In recent years, there has been growing interest in using precipitable water vapor (PWV) derived from global positioning system (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features such as Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal, and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a 4-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.

Near ground radio wave propagation is examined in a tropical plantation experimentally at VHF and UHF bands. The propagation loss with forest depth is empirically analyzed using an integrated model combining both the foliage induced effect and the ground effect. Several well-known empirical foliage models are compared and analyzed. It is observed that the fitted ITU-R model taking into account the ground reflection, can provide a close approximation to the path loss in a tropical palm plantation. However, the accuracy of this model becomes poor when lateral wave dominates in the VHF band. A modification to the ITU-R model is then proposed taking into consideration the lateral wave effect. The modified model is verified using measured and published data, and found to have higher accuracy for large foliage depth in the VHF band as compared to the existing empirical foliage models.

Microstrip line (MLIN)-based glucose sensors have been used for noninvasive glucose monitoring. However, the current MLIN-based solutions do not provide enough sensitivity. The reason for this is that they rely on the fringing field of an MLIN that leads to a shallow penetration depth and thus low sensitivity. In this paper, we propose a sensitive MLIN-based glucose sensor using the main field. The idea is illustrated using the following three steps. Step 1 is sensing the glucose level by using the main field, which uses the material under test (MUT) as the substrate of an MLIN terminated with a load; Step 2 is identifying the contribution of different parameters and different components of parameters to sensitivity, which leads to Step 3, in which different parameters and/or different components of parameters are crosschecked for sensing. Two frequency bands, 100 to 500 MHz and 1.4 to 1.9 GHz, were investigated. In Step 1, the main field configuration was compared with its fringing field counterpart. It is shown that the proposed MLIN configuration presents an average sensitivity of 1.8×10 ^-3 dB/(mg/dL) in terms of the reflection coefficient that is more than 10 times higher than that of its counterpart. In Step 2, different components of the reflection coefficient and the input impedance of the proposed configuration were examined, and their diverse contributions to the sensitivity were shown, which allows crosschecking to improve sensing accuracy. This was successfully shown in Step 3, in which algorithms for crosschecking were proposed, and the sensing accuracy was examined. In terms of the optimal sensing frequency band, 1.4 to 1.9 GHz shows higher sensitivity than 100 to 500 MHz, where the molecules may interact the most with the waves. This implies that the interaction between a wave and the sensing structure with the MUT contributes more to sensitivity than the direct interaction between the wave and the MUT. The radiofrequency safety of the proposed configuration was examined by checking the specific absorption rate of the MUT with the proposed structure. This paper offers a new avenue for the development of an MLIN-based noninvasive glucose sensor for continuous monitoring with high sensitivity in the near future. It can be used both as a single sensor and in a multiple-sensor glucose monitoring system.

In this paper, a simple algorithm is proposed to perform the nowcasting of rainfall in the tropical region. The algorithm applies global positioning system-derived precipitable water vapor (PWV) values and its second derivative for the short-term prediction of rainfall. The proposed algorithm incorporates the seasonal dependency of PWV values for the prediction of a rain event in the coming 5 min based on the past 30 min of PWV data. This proposed algorithm is based on the statistical study of four-year PWV and rainfall data from a station in Singapore and is validated using two-year independent data for the same station. The results show that the algorithm can achieve an average true detection rate and a false alarm rate of 87.7% and 38.6%, respectively. To analyze the applicability of the proposed algorithm, further validations are done using one-year data from one independent station from Singapore and two-year data from one station from Brazil. It is shown that the proposed algorithm performs well for both the independent stations. For the station from Brazil, the average true detection and false alarm rates are around 84.7% and 37%, respectively. All these observations suggest that the proposed algorithm is reliable and works well with a good detection rate.

This paper presents an experimental study of air-to-ground channels over sea surface at the C-band (5.7 GHz) with low airborne altitudes (0.37-1.83 km) through wideband channel measurements. In this paper, the multipath statistics and the propagation loss at different airborne altitudes are estimated and analyzed. It is observed that about 95% (86%) of the measured channel responses can be represented by the 3-ray (2-ray) multipath model. As the airborne altitude decreases, there is a higher probability for the appearance of multipath components. Moreover, it is found that the evaporation duct and elevated duct over the sea surface are the two important factors that can significantly affect the over-water air-to-ground communication link. These ducts can also decrease the rate of radio-wave attenuation, i.e., a decrease in path-loss exponent n in the log-distance path-loss models.