长骨中振动声激发超声导波的方法

为了实现一定频段内任意低频下在长骨中激励导波信号,本文提出一种采用聚焦高频(5 MHz)超声换能器在长骨皮质骨中激发低频(150 kHz)超声导波的振动声方法.首先介绍了板状超声导波理论和双声束共聚焦法与单声束调幅法激发振动声的基本原理;进而采用三维有限元仿真方法分析振动声激发低频超声导波的基本现象,然后结合牛胫骨板离体实验,验证振动声激发低频超声导波的可行性.结果均表明,双声束共焦与单声束振动超声均可在骨板中激发低频超声导波.相关研究方法有助于提高空间域长骨中超声导波测量精度,以及在一定频段内实现任意频率激励等,对发展低频超声导波在体测量长骨皮质骨的新技术具有一定的指导意义.     

时频方法分析长骨中的超声导波及皮质骨厚度

超声导波在长骨中传播时,接收信号中含有相互叠加的多个导波模式。本文提出将希尔伯特-黄变换(HHT)用于分析叠加的多模式导波信号,将其分解成许多单个独立的模式,然后对分解出的模式求得其对应频率下的群速度,并与短时傅里叶变换所得的结果进行比较。通过与理论计算结果比较,可得到长骨皮质骨的厚度。研究结果表明,实验得到的厚度与实际厚度一致。说明通过测量导波模式的速度可以评价皮质骨的厚度,也说明HHT方法是一种识别叠加多模式导波信号的有效方法。     

基于自适应多尺度时频熵的遥测振动信号异常检测方法

针对遥测振动信号频域成份复杂、非平稳非线性和强噪声特性,提出一种基于自适应多尺度时频熵的遥测振动信号异常检测方法;首先对采集到的遥测振动信号进行零漂修正和趋势项消除,然后采用自适应分解方法对信号进行多尺度分解,得到若干分量,利用相关系数剔除虚假分量;接下来用筛选出的分量作时频分布,对时频分布进行多层多尺度划分,计算相应尺度频段内信号的分形维数,依据分形维数的大小自适应地确定各频段的时频划分尺度;最后计算时频平面的自适应多尺度时频熵,通过时频熵的变化情况对遥测振动信号进行异常检测;实测数据验证了该方法的有效性。     

次声信号的时频信号能量统计检测方法

次声信号检测是次声事件监测的重要环节,常用的短时平均与长时平均之比STA/LTA(Short Term Average to Long Term Average)和著名的逐次多通道互相关方法PMCC(the Progressive Multiple Cross-Correlation)误检率都比较高.为此,提出一种时频域信号能量统计的次声信号检测方法,其基本原理是根据目标信号与噪声的频域分布差异,采用窄带分频技术将待检波形变换到时频域,按时频格点计算局部平均能量与全局平均能量之比,当比值大于设定阈值时,认为该格点属于目标信号的一部分。以国际监测系统次声台站监测数据为对象进行了实验研究,并与PMCC检测结果进行了比较,结果表明所提出的信号检测方法对微弱次声信号检测能力强、误检信号少.     

骨折长骨中超声导波传播特性的仿真研究

采用超声导波评价长骨骨折已成为近两年来的一个研究热点.本文采用混合边界元方法(HBEM)对超声导波在骨裂长骨中的传播情况以及各导波模式的反射系数和透射系数进行了数值分析.研究结果表明,入射导波模式经由裂纹处模式转换后依然保持为主要接收模式不变.对某一裂纹,各模式透射系数常在相近的频率点上达到局部峰值.对于不同深宽比(d/w)裂纹,某些模式透射系数曲线局部峰值所对应的频率存在着相互错开的现象,这些结果可用于选择最优入射导波模式和频率,以便更好地定量评价骨质以及骨裂状况.     

超声导波评价长骨骨折的仿体实验研究

定量超声评价长骨骨折已成为近年来的研究热点之一,超声导波模式转换理论可被应用于长骨骨折状况的评价。采用三维时域有限差分法分析骨折长骨中导波模式转换的基本规律,提取模式转换定量评价参数;进而结合亚克力管仿体实验,分析不同裂纹程度下导波模式的转换情况。仿真与仿体实验表明,导波模式能量参数与裂纹程度具有很好的相关性,可用于评价长骨骨折状况。     

超声导波的频散补偿与模式分离算法研究

超声导波已被广泛应用于无损检测与评价,针对超声导波应用中普遍存在的模式混叠与频散问题,本文在采用频谱相位与信号时延函数建模导波频散信号传递系统的基础上,提出了一种单一模式的频散补偿算法。进一步地,通过将选择性频散补偿技术与导波模式分离相结合,本文算法克服了传统补偿算法所不能解决的多模式导波频散问题。板中的Lamb波A0,A1和S0混合模式仿真分析表明,本算法不仅可用于单一导波模式信号合成与频散补偿,而且可在实现多模式混合导波信号选择性频散补偿基础上,解决多模式导波信号中单一模式提取与分离。本文研究有助于超声导波多模式频散信号的分析与处理.     

时频分布重排方法在水下目标回声识别中的应用

特征提取是目标识别的关键技术,本文提出了一种基于时频分布重排的Wigner-Ville分布(WD)特征提取方法。选择WD作为研究问题的切入点,使用核函数方法,通过平滑伪Wigner-Ville分布(SPWD)抑制交叉项;为解决引入核函数导致信号时频聚集性下降问题,我们采用时频重排方法来增强目标的时频聚集性,最后用海上实验数据对本文方法进行了有效性检验。实验结果表明,本文提出的SPWD时频重排特征提取方法具有:(1)可有效抑制WD固有的交叉项;(2)增强目标回声信号时频聚集性;(3)可明显地提高水下目标识别率。该方法具有一定的应用推广价值,为解决水下目标识别问题提供了很好的思路。     

运动目标声信号的时频特征研究

提高声被动目标定位精度需要研究运动目标在有风条件下目标声信号的特征。本文从声学波动方程出发，对无风和有常速风情况下的波动方程进行了求解，得到了运动目标声信号的延迟关系；分析了解的主要影响项，并得到了其解析信号；用解析信号对运动目标声信号的延迟特性和时频特征进行了仿真计算。     

宽吻海豚Click信号的时频滤波检测方法

针对宽吻海豚Click信号检测提出了一种在信号时频图中基于Gabor滤波器的检测方法。该方法首先对声信号进行分段处理,计算每一段信号的时频图;然后设计Gabor滤波器,提取时频图中垂直方向的线条;对Gabor滤波处理后的时频图进行自适应阈值处理,提取时频图中能量较强的区域;最后通过连通域分析确定Click信号的位置.仿真合成不同信噪比的测试信号,本文算法在Click信号和背景噪声平均功率比为15 dB的情况下,Click信号的找全率达到了99%,错误率为0%;对实际采集的声信号进行Click信号检测,找全率为100%。本文方法预期为海豚观测和海豚生物学行为的研究提供一定的技术支持。     

Analysis of ultrasonic guided waves propagating in long bones and cortical thickness based on time-frequency methods

The ultrasonic signals in long bones contain multiple guided modes which are mutual superposed.The velocities of guided waves in long bones are very sensitive to cortical thickness (CTh).In this paper,Hilbert-Huang transform(HHT) was proposed to analyze multi-mode guided waves,which can decompose superposed waves into many independent modes.Then the group velocity of each mode was obtained at corresponding frequency,which was compared with the results of short time Fourier transform(STFT).The CTh was also obtained by comparing with the theoretical calculation.The results showed that the experimental determined thickness was in agreement with the actual CTh,indicating that measuring the velocity of the guided mode can be used to estimate the CTh.The HHT is an effective method to identify multimode guided waves.     

Detection of long bones based on Barker code excited ultrasonic guided waves

Single pulse excited ultrasonic guided wave surfers high attenuation during the propagation in long bones.This results in small amplitude and low signal-to-noise ratio(SNR)of measured signals.Thus,the Barker code excitation is introduced into long bone detection to improve the quality of received signals,due to its efficiency in increasing amplitude and SNR.Both simulation and in vitro experiment were performed,and the results were decoded by the weighted match filter(WMF) and the finite impulse response- least squares inverse filter(FIRLSIF),respectively.The comparison between the results of Barker code excitation and sine pulse excitation was presented.For 13-bit Barker code excitation,WMF produced 13 times larger amplitude than sine pulse excitation,while FIR-LSIF achieved higher peak-sidelobe-level(PSL) of —63.59 dB and better performance in noise suppression.The results show that the Barker code excited guided waves have the potential to be applied to the long bone detection.     

Joint spectrogram segmentation and ridge-extraction method for separating multimodal guided waves in long bones

Ultrasonic guided waves (GWs) can be used to evaluate long bones effectively because of the ability to provide the information of the whole bone. In this study, a joint spectrogram segmentation and ridge-extraction (JSSRE) method was proposed to separate multiple modes in long bones. First, the Gabor time-frequency transform was applied to obtain the spectrogram of multimodal signals. Then, a multi-class image segmentation algorithm was used to find the corresponding region of each mode in the spectrogram, including an improved watershed transform and a region growing procedure. Finally, the ridges were extracted and the time domain signals representing individual modes were reconstructed from these ridges in each region. The validations of this method were discussed by simulated multimodal signals with different signal-to-noise ratios (SNR). The correlation coefficients between the original signals without noise and the reconstructed signals were calculated to analyze the results quantitatively. The results showed that the extracted ridges were in good agreement with generated theoretical dispersion curves, and the reconstructed signals were highly related to the original signals, even under the SNR=3 dB situation.     

一种基于人工蜂群算法的混沌信号盲分离方法

混沌信号所固有的非周期、宽带频谱和对初值极度敏感等特性使得对这类信号进行盲分离极为困难. 针对这一问题, 提出一种新的盲分离方法, 该方法通过相空间重构来构造代价函数, 将混沌信号的盲分离转化为一个无约束优化问题, 并利用人工蜂群算法进行求解. 不同于现有的独立成分分析方法仅使用混合信号的统计特性来解决分离问题, 该方法能充分利用混合信号内在的动态特性, 因而在处理混沌信号这种确定性信号时能获得更好的分离效果. 此外, 正交矩阵的参数化表示有效地降低了盲分离问题的复杂性, 使优化过程能快速收敛. 实验结果表明, 该方法具有较快的收敛速度和较高的数值精度, 在分离混沌信号时其整体性能优于现有的几种盲分离方法. 同时, 在分离混沌-高斯混合信号的实验中该方法也展现出优异良好的性能, 这表明该文的方法有应用潜力.     

基于声源方位信息和非线性时频掩蔽的语音盲提取算法

针对欠定卷积混合的语音信号模型,提出一种基于声源方位信息和非线性时频掩蔽的语音盲提取算法。首先对低频段混合语音信号进行时频分析估计瞬时相对时延(ITD)并采用势函数聚类分析方法估计出声源个数及其ITD,接着锁定目标提取准确的目标语音方位信息,最后利用独立语音在时频域上的近似W一分离正交性,采用非线性时频掩蔽的方法提取目标语音。仿真实验表明,该方法能锁定任意感兴趣目标方位,能有效提取目标语音,文中实验条件下信噪比增益平均达9.5 dB。     

Performance of blind source separation algorithms for fMRI analysis using a group ICA method

Independent component analysis (ICA) is a popular blind source separation technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist; however, the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely, information maximization, maximization of non-Gaussianity, joint diagonalization of cross-cumulant matrices and second-order correlation-based methods, when they are applied to fMRI data from subjects performing a visuo-motor task. We use a group ICA method to study variability among different ICA algorithms, and we propose several analysis techniques to evaluate their performance. We compare how different ICA algorithms estimate activations in expected neuronal areas. The results demonstrate that the ICA algorithms using higher-order statistical information prove to be quite consistent for fMRI data analysis. Infomax, FastICA and joint approximate diagonalization of eigenmatrices (JADE) all yield reliable results, with each having its strengths in specific areas. Eigenvalue decomposition (EVD), an algorithm using second-order statistics, does not perform reliably for fMRI data. Additionally, for iterative ICA algorithms, it is important to investigate the variability of estimates from different runs. We test the consistency of the iterative algorithms Infomax and FastICA by running the algorithm a number of times with different initializations, and we note that they yield consistent results over these multiple runs. Our results greatly improve our confidence in the consistency of ICA for fMRI data analysis.     

基于压缩感知的欠定源信号恢复算法比较

构建了基于压缩感知的欠定盲源分离源信号恢复模型,比较研究了基于互补匹配追踪算法(CMP)、基于L1范数的互补匹配追踪算法(L1CMP)和基于修正牛顿的径向基函数算法(NRASR)实现欠定源信号恢复的应用效果。结果表明：源信号时域充分稀疏情况下,CMP,L1CMP和NRASR的恢复效果接近,但L1CMP算法计算复杂度最低;变换域充分稀疏情况下,CMP和L1CMP恢复效果接近,NRASR恢复效果较差;时域非充分稀疏情况下,CMP效果较差,L1CMP和NRASR效果接近。综合考虑,L1CMP算法效果最佳;在观测信号数和源数较少的情况下,算法在时域恢复信号精度会下降;稀疏表示法结合压缩感知重构能够提高源信号恢复的效果。     

Impact of the fracture on low-order guided waves in long bone

Using ultrasonic guided waves to assess long bone fractures and fracture healing has become a promising diagnostic issue.But the multimode overlap of the guided waves challenges the quantitative evaluation and clinical application.In the preformed study,in order to simplify the signal interpretation,the low-frequency sinusoidial signals were used to only excite S0 and A0 modes in fractured long bones.The amplitudes of S0 and A0 modes were numerically analyzed with variation in crack width and fracture angle.Numerical simulation,based on the two-dimension finite-difference time-domain(2D-FDTD) reveals that both S0 and A0 amplitudes decrease with the fracture widening.However,the increase in fracture angle gradually enhances the A0 amplitude,while with respect to the S0 mode,its amplitude shows a non-monotonic trend to the variation in fracture angle with a turning point around 45°.The amplitude ratio between S0 and A0 can reflect the variations in crack width and fracture angles.The simulation illustrates that ultrasonic guided S0 and A0 modes are sensitive to the degree of both vertical and oblique fractures in the long cortical bone.These findings may be helpful for fractures diagnosis and healing evaluation of the long bone.     

车载场景结合盲源分离与多说话人状态判决的语音抽取

在车载分布式传声器阵列场景中,结合盲源分离TRINICON (Triple-N ICA for convolutive mixtures)算法与多说话人状态判决实现期望语音抽取。根据分布式传声器阵列与声源的相对位置关系,设计特定的盲源分离初始化条件以保证输出通道与声源的映射关系;根据分布式传声器阵列的频响特点,设计特征矢量来进行多说话人判决,并将判决结果引入TRINICON算法参数迭代过程。在使用实际车载录音数据的仿真评测中,所提方法在不同信噪比下有较高的鲁棒性,可有效提升TRINICON算法的收敛速度和语音信号的信扰比,且可以确保准确的通道映射。评测结果表明该方法可以在车载场景中有效抽取出期望语音,为车载复杂场景下的声信息提取提供了一种可靠且收敛快速的解决方法。