Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

A Novel Counter Sheet-Flow Sandwich Cell Culture Device For Mammalian Cell Growth In Space

Cell culture and growth in space is crucial to understand the cellular responses under microgravity. The effects of microgravity were coupled with such environment restrictions as medium perfusion, in which the underlying mechanism has been poorly understood. In the present work, a customer-made counter sheet-flow sandwich cell culture device was developed upon a biomechanical concept from fish gill breathing. The sandwich culture unit consists of two side chambers where the medium flow is counter-directional, a central chamber where the cells are cultured, and two porous polycarbonate membranes between side and central chambers. Flow dynamics analysis revealed the symmetrical velocity profile and uniform low shear rate distribution of flowing medium inside the central culture chamber, which promotes sufficient mass transport and nutrient supply for mammalian cell growth. An on-orbit experiment performed on a recovery satellite was used to validate the availability of the device.

Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.