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Unmanned aircraft systems service suppliers adhere to interoperability standards that require unmanned aircraft operators to submit an operational intent, which describes the planned flight path in four-dimensional space. To ensure fairness, the central database follows a first-come, first-served approach, accepting new operational intents as long as they do not conflict with any active ones. However, creating a viable operational intent is challenging due to moving obstacles. This paper introduces an innovative optimization-based procedure to automate the intent filing process. It utilizes a stacked hexagonal tessellation to model the airspace, offering adjustable granularity. Path finding is accomplished using integer programming on the hex grid. The integer program is solved on a grid canvas that includes only necessary cells, striking a balance between computational efficiency and optimality. Simulation experiments demonstrate the procedure’s effectiveness in generating feasible trajectories, even in scenarios with dense, omnidirectional air traffic. This procedure has the potential to become the foundational software core for low-altitude air traffic management systems, providing strategic deconfliction and constraint management services. 

We study the problem of detecting abnormal inactivities within a single-occupied household based on smart meter readings. Such abnormal events include immobilizing medical conditions or sudden deaths of elderly or disabled occupants who live alone, the delayed discovery of which poses realistic social concerns as the population ages. Two novel unsupervised learning algorithms are developed and compared: one is based on nested dynamic time warping (DTW) distances and the other based on Mahalanobis distance with problem-specific features. Both algorithms are able to cold-start from limited historical data and perform well without extended parameter tuning. In addition, the algorithms are small profile in terms of data usage and computational need, and thus are suitable for implementation on smart meter hardware. The proposed methods have been thoroughly validated against real data sets with simulated target scenarios and have exhibited satisfactory performance. An implementation scheme on smart meter hardware is also discussed.