基于行列式随机循环的压缩感知测量矩阵研究

压缩感知理论,从信号的自身特性出发,通过变换作用域和线性投影实现对信号的采样和压缩。测量矩阵是该理论中获得最优测量,实现精确重构的关键。本文在介绍常用测量矩阵的基础上,重点研究了结构化测量矩阵。鉴于测量矩阵设计的最重要的原则是降低矩阵元素间的相干性,本文借鉴循环矩阵和广义轮换矩阵的优点,提出了采用均匀随机数对结构化测量矩阵进行随机循环的构造方法。仿真实验表明新矩阵在信号重建上具有更好的性能。     

One bit feedback for quasi-orthogonal space-time block codes based on circulant matrix

During the last few years, a number of Quasi- Orthogonal Space-Time Block Codes (QOSTBC) have been proposed for using in multiple transmit antennas systems. In this letter, based on circulant matrix, we propose a novel method of extending any QOSTBC constructed for 4 transmit antennas to a closed-loop scheme. We show that with the aid of multiplying the entries of QOSTBC code words by the appropriate phase factors which depend on the channel information, the proposed scheme can improve its transmit diversity with one bit feedback. The performances of the proposed scenario extended from Jafarkhani?s QOSTBC as well as its optimal constellation rotated scheme are analyzed. The simulation results suggest that there is a significant Eb/No advantage in the proposed scheme which is able to be designed easily.     

Background subtraction in surface-wave holography

Surface-wave holographic techniques can be used to detect the presence of small defects. The detection limit is usually determined not by signal to noise considerations, but rather by the presence of spurious background signals, such as bulk waves. The letter describes a technique for eliminating this background, and demonstrates the improvement in the detection limit.     

Background subtraction using generalised Gaussian family model

Proposed is a robust method to detect foreground regions from colour video sequences using a generalised Gaussian family model and multiple thresholds. Experiments show that the proposed algorithm works better than conventional approaches in various environments.     

LDPC block and convolutional codes based on circulant matrices

A class of algebraically structured quasi-cyclic (QC) low-density parity-check (LDPC) codes and their convolutional counterparts is presented. The QC codes are described by sparse parity-check matrices comprised of blocks of circulant matrices. The sparse parity-check representation allows for practical graph-based iterative message-passing decoding. Based on the algebraic structure, bounds on the girth and minimum distance of the codes are found, and several possible encoding techniques are described. The performance of the QC LDPC block codes compares favorably with that of randomly constructed LDPC codes for short to moderate block lengths. The performance of the LDPC convolutional codes is superior to that of the QC codes on which they are based; this performance is the limiting performance obtained by increasing the circulant size of the base QC code. Finally, a continuous decoding procedure for the LDPC convolutional codes is described.     

BIBD循环置换矩阵的多进制LDPC码构造

提出了基于均衡不完全区组设计(Balanced Incomplete Block Design,BIBD)的多进制准循环LDPC(Low-Density Parity-Check)码代数构造方法。在该构造方法中提出了广义多进制位置向量的概念,并根据广义多进制位置向量和BIBD法对指数矩阵进行广义二维扩展,构造出具有循环置换子矩阵的多进制校验矩阵,由此得到girth不小于6的多进制LDPC码。仿真结果表明,采用FFT-QSPA(基于快速傅里叶变换的多进制和积算法)对构造出的LDPC码进行译码,在AWGN信道下相比于同参数的RS码来说可以取得明显的编码增益,并且优于多进制Mackay码。     

多次曝光散斑干涉法实现光学图像相减

本文给出了利用重铬酸盐明胶全息干板可以多次曝光记录的特性,实现散斑多次曝光图像相减的方法.这种方法简单方便,且具有高质量的输出结果.实验与理论分析很好符合.     

基于改进谱减法的语音增强

针对传统谱减法具有残余音乐噪声过强,清音部分损失严重的缺点,提出了一种利用语音信号的短时平均幅度差特征(AMDF)并结合短时平均幅度(AM)的语音检测算法,在原始语音估计式中引入了参数α和β,对传统的谱减法进行改进.根据采集的真实带噪语音数据,将传统谱减法和改进的谱减法结果进行了比较分析.在研究基于改进的谱减法的语音增强算法基础上,构建了以TMS320VC5509和TLV320A1C23 Codec为核心器件的实时系统,能够有效地提高语音信噪比,信号的信噪比由12.2 dB提高到了4.0 dB,改善了语音质量.     

基于改进谱减法的语音增强

针对传统谱减法具有残余音乐噪声过强,清音部分损失严重的缺点,提出了一种利用语音信号的短时平均幅度差特征（AMDF）并结合短时平均幅度（AM）的语音检测算法,在原始语音估计式中引入了参数α和β,对传统的谱减法进行改进。根据采集的真实带噪语音数据,将传统谱减法和改进的谱减法结果进行了比较分析。在研究基于改进的谱减法的语音增强算法基础上,构建了以TMS320VC5509和TLV320AIC23 Codec为核心器件的实时系统,能够有效地提高语音信噪比．信号的信噪比由12．2dB提高到了4．0dB．改善了语音质量。     

用干涉滤波技术实现图像相减

本文提出了一种用干涉及滤波技术实现图像相减的简单方法。将两相减图像放在透镜的前面,在平行光照射下,在透镜的后焦面上得到两图像的傅氏谱的干涉,通过适当的滤波,可滤去图像的相同部分,从而在输出得到它们的相减。     

一种基于改进的谱减法的语音增强算法

为改善语音质量,提高语音识别系统的性能,提出了一种新的基于改进的谱减法的语音增强算法。新算法在所有噪声都能转化为加性高斯白噪声的基础上,依据高斯白噪声幅度谱服从瑞利分布的特点,对噪声幅度谱进行精确的估计,并采用频带方差进行端点检测以及时检出语音。仿真分析表明：该方法能够较好地抑制噪声,噪声消除效果较传统算法具有明显的提高。     

Spectral subtraction based on phonetic dependency and maskingeffects

A speech state-dependent spectral subtraction method to regulate the blind spectral subtraction for improved enhancement is proposed. In this method a modified subtraction rule is applied over the speech selectively contingent to the speech state being voiced or unvoiced, in an effort to incorporate the acoustic characteristics of phonemes. The aim is to remedy the subtraction induced signal distortion attained by two state-dependent procedures: spectrum sharpening and minimum spectral bound. In order to remove the residual noise, the proposed method employs a procedure utilising the masking effect. The proposed spectral subtraction, including state-dependent subtraction and residual noise reduction using the masking threshold shows effectiveness in terms of the compensation of spectral distortion in the unvoiced region as well as residual noise reduction     

基于多频带谱减法的抗噪声语音识别研究

为了减少在噪声环境下测试条件与训练条件不匹配导致的语音识别性能下降,提出了一种结合多频带谱减法的抗噪声语音识别系统。首先提取带噪语音的前几帧作为估计的噪声信号,将带噪语音、估计的噪声信号按频率划分M个互不相交的频带,然后根据每个频带内带噪语音与估计的噪声信号的性噪比,来确定该频带噪声的谱减参数。语音增强作为前端处理,与语音识别器级连构成抗噪声语音识别系统。通过实验仿真表明,基于多频带谱减法的抗噪声语音识别系统在不同信噪比不同类型的噪声下,识别性能明显优于基本谱减法。     

基于谱减算法语音增强的研究

谱减法是处理宽带噪声较为传统和有效的方法,它的运算量较小,容易实时实现,增强效果也较好.比较详细地叙述了谱减法的基本原理和降低音乐噪声的方法.     

NEW CONSTRUCTIONS OF LDPC CODES BASED ON CIRCULANT PERMUTATION MATRICES FOR AWGN CHANNEL

Two new design approaches for constructing Low-Density Parity-Check （LDPC） codes are proposed. One is used to design regular Quasi-Cyclic LDPC （QC-LDPC） codes with girth at least 8. The other is used to design irregular LDPC codes. Both of their parity-check matrices are composed of Circulant Permutation Matrices （CPMs）. When iteratively decoded with the Sum-Product Algorithm （SPA）, these proposed codes exhibit good performances over the AWGN channel.     

基于频谱减法的语音去噪算法研究

语音增强技术是音频信号处理中的重要部分,频谱减法是目前在语音增强技术中最常用的方法之一。针对传统频谱减法会产生音乐噪声并无法消除音乐噪声的不足之处及高频噪声干扰比较严重的情况下频谱减法效果差的情况,采用了在频谱减法之后进行LMS滤波以降低音乐噪声对语音质量的影响和低通滤波以滤除脉冲干扰。根据仿真结果表明,改进扩展频谱减法能够有效降低音乐噪声和尖锐的高频兹兹声,从而提高信噪比,达到语音增强的目的。     

图像相减的一种新模型

借用光学滤波系统理论,对对称图像相减的数学模型进行推广,得出了傅里叶变换实现非对称放置两个图像相减的数学表达式,诠释了其物理意义,规范了其适用范围和使用条件。计算机模拟实验验证了结论的可靠性与可行性。该结论对信息处理、图像识别等具有实用价值。     

Electromyography signal segmentation method based on spectral subtraction backtracking

Surface electromyography (EMG) is a bioelectrical signal that recognizes speech contents in a non-acoustic form. Activity detection is an important research direction in EMG research. However, in the low signal-to-noise ratio (SNR) environment, it is difficult for traditional methods to obtain accurate active signals. This paper proposes a new energy-based spectral subtraction backtracking (E-SSB) method to segment EMG active signal in the low SNR environment. Compared with traditional energy detection, the algorithm in this paper adds spectral subtraction (SS) to filter out the clutter, and raises a retrospective idea to improve the classification performance. The experiment results show the proposed activity detection method is more effective than other methods in the low SNR environment.     

Synthesis of multivariate stationary series with prescribed marginal distributions and covariance using circulant matrix embedding

The problem of synthesizing multivariate stationary series Y[n]=(Y₁[n],…,Y_P[n])^T, n∈Z, with prescribed non-Gaussian marginal distributions, and a targeted covariance structure, is addressed. The focus is on constructions based on a memoryless transformation Y_p[n]=f_p(X_p[n]) of a multivariate stationary Gaussian series X[n]=(X₁[n],…,X_P[n])^T. The mapping between the targeted covariance and that of the Gaussian series is expressed via Hermite expansions. The various choices of the transforms f_p for a prescribed marginal distribution are discussed in a comprehensive manner. The interplay between the targeted marginal distributions, the choice of the transforms f_p, and on the resulting reachability of the targeted covariance, is discussed theoretically and illustrated on examples. Also, an original practical procedure warranting positive definiteness for the transformed covariance at the price of approximating the targeted covariance is proposed, based on a simple and natural modification of the popular circulant matrix embedding technique. The applications of the proposed methodology are also discussed in the context of network traffic modeling. Matlab codes implementing the proposed synthesis procedure are publicly available at http://www.hermir.org.     

采用矩阵累乘的自相关矩阵构造

提出了一种适用于低信噪比情况下提取扩频信号特征参数的算法。该算法通过对被测信号的多次采样、分段累乘,扩大了待分解信号的样本数,降低了噪声的影响,从而能够获得比传统子空间分解算法更好的性能。通过分段累乘构造的自相关矩阵,对其进行特征值分解后,表现出对噪声不敏感的特性,在一定程度上克服了常规方法的噪声敏感缺点。对算法的仿真计算表明,该方法应用在低信噪比的通信环境下,信号特征值不会被噪声湮没,解决了传统子空间方法在低信噪比条件下的分辨率不足的问题。该算法的提出对低信噪比条件下的扩频信号处理和参数检测有重要的工程和实际意义。