Wireless channel extraction analyzing based on graph theory

Wireless channels comprise various signal characteristics that correspond to different features. This research applies digital signal processing to first excavate and categorize various features found in the channel data. Then, borrowing from graph theory, fast clustering analysis and decision tree modeling are introduced to identify unique “fingerprint” characteristics. Finally, two scenarios were tested using artificial neural networks to identify and verify their applicability in different geographical locations.     

Maintaining strong mutual visibility of an evader moving over the reduced visibility graph

In this paper, we address the problem of determining whether a mobile robot, called the pursuer, is able to maintain strong mutual visibility (a visibility notion between regions over a convex partition of the environment) of an antagonist agent, called the evader. We frame the problem as a non cooperative game. We consider the case in which the pursuer and the evader move at bounded speed, traveling in a known polygonal environment with or without holes, and in which there are no restrictions as to the distance that might separate the agents. Unlike our previous efforts (Murrieta-Cid et al. in Int J Robot Res 26:233–253, 2007), we give special attention to the combinatorial problem that arises when searching for a solution through visiting several locations in an environment with obstacles. In this paper we take a step further, namely, we assume an antagonistic evader who moves continuously and unpredictably, but with a constraint over its set of admissible motion policies, as the evader moves in the shortest-path roadmap, also called the reduced visibility graph (RVG). The pursuer does not know which among the possible paths over the RVG the evader will choose, but the pursuer is free to move within all the environment. We provide a constructive method to solve the decision problem of determining whether or not the pursuer is able to maintain strong mutual visibility of the evader. This method is based on an algorithm that computes the safe areas (areas that keep evader surveillance) at all times. We prove decidability of this problem, and provide a complexity measure to this evader surveillance game; both contributions hold for any general polygonal environment that might or not contain holes. All our algorithms have been implemented and we show simulation results.     

Multi-contact vertical ladder climbing with an HRP-2 humanoid

We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites.     

A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation

This paper introduces a novel hybrid adaptive cuckoo search (HACS) algorithm to establish the parameters of chaotic systems. In order to balance and enhance the accuracy and convergence rate of the basic cuckoo search (CS) algorithm, the adaptive parameters adjusting operation is presented to tune the parameters properly. Besides, the exploitation capability of the CS algorithm is enhanced a lot by integrating the orthogonal design strategy. The functionality of the HACS algorithm is tested through the Lorenz system under the noise-free and noise-corrupted conditions, respectively. The numerical results demonstrate that the algorithm can estimate parameters efficiently and accurately, and the capability of noise immunity is also powerful. Compared with the basic CS algorithm, genetic algorithm, and particle swarm optimization algorithm, the HACS algorithm is energy efficient and superior.