Abstract-Frequency-tuned saliency detection analyzes image saliency from the perspective of frequency domain and fully combines image segmentation method, which outputs welldefined boundaries of salient objects. However, the method ignores spatial relationships across image parts. This paper proposes an improved saliency detection method on the basis of the frequency-tuned method. In this method, we first segment the input image into regions and then analyze the image from the frequency domain. After that, we preprocess it using Gaussian filter to eliminate noise and coding artifacts. For each region, we can get saliency map in region-level based on region contrast. Finally, salient regions are selected by "winner-take-all" (WTA) neural network and Inhibition of Return(IOR) mechanism. The proposed salient region detection algorithm combines the virtues of frequency-tuning and region contrast. The experimental results show the feasibility and validity of this algorithm.

The symbolic network adds the emotional information of the relationship, that is, the "+" and "-" information of the edge, which greatly enhances the modeling ability and has wide application in many fields. Weak unbalance is an important indicator to measure the network tension. This paper starts from the weak structural equilibrium theorem, and integrates the work of predecessors, and proposes the weak unbalanced algorithm EAWSB based on evolutionary algorithm. Experiments on the large symbolic networks Epinions, Slashdot and WikiElections show the effectiveness and efficiency of the proposed method. In EAWSB, this paper proposes a compression-based indirect representation method, which effectively reduces the size of the genotype space, thus making the algorithm search more complete and easier to get better solutions.Network is a general model of many complex systems. It represents the things in the system with nodes and the relations between things with edges. Starting from the emotional attributes of the side, the network can be divided into symbolic networks [Easley and Kleinberg (2019)] and unsigned networks. It is widely used in politics [Ghosn, Palmer and Bremier (2004)], society [Wasserman and Faust (1994)], biology [Parisien, Anderson and Eliasmith (2008)], e-commerce [Zolfaghar and Aghaie (2010)], cyberspace [Burke and Kraut (2008)], etc. applications. Structural balance theory is the basic theory in symbolic networks. It was first proposed by Heider [Fritz (1946)] from the perspective of social psychology in the 1940s. Cartwright

In synthetic aperture radar (SAR), increasing the pulse width of signal is an effective way to achieve high signal-to-noise ratio (SNR) imaging. However, when the pulse repetition frequency (PRF) is fixed, increasing the pulse width will reduce the maximum unambiguous swath width of SAR. In order to solve this contradiction, a method based on continuous pulse coding (CPC) which increases the average transmit power by multiple pulses with varying high PRF is proposed in this paper. Due to the small interval between pulses, the echo will have range ambiguity and occlusion problems. To obtain the complete echo, we firstly use the form of CPC signal and the swath width of radar to construct a linear-equation-set to model the ambiguous echo of each receiving window. Next, the established linear-equations are split according to the distribution law of receiving window. Finally, the echo energy accumulation of multiple pulses is accomplished by solving the split sub-linear-equations. Therefore, the echo with both swath width corresponding to narrow pulse and high SNR corresponding to wide pulse is obtained to realize high signal-to-noise ratio and wide swath (HSNR-WS) SAR imaging. The experimental results have confirmed the effectiveness of the method proposed in this paper.Index Terms-Continuous pulse coding (CPC); decoding; high signal-to-noise ratio and wide swath (HSNR-WS); synthetic aperture radar (SAR).

We presented 3D fusion technique based on wavelet transform for analyzing 3D dataset of gravity and magnetic inversion intuitively and comprehensively. The technique expands the conventional 2D image fusion technique based on wavelet transform to 3D case, including using 3D wavelet decomposition and reconstruction to replace 2D ones and reforming the fusion rules of high and low frequency components in 3D field. The disciplines of some crucial parameters related to the 3D fusion technique were provided, so that bring some convenient to use this techinique. The synthetic data test showed that the 3D fusion technique is effetive and reliable.

In geophysics exploration, using gradient tensor instead of the full magnetic field gradient has many advantages, which magnetic gradient tensor data to better describe small anomalies. However, the measurement of magnetic gradiometer contains a very complex motion noise, separating the motion noise from the signal component is a large challenge. In this paper, we show the expression for the magnetic gradient tensor, and then through model tests proved the Kalman filter good filtering effect.