Small objects only occupy a few pixels in an image, which results low performance of small object detection for existing algorithms. Therefore, the authors propose dense sampling and detail enhancement network (DSDE-Net) to address this issue. The contains module used increase resolution feature maps expand receptive field, includes atrous spatial pyramid pooling coordinate attention mechanism ...

With the development of digital radio frequency memory technology, main-lobe deception jamming represented by interrupted-sampling repeater (ISRJ) poses a severe challenge to radar. Traditional antijamming methods usually need estimate parameters and have risk losing target information. For above problems, this article proposes deep neural network-based ISRJ recognition detection method which c...

Sampling hidden populations is particularly challenging using standard sampling methods mainly because of the lack of a sampling frame. Respondent-driven sampling (RDS) is an alternative methodology that exploits the social contacts between peers to reach and weight individuals in these hard-to-reach populations. It is a snowball sampling procedure where the weight of the respondents is adjuste...

Abstract Graph sampling methods have been used to reduce the size and complexity of big complex networks for graph mining visualization. However, existing often fail preserve connectivity important structures original graph. This paper introduces a new divide conquer approach spectral based on connectivity, called BC Tree (i.e., decomposition connected into biconnected components) sparsificatio...

Abstract Purpose Network analysis has promised to inform clinical practice about what should be prioritised in treating different psychological disorders. However, the pure phenomenological approach adopted by network did not help advance this goal considerably. We propose a theoretical based on cognitive model of psychopathology. Method used multivariate vector-autoregression analyse networks ...