Frequency invariant beamforming via jointly optimizing spatial and frequency responses

An approach to designing broadband frequency invariant beamformer based on finite impulse response (FIR) filters via jointly optimizing the spatial and frequency responses is proposed. The beam responses are jointly optimized to satisfy both spatial and frequency domain specifications by designing a bank of FIR filters corresponding to the input channels. It minimizes the maximum error between the designed beam pattern and the desired one in the mainlobe area over the working frequency band, and guarantees the sidelobes in the passband and the beam magnitude responses in the stopband to be below some given threshold values. White noise gain constraint is used to improve the robustness of the beamformer against random errors. The beam patterns are expressed as a linear function of FIR filter impulse responses, and the design problem is formulated as the second-order cone programming (SOCP), which can be solved efficiently via the well-established interior point methods. Results of computer simulation and lake-experiment for a twelve-element semicircular array demonstrate superior performance of this approach in comparison to the existing approaches.     

Ability to detect weak effective seismic signals by utilizing chaotic vibrator system

Taking the advantage of CVS (Chaotic Vibrator System) sensitivity of large-scale periodic phase-state response to quasi-periodic or periodic signals,a series of numerical experiments were made to understand the ability of CVS to detect weak effective seismic signals in the common-shot seismic record distorted by strong stochastic noise. The re-sults demonstrate that the large-scale periodic phase-states of CVS are correlated with the signal composition of the quasi-periodic wavelet sequence constructing from horizontal moveout of seismic events,noise strength and the noise distortion de-gree to signal. For the same kind of events,the higher the noise distortion degree is,the lower the detectable SNR can be reached by CVS. For seismic data with the same noise distortion degree,the closer the scanning seismic velocity (the trial moveout ve-locity) approaches to the accurate velocity,the higher the detectable SNR can be reached by CVS. More-over,the truncating scanning velocities form an asymmetric belt,which indirectly makes CVS achieve a large-scale periodic phase-state and then the ratio of wavelet distortion coefficients in events can be a biggish variable scope.     

Method for signal decomposition and denoising based on nonuniform cosine-modulated filter banks

In this paper, a novel method for signal decomposition and denoising is proposed based on a nonuniform filter bank （NUFB）, which is derived from a uniform filter bank. With this method, the signal is firstly decomposed into M subbands using a uniform filter bank. Then according to their energy distribution, the corresponding consecutive filters are merged to compose the nonuniform filters. With the resulting NUFB, the signal can be readily matched and flexibly decomposed according to its power spectrum distribution. As another advantage, this method can be used to detect and remove the narrow-band noise from the corrupted signal. To verify the proposed method, a simulation of extracting the main information of an audio signal and removing its glitch is given.     

Matched field noise suppression： Principle with application to towed hydrophone line array

Discrete noise source suppression in underwater acoustic channel has attracted great attention in recent years. The paper proposes a new principle for dealing with the problem. This new principle is called matched field noise suppression (MFNS). Based on a previous work of the authors group, a full understanding about how a discrete noise source shows effects on the performance of a towed hydrophone line array has been obtained. In light of that finding,MFNS is proposed, which explores and utilizes the characteristics of the noise transmission channel to achieve much greater suppression of the noise in comparison with existing approaches. MFNS combines the concept of matched field processing (MFP) and optimal sensor array processing (OSAP) together to suppress the discrete noise source and to maintain an optimal beam for receiving far-field wanted plane wave signals. A MFNS beam-former is deduced in constraint with signal plane-wave response being unit and noise matched field response being zero. A dosed-form solution of the weight vector for the beam-former is given. Computer simulation results agree well to the theoretical analysis.     

Enhancing the quality metric of protein microarray image

The novel method of improving the quality metric of protein microarray image presented in this paper reduces impulse noise by using an adaptive median filter that employs the switching scheme based on local statistics characters; and achieves the impulse detection by using the difference between the standard deviation of the pixels within the filter window and the current pixel of concern. It also uses a top-hat filter to correct the background variation. In order to decrease time consumption, the top-hat filter core is cross structure. The experimental results showed that, for a protein microarray image contaminated by impulse noise and with slow background variation, the new method can significantly increase the signal-to-noise ratio, correct the trends in the background, and enhance the flatness of the background and the consistency of the signal intensity.     

Research on variable block size motion estimation algorithm for airborne image

A fast motion estimation algorithm for variable block-size using the ＂line scan and block merge procedure＂ is proposed for airborne image compression modules.Full hardware implementation via FPGA is discussed in detail.The proposed pipelined architecture based on the line scan algorithm is capable of calculating the required 41 motion vectors of various size blocks supported by H.264 within a 16 × 16 block in parallel.An adaptive rate distortion cost function is used for various size block decision.The motion vectors of adjacent small blocks are merged to predict the motion vectors of larger blocks for reducing computation.Experimental results show that our proposed method has lower computational complexity than full search algorithm with slight quality decrease and little bit rate increase.Due to the high real-time processing speed it can be easily realized in hardware.     

Approximately optimal tracking control for discrete time-delay systems with disturbances

Optimal tracking control （OTC） for discrete time-delay systems affected by persistent disturbances with quadratic performance index is considered. By introducing a sensitivity parameter, the original OTC problem is transformed into a series of two-point boundary value （TPBV） problems without time-advance or time-delay terms. The obtained OTC law consists of analytic feedforward and feedback terms and a compensation term which is the sum of an infinite series of adjoint vectors. The analytic feedforward and feedback terms can be found by solving a Riccati matrix equation and two Stein matrix equations. The compensation term can be obtained by using an iteration formula of the adjoint vectors. Observers are constructed to make the approximate OTC law physically realizable. A simulation example shows that the approximate approach is effective in tracking the reference input and robust with respect to exogenous persistent disturbances.     

An improved method to detect remote sensing image targets captured by sensor network

In order to detect targets from the hyper-spectral images captured by unmanned aerial vehicles, the images are moved into a new characteristic space with greater divisibility by making use of the manifold learning theory. On this basis, a furation impulse response (FIR) filter is developed. The output energy can be minimized after images passing through a FIR filter. The target pixel and the background pixel are distinguished according to the restrained conditions. This method can effectively suppress noises and detect sub-pixel targets in the hyper-spectral remote sensing image of unknown background spectrum.     

A new method for compression-rebuilding of IR spectra

This work presents a new spectral data compression-rebuilding technique to translate the full IR spectral data into compact codes based on the analysis and comprehension encoding approach. This method has been successfully applied to a sample set of 505 IR spectra randomly picked from 100 000 spectra. The results show that the compression ratio reaches 12.7:1 under a very weak curve distortion. The choice of the number and shape of the basis functions is flexible. The IR spectra can be compressed in a fixed data size in fulfilling the distortion criteria. The data after compression have no significance in the sense of 蜶 spectra. To recover the original spectra, a specific algorithm must be applied. So the method can be used as a cryptic tool. Furthermore, the method can be applied to the compression of other complex curve by utilizing some of proper basis functions.     

Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China

Observations of atmospheric carbon dioxide (CO2 ) from satellites offer new data sources to understand global carbon cycling. The correlation structure of satellite-observed CO2 can be analyzed and modeled by geostatistical methods, and CO2 values at unsampled locations can be predicted with a correlation model. Conventional geostatistical analysis only investigates the spatial correlation of CO2 , and does not consider temporal variation in the satellite-observed CO2 data. In this paper, a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO2 in China. The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component. The variogram result indicates a significant degree of temporal correlation within satellite-observed CO2 data sets in China. Prediction of monthly CO2 using the spatiotemporal variogram model and spacetime kriging procedure is implemented. The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique. The spatiotemporal approach gives better results, with higher correlation coefficient (r2 ), and less mean absolute prediction error and root mean square error. Moreover, the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO2 than those from the spatial-only approach.     

均匀磁场激励磁感应成像滤波反投影算法

通过亥姆霍兹线圈在中心区域建立正弦均匀的激励磁场,磁场方向近似直线,有助于简化逆问题的复杂度.建立了8通道磁感应测量系统仿真模型,采用滤波反投影算法重构出电导率分布.在滤波反投影算法中,首先用线性插值补充检测数据,然后用Hamming滤波器对数据进行滤波,同时加入窗口滤波器减少了涡流磁场发散对周围线圈的影响.加入不同噪声比的噪声干扰,测试了算法对噪声的抑制能力,实验结果表明该模型下运用反投影滤波算法可以重构出电导率的分布.     

彩色视频图像卡通风格化研究与实现

提出一种彩色视频图像卡通风格化的新方法,将均值漂移滤波与高斯图像金字塔结合起来使用,在对彩色视频图像进行颜色聚类和噪声消除的同时,提高了处理的速度.运用DoG算子对滤波后的图像进行边缘检测;再对移滤波后的图像进行色彩量化;最后将量化后的图像与边缘图像进行融合.融合时根据边缘处梯度的大小设置边缘颜色的深度,使得最终得到的卡通化图像不仅在高对比度区域得到加强而且减少了噪声,边缘更加自然.实验结果表明:本算法与传统的双边滤波和DoG算子相比,获得了更好的卡通风格化效果和更快的运行速度,并且可以直接将输入的视频文件转换为卡通化的视频文件输出.     

DT-MRI张量值图像的一种加权方向距离滤波方法

为了正则化扩散张量磁共振成像所产生的扩散张量场,提出了一种非线性张量值加权方向距离滤波方法.首先通过张量间的二次点积定义了张量值图像的方向滤波,然后结合张量中值滤波引入了一个联合最小化准则,在此准则的基础上给出张量值方向距离滤波器的一般结构,并使用权重矢量在距离和方向域上控制滤波器输出,获得更加稳健的处理结果.在合成二阶张量场和真实扩散张量数据集上进行了实验.客观结果比较和滤波图像的主观评估表明:该滤波方法可在保持张量场细节不变的同时有效地消除噪声,是磁共振扩散张量场的一种有价值的滤波工具.     

一种抑制语音噪声的最佳约束线性估计器

本文提出一种最佳约束线性估计器，用于抑制语音信号不 相关加性噪声。该估计器是在给定信号失真度下，使噪声能量最小为准则来设计对真实语音信号的约束线性估计。文中证明这种最佳的约束线性估计器是一种输入噪声电平地的Ｗｉｅｎｅｒ滤波器。     

一种基于Rank变换的改进中值滤波

针对传统中值滤波对孤立噪声点和连续噪声不能有效滤波等问题,提出了一种改进算法：首先通过Rank变换,找出图像中所有的孤立噪声点,然后遍历图像对孤立的噪声点采用中值滤波,最大限度保持图像细节,有效地解决了在抑制图像噪声和保护图像细节方面的矛盾,对图像中孤立噪声,蕞唷较好的滤波效果;随后,进一步针对少量连续噪声点的情况,采用迭代算法对上述改进中值滤波算法结果进行处理,来解决连续噪声的滤波问题．试验结果表明,本文算法滤波后的图像效果明显好于传统滤波方法,能够有效地去噪,并能较好地保持图像细节和边缘．     

多结构元素多尺度的数学形态学图像边缘检测算法

边缘检测是图象处理与模式识别的一个重要图象预处理过程。传统的边缘提取方法如Sobel,Prewitt和Canny等非常有效但对噪声非常敏感。形态学边缘检测目前已成一个研究热点,但大多算法采用单一结构元素,很难对复杂边界进行有效的处理。因此我们提出一种基于多结构元素多尺度的数学形态学边缘检测算法,先用多尺度结构元素交替顺序形态开-闭平滑图象以去噪,再用多结构元素对不同方向的边缘进行提取,最后把各方向边缘融合得到图象边缘。实验结果表明,提出的算法不仅有很强的抗噪性,而且很有效的提取图象的边缘。     

基于运动矢量的视频去抖动算法

采用新的几何分析方法建立了视频去抖动处理的数学模型,提出一种基于运动矢量的视频去抖动算法.采用新的快速M鲁棒估计法获得摄像机全局运动参数集;滤波该参数集滤除随机抖动带来的运动噪声.为了提高算法的可靠性,在全局运动估计之前对原始运动矢量进行了时空滤波;在运动校正阶段,引入了"重同步"机制防止差错累积.实验表明,该算法具有良好的去抖动效果,能够用于实时处理.     

一种新的脉冲噪声图像恢复方法

为消除图像中的脉冲噪声, 提出一种窗口自适应开关中值滤波方法. 利用BP神经网络将图像中的每个像素点分类为信号点或噪声点, 再采用改进的中值滤波器对检测后的图像进行滤波处理. 根据噪声检测结果, 滤波器自适应调整窗口大小并选择性取样, 逐点滤波消除图像中的噪声. 该方法在抑制脉冲噪声、 保护图像细节方面均优于以往基于中值滤波的法, 即使在图像遭受70%噪声污染的极端情况下, 仍能得 到很好恢复.     

基于复合稀疏去噪的沙漠地震噪声消减

沙漠地震噪声既含有低频噪声又含有高斯白噪声,一般方法很难同时去除这两种噪声,针对此问题提出了使用复合稀疏去噪同时压制沙漠地震噪声中的低频噪声和高斯白噪声的方法。在复合稀疏去噪中将平方损失作为损失函数的保真度约束,将信号和信号一阶导数的 L1 范数作为损失函数的稀疏约束,然后通过最小化损失函数去除信号中的高斯白噪声。为同时去除低频噪声,复合稀疏去噪中联合使用了低通滤波器,即可一次性去除低频噪声和高斯白噪声。分别对模拟地震数据和实际地震数据进行仿真实验,实验表明该方法可有效地同时压制沙漠地震数据中的低频噪声和高斯白噪声,复合稀疏去噪的保幅略好于带通滤波器,去噪后带通滤波器会产生较严重失真,而复合稀疏去噪的失真较小。     

基于四元数EKF算法的小型无人机姿态估计

针对小型无人机设计的姿态测量系统,提出一种用于小型无人机姿态估计的四元数扩展Kalman滤波算法.该算法通过建立四元数姿态运动模型和航姿传感器测量模型,构建了以四元数和陀螺仪随机漂移为状态向量、以加速度计测量值和磁阻仪解算的航向角为观测向量的扩展Kalman滤波器,并设计了自适应测量噪声协方差矩阵修正法.实验结果表明,该算法不但解决了微机电系统惯性器件用于载体姿态测量时精度低、易发散、易被干扰的问题,而且显著减小了陀螺仪随机漂移对姿态估计的影响,有效提高了姿态估计的精度.