Synthetic aperture radar (SAR) is an imaging system which provides high-resolution images of earth surface. Nowadays there is an ever-growing interest in the SAR data compression because of the huge resources which re...
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The target recognition accuracy of remote sensing images is not satisfied. The labels of images acquisition and recollecting are difficult and expensive. In order to solve the problem, we introduce transfer learning i...
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In this paper we propose a novel spectral clustering algorithm called Immune Greedy Spectral Clustering Algorithm, which introduces immune clone selection algorithm instead of greedy selection to choose a subset befor...
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It is commonplace for the lack of labeled data in novel domains on medical image computer-aided diagnosis but there have been some labeled data or prior knowledge in old correlative domains. In this paper, instance-tr...
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Contour extraction is an important task in image processing and computer vision. The contextual modulation is a universal phenomenon in the primary visual cortex (VI). A biologically motivated computational model is p...
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Contour extraction is an important task in image processing and computer vision. The contextual modulation is a universal phenomenon in the primary visual cortex (VI). A biologically motivated computational model is presented for contour extraction in this paper. Two mechanisms of contextual modulation, surround suppression and collinear facilitation, are integrated in this model. We obtain good results via this model to extract contours from images with noise and texture backgrounds. This work provides a biologically motivated approach with great potential for computer vision.
This chapter proposes a novel spectral clustering ensemble method for unsupervised land cover classification of PolSAR data. This method increases the diversity to overcome the instability results caused by the random...
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The degradation of AlGaN/GaN high electron mobility transistors (HEMTs) has a close relationship with a model of traps in AlGaN barriers as a result of high electric field. We mainly discuss the impacts of strong el...
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The degradation of AlGaN/GaN high electron mobility transistors (HEMTs) has a close relationship with a model of traps in AlGaN barriers as a result of high electric field. We mainly discuss the impacts of strong electrical field on the AlGaN barrier thickness of AlGaN/GaN HEMTs. It is found that the device with a thin AlGaN barrier layer is more easily degraded. We study the degradation of four parameters, i.e. the gate series resistance RGate, channel resistance R channel, gate current IG,off at VGS=-5 and VDS=0.1 V, and drain current ID,max at VGS=2 and VDS=5 V. In addition, the degradation mechanisms of the device electrical parameters are also investigated in detail.
Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm (IDCMA). IDCMA is unique in that its f...
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Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm (IDCMA). IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals (antibodies) are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant (active) antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.
In the past few years, multiobjective clustering has been one of the most successful techniques in the field of computer vision and data clustering. This paper proposes a novel unsupervised approach for synthetic aper...
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The performance of the traditional clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, Projection Pursuit w...
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