Rainfall Monitoring Using a Microwave Links Network:A Long-Term Experiment in East China

The first long-term rainfall monitoring experiment using the commercial microwave links(CMLs)network in East China is introduced.The network,located in Jiangyin,Jiangsu Province,consists of 49 links with frequencies ranging from 15 GHz to 26 GHz and lengths from 1.14 km to 4.78 km.An OTT PARSIVEL disdrometer is deployed to refine the local rain-induced attenuation relationship,and the CML observations are compared simultaneously with five rain gauges.The inversion parameters of the CML are optimized by minimizing the error of the accumulated rainfall of historical rainfall events.The inversion results show that the daily accumulated rainfall retrieved by the CMLs agrees well with the rain gauge measurements.As an opportunistic approach to monitor near-surface rainfall with high spatiotemporal representativeness and accuracy,the CML network can be used to monitor and forecast urban flood disasters,especially in regions where the widepread deployment of conventional meteorological instruments is impractical.

Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition

Accurately measuring meteorological visibility is an important factor in road, sea, rail, and air transportation safety, especially under visibility-reducing weather events. This paper deals with the application of Machine Learning methods to estimate meteorological visibility in dusty conditions, from the power levels of commercial microwave links and weather data including temperature, dew point, wind speed, wind direction, and atmospheric pressure. Three well-known Machine Learning methods are investigated: Decision Trees, Random Forest, and Support Vector Machines. The correlation coefficient and the mean square error, between the visibility distances estimated by Machine Learning methods and those provided by Burkina Faso weather services are computed. Except for the SVM method, all the other methods give a correlation coefficient greater than 0.90. The Random Forest method presents the best result both in terms of correlation coefficient (0.97) and means square error (0.60). For this last method, the best variables that explain the model are selected by evaluating the weight of each variable in the model. The best performance is obtained by considering the attenuation of the microwave signal and the dew point.

Wireless actuation and control of ionic polymer-metal composite actuator using a microwave link

The ionic polymer–metal composite(IPMC),a type of electroactive polymer(EAP)actuator,has created a unique opportunity to design robots that mimic the motion of biological systems due to its soft structure and operation at a low voltage.Although this polymer actuator has strong potential for a next-generation artificial muscle actuator,it has been observed by many researchers that supplying actuation voltages in multiple locations is challenging.In robotic applications,a tethered operation is prohibited and the battery weight can be critical for actual implementation.In this research,the remote unit can provide necessary power and control signals to the target mobile robot units actuated by IPMCs.This research addresses a novel approach of using a wireless power link between the IPMC and a remote unit using microstrip patch antennas designed on the electrode surface of the IPMC for transmitting the power.Frequency modulation of the microwave is proposed to selectively actuate a particular portion of the IPMC where the matching patch antenna pattern is located.This approach can be especially useful for long-term operation of small-scale locomotion units and avoids problems caused by complex internal wiring often observed in various types of biologically inspired robots.