87 research outputs found
Thomson backscattering in combined two laser and magnetic field
The Thomson backscattering of an electron moving in combined fields is
studied by a dynamically assisted mechanism. The combined fields are composed
of two co-propagating laser fields and a magnetic field, where the first laser
field is strong and low-frequency while the second is weak and high-frequency,
relatively. The dependence of fundamental frequency of emission on the ratio of
incident laser high-to-low frequency is presented and the spectrum of
backscattering is obtained. It is found that, with a magnetic field, the peak
of the spectrum and the corresponding radiation frequency are significantly
larger in case of two-laser than that in case of only one laser. They are also
improved obviously as the frequency of the weak laser field. Another finding is
the nonlinear correlation between the emission intensity of the backscattering
and the intensity of the weak laser field. These results provide a new
possibility to adjust and control the spectrum by changing the ratios of
frequency and intensity of the two laser fields.Comment: 13 pages, 4 figure
Core Point Pixel-Level Localization by Fingerprint Features in Spatial Domain
Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples
Extreme sparse multinomial logistic regression : a fast and robust framework for hyperspectral image classification
Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework
Extreme sparse multinomial logistic regression: a fast and robust framework for hyperspectral image classification.
Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework
Oxygen dissociation on the C3N monolayer: A first-principles study
The oxygen dissociation and the oxidized structure on the pristine C3N
monolayer in exposure to air are the inevitably critical issues for the C3N
engineering and surface functionalization yet have not been revealed in detail.
Using the first-principles calculations, we have systematically investigated
the possible O2 adsorption sites, various O2 dissociation pathways and the
oxidized structures. It is demonstrated that the pristine C3N monolayer shows
more O2 physisorption sites and exhibits stronger O2 adsorption than the
pristine graphene. Among various dissociation pathways, the most preferable one
is a two-step process involving an intermediate state with the chemisorbed O2
and the barrier is lower than that on the pristine graphene, indicating that
the pristine C3N monolayer is more susceptible to oxidation than the pristine
graphene. Furthermore, we found that the most stable oxidized structure is not
produced by the most preferable dissociation pathway but generated from a
direct dissociation process. These results can be generalized into a wide range
of temperatures and pressures using ab initio atomistic thermodynamics. Our
findings deepen the understanding of the chemical stability of 2D crystalline
carbon nitrides under ambient conditions, and could provide insights into the
tailoring of the surface chemical structures via doping and oxidation.Comment: 23 pages,8 figure
Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been recently proposed for feature extraction in hyperspectral remote sensing. With the help of hidden nodes in deep layers, a high-level abstraction is achieved for data reduction whilst maintaining the key information of the data. As hidden nodes in SAEs have to deal simultaneously with hundreds of features from hypercubes as inputs, this increases the complexity of the process and leads to limited abstraction and performance. As such, segmented SAE (S-SAE) is proposed by confronting the original features into smaller data segments, which are separately processed by different smaller SAEs. This has resulted in reduced complexity but improved efficacy of data abstraction and accuracy of data classification
Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectralâspatial classification of hyperspectral images.
Although extreme learning machine (ELM) has successfully been applied to a number of pattern recognition problems, only with the original ELM it can hardly yield high accuracy for the classification of hyperspectral images (HSIs) due to two main drawbacks. The first is due to the randomly generated initial weights and bias, which cannot guarantee optimal output of ELM. The second is the lack of spatial information in the classifier as the conventional ELM only utilizes spectral information for classification of HSI. To tackle these two problems, a new framework for ELM-based spectral-spatial classification of HSI is proposed, where probabilistic modeling with sparse representation and weighted composite features (WCFs) is employed to derive the optimized output weights and extract spatial features. First, ELM is represented as a concave logarithmic-likelihood function under statistical modeling using the maximum a posteriori estimator. Second, sparse representation is applied to the Laplacian prior to efficiently determine a logarithmic posterior with a unique maximum in order to solve the ill-posed problem of ELM. The variable splitting and the augmented Lagrangian are subsequently used to further reduce the computation complexity of the proposed algorithm. Third, the spatial information is extracted using the WCFs to construct the spectral-spatial classification framework. In addition, the lower bound of the proposed method is derived by a rigorous mathematical proof. Experimental results on three publicly available HSI data sets demonstrate that the proposed methodology outperforms ELM and also a number of state-of-the-art approaches
MIMR-DGSA: unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm
Band selection plays an important role in hyperspectral data analysis as it can improve the performance of data analysis without losing information about the constitution of the underlying data. We propose a MIMR-DGSA algorithm for band selection by following the Maximum-Information-Minimum-Redundancy (MIMR) criterion that maximises the information carried by individual features of a subset and minimises redundant information between them. Subsets are generated with a modified Discrete Gravitational Search Algorithm (DGSA) where we definine a neighbourhood concept for feature subsets. A fast algorithm for pairwise mutual information calculation that incorporates variable bandwidths of hyperspectral bands called VarBWFastMI is also developed. Classification results on three hyperspectral remote sensing datasets show that the proposed MIMR-DGSA performs similar to the original MIMR with Clonal Selection Algorithm (CSA) but is computationally more efficient and easier to handle as it has fewer parameters for tuning
Approximate affine linear relationship between L1 norm objective functional values and L2 norm constraint bounds
For an optimization problem with an norm objective function subject to an norm inequality constraint, this paper shows that there is an approximately linear relationship between the norm objective functional values and the norm specifications. This relationship is verified through the use of random and real world industrial data. The obtained results can be employed for 1) estimating the norm output objective functional value without solving the optimization problem numerically; 2) providing an insight for defining the norm specification in which a simple method is proposed in this paper; and 3) testing whether the obtained solutions are the globally optimal solutions or not. These advantages are demonstrated via the use of random data
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