,提高16QAM通信系统的传输性能。

基于HPLC-QAMS多组分定量联合多元统计分析及加权TOPSIS法的畲药树参综合品质评价

目的建立同步检测畲药树参中紫丁香苷、绿原酸、芥子醛葡萄糖苷、松柏醇、芦丁、山柰酚-3-O-芸香糖苷、3,4-O-二咖啡酰基奎宁酸、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸含量的高效液相色谱一测多评(HPLC-QAMS)方法,并采用多元统计分析及加权优劣解距离(technique for order preference by similarity to ideal solution method,TOPSIS)法对其品质进行综合评价。方法以Waters Xbridge C 18色谱柱;乙腈-0.05%甲酸溶液为流动相,梯度洗脱;检测波长260 nm。以山柰酚-3-O-芸香糖苷为参照物,建立内参物与其他8个待测成分的相对校正因子(relative correction factor,RCF),进行RCF耐用性考察及色谱峰定位,同时与外标法实测结果进行对比,验证HPLC-QAMS法准确性和可靠性。运用主成分分析(principal component analysis,PCA)、正交偏最小二乘法-判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)等多元统计分析以及W-TOPSIS法对9个成分HPLC-QAMS法含量结果的相关性进行分析,挖掘影响畲药树参产品质量的主要潜在标志物,建立畲药树参综合质量优劣评价方法。结果9种成分分别在3.27~81.75μg/mL、9.85~246.25μg/mL、0.43~0.75μg/mL、0.31~7.75μg/mL、1.58~39.50μg/mL、0.59~14.75μg/mL、1.26~31.50μg/mL、4.55~113.75μg/mL和1.98~49.50μg/mL范围内线性关系良好,平均加样回收率96.82%~100.07%(RSD<2.0%);HPLC-QAMS和外标法(ESM)含量测定结果差异无统计学意义(P>0.05),HPLC-QAMS法可用于畲药树参多组分定量控制;多元统计分析结果显示,前2个主成分累计方差贡献率89.589%,绿原酸、紫丁香苷、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸是影响畲药树参产品质量的主要潜在标志物;加权TOPSIS法结果显示浙江地区所得畲药树参质量最优,其次为江西、安徽、湖南和湖北产树参,云南和贵州产树参位于排名后4位。结论所建立的HPLC-QAMS多组分定量控制方法,操作便捷、结果准确;多元统计分析联合加权TOPSIS法全面客观,可用于畲药树参品质的综合评价。

基于QAM的协作通信系统物理层认证技术

针对两用户协作通信系统的身份认证问题,提出了一种基于正交幅度调制(QAM)的协作物理层认证机制。在该机制中,两个单天线用户分别使用同相与正交分量传输自己及伙伴的消息和标签,从而实现两用户消息和标签的唯一分解。基站采用最大比合并检测用户消息和标签,并通过标签对比完成物理层身份认证。在等功率分配且用户上行信道对称的典型场景下,给出了消息和标签的误码率闭合表达式。结果表明:在信噪比(SNR)为6~15 dB时,所提机制的认证概率与非协作物理层认证相比提高了12%~20%,非法攻击认证概率仅保持在10^(-3)量级,所提认证机制具有更好的身份认证性能。

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

基于HPLC-QAMS测定两种寄主肉苁蓉中6种苯乙醇苷成分含量

建立HPLC-QAMS同时测定梭梭-肉苁蓉和四翅滨藜-肉苁蓉醇提液中松果菊苷、毛蕊花糖苷、异毛蕊花糖、肉苁蓉苷A、管花苷A和2′-乙酰基毛蕊花糖苷的含量。HPLC法测定6种苯乙醇苷成分在各自线性范围内线性关系良好(r≥0.9995),方法学考察RSD均<3.0%,平均加样回收率为98.616%~100.203%,RSD为1.645%~2.602%。以松果菊苷为内参物,计算其它5个成分的RCF分别为0.8839、1.0038、0.7510、0.8430和0.8515。利用RCF计算两种寄主肉苁蓉6种苯乙醇苷成分含量,QAMS与ESM测得的两种寄主肉苁蓉各成分含量无明显差异。结果表明,建立的HPLC-QAMS测定肉苁蓉醇提取液中6种苯乙醇苷成分的方法稳定可靠,可用于其质量控制及药效基础研究。

QAMS多组分定量联合PCA、OPLS-DA及GRA分析法评价白花蛇舌草的质量

目的:采用高效液相-一测多评(HPLC-QAMS)法联合化学计量学及灰色关联度分析法评价白花蛇舌草质量。方法:采用Alltima C_(18)色谱柱(5.0μm,250.0 mm×4.6 mm),0.2%磷酸-乙腈为流动相梯度洗脱;以芦丁为参照物,建立其与京尼平苷酸、京尼平苷、车叶草苷酸、车叶草苷、2-羟基-3-甲基蒽醌、1,2-二羟基-3-甲基蒽醌、槲皮素、山柰酚、齐墩果酸、熊果酸、豆甾醇和β-谷甾醇的相对校正因子并进行校正因子耐用性考察。同时采用ESM和QAMS法测定收集到的16批白花蛇舌草中该13种成分的含量,再运用统计软件进行化学计量学及灰色关联度分析。结果:13种成分方法学验证均符合2020年版《中华人民共和国药典》要求。京尼平苷酸、京尼平苷、车叶草苷酸、车叶草苷、2-羟基-3-甲基蒽醌、1,2-二羟基-3-甲基蒽醌、槲皮素、山柰酚、齐墩果酸、熊果酸、豆甾醇、β-谷甾醇与芦丁的平均相对校正因子分别为0.9489、0.7033、0.7824、1.1359、0.5845、0.8005、0.8933、1.0683、0.7406、0.8640、0.6745、0.5424。两种方法测定结果比较,差异无统计学意义(P>0.05)。化学计量学方法显示16批白花蛇舌草聚为3类,呈现一定的产区差异。芦丁、车叶草苷酸、京尼平苷和齐墩果酸是影响白花蛇舌草产品质量的主要潜在标志物。GRA法分析结果显示7个省中浙江和江西地区所得白花蛇舌草质量最优。结论:本试验所建立的方法操作便捷、结果准确,结合化学计量学及GRA方法可用于白花蛇舌草质量的综合评价。

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

一种5G FBMC-16QAM抗干扰系统仿真研究

为了解决5G通信系统中存在的干扰问题,提出一种将多载波技术(FBMC)与正交幅度调制(QAM)技术相结合的方法,可以更好地解决相邻子载波干扰问题,并提高功率效率、降低带外衰减。进而设计一种基于5G FBMC系统的16QAM调制解调器,并通过星座图和FBMC-16QAM调制解调数据的误码率仿真曲线进行分析与比较,结果表明:随着信噪比的增加,误码率逐渐降低。此外,在信噪比相同的情况下,随着进制数的增加,误码率也逐渐增加。

Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

一种新的叠加64QAM星座设计方案

为了提升可见光通信系统的传输速率和克服信道相关性,在2×2多输入多输出可见光通信系统中提出一种叠加64阶正交幅度调制(64QAM)星座方案。首先,在发射端生成两路几何整型的8QAM信号,通过两个发光二极管(LED)发送;其次,在自由空间传输到接收端线性叠加得到一个方形的64QAM星座信号。实验表明,所提方案误码率更低、电压峰峰值工作范围更广,相较于现有方案的发射端信号具有更大的最小欧式距离和较低的峰均功率比值,因此抗噪声能力更强,能有效降低发射端LED产生非线性失真风险。理论仿真和实验都证明了其优越性。

基于HPLC-QAMS多指标成分定量测定联合化学计量学的甘露消渴胶囊质量评价

目的建立高效液相一测多评法(high performance liquid chromatography-quantitative analysis of multi-components by single marker,HPLC-QAMS)定量测定甘露消渴胶囊中毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷、莫诺苷、马钱苷、山茱萸新苷、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B的含量,并结合化学计量学对3个厂家的产品质量进行评价。方法采用高效液相色谱仪,流动相选择乙腈-0.2%磷酸,色谱柱为Ultimate XB-C_(18)(250 mm×4.6 mm,5μm)进行梯度洗脱。检测波长分别为330 nm(检测毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷)、240 nm(检测莫诺苷、马钱苷、山茱萸新苷)和208 nm(检测泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B)。以马钱苷为内参物,建立其他10种成分的相对校正因子,并计算各成分含量。采用SPSS 26.0统计软件对甘露消渴胶囊中11种成分含量测定结果进行聚类分析和主成分分析。结果毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷、莫诺苷、马钱苷、山茱萸新苷、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B分别在7.51~375.50、3.37~168.50、0.46~23.00、1.08~54.00、9.60~480.00、4.77~238.50、1.64~82.00、0.76~38.00、1.95~97.50、0.93~46.50和6.69~334.50μg·mL^(-1)范围内线性关系良好,相关系数(r=0.9991~0.9995),加样回收率及相应的RSD依次为99.11%(1.24%)、98.66%(1.45%)、97.71%(1.60%)、96.98%(0.93%)、100.20%(0.65%)、98.54%(1.18%)、97.90%(1.34%)、96.95%(1.07%)、98.47%(0.94%)、99.10%(0.89%)和100.08%(0.59%)。15批甘露消渴胶囊聚为3类,经主成分分析得4个主成分的累积贡献率达到85.338%。结论所建立的HPLC-QAMS法结合化学计量学可综合评价甘露消渴胶囊的质量。

适合高阶QAM信号的加权多模盲均衡算法

该文提出了一种加权多模盲均衡算法。该算法结合了多模盲均衡算法和判决引导算法的各自优势,利用由判决符号的指数幂构成的加权项调整代价函数中的模值。在均衡器系数迭代过程中,加权项不仅随着判决符号自适应地改变,还可以根据MSE估计值作更精确地调整。理论分析和仿真结果表明,与多模盲均衡算法等其它算法相比,该文提出的算法在同等条件下可以获得更快的收敛速度和更低的稳态收敛残差,更适用于高阶QAM信号。

适用于高阶QAM信号的盲均衡算法

为了提高高阶QAM信号盲均衡性能,给出了改进的WMMA盲均衡算法和基于多模的混合盲均衡算法。改进的WMMA盲均衡算法采用新的加权因子选取方法,降低了稳态误差,提高了算法对不同调制阶数的适应能力;此外,采用了基于峰度的变步长方法,能够提高均衡器收敛速度。基于多模的混合盲均衡算法将改进的WMMA盲均衡算法和判决引导算法有机结合,构造出新的混合盲均衡代价函数,在保证较低稳态误差的前提下,能够大幅度提高均衡器的收敛速度。理论分析和仿真结果表明,2种算法对信号星座图的形状不敏感,稳态误差和收敛速度较其他算法均有改善。

基于QAM的FSO-OFDM系统性能分析

由于OFDM调制技术能有效抵御大气散射效应,提出了频带FSO-OFDM传输的系统方案。在建立大气散射信道模型之后,推导了由散射造成的光强衰减系数及光强起伏方差和系统误符号率(SER)间的关系。以雾天和雨天为例,通过仿真实验验证了其正确性。结果表明:毛毛雨、广延雨和雷暴雨散射造成的光强起伏方差分别为2.4×10-2、3.044 1×10-4和8.776 2×10-5。当误符号率Pe=10-5、子载波N=32时,相对于毛毛雨,广延雨天和雷暴雨天的误符号率分别降低了12 dB、15 dB。虽然平流雾较辐射雾散射造成的光强衰减大,但它们的光强起伏方差均较小。当误符号率Pe=10-3,子载波N=32时,相对于平流雾,辐射雾的误符号率降低了10 dB。因此,在散射信道中系统误符号率主要取决于散射造成的光强起伏方差,其次取决于散射造成的光强度衰减系数。

适用于高阶QAM信号的多模混合盲均衡算法

为了提高高阶QAM信号的盲均衡性能,本文提出了一种新的多模混合盲均衡算法。该算法首先对加权多模盲均衡算法进行改进,提出了新的加权因子选取方法;然后将其与判决引导算法有机结合,构造出新的多模混合盲均衡代价函数对均衡器系数自适应更新。与其他盲均衡算法相比,该算法不仅降低了均衡后的稳态误差,增强了对不同调制阶数的适应能力,而且大幅度提高了均衡器的收敛速度。仿真结果验证了该算法的有效性。

全数字多星座图、可变符号率QAM调制器

本文给出了一种适用于数字有线电视（DVB-C）的可变符号速率（4～8M波特率）、可变星座点数（M=4，16，32，64，128，256）的全数字化MQAM调制方法。并对算法实现的主要模块进行了分析与设计。最后用实测指标验证了该MQAM调制方法的有效性与优越性。

变速率QAM调制

本文采用ＤＳＰ芯片利用软件实现了对变速率ＱＡＭ基带信号的处理。变速率ＱＡＭ就是根据衰落信道的传输特性来自发地调节信号的调制电平数，使信号的误码率在可以接受的条件下尽可能地提高信息传输速率。仿真结果表明：在高信噪比情况下，保持同样的误码率，变速率ＱＡＭ的频谱利用率可以比４ＤＰＳＫ提高一倍以上。

全数字高阶QAM解调系统的设计

给出了一种适合高阶256QAM的全数字QAM解调方案,提出自适应改变环路带宽的极性判决载波恢复算法以及基于CMA算法和SGA算法的盲均衡算法,并且结合内插滤波器对整个解调系统进行了优化。算法用FPGA实现并成功应用到高阶QAM接收机中。