• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Infrared, Millimeter, and Terahertz Waves >Dispersion-independent terahertz classification based on Geometric Algebra for substance detection
【24h】

Dispersion-independent terahertz classification based on Geometric Algebra for substance detection

机译：基于几何代数的与色散无关的太赫兹分类用于物质检测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We demonstrate and validate Geometric Algebra (GA) based terahertz (THz) signal classification of various powders in tablet form of various thicknesses, and compare the results with a conventional Support Vector Machine (SVM) approach. By using geometric algebra we can perform classification independently of dispersion and hence independently of the transmission path length through the sample. In principle, it may be possible to extend the GA coordinate-free transformation to other types of pulsed signals, such as pulsed microwaves or even acoustic signals in such fields as seismology. The classifier is available for download at Github, https://github.com/swuzhousl/ Shengling-zhou/blob/geometric-algebra-classifier/GAclassifier/.
机译：我们演示并验证了各种厚度的片剂形式的各种粉末的基于几何代数（GA）的太赫兹（THz）信号分类，并将结果与​​传统的支持向量机（SVM）方法进行了比较。通过使用几何代数，我们可以进行独立于色散的分类，从而独立于通过样本的传输路径长度进行分类。原则上，可以将GA无坐标转换扩展到其他类型的脉冲信号，例如在诸如地震学等领域的脉冲微波或什至是声信号。可从Github上下载分类器，网址为https://github.com/swuzhousl/shengling-zhou/blob/geometric-algebra-classifier/GAclassifier/。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号