Evolving Network Model with Local-Area Preference for Mobile Ad Hoc Network

To accurately describe the evolving features of Mobile Ad Hoc Networks (MANETs) and to improve the performance of such networks, an evolving topology model with local-area preference is proposed. The aim of the model, which is analyzed by the mean field theory, is to optimize network structures based on users' behaviors in MANETs. The analysis results indicate that the network generated by this evolving model is a kind of scale-free network. This evolving model can improve the fault-tolerance performance of networks by balancing the connectivity and two factors, i.e., the remaining energy and the distance to nodes. The simulation results show that the evolving topology model has superior performance in reducing the traffic load and the energy consumption, prolonging network lifetime and improving the scalability of networks. It is an available approach for establishing and analyzing actual MANETs.

Blind Image Quality Assessment by Pairwise Ranking Image Series

Image quality assessment(IQA)is constantly innovating,but there are still three types of stickers that have not been resolved:the“content sticker”-limitation of training set,the“annotation sticker”-subjective instability in opinion scores and the“distortion sticker”-disordered distortion settings.In this paper,a No-Reference Image Quality Assessment(NR IQA)approach is proposed to deal with the problems.For“content sticker”,we introduce the idea of pairwise comparison and generate a largescale ranking set to pre-train the network;For“annotation sticker”,the absolute noise-containing subjective scores are transformed into ranking comparison results,and we design an indirect unsupervised regression based on EigenValue Decomposition(EVD);For“distortion sticker”,we propose a perception-based distortion classification method,which makes the distortion types clear and refined.Experiments have proved that our NR IQA approach Experiments show that the algorithm performs well and has good generalization ability.Furthermore,the proposed perception based distortion classification method would be able to provide insights on how the visual related studies may be developed and to broaden our understanding of human visual system.

The abundance effect on network nestedness is stronger for parasitic than herbivory interactions

It has been suggested that the importance of network architecture to species diversity and stability should be based on preference networks(comprised of niche differentiations),rather than observational networks,because species abundance may significantly affect interaction frequencies.Considering that resource abundance is usually greater for herbivores than parasites,we hypothesize that the abundance effect is stronger for parasitic than herbivory interactions.To test this hypothesis,we collected 80 quantitative observational networks including 34 herbivorous and 46 parasitic networks from the published literature,and derived preference networks by removing the effects of species abundance.We then determined the network nestedness using both weighted NODF and spectral radius.We also determined species degree distribution,interaction evenness,weighted connectance and robustness for both observational and preference networks.The observational networks(including both herbivory and parasitic networks)were more nested judged by weighted NODF than spectral radius.Preference networks were less nested for parasitic than herbivory networks in terms of both weighted NODF and spectral radius,possibly because removing the abundance effect increased interaction evenness.These trends indicate that the abundance effect on network nestedness is stronger for parasitic than herbivory networks.Weighted connectance and robustness were greater in most preference networks than observational networks,indicating that preference networks may have high network stability and community persistence compared with observational ones.The data indicate that future network analyses should not only address the structural difference between mutualistic and antagonistic interactions,but also between herbivory and parasitic interactions.