Frequency-hopping transmitter fingerprint feature recognition with kernel projection and joint representation

Frequency-hopping(FH)is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems because of its inherent capability of low interception,good confidentiality,and strong antiinterference.However,non-cooperation FH transmitter classification is a significant and challenging issue for FH transmitter fingerprint feature recognition,since it not only is sensitive to noise but also has non-linear,non-Gaussian,and non-stability characteristics,which make it difficult to guarantee the classification in the original signal space.Some existing classifiers,such as the sparse representation classifier(SRC),generally use an individual representation rather than all the samples to classify the test data,which over-emphasizes sparsity but ignores the collaborative relationship among the given set of samples.To address these problems,we propose a novel classifier,called the kernel joint representation classifier(KJRC),for FH transmitter fingerprint feature recognition,by integrating kernel projection,collaborative feature representation,and classifier learning into a joint framework.Extensive experiments on real-world FH signals demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art recognition methods.

Non Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion

Medical Image Fusion is the synthesizing technology for fusing multi-modal medical information using mathematical procedures to generate better visual on the image content and high-quality image output.Medical image fusion represents an indispensible role infixing major solutions for the complicated medical predicaments,while the recent research results have an enhanced affinity towards the preservation of medical image details,leaving color distortion and halo artifacts to remain unaddressed.This paper proposes a novel method of fusing Computer Tomography(CT)and Magnetic Resonance Imaging(MRI)using a hybrid model of Non Sub-sampled Contourlet Transform(NSCT)and Joint Sparse Representation(JSR).This model gratifies the need for precise integration of medical images of different modalities,which is an essential requirement in the diagnosing process towards clinical activities and treating the patients accordingly.In the proposed model,the medical image is decomposed using NSCT which is an efficient shift variant decomposition transformation method.JSR is exercised to extricate the common features of the medical image for the fusion process.The performance analysis of the proposed system proves that the proposed image fusion technique for medical image fusion is more efficient,provides better results,and a high level of distinctness by integrating the advantages of complementary images.The comparative analysis proves that the proposed technique exhibits better-quality than the existing medical image fusion practices.

A Depth Video Coding In-Loop Median Filter Based on Joint Weighted Sparse Representation

The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representation-based median filter as the in-loop filter in depth video codec. It constructs depth candidate set which contains relevant neighboring depth pixel based on depth and intensity similarity weighted sparse coding, then the median operation is performed on this set to select a neighboring depth pixel as the result of the filtering. The experimental results indicate that the depth bitrate is reduced by about 9% compared with anchor method. It is confirmed that the proposed method is more effective in reducing the required depth bitrates for a given synthesis quality level.