Recursive nearest neighbor search in a sparse and multiscale domain for comparing audio signals

We investigate recursive nearest neighbor search in a sparse domain at the scale of audio signals. Essentially, to approximate the cosine distance between the signals we make pairwise comparisons between the elements of localized sparse models built from large and redundant multiscale dictionaries of time-frequency atoms. Theoretically, error bounds on these approximations provide efficient means for quickly reducing the search space to the nearest neighborhood of a given data; but we demonstrate here that the best bound defined thus far involving a probabilistic assumption does not provide a practical approach for comparing audio signals with respect to this distance measure. Our experiments show, however, that regardless of these non-discriminative bounds, we only need to make a few atom pair comparisons to reveal, e.g., the origin of an excerpted signal, or melodies with similar time-frequency structures.     

基于代理函数优化的稀疏性字典学习

稀疏性字典学习是指对在某个已知的基字典上具有稀疏表示的字典的学习.论文利用块松弛思想,将稀疏性字典学习问题转化为字典和系数的分别优化问题,利用代理函数优化方法分别对固定字典和固定系数情况下的目标函数进行优化处理,得到固定字典情况下的系数更新算法和固定系数情况下的字典更新算法,进而得到稀疏性字典学习算法.理论分析说明了本...     

基于数据挖掘技术的字典生成方法

随着信息技术和数据技术的飞速发展,从大量的数据中获取有用的信息和知识变得越来越重要。聚类分析和关联规则挖掘是数据挖掘中的核心技术。文中基于数据挖掘技术和口令因子的概念,提出一种崭新的用户口令字典生成方法,通过对原始的用户口令进行数据挖掘,分析在用户口令中所隐含的口令因子关联规则,将所获得的口令因子关联规则成功地应用在用户口令字典生成中,并在WinRAR文件破解中取得了良好的效果。     

Single-image super-resolution with joint-optimization of TV regularization and sparse representation

A super-resolution (SR) reconstruction framework is proposed using regularization restoration combined with learning-based resolution enhancement via sparse representation. With the viewpoint of conventional learning methods, the original image can be split into low frequency (LF) and high frequency (HF) components. The reconstruction mainly focuses on the process of HF part, while the LF one is founded simply by typical interpolation function. For the severely blurred single-image, we first use regularization restoration technology to recover it. Then the regularized output remarkably betters the quality of LF used in traditional learning-based methods. Last, image resolution enhancement with characteristic of edge preserving can implement based on the acquired relatively sharp intermediate image and the pre-constructed over-complete dictionary for sparse representation. Specifically, the regularization can favorably weaken the dependence of atoms on the course of degradation. With both techniques, we can noticeably eliminate the blur and the edge artifacts in the enlarged image simultaneously. Various experimental results demonstrate that the proposed approach can produce visually pleasing resolution for severely blurred image.     

结合字典稀疏表示和非局部相似性的自适应压缩成像算法

如何以较少的观测值重构出高质量的图像是压缩成像系统的一个关键问题.本文根据图像块随机投影能量大小分布特点,提出了一种新的自适应采样方式以及针对自适应采样的有效重构算法.重构时利用了图像在字典下的稀疏表示原理和图像的非局部相似性先验知识.为实现图像的稀疏表示,文中构造了由多个方向字典和一个正交DCT字典组成的冗余字典,并用l1范数作为约束条件求解稀疏优化问题.由于充分利用了图像块的局部特性和图像的非局部特性,本文的压缩成像算法在低采样率下能重构出较高质量的图像.     

图像过完备稀疏表示理论及应用综述

超完备字典稀疏表示作为一种有效表示模型,广泛应用于各种信号和图像处理任务中。介绍了稀疏表示的理论框架以及主要研究方向,分别从稀疏表示的可重构性和区分性两方面对其在图像处理及图像分析领域的应用进行综述。     

水声通信数据无损压缩方法的对比研究

为了选择适合水声通信数据无损压缩的算法,对哈夫曼压缩算法和LZ77压缩算法进行了对比研究。通过C语言编程实现两种算法的压缩,并利用水声通信数据获得压缩结果。对两种算法的压缩率和压缩效率对比分析之后,得出结论：对于水声信号,使用哈夫曼算法将获得更好的压缩率和压缩速率。尤其是哈夫曼算法的压缩速率远远优于LZ77算法。     

基于口令认证的RFID系统安全协议及其BAN逻辑分析

本文提出一种新的基于口令认证的RFID系统安全协议.该方法充分利用RFID低等级标签提供的有限资源:访问口令(PW)、标签的标识码(ID)和伪随机函数等建立RFID系统读写器和标签双向认证的安全协议,对该协议抵抗各种攻击的安全性进行理论分析并对该协议的认证功能进行BAN逻辑的形式化分析.结果表明该协议能够有效抵御在线和离线字典攻击、伪装攻击、重放攻击以及流量分析和跟踪攻击,因而解决了RFID系统的安全问题.