This article presents an Unmanned Underwater Vehicle simulator named Simu2VITA, which was designed to be rapid to set up, easy to use, and simple to modify the vehicle’s parameters. Simulation of the vehicle dynamics is divided into three main Modules: the Actuator Module, the Allocation Module and the Dynamics Model. The Actuator Module is responsible for the simulation of actuators such as propellers and fins, the Allocation Module translates the action of the actuators into forces and torques acting on the vehicle and the Dynamics Module implements the dynamics equations of the vehicle. Simu2VITA implements the dynamics of the actuators and of the rigid body of the vehicle using the MATLAB/Simulink^® framework. To show the usefulness of the Simu2VITA simulator, simulation results are presented for an unmanned underwater vehicle navigating inside a fully flooded tunnel and then compared with sensor data collected when the real vehicle performed the same mission using the controllers designed employing the simulator.

Hydroelectric power plants often make use of tunnels to redirect the flow of water to the plant power house. Such tunnels are often flooded and can span considerable distances. Periodical inspections of such tunnels are highly desirable since a tunnel collapse will be catastrophic, disrupting the power plant operation. In many cases, the use of Unmanned Underwater Vehicles (UUVs) equipped with mechanical profiling sonars is a suitable and affordable way to gather data to generate 3D mapping of flooded tunnels. In this paper, we study the resolution of 3D tunnel maps generated by one or more mechanical profiling sonars working in tandem, considering synchronization and occlusion problems. The article derives the analytical equations to estimate the sampling of the underwater tunnels using mechanical profiling sonars (scanning sonars). Experiments in a simulated environment using up to four sensors simultaneously are presented. We also report experimental results obtained by a UUV inside a large power plant tunnel, together with a first map of this environment using a single sonar sensor.

Singular Spectrum Analysis (SSA) is a non parametric approach that can be used to decompose a time series into trends, oscillations and noise. In online applications, the SSA algorithm must be recalculated for each new sample available. The so called Causal SSA have been used in this context. This paper aims to contribute to the application of Causal SSA as an indicator for trading rules design by means of comparing them to previously proposed ones based on classic indicators. Using historical time series from some of the major Brazilian stocks, different simulations are carried out. Some performance metrics are applied along with several statistical tests, thereby supporting a detailed analysis of the experimental results obtained. The results analysis indicates that, no matter what technical trading rules, it is worth to apply the SSA method rather than classic approaches.