基于PRI谱的双门限雷达信号分选算法及其硬件平台设计

针对修正的PRI变换法无法分选重频参差雷达信号的缺点,提出了一种基于PRI谱的双门限雷达信号分选算法.该算法先对PRI谱设定双门限检测各个准PRI,再结合组合查看及序列搜索提取各个PRI.同时,构建了一种预分选、主分选结合的新型分选平台.预分选利用带有PowerPC的FPGA构架片上系统,完成对脉冲雷达信号的实时测量、AOA分类及数据存储;主分选采用DSP专职完成其他维参数的分选算法.谊分选新算法在保留抑制PRI子谐波优点的同时,对重频固定、抖动和参差的雷达脉冲信号都有较好的检测结果.视频信号源的大量测试表明,谊分选平台对普通雷达、PRI抖动和参差雷达、频率捷变雷达及一些特殊雷达均具有较好的分选能力.     

文本分类技术研究进展

介绍文本分类的研究背景、关键技术;总结经典文本分类方法;讨论目前新涌现的分本分类模型及面临的问题,并对文本分类趋势进行展望。     

在VS2003.NET环境下的GDI+开发方法

本文以图片的处理流程为主线,介绍了在VS2003.NET的环境下GDI+的使用方法。详细介绍了GDI+环境的搭建过程、GDI+绘图方法。其中包括图片导入、图片显示以及图片的保存方法,并给出了相关代码示例。     

某型导弹电子舱地面数据采集分析系统实现

针对脱离弹体后的电子舱无法进行实时数据采集分析的这一问题,提出使用VI和FPGA配合的独立模拟测试平台方案,利用FPGA模拟各类故障检测信号并且使用直接注入至电子舱的方法,用以解决在保证使电子舱加电正常运转的前提下使用VI技术实现独立采集和检测电子舱各类参数和性能指标目的;经实际检测结果验证,该数据采集分析系统能够独立地完成电子舱各类故障模拟并且能实时采集、记录、分析和显示电子舱的若干相应信号,为电子舱的性能分析、评价和故障定位分析等提供了重要的数据参考。     

一种基于功率谱重心的自适应特征信息提取方法

提出一种基于功率谱重心的自适应特征信息提取方法.该方法通过对不同状态转换过程中的灵敏度分布进行分析获得特征功率谱,然后计算特征功率谱的重心频率以及自适应加权系数,对特征功率谱进行自适应加权计算获得特征信息.文中给出了将自适应特征信息提取方法应用于磨煤机噪声信号特征提取的实例,对比分析了采用不同特征信息提取方法的效果.仿真结果表明,所提出的自适应特征信息提取方法能够准确剔除无效信息,自主适应运行工况变化,具有较好的灵敏度和线性度,为提高软测量模型的预测精度和泛化性能提供了保证.     

Spin dynamics and implications for superconductivity: some problems with thed-wave scenario

We review the spin dynamics of the normal state of the cuprates with special emphasis on neutron data in both the YBa₂Cu₃O_7– and La_2–x Sr _x CuO₄ systems. When realistic models of the Fermi surface shapes are incorporated, along with a moderate degree of spin fluctuations, we find good semiquantitative agreement with experiment for both cuprates. Building on the success of this Fermi-liquid-based scheme, we explore the implications ford-wave pairing from a number of vantage points. We conclude that our present experimental and theoretical understanding is inadequate to confirm or refute thed-wave scenario.     

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.     

Ka频段卫通相控阵封装天线设计与实现

针对卫星通信对终端天线低剖面、低功耗、极化可变及发射控制等需求,采用平板相控阵架构、极化可变阵列、稀疏布阵等技术,研制了一台基于单板集成封装技术的Ka频段卫星通信相控阵天线。该天线满足高轨同步卫星宽带应用需求和适航符合性验证试验DO-160G要求,经长时间跑车和飞行试验,其性能优良,可靠性高。     

Effect of Sintering Temperature on Magnetic and Electrical Properties of GdBaCo<Subscript>2</Subscript><Emphasis Type="Italic">O</Emphasis><Subscript>5 + <Emphasis Type="Italic">δ</Emphasis></Subscript>

In this study, we report the results of an investigation into the sintering temperature dependence of magnetic and transport properties for GdBaCo₂ O _{5 + δ} synthesized through a sol-gel method. The lowering of sintering temperature leads to the increase of oxygen content and the reduction of grain size. The increase of oxygen content results in the enhancement of magnetic interactions and the weakening of Coulomb repulsion effect, while the reduction of grain size improves the magnetoresistance effect. Metal-insulator transition accompanied with spin-state transition is observed in all samples.