SUMMARYA small-scale experimental setup for autonomous target tracking of a surface vessel in the presence of obstacles is presented. The experiments are performed in simulated rough seas through wave, current, and wind generation in a small indoor pool. Absolute position of the agent and the target as well as the obstacle size and position are provided through an overhead camera by detecting color light emitting diodes installed on all objects. Ordinary differential equations with stable limit-cycle solutions are used to define transitional trajectories around obstacles based on the camera data. A sliding mode control law is implemented for real-time tracking control which is capable of rejecting large disturbances from the generated waves and wind. The sliding mode control signals are sent to wireless receivers on the autonomous vessel where a proportional integral speed controller maintains the commanded speed. A special scaling method is presented to show that the environmental forces are similar to those of moderate through high sea states. Several experiments are presented where the autonomous vessel catches and follows a target boat moving in arbitrary trajectories in both the presence and absence of obstacles.

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These days, health care systems such as pharmacies and drugstores normally produce high volumes of data. Consequently, utilizing data mining methods in health care systems has become a conventional process. In this research, Apriori algorithm has been applied to perform data mining using the data obtained from the prescriptions ordered within a pharmacy. Ten association rules were achieved from the assigned pharmaceutical drugs in those prescriptions using the aforementioned Apriori algorithm. The accuracy of these rules is also manually studied and reviewed by a physician. Among these association rules, Vitamin D and Calcium pills are the most interrelated medications, and Omeprazole and Metronidazole rankd second in terms of association. The results of this study provide useful feedback information about associations among drugs.

A novel trajectory tracking sliding mode control law for general planar underactuated autonomous vessel models is presented where all six position and velocity states are asymptotically stabilized. The approach is based on defining a transitional trajectory vector function which can be used to reduce the sixth order system to a fourth order one with two control inputs. It is then shown that the stabilization of the reduced order system guarantees asymptotic stability of all six system states where the only restriction for reference trajectory is that it must satisfy the vessel's nonholonomic constraint. The most important advantages of the approach are that it does not require any specific structure for the forcing functions such as hydrodynamic damping, it is robust to modeling uncertainties and disturbances, and it can be applied to models with diagonal and non-diagonal mass matrices. Simulation results are presented for an autonomous surface vessel.

A novel nonlinear trajectory tracking controller for underactuated unmanned surface vessels is presented. A comprehensive planar model of the vessel with two control inputs is considered such that the system is represented by the equations of motion comprised of two double integrators subject to a second-order nonholonomic constraint. Given a target trajectory, a transitional desired trajectory is generated that uniformly satisfies the nonholonomic constraint and actuator saturation constraints. The system error dynamics is then modeled using the equations of motion and the transitional desired trajectory. A finite time sliding mode control law is developed to stabilize the yaw rotation which is robust to model uncertainties and disturbances. Consequently, the resulting reduced-order system is asymptotically stabilized via the surge force. Examples are presented and demonstrate that the approach provides trajectories and tracking control inputs which are suitable for real world applications.