Model-based detector and extraction of weak signal frequencies from chaotic data

Detecting a weak signal from chaotic time series is of general interest in science and engineering. In this work we introduce and investigate a signal detection algorithm for which chaos theory, nonlinear dynamical reconstruction techniques, neural networks, and time-frequency analysis are put together in a synergistic manner. By applying the scheme to numerical simulation and different experimental measurement data sets (Henon map, chaotic circuit, and NH(3) laser data sets), we demonstrate that weak signals hidden beneath the noise floor can be detected by using a model-based detector. Particularly, the signal frequencies can be extracted accurately in the time-frequency space. By comparing the model-based method with the standard denoising wavelet technique as well as supervised principal components analysis detector, we further show that the nonlinear dynamics and neural network-based approach performs better in extracting frequencies of weak signals hidden in chaotic time series.     

A novel local PCA-based method for detecting activation signals in fMRI.

A novel local principal component analysis (LPCA) technique is presented for activation signal detection in functional magnetic resonance imaging (fMRI) without explicit knowledge about the shape of the model activation signal. Unlike the traditional PCA methods, our LPCA algorithm is based on a measure of separation between two clusters formed by the signal segments in active periods and inactive periods, which is computed in an eigen-subspace. In addition, we only applied PCA to the temporal sequence of each individual voxel instead of applying PCA to the fMRI data set. In our algorithm, we first applied a linear regression procedure to alleviate the baseline drift artifact. Then, the baseline-corrected temporal signals were partitioned into active and inactive segments according to the paradigm used for the fMRI data acquisition. Principal components were computed from all these segments for each voxel by PCA. By projecting the segments of each voxel onto a linear subspace formed by the corresponding most dominant principal components, two separate clusters were formed from active and inactive segments. An activation measure was defined based on the degree of separation between these two clusters in the projection space. We show experimental results on the activation signal detection from various sets of fMRI data with different types of stimulation by using the proposed LPCA algorithm and the standard t-test method for comparison. Our experiments indicate that the LPCA algorithm in general provides substantial signal-to-noise ratio improvement over the t-test method.     

Respiratory noise correction using phase information

Respiratory noise is a confounding factor in functional magnetic resonance imaging (MRI) data analysis. A novel method called Respiratory noise Correction using Phase information is proposed to retrospectively correct for the respiratory noise in functional MRI (fMRI) time series. It is demonstrated that the respiratory movement and the phase of functional MRI images are highly correlated in time. The signal fluctuation due to respiratory movements can be effectively estimated from the phase variation and removed from the functional MRI time series using a Wiener filtering technique. In our experiments, this new method is compared with RETROICOR, which requires recording respiration signal simultaneously in an fMRI experiment. The two techniques show comparable performance with respect to the respiratory noise correction for fMRI time series. However, this technique is more advantageous because there is no need for monitoring the subjects’ respiration or changing functional MRI protocols. This technique is also potentially useful for correcting respiratory noise from abnormal breathing or when the respiration is not periodic.     

Principal component analysis for emergent acoustic signal detection with supporting simulation results

A method is introduced that uses principal component analysis (PCA) to detect emergent acoustic signals. Emergent signal detection is frequently used in radar applications to detect signals of interest in background clutter and in cognitive radio to detect the primary user in a frequency band. The method presented differs from other standard techniques in that the detection of the signal of interest is accomplished by detecting a change in the covariance between two channels of data instead of detecting the change in statistics of a single channel of data. For this paper, PCA is able to detect emergent acoustic signals by detecting when there is a change in the eigenvalue subspace of the covariance matrix caused by the addition of the signal of interest. The algorithm's performance is compared to an energy detector and the Neyman-Pearson theorem. Acoustic simulations were used to verify the performance of the algorithm. Simulations were also used to examine the effectiveness of the algorithm under various signal-to-interferer and signal-to-noise ratios, and using various test signals.     

Multichannel wavelength multicasting for two QPSK signals based on FWM in SOA

We experimentally demonstrate multichannel wavelength multicasting for two nonreturn-to-zero quadrature phase-shift keying(NRZ-QPSK) channels based on four-wave mixing(FWM) in semiconductor optical amplifier(SOA). Through the interaction with the two pumps in SOA, the input two 25 Gb/s NRZ-QPSK channels are successfully simultaneously multicast to five and two new wavelengths, respectively. All the multicast channels are with a power penalty less than 2.5 d B at a bit error rate(BER) of 10-3. A characterization of the system performance using conversion efficiency and BER as figures-of-merit in terms of pump and signal powers is also presented. The results indicate that the pump and signal powers can be optimized to eliminate the introduced deleterious nonlinear components. The wavelengths of the two NRZ-QPSK channels and the two pumps need to be specified to avoid the crosstalk induced by high-order FWM.     

A detecting method for line-spectrum target by fusing output DC jump to fluctuations ratio of sub-band spatial spectrum and beam space

The low frequency line components of the radiated noise from an underwater target usually have both high spectrum level and sustained stability.This feature could be used to increase the detection performance of conventional broadband energy integration method.The required spectrum level is theoretically discussed when the detection performance of the known line detection is better than that of broadband energy integration method.Under the condition of the target can be detected in line spectrum band,the relationship between the line spectrum level and signal to noise ratio(SNR)is also discussed.This paper proposes a line spectrum target detection method that a matrix using DC jump to fluctuations ratios of sub-band spatial spectrum and beam space output is constructed.This matrix acts as a filter that the line spectrum target with certain frequency and azimuth is passed at most.By fusing with the other sub band results,the conventional detection performance can be improved.At the same time,the applicable condition and detection performance are analyzed in the paper.The simulation and the sea trial data processing results show that the algorithm can effectively extract weak goal line spectrum target under the condition of multi-interference.The algorithm doesn't need multi-frame statistics and the detection performance is closer to the optimal line spectrum method.     

基于水声信道传播时延排序的分层空时信号检测

基于分层空时编码的多输入多输出技术是一种极具潜力的高速水声通信技术, 但要实现这种潜力需要复杂的空时信号处理方法, 以抵消来自水声信道的多径干扰和异步到达干扰, 以及叠加在接收端的各层信号之间的干扰. 对低复杂度的空时信号处理方案进行了研究, 提出了一种基于子信道传播时延排序的有序连续干扰抵消信号检测算法, 利用子信道间的传播时延差, 实现可使差错概率最小的最佳检测排序; 给出了利用信道估计, 以极低的计算量确定排序的方法, 从而可以大幅降低信号检测的计算复杂度. 采用低复杂度的单载波频域均衡来抵消水声信道中的码间干扰和异步到达干扰. 仿真结果表明, 基于时延排序的信号处理算法可以获得检测性能的改善, 而且性能增益在高数据率时更加显著. 研究结果表明, 采用有效的信号处理方法可使水声信道中造成信号检测干扰的传播时延成为改善系统性能的有利因素.     

Infrared small target enhancement via phase spectrum of Quaternion Fourier Transform

Small target enhancement is one of the crucial stages in infrared small target detection. In this paper, we propose a new method using phase spectrum of Quaternion Fourier Transform to enhance small targets while suppressing backgrounds for infrared images. This is inspired by the property that regularly Gaussian-like shape small targets could be considered as attractively salient signal in infrared images and the location information of such signal is implicitly contained in the phase spectrum from frequency domain. Formally, in the proposed method, we adopt the phase spectrum of Quaternion Fourier Transform instead of using traditional Fourier Transform to enhance the targets since the quaternion provides at most four data channels than only one for the latter, which could be helpful to broad types of background clutters by adding more information. For the construction of the quaternion, we present a second-order directional derivative filter via facet model to compute four second order directional derivative maps from four directions respectively as the four data channels. This filter is used to suppress noises and distinguish the targets and backgrounds into separably different textures so that it would boost the robustness of small target enhancement. In experiments, some typical infrared images with various scenes are tested to validate the effectiveness of the proposed method. The results demonstrate that our method actually has good performance and outperforms several state-of-the-art methods, which can be further used for infrared small target detection and tracking.     

多基地空时码探测信号设计及时反相关检测技术

为实现浅海复杂环境下的多基地声纳多源目标回波分辨,本文设计了一种适用于多入多出垂直阵信道环境下的空时码探测信号,并针对倾斜垂直阵的多途子信道差异问题,提出了信号的时反相关检测技术.空时码探测信号采用伪随机信号调制,具有良好的正交性,能在抗子信道严重衰落的同时,分辨多源目标回波.垂直阵受水流冲击,呈倾斜状态时,其多途子信道不一致性会导致各子信道传递信号无法在接收端聚焦,使阵列增益受损,同时导致时延测量能力下降和信号判决错误率上升,为此本文设计了信道训练信号用以估计多途子信道环境,通过虚拟时间反转镜获得子信道不一致条件下的最佳匹配检测信号,实现对接收信号的时反相关检测.仿真结果表明,本文所设计的探测信号和检测方法,能够克服复杂的信道条件和多途子信道不一致性引起的检测问题,满足多基地声纳探测需求,实现多源目标回波分辨.     

变分模态分量降噪后重构的水声弱信号检测

针对能量检测法在低信噪比下对非合作水声探测信号的检测性能显著下降的问题,提出了一种组合变分模态分解和小波变换降噪重构的信号检测方法。以信号分解出的各个本征模态函数的近似熵与互相关系数比值作为分量分类参数,将所得分量分为信号分量、含噪信号分量与噪声分量,然后利用第二代小波变换对含噪信号分量降噪后与信号分量组成重构信号,最后对重构信号进行检测。数值仿真结果表明该方法可以在无先验信息的情况下对CW和LFM信号自适应降噪,信噪比0 dB以下时CW信号重构后信噪比提升约12 dB,宽带LFM信号信噪比提升约8～9 dB,有效提升了低虚警概率下信号的检测概率。湖试结果表明,虚警概率为0.1时检测概率可提升至0.9以上,验证了该方法的有效性。     

基于最优滤波器的强混沌背景中谐波信号检测方法研究

强混沌背景中的微弱谐波信号检测有重要的工程研究意义. 目前的检测方法主要是基于Takens理论的混沌相空间重构方法, 然而这些方法往往对信干噪比要求高, 且对高斯白噪声敏感等. 本文注意到混沌信号的二阶统计特性是不变的, 根据这个特点提出了一种基于最优滤波器的强混沌背景中的微弱谐波信号检测方法. 该方法首先构建一个数据矩阵, 在频域上对每个频率通道分别检测谐波信号, 从而将信号检测问题转化为最优化问题, 然后利用最优化理论设计滤波器, 使待检测频率通道的信号增益保持不变, 而尽量抑制其他频率通道的信号, 最后通过判断每一频率通道的输出信干噪比来检测谐波信号. 与传统方法相比, 本文方法有如下优点: 1)可以检测更低信干噪比下的微弱谐波信号; 2)可检测的信号幅度范围更大; 3)抗白噪声性能更强. 仿真结果证明了本文方法的有效性.     

一种公共网络攻击数据挖掘智能算法研究

公共网络的开放性和自组织特性导致网络容易受到病毒干扰和入侵攻击,对攻击数据的准确高效挖掘能确保网络安全。传统方法采用时频指向性波束特征聚类方法实现攻击数据挖掘,在信噪比较低时攻击数据准确挖掘概率较低。提出一种基于自适应滤波检测和时频特征提取的公共网络攻击数据挖掘智能算法。首先进行公共网络攻击数据的信号拟合和时间序列分析,对含噪的攻击数据拟合信号进行自适应滤波检测,提高信号纯度,对滤波输出数据进行时频特征提取,实现攻击数据的准确挖掘。仿真结果表明,采用该算法进行网络攻击数据挖掘,对攻击数据特征的准确检测性能较高,对干扰的抑制性能较强,能有效实现网络安全防御。     

Full color ghost imaging by using both time and code division multiplexing technologies

We propose a new full color ghost imaging scheme using both time and code division multiplexing technologies. In the scheme, the speckle patterns of three colors (red, green and blue) are modulated with different time slots and codes. The light intensity is sampled by one bucket detector. Then based on the modulated time slots and codes, we can effectively and simultaneously extract three detection component signals corresponding to three color components of objects from the sampling signal of the bucket detector. Finally, three component images resulting from the three component detection signals can be synthesized into a full color image. The experimental results verify the feasibility of our scheme under the limit of the number of time slots and codes. Moreover, our scheme reduces the number of bucket detectors and can realize high quality imaging even in a noisy environment.     

Multisubject activation detection in fMRI by testing correlation of data with a signal subspace

In this article, a generalized likelihood ratio test is proposed to assess the correlation between multisubject functional MRI (fMRI) time series and bases of a signal subspace for detecting the existence of group activation in each voxel of the brain. The signal subspace is generated by a design matrix using the time series of the desired effects. The proposed method leads to testing the product of eigenvalues of a specific matrix. The eigenvector corresponding to the largest eigenvalue is the weighting vector for the linear combination of time series of various subjects that has the maximum correlation with the signal subspace. In another method, namely, canonical correlation analysis, the largest eigenvalue of the above matrix is tested for activation detection. Surrogate data on resting state (no activation) are generated by randomization and used to estimate the statistical distribution of these parameters under the null hypothesis condition. A postprocessing step is applied to prevent false detection of voxels that are not sufficiently active (among subjects) by defining a minimum ratio for the active population. The proposed methods are applied on simulated and experimental fMRI data, and the results are compared with those of the general linear model (GLM; using the SPM and FMRISTAT toolboxes). The proposed methods showed higher detection sensitivity as compared with the GLM for activation detection in simulated data. Similarly, they detected more activated regions than did the GLM from multisubject experimental fMRI data on a visual (sensorimotor) event-related task.     

基于主成分分析法的高性能波长计研究与实现

波长探测作为科学技术中的一个基本工具,在分析化学、生物传感和光学通信等众多领域具有重要作用。从多模光纤信号传输理论出发,在绝热、准直模型中建立基于模式干涉效应的强度干涉图像理论;实验测量时,在光纤尾端引入缓变（斜率约为0.01）锥形区域设计,保证收集到侧面辐射信号的同时,也近似满足理论模型和数值仿真结果。在搭建的显微共焦成像系统中,连续扫描窄带激光器进行干涉图像存储,经过区域选取、向量拼接以及奇异值分解等步骤得到由器件特性决定的校样矩阵。波长的探测过程共分为两个步骤：在工作带宽内纳米量级粗略扫描光波长得到粗略校准矩阵,一维待测信号强度图像与之进行内积相关性运算后,选取数值最高的波长值作为预估波长单元;在此基础上精细扫描得到精细校样矩阵,选取三个最大主成分并定义与波长欧氏距离最小值确定最终探测波长。采用内积相关性运算联合主成分分析法不仅可以将波长探测分辨率提高到20 pm、准确率达到96.7%,探测效率较其他光谱重建算法提高50倍。实验证实该波长计工作范围至少为400～700 nm,器件尺寸仅为π×(20 μm)2×0.5 mm。该器件在高性能、便携式和低成本方面较同类器件有较大提升,在光谱重建效率也集成了高效算法,能够广泛应用于光纤传输系统的波长实时探测。     

一种基于非完整二维相空间分量置换的混沌检测方法

由于混沌时间序列和随机过程具有很多类似的性质, 因而在实际中很难将两者区分开来. 混沌信号检测与识别是混沌时间序列分析中一个重要的课题. 混沌信号是由确定性的混沌映射或混沌系统产生的, 相比于高斯白噪声序列, 其在非完整的二维相空间中表现出更加丰富的结构特性. 本文通过研究混沌时间序列和高斯白噪声序列在非完整二维相空间中的分布特性, 利用混沌信号的非线性动力学特性, 提出了一种基于非完整二维相空间分量置换的混沌信号检测方法. 该方法首先由接收序列得到非完整的二维相空间, 基于第一维分量大小关系实现对第二维分量的置换与分组, 进一步求得F检验统计量. 然后利用混沌系统的局部特性, 获取非完整二维相空间的动力学结构信息, 实现对混沌序列的有效检测. 在高斯白噪声条件下对多种混沌信号进行了信号检测的数值仿真. 仿真结果表明: 相比置换熵检测, 本文所提算法所需数据量小、计算简单以及具有更低的时间复杂度, 同时对噪声具有更好的鲁棒性.     

Dynamic photoelastic analysis by optical heterodyne polarimetry

This paper describes the mapping of the spatiotemporal principal stress distribution evolved with time in an epoxy photoelastic sample. In the optical heterodyne polarimeter exploited, the signal beam of light transmitted by the sample under continuously loaded condition is photomixed with the local oscillator beam of light made up of orthogonal linearly polarized two-frequency components. Every pixel of a MOS video camera used generates a beat photocurrent that possesses the two orthogonal field components of the elliptically polarized signal beam. The spatiotemporal principal stress distributions can be uniquely determined simultaneously and independently from these two orthogonal field components, and are successfully mapped in a time-sequential form. The spatial and temporal resolutions in the maps are 0.18 mm and 2.9 ms, respectively.     

Communication techniques and coding for atmospheric turbulence channels

In free-space optical communication links, atmospheric turbulence causes fluctuations in both the intensity and the phase of the received light signal, impairing link performance. In this paper, we describe several communication techniques to mitigate turbulence-induced intensity fluctuations, i.e., signal fading. These techniques are applicable in the regime in which the receiver aperture is smaller than the correlation length of the fading, and the observation interval is shorter than the correlation time of the fading. We assume that the receiver has no knowledge of the instantaneous fading state. The techniques we consider are based on the statistical properties of fading, as functions of both temporal and spatial coordinates. Our approaches can be divided into two categories: temporal domain techniques and spatial domain techniques. In the spatial domain techniques, one must employ at least two receivers to collect the signal light at different positions or from different spatial angles. Spatial diversity reception with multiple receivers can be used to overcome turbulence-induced fading. When it is not possible to place the receivers sufficiently far apart, the fading at different receivers is correlated, reducing the diversity gain. We describe a ML detection technique to reduce the diversity gain penalty caused by such fading correlation. In the temporal domain techniques, one employs a single receiver. When the receiver knows only the marginal statistics of the fading, a symbol-by-symbol ML detector can be used to optimize performance. When the receiver also knows the temporal correlation of the fading, maximum-likelihood sequence detection (MLSD) can be employed, yielding a further performance improvement, but at the cost of very high complexity. We describe two reduced-complexity implementations of the MLSD, which make use of a single-step Markov chain model for the fading correlation in conjunction with per-survivor processing. Next, we also investigate the performance of using error-control coding and pilot symbol-assisted detection schemes through atmospheric turbulence channels.     

基于FastICA的高光谱图像目标分割

针对高光谱图像目标识别与分类的应用背景,提出了一种基于快速独立成分分析的高光谱图像目标分割算法.通过引入虚拟维数对图像中的目标端元数量进行估计,利用基于非监督正交子空间投影的异常端元提取算法自动获取目标端元光谱,并将其作为快速独立成分分析的初始混合矩阵.采用最小噪声分量变换对原始数据进行降维,利用快速独立成分分析从降维后的主成分中依次提取出图像中的独立分量.最后,对各独立分量进行恒虚警率检测与形态学滤波,从而得到最终的目标分割结果.对AVIRIS型高光谱图像的实验结果表明,该方法可有效探测出图像中的目标,并可获得较好的分割结果.     

高光谱图像中基于端元提取的小目标检测算法

针对高光谱图像中小目标检测问题,提出了一种基于端元提取的目标检测算法。该算法利用主成分分析的变换矩阵来构造投影算子,把原始图像投影到该算子构成的正交子空间后,大概率的背景信息得到抑制,从而突出了小概率的目标;在完成背景信息抑制的基础上,利用迭代误差分析方法进行端元的自动提取;根据所提取出的目标端元的光谱,结合光谱角度匹配技术完成目标物的检测。为了验证新方法的有效性,利用高光谱数据进行了实验研究,并与经典的RX算法的检测结果相比较。实验结果表明提出的基于端元提取的算法不需要目标的任何先验知识就能达到比较好的目标探测效果,对RX算法检测效果不太理想的小目标也能准确识别。