Artificial immune systems （AIS） are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clona...
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Artificial immune systems （AIS） are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multipleaccess communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.
This paper developed two learning procedure, respectively, based on the orthogonal least squares (OLS) method and the "Innovation- Contribution" criterion (ICc) proposed newly. The orthogonal use of the step...
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Because of noise and clutter, the infrared target detection even becomes more difficult. In this paper, we present an automatic seed selection method based on an improved mountain cluster algorithm to be employed in i...
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A method for real-time fast detection and symptom analysis of cracks in highway asphalt pavement is proposed. At the first step, the fissure characteristic of the acquired and de-noised pavement images is analyzed and...
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A double recursive algorithm based on fuzzy entropy for image thresholding is proposed. The inner recursive step is to calculate the threshold between the given gray level interval, and the outer recursive step is to ...
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This paper proposes a new image denoising method based on the NonsubSampled Contourlet Transform (NSCT) and the bivariate model under the framework of Bayesian MAP estimation theory. The proposed algorithm uses the NS...
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This paper proposes a new image denoising method based on the NonsubSampled Contourlet Transform (NSCT) and the bivariate model under the framework of Bayesian MAP estimation theory. The proposed algorithm uses the NSCT's advantages of translation-invariant and multidirection-selectivity, exploits the intra-scale and inter-scale correlations of NSCT coefficients, and elaborates the method of noise estimation. Compared with some current outstanding denoising methods, the simulation results and analysis show that the proposed algorithm obviously outperforms in both Peak Signal-to-Noise Ratio (PSNR) and visual quality, and effectively preserves detail and texture information of original images.
Bandelet transform is an efficient image sparse representation approach which can adaptively approximate the geometrical regularity of image structures. In this paper, a multi-bandelets based method for SAR image comp...
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This paper studies a class of stochastic nonlinear interconnected systemswith unknown parameters involved. By employing the stochastic Lyapunov-like theorem and the back-stepping design technique, a decentralized cont...
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A novel analytic approach is presented to study the population of excitatory and inhibitory spiking neurons in this paper. The evolution in time of the population dynamic equation is determined by a partial differenti...
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A novel analytic approach is presented to study the population of excitatory and inhibitory spiking neurons in this paper. The evolution in time of the population dynamic equation is determined by a partial differential equation. A new function is proposed to characterize the population of excitatory and inhibitory spiking neurons, which is different from the population density function discussed by most researchers. And a novel evolution equation, which is a nonhomogeneous parabolic type equation, is derived. From this, the stationary solution and the firing rate of the stationary states are given. Last, by the Fourier transform, the time dependent solution is also obtained. This method can be used to analyze the various dynamic behaviors of neuronal populations.
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