An adaptive time delay estimati on algorithm based onquadratic weighting of the frequency domain

Time delay estimation (TDE) plays an important role in many engineering appli-cations. A new time delay estimation configuration, the quadratic weighting of the frequency domain adaptive TDE model, is put forward. The quadratic weighting of the frequency domainSCOT (Smoothed Coherence Transform) and ML (Maximum Likelihood) adaptive TDE algo-rithms are presented, respectively. The variance of the quadratic weighting of the frequency domain SCOT algorithm is derived. Then the proposed algorithms are applied in the TDE of helicopter passive acoustic location. The simulation results are presented which verify that the proposed algorithm has better performance in the low signal to noise ratio.     

An adaptive filter-based method for robust, automatic detection and frequency estimation of whistles

This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances.     

基于自适应像素级注意力模型的场景深度估计

深度估计是传统的计算机视觉任务,在理解三维场景中起着至关重要的作用。基于单目图像的深度估计任务的困难在于如何提取图像特征中大范围依赖的上下文信息,提出了自适应的上下文聚合网络（adaptive context aggregation network,ACANet）用于解决该问题。该方法基于有监督的自注意力模型(supervised self-attention,SSA),能够自适应地学习任意像素之间的具有任务特性的相似性以模拟连续的上下文信息,并通过模型学习的注意力权重分布用来聚合提取的图像特征。将单目深度估计任务设计为像素级的多分类问题,经过设计的注意力损失函数减少RGB图像和深度图的语义不一致性,通过生成的像素级注意力权重对由位置索引的特征进行全局池化。最后提出一种软性有序推理算法（soft ordinal inference,SOI）,充分利用网络的预测置信度,将离散的深度标签转化为平滑连续的深度图,并且提高了准确率（rmse下降了3%）。在公开的单目深度估计基准数据集NYU Depth V2上的实验结果表明:rmse指标为0.490,阈值指标为82.8%,取得了较好的结果,证明了本文提出的算法的优越性。     

基于自适应频率不变响应基阵的宽带信号方位估计

提出一种设计频率不变响应基阵的自适应模拟方法，并将其用于构造波束形成矩阵来实现宽带信号的聚焦。该方法不仅保持了波束域方位估计算法的运算量小、分辨率高等优点，还具有相关信号子空间能解相关源的能力，且对阵形及各阵元的指向性也没有任何限制。仿真结果说明了本文方法的有效性。     

二次加权频域自适应时延估计算法与应用

时延估计问题在许多工程应用中具有重要的意义。本文提出了一种二次加权频域自适应时延估计模型,给出了二次加权频域SCOT和ML自适应时延估计算法,推导了二次加权频域SCOT自适应时延估计的方差。把该算法应用于对武装直升机的声测无源定位中,用实测直升机噪声数据进行了计算机仿真。结果表明,在低信噪比情况下,二次加权频域自适应时延估计具有更好的时延估计性能。     

利用先验估计自适应加窗的准静态超声弹性成像位移估计算法

准静态弹性成像技术是基于组织压缩前和压缩后超声回波射频信号进行组织运动重构的弹性成像技术。提出了一种基于先验估计的自适应窗函数算法,在位移估计过程中,使用已估计的临近窗的时延值作为先验信息,自动调整截取压缩后射频信号段的截取窗函数,提高了互相关运算所需的压缩前和压缩后信号段之间的相关性。仿真实验结果表明,该算法不仅大大提高了成像速度,而且提高了信噪比较低时的成像质量,同时该算法具有更宽的应变通带。     

一种改进的非整数自适应时延估计方法*

针对基于时延估计的机电设备故障声定位中的低信噪比和脉冲噪声情况,用α稳定分布建模噪声,改进了非整数自适应时延估计方法。共变相关法对观测序列进行时延估计粗测,将得到的估计值作为非整数自适应时延估计器的初值;将共变相关法中共变序列作为时延估计器的输入信号,在最小平均p范数准则下迭代得到非整数时延估计值。共变序列保留了原始序列间的时延信息,削弱了不相关的噪声。计算机仿真对比实验验证了改进的方法在脉冲环境和低信噪比条件下有更好的性能。     

一种维纳加权频域自适应时延估计算法

提出了一种基于频域自适应滤波的维纳加权时延估计算法。该算法首先实现了对输入信号的维纳自适应滤波预处理,然后在频域实现了自适应时延估计。该算法在将相位数据拟合时,做了一次加权处理,有效地改善了时延估计的精度和自适应收敛速度。最后用实测的直升机噪声数据进行了仿真,验证了新算法的有效性。     

基频估计算法研究平均骨小梁间距

超声背散射信号能提供松质骨微结构信息从而能有效的诊断骨质疏松,而平均骨小梁间距(MTBS)是表征松质骨微结构的重要参数之一。本文提出了一种基频估计的MTBS估计算法,对仿真信号和离体牛胫骨松质骨中的超声背散射信号分别进行分析处理,并和现有的两种算法(简易反向滤波跟踪(SIFT)算法和AR倒谱法)进行比较。实验结果表明基频估计算法对噪声和随机散射回波有更好的鲁棒性,在信噪比较低和随机散射回波幅度较大时估计值更加精确,且有很好的方差性能。充分说明了本文算法是一种有效的MTBS估计算法。     

Study on the mean trabecular bone spacing based on fundamental frequency estimation method

Ultrasonic backscatter signals from the cancellous bone can be used to diagnose osteoporosis effectively due to its ability to provide the information of bone microstructure. Mean trabecular bone spacing(MTBS)is one of the important parameters for characterization of bone microstructure.This paper proposed a MTBS estimating method based on the fundamental frequency estimation,which was applied to backscatter signals from simulations, and in vitro bovine trabeculae.The estimated MTBS were compared with those of simplified inverse filter tracking(SIFT)algorithm and autoregressive(AR)cepstrum method.The results demonstrated that the proposed method is very robust for the MTBS estimation with more precise estimates and smaller estimated variance in the presence of a small signal-to-noise ratio (SNR),and a large scattering strength ratio of diffuse scatterers to regular ones.     

An automatic frequency domain modal parameter estimation algorithm

In this article, a frequency-domain modal parameter estimation method is proposed. The algorithm automatically separates physical poles from mathematical ones. An important issue in the automatization of the algorithm is the inclusion of noise information to estimate the standard deviations of the poles. These standard deviations are used (together with other features) as the inputs of a fuzzy clustering algorithm. The clustering algorithm then classifies the poles into the mathematical and physical ones. The method requires no user interaction, and a parameter is available quantifying the success of the classification.     

基于自适应频域滤波的红外弱小目标检测技术

研究复杂背景下弱小目标检测问题对提高靶场光电设备探测能力具有重要意义.根据红外图像的背景复杂程度,提出一种自适应高斯高通滤波算法.该算法利用改进中值滤波器对图像进行降噪,采用图像方差加权熵,定量描述红外图像背景复杂程度,根据图像背景复杂程度自动调整滤波器截止频率,实现不同背景下红外弱小目标自动检测,并利用靶场实测光电图像对算法进行了验证.实验结果表明该算法能够有效地在不同图像背景下检测到弱小目标.     

基于正交频分复用的水声自适应通信系统性能仿真研究

本文从平均谱效率的角度分析并讨论了基于正交频分复用(Orthogonal Frequency Division Multiplex,OFDM)技术的水声自适应通信系统的通信性能。在两种典型的水声统计信道假设下,根据信道状态信息的不同精确程度,针对不同的调制方式,对水声自适应通信系统的平均谱效率进行了理论推导,并给出了部分性能函数的解析形式,而后从多个角度给出了较为全面的数值仿真分析结果。仿真结果显示:当目标误码率为10-3时,水声自适应通信系统可以在相同发射功率下,显著地改善通信性能(约3 bit/s/Hz);在同样的目标误码率下,如果维持传输性能不变,自适应通信系统可以显著节省发射功率(约10 dB)。在信道状态信息不确知的情况下,当信道估计误差大于-15 dB,或多普勒补偿后信道的扩展因子大于0.03时,自适应系统的性能才会显著劣化。此外,在统计意义上,自适应通信方法对于系统性能的改善程度随通信距离的增加而增大。     

Robust and accurate fundamental frequency estimation based on dominant harmonic components

This paper presents a new method for robust and accurate fundamental frequency (F0) estimation in the presence of background noise and spectral distortion. Degree of dominance and dominance spectrum are defined based on instantaneous frequencies. The degree of dominance allows one to evaluate the magnitude of individual harmonic components of the speech signals relative to background noise while reducing the influence of spectral distortion. The fundamental frequency is more accurately estimated from reliable harmonic components which are easy to select given the dominance spectra. Experiments are performed using white and babble background noise with and without spectral distortion as produced by a SRAEN filter. The results show that the present method is better than previously reported methods in terms of both gross and fine F0 errors.     

一种基于自适应权值的立体匹配方法

提出了一种新的自适应权值的立体匹配方法,在匹配中无需逐像素确定其支持窗口的尺寸。首先根据像素间的相似性和邻近性对匹配窗口内每一像素的支持权值进行调整,使与待匹配点位于同一区域的像素权值增大,然后在匹配的代价函数中引入视差平滑性约束项,从而获得最终视差。在Middlebury提供的标准图像上进行了测试。实验结果表明,该方法可以获得良好的视差图。     

基于实时主动声定位的快速测频算法

针对爆炸试验中冲击波超压传感器位置定位的问题,采用陆上实时主动声定位系统对爆炸试验中冲击波超压传感器位置进行定位。系统中对主动发出声信号频率为已知特定几种频率之一的单频信号的快速实时频率检测,提出了一种快速频率检测算法。算法充分利用信号频率为已知特定几种频率之一这一特定条件,并结合离散时间傅里叶变换(DTFT)与离散傅里叶变换(DFT)的关系以及帕塞瓦尔定理,得出快速频率检测的表达式。经实验验证和计算分析,算法检测频率精准可靠,计算量比其他快速测频算法小得多,更适用于军事紧急状态下陆上实时主动声定位中主动发声和实时定位条件下的快速测频。     

基于谐波和噪声能量改进的语音截止频率轮廓估计

为了对语音谐波/噪声模型中的语音截止频率轮廓进行更好的描述,本文提出了一种基于谐波和噪声能量改进的语音截止频率轮廓估计算法。改进算法对累积谐波和噪声能量函数进行对应谐波处的功率谱加权,并且在语音截止频率轨迹的平滑部分采用形态滤波的手段。实验表明,与原算法相比,通过改进算法得到的截止频率轮廓在语谱图上标注得更为准确,在主观评分测试中改进算法也获得了优于原算法的测试评分。改进算法比原算法能够更加准确的对语音截止频率轮廓进行估计,从而使得语音谐波/噪声模型在语音编码、语音合成及识别方面具有更为有效的应用。     

基于红外和可见光图像逐级自适应融合的场景深度估计

从图像中恢复场景的深度是计算机视觉领域中的一个关键问题。考虑到单一类型图像在深度估计中受场景不同光照的限制,提出了基于红外和可见光图像逐级自适应融合的场景深度估计方法（PF-CNN）。该方法包括双流滤波器部分耦合网络、自适应多模态特征融合网络以及自适应逐级特征融合网络。在双流卷积中红外和可见光图像的滤波器部分耦合使两者特征得到增强;自适应多模态特征融合网络学习红外和可见光图像的残差特征并将两者自适应加权融合,充分利用两者的互补信息;逐级特征融合网络学习多层融合特征的结合,充分利用不同卷积层的不同特征。实验结果表明:PF-CNN在测试集上获得了较好的效果,将阈值指标提高了5%,明显优于其他方法。     

Fast intersections on nested tetrahedrons (FINT): An algorithm for adaptive finite element based distributed parameter estimation

A variety of biomedical imaging techniques such as optical and fluorescence tomography, electrical impedance tomography, and ultrasound imaging can be cast as inverse problems, wherein image reconstruction involves the estimation of spatially distributed parameter(s) of the PDE system describing the physics of the imaging process. Finite element discretization of imaged domain with tetrahedral elements is a popular way of solving the forward and inverse imaging problems on complicated geometries. A dual-adaptive mesh-based approach wherein, one mesh is used for solving the forward imaging problem and the other mesh used for iteratively estimating the unknown distributed parameter, can result in high resolution image reconstruction at minimum computation effort, if both the meshes are allowed to adapt independently. Till date, no efficient method has been reported to identify and resolve intersection between tetrahedrons in independently refined or coarsened dual meshes. Herein, we report a fast and robust algorithm to identify and resolve intersection of tetrahedrons within nested dual meshes generated by 8-similar subtetrahedron subdivision scheme. The algorithm exploits finite element weight functions and gives rise to a set of weight functions on each vertex of disjoint tetrahedron pieces that completely cover up the intersection region of two tetrahedrons. The procedure enables fully adaptive tetrahedral finite elements by supporting independent refinement and coarsening of each individual mesh while preserving fast identification and resolution of intersection. The computational efficiency of the algorithm is demonstrated by diffuse photon density wave solutions obtained from a single- and a dual-mesh, and by reconstructing a fluorescent inclusion in simulated phantom from boundary frequency domain fluorescence measurements.     

An adaptive quarter-wave tube that uses the sliding-Goertzel algorithm for estimation of phase

This paper describes an adaptive quarter wave tube used to attenuate a tone from the exhaust noise of a large diesel engine. A sliding-Goertzel algorithm was used to calculate the phase angle of the transfer function between a microphone in the adaptive quarter wave tube and in the main exhaust duct. The control system adjusted the length of the adaptive quarter wave tube until the phase angle was −90° and caused the sound pressure level at the cylinder firing frequency in the exhaust duct to be minimized. The system was able to adapt to changes in engine speed, exhaust gas temperature, and load applied to the engine. The results demonstrate that the sliding-Goertzel algorithm can be used effectively to estimate the phase angle in an adaptive–passive acoustic control system.