脉搏血氧饱和度远程实时监护系统设计

为实现医生同时对多个患者的血氧饱和度的远程实时监护,设计了一种基于因特网的脉搏血氧远程实时监护系统.该系统分为患者端和医生端.患者端采用以C8051F320为处理核心的血氧检测电路提取相关血氧信号,通过USB传输到上位机进行处理、分析和显示;并通过因特网将数据实时发送至远程医生端显示、存储.实验测试表明,该系统具有延时小、可靠性较高和较大的应用前景等优点.     

浅谈应用软件中自动化测试的实现

本文就现今软件测试中存在的自动化检测过早退化及消亡的现象展开讨论,探讨其消亡原因以及对策,并就软件自动化测试技术的选择进行研究,从而实现应用软件自动化测试驶入"测试的快车道".     

医学院校中计算机基础教学面临的问题及建议

论述了在医学院校中计算机文化基础教学中存在的一些问题,就如何提高教学效果、培养学生的能力,对大学计算机基础教学模式提出了一些切实可行的探索。从教学对象、教学方式等几个方面给出一些建议,力争提高计算机基础课程的教学质量和教学效果。     

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。     

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.