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On the basis of the fundamental differential evolution (DE), this paper puts forward several improved DE algorithms to find a balance between global and local search and get optimal solutions through rapid convergence. Meanwhile, a random mutation mechanism is adopted to process individuals that show stagnation behaviour. After that, a series of frequently-used benchmark test functions are used to test the performance of the fundamental and improved DE algorithms. After a comparative analysis of several algorithms, the paper realizes its desired effects by applying them to the calculation of single and multiple objective functions. [ABSTRACT FROM AUTHOR]

Digital image watermarking is one of the proposed solutions for copyright protection of multimedia data. This technique is better than Digital Signatures and other methods because it does not increase overhead. Watermarking adds the additional requirement of robustness. To improve the robustness of digital image watermarking method based on the image frequency, this paper adopts the intermediate frequency to embed the watermarking and proposes an digital image watermarking algorithm based on robust principal component analysis (RPCA) and discrete cosine transform (DCT). Firstly, the high frequency part and the low frequency part of the image are extracted by the RPCA algorithm. Because the high frequency part has complex statistical characteristics, this paper processes the high frequency part with "8×8" DCT method to obtain intermediate frequency coefficients and then the watermarking information is embedded into the obtained intermediate frequency coefficients. The experimental results show that the proposed algorithm leads to satisfactory robustness to the attacks of impulse noise and cropping. [ABSTRACT FROM AUTHOR]

Biclustering algorithm is proposed for discovering matrix with biological significance in gene expression data matrix and it is used widely in machine learning which can cluster the row and column of matrix. In order to further improve the performance of biclustering algorithm, this paper proposes a semisupervised clustering algorithm based on variational Bayesian. Firstly, it introduces supplementary information of row and column for biclustering process and represents corresponding joint distribution probability model. In addition, it estimates the parameter of joint distribution probability model based on variational Bayesian learning method. Finally, it estimates the performance of proposed algorithm through synthesized data and real gene expression data set. Experiments show that normalized mutual information of this paper's new method is better than relevant biclustering algorithms for biclustering analysis. [ABSTRACT FROM AUTHOR]

In communication between planes and satellites, Optical Beamforming Networks (OBFNs), which rely on many small and flat Phased Array Antennas (PAAs), need to be tuned in order to receive signals from specific angles. In this paper, we develop a deep neural network representation of tuning OBFNs. The problem of tuning an OBFN is in many aspects similar to training a deep neural network. We present a way to exploit the special structure of OBFNs into deep neural network and an algorithm for tuning OBFNs based on feedback that can be easily measured in real system. Training data, which consists of full signals, can be measured, and therefore is used in this paper. For pilot signals, the desired signal is known explicitly. Given the configuration of OBFNs and all nominal parameters required, it was verified in simulation that the deep neural network can be used to tune large scale OBFNs for any desired delays. [ABSTRACT FROM AUTHOR]

As the internet keeping developing, face recognition has become a research hotspot in the field of biometrics. This paper proposes an improved face recognition algorithm that reduces the influence of illumination and posture variations. First, face images are transformed by using the improved contourlet transform method to get low frequency sub-band images and high frequency sub-band images. Then this paper uses the principal component analysis to extract main features. Finally, combines these statistic features together as feature vector and recognize face images. Analysis, experiment and proof on the ORL face database and the Yale face database show that this algorithm is better able to recognize faces, reduce the influence of illumination and posture variations and increase the efficiency of face recognition. [ABSTRACT FROM AUTHOR]

How to effectively store and transmit such multi-media files as image and video has become a research hotspot. The traditional compression algorithms have a relatively low compression ratio and bad quality of decoded image, at present, the fractal image compression method with a higher compression ratio fails to meet the requirements of the practical applications in the quality of the compressed image as well as the coding and decoding time.This paper integrates fractal thought and multi-wavelet transform and proposes a fractal image compression algorithm based on multi-wavelet transform. To transform the image model into a combination of relevant elements in the frequency domain instead of merely building on the foundation of the neighborhood gray-scale correlation has the ability to code larger image blocks, eliminates the possibility of global correlation in the image and improves the coding speed of the existing fractal image compression algorithm. The experimental result shows that the algorithm proposed in this paper can accelerate the coding speed of the present fractal image compression and have certain selfadaptivity while slightly reducing the quality of decoding image. [ABSTRACT FROM AUTHOR]

In space information network, the long delay and high link error rate are the most different characteristics from the traditional ground network. Based on the queuing game theory, a novel congestion control is proposed in this paper. It assumes that the users pay an admission fee for entering the queue, and then cost during the waiting time, at last they obtain a benefit from the node when they are serviced completely. This paper not only designs a variable admission fee in order that the proposed algorithm is more suitable for the space environment, but also considers the ending profits of the game which is described by a discount rate. The simulation result shows the proposed algorithm improve the network performance. [ABSTRACT FROM AUTHOR]

Because of the influence from geographical location, weather and other kinds of circumstances in monitored areas, the shift of the node location and non-uniform distribution, this paper proposed an improved DV-Hop location algorithm. First of all, the package structure by changing the anchor nodes to reduce the number of hops data acquisition phase node data storage; introducing weights to the average hop distance calculation phase the original average hop distance calculation method was improved, and between the node and anchor node distance calculated on the basis of reference anchor nodes are different; then, iterative refinement of node localization stage through the use of multilateral measurement method and Taylor series. Finally, simulation experiment of this method, and compared with the existing methods, the results prove that the method in this paper can greatly reduce positioning errors without adding hardware equipment and network traffic, improve the positioning accuracy, a better solution to the problem of node localization networking monitoring area. [ABSTRACT FROM AUTHOR]

In order to solve being sensitive to the initial weights, slow convergence, being easy to fall into local minimum and other problems of the BP neural network, this paper introduces the Particle Swarm Optimization algorithm into the Artificial Neural Network training, and construct a BP neural network model optimized by the particle swarm optimization. This method can speed up the convergence and improve the prediction accuracy. Through the analysis of the main factors on the cost of transmission line project, dig out the path and lead factors, topography and meteorological factors, the tower and the tower base materials and other factors. Use the PSO-BP model for the cost forecasting of transmission line project based on historical project data. The result shows that the method can predict the cost effectively. Compared with the traditional BP neural network, the method can predict with higher accuracy, and can be generalized and applied in cost forecasting of actual projects. [ABSTRACT FROM AUTHOR]

Owing to the worldwide increase of electronic communications and transactions, security and encryption speed have become essential elements of all systems and applications. Advanced Encryption Standard (AES) is one of the most widely secure and used symmetric encryption algorithms today. In this paper, we present high throughput full-pipelining implementation of AES algorithm, using the least amount of possible hardware. Substitution box (S-box) is the only non-linear step in this algorithm. It is the most complicated and costly part of the system. Consideration on high delay and hardware cost required for this transformation, we proposed pipelined S-box implementation by using composite field, to deal with the critical path and the occupied memory. In addition, efficient key expansion architecture suitable for our proposed AES design is also presented. The implementation has been successfully done by Virtex-5 XC5VLX85 and Virtex-6 XC6VLX75T Field Programmable Gate Array (FPGA) devices using Xilinx ISE 14.7. Our high throughput AES design achieves a data encryption rate of 108.69 Gbps and uses only 6361 slices. Compared to the best previous works, this implementation improves data throughput by 5.6% and reduces the occupied memory by 77.69%. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is self-organizing network; it consists of a large number of sensor nodes with perception, calculation ability and communication ability. Generally, the floor, walls or people moving has an effect on indoor localization, so it will result in multi-path phenomena and decrease signal strength; also the received signal strength indicator (RSSI) is unable to gain higher accuracy of positioning. In order to improve the WSN positioning accuracy in indoor condition, in this paper, we firstly propose krill swarm algorithm used for WSN localization. We detailed analyze the multilateral measurement method in range-free distance vector-hop (DV-hop) localization algorithm. The position problem can be transformed into a global optimization problem. Then, we adequately utilize the advantage of calculating optimization problem and apply the krill swarm algorithm into the stage of estimating unknown node coordinates in DV-hop algorithm to realize localization. Finally, the simulation experience results show that the localization with krill swarm algorithm has an obviously higher positioning precision and accuracy stability with different anchor node proportion and nodes. [ABSTRACT FROM AUTHOR]

In order to obtain ideal moving target detection results, a kind of moving target detection algorithm of improved mixture Gaussian model is proposed in this paper on the basis of analyzing the deficiency of traditional Mixture Gaussian model. Firstly, in this algorithm, the update rate is increased to better adapt to the change of environment and the idea of pixel connected region is introduced to remove the noise, then the invariance property of chroma is adopted to avoid arising of “shadow” phenomenon; finally, simulation comparison experiment is used to test its performance. The experiment results show that this algorithm can generate reliable background image and accurately and completely detect the moving target to obtain more superior detection results of comparison algorithm, which can meet the practical application requirement of target tracking. [ABSTRACT FROM AUTHOR]

In this research, we describe some key issues of distribution route optimization with express large data. It includes data warehouse establishment for storing user and map information, establishment of the real distribution network topology, establishment of the algorithm and model of the recommended delivery route, development of the visualization tools with the help of GIS tools to form the regional operation board. This paper aims to make a global grasp of the route optimization problem of express delivery. It provides a reference for the optimization problem of express delivery routes in any other areas. [ABSTRACT FROM AUTHOR]

Polarimetric SAR (PolSAR) has played more and more important roles in earth observation. Polarimetric SAR image classification is one of the key problems in the PolSAR image interpretation. In this paper, based on the scattering properties of fully polarimetric SAR data, combing the statistical characteristics and neighborhood information, an efficient unsupervised method of fully polarimetric SAR image classification is proposed. In the method, polarimetric scattering characteristics of fully polarimetric SAR image is used, and in the denoised total power image of polarimetric SAR, SPAN (the total polarimetric power), the texture features of gray level co–occurrence matrix are extracted at the same time. Finally, the polarimetric information and texture information are combined for fully polarimetric SAR Image classification with clustering algorithm. The experimental results show that better classification results can be obtained in the Radarsat-2 data with the proposed method. [ABSTRACT FROM AUTHOR]

With the development and application of service computing technology, quality of service has become one of the important bases for the selection of web services in service composition. In order to avoid the influence of functional difference of services on the QoS of services in the service composition, this paper proposes a two-layer QoS model service selection algorithm based on feature grouping. First of all, in the process of service composition, the services are grouped according to their function, then the services in the same group are further grouped by QoS, which includes common QoS and reputation degree QoS based on the subjective and objective factors. The reliability of service is defined to express the common QoS, while a kind of evaluation method of reputation degree is raised to express the reputation degree QoS. Experimental results show that this method can obtain web services with high reliability and high credit standing; meanwhile it can increase the success rate of web service composition. [ABSTRACT FROM AUTHOR]

The traditional RBF neural network has the problem of slow training speed and low efficiency, this paper puts forward the algorithm of improvement of RBF neural network by AdaBoost algorithm combined with PSO, to expand the application range of the RBF neural network. Firstly, it preprocesses the sample data in training set, and initialize the weights of test data; Secondly, it optimizes and chooses different implied layer functions and network learning parameters by using the improved PSO algorithm, to produce different types of RBF weak predictor, and repeatedly train the sample data by using Matlab tools; Finally, it constructs multiple generated RBF weak predictors to strong predictors by using AdaBoost iterative algorithm. It chooses data sets of UCI database to do the simulation experiment, and the simulation results show that the proposed algorithm further reduces the mean absolute error, compared with the traditional RBF neural network prediction, the experiment has improved the prediction precision of the network, to provide a reference for RBF neural network prediction. [ABSTRACT FROM AUTHOR]

As a highly concerned research topic in the field of data mining, association rules have achieved good results at present. However, in practical application, the expanding of database scale coupled with the increasing of the amount of data leads to the decrease of efficiency in algorithm mining, which limits its own application. The paper modifies the data mining algorithm based on association rules and explores the simulation of this algorithm, intending to provide some reference for the improvement and simulation of association rules data mining algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the drawbacks of current harmonic frequency estimation algorithm in power system are pointed out. In order to improve the detection accuracy and reduce the computational complexity, a new harmonic estimation algorithm for power system is proposed by combining the ESPRIT algorithm with linear neural network. Theoretic analysis and simulation experiments indicated that the new algorithm has super resolution characteristics, ideally suited for harmonic analysis in power system. [ABSTRACT FROM AUTHOR]

Multi-tenant main memory index is an important tool to accelerate data access to software as a service. Establishing main memory indexes for each tenant occupies lots of memory space and results in performance bottleneck. The data schemas and access patterns of different tenants are similar, which provides the conditions for tenants storing their index entries with shared structure in main memory. In the paper, the designed structure of main memory index puts the indexes of different tenants on one tree to achieve the effects of saving memory space. Meanwhile, tenant placement algorithm in the cloud is proposed. Its places the tenants with similar indexes on the same node to further optimize the main memory space. [ABSTRACT FROM AUTHOR]

Directed against the characteristics of computational intensity of fractal image compression encoding, a serial-parallel transfer mechanism is built for encoding procedures. By utilizing the properties of single instruction and multithreading execution of compute unified device architecture (CUDA), the parallel computational model of fractal encoding is built on the graphic processor unit (GPU) in order to parallelize the considerably time-consuming serial execution process of searching for the block of best match. The experimental result indicates, the algorithm in this paper shortens the encoding time to the millisecond scale and significantly boosts the execution efficiency of fractal image encoding algorithm while keeping the decoded image in good quality. [ABSTRACT FROM AUTHOR]

The big cities in the world always face the traffic jam. This problem is caused by the increasing number of vehicle from time to time and the increase of vehicle is not anticipated with the development of adequate new road section. One important aspect in the traffic management concept is the need of traffic density data of every road section. Therefore, the purpose of this paper is to analyze the possibility of optimization on the use of video file recorded from CCTV camera for visual observation and tool for counting traffic density. The used method in this paper is adaptive background extraction with Gaussian Mixture Models algorithm. It is expected to be the alternative solution to get traffic density data with a quite adequate accuracy as one of aspects for decision making process in the traffic engineering. [ABSTRACT FROM AUTHOR]

Size fitting problem is a main obstacle to large scale online garment sales. It is the difficult to customers to find the fit garments when they couldn't try on. In this paper, we present an algorithm base on wavelet neural network to help customer choosing their clothing specifications automatically. After the reasonable wavelet function is selected, we established the model structure and the initial parameters. The wavelet neural network is trained by the body measures and the result of AHP algorithm after normalization. The new data are used to test the network. As a result, the error from wavelet neural network is smaller, and the prediction accuracy is proved than that from the algorithm based on traditional BP network. [ABSTRACT FROM AUTHOR]

Current methods of service selection based on quality of service (QoS) usually focus on a single service request at a time, or let the users in a waiting queue wait for Web services when the same functional Web service has more than one requests, and then choose the Web service with the best QoS for the current request according to its own needs. However, there are multiple service requests for the same functional web service at a time in practice and we cannot choose the best service for users every time because of the service's load. This paper aims at solving the Web Services selection for concurrent requests and developing a global optimal selection method for multiple similar service requesters to optimize the system resources. It proposes the improved social cognitive (ISCO) algorithm which uses genetic algorithm for observational learning and uses deviating degree to evaluate the solution. Furthermore, to enhance the efficiency of ISCO, the elite strategy is used in ISCO algorithm. We evaluate performance of the ISCO algorithm and the selection method through simulations. The simulation results demonstrate that the ISCO is valid for optimization problems with discrete data and more effective than ACO and GA. [ABSTRACT FROM AUTHOR]

Currently, the method of optimizing the wavelet neural network with particle swarm plays a certain role in improving the convergence speed and accuracy; however, it is not a good solution for problems of turning into local extrema and poor global search ability. To solve these problems, this paper, based on the particle swarm optimization, puts forward an improved method, which is introducing the chaos mechanism into the algorithm of chaotic particle swarm optimization. Through a series of comparative simulation experiments, it proves that applying this algorithm to optimize the wavelet neural network can successfully solve the problems of turning into local extrema, and improve the convergence speed of the network, in the meantime, reduce the output error and improve the search ability of the algorithm. In general, it helps a lot to improve the overall performance of the wavelet neural network. [ABSTRACT FROM AUTHOR]

This paper presents one new algorithm for local community discovery. It employs a new vertex selection strategy which considers not only the boundary structure of candidate local community but also the probability which the investigated vertex will return to the candidate local community. A local random walk is adopted to compute this return probability which does not require the global information. We choose four algorithms for comparison which are the best ones existed by far. For better evaluation, the datasets include not only the computer generated graphs in standard benchmark but also the real-world networks which are classical ones in global community discovery. The experimental results show our algorithm outperforms the other ones on the computer generated graphs. The performance of our algorithm is approximately the same with the algorithm proposed by Luo, Wang and Promislow on realworld networks. [ABSTRACT FROM AUTHOR]

Due to the motion control system of electromagnetic linear actuator (EMLA) is a nonlinear system with poor controllability; single control strategy has been difficult to meet the requirements of its control. A compound control strategy based on inverse system control (ISC) and proportional-integral (PI) is designed in this paper. Switching between two algorithms, which is based on the fuzzy rules, prevents the control algorithm to jitter and jump. System model is built under Matlab/Simulink to do simulation analysis. The designed controller is integrated into the system simulation model and the system software of digital signal processor (DSP) controller. Simulation and test results show that the compound control strategy using fuzzy switching rules achieves the smooth transition of two control algorithms, and the goal of any position location, and continuous adjustment in 0~4mm lift. Positioning accuracy is up to ± 0.02mm, while the response time is less than 10ms. [ABSTRACT FROM AUTHOR]

A new scheduling strategy of terminal system virtual link (VL) is put forward, which is based on the short-frame prior scheduling and integrated with weighted round robin (WRR).The new approach, ensured the priority of the important short-frame and balanced the delay upper bound of other different priority signals. Based on the theory of network calculus, the paper derived the delay upper bound of different virtual links through the new combinatorial scheduling strategy and researched the relation among the maximum delay upper bound, weight ratio and frame length. The AFDX network model based on OPNET is established, the terminal system data transmission delay under the three scheduling strategy is simulated and compared. The results show that, the new scheduling strategy can reduce the maximum delay time of short-frame virtual link, and improve the processing bandwidth of relatively important task in long-frame data transmission. Above all, it is feasible and applicable to airborne network with more shortframe and different priority data flows. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of Gaussian mixture model background method, a moving object detection method mixed with adaptive iterative block and interval frame difference method in the Gaussian mixture model is proposed. In this method, the video sequences are divided into different size pieces in order to reduce the amount of calculation of the algorithm. It not only effectively solves the problem that the traditional Gaussian mixture model algorithm cannot detect large and slow moving object accurately, but also solves empty and no connection problems due to the introduction of block thought. The experimental results show that the improved algorithm has faster processing speed, better effect and better environment adaptability compared with the background of the Gaussian mixture model method. And it can detect moving object more accurately and completely. [ABSTRACT FROM AUTHOR]

The data mining is a massive, incomplete and random data. People don't know how to extract the data in advance because of the large amount of data of the network in the database, and the heterogeneous and dynamic characteristic, which makes the extraction process difficult. Data mining needs effective calculation. Cloud computing, as a business computing model, can assign a large number of calculate tasks. Based on users’ need, it can allocate the computing, storage and application ability, and can solve massive computing problems effectively in data mining. According to the sequence of item location and the growth algorithm of SPM - LIPI mining, the algorithm discussed in this paper can improve the traditional algorithm, and form the same item at the end of the sequence position information table. It can also avoid repeated scans of the database mining through trimming by the database. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is new data acquisition algorithm that can be used for compression. CS theory certifies that signals can be recovered from fewer samples than Nyquist rate. On this paper, the compressive sensing technique is applied for data compression on our weather monitoring system. On this weather monitoring system, compression using compressive sensing with fewer samples or measurements means minimizing sensing and overall energy cost. Our focus on this paper lies in the selection of matrix for representation basis under which the weather data are sparsely represented. Results from simulation show that the using of DCT (Discrete Cosine Transform) as representation basis has a better performance on weather data recovery compared with other transform methods such as Walsh-Hadamard Transform (WHT) and Discrete Wavelet Transform (DWT). [ABSTRACT FROM AUTHOR]

Wireless network state varies with the surrounding environment, however, the existing resource allocation algorithm cannot adapt to the varying network state, which results to the underutilization of frequency and power resource. Therefore, in this paper, we propose an adaptive resource allocation algorithm which can efficiently adapt to the varying network state by building an optimal mathematical model and then changing the weighted value of the objective function. Furthermore, the optimal allocation of subcarrier and power is derived by using the Lagrange dual decomposition and the subgradient method. Simulation results show that the proposed algorithm can adaptively allocate the resource to the users according to the varying user density which represents the network state. [ABSTRACT FROM AUTHOR]

Author name disambiguation (AND) is a challenging task for scholars who mine bibliographic information for scientific knowledge. A constructive approach for resolving name ambiguity is to use computer algorithms to identify author names. Some algorithm-based disambiguation methods have been developed by computer and data scientists. Among them, supervised machine learning has been stated to produce decent to very accurate disambiguation results. This paper presents a combination of principal component analysis (PCA) as a feature reduction and deep neural networks (DNNs), as a supervised algorithm for classifying AND problems. The raw data is grouped into four classes, i.e., synonyms, homonyms, homonymssynonyms, and non-homonyms-synonyms classification. We have taken into account several hyperparameters tuning, such as learning rate, batch size, number of the neuron and hidden units, and analyzed their impact on the accuracy of results. To the best of our knowledge, there are no previous studies with such a scheme. The proposed DNNs are validated with other ML techniques such as Naïve Bayes, random forest (RF), and support vector machine (SVM) to produce a good classifier. By exploring the result in all data, our proposed DNNs classifier has an outperformed other ML technique, with accuracy, precision, recall, and F1-score, which is 99.98%, 97.98%, 97.86%, and 99.99%, respectively. In the future, this approach can be easily extended to any dataset and any bibliographic records provider. [ABSTRACT FROM AUTHOR]

This paper presents optimal reconfiguration of radial distribution network with integration of distributed energy resources (DER) using modified particle swarm optimization (PSO) algorithm. The advantages of integration of DER in distribution system are minimizing power losses, improving voltage profiles and load factors, eliminating system upgrades, and reducing environmental impacts. However, the presence of DER could also cause technical problems in voltage quality and system protection. Optimal reconfiguration of distribution network is subjected to minimize power loss and to improve voltage profile in order to enhance the efficiency the distribution system. In this study, reconfiguration method is based on an improved PSO. The method has been tested in a 60-bus Bantul distribution network of Yogyakarta Special Region province, Indonesia. The simulation results show that optimal reconfiguration of the network with integration of DER has successfully enhancing the efficiency of the distribution system. [ABSTRACT FROM AUTHOR]

One of the major problems that occur in text localization process is the issue of color similarity between text and background image. The limitation of localization algorithms due to high color similarity is highlighted in several research papers. Hence, this research focuses towards the improvement of text localizing capability in high color background image similarity by introducing an adaptive text localization algorithm (ATLAS). ATLAS is an edge-based text localization algorithm that consists of two parts. Text- Background Similarity Index (TBSI) being the first part of ATLAS, measures the similarity index of every text region while the second, Multi Adaptive Threshold (MAT), performs multiple adaptive thresholds calculation using size filtration and degree deviation for locating the possible text region. In this research, ATLAS is verified and compared with other localization techniques based on two parameters, localizing strength and precision. The experiment has been implemented and verified using two types of datasets, generated text color spectrum dataset and Document Analysis and Recognition dataset (ICDAR). The result shows ATLAS has significant improvement on localizing strength and slight improvement on precision compared with other localization algorithms in high color text-background image. [ABSTRACT FROM AUTHOR]

This paper describes the development of radar object detection using two dimensional constant false alarm rate (2D-CFAR). Objective of this development is to minimize noise detection if compared with the previous algorithm that uses one dimensional constant false alarm rate (1D-CFAR) algorithm such as order-statistic (OS) CFAR, cell-averaging (CA) CFAR, AND logic (AND) CFAR and variability index (VI) CFAR where has been implemented on coastal surveillance radar. The optimum detection result in coastal surveillance radar testing when Pfa set to 1e-2, Kth set to 3/4*Nwindow and Guard Cell set to 0. Principle of 2D-CFAR algorithm is combining of two CFAR algorithms for each array data of azimuth and range. Order statistic (OS) CFAR algoritm is implemented on this 2D-CFAR by fusion rule of AND logic.The algorithm of 2D-CFAR is developed using Microsoft Visual C++ 2008 and the output of 2D-CFAR is plotted on PPI scope radar using GDI+ library. The result of 2D-CFAR development shows that 2D-CFAR can minimize noise detected if compared with 1D-CFAR with the same parameter of CFAR. Best performance of 2D- CFAR in object detection when Nwindow set to 128. The time of software processing of 2D-CFAR is about two times longer than the 1D-CFAR. [ABSTRACT FROM AUTHOR]

Many engineering problems are the complex optimization problems with the large numbers of global andlocal optima. Due to its complexity, general particle swarm optimization method inclines towards stagnation phenomena in the later stage of evolution, which leads to premature convergence. Therefore, a highly efficient particle swarm optimizer is proposed in this paper, which employ the dynamic transitionstrategy ofinertia factor, search space boundary andsearchvelocitythresholdbased on individual cognitionin each cycle to plan large-scale space global search and refined local search as a whole according to the fitness change of swarm in optimization process of the engineering problems, and to improve convergence precision, avoid premature problem, economize computational expenses, and obtain global optimum. Several complex benchmark functions are used to testify the new algorithm and the results showed clearly the revised algorithm can rapidly converge at high quality solutions. [ABSTRACT FROM AUTHOR]

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The tracking of vehicles using RFID was developed consider three major problems, i.e. traffic signal timing, congestion on road and theft of vehicles. This paper describes an algorithm for predicting the speed of traffic light violators. The novel algorithm is implemented in the form of a simulation program. The traffic light system is equipped RFID reader as the main tool for identifying the vehicle's RFID tags. In the simulation, the vehicle's ID is generated using a random number generation. The distribution of the vehicle's numbers is uniform and the expected number of vehicles violated the traffic light is not more than 3 % of the total number generated. The timing accuracy for speed traffic lights violator is 100 milli seconds. From the simulation conducted, the algorithm has performed as expected. It is able to predict the speed of traffic light violators ranging from 5 km/h up to 80 km/h in real-time. [ABSTRACT FROM AUTHOR]

In the existing cloud computing environment, batch scheduling strategies mainly focus on the management of resources allocation. This paper provides the task scheduling algorithm based on service quality which fully considers priority and scheduling deadline. The improved algorithm combines the advantages of Min-min algorithm with higher throughput and linear programming with global optimization, considers not only all the tasks but also the high priority tasks. The experiment result shows that compared with the Min-min and DBCT the completed tasks of the improved algorithm increase about 10.6% and 22.0%, on the other hand the completed high priority tasks also increases approximately 20% and 40%. [ABSTRACT FROM AUTHOR]

The community detection problem is modeled as multi-objective optimization problem, and a classic NSGA-II (nondominated sorting genetic algorithm) is adopted to optimize this problem, which overcomes the resolution problem in the process of modularity density optimization and the parameter adjustment in the process of general modularity density optimization. In this case, a set of Pareto solutions with different partitioning results can be obtained in one time, which can be chosen by the decision maker. Besides that, the crossover and mutation operators take the neighborhood information of the vertices of networks into consideration, which matches up with the property of real world complex networks. The graph based on coding scheme confirms the self-adjustment of the community numbers, rather than sets up in advance. All the experiment results indicate that NSGA-II based algorithm can detect the construction of community effectively. [ABSTRACT FROM AUTHOR]

Feature selection of text is one of the basic matters for intelligent classification of text. Textual feature generating algorithm adopts weighted textual vector space model generally at present. This model uses BP network evaluation function to calculate weight value of single feature and textual feature redundancy generated in this algorithm is high generally. For this problem, a textual feature generating algorithm based on clustering weighting is adopted. This new method conducts initial weighted treatment for feature candidate set first of all and then conducts further weighted treatment of features through semantic and information entropy and it removes redundancy features with features clustering at last. Experiment shows that the average classification accuracy rate of this algorithm is about 5% higher than that of traditional BP network algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an AR.Drone is flown autonomously from the initial position (x,y,z) to the desired position called waypoint using Fuzzy Logic Controller (FLC). The FLC consists of three control loops which are pitch control loop, roll control loop and vertical rate control loop. For each control loop, desired position and real position are used as inputs of the FLC, while pitch, roll and vertical rate are used as output respectively. The algorithm is realised in three flight schemes and the navigation data is recorded. The first flight scheme: a desired x-position of AR.Drone will be reached first followed by a desired y-position, and lastly a desired z-position. The second flight scheme: a desired x-position and y-position will be reached simultaneously followed by a desired z-position. The third flight scheme: AR.Drone flies towards to desired position simultaneously. The results show that the AR.Drone can reach the waypoint with the three schemes well. However, the flight scheme straight towards the waypoint with the FLC working simultaneously is the most satisfying one. [ABSTRACT FROM AUTHOR]

The Dedicated Short-Range Communication (DSRC) has been widely accepted as a promising wireless technology for enhancing traffic safety. In such DSRC-based vehicle-to-vehicle (V2V) communication systems, because of the extremely time-varying characteristic of wireless propagation channels, accurate channel estimation is essential for reliable information exchange between vehicles. In this paper, the characteristics of the propagation channel and several traditional channel estimation schemes for V2V communications are reviewed. Then, a delay-based channel-frequency-response decomposition scheme is proposed to estimate and predict the double-selective V2V channel while adhering to the IEEE 802.11p standard. The proposed method achieves a more favorable performance than the traditional methods in V2V scenarios by combining the least square estimation in the frequency domain with the linear prediction in time domain. The performance advantages of the proposed scheme are verified by the simulation results from three typical scenarios. Furthermore, a reference design on a field-programmable gate array for the proposed channel estimation scheme is presented for the purpose of demonstrating its implementation feasibility and complexity. [ABSTRACT FROM AUTHOR]

Test generation algorithm based on the SVM (support vector machine) generates test signals derived from the sample space of the output responses of the analog DUT. When the responses of the normal circuits are similar to those of the faulty circuits (i.e., the latter have only small parametric faults), the sample space is mixed and traditional algorithms have difficulty distinguishing the two groups. However, the SVM provides an effective result. The sample space contains redundant data, because successive impulse-response samples may get quite close. The redundancy will waste the needless computational load. So we propose three difference methods to compress the sample space. The compressing sample space methods are Equidistant compressional method, k-nearest neighbors method and maximal difference method. Numerical experiments prove that maximal difference method can ensure the precision of the test generation. [ABSTRACT FROM AUTHOR]

In order to process small components of original image under the microscope, firstly, this paper adopts median filtering algorithm to enhance targets; and the targets are sharpened by using lateral inhibition algorithm, the edge of targets is outlined. In order to get reliable target region, adaptive threshold segmentation algorithm is used to extract need target region, and characteristics of target is used to distinguish multiple targets. Based on the chip resistor, one tiny component, in the captured image, we judge if the chip resistor is qualified by calculating the pixels area values. The experimental results show that, the image processing algorithm and qualified detection algorithm is reasonable, which provides the theoretical basis and implementation method of effective target extraction and further qualified test. [ABSTRACT FROM AUTHOR]

In bistatic inverse synthetic aperture radar (Bi-ISAR) system, its image resolution is lower than monostatic ISAR system. In order to solve this problem, the linear prognosis algorithm is adopted in the imaging process and the imaging algorithm based on linear prognosis is proposed. Space target Bi-ISAR imaging is taken as example in the research. The one-dimensional range profile is created through pulse compression mehod. Before the azimuth compression, burg entropy maximum algorithm in Levions recursive method is used to estimate the prognosis coefficients and the azimuth echo data. Then Fourier transformation is used to compress the azimuth data in order to get the high resolution azimuth image. This imaging method can obtain the two-dimensional image with the resolution equal to the monostatic ISAR or even higher than it. Simulation experiments have verified the effectiveness and availability of the algorithm. [ABSTRACT FROM AUTHOR]

For guaranteeing all multicast destination nodes receiving the source information with their maximal flow respectively and obtaining the network maximal throughput, a heuristic algorithm based on network coding, Maximal Flow for Single-source Two-destinations Multicast (MFSTM) is proposed to maximize the network throughput. By calculating the each destination's maximal flow, the number of linkdisjoint paths which equals to destination's maximal flow, are searched for each destination to construct the network coding graph. A heuristic algorithm based on network coding is designed to delete the redundant link in the network coding graph and guarantee the network throughput maximization. Comparing the traditional maximal multicast stream algorithm based on network coding, the simulation results show that the MFSTM algorithm makes two destinations receive the information at the speed of their maximal flow respectively, and decode the source node information at each destination node successfully. [ABSTRACT FROM AUTHOR]

One of the most important problems of clustering algorithms in mobile ad-hoc networks (MANETs) is the relatively low stability in generated clusters which are resulted by rapid clusters destruction and high energy consumption in performing the re-clustering processes. Many algorithms have been provided to increase the clusters stability of which the most significant are weight-based algorithms. In weight-based algorithms, only limited information of each node is used to determine its weight and it causes that the best possible option for cluster-head is not selected. The purpose of this paper is providing one weight-based algorithm in which each node's weight determination is performed not only by using its node information but also its neighbor's nodes information and this work is performed by determining the links' weight between nodes that provide connections between nodes. Via this method, the best possible options can be selected as cluster-head. In simulations and performed experiments, it is revealed that the generated clusters by our proposed algorithms have very high stability. [ABSTRACT FROM AUTHOR]

In order to better extract the focused regions and effectively improve the quality of the fused image, a novel multi-focus image fusion scheme withsparse feature basedpulse coupled neural network (PCNN) is proposed. The registered source images are decomposed into principal matrices and sparse matrices by robust principal component analysis (RPCA).The salient features of the sparse matrices construct the sparse feature space of the source images. The sparse features are used to motivate the PCNN neurons. The focused regions of the source images are detected by the output of the PCNN and integrated to construct the final fused image. Experimental results showthat the proposed scheme works better in extracting the focused regions and improving the fusion quality compared to the other existing fusion methods in both spatial and transform domain. [ABSTRACT FROM AUTHOR]

The process of introducing emotion can be improved through three-dimensional (3D) tutoring system. The problem that still not solved is how to provide realistic tutor (avatar) in virtual environment. This paper propose an approach to teach children on understanding emotion sensation through facial expression and sense of touch (haptic).The algorithm is created by calculating constant factor (f) based on maximum value of RGB and magnitude force then magnitude force range will be associated into particular colour. The Integration process will be started from rendering the facial expression then followed by adjusting the vibration power to emotion value. The result that achieved on experiment, it show around 71% students agree with the classification of magnitude force into emotion representation. Respondents commented that high magnitude force create similar sensation when respondents feel anger, while low magnitude force is more relaxing to respondents. Respondents also said that haptic and facial expression is very interactive and realistic. [ABSTRACT FROM AUTHOR]