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The reliability of support vector machines for classifying multi-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for urban land cover in Wuhan, Hubei province of China. Firstly, radiation rectification, normalization processing and geometry registration are made between the bi-temporal images. Secondly, SVM approach is used in...
In hyerspectral remote sensing community, sparse representation based classification (SRC) is a novel concept — a testing pixel is linearly represented by labeled data, and weight coefficients are often solved by an ℓ1-norm minimization. In this work, an extension of SRC is proposed by imposing an adaptive similarity measurement between the testing pixel and labeled data on the ℓ1-norm penalty, named...
Neural decoding is a procedure to acquire intended movement information from neural activity and generate movement commands to control external devices such as intelligent prostheses. In this study, monkey Astra was trained to accomplish a 3-D reach-to-grasp task, and we recorded neural signals from its primary motor cortex (M1) during the task. The task-related cells were divided into four classes...
In hyperspectral imagery, there exist homogeneous regions where neighboring pixels tend to belong to the same class with high probability. However, even though neighboring pixels are from the same material, their spectral characteristics may be different due to various factors, such as internal instrument noise or atmospheric scattering, which results in misclassification. In this work, the proposed...
This study aims to detect the urban expansion in the Greater Toronto Area (GTA) in the period of 29 years lasting from 1985 to 2013 using the optical remote sensing data. A time series study is carried out and the change of the urban area and non-urban area is analyzed bi-temporally and multi-temporally by using the post-classification comparison method. Landsat images can been used to examine the...
Single Nucleotide Polymorphisms (SNPs) are the most common variants in the human genome. Disease analysis costs can be reduced by selecting meaningful SNPs, i.e., tagging the SNP selection. We propose a method, called chaos particle swarm optimization (CPSO), to select tag SNPs, and use linkage disequilibrium (LD) and the K-nearest neighbor (K-NN) method to respectively reduce and evaluate the tag...
During gene expression, transcription factors are unable to bind to a transcription binding site (TFBS) involved in regulation if DNA methylation has occurred at the TFBS. Methyl-CpG-binding proteins may also occupy the TFBS and prevent the functioning of a transcription factor. Thus, the methylation status of CpG sites is an important issue when trying to understand gene regulation and shows strong...
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