Towards a face recognition method based on uncorrelated discriminant sparse preserving projection

Feature extraction has always been an important step in face recognition, the quality of which directly determines recognition result. Based on making full use of advantages of Sparse Preserving Projection (SPP) on feature extraction, the discriminant information was introduced into SPP to arrive at a novel supervised feather extraction method that named Uncorrelated Discriminant SPP (UDSPP) algorithm. The obtained projection with the method by sparse preserving intra-class and maximizing distance inter-class can effectively express discriminant information, while preserving local neighbor relationship. Moreover, statistics uncorrelated constraint was also added to decrease redundancy among feature vectors so as to obtain more information as possible with little vectors as possible. The experimental results show that the recognition rate improved compared with SPP. The method is also superior to recognition methods based on Euclidean distance in processing face database in light.     

A directly addressed monolithic LED array as a projection source

Abstract— Light‐emitting‐diode arrays have been fabricated and demonstrated by several groups in a variety of applications in which structured illumination is desired, such as direct‐view displays and fluorescence detection. State‐of‐the‐art LEDs can achieve a high optical power density, exceeding 40 W/cm². This allows the use of such LEDs, if patterned into an array, as a light and pattern source for projection displays. As a proof of principle, a projection source using a monolithically integrated patterned LED array at a power density of 0.69 W/cm², a source brightness of 21,000,000 cd/m², and a projected brightness of 100,000 cd/m² is reported. It is shown that an improved version of such a display would allow significantly better power consumption and overall system efficiency than permitted by current projection‐display concepts.     

混合MIMO相控阵雷达的交错稀疏阵列设计

针对常规的发射子阵分割会使混合MIMO相控阵雷达的孔径减小和馈电网络复杂度增加的问题,提出一种交错稀疏的发射子阵分割方法,通过寻求最大输出信干噪比的方式获得最佳的阵列结构.首先构建交错稀疏结构下的混合MIMO相控阵雷达模型,运用序列凸近似方法将非凸的目标函数转化为凸函数;然后通过凸优化分别求解出一维和二维混合MIMO相控阵雷达在阵元数目固定和阵元数目作为变量的情况下的最佳阵列结构;最后,通过仿真表明所提出方法不仅可以获得较大的信干噪比和较低的旁瓣电平值,且较常规的子阵分割方式能获得更高的波达方向估计精度.     

稀疏信道下OFDM信号的迭代检测方法

提出了一种适用于稀疏信道环境的OFDM信号迭代检测方法。该方法采用导频进行初始化信道估计,使用数学期望最大（EM）算法迭代检测OFDM信号。该方法需要已知信道的路径时延信息、噪声方差和信道冲击响应的协方差矩阵,因此同时给出了这些参数的估计方法。仿真结果证明了这种迭代算法的有效性,而且使用估计的信道参数时的系统性能和理想参数条件下的性能接近。     

基于非常稀疏随机投影的图像重建方法

将非常稀疏随机投影引入可压缩传感CS（Compressed Sensing）理论,提出一种新的CS测量矩阵：非常稀疏投影矩阵。利用非常稀疏投影分布的渐近正态性,证明了新的矩阵满足CS测量矩阵的必要条件。该矩阵由于其构成的非常稀疏性大大简化了图像重建过程中的投影计算,从而提高重建速度。实验结果表明非常稀疏投影矩阵在满足一定测量数目要求的条件下可以精确重建。最后给出了新的测量矩阵与一般采用的高斯和贝努里测量矩阵的重建结果比较和分析。     

基于稀疏表示的系统辨识方法

基于信号的稀疏表示理论提出一种线性时不变系统辨识方法.该方法利用线性调频信号作为线性时不变系统激励输入信号,在利用传统方法进行系统辨识前利用稀疏分解算法对系统输出信号进行噪声处理.线性调频信号具有较好的时频聚集特性,线性时不变系统的输出也将具有很好的时频特征,利用基于Gabor字典的稀疏分解将能有效地提取输出信号中的有效分量,滤除其中的噪声成分,提高系统辨识的精度.仿真实验表明,本文提出的方法在低信噪比情况下,辨识效果好于传统方法.     

十字形二维稀疏混合MIMO相控阵雷达收发阵列设计

针对二维混合多输入多输出(MIMO)相控阵雷达发射子阵分割带来的自由度损失进而影响雷达系统参数估计性能的问题,提出一种基于十字阵的二维稀疏混合MIMO相控阵雷达收发阵列设计方法.首先,结合稀疏阵列-共轭嵌套阵对混合MIMO相控阵雷达的收发端进行稀疏设计;其次,对混合MIMO相控阵雷达产生的合阵进行做差处理,得到阵元位置差的差异阵列;最后,通过空间平滑处理进行波达方向估计.仿真实验表明,相较于传统的二维混合MIMO相控阵雷达,所提出方法仅利用两个互相垂直的一维线阵便可形成阵列的二维平面扩展,同时,在不增加阵元个数的前提下可有效扩展雷达阵列虚拟阵元数目,提高阵列的自由度以及波达方向估计性能.     

Double articulation analyzer with deep sparse autoencoder for unsupervised word discovery from speech signals

Direct word discovery from audio speech signals is a very difficult and challenging problem for a developmental robot. Human infants are able to discover words directly from speech signals, and, to understand human infants’ developmental capability using a constructive approach, it is very important to build a machine learning system that can acquire knowledge about words and phonemes, i.e. a language model and an acoustic model, autonomously in an unsupervised manner. To achieve this, the nonparametric Bayesian double articulation analyzer (NPB-DAA) with the deep sparse autoencoder (DSAE) is proposed in this paper. The NPB-DAA has been proposed to achieve totally unsupervised direct word discovery from speech signals. However, the performance was still unsatisfactory, although it outperformed pre-existing unsupervised learning methods. In this paper, we integrate the NPB-DAA with the DSAE, which is a neural network model that can be trained in an unsupervised manner, and demonstrate its performance through an experiment about direct word discovery from auditory speech signals. The experiment shows that the combined method, the NPB-DAA with the DSAE, outperforms pre-existing unsupervised learning methods, and shows state-of-the-art performance. It is also shown that the proposed method outperforms several standard speech recognizer-based methods with true word dictionaries.     

An optimal parallel algorithm for sparse matrix bandwidth reduction on a linear array

A simple and efficient algorithm for the bandwidth reduction of sparse symmetric matrices is proposed. It involves column-row permutations and is well-suited to map onto the linear array topology of the SIMD architectures. The efficiency of the algorithm is compared with the other existing algorithms. The interconnectivity and the memory requirement of the linear array are discussed and the complexity of its layout area is derived. The parallel version of the algorithm mapped onto the linear array is then introduced and is explained with the help of an example. The optimality of the parallel algorithm is proved by deriving the time complexities of the algorithm on a single processor and the linear array.     

基于遗传算法的MIMO发射接收阵列联合优化研究

为了对MIMO发射接收阵列进行联合波束优化,提出了一种采用遗传算法对MIMO收发阵列联合优化的方法.分析了MIMO发射接收阵列联合优化的必要性,建立了MIMO稀疏阵列信号模型,给出了MIMO稀疏阵虚拟收发波束形成的方法.提出了基于遗传算法的MIMO阵列优化方法,包括适应度函数设计、编码以及交叉变异方法等,优化目的为保持主瓣宽度不变的情况下,尽可能抑制旁瓣水平.仿真结果表明了该算法的有效性.     

基于稀疏排列的LPP和ELM的人脸识别

在人脸图像识别中人脸图像数据中有很多是稀疏的,对于稀疏数据的降维是流形学习算法面临的一个问题。为了有效地从高维图像数据中提取人脸图像的敏感信息,提高人脸识别的速度,文章提出了一种基于流形学习的有监督稀疏排列的局部保持投影算法(SSLPP)的极端学习机(ELM)。     

稀疏信号欠Nyquist采样与重构研究现状分析

传统的信号采样理论要求采样频率需高于Nyquist频率(信号最高频率)的两倍,给射频及超宽带领域的信号采样、存储和传输造成巨大压力。近年来针对可以进行稀疏表示的信号,欠Nyquist采样技术取得了很大的发展,大大降低了精确重构原始信号所需的采样率。综述性地归纳了目前可以进行欠Nyquist采样的信号模型,将出现的各种欠Nyquist采样总结为一个基本模型,并针对频域稀疏和时域稀疏信号分别对采样模型及原理进行了阐述,分析了采样信号的子空间探测和重建方法,讨论了稀疏信号欠Nyquist采样与重构的实现与应用现状,并对研究前景进行了展望。     

Double-characters detection of nonlinear frequency modulated signals based on FRFT

In many practical applications,signals to be detected are unknown nonlinear frequency modulated (FM)and are corrupted by strong noise.The phase histories of the nonlinear FM signals are assumed to be unknown smooth functions of time,which are usually poorly modeled or cannot be modeled at all by a small number of parameters.Because of the lack of phase model,a nonparametric detection method is proposed based on successive fractional Fourier transform and double-characters detection.The detection process goe...     

基于FRFT的非线性调频信号双特征检测方法

强噪声中的未知非线性调频信号检测一直是实际检测领域中的一个难点问题.非线性调频信号的相位历史是关于时间的未知平滑函数,并且该函数不能或者很难用少量参数来建模.由于相位模型的缺失,我们提出一种基于接连分数阶Fourier变换和双特征检测的非参数化检测方法.检测方案包含3部分:首先,计算一个周期内接连角度的分数阶Fourier变换,将这些不同角度的变换结果构造成二维图像;然后,利用硬阈值处理获得二值图像,并利用多重中值滤波来去除该二值图像中的孤立噪声点,从而获得精炼的二值图像;最后,根据精炼图像提取2个互补的检测统计量,并通过双特征检测器判决目标是否存在.本文针对3类不同阶的多项式相位信号以及正弦相位信号的对比实验结果表明,提出的方法是有效且鲁棒的,并且获得了满意的检测性能.     

Effectiveness of Teager energy operator for epoch detection from speech signals

In this paper, we try to present the problem of epoch detection from a different perspective that not only deals with estimation of epoch instances (i.e., glottal activity) but also with quantification of the absence of epochs (i.e., no glottal activity) in the unvoiced regions of speech signal. Most of the epoch detection methods perform significantly well in the voiced regions of speech but are not robust enough in the unvoiced regions of speech, i.e., they detect a number of pseudo epochs in the unvoiced regions of speech. We propose a simple method based on Teager Energy Operator (TEO) which not only determines the epochs in voiced region (due to its superior temporal resolution and its ability to capture airflow properties through the glottis) but also is very effective in unvoiced region. Recently proposed methods such as 0-Hz resonator-based method and DYPSA method gave a combined rate (CR) (for detecting epochs in voiced and unvoiced regions of speech) of 74.7% and 60%, respectively and a pseudo epoch rate (PER) (i.e., spurious epochs in the unvoiced regions of speech) of 62.9% and 54.04%, respectively. On the other hand, our proposed method gave a CR and PER of 87% and 0.27%, respectively. This result suggests that the proposed method captures glottal activity more efficiently both in voiced and unvoiced regions of speech signal. The performance of the proposed method is demonstrated using publicly available CMU-Arctic database using the epoch information from the electro-glottograph (EGG) as reference signal to serve as ground truth for estimation of glottal closure instants (GCI). Due to the noise suppression capability of TEO, the proposed method has almost no or little effect (i.e., robust) against signal degradations like white, babble, high frequency and vehicle noises as compared to 0-Hz resonator and DYPSA methods.     

基于稀疏表示的联合多通道肌电信号手势识别

为了改善传统肌电信号手势识别过程的复杂性,将稀疏表示用于表面肌电信号手势识别。针对不同的动作模式下对表面肌电信号提取的特征总是有较大的差异,稀疏表示的过程可以将差异放大,从而改善分类效果。本文首先采集不同手势的多通道表面肌电信号,然后将多通道信号进行切割;通过求解测试样本在训练矩阵下的稀释表示,达到分类识别的目的,并通过实验仿真验证了算法的可行性和优越性。     

基于低秩投影中稀疏误差矩阵分析的视觉跟踪

单目标跟踪是计算机视觉的重要组成部分,其鲁棒性一直受到目标遮挡、光照变化、目标尺度变化等因素的制约。针对这个问题,提出了基于低秩投影中稀疏误差矩阵分析的视觉跟踪算法。为了克服模板漂移对跟踪的影响,采用目标模板和候选目标的相似性关系动态选择目标模板的更新方式。在粒子滤波的框架下,利用鲁棒主成分分析和低秩投影原理求得候选目标的稀疏误差矩阵,根据稀疏误差矩阵的边缘信息和平滑度信息实现对下一帧目标的观测似然估计。在多个视频序列上的实验表明,算法具有较好的鲁棒性。     

短波宽带阵列信号源的设计与实现

为了在实验室给宽带阵列信号处理设备的研制、维护提供一个较好的仿真环境,介绍了一种基于多片DSP芯片ADSP-TS201S和数字上变频器AD9857构成的宽带阵列信号源,模拟实现了最大带宽为4MHz的短波宽带阵列信号。主要包括系统设计方案、系统硬件设计和DSP软件设计等。     

Blind estimation of channel parameters and source components for EEG signals: a sparse factorization approach

In this paper, we use a two-stage sparse factorization approach for blindly estimating the channel parameters and then estimating source components for electroencephalogram (EEG) signals. EEG signals are assumed to be linear mixtures of source components, artifacts, etc. Therefore, a raw EEG data matrix can be factored into the product of two matrices, one of which represents the mixing matrix and the other the source component matrix. Furthermore, the components are sparse in the time-frequency domain, i.e., the factorization is a sparse factorization in the time frequency domain. It is a challenging task to estimate the mixing matrix. Our extensive analysis and computational results, which were based on many sets of EEG data, not only provide firm evidences supporting the above assumption, but also prompt us to propose a new algorithm for estimating the mixing matrix. After the mixing matrix is estimated, the source components are estimated in the time frequency domain using a linear programming method. In an example of the potential applications of our approach, we analyzed the EEG data that was obtained from a modified Sternberg memory experiment. Two almost uncorrelated components obtained by applying the sparse factorization method were selected for phase synchronization analysis. Several interesting findings were obtained, especially that memory-related synchronization and desynchronization appear in the alpha band, and that the strength of alpha band synchronization is related to memory performance.     

Distributed detection of sparse signals with censoring sensors in clustered sensor networks

In this paper, we explore the distributed detection of sparse signals in energy-limited clustered sensor networks (CSNs). For this problem, the centralized detector based on locally most powerful test (LMPT) methodology that uses the analog data transmitted by all the sensor nodes in CSNs can be easily realized according to the prior work. However, for the centralized LMPT detector, the energy consumption caused by data transmission is excessively high, which makes its implementation in CSNs with limited energy supply impractical. To address this issue, we propose a new detector by combining the advantages of censoring and LMPT strategies, in which both the cluster head (CLH) nodes and the ordinary (ORD) nodes only send data deemed to be informative enough and the fusion center (FC) fuses the received data based on LMPT methodology. The detection performance of the proposed detector, characterized by Fisher Information, is analyzed in the asymptotic regime. Also, we analytically derive the relationship between the detection performance of the proposed censoring-based LMPT (cens-LMPT) detector and the communication rates, both of which are controlled by the censoring thresholds. We present an illustrative example by considering the detection problem with 2-CSNs, i.e., CSNs in which each cluster contains two nodes, and provide corresponding theoretical analysis and simulation results.