Combining signal processing and machine learning techniques for real time measurement of raindrops

被引：8

作者：

Denby, B ^{[1
]}

Prévotet, JC

Garda, P

Granado, B

Barthes, L

Golé, P

Lavergnat, J

Delahaye, JY

机构：

[1] Univ Versailles, Lab Instruments & Syst, Paris 05, France

[2] Ctr Etud Environm Terrestre & Planataires, Velizy Villacoublay, France

[3] Univ Paris 06, Lab Instruments & Syst, Paris 05, France

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2001年 / 50卷 / 06期

关键词：

machine learning; meteorology; neural networks; optical disdrometer; real time instrumentation; telecommunications;

D O I：

10.1109/19.982973

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The data acquisition system for a new type of optical disdrometer is presented. As the device must measure sizes and velocities of raindrops as small as 0.1 mm diameter in real time in the presence of high noise and a variable baseline, algorithm design has been a challenge. The combining of standard signal processing techniques and machine learning methods (in this case, a neural network) has been essential to obtaining good performance.

引用

页码：1717 / 1724

页数：8

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