Vegetative and reproductive morphology of Kallymenia patens (Kallymeniaceae, Rhodophyta) in the Mediterranean Sea

Reproductive morphology of the Mediterranean red alga Kallymenia patens is described for the first time, confirming its position in the genus. K. patens is characterized by a non-procarpic female reproductive apparatus, carpogonial branch systems consisting of supporting cells bearing both three-celled carpogonial branches and subsidiary cells that lack a hypogynous cell and carpogonium; fusion cells develop numerous connecting filaments, and tetrasporangia are scattered over the thallus and are probably cruciately divided. Old fertile spathulate specimens of K. patens are morphologically similar to K. spathulata, but they can be distinguished by the length of spathulated proliferations (up to 0.6 cm and 6 cm, respectively), the length of inner cortical cells (up to 70 and 30 μm, respectively), and the gonimoblast location (in proliferations from the perennial part of the blade and over all the thallus surface, respectively)

An hybrid methodology for RL-based behavior coordination in a target following mission with an AUV

Proposes a behavior-based scheme for high-level control of autonomous underwater vehicles (AUVs). Two main characteristics can be highlighted in the control scheme. Behavior coordination is done through a hybrid methodology, which takes in advantages of the robustness and modularity in competitive approaches, as well as optimized trajectories

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Dam wall detection and tracking using a Mechanically Scanned Imaging Sonar

In dam inspection tasks, an underwater robot has to grab images while surveying the wall meanwhile maintaining a certain distance and relative orientation. This paper proposes the use of an MSIS (mechanically scanned imaging sonar) for relative positioning of a robot with respect to the wall. An imaging sonar gathers polar image scans from which depth images (range & bearing) are generated. Depth scans are first processed to extract a line corresponding to the wall (with the Hough transform), which is then tracked by means of an EKF (Extended Kalman Filter) using a static motion model and an implicit measurement equation associating the sensed points to the candidate line. The line estimate is referenced to the robot fixed frame and represented in polar coordinates (rho&thetas) which directly corresponds to the actual distance and relative orientation of the robot with respect to the wall. The proposed system has been tested in simulation as well as in water tank conditions

The morphology and reproductive structures of Mediterranean species of the genus Nemastoma J. Agardh, nom. cons. (Nemastomataceae, Nemastomatales): Nemastoma dichotomum and N. dumontioides

The morphology and reproductive structures of Mediterranean species of the genus Nemastoma J. Agardh, nom. cons. (Nemastomataceae, Nemastomatales): Nemastoma dichotomum and N. dumontioides

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach