In the world of multimedia communication, there are lots of scenarios which require a higher protection level for some important data. On the basis of the expanding window fountain(EWF) codes, a new unequal error prot...
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In the world of multimedia communication, there are lots of scenarios which require a higher protection level for some important data. On the basis of the expanding window fountain(EWF) codes, a new unequal error protection algorithm based on fountain codes is proposed in this paper. The higher priority protection bits are chosen as the input set of the fountain code with degree 1 and a portion of bits with degree 2. The EWF algorithm is still employed to encode the rest bits, which has greater than 2 degree distribution. Our algorithm not only improves the performance of less important bit, at the same time,it also improves the performance of the more important *** simulation results show that, compared with the previous expanding window fountain codes, the proposed algorithm can provide a superior unequal error protection.
As the third-generation neural network technology, pulse coupled neural network (PCNN) had used in many fields successfully, but it hindered its popularize that so many parameters of the PCNN need to be set up. This p...
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Multimedia data is usually represented with different low-level features, and different types of multimedia data, namely multimodal data, often coexist in many data sources. It is interesting and challenging to learn ...
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The efficiency and performance of the Twin Support Vector Machines(TWSVM) are better than the traditional support vector machines when it deals with the problems. However, it also has the problem of selecting kernel f...
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The efficiency and performance of the Twin Support Vector Machines(TWSVM) are better than the traditional support vector machines when it deals with the problems. However, it also has the problem of selecting kernel functions. Generally, TWSVM selects the Gaussian radial basis kernel function. Although it has a strong learning ability, its generalization ability is relatively weak. In a certain extent, this will limit the performance of TWSVM. In order to solve the problem of selecting kernel functions in TWSVM, we propose the twin support vector machines based on the mixed kernel function(MK-TWSVM) in this paper. To make full use of the learning ability of local kernel functions and the excellent generalization ability of global kernel functions, MK-TWSVM selects a global kernel function and a local kernel function to construct a mixed kernel function which has the better performance. The experimental results indicate that the mixed kernel function makes TWSVM have the good learning ability and generalization ability. So it improves the performance of TWSVM.
With the widespread use of mobile devices, the location-based service (LBS) applications become increasingly popular, which introduces the new security challenge to protect user's location privacy. On one hand, a ...
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For low-rank recovery and error correction, Low-Rank Representation (LRR) row-reconstructs given data matrix X by seeking a low-rank representation, while Inductive Robust Principal Component Analysis (IRPCA) aims to ...
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ISBN:
(纸本)9781479914821
For low-rank recovery and error correction, Low-Rank Representation (LRR) row-reconstructs given data matrix X by seeking a low-rank representation, while Inductive Robust Principal Component Analysis (IRPCA) aims to calculate a low-rank projection to column-reconstruct X. But either column or row information of Xis lost by LRR and IRPCA. In addition, the matrix X itself is chosen as the dictionary by LRR, but (grossly) corrupted entries may greatly depress its performance. To solve these issues, we propose a simultaneous low-rank representation and dictionary learning framework termed Tensor LRR (TLRR) for robust bilinear recovery. TLRR reconstructs given matrix X along both row and column directions by computing a pair of low-rank matrices alternately from a nuclear norm minimization problem for constructing a low-rank tensor subspace. As a result, TLRR in the optimizations can be regarded as enhanced IRPCA with noises removed by low-rank representation, and can also be considered as enhanced LRR with a clean informative dictionary using a low-rank projection. The comparison with other criteria shows that TLRR exhibits certain advantages, for instance strong generalization power and robustness enhancement to the missing values. Simulations verified the validity of TLRR for recovery.
Nowadays it is difficult to get overall sentiment orientation of the comment text. To solve this problem, in the paper, the method of multi-document sentiment summarization based on model Latent Dirichlet Allocation (...
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Aiming at the problem of constructing huge amounts of projected databases in PrefixSpan algorithm, this paper proposes an Improved PrefixSpan algorithm for Mining Sequential Patterns, called BLSPM algorithm (based on ...
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Aiming at the problem of constructing huge amounts of projected databases in PrefixSpan algorithm, this paper proposes an Improved PrefixSpan algorithm for Mining Sequential Patterns, called BLSPM algorithm (based on bi-level Sequential Patterns Mining). The algorithm use duplicated projection and certain specific sequential patterns pruning, reduce the scale of projected databases and the runtime of scanning projected databases, thus, the efficiency of algorithm could be raised up greatly, and all needed sequential patterns are obtained. Experiment results shows that BLSPM algorithm is more efficient than PrefixSpan algorithm in large databases.
To solve the problem of authorization inflexibility and the problem of coarse-grained attribute revocation exist in the attribute based access control model, a kind of improved attribute based access control model whi...
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To improve the accuracy of image retrieval, a two-stage method for annotation-based image retrieval is proposed. In the approach, images are ranked using their attached text information at the first stage, and Content...
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