In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can...
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A sequence {ai |1 ≤ i ≤ k} of integers is a weak Sidon sequence if the sums ai + aj are all different for any i i |1 ≤ i ≤ k} such that 1 ≤ a1k ≤ n. Let the weak Sidon number G(k) = min{n | g(n) = k}. In this no...
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A sequence {ai |1 ≤ i ≤ k} of integers is a weak Sidon sequence if the sums ai + aj are all different for any i i |1 ≤ i ≤ k} such that 1 ≤ a1k ≤ n. Let the weak Sidon number G(k) = min{n | g(n) = k}. In this note, g(n) and G(k) are studied, and g(n) is computed for n ≤ 172, based on which the weak Sidon number G(k) is determined for up to k = 17.
A deep Neural Network model was trained to classify the facial expression in unconstrained images, which comprises nine layers, including input layer, convolutional layer, pooling layer, fully connected layers and out...
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This paper proposes a new algorithm for imaging moving targets via estimating their motion parameters with dual channel synthetic aperture radar. Displaced phase center array (DPCA) antennas are used for clutter cance...
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ISBN:
(纸本)9781479929948
This paper proposes a new algorithm for imaging moving targets via estimating their motion parameters with dual channel synthetic aperture radar. Displaced phase center array (DPCA) antennas are used for clutter cancelation through which moving targets are detected. The range motion of a moving target induces a Doppler centroid shift and additional range walk, while the along-track motion changes the Doppler modulation rate. The Doppler centroid shift dislocates the moving target from its actual azimuth position, and the additional range walk deteriorates the imaging resolution. The slope angle of the range walk trajectory is estimated via Hough transform, then the range velocity can be obtained. Doppler parameters of the moving targets are estimated with the fractional Fourier transform (FrFT). In this way, both target range and azimuth velocities are acquired. Next, the moving targets are focused with one uniform imaging algorithm. Simulated data processing results are provided to demonstrate the effectiveness of the proposed algorithm.
Deconvolution is known as an ill-posed problem. In order to solve such a problem, a regularization method is needed to constrain the solution space and find a plausible and stable solution. In practice, it is very com...
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Deconvolution is known as an ill-posed problem. In order to solve such a problem, a regularization method is needed to constrain the solution space and find a plausible and stable solution. In practice, it is very computation intensive when using cross-validation method to select the regularization parameter. In this paper, we present an adaptive regularization method to find the optimal regularization parameter value and represent the trade-off between model fitness of the data and the smoothness of the extracted signal. Spectral signal extraction experimental results demonstrate that the time complexity the proposed method is much lower than the one without adaptive regularization and is convenient for users also. And quantitative performance analysis show that the proposed intelligent approach performs better than that of current deconvolution extraction method and other extraction method used in the Large Area Multi-Objects Fiber Spectroscopy Telescope spectral signal processing pipeline.
Optimization in noisy environments is regard as a favorite application domains of genetic algorithms. Different methods for reducing the influence of noise are presented and discussed. A new fitness evaluation method ...
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ISBN:
(纸本)9781479969302
Optimization in noisy environments is regard as a favorite application domains of genetic algorithms. Different methods for reducing the influence of noise are presented and discussed. A new fitness evaluation method is proposed that reevaluates all survival individuals each generation. Compared with re-sampling and population sizing, the new evaluation approach shows higher probability of searching to the global extremum area and precision of convergence. These results demonstrate that the proposed method is effective for reducing noise effects.
Point matching is an important component of image registration. Recent years, Coherent Point Drift (CPD) method becomes a very popular point matching approach. CPD treats point matching as a probability estimation pro...
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Point matching is an important component of image registration. Recent years, Coherent Point Drift (CPD) method becomes a very popular point matching approach. CPD treats point matching as a probability estimation problem and speeds up the process of matching a lot. In this method, one set of points are thought to be sampled from a Gaussian Mixture Model (GMM), which is centered by the other set of points. However, CPD is sensitive to outliers and noises, especially when the noise ratio increased or the number of outliers gets much high. To deal with this problem, we introduce shape context into the step of searching for matching points and then improve the form of prior probabilities of GMM in this paper. The main idea of our method is that if the most points in a data set are likely to be matched to a particular centroid, this Gaussian component should be have a more influence to GMM. Therefore, we set prior probability of GMM with the similarity between GMM components and the data set. And the computation of similarity is based on shape context. The experiments on 2D and 3D images show that when noise ratio is low, our method performs as well as CPD does, but as the ratio increased, our method is more robust and satisfactory than CPD.
In this paper, we try to deal with the problem of shadow detection from static images and video sequences. In instead to considering individual regions separately, we use relative illumination conditions between segme...
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In this paper, we investigate the efficiency and scalability of Gaussian mixture model based learning algorithm for the detection of Near-Earth objects in large scale astronomy image data. We propose an effective sche...
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ISBN:
(纸本)9781479938414
In this paper, we investigate the efficiency and scalability of Gaussian mixture model based learning algorithm for the detection of Near-Earth objects in large scale astronomy image data. We propose an effective scheme to reduce the computational complexity of current learning algorithm, this is achieved by adopting the perceptual image hashing method. Our proposed scheme is validated on raw astronomy image data. The experiment results illustrate that both efficiency and scalability are improved significantly in astronomical scenario and other scenario.
An image compression-encryption algorithm based on 2-D compressive sensing is proposed, which can accomplish encryption and compression simultaneously. The measurements are performed in two directi-ons and the measure...
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