A Descriptive Model of Robot Team and the Dynamic Evolution of Robot Team Cooperation

At present, the research on robot team cooperation is still in qualitative analysis phase and lacks the description model that can quantitatively describe the dynamical evolution of team cooperative relationships with constantly changeable task demand in Multi-robot field. First this paper whole and static describes organization model HWROM of robot team, then uses Markov course and Bayesian theorem for reference, dynamical describes the team cooperative relationships building. Finally from cooperative entity layer, ability layer and relative layer we research team formation and cooperative mechanism, and discuss how to optimize relative action sets during the evolution. The dynamic evolution model of robot team and cooperative relationships between robot teams proposed and described in this paper can not only generalize the robot team as a whole, but also depict the dynamic evolving process quantitatively. Users can also make the prediction of the cooperative relationship and the action of the robot team encountering new demands based on this model. Journal web page & a lot of robotic related papers www.ars-journal.co

Unidirectional emission and nanoparticle detection in a deformed circular square resonator

We propose a novel deformed square resonator which has four asymmetric circular sides. Photons leak out from specific points, depending on the interplay between stable islands and unstable manifolds in phase space. By carefully breaking the mirror reflection symmetry, optical modes with strong chirality approaching 1 and unidirectional emission can be achieved simultaneously. Upon binding of a nanoparticle, the far-field emission pattern of the deformed microcavity changes drastically. Due to the EP point of the degenerate mode pairs in the deformed cavity, chirality-based far-field detection of nanoparticles with ultra-small size can be realized

NG2 cells response to axonal alteration in the spinal cord white matter in mice with genetic disruption of neurofilament light subunit expression

<p>Abstract</p> <p>Background</p> <p>Chondroitin sulphate proteoglycan (NG2) expressing cells, morphologically characterized by multi-branched processes and small cell bodies, are the 4^thcommonest cell population of non-neuronal cell type in the central nervous system (CNS). They can interact with nodes of Ranvier, receive synaptic input, generate action potential and respond to some pathological stimuli, but the function of the cells is still unclear. We assumed the NG2 cells may play an active role in neuropathogenesis and aimed to determine if NG2 cells could sense and response to the alterations in the axonal contents caused by disruption of neurofilament light subunit (NFL) expression.</p> <p>Results</p> <p>In the early neuropathological development stage, our study showed that the diameter of axons of upper motor neurons of NFL-/- mice decreased significantly while the thickness of their myelin sheath increased remarkably. Although there was an obvious morphological distortion in axons with occasionally partial demyelination, no obvious changes in expression of myelin proteins was detected. Parallel to these changes in the axons and their myelination, the processes of NG2 cells were disconnected from the nodes of Ranvier and extended further, suggesting that these cells in the spinal cord white matter could sense the alteration in axonal contents caused by disruption of NFL expression before astrocytic and microglial activation.</p> <p>Conclusion</p> <p>The structural configuration determined by the NFL gene may be important for maintenance of normal morphology of myelinated axons. The NG2 cells might serve as an early sensor for the delivery of information from impaired neurons to the local environment.</p

A Biological Hierarchical Model Based Underwater Moving Object Detection

Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results