Vision-based landing of a simulated unmanned aerial vehicle with fast reinforcement learning

Landing is one of the difficult challenges for an unmanned aerial vehicle (UAV). In this paper, we propose a vision-based landing approach for an autonomous UAV using reinforcement learning (RL). The autonomous UAV learns the landing skill from scratch by interacting with the environment. The reinforcement learning algorithm explored and extended in this study is Least-Squares Policy Iteration (LSPI) to gain a fast learning process and a smooth landing trajectory. The proposed approach has been tested with a simulated quadrocopter in an extended version of the USARSim Unified System for Automation and Robot Simulation) environment. Results showed that LSPI learned the landing skill very quickly, requiring less than 142 trials

Password security through Negative Filtering

the purpose of an authentication system is to identify and verify incoming authentication requests comparing with some form of (stored) user identity. This stored user profile is at risk of being hacked and exploited by the attackers. The Negative Filtering or Negative Authentication (NA) approach utilizes a form of complement profiles which resembles the censoring and maturation process of T-cells in the immune system. The scope and applicability issues of this approach in the context of existing (positive) authentication systems have been discussed. The negative authentication is implemented using a real-valued negative selection algorithm [1]. The performance of the technique along with security considerations has been analyzed and feasible configuration settings are reported. © 2010 IEEE