The cyclic codes with parity check polynomial the reciprocal of the characteristic polynomial of the Fibonacci recurrence over a prime finite field are shown to have either one weight or two weights. When these codes ...
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In this paper, we prove existence of optimal complementary dual codes (LCD codes) over large finite fields. We also give methods to generate orthogonal matrices over finite fields and then apply them to construct LCD ...
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This paper focus on the subgraph isomorphism (SI) problem. We present an improved genetic algorithm, a heuristic method to search the optimal solution. The contribution of this paper is that we design a dedicated cros...
This paper focus on the subgraph isomorphism (SI) problem. We present an improved genetic algorithm, a heuristic method to search the optimal solution. The contribution of this paper is that we design a dedicated crossover algorithm and a new fitness function to measure the evolution process. Experiments show our improved genetic algorithm performs better than other heuristic methods. For a large graph, such as a subgraph of 40 nodes, our algorithm outperforms the traditional tree search algorithms. We find that the performance of our improved genetic algorithm does not decrease as the number of nodes in prototype graphs.
Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we pre...
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Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we present a multiview metric learning framework for multi-view video summarization. It combines the advantages of maximum margin clustering with the disagreement minimization criterion. The learning framework thus has the ability to find a metric that best separates the input data, and meanwhile to force the learned metric to maintain underlying intrinsic structure of data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.
We consider the phase noise filtering problem for Interferometric Synthetic Aperture Radar(In SAR)using a total variation regularized complex linear least squares formulation. Although the original formulation is conv...
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We consider the phase noise filtering problem for Interferometric Synthetic Aperture Radar(In SAR)using a total variation regularized complex linear least squares formulation. Although the original formulation is convex, solving it directly with the standard CVX package is time consuming due to the large problem size. In this paper, we introduce the effective and efficient alternating direction method of multipliers(ADMM) to solve the equivalent well-defined complex formulation for the real and imaginary parts of the optimization *** the iteration complexity and the computational complexity of the ADMM are established in the forms of theorems for our In SAR phase noise problem. Simulation results based on simulated and measured data show that this new In SAR phase noise reduction method not only is 3 orders of magnitude faster than the standard CVX solver, but also has a much better performance than the several existing phase filtering methods.
In this paper, we present a robust face recognition method with combined locality-sensitive sparsity and group sparsity constraint. The group sparsity constraint is designed to utilize the grouped structure informatio...
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A kind of hybrid surface plasmonic waveguide based on the nonlinear media of Si-NC/SiO2 was designed. The dependence of the distribution of longitudinal energy flux density, the effective refractive index, the propaga...
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Community structure is one of the most important features in real networks and reveals the internal organization of the vertices. Uncovering accurate community structure is effective for understanding and exploiting n...
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Community structure is one of the most important features in real networks and reveals the internal organization of the vertices. Uncovering accurate community structure is effective for understanding and exploiting networks. Tolerance Granulation based Community Detection Algorithm(TGCDA) is proposed in this paper, which uses tolerance relation(namely tolerance granulation) to granulate a network hierarchically. Firstly, TGCDA relies on the tolerance relation among vertices to form an initial granule set. Then granules in this set which satisfied granulation coefficient are hierarchically merged by tolerance granulation operation. The process is finished till the granule set includes one granule. Finally, select a granule set with maximum granulation criterion to handle overlapping vertices among some granules. The overlapping vertices are merged into corresponding granules based on their degrees of affiliation to realize the community partition of complex networks. The final granules are regarded as communities so that the granulation for a network is actually the community partition of the *** on several datasets show our algorithm is effective and it can identify the community structure more accurately. On real world networks, TGCDA achieves Normalized Mutual Information(NMI) accuracy 17.55% higher than NFA averagely and on synthetic random networks, the NMI accuracy is also improved. For some networks which have a clear community structure, TGCDA is more effective and can detect more accurate community structure than other algorithms.
How to fast, accurately and robustly recognize wheat diseases, particularly for those diseases with mild-to-moderate severity, is a challenge for prevention and control of crop disease timely. In this study, image pro...
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How to fast, accurately and robustly recognize wheat diseases, particularly for those diseases with mild-to-moderate severity, is a challenge for prevention and control of crop disease timely. In this study, image processing technique was applied to segment the infected regions of disease leaves. Twenty disease features were extracted, and eighteen larger weight features were selected by Relief-F algorithm to generate the models of Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Back Propagation Neural Network (BPNN). Subsequently, these models were used to identify two kinds of wheat diseases, namely, wheat stripe rust and powdery mildew. Total 136 samples, including 68 training samples and 68 test samples with different infection severities were used to study the recognition capabilities of the three models. Results showed that high predictive accuracies in identification of two wheat diseases with varying severity for all three models. Overall accuracy of RVM was 89.71%, which was superior to 83.82% of SVM and inferior to 92.64% of BPNN. Meanwhile, the recognition accuracies of SVM, RVM and BPNN models for mild-to-moderate disease were 83.33%, 88.33% and 91.67%, respectively. The prediction time of RVM was less than those of SVM and BPNN, with differences as large as 7.96 and 31.68 times, respectively. Therefore, RVM appeared to be the most suitable for real-time identifying wheat leaf diseases among the three models, which can provide important technical support for wheat diseases management.
The subwavelength metal grating is designed and simulated by using the finite difference time domain (FDTD) algorithm. The transmission characteristics of subwavelength metal grating structure are studied based on the...
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