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This paper proposed a image fog removal method. The method based on the disinhibition properties of retinal neurons concentric receptive field whose function is three Gaussians. Firstly, we enhance original fog image by using contrast limited global histogram equalization. Secondly, a local image enhancement method is carried out to restore the image's details and deepness information. According to...
The resolution of ultra-wideband inverse synthetic aperture radar (UWB ISAR) imaging cannot be improved directly due to the incongruent point spread function (PSF) for each imaging point. In this paper, we use the correlation function of the real (or imaginary) part of the measured one-dimensional PSF to detect the edge of image blocks reconstructed through the traditional method and to detect the...
A high-efficiency time delay estimation method is presented to obtain the accurate loop delay (including integer and fractional loop delay) between the input signal and the time delayed feedback signal in digital predistortion subsystem within the same process. Compared with the conventional, a sampling position adjusting scheme for complex signals using the piecewise-parabolic filter assisted by...
Association rule is one of the key techniques for data mining and knowledge discovery in databases. Before mining association rules from numerical data, however, the variable domains are required to be partitioned into sections first (i.e. the data should be discretized), which will directly affect the quality of association rules to be generated. But it is infeasible to find the best combination...
Bag-of-features has become very popular in Image classification. Offline codebook learning has to limit the number of training sample concerned with memory, and it influences classification accuracy to some extent. We propose an online sparse learning algorithm, which utilizes the reconstruction error to update the current codebook. It can capture salient properties of images in real-time. Most of...
Intrusion detection systems (IDS) usually trigger a great number of alarm messages that frequently overwhelm their human operators. Hierarchically clustering technique is able to help IDS operators to get meaningful overviews from the great number of alarms. A dilemma is encountered when the clusters are generated. If the clusters are obtained one by one, they cannot be prevented from overlapping...
The k-means clustering problem is a famous problem with a variety of applications. It can be summarized as finding the best k representative centers for an input data set. K-means algorithm and its variations are known to be fast approximation iterative algorithms to the problem. However, several studies have shown that the genetic algorithm (GA) performs more favorably. In this paper, a new crossover...
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