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Device-free localization (DFL) is expected to detect and locate a person by measuring the changes of received signals in wireless sensor networks without the need of any device. Fingerprint-based DFL in changeable environments has attracted wide attenuation in recent years. However, the accuracy of fingerprint-based localization could be improved further in changing environments. In this paper, we...
Automatic modulation classification (AMC) plays a key role in cognitive radar, cognitive radio and some other civilian and military fields to identify the type of modulation. In this paper, a deep learning based modulation classification method is developed for discriminating digital modulated signals. This proposed method uses a stacked sparse auto-encoders to extract features from ambiguity function...
This paper presents an improved unsupervised feature learning (UFL) pipeline to discover intrinsic structures of local image patches as well as learn good feature representations automatically for image scenes. In our method, the original image patch vectors embedded in the high-dimensional pixel space are first mapped into a low-dimensional intrinsic space by linear manifold techniques, and then...
In order to enhance the employability of college students, this paper analyzes identification competency of college students using the competency model. By contrasting the competency model of IT companies, identification competency of certain college students major in communication are investigated respectively from good thinking, achievement orientation, teamwork, learning ability, tenacity and initiative...
In this paper, we attempt to solve the efficiency problem of PolSAR scene classification with non-parametric classifier. We employ the tree-structure based search strategy to perform fast approximate nearest neighbour search by introducing the multiple randomized kd-tree and hierarchical kmeans-tree into ONBNN classifier. The experimental results on RadarSat-2 PolSAR dataset demonstrate that our method...
In this paper, we present a study of extracting urban areas from Polarimetric Synthetic Aperture Radar (PolSAR) images using only positive samples. We solve this problem by learning a standard binary classifier (urban/non-urban) given an incomplete set of positive samples (urban) and a set of unlabeled samples (some of which are urban and some of which are non-urban) based on the work of Elkan and...
The paper proposes a fast and accurate semantic segmentation approach for a large Polarimetric SAR (PolSAR) image using Conditional Random Fields (CRFs). It efficiently incorporates the polarimetric signatures, texture and intensity features into a unite CRFs model, and employs a fast max-margin training method for parameters learning. Experiments on RadarSat-2 PolSAR data in Flevoland test site demonstrate...
A hierarchical boosting algorithm based on feature selection is proposed for Synthetic Aperture Radar (SAR) image retrieval here. Motivated by Joint Boost and Shared feature frameworks, category combinations are adopted as the training and classification set of a hierarchical boosting-based classification frameworkpsilas middle layer. It has superiorities over the classical method which combines Boosting...
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