In order to improve the coverage of wireless sensor networks and reduce the energy consumption of node movement in secondary deployment, an improved coverage optimization algorithm based on improved Salpa swarm Intelligent algorithm (ATSSA) is proposed. Firstly, the population is initialized using tent chaotic sequence to enhance the optimization ability of the algorithm. Secondly, the T-distribution mutation is added to the update formula of the leaders for improving the ability to jump out of the local optimal value. Finally, an adaptive formula for updating the position of the follower is proposed, which not only guarantees the local searching ability of the algorithm in the late iteration period, but also improves the global searching ability of the algorithm in the early iteration period. The experimental results show that ATSSA algorithm can improve the coverage of the wireless sensor networks and reduce deployment costs compared with other algorithms, when it is used in the wireless sensor networks.

无线传感器网络(Wireless Sensor Networks,WSN)的路由协议是无线传感器网络领域中的一个研究热点.针对LEACH协议的不足,提出一种基于自适应t分布改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)的改进LEACH协议(LEACH?ISSA),以解决随机簇首选择、节点分布以及LEACH能耗不均的问题.通过LEACH协议对LEACH?ISSA进行初步分簇后,排除低能节点以生成候选簇首;然后结合剩余能量、节点之间的距离和相邻节点因子,建立适应度函数,以ISSA算法优化候选簇首的选择.实验结果表明,与LEACH协议相比,LEACH?ISSA协议的集群效果得到优化,簇首的能耗降低,集群中节点的能耗得以均衡,网络生命周期有效延长.

Rubber trees along the southeast coast of China always suffer severe damage from hurricanes. Quantitative assessments of the capacity for wind resistance of various rubber tree clones are currently lacking. We focus on a vulnerability assessment of rubber trees of different clones under wind disturbance impacts by employing multidisciplinary approaches incorporating scanned points, aerodynamics, machine learning and computer graphics. Point cloud data from two typical rubber trees belonging to different clones (PR107 and CATAS 7-20-59) were collected using terrestrial laser scanning, and a connection chain of tree skeletons was constructed using a clustering algorithm of machine learning. The concept of foliage clumps based on the trunk and first-order branches was first proposed to optimize rubber tree plot 3D modelling for simulating the wind field and assessing the wind-related parameters. The results from the obtained phenotypic traits show that the variable leaf area index and included angle between the branches and trunk result in variations in the topological structure and gap fraction of tree crowns, respectively, which are the major influencing factors relevant to the rubber tree’s capacity to resist hurricane strikes. The aerodynamics analysis showed that the maximum dynamic pressure, wind velocity and turbulent intensity of the wind-related parameters in rubber tree plots of clone PR107 (300 Pa, 30 m/s and 15%) are larger than that in rubber tree plots of clone CATAS-7-20-59 (120 Pa, 18 m/s and 5%), which results in a higher probability of local strong cyclone occurrence and a higher vulnerability to hurricane damage.

l1-minimization algorithm is one of the hot topics in the signal processing and optimization com-munities in solving the sparsest matrix .Compared with the traditional principal component analysis using l2 norm ,the l1 norm only calculates the main characteristics matrix of the image ,which is more robust to noise and abnormal data .While it is used too few in wood identification .The local binary pattern (LBP) texture analysis operator is defined as a gray-scale invariant texture measure .LBP algorithm is important in view point of pattern classification ,and can be used to extract three-layer cross-sectional features of different wood RGB images data .And then a fast l1 norm algorithm is used to implement fast and accu-rate identification to judge whether the wood surface has defects or not ,and where defects locate .Many experiments indicte that fast l1 algorithm combined with LBP can get correction of 0 .931 for defect loca-tion in wood surface .

传感器节点能量有限一直以来都是无线传感器网络的关键所在.针对该问题对传统的GAF(geographic adaptive fi-delity,GAF)算法进行了改进.改进的GAF算法引入了支持向量回归机(Support Vector Regression,SVR)来优化虚拟单元格的划分,同时将正方形网格改为圆形区域;另外,通过改变圆形区域的半径来加强相邻区域的连通性.结果显示,与传统的GAF算法相比,改进后的算法具有更大的优势,降低了节点能耗.

The uneven distribution of cluster-heads and the uncertainty of the number of cluster-heads in every loop cause defects in the LEACH algorithm.In this paper,the optimization from two different aspects based on the above problems is proposed.The first one is the optimization based on the SVM,that is,taking the location of the node into consideration and searching for supporting vectors to divide the wireless sensor network(WNS)into several zones,which can relieve the inefficiency caused by the unbalanced distribution of the cluster heads.The second one is to improve the cluster-heads selection mechanism in the LEACH algorithm,that is to say,solving inefficiency caused by the uncertainty of the number of cluster-heads in every loop through defining the number of cluster-heads in every single loop.From the analysis of the statistics and data collected from the simulation experiments of the improved LEACH algorithms,the conclusion can be drawn that the optimized algorithm can prolong the life cycle of WNS effectively as well as decrease the energy consumption of WNS obviously.

Explore the RSSI (Received Signal Strength Indicator) technology, understand its location principle and verify its accu-racy, after several field measurements at different distances to collect multiple sets of received signal strength indicator, through mathematical software, used linear regression and two kinds of regression polynomial regression analysis, researched their rele-vance, and then compared, verified its accuracy. Chose the more in line with the analysis method of the original data, and ac-quired the relationship between RSSI and distance.

主流的图结构数据分类算法大都是基于频繁子结构挖掘策略.这一策略必然导致对全局数据空间的不断重复搜索,从而使得该领域相关算法的效率较低,无法满足特定要求.针对此类算法的不足,采用分而治之方法,设计出一种模块化数据空间和利用Hash链表存取地址及支持度的算法.将原始数据库按照规则划分为有限的子模块,利用gSpan算法对各个模块进行操作获取局部频繁子模式,再利用Hash函数将各模块挖掘结果映射出唯一存储地址,同时记录其相应支持度构成Hash链表,最后得到全局频繁子模式并构造图数据分类器.算法避免了对全局空间的重复搜索,从而大幅度提升了执行效率；也使得模块化后的数据可以一次性装入内存,从而节省了内存开销.实验表明,新算法在分类模型塑造环节的效率较之于主流图分类算法提升了1.2～3.2倍,同时分类准确率没有下降.

The the existing farmland climate elements mainly rely on to obtain artificial,time-consuming.The special instru ments measurable compare a single,complex operation.This equipment is based MSP430 the microclimate acquisition system,measurement speed,high precision,ultra-low power consumption and can be powered by the device itself under the condi tions of long-term measurement data.The device is equipped with a data memory,you can store data directly through the USB port,export,and greatly improve measurement efficiency.

A complete texture image retrieval system includes two techniques: texture feature extraction and similarity measurement. Specifically, similarity measurement is a key problem for texture image retrieval study. In this paper, we present an effective similarity measurement formula. The MIT vision texture database, the Brodatz texture database, and the Outex texture database were used to verify the retrieval performance of the proposed similarity measurement method. Dual-tree complex wavelet transform and nonsubsampled contourlet transform were used to extract texture features. Experimental results show that the proposed similarity measurement method achieves better retrieval performance than some existing similarity measurement methods.

In order to eliminate the effect of image rotation on image retrieval,a novel rotation-invariant texture image retrieval algorithm is presented based on the nonsubsampled contourlet transform(NSCT),gray level concurrence matrix(GLCM)and novel similarity measurement.The NSCT has anisotropy and translation invariability.The GLCM reflects the direction,adjacency spacing relationship and range of variance change of the image.The rotation-invariant features are achieved by calculating the average energy and average standard deviation of all subbands at each NSCT scale,the mean and covariance of the second moment angle,inertia entropy,inertia moment,contrast points moment of the GLCM.A novel similarity measure is presented to improve the retrieval performance of texture images.Experimental results demonstrate that:compared with the dual tree-complex wavelet transform based approach,the image retrieval algorithm improves the retrieval accuracy from 73.28% to 80.71% for the rotated database of 640 images.

The deployment of sensor nodes was formalized as a combinatorial optimization problem,and the network coverage was used as the objective function.For the model this paper proposed a hybrid algorithm of artificial fish swarm algorithm(AFSA) and particle swarm optimization(PSO) by combining the advantages of the two algorithms.Particle swarm optimization can achieve the effective local search,and artificial fish swarm algorithm can enhance the ability of global optimization.The AFSA-PSO hybrid algorithm proposed in this paper has the advantages of both.The simulation results show that AFSA-PSO hybrid algorithm is superior to the artificial fish swarm algorithm and particle swarm optimization algorithm,can effectively improve network coverage with fewer iterations.

In wireless sensor networks,the wireless sensor nodes have to cover the area to be monitored effectively.In order to reduce the coverage holes and improve the coverage rate in wireless sensor networks,this paper proposed a new deployment strategy of wireless sensor network nodes based on improved particle swarm optimization.Taken network coverage as the fitness function,the deployment of sensor nodes would be formalized as an objective optimization problem.By employing the k-means clustering algorithm,the population was divided into several sub-populations.In addition,the population was re-divided into new sub-populations dynamically,which could weaken particles on the pursuit of local optima,realize the improvement of basic PSO algorithm,effectively solve the premature problem of basic PSO algorithm,and accelerate the convergence of the algorithm.Experimental results show that this deployment strategy can reduce the coverage holes in wireless sensor networks as much as possible and effectively improve the network coverage rate.Compared with the results of elementary particle swarm optimization,the conventional genetic algorithm and swarm optimization algorithm,its coverage rate was increased by 4.11%,9.75% and 5.25%.

This paper presents a novel algorithm for texture image retrieval based on nonsubsampled contourlet(NSCT) and Dual-tree complex wavelet transform(DT-CWT). Nonsubsampled contourlet transform has anisotropy and translation invariability. DT-CWT not only has good localization in time-frequency domain, but also has approximate translation invariant, more directivity and limited data redundancy. Texture features based on NSCT and DT-CWT can include more information of image. This paper also presents a novel similarity measure. Experimental results demonstrate that the proposed approach improves average retrieval accuracy.