Based on the fractional discrete cosine transform(DCT) via polynomial interpolation(PI-Fr DCT) and the dependent scrambling and diffusion(DSD), an image encryption algorithm is proposed. Under certain conditions, the introduction of PI-Fr DCT reduces computational complexity compared with fractional DCT(Fr DCT). By using a sigmoid function, the encrypted results are limited within a range from 0 to 255. The real-valued output of PI-Fr DCT is beneficial to the storage, display and transmission of the cipher-text. During the stage of confusion and diffusion, the values of all PI-Fr DCT coefficients change simultaneously as their locations are replaced. DSD enhances the scrambling and diffusion level of encrypted images and provides nonlinearity to the encryption system. Simulation results demonstrate that the proposed encryption algorithm is feasible, effective and secure.
锁相是指系统的响应与周期性刺激的特定相位同步的物理现象.听觉神经的锁相对揭示人的听觉认知基本的神经机理及改善听觉感知有重要意义.然而,现有研究主要集中于心理物理方法和幅度谱分析,不能有效区分包络响应和时域细节结构响应,不能直观反映神经锁相.本文主要利用拔靴法和离散傅里叶变换,提出了基于样本熵的时域细节结构频率跟随响应(temporal-fine-structure-related frequency following response,FFR_T)的神经锁相值(phase locking value,PLV)计算方法,用于分析神经物理实验数据.两个脑电实验结果表明:FFR_T的PLV样本熵显著大于包络相关频率跟随响应(envelope-related frequency following response,FFR_E)的PLV,且二者正交独立,新方法能有效地分别反映听觉系统对包络和时间细节结构的锁相机理;基频处的响应主要来源于FFR_E的锁相;基频处,不可分辨谐波成分包络的锁相能力优于对可分辨谐波;基频缺失时,畸变产物是不同的听觉神经通路的FFR_E的混合;谐波处,FFR_E集中于低频,FFR_T则集中于中、高频;听觉神经元锁相能力与声源的频率可分辨性相关.FFR_T的PLV方法克服了现有FFR分析的局限性,可用于深入研究听觉神经机理.
The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) was proposed. The graph regularized sparse coding showed the potential in maintaining the geometrical information of the data. In this study, it was incorporated with two-level Bregman iterative procedure that updated the data term in outer-level and learned dictionary in innerlevel. Moreover,the graph regularized sparse coding and simple dictionary updating stages derived by the inner minimization made the proposed algorithm converge in few iterations, meanwhile achieving superior reconstruction performance. Extensive experimental results have demonstrated GSCMRI can consistently recover both real-valued MR images and complex-valued MR data efficiently,and outperform the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.
