Geomagnetic-based indoor positioning has drawn a great attention from academia and industry due to its advantage of being operable without infrastructure support and its reliable signal characteristics. However, it must overcome the problems of ambiguity that originate with the nature of geomagnetic data. Most studies manage this problem by incorporating particle filters along with inertial sensors. However, they cannot yield reliable positioning results because the inertial sensors in smartphones cannot precisely predict the movement of users. There have been attempts to recognize the magnetic sequence pattern, but these attempts are proven only in a one-dimensional space, because magnetic intensity fluctuates severely with even a slight change of locations. This paper proposes accurate magnetic indoor localization using deep learning (AMID), an indoor positioning system that recognizes magnetic sequence patterns using a deep neural network. Features are extracted from magnetic sequences, and then the deep neural network is used for classifying the sequences by patterns that are generated by nearby magnetic landmarks. Locations are estimated by detecting the landmarks. AMID manifested the proposed features and deep learning as an outstanding classifier, revealing the potential of accurate magnetic positioning with smartphone sensors alone. The landmark detection accuracy was over 80% in a two-dimensional environment.
We study the configurational and dynamic properties of a long polymer chain during the process of penetration of the chain through the thin and porous membrane.The mobility of a polymer in this state depends strongly on the topological restrictions imposed by the membrane.The largest mobility have configurations in which the polymer chain crosses the membrane only once and diffuses through a single pore from one side of the membrane to the other. This configuration may have small statistical weight compared to other configurations with low mobility in which the polymer chain crosses the membrane many times. The relative weight of these configurations depends also on the chemical potential of the membrane. We find the configurations ~vhich give the main contribution to the transport and calculate the overall permeability of the membrane for Zimm and Rouse mechanisms of polymer dynamics.1.
We present a new model for the motion of a megabase-long DNA molecule undergoing gel electrophoresis. We assume that the dynamics of large segments of DNA is almost deterministic and can be described by a set of simple mechanical equations. This allows the numerical study of gel electrophoresis of ultra-high molecular weight DNA. A strong electric field forces DNA in a gel into a tree-like structure with branches-loops of different sizes. We determined the loop-size distribution function. This distribution has a power law form, confirming the hypothesis of the statistical self-similarity of a moving polymer. We find periodic configuration changes in the motion of a circular polymer, with the average period proportional to the molecular weight. During the period, a polymer goes through three distinct phases: a simple V-shape configuration, a growing tree, and a decaying tree. For a linear polymer this periodicity is much less pronounced because of additional perturbations to the dynamics caused by free ends. A circular polymer stays in a simple V-shaped configuration about 30% of the time, independent of molecular weight (10% for a linear polymer).
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