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After the next-generation sequencing technology, long-read sequencing technologies were developed. Both the advantages and the drawbacks of long-read sequencing technologies are obvious. Here, we take advantages of long-read sequencing technology, MinION, to develop a comprehensive and user-friendly pipeline, NanoAsPipe, to identify the transcriptomic profiles, isoform profiles, and alternative splicing...
Deep learning techniques have been demonstrated to be a powerful tool to learn features of images automatically. In this paper, a novel deep learning structure, i.e., deep stacked neural network (DSNN), is constructed to extract different levels of deep features of hyperspectral images. Specifically, convolutional neural network (CNN) is used as basic units in the proposed DSNN for feature extraction...
In this paper, the tensor-based offset-sparsity decomposition (TOSD) method, or low-rank and sparse decomposition, is applied to hyperspectral imagery, where the low-rank tensor is considered to be enhanced or pruned data and used for classification. In the tensor form of dataset, all the information of the original 3D data cube, includes spatial and spectral information, can be better reserved. To...
Deep learning techniques have brought in revolutionary achievements for feature learning of images. In this paper, a novel structure of 3-Dimensional Convolutional AutoEncoder (3D-CAE) is proposed for hyperspectral spatial-spectral feature learning, in which the spatial context is considered by constructing a 3-Dimensional input using pixels in a spatial neighborhood. All the parameters involved in...
Graph embedding, as a dimensionality reduction framework, has already drawn great attention in hyperspectral image analysis. Taking locality preserving projection (LPP) as example, LPP utilizes typical Euclidean distance in heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with...
This paper presents a novel deep convolutional feature fusion (ConvFF) approach for high-resolution scene classification, characterizing the well-known deep convolutional neural network (ConvNet) approach. The proposed ConvFF approach starts by generating an initial feature representation of the original scenes under exploration from two deep ConvNets pre-trained on two different large amount of labeled...
An interesting target detection framework with transferred deep convolutional neural network (CNN) is proposed. For CNN, many labeled samples are needed to train the multi-layer network. However, for target detection tasks, only few target spectral signatures are available, or they are unknown in anomaly detection. In this work, we employ a reference data and further generate pixel-pairs to enlarge...
A novel spectral-spatial joint multiscale approach is developed to address the multi-class change detection problem in bitemporal multispectral remote sensing images. The proposed approach is based on a multiscale morphological compressed change vector analysis (M2C2VA), which extend the state-of-the-art spectrum-based compressed change vector analysis (C2VA) while preserving more geometrical details...
The paper investigates representation-based classification for multispectral imagery. Due to the limited spectral dimension, the performance may be limited, and, in general, it is difficult to discriminate different classes using multispectral imagery. Nonlinear band generation method is proposed to use which can provide additional spectral information for multispectral classification. Two classifiers,...
Conduction process in dielectric liquids has been studied for many years because of its importance in understanding the process of dielectric breakdown. As a key parameter in conduction process, ion mobility has been measured by various methods. The reversal polarity method has been widely used to accurately measure the ion mobility in the dielectric liquids. In this paper, the effect of electrical...
High-throughput next generation sequencing of cDNA, i.e. RNA-Seq, presents an unprecedented resource for characterizing the alternative splicing (AS) in complex eukaryotic transcriptomes. Accumulating evidences indicate that AS is developmentally regulated, but the precise responses of AS event to development is not well understood. Here, we describe a new method, based on an adjusted beta-distribution...
Semisupervised learning is very useful for hyperspectral image classification, when the number of labeled samples is insufficient. In this paper, a simple selftraining method with spatial majority filtering is proposed to find unlabeled samples that can assist classifier training. It is based on the assumption that an unlabeled pixel, after initial classification, that has the same class label of...
Curved planar reformation (CPR) is an effective way to inspect and monitor the inner information of vessel. Compared to three-dimensional (3D) vessel visualization technologies, it can diagnose vascular diseases such as stenosis, calcification and atherosclerotic plaque, the extensions of this technique help improve medical image analysis and surgical consultation. As traditional straightened CPR...
Nanoparticles have manifested the potential to enhance the breakdown performance of transformer oil. The AC and positive lightening impulse breakdown strengths of the oil samples with and without nanoparticles were measured according to IEC standard methods. The test results indicate that addition of silica nanoparticles can enhance the ac breakdown strength of transformer oil. Additionally, the mean...
The insulation of dielectric liquids is of great importance for safe operation of transformers. It is generally recognized that the breakdown happens when streamers completely bridge two conductors inside the transformer, leading to insulation failure. The propagation shapes of streamers can be obtained with the help of optical technology and be analyzed qualitatively. Fractal dimension has been increasingly...
Accurately measurement of ion mobility behavior in transformer oil is critical for reasonable designing and safely operating HVDC transformers due to its electric field distribution determined by the ion migration in the oil. In this paper, we aim to investigate the effect of electrode configuration on the testing accuracy of ion mobility via the reversal polarity method. Both parallel plane electrode...
The potential hazard of heavy metals in reclaimed mine soil has influenced on the human health. The inversion analysis of hyperspectral data can be used to estimate heavy metal content of the soil effectively. In this paper, the characteristic bands are extracted by spectral pretreatment, including Savitzky-Golay (SG), Standard Normal Variety (SNV), First Derivative (FD), Second Derivative (SD), or...
Mixed pixels in remotely sensed imagery degrade its value in practical use. Sub-pixel mapping is a promising technique to solve this problem. It can generate a fine resolution land cover map from coarse resolution fractional images by predicting spatial locations of land cover classes at sub-pixel scale. However, accuracy is often limited. When the scale factor is large, the sub-pixel distribution...
Nonnegative least squares, a state-of-the-art approach to endmember abundance estimation in the hyperspectral-unmixing problem, is coupled with random projection employed for dimensionality reduction. Both Hadamard- and Gaussian-based projections are considered. Experimental results reveal that random projections can significantly reduce data volume without detrimentally affecting the accuracy of...
Representation-based classification has gained great interest recently. In this paper, we present a novel joint low rank and sparse representation-based classification (JLRSRC) method for hyperspectral imagery. For a testing set, JLRSRC seeks weight coefficients to represent a testing pixel as linear combination of atoms in an over-complete dictionary. Since the low rank model is capable of preserving...
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