A Simulation Framework to Support the Design and Evaluation of Adaptive Scanning for Phased-Array Weather Radars

We propose a simulation framework that can be used to design and evaluate the performance of adaptive scanning algorithms on different phased-array weather radar designs. The simulator is proposed as tool to 1) compare the performance of different adaptive scanning algorithms on the same weather event, 2) evaluate the performance of a given adaptive scanning algorithm on several weather events, and 3) evaluate the performance of a given adaptive scanning algorithm on a given weather event using different radar designs. We illustrate the capabilities of the proposed framework to design and evaluate the performance of adaptive algorithms aimed at reducing the update time using adaptive scanning. The example concept of operations is based on a fast low-fidelity surveillance scan and a high-fidelity adaptive scan. The flexibility of the proposed simulation framework is tested using two phased-array-radar designs and three complementary adaptive scanning algorithms: focused observations, beam clustering, and dwell tailoring. Based on a significant weather event observed by an operational NEXRAD radar, our experimental results consist of radar data that were simulated as if the same event had been observed by arbitrary combinations of radar systems and adaptive scanning configurations. Results show that simulated fields of radar data capture the main data-quality impacts from the use of adaptive scanning and can be used to obtain quantitative metrics and for qualitative comparison and evaluation by forecasters. That is, the proposed simulator could provide an effective interface with meteorologists and could support the development of concepts of operations that are based on adaptive scanning to meet the evolutionary observational needs of the U.S. National Weather Service.

Simultaneous Arrival Matching for New Spatial Crowdsourcing Platforms

In recent years, 3D spatial crowdsourcing platforms become popular, in which users and workers travel together to their assigned workplaces for services, such as InterestingSport and Nanguache. A typical problem over 3D spatial crowdsourcing platforms is to match users with suitable workers and workplaces. Existing studies all ignored that the workers and users assigned to the same workplace should arrive almost at the same time, which is very practical in the real world. Thus, in this paper, we propose a new Simultaneous Arrival Matching (SAM), which enables workers and users to arrive at their assigned workplace within a given tolerant time. We find that the new considered arriving time constraint breaks the monotonic additivity of the result set. Thus, it brings a large challenge in designing effective and efficient algorithms for the SAM. We design Sliding Window algorithm and Threshold Scanning algorithm to solve the SAM. We conduct the experiments on real and synthetic datasets, experimental results show the effectiveness and efficiency of our algorithms.