An improved automatic time-of-flight picker for medical ultrasound tomography

Objective and motivationTime-of-flight (TOF) tomography used by a clinical ultrasound tomography device can efficiently and reliably produce sound-speed images of the breast for cancer diagnosis. Accurate picking of TOFs of transmitted ultrasound signals is extremely important to ensure high-resolution and high-quality ultrasound sound-speed tomograms. Since manually picking is time-consuming for large datasets, we developed an improved automatic TOF picker based on the Akaike information criterion (AIC), as described in this paper.MethodsWe make use of an approach termed multi-model inference (model averaging), based on the calculated AIC values, to improve the accuracy of TOF picks. By using multi-model inference, our picking method incorporates all the information near the TOF of ultrasound signals. Median filtering and reciprocal pair comparison are also incorporated in our AIC picker to effectively remove outliers.ResultsWe validate our AIC picker using synthetic ultrasound waveforms, and demonstrate that our automatic TOF picker can accurately pick TOFs in the presence of random noise with absolute amplitudes up to 80% of the maximum absolute signal amplitude. We apply the new method to 1160 in vivo breast ultrasound waveforms, and compare the picked TOFs with manual picks and amplitude threshold picks. The mean value and standard deviation between our TOF picker and manual picking are 0.4 μs and 0.29 μs, while for amplitude threshold picker the values are 1.02 μs and 0.9 μs, respectively. Tomograms for in vivo breast data with high signal-to-noise ratio (SNR) (∼25 dB) and low SNR (∼18 dB) clearly demonstrate that our AIC picker is much less sensitive to the SNRs of the data, compared to the amplitude threshold picker.Discussion and conclusionsThe picking routine developed here is aimed at determining reliable quantitative values, necessary for adding diagnostic information to our clinical ultrasound tomography device - CURE. It has been successfully adopted into CURE, and allows us to generate such values reliably. We demonstrate that in vivo sound-speed tomograms with our TOF picks significantly improve the reconstruction accuracy and reduce image artifacts.     

New automatic localization technique of acoustic emission signals in thin metal plates

In acoustic emission (AE) measurement, the information of the arrival time is very important for event location, event identification and source mechanism analysis. Manual picks are time-consuming and sometimes subjective, especially in the case of large volumes of digital data. Various techniques have been presented in the literature and are routinely used in practice such as amplitude threshold, analysis of the long-term average/short-term average (LTA/STA), high-order statistics or artificial neural networks.A new automatic determination technique of the first arrival times of AE signals is presented for thin metal plates. Based on Akaike’s information criterion, proposed algorithm of the first arrival detection uses a specific characteristic function, which is sensitive to change of frequency in contrast to others such as envelope of the signal. The approach is applied to data sets of three different tests. Reliable results show the potential of our approach.     

Automatic detection of bioacoustics impulses based on kurtosis under weak signal to noise ratio

Passive acoustic monitoring (PAM) of marine mammal vocalizations has been efficiently used in a wide set of applications ranging from marine wildlife surveys to risk mitigation of military sonar emissions. The primary use of PAM is for detecting bioemissions, a good proportion of which are impulse sounds or clicks. A click detection algorithm based on kurtosis estimation is proposed as a general automatic click detector. The detector works under the assumption that click trains are embedded in stochastic but Gaussian noise. Under this assumption, kurtosis is used as a statistical test for detection. The algorithm explores acoustic sequences with the optimal frequency bandwidth for focusing on impulse sounds. The detector is successfully applied to field observations, and operates under weak signal to noise ratios and in presence of stochastic background noise. The algorithm adapts to varying click center frequency. Kurtosis appears as a promising approach to detect click trains, alone or in combination with other clicks detector, and to isolate individual clicks.     

基于声学扫描振镜的超声/光声双模态成像技术

超声/光声双模态成像技术因其同时兼具超声的高分辨率结构成像和光声的高对比度功能成像优势,极大地推动了光声成像技术的临床应用推广.传统超声/光声双模态成像技术多基于超声成像所用阵列探头同时收集光声信号,系统结构紧凑且无需图像配准,操作便捷.但该类设备使用阵列探头和多通道数据采集,使得其成本较高;且成像结果易受通道一致性差异影响.本文提出了一种基于声学扫描振镜的超声/光声双模态成像技术,该技术采用单个超声换能器结合一维声学扫描振镜进行快速声束扫描,实现超声/光声双模态成像,是一种小型化、低成本的双模态快速成像技术.本文开展了系列仿体和活体成像研究,实验结果表明:系统有效成像范围为15.6 mm,超声和光声成像B扫描速度分别为1.0 s^–1和0.1 s^–1 (光声成像速度主要受制于脉冲激光器重复频率).基于本文所提技术研究,有助于进一步推动超声/光声双模态成像技术的临床转化和普及;也为基于超声信号检测的多模态成像技术提供了一种低成本、小型化和快速声信号检测的参考方案.     

Detecting Doppler ultrasound embolic signals using the wavelet-based feature extraction algorithm

The non-invasive detection of circulating emboli with the Doppler ultrasound technique is of active significance in clinical applications. In order to eliminate drawbacks of artifacts brought by the movement of probes or patients and detect emboli accurately, relevant feature parameters are extracted from two angles of the wavelet transform of Doppler signals. The singularity of the signal waveform is analyzed based on its wavelet scalogram; then transverse and longitudinal parameters are extracted to represent the scalogram characteristics. A novel method is proposed based on the adaptive wavelet packet basis, from which several parameters such as the energy, the scale, etc. are extracted to represent the optimized signal approximation features. With all feature parameters in two aspects, a classification system is established for Doppler Ultrasound embolic signals by solving the generalized Fisher discriminant plane. From experiments on 300 simulated and 298 clinical Doppler ultrasound signals of cerebral arteries, it is shown that the proposed system can achieve the emboli detection rates of 99.0% and 98.5% for the training set and the testing set respectively. Therefore this method makes an improve- ment of emboli detection compared to traditional in the automatic detection of clinical emboli. methods and has the possibility to be applied     

The Relationship of Cavitation to the Negative Acoustic Pressure Amplitude in Ultrasonic Therapy

The relationship between the cavitation and acoustic peak negative pressure in the high-intensity focused ultrasound(HIFU)Held is analyzed in water and tissue phantom.The peak negative pressure at the focus is determined by a hybrid approach combining the measurement with the simulation.The spheroidal beam equation is utilized to describe the nonlinear acoustic propagation.The waveform at the focus is measured by a fiber optic probe hydrophone in water.The relationship between the source pressure amplitude and the excitation voltage is determined by fitting the measured ratio of the second harmonic to the fundamental component at the focus,based on the model simulation.Then the focal negative pressure is calculated for arbitrary voltage excitation in water and tissue phantom.A portable B-mode ultrasound scanner is applied to monitor HIFU-induced cavitation in real time,and a passive cavitation detection(PCD)system is used to acquire the bubble scattering signals in the HIFU focal volume for the cavitation quantification.The results show that:(1)unstable cavitation starts to appear in degassed water when the peak negative pressure of HIFU signals reaches 13.5 MPa;and(2)the cavitation activity can be detected in tissue phantom by B-mode images and in the PCD system with HIFU peak negative pressures of 9.0 MPa and 7.8 MPa,respectively,which suggests that real-time B-mode images could be used to monitor the cavitation activity in two dimensions,while PCD systems are more sensitive to detect scattering and emission signals from cavitation bubbles.     

Passive cavitation mapping for localization and tracking of bubble dynamics

Current acoustic techniques for studying cavitation dynamics are only readily applicable to single-bubble activity, while optical methods can only be used in transparent media. However, multi-bubble cavitation often occurs in opaque media such as biological tissue. Here, the signals received passively by each of the 64 channels of a diagnostic ultrasound array are used to localize and separate emissions from several bubble clusters cavitating in agar gel, thereby providing a method of observing cavitation dynamics. The method has a high spatiotemporal resolution and is applicable to cavitation in opaque media.     

Electromotile hearing: acoustic tones mask psychophysical response to high-frequency electrical stimulation of intact guinea pig cochleae

When sinusoidal electric stimulation is applied to the intact cochlea, a frequency-specific acoustic emission can be recorded in the ear canal. Acoustic emissions are produced by basilar membrane motion, and have been used to suggest a corresponding acoustic sensation termed "electromotile hearing." Electromotile hearing has been specifically attributed to electric stimulation of outer hair cells in the intact organ of Corti. To determine the nature of the auditory perception produced by electric stimulation of a cochlea with intact outer hair cells, guinea pigs were tested in a psychophysical task. First, subjects were trained to report detection of sinusoidal acoustic stimuli and dynamic range was assessed using response latency. Subjects were then implanted with a ball electrode placed into scala tympani. Following the surgical implant procedure, subjects were transferred to a task in which acoustic signals were replaced by sinusoidal electric stimulation, and dynamic range was assessed again. Finally, the ability of acoustic pure-tone stimuli to mask the detection of the electric signals was assessed. Based on the masking effects, it is concluded that sinusoidal electric stimulation of the intact cochlea results in perception of a tonal (rather than a broadband or noisy) sound at a frequency of 8 kHz or above.     

Acoustic detection and classification of river boats

We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces false alarms rate. The proposed algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real time detection while minimizing the false alarms rate.     

Modeling cochlear dynamics: interrelation between cochlea mechanics and psychoacoustics

A model of the cochlea was used to bridge the gap between model approaches commonly used to investigate phenomena related to otoacoustic emissions and more filter-based model approaches often used in psychoacoustics. In the present study, a nonlinear and active one-dimensional transmission line model was developed that accounts for several aspects of physiological data with a single fixed parameter set. The model shows plausible excitation patterns and an input-output function similar to the linear-compressive-linear function as hypothesized in psychoacoustics. The model shows realistic results in a two-tone suppression paradigm and a plausible growth function of the 2f(1)-f(2) component of distortion product otoacoustic emissions. Finestructure was found in simulated stimulus-frequency otoacoustic emissions (SFOAE) with realistic levels and rapid phase rotation. A plausible "threshold in quiet" including finestructure and spontaneous otoacoustic emissions (SOAE) could be simulated. It is further shown that psychoacoustical data of modulation detection near threshold can be explained by the mechanical dynamics of the modeled healthy cochlea. It is discussed that such a model can be used to investigate the representation of acoustic signals in healthy and impaired cochleae at this early stage of the auditory pathway for both, physiological as well as psychoacoustical paradigms.     

基于经验模态分解和支持向量机的车型声频识别

车型自动识别是智能交通系统的重要组成部分。针对现有车型识别存在的问题,提出利用经验模态分解和支持向量机的车型声频识别方法。将车辆行驶的声音信号进行分解,以分解不同模态的能量作为特征向量,并以此作为训练样本对支持向量机构成的车型识别器进行训练,通过对小汽车和卡车的声音信号处理结果表明:利用车辆声音信号能够正确识别不同的车型,识别准确率达95%,是车型识别的有效方法。     

Using an infrasonic method to monitor the destruction of glaciers in Arctic conditions

We study the application of an infrasonic method to detect infrasonic acoustic emission caused by the destruction of glaciers in the Arctic. We consider the main approaches and methods for automatic signal detection from the data of infrasonic microarrays from the viewpoint of their practical use in conditions of frequent and significant variations in the noise level characteristics of the Arctic coast. We propose a novel method for the automatic detection of infrasonic events based on representation of a plane wave signal and adaptive estimation of the noise level. The method makes it possible to detect signals with a small number of sensors (up to three) in the specific conditions of the Arctic coast. We present the results of infrasonic monitoring of the destruction of Icefjord outlet glaciers (Spitsbergen archipelago) carried out by the Kola Branch of the RAS Geophysical Survey in 2011–2012.     

Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification

This paper proposes a novel technique for automatic ultrasound non-destructive foreign body (FB) detection and classification. A signal registration process is introduced to eliminate shift variations commonly encountered in ultrasound signals. Information theory based methods are then developed for wavelet basis selection and feature extraction to facilitate robust FB classification. Probabilistic neural networks are used for FB classification. Experimental results confirm that the wavelet basis selected by the proposed method improves the FB classification accuracy. It is concluded that low order wavelet bases have better ability to distinguish classes with great similarities than their higher order counterparts, while the reverse is true for more divergent classes.     

Detection of speech landmarks: use of temporal information

Studies by Shannon et al. [Science, 270, 303-304 (1995)], Van Tasell et al. [J. Acoust. Soc. Am. 82, 1152-1161 (1987)], and others show that human listeners can understand important aspects of the speech signal when spectral shape has been significantly degraded. These experiments suggest that temporal information is particularly important in human speech perception when the speech signal is heavily degraded. In this study, a system is developed that extracts linguistically relevant temporal information that can be used in the front end of an automatic speech recognition system. The parameters targeted include energy onset and offsets (computed using an adaptive algorithm) and measures of periodic and aperiodic content; together these are used to find abrupt acoustic events which signify landmarks. Overall detection rates for strongly robust events, robust events, and weak events in a portion of the TIMIT test database are 98.9%, 94.7%, and 52.1%, respectively. Error rates increase by less than 5% when the speech signals are spectrally impoverished. Use of the four temporal parameters as the front end of a hidden Markov model (HMM)-based system for the automatic recognition of the manner classes "sonorant," "fricative," "stop," and "silence" results in the same recognition accuracy achieved when the standard 39 cepstral-based parameters are used, 70.1%. The combination of the temporal parameters and cepstral parameters results in an accuracy of 74.8%.     

Acoustic features based on auditory model and adaptive fractional Fourier transform for speech recognition

It is well known that auditory system of human beings has excellent performance which automatic speech recognition(ASR) systems can’t match,and fractional Fourier transform (FrFT) has unique advantages in non-stationary signal processing.In this paper,the Gammatone filterbank is applied to speech signals for front-end temporal filtering,and then acoustic features of the output subband signals are extracted based on fractional Fourier transform. Considering the critical effect of transform order for FrFT,an order adaptation method based on the instantaneous frequency is proposed,and its performance is compared with the method based on ambiguity function.ASR experiments are conducted on clean and noisy Putonghua digits,and the results show that the proposed features achieve significantly higher recognition rate than the MFCC baseline,and the order adaptation method based on instantaneous frequency has much lower complexity than that based on ambiguity function.Further more,the FrFT-based features achieve the highest recognition rate using the proposed order adaptation method.     

中高分辨力遥感图像中飞机目标自动识别算法研究

提出了一种中高分辨力的航空航天遥感图像中飞机目标快速自动识别的新算法。在分割和分类过程中充分利用飞机目标的先验知识,提出了一种改进区域分割方法,并应用树分类器对飞机目标进行自动识别。所提出的改进区域分割方法较好地实现了区域分割中阈值的准确自动选取,克服了复杂背景图像中小目标的全局阈值自动分割的失效问题。采用二叉树分类器,通过提取简单的目标几何特征,分层进行种类识别,提高了识别速度,降低了漏检率和虚警率。运用该方法进行了实验。结果表明,识别率达到了100%。     

A generalized power-law detection algorithm for humpback whale vocalizations

Conventional detection of humpback vocalizations is often based on frequency summation of band-limited spectrograms under the assumption that energy (square of the Fourier amplitude) is the appropriate metric. Power-law detectors allow for a higher power of the Fourier amplitude, appropriate when the signal occupies a limited but unknown subset of these frequencies. Shipping noise is non-stationary and colored and problematic for many marine mammal detection algorithms. Modifications to the standard power-law form are introduced to minimize the effects of this noise. These same modifications also allow for a fixed detection threshold, applicable to broadly varying ocean acoustic environments. The detection algorithm is general enough to detect all types of humpback vocalizations. Tests presented in this paper show this algorithm matches human detection performance with an acceptably small probability of false alarms (P(FA) < 6%) for even the noisiest environments. The detector outperforms energy detection techniques, providing a probability of detection P(D) = 95% for P(FA) < 5% for three acoustic deployments, compared to P(FA) > 40% for two energy-based techniques. The generalized power-law detector also can be used for basic parameter estimation and can be adapted for other types of transient sounds.     

A semi-automatic processing technique for elastic-wave laboratory data

This paper addresses the problem of determining the onset of transient signals such as seismograms, acoustic emissions or ultrasonic signals. Usual manual techniques of onset-time picking are time consuming when numerous measurements are available. This may occur when dealing with (i) anisotropic rocks requiring many elastic wave velocities measurements in the laboratory, (ii) 4-D seismic field data or (iii) laboratory acoustic emissions data. We present a semi-automatic processing technique devoted to the study of case (i). It is based on ultrasonic signal analysis by wavelet transform and an onset-time picking procedure combining Akaike Information Criterion and cross-correlation method. The first step consists in extracting, from the whole experimentally recorded signal, the frequency component corresponding to the perturbation induced by a typical ultrasonic transducer in the laboratory. The second step is dedicated to the onset-time picking of the phase arrival in the extracted signal. The use of this processing technique based on mathematical arguments reduces human subjectivity. Main outcomes are: (i) increase of signal-to-noise ratio; (ii) measurement of elastic wave velocities at prescribed central frequency; (iii) drastic increase of efficiency in wave data processing; and (iv) increase in reliability (repeatability) of wave data acquisition.     

Fiber Sensing of Micro -Crack

Optical fiber sensors are used for sensing micro -cracking in composite and metal materials in aerospace applications.The sensing mechanism is based on the detection of acoustic emission signals, which are known to emanate from micro-cracks when they grow under further loading. The sensor head consists of a fiber Bragg grating that is capable of detecting acoustic emission signals generated by pencil lead breaking, of frequencies up to 200 kHz.     

Reverberation cancellation in a closed test section of a wind tunnel using a multi-microphone cesptral method

Nowadays, although aerodynamic data are still primarily sought after during wind tunnel tests, reliable acoustic measurements also become a priority for aircraft designers. In order to gather both kinds of data, aerodynamic and acoustic tests are carried out simultaneously under the same closed test section. This solution has two major drawbacks: the acoustic signals delivered by microphones may be corrupted by the boundary layer expanding on the wind tunnel walls and by the reverberant noise originating from reflective surfaces. Technological solutions can be deployed to reduce the corruption of the signals by the wind tunnel background noise. Methods based on the power cepstrum can be used to reduce reverberation effects by removing the quefrencies due to the echoes in the cepstral domain.