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Spectrum sharing in radar bands with interference forecasting for enhanced radar protection can help design proactive resource allocation solutions which can achieve high data rates for wireless communication networks on one hand and help protect the incumbent radar systems. We consider radar spectrum sharing in 5.6GHz where a weather radar operates as a primary system and the dominant secondary system is an enterprise network consisting of access points (APs) in a university campus. Our work models transmit the power of the APs as a time series with multinomial distribution based on real collected data. The aggregated interference due to the transmissions from the APs at the radar is forecasted using a long short-term memory (LSTM) based neural network. Monte Carlo dropout is utilized to generate prediction intervals that capture the uncertainties in the interference from the APs. Finally, by using both average and upper limits of predicted interference time series a cloud-assisted efficient sharing and radar protection algorithm is proposed. Tracking the rotating radar is not required in the proposed system. The results show that the proposed efficient sharing and radar protection system ensures better radar protection and increased throughput for wireless communication users.

To facilitate efficient cloud managed resource allocation solutions, collection of key wireless metrics from multiple access points (APs) at different locations within a given area is required. In unlicensed shared spectrum bands collection of metric data can be a challenging task for a cloud manager as independent self-interested APs can operate in these bands in the same area. We propose to design an intelligent crowdsourcing solution that incentivizes independent APs to truthfully measure/report data relating to their wireless channel utilization (CU). Our work focuses on challenging scenarios where independent APs can take advantage of recurring patterns in CU data by utilizing distribution aware strategies to obtain higher reward payments. We design truthful reporting methods that utilize logarithmic and quadratic scoring rules for reward payments to the APs. We show that when measurement computation costs are considered then under certain scenarios these scoring rules no longer ensure incentive compatibility. To address this, we present a novel reward function which incorporates a distribution aware penalty cost that charges APs for distorting reports based on recurring patterns. Along with synthetic data, we also use real CU data values crowdsourced using multiple independent measuring/reporting devices deployed by us in the University of Oulu.

Objective: The objective of study was to find out serum uric acid level in normal andpreeclamptic pregnant women of third trimester visiting outpatient department of obstetrics and gynecology of Bahawal Victoria Hospital, Bahawalpur. Methodology: It was a cross sectional descriptive study conducted form July 2018 to June 2019. All primigravida women of age 18-35 years in third trimester of singleton pregnancy attending in Obstetrics and Gynecology Outpatient Department of Bahawal Victoria Hospital in study duration were included in the study. Statistical analysis was performed by using SPSS version 14. Chi-square test was performed to find the statistical difference regarding uric acid distribution between groups and ‘p’ value <0.05 was considered as a lowest level of significance. Results: Out of total 1212 women 84.6% were normal and 15.4% had preeclampsia. In our study out of 187 preeclamptic women, 63.6% had raised serum uric acid level and out of 268 normal pregnant women uric acid level was raised in only 39.5%. Results were found statistically significant. Conclusion: Results of our study suggest that serum uric acid level in pregnant women can be used as a useful and inexpensive marker in prediction of preeclampsia and preventive measures can be taken accordingly.

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