Multi-robot tracking of mobile target is studied in the paper, which is based on the communication and sensors. For an independent tracking robot, the processes are separated into three layers and four tasks, and allo...
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Multi-robot tracking of mobile target is studied in the paper, which is based on the communication and sensors. For an independent tracking robot, the processes are separated into three layers and four tasks, and allocated to different robots for distinct roles in tracking, which is named the Distributed Decision control System (DDCS). After that, two tracking models, centralized and distributed models, are designed for multi-robot tracking. Furthermore, a Proportional Navigation Guidance Law (PNGL) and l-ϕ formation control algorithm are mentioned to realize the robot motion control. At last the simulation has shown the feasibility and validity of both models.
To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrö...
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To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrödinger Equation is proposed. Our Method is based on computing the numerical solutions of initial value problem for second order nonlinear Schrödinger equation by using discrete Fourier Transformation. Schrödinger transformation of image is first given. We compute the probability P(b,a) that a particle moves from a point a to another point b according to I-Type Schrödinger transformation of image and obtain boundary of object by using quantum contour model.
In this paper, we presented a ringing metric to evaluate the quality of images restored using iterative image restoration algorithms. A ringing metrics is used to assessment the restored images based on the Gabor filt...
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In this paper, we presented a ringing metric to evaluate the quality of images restored using iterative image restoration algorithms. A ringing metrics is used to assessment the restored images based on the Gabor filter. The experimental results validate the proposed method perform well over a wide range of restoration image ringing levels assessment. And the proposed model has given good agreement with observer ratings obtained in subjective experiments.
In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin clonal selection algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and i...
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In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin clonal selection algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and improves antibody population by three operations: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. By introducing Baldwin effect, BCSA can make the most of experience of antibodies, accelerate the convergence, and obtain the global optimization quickly. In experiments, BCSA is tested on four types of functions and compared with the clonal selection algorithm and other optimization methods. Experimental results indicate that BCSA achieves a good performance, and is also an effective and robust technique for optimization.
A method for multi-classifier ensemble of Support Vector Machine ensemble (SVMs) and Kernel Matching Pursuit Ensemble (KMPs) is proposed. Support Vector Machine has advantage in solving classification problem of high ...
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A method for multi-classifier ensemble of Support Vector Machine ensemble (SVMs) and Kernel Matching Pursuit Ensemble (KMPs) is proposed. Support Vector Machine has advantage in solving classification problem of high dimension and small size dataset, and Kernel Matching Pursuit has almost classified performance and the more sparsely solution as comprised with the SVM. So the SVM and the KMP are mix boosted in this paper, which can decrease generalization errors of the single classifier ensemble and improve ensemble classification accuracy by increasing diversity between ensemble individuals. The experiments show that the proposed method can shorten running time and improve classification accuracy compared with individual SVMs or KMPs.
Support vector machine, a universal method for learning from data, gains its development based on statistical learning theory. It shows many advantages in solving nonlinearly small sample and high dimensional problems...
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Support vector machine, a universal method for learning from data, gains its development based on statistical learning theory. It shows many advantages in solving nonlinearly small sample and high dimensional problems of pattern recognition. Only a part of samples or support vectors (SVs) plays an important role in the final decision function. But SVs could not be obtained in advance until a quadratic programming is performed. In this paper, we use K-nearest neighbour method to extract a boundary vector set which may contain SVs. The number of the boundary set is smaller than the whole training set. Consequently it reduces the training samples, speeds up the training of support vector machine.
A progressive image compression scheme is investigated using reversible integer discrete cosine transform (RDCT) which is derived from the matrix factorization theory. Previous techniques based on DCT suffer from bad ...
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A progressive image compression scheme is investigated using reversible integer discrete cosine transform (RDCT) which is derived from the matrix factorization theory. Previous techniques based on DCT suffer from bad performance in lossy image compression compared with wavelet image codec. And lossless compression methods such as IntDCT, I2I-DCT and so on could not compare with JPEG-LS or integer discrete wavelet transform (DWT) based codec. In this paper, lossy to lossless image compression can be implemented by our proposed scheme which consists of RDCT, coefficients reorganization, bit plane encoding, and reversible integer pre- and post-filters. Simulation results show that our method is competitive against JPEG-LS and JPEG2000 in lossless compression. Moreover, our method outperforms JPEG2000 (reversible 5/3 filter) for lossy compression, and the performance is even comparable with JPEG2000 which adopted irreversible 9/7 floating-point filter (9/7F filter).
Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin clonal selection algorithm (BCSA), is proposed to solve the optimal appro...
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Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin clonal selection algorithm (BCSA), is proposed to solve the optimal approximation of linear systems. For engineering computing, the novel algorithm adopts three operations to evolve and improve the population: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new algorithm have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm, multi-agent genetic algorithm and artificial immune response algorithm.
In this paper, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the infor...
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In this paper, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compared LCSA with the clonal selection algorithm (CSA) in solving twenty benchmark problems to test the performance of LCSA. The results demonstrate that LCSA is effective and efficient in solving numerical optimization problems.
We consider the problem of looking for small universal spiking neural P systems with exhaustive use of rules, which was formulated as an open problem by Gheorghe Paun in a survey paper. Here, spiking neural P systems ...
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We consider the problem of looking for small universal spiking neural P systems with exhaustive use of rules, which was formulated as an open problem by Gheorghe Paun in a survey paper. Here, spiking neural P systems are used in two versions: as devices computing functions and as devices generating sets of numbers, with two ways of encoding the result of a computation. As devices of computing functions, if we associate the result with the distance between the first two spikes emitted by the output neuron, we produce a universal computing spiking neural P system with exhaustive use of rules (without delay) having 125 neurons; if we introduce the usual way of defining the result of a computation in membrane systems to encode the result, namely, the number of spikes emitted during a computation, then a universal computing system (without delay) with 126 neurons is also obtained in the sense of the exhaustive use of rules. For spiking neural P systems used as generators of sets of numbers, we construct a universal system (without delay) by using 128 neurons under the first way of defining the computation result, and a system (without delay) by using 127 neurons under the second way of defining the computation result.
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