A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient *** iterative method is introduced to s...
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A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient *** iterative method is introduced to solve this equation *** unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density *** simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.
Scale is a major concept in many sciences concerned with human activities and physical processes occurring in the world, and directly related to many investigations of spatial objects, including the procedure of spati...
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Scale is a major concept in many sciences concerned with human activities and physical processes occurring in the world, and directly related to many investigations of spatial objects, including the procedure of spatial data mining. In this paper, we attempt to apply the spatial data mining to the field of coal mining, and the technical notion is to generate patterns or rules by means of different scale databases that depict the same subject. The whole research procedure gives readers an understanding of how processes operate at different scales and how they can be linked across scales. At the same time, our study actually presents a new method of image mining as well.
We propose a novel method for joint probabilistic constrained robust beamforming and antenna selection used in cognitive radio (CR) networks. Assuming complex Gaussian distributed channel state information (CSI) error...
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We propose a novel method for joint probabilistic constrained robust beamforming and antenna selection used in cognitive radio (CR) networks. Assuming complex Gaussian distributed channel state information (CSI) errors, the Bernstein-type inequalities are used to transform the no closed-form probabilistic constrained into the deterministic forms. Moreover, l1-norm is introduced as the closest convex approximation of ℓ0-norm. So, the original NP-hard optimal problem can be relaxed to as a tractable convex optimization problem. A computationally efficient and near-optimal solution is obtained by a iteratively re-weighted algorithm. Simulations show that the proposed algorithm meet prescribed service levels at a relatively small excess transmission power in a number of transmitter reduction scenarios.
Concept generalization under incomplete domain theory is a very important research aspect in artificial intelligence. Current multilayer perceptron and EBL (explanation based learning) approaches cannot deal with it e...
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ISBN:
(纸本)0780342534
Concept generalization under incomplete domain theory is a very important research aspect in artificial intelligence. Current multilayer perceptron and EBL (explanation based learning) approaches cannot deal with it effectively. We present a new method, hybrid multilayer perceptron/EBL approach for concept generalization, which can deal with concept generalization more effectively.
Signal peptides are significant important in targeting the translocation of integral membrane proteins and secretory proteins. Due the high similarity between the transmembrane helices and signal peptides, classifiers...
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ISBN:
(纸本)9781509037117
Signal peptides are significant important in targeting the translocation of integral membrane proteins and secretory proteins. Due the high similarity between the transmembrane helices and signal peptides, classifiers have limit ability to discriminate the signal peptides from the transmembrane helices. To solve this problem, the protein functional domain information is applied in this method. For accurately identify the cleavage sites along the sequence, a subset of potential cleavage sites was firstly screened out by statistical machine learning rules, and then the final unique site was picked out according to its evolution conservation score. This method has been benchmarked on multiple datasets and the experimental results have shown its superiority.
In this paper, a novel approach for image visual saliency detection is proposed from both the salient object (foreground) and the background perspective. To better highlight the salient object, we start from what is a...
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ISBN:
(纸本)9781479957521
In this paper, a novel approach for image visual saliency detection is proposed from both the salient object (foreground) and the background perspective. To better highlight the salient object, we start from what is a salient object and adopt priors including contrast prior and center prior to measure the dissimilarity between different image elements. To better suppress the background, we focus on what is the background and measure the pixel-wise saliency by the minimum seam cost where the seam is an optimal 8-connected path from the pixel to some boundary pixel. The final saliency map is obtained by the combination of two measure systems which leads to the goal of both highlighting the salient object and suppressing the background. Both qualitative and quantitative experiments conducted on a benchmark dataset show that our approach outperforms seven state-of-the-art methods.
＜正＞A new neural network for imagerecognition is proposed in this *** many cases of classification the number of class "dimensions is much larger than the number of classes and classes are independent linearly...
＜正＞A new neural network for imagerecognition is proposed in this *** many cases of classification the number of class "dimensions is much larger than the number of classes and classes are independent linearly of each other.A mapping from the class space into a new orthogonality space is used *** the basis of mapping a Hopfield neural network as classifer is proposed *** results of computer simulation show the high performance of the method.
GRABAC NN model,which means Gradient Radical Basis Cell Neural Network,is a kind of generalized neural *** with Hopfield-type models,it has much more dynamic qualitative behavior,that is all tracks converge one of the...
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GRABAC NN model,which means Gradient Radical Basis Cell Neural Network,is a kind of generalized neural *** with Hopfield-type models,it has much more dynamic qualitative behavior,that is all tracks converge one of the fixpoints,its complexity of time and space are O(mn) other than O(nn),and unlimited memory capacity can be obtained,in
Coherence-enhancing diffusion (CED), based on analysis of oriented structures, has been extensively used in imageprocessing. This diffusion filtering can keep some junctions and close broken linear structures, but it...
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Linear Discriminant Analysis (LDA) is frequently used for dimension reduction and has been successfully utilized in many applications, especially face recognition. In classical LDA, however, the definition of the betw...
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