A new adaptive co-site broadband interference cancellation method with auxiliary channel

With the boom of the communication systems on some independent platforms(such as satellites,space stations,airplanes,and vessels),co-site interference is becoming prominent.The adaptive interference cancellation method has been adopted to solve the co-site interference problem.But the broadband interference cancellation performance of traditional Adaptive Co-site Interference Cancellation System(ACICS)with large delay mismatching and antenna sway is relatively poor.This study put forward an Adaptive Co-site Broadband Interference Cancellation System With Two Auxiliary Channels(ACBICS-2A).The system model was established,and the steady state weights and Interference Cancellation Ratio(ICR)were deduced by solving a time-varying differential equation.The relationship of ICR,system gain,modulation factor,interference signal bandwidth and delay mismatching degree was acquired through an in-depth analysis.Compared with traditional adaptive interference cancellation system,the proposed ACBICS-2A can improve broadband interference cancellation ability remarkably with large delay mismatching and antenna sway for the effect of auxiliary channel.The maximum improved ICR is more than 25 dB.Finally,the theoretical and simulation results were verified by experiments.

A robust sparse representation algorithm based on adaptive joint dictionary

Sparse representation based on dictionary construction and learning methods have aroused interests in the field of face recognition.Aiming at the shortcomings of face feature dictionary not‘clean’and noise interference dictionary not‘representative’in sparse representation classification model,a new method named as robust sparse representation is proposed based on adaptive joint dictionary(RSR-AJD).First,a fast lowrank subspace recovery algorithm based on LogDet function(Fast LRSR-LogDet)is proposed for accurate low-rank facial intrinsic dictionary representing the similar structure of human face and low computational complexity.Then,the Iteratively Reweighted Robust Principal Component Analysis(IRRPCA)algorithm is used to get a more precise occlusion dictionary for depicting the possible discontinuous interference information attached to human face such as glasses occlusion or scarf occlusion etc.Finally,the above Fast LRSR-LogDet algorithm and IRRPCA algorithm are adopted to construct the adaptive joint dictionary,which includes the low-rank facial intrinsic dictionary,the occlusion dictionary and the remaining intra-class variant dictionary for robust sparse coding.Experiments conducted on four popular databases(AR,Extended Yale B,LFW,and Pubfig)verify the robustness and effectiveness of the authors’method.

A weighted block cooperative sparse representation algorithm based on visual saliency dictionary

Unconstrained face images are interfered by many factors such as illumination,posture,expression,occlusion,age,accessories and so on,resulting in the randomness of the noise pollution implied in the original samples.In order to improve the sample quality,a weighted block cooperative sparse representation algorithm is proposed based on visual saliency dictionary.First,the algorithm uses the biological visual attention mechanism to quickly and accurately obtain the face salient target and constructs the visual salient dictionary.Then,a block cooperation framework is presented to perform sparse coding for different local structures of human face,and the weighted regular term is introduced in the sparse representation process to enhance the identification of information hidden in the coding coefficients.Finally,by synthesising the sparse representation results of all visual salient block dictionaries,the global coding residual is obtained and the class label is given.The experimental results on four databases,that is,AR,extended Yale B,LFW and PubFig,indicate that the combination of visual saliency dictionary,block cooperative sparse representation and weighted constraint coding can effectively enhance the accuracy of sparse representation of the samples to be tested and improve the performance of unconstrained face recognition.

Image-denoising algorithm based on improved K-singular value decomposition and atom optimization

The traditional K-singular value decomposition(K-SVD)algorithm has poor imagedenoising performance under strong noise.An image-denoising algorithm is proposed based on improved K-SVD and dictionary atom optimization.First,a correlation coefficient-matching criterion is used to obtain a sparser representation of the image dictionary.The dictionary noise atom is detected according to structural complexity and noise intensity and removed to optimize the dictionary.Then,non-local regularity is incorporated into the denoising model to further improve image-denoising performance.Results of the simulated dictionary recovery problem and application on a transmission line dataset show that the proposed algorithm improves the smoothness of homogeneous regions while retaining details such as texture and edge.

卤过氧化物酶在绿色卤化反应中的研究进展

卤化反应是一类极其重要的有机合成反应,在实验室研究和化工生产领域占据重要地位.传统卤化反应因存在使用有毒有害试剂、反应缺乏选择性等问题而亟待改进,生物酶催化策略则为突破上述瓶颈提供了可能.自然界已经进化出多种可对有机物中催化引入卤素的卤化酶.酶催化卤化反应的突出优势在于常温常压下,可使用来源温和的卤素进行高效的催化反应.催化范围包括卤化、羟卤化、卤环合和氧化脱羧等多种具有挑战性的反应.鉴于酶催化卤化反应展示出巨大的潜力,从催化活性、酶稳定性、底物浓度、催化范围等几个方面着重介绍了卤过氧化物酶在绿色卤化反应中的最新研究进展,为进一步开发绿色的卤化酶催化卤化反应提供参考.