Integration of spatial attractions between and within pixels for sub-pixel mapping

As a promising technique to enhance the spatial reso- lution of remote sensing imagery, sub-pixel mapping is processed based on the spatial dependence theory with the assumption that the land cover is spatially dependent both within pixels and be- tween them. The spatial attraction is used as a tool to describe the dependence. First, the spatial attractions between pixels, sub- pixel/pixel spatial attraction model （SPSAM）, are described by the modified SPSAM （MSPSAM） that estimates the attractions accord- ing to the distribution of sub-pixels within neighboring pixels. Then a mixed spatial attraction model （MSAM） for sub-pixel mapping is proposed that integrates the spatial attractions both within pix- els and between them. According to the expression of the MSAM maximumising the spatial attraction, the genetic algorithm is em- ployed to search the optimum solution and generate the sub-pixel mapping results. Experiments show that compared with SPSAM, MSPSAM and pixel swapping algorithm modified by initialization from SPSAM （MPS）, MSAM can provide higher accuracy and more rational sub-pixel mapping results.

Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes significant.Motivated by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected vehicle.Aiming at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making mechanism.In addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access network.This paper proposes a novel framework where the bio-inspired mechanism is combined with TOPSIS.In a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global sense.The experimental results confirm that the proposed method performs better when compared with conventional schemes.

Research on Global Competition and Acoustic Search Algorithm Based on Random Attraction

In view of complex optimization problems with high nonlinearity and multiple extremums,harmony search algorithm has problems of slow convergence speed,being easy to be stalled and difficult to combine global search with local search.Therefore,a global competitive harmonic search algorithm based on stochastic attraction is proposed.Firstly,by introducing the stochastic attraction model to adjust the harmonic vector,the harmonic search algorithm was greatly improved and prone to fall into the local optimum.Secondly,the competitive search strategy was made to generate two harmony vectors for each iteration and make competitive selection.The adaptive global adjustment and local search strategy were designed to effectively balance the global and local search capabilities of the algorithm.The typical test function was used to test the algorithm.The experimental results show that the algorithm has high precision and the ability to find the global optimum compared with the existing algorithms.The overall accuracy is increased by more than 50%.