Instituto Politécnico Nacional

About: Instituto Politécnico Nacional is a education organization based out in Mexico City, Mexico. It is known for research contribution in the topics: Population & Control theory. The organization has 43351 authors who have published 63315 publications receiving 938532 citations. The organization is also known as: Instituto Politécnico Nacional & Instituto Politecnico Nacional.

Description logics of minimal knowledge and negation as failure

Abstract: We present description logics of minimal knowledge and negation as failure (MKNF-DLs), which augment description logics with modal operators interpreted according to Lifschitz's nonmonotonic logic MKNF. We show the usefulness of MKNF-DLs for a formal characterization of a wide variety of nonmonotonic features that are both commonly available inframe-based systems, and needed in the development of practical knowledge-based applications: defaults, integrity constraints, role, and concept closure. In addition, we provide a correct and terminating calculus for query answering in a very expressive MKNF-DL.

Can Two-Photon Correlation of Chaotic Light Be Considered as Correlation of Intensity Fluctuations?

Abstract: Two-photon correlation phenomena, including the historical experiment of Hanbury Brown and Twiss, may have to be described quantum mechanically, regardless of whether the source of radiation is classical or quantum. Supporting this point, we present a ghost imaging type of second-order spatial correlation experiment of chaotic light to show that the classical understanding based on the concept of statistical intensity fluctuations does not give a correct interpretation for the observation. From a practical point of view, this experiment demonstrates the possibility of having high contrast lensless two-photon imaging with chaotic light, suggesting imaging applications for radiations for which no effective lens is available.

Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles

Abstract: The BPF proposal ensures a feasible, optimal and safe path for robot navigation.The results of BPF overcomes APF and other EAPF methods like those based in GAs.The BPF is quite faster in optimization leading to reduction in computation burden.The BPF running in parallel mode is the most suitable to fulfill local and global controllability.The BPF is capable to work in offline and online mode with static and dynamic obstacles. In this paper, optimal paths in environments with static and dynamic obstacles for a mobile robot (MR) are computed using a new method for path planning. The proposed method called Bacterial Potential Field (BPF) ensures a feasible, optimal and safe path. This novel proposal makes use of the Artificial Potential Field (APF) method with a Bacterial Evolutionary Algorithm (BEA) to obtain an enhanced flexible path planner method taking all the advantages of using the APF method, strongly reducing its disadvantages. Comparative experiments for sequential and parallel implementations of the BPF method against the classic APF method, as well as with the Pseudo-Bacterial Potential Field (PBPF) method, and with the Genetic Potential Field (GPF) method, all of them based on evolutionary computation to optimize the APF parameters, were achieved. A simulation platform that uses an MR realistic model was designed to test the path planning algorithms. In general terms, it was demonstrated that the BPF outperforms the APF, GPF, and the PBPF methods by reducing the computational time to find the optimal path at least by a factor of 1.59. These results have a positive impact in the ability of the BPF path planning method to satisfy local and global controllability in dynamic complex environments, avoiding collisions with objects that will interfere the navigation of the MR.