The Autonomous Attack Aviation Problem

An autonomous unmanned combat aerial vehicle (AUCAV) performing an air-to-ground attack mission must make sequential targeting and routing decisions under uncertainty. We formulate a Markov decision process model of this autonomous attack aviation problem (A3P) and solve it using an approximate dynamic programming (ADP) approach. We develop an approximate policy iteration algorithm that implements a least squares temporal difference learning mechanism to solve the A3P. Basis functions are developed and tested for application within the ADP algorithm. The ADP policy is compared to a benchmark policy, the DROP policy, which is determined by repeatedly solving a deterministic orienteering problem as the system evolves. Designed computational experiments of eight problem instances are conducted to compare the two policies with respect to their quality of solution, computational efficiency, and robustness. The ADP policy is superior in 2 of 8 problem instances - those instances with less AUCAV fuel and a low target arrival rate - whereas the DROP policy is superior in 6 of 8 problem instances. The ADP policy outperforms the DROP policy with respect to computational efficiency in all problem instances

Creating drag and lift curves from soccer trajectories

Trajectory analysis is an alternative to using wind tunnels to measure a soccer balls aerodynamic properties. It has advantages over wind tunnel testing such as being more representative of game play. However, previous work has not presented a method that produces complete, speed -dependent drag and lift coefficients. Four high-speed cameras in stereo-calibrated pairs were used to measure the spatial co-ordinates for 29 separate soccer trajectories. Those trajectories span a range of launch speeds from 9.3 m/s to 29.9 m/s. That range encompasses low-speed laminar flow of air over a soccer ball, through the drag crises where air flow is both laminar and turbulent, and up to high-speed turbulent air flow. Results from trajectory analysis were combined to give speed-dependent drag and lift coefficient curves for the entire range of speeds found in the 29 trajectories. Average root mean square error between measured and modelled trajectory was 0.028 m horizontally and 0.034 m vertically. The drag and lift crises can be observed in the plots of drag and lift coefficients respectively

Histology of the Porous Oculomotorius: Relevance to Anterior Skull Base Approaches

Objective  Mobilization of cranial nerve III (CNIII) at its dural entry site is commonly described to avoid damage from stretching during approaches to the parasellar, infrachiasmatic, posterior clinoid, and cavernous sinus regions. The histologic relationships of CNIII as it traverses the dura, and the associated surgical implications are nonetheless poorly described. We herein assess the histology of the CNIII-dura interface as it relates to surgical mobilization of the nerve. Methods  A fronto-orbitozygomatic temporopolar approach was performed on six adult cadaveric specimens. The CNIII-dural entry site was resected and histologically processed. The nerve-tissue planes were assessed by a neuropathologist. Results  Histologic analysis demonstrated that CNIII remained separate from the dura within the oculomotor cistern (porous oculomotorius up to the oculomotor foramen). Fusion of the epineurium of CNIII and the connective tissue of the dura was seen at the level of the foramen, with no clear histologic plane identified between these structures. Conclusion  CNIII may be directly mobilized within the oculomotor cistern, while dissections of CNIII distal to the oculomotor foramen should maintain a thin layer of connective tissue on the nerve