Position information of individual nodes is very useful in implementing functions such as routing, querying and many applications. Researchers have proposed several location techniques but only a few relative location algorithms for mobile wireless sensor networks (WSNs). Although mobility would appear to make localization more difficult, this paper introduces a distributed 3 dimension relative localization algorithm for the mobile WSNs. This approach is an effort in finding a solution to positioning problem for highly dynamic nodes deployed randomly in a complex environment and the 3 dimension coordinate provided can be satisfied with great applications. The distributed coordinate transformation algorithm takes lower convergence times in the establishment of the global coordinate system.
Hyperspectral reflectance (350–2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application. Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters, comprising leaf area index (LAI; m2 green leaf area m−2 soil) and green leaf chlorophyll density (GLCD; mg chlorophyll m−2 soil), using stepwise multiple regression (SMR) models and support vector machines (SVMs). Four transformations of the rice canopy data were made, comprising reflectances (R), first-order derivative reflectances (D1), second-order derivative reflectances (D2), and logarithm transformation of reflectances (LOG). The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI, with a root mean square error (RMSE) of 1.0496 LAI units. The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD, with an RMSE of 523.0741 mg m−2. The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters, but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data.
Localization is a fundamental problem in wireless sensor networks (WSN). Many applications of WSN and middleware such as router require the sensor nodes to obtain their locations. Most existing localization algorithms use some mobile anchor nodes (e.g., equipped with GPS) to transmit beacons with their own coordinates for localizing other nodes. These algorithms do not need too much cost but obtain higher localization precision according to the mobile path. In the case path planning of the mobile anchor is the fundamental problem to be solved. In this paper we first study the path planning of the mobile anchor in localization for wireless sensor networks using graph theory. We regard wireless sensor networks as a connected undirected graph and then the path planning problem is translated into having a Spanning Tree and traversing graph. The paper proposes Breadth-First (BRF) and Backtracking Greedy (BTG) algorithms for Spanning Tree. The BRF and BTG algorithms provide robust localization for WSN under the random distribution and obtain higher localization precision in the simulations and real experiments.
This paper analyzes the evolution rule from the traditional scan-line filling algorithm to its enhanced ones,points out that there are redundant reading operations in these algorithms which are due to the two-scan-line only comparision.To save more context information,this paper creates a new concept called maximum tree which considers maximum adjacent scan lines together as an integral whole in order to reduce reading operations,classifies their length relations into nine different cases,introduces a deterministic finite automata to reduce comparisions,proposes the maximum-tree filling algorithm.At last,this paper compares the efficiency of different algorithms and shows that the new one is enhanced considerably.
Random pre-distribution of secret key is one of the most practical schemes in wireless Ad hoc networks. A pair-wise key pre-distribution scheme for Ad hoc networks is proposed. With the advantage of oneway Hash function, the key pool is composed of Hash chain. The nodes, only by pre-distributing a few of secret keys, could establish pair-wise keys amongst its neighboring nodes. This scheme is of some properties, including less storage size and computing overhead, full connectivity, dynamic node and key management. The theoretical analysis shows that the proposed scheme performs well in terms of efficiency and security strength, and is fairly suitable for Ad hoc networks.
With the development of computer technology, network security has become an important issue of concern. In view of the growing number of network security threats and the current intrusion detection system development, this paper gives a new model of anomaly intrusion detection based on clustering algorithm. Because of the k-means algorithm's shortcomings about dependence and complexity, the paper puts forward an improved clustering algorithm through studying on the traditional means clustering algorithm. The new algorithm learns the strong points from the k-medoids and improved relations trilateral triangle theorem. The experiments proved that the new algorithm could improve accuracy of data classification and detection efficiency significantly. The results show that this algorithm achieves the desired objectives with a high detection rate and high efficiency.
Based on Perception Linear Prediction (PLP), an approach to extract features from underwater target signal is presented. The method is the simulation of hearing property of human beings. Through the auditory psychology, three auditory spectrums are estimated, and they are Critical band, Equal-loudness curve and Intensity-loudness power. Then, a twelve-dimension feature vector is obtained. The vector is also a twelve-order all-pole model and it is robust. With the feature vector , the training and recognition processes are performed. The real sea experiments prove that human ear is at different level of sensitivity with different frequency bands where six kinds of radiated noises exist respectively, that the underwater target features are robust, that the dimension is relatively lower and the computation is less expensive and the recognition ratio may arrive 91% to six kinds of underwater target noise.
College PE serves as an important platform to promote college students' comprehensive quality and its goal is to realize fitness and develop students' sense of sports.For quite a long time,there has been a biased phenomenon of evaluating students in an over quantitative,summative and absolute way in their table tennis courses and the evaluation form and subject have been simple.In the new evaluation index system of learning,students are encouraged to participate in the evaluation,individual differences of students are brought into focus,relative and process evaluations are advocated,competition and the teaching of doubles are highlighted and test validity is improved.Experiments indicate that the new index system is of good application effect and students' sense of participation is strengthened both in and outside the classrooms.
Micromation/miniaturization is one of the most important trends of the world's UAVs.In this paper,a novel micro/mini four rotor vehicle(quadrotor) was summarized.At first,the concept and characteristics of the quadrotor were proposed.Then the status of the micro/mini quadrotor was introduced and its fabrication and flight control were summarized.The trends of the mini quadrotor's development were described in detail.Further more,the associated critical technologies and future development were presented.The research status of the quadrotor developed by the Robot Lab in National University of Defense Technology was also described.
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