Spectrogram denoising and automated extraction of the fundamental frequency variation of dolphin whistles

Marine mammal vocalizations are often analyzed using time-frequency representations (TFRs) which highlight their nonstationarities. One commonly used TFR is the spectrogram. The characteristic spectrogram time-frequency (TF) contours of marine mammal vocalizations play a significant role in whistle classification and individual or group identification. A major hurdle in the robust automated extraction of TF contours from spectrograms is underwater noise. An image-based algorithm has been developed for denoising and extraction of TF contours from noisy underwater recordings. An objective procedure for measuring the accuracy of extracted spectrogram contours is also proposed. This method is shown to perform well when dealing with the challenging problem of denoising broadband transients commonly encountered in warm shallow waters inhabited by snapping shrimp. Furthermore, it would also be useful with other types of broadband transient noise.     

Tracking fin whale calls offshore the Galicia Margin, North East Atlantic Ocean

Data recorded during a temporary deployment of ocean bottom seismometers (OBSs) are used in this study to monitor the presence of fin whales around the array. In the summer of 2003, ten OBSs were placed 250 km from the NW coast of Iberia in the Galicia Margin, NE Atlantic Ocean for a period of one month. The recorded data set provided a large variety of signals, including fin whale vocalizations identified by their specific acoustic signature. The use of a dense array of seafloor receivers allowed investigation into the locations and tracks of the signal-generating whales using a seismological hypocentral location code. Individual pulses of different sequences have been chosen to study such tracks. Problems related to the correct identification of pulses, discrimination between direct and multiple arrivals, and the presence of more than one individual have been considered prior to location. Fin calls were concentrated in the last two weeks of the deployment and the locations were spread around the area covered by the array. These results illustrate that, besides its classical seismological aim, deployment of semipermanent seafloor seismic arrays can also provide valuable data for marine mammal behavior studies.     

Compression of Cuvier’s beaked whale (Ziphius cavirostris) vocalizations using fast orthogonal search

Remote sensing of beaked whale vocalizations poses significant problems due to limited communications bandwidths. Many beaked whales vocalize (click) at frequencies up to 50 kHz Hence high bandwidth sampling (typically 100+ kHz) and processing is required in order to detect the clicks, but transmitting the data from a remote sensor using a low-bandwidth (4800 baud) satellite link results in a real-time bottleneck. Even if auto-detection algorithms were used on the remote sensor, some data would need to be relayed to a human operator to verify the classification. Hence, the ability to compress the data in a manner that does not impede the ability to detect and classify the transient signal is required. Typical audio compression techniques have a maximum sampling rate of 48 kHz which is too low to collect beaked whale clicks and still obey the Nyquist criterion. In addition, audio compression algorithms also have a psycho-acoustic model that aids in the compression of the signal but distorts the audio signal.This paper presents a compression algorithm that uses a non-linear modelling technique called fast orthogonal search (FOS) to create a functional expansion of the acoustic data. The candidate functions used in the functional expansion are transient signals that model Cuvier’s beaked whale (Ziphius cavirostris) clicks as well as sinusoidal functions for modelling whale songs. A compression ratio of 93 is achieved by transmitting candidate identification numbers and weights for only the candidate functions that are chosen by the FOS algorithm. The acoustic signal is recreated using the weights and candidate numbers transmitted. The reconstructed time series is used as an input to a band-limited energy detector for whale vocalizations. The raw data and the reconstructed data have comparable probability of detection and missed detections, with fewer false alarms for the reconstructed signal.     

Vocal production mechanisms in the budgerigar (Melopsittacus undulatus): the presence and implications of amplitude modulation.

In this paper acoustic evidence is presented for the presence of amplitude modulation in budgerigar (Melopsittacus undulatus) contact calls and learned English vocalizations. Previously, acoustic analyses of budgerigar vocalizations have consisted solely of visual inspection of spectrograms or power spectra (derived from Fourier transformation). Such analyses have led researchers to conclude that budgerigar vocalizations are primarily frequency-modulated, harmonic vocalizations. Although budgerigar calls have been shown to contain regions that are modulated in amplitude, the implications of this fact have been largely ignored. Amplitude modulation, the nonlinear interaction between two separate signals that results in the creation of new, heterodyne (sum and difference) frequencies, can produce a very complex Fourier spectrum that may resemble that produced by a harmonic vocalization. In this paper, the acoustic principles necessary for identifying amplitude modulation present in signals are outlined, and followed by data demonstrating that amplitude modulation is a prominent feature not only of natural budgerigar contact calls, but also of their learned English vocalizations. It is illustrated how analyzing a vocalization that contains amplitude modulation as if it were harmonic can result in misinterpretations of the acoustic and physical properties of the sound and sound source. The implications of amplitude modulation for studies of the ontogenetic, physical, and neural basis of budgerigar vocalizations are discussed, and a potential model for how the budgerigar syrinx may function to produce amplitude modulation is proposed.     

基于拉曼光谱技术的海水微塑料快速识别技术研究

近年来由于塑料的大量使用和排放,这些塑料经环境作用破碎变成微塑料大量汇聚到海洋中,导致海洋中聚集大量微塑料。微塑料形状较小,难以识别其来源与种类。激光拉曼探测技术具有快速、无损、且各物质指纹峰明显易被精确识别等优点,近年来被广泛应用。本文基于拉曼光谱探测技术,提出了一种结合小波处理、随机森林算法实现海水中微塑料快速识别的智能分类方法。针对六种典型的海水微塑料标准样品（丙烯腈(A)-丁二烯(B)-苯乙烯(S)的三元共聚物（ABS）、聚酰胺（PA）、聚对苯二甲酸乙二醇酯（PET）、聚丙烯（PP）、聚苯乙烯（PS）、聚氯乙烯（PVC））,采用激光拉曼探测技术进行光谱数据收集,对获取的拉曼光谱采用小波基为DB7、分解次数为3的小波,标准差归一化进行了拉曼光谱预处理。为了提高识别速度,同时还需要对光谱数据进行数据压缩预处理,分别进行了数据压缩点为64,128,256,512和1 024点的数据压缩比较,它们的决策树算法识别精度分别为91.51%,91.67%,92.35%,93.17%和93.21%,随机森林算法识别精度分别为93.12%,93.92%,94.83%,96.81%和96.81%,实验结果表明,微塑料的拉曼光谱压缩为512点时为效率和精度的最佳压缩点,可以为实际工程应用中微塑料拉曼数据压缩提供参考。分别采用决策树、随机森林两种算法进行微塑料拉曼光谱识别研究。研究结果表明,基于拉曼光谱数据,随机森林算法的识别微塑料交叉验证精度高于决策树算法。为进一步提高识别精度,进行了模型参数（折次k）优化研究,采用经过优化后的模型参数（k=20）,随机森林算法识别微塑料的交叉验证精度可以达到97.24%。可以为实际海水中微塑料的快速识别提供技术参考。     

The use of roving discs and orthogonal natural frequencies for crack identification and location in rotors

A variety of approaches that have been developed for the identification and localisation of cracks in a rotor system, which exploit natural frequencies, require a finite element model to obtain the natural frequencies of the intact rotor as baseline data. In fact, such approaches can give erroneous results about the location and depth of a crack if an inaccurate finite element model is used to represent an uncracked model. A new approach for the identification and localisation of cracks in rotor systems, which does not require the use of the natural frequencies of an intact rotor as a baseline data, is presented in this paper. The approach, named orthogonal natural frequencies (ONFs), is based only on the natural frequencies of the non-rotating cracked rotor in the two lateral bending vibration x–z and y–z planes. The approach uses the cracked natural frequencies in the horizontal x–z plane as the reference data instead of the intact natural frequencies. Also, a roving disc is traversed along the rotor in order to enhance the dynamics of the rotor at the cracked locations. At each spatial location of the roving disc, the two ONFs of the rotor–disc system are determined from which the corresponding ONF ratio is computed. The ONF ratios are normalised by the maximum ONF ratio to obtain normalised orthogonal natural frequency curves (NONFCs). The non-rotating cracked rotor is simulated by the finite element method using the Bernoulli–Euler beam theory. The unique characteristics of the proposed approach are the sharp, notched peaks at the crack locations but rounded peaks at non-cracked locations. These features facilitate the unambiguous identification and locations of cracks in rotors. The effects of crack depth, crack location, and mass of a roving disc are investigated. The results show that the proposed method has a great potential in the identification and localisation of cracks in a non-rotating cracked rotor.     

Data assimilation using Ensemble Transform Kalman Filter (ETKF) in ROMS model for Indian Ocean

Study of Oceans dynamics and forecast is crucial as it influences the regional climate and other marine activities. Forecasting oceanographic states like sea surface currents, Sea surface temperature (SST) and mixed layer depth at different time scales is extremely important for these activities. These forecasts are generated by various ocean general circulation models (OGCM). One such model is the Regional Ocean Modelling System (ROMS). Though ROMS can simulate several features of ocean, it cannot reproduce the thermocline of the ocean properly. Solution to this problem is to incorporates data assimilation (DA) in the model. DA system using Ensemble Transform Kalman Filter (ETKF) has been developed for ROMS model to improve the accuracy of the model forecast. To assimilate data temperature and salinity from ARGO data has been used as observation. Assimilated temperature and salinity without localization shows oscillations compared to the model run without assimilation for India Ocean. Same was also found for u and v-velocity fields. With localization we found that the state variables are diverging within the localization scale.     

自适应分段时域质心特征在鱼类识别中的应用

提出了一种基于时域质心的时域自适应分段方法。该方法以时域质心为依据对信号的时域进行划分,在划分的各个子段内计算时域质心,并将其作为下一层划分的分割点。各个子段内的时域质心反映了信号的能量分布特性,可作为识别特征量。对三种常见的不同形状的鱼类进行了水池试验,提取自适应分段时域质心特征,并使用BP神经网络分类器成功进行了分类。结果表明:利用自适应分段时域质心特征可对不同形状的鱼类进行识别,且具有较高的识别率。     

Multi-camera digital image correlation method with distributed fields of view

A multi-camera digital image correlation (DIC) method and system for measurements of large engineering objects with distributed, non-overlapping areas of interest are described. The data obtained with individual 3D DIC systems are stitched by an algorithm which utilizes the positions of fiducial markers determined simultaneously by Stereo-DIC units and laser tracker. The proposed calibration method enables reliable determination of transformations between local (3D DIC) and global coordinate systems. The applicability of the method was proven during in-situ measurements of a hall made of arch-shaped (18 m span) self-supporting metal-plates. The proposed method is highly recommended for 3D measurements of shape and displacements of large and complex engineering objects made from multiple directions and it provides the suitable accuracy of data for further advanced structural integrity analysis of such objects.     

High resolution 3-D shapes of fish schools: A new method to use the water column backscatter from hydrographic MultiBeam Echo Sounders

The Multi-Beam Echo Sounder (MBES) is a tool that allows getting information about bathymetric, morphological and compositional characteristics of the seabed surface. Although this is still one of the most important features in hydrography, MBES can now also discriminate the acoustic imaging of the water mass by recording sampled reflectivity measurements along each beam. In this paper, two MBES, namely the Kongsberg Simrad EM3002D (300 kHz) and the Reson Seabed8125-H (455 kHz), which are generally employed to study seafloor characteristics, have been used for the identification of fish schools in the water column at very high resolution. In particular, a methodology to identify and analyse fish schools through the Water Column Backscatter (WCB) is presented. This methodology made it possible to obtain a wide variety of fish schools shape in 3-D (in our case, anchovies and sardines), which allowed a first study on the ecological behaviour of the schools according to their shape. All data were collected along with Kongsberg Simrad EK60 scientific echo sounder, allowing also a comparison of different approaches to fish schools identification. The data presented in this paper were collected during two oceanographic surveys within the project MEDIAS (MEDiteranean International Acoustic Survey), Ancheva 2010 and Ancheva 2011. Beyond the mere visualization of school shapes, we also attempted to gain numerical values of the school volumes and to realize a more detailed study of anchovies and sardines school shapes. In line with the current literature, this study showed that there are three main factors influencing the shape of a school, namely the depth of the bottom, the time of the day when data are acquired and the density of individuals in the investigated area.     

Use of random matrix theory in the discovery of resting state brain networks

Connectomics identifies brain networks in vivo in resting state functional MRI. However, the presence of noise produces spurious identification of brain networks, which have low test-retest reliability. A Network Based Statistics approach to network identification has been previously proposed that affords much better statistical power relative to Bonferroni method but nevertheless provides a sufficiently conservative, family-wise control for false positives. We propose the use of Random Matrix Theory (RMT) to discover brain networks and to associate those networks with demographic and clinical variables. We parcellated the brain into cortical and subcortical regions using either an anatomical or a functional brain atlas. We applied RMT to study functional connectivity across brain regions by first computing the correlation matrix for time courses in those brain regions and then identifying eigenvalues that deviate from the theoretical random distribution that RMT predicts, on the assumption that real brain networks would produce eigenvalues that differ significantly from the random distribution. We assessed the specificity and test-retest reliability of identified networks through application of this RMT-based approach to (1) synthetic data generated under the null-hypothesis, (2) resting state functional MRI data from 4 real-world cohorts of patients and healthy controls, and (3) synthetic data generated by the addition of increasing amounts of noise to real-world datasets. Our findings showed that RMT method was robust to the atlas used for parcellating the brain and did not discover a brain network in synthetic data when in fact a network was not present (i.e., specificity was high); RMT-identified networks in the real-world dataset had high test-retest reliability; and RMT-based method consistently discovered the same network in the presence of increasing noise in the real-world dataset.     

Eigen-coordinates: New method of analytical functions identification in experimental measurements

A new method of identification of the analytical curves describing experimental data is suggested. It is based on the presentation of the wide class of analytical functions in the so-calledeigen-coordinates where the function takes the form of sloping lines. The slopes are related to the initial set of fitting parameters and can be found from the linear least square procedure. The method suggested has been checked in detail on the identification of a free induction signal and main statistical distributions which are widely used in physics, technics and applied statistics. The eigen-coordinates for some relaxation functions have been obtained as well.     

IDENTIFICATION OF MODAL PARAMETERS FROM MEASURED OUTPUT DATA USING VECTOR BACKWARD AUTOREGRESSIVE MODEL

In this paper, a modal identification system that is based on the vector backward autoregressive (VBAR) model has been developed for the identification of natural frequencies, damping ratios and mode shapes of structures from measured output data. The modal identification using forward autoregressive approach has some problems in discriminating the structure modes from spurious modes. On the contrary, the VBAR approach provides a determinate boundary for the separation of system modes from spurious modes, and an eigenvalue filter for the selection of physical modes is existent in the proposed method. For convenience of application, the backward state equation established from VBAR model is transformed into a forward state equation, which is termed as transformed VFAR model in this paper. In addition, the extraction of equivalent system matrix of state equation of motion for structures from the transformed VFAR model has been developed, and then the normal modes can be calculated from the identified equivalent system matrix. Two examples of modal identification are carried out to demonstrate the availability and effectiveness of the proposed backward approach: (1) Numerical modal identification for a three-degree-of-freedom dynamic system with noise level in 20% of r.m.s of measured output data; (2) experimental modal identification of a cantilever beam. Finally, to show the advantage of the proposed VBAR approach on the selection of physical modes, the modal identification by stochastic subspace method was performed. The results from both methods are compared.     

Theoretical detection ranges for acoustic based manatee avoidance technology

The West Indian manatee (Trichechus manatus latirostris) has become endangered partly because of watercraft collisions in Florida's coastal waterways. To reduce the number of collisions, warning systems based upon detecting manatee vocalizations have been proposed. One aspect of the feasibility of an acoustically based warning system relies upon the distance at which a manatee vocalization is detectable. Assuming a mixed spreading model, this paper presents a theoretical analysis of the system detection capabilities operating within various background and watercraft noise conditions. This study combines measured source levels of manatee vocalizations with the modeled acoustic properties of manatee habitats to develop a method for determining the detection range and hydrophone spacing requirements for acoustic based manatee avoidance technologies. In quiet environments (background noise approximately 70 dB) it was estimated that manatee vocalizations are detectable at approximately 250 m, with a 6 dB detection threshold, In louder environments (background noise approximately 100dB) the detection range drops to 2.5 m. In a habitat with 90 dB of background noise, a passing boat with a maximum noise floor of 120 dB would be the limiting factor when it is within approximately 100 m of a hydrophone. The detection range was also found to be strongly dependent on the manatee vocalization source level.     

海洋大气气溶胶光学模型参数的相关性研究

气溶胶光学模型参数在气溶胶遥感和气候强迫研究中都具有重要的作用.通过对全球近90个气溶胶自动观测网(AERONET)的海洋站点数据进行筛选、分类和分析,发现了气溶胶模型的中值半径及其标准偏差间的负相关性,并给出了经验关系.利用该关系对现行中分辩率成像光谱仪(MODIS)海洋气溶胶模型进行了评估,并指出了该模型存在的不足...     

How many parameters can be identified by adaptive synchronization in chaotic systems?

Five interesting experiments have been done for a class of chaos synchronization systems with unknown parameters and unknown control directions. And three important conclusions about parameters identification have been made. First, a necessary and sufficient condition for parameters identification is obtained. Second, a Nussbaum method is proposed to solve the problem of unknown control direction. Third, the adaptive method is not infinitely effective considered for our current ability of computation and simulation algorithm.     

汽车自动变速箱油的近红外光谱识别研究

利用自编码网络(autoencoder network, AN)流形学习和稀疏表示(sparse representation, SR)方法对汽车变速箱油进行近红外光谱品种识别研究。以壳牌、美孚、嘉实多、上海大众和上海通用五种变速箱油为对象,利用AN方法对600～1800 nm近红外光谱数据进行非线性降维,获取10个特征变量。每种变速箱油选取30个样本(共150个样本)作为训练样本,每种30个样本(共150个样本)作为测试样本。所有训练样本的特征变量组成了稀疏表示方法的整体训练样本矩阵,将变速箱油品种分类识别问题转化为一个求解待识别测试样本对于整体训练样本矩阵的稀疏表示问题,通过求解L-1范数意义下的最优化问题来实现。经过主成分分析(principal component analysis,PCA)和AN降维后,分别利用线性判断分析法(linear discriminant analysis,LDA)、偏最小二乘支持向量机法(least squares-support vector machine,LS-SVM)和本文提出的稀疏表示分类算法进行分类比较。结果表明,结合自编码网络和稀疏表示方法对五种汽车变速箱油品种的平均识别准确率达97.33%,为汽车变速箱油品种近红外光谱快速准确识别提供了有效的新途径。     

Experimental identification of viscous damping in linear vibration

This paper is concerned with the experimental evaluation of the performance of viscous damping identification methods in linear vibration theory. Both existing and some new methods proposed by the present authors [A.S. Phani, J. Woodhouse, Viscous damping identification in linear vibration, Journal of Sound and Vibration 303 (3–5) (2007) 475–500] are applied to experimental data measured on two test structures: a coupled three cantilever beam with moderate modal overlap and a free–free beam with low modal overlap. The performance of each method is quantified and compared based on three norms and the best methods are identified. The role of complex modes in damping identification from vibration measurements is critically assessed.     

不确定海洋环境中的模态子空间重构稳健定位方法

实际的海洋是一个不确定的声传播环境,常规的匹配场方法在进行目标定位时会遇到环境失配的问题,导致定位性能下降.在不确定的海洋环境中,声场传播中的一部分简正波模态受到声场不确定性的影响较小.基于此,本文提出了一种模态子空间重构的稳健定位方法.该方法使用稳定的模态来重构拷贝场向量,相比于常规匹配场定位方法中使用全阶模态来构造拷贝场向量,其定位结果更加稳健.利用计算机仿真数据和海试数据进行了定位性能分析,并给出了常规匹配场定位方法和稳健最大似然定位方法作为对比.研究结果表明:1)不确定海洋环境中,常规匹配场定位方法即使在较高的信噪比条件下其定位性能也较差.2)模态子空间重构定位方法的性能优于常规匹配场定位方法和稳健最大似然方法.     

利用海洋环境噪声直接提取海底反射系数

提出了一种利用海洋环境噪声直接(非迭代)提取海底反射系数的方法,基于海洋环境噪声垂直相关函数与方向密度函数的相似性,结合海洋环境噪声射线理论,由两个垂直排列的水听器实现海底反射系数的被动获取.方法适应性的仿真分析说明较高海况下且近场无航船干扰的海洋环境噪声数据有利于方法的实现;对于两层海底,海洋环境噪声垂直相干函数经高...